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What is trip generation.

One of the first steps in a traffic impact study is trip generation where traffic to and from the proposed development, or a nearby off-site development, is forecasted. Normally it will be based on the land use type and development size, number of employees, or dwelling units depending on the land use type. The primary source of the trip generation information comes from the Institute of Transportation Engineers Trip Generation Manual . It contains instructions and about 2,000 pages of data from small studies at various developments to establish equations that associate an independent variable with the trips counted at the site. The independent variable should be the cause of the variation in trips from a site and easily measured or forecasted, like the number of homes in a subdivision. Many of the land uses have more than one independent variable to choose from, such as office where the square foot or the number of employees can be used. Trips can be estimated on a daily or hourly basis for the street peak and generator peaks and are one-way. A round trip is counted as two one-way trips. Of course, there are separate equations for weekdays, Saturdays and Sundays. The street peak is when the traffic on the adjacent street is highest, typically between 7:00 AM and 9:00 AM and 4:00 and 6:00 PM. The generator peak may or may not occur at the same time. For example, fast-food restaurants peak around lunch time.

Once the total number of trips in and out of a development is forecasted for a particular time, the next step is estimate the number of internal trips if there is more than one land use that can be accessed without going back on to the street system. This is very typical in mixed use developments that may have retail, office and residential uses. Internal trips can be tricky to estimate, but generally reduce the impact of the development on the public roads when a motorist can visit a restaurant, bank, grocery store and dry cleaner without going back on the public roads. They can park and walk or drive between the land uses as long as they don’t use public roads to do so. The ITE Handbook has a method that can be used to estimate internal trips, and it can be supplemented with data from NCHRP Report 684 . In some studies that include the site’s internal road network, it may be necessary to assign the internal trips rather than just subtracting them from the external trips.

After reducing the total trips by the number of internal trips, the next step is to divide the remaining trips into primary, pass-by and diverted link trips.

Primary trips are those where the motorist only goal in getting the vehicle was to come to the development and then return to where they came from. These are estimated by forecasting the pass-by trips and the diverted link trips, and then subtracting them from the total trip minus the internal trips.

Pass-by trips are those trips already on the roadways immediately adjacent to the site, but altering their path at the driveway to visit the site. Pass-by trips differ from diverted link trips in that the diverted link trips would be on nearby streets whereas pass-by trips would be on the adjacent streets with driveways. Pass-by trips require a development driveway on the street where the motorist would have been driving on anyway. See the red line above.

Diverted link trips are those that would have been on the roadway network anyway, but alter their path to visit the site. For example, for a gas station at an interchange, diverted link trips are those that would come off the freeway and then go back to the freeway in their initial direction. See the blue line above. They are not pass-by trips since the site doesn’t have a driveway to freeway.

Occasionally mass transit, walking, biking or even horse and buggies can come into the mix as well, but for most traffic impact studies in Indiana and Kentucky, the assumption is that the data in the ITE Trip Generation Manual reflects the same conditions as the proposed development. In some cases, the trips need to be assigned to the road network before determining the mass transit percentages since they may vary by location due to the availability of mass transit. Distance also needs to be considered for walking and biking.

Yarger Engineering offers free initial consultations, and we would be glad to discuss your situation. Call us at 317-475-1100 or email us !

Fundamentals of Transportation/Trip Generation

Trip Generation is the first step in the conventional four-step transportation forecasting process (followed by Destination Choice , Mode Choice , and Route Choice ), widely used for forecasting travel demands. It predicts the number of trips originating in or destined for a particular traffic analysis zone.

Every trip has two ends, and we need to know where both of them are. The first part is determining how many trips originate in a zone and the second part is how many trips are destined for a zone. Because land use can be divided into two broad category (residential and non-residential) we have models that are household based and non-household based (e.g. a function of number of jobs or retail activity).

For the residential side of things, trip generation is thought of as a function of the social and economic attributes of households (households and housing units are very similar measures, but sometimes housing units have no households, and sometimes they contain multiple households, clearly housing units are easier to measure, and those are often used instead for models, it is important to be clear which assumption you are using).

At the level of the traffic analysis zone, the language is that of land uses "producing" or attracting trips, where by assumption trips are "produced" by households and "attracted" to non-households. Production and attractions differ from origins and destinations. Trips are produced by households even when they are returning home (that is, when the household is a destination). Again it is important to be clear what assumptions you are using.

  • 1 Activities
  • 2.1 Home-end
  • 2.2 Work-end
  • 2.3 Shop-end
  • 3 Input Data
  • 4.1 Home-end
  • 4.2 Non-home-end
  • 5 Normalization
  • 6 Sample Problems
  • 7 Variables
  • 8 Abbreviations
  • 9 External Exercises
  • 10 Additional Problems
  • 11 End Notes
  • 12 Further reading
  • 14 References

Activities [ edit | edit source ]

People engage in activities, these activities are the "purpose" of the trip. Major activities are home, work, shop, school, eating out, socializing, recreating, and serving passengers (picking up and dropping off). There are numerous other activities that people engage on a less than daily or even weekly basis, such as going to the doctor, banking, etc. Often less frequent categories are dropped and lumped into the catchall "Other".

Every trip has two ends, an origin and a destination. Trips are categorized by purposes , the activity undertaken at a destination location.

Some observations:

  • Men and women behave differently on average, splitting responsibilities within households, and engaging in different activities,
  • Most trips are not work trips, though work trips are important because of their peaked nature (and because they tend to be longer in both distance and travel time),
  • The vast majority of trips are not people going to (or from) work.

People engage in activities in sequence, and may chain their trips. In the Figure below, the trip-maker is traveling from home to work to shop to eating out and then returning home.

trip engineering definition

Specifying Models [ edit | edit source ]

How do we predict how many trips will be generated by a zone? The number of trips originating from or destined to a purpose in a zone are described by trip rates (a cross-classification by age or demographics is often used) or equations. First, we need to identify what we think the relevant variables are.

Home-end [ edit | edit source ]

The total number of trips leaving or returning to homes in a zone may be described as a function of:

{\displaystyle T_{h}=f(housing\ units,\ household\ size,\ age,\ income,\ accessibility,\ vehicle\ ownership).\,\!}

Home-End Trips are sometimes functions of:

  • Housing Units
  • Household Size
  • Accessibility
  • Vehicle Ownership
  • Other Home-Based Elements

Work-end [ edit | edit source ]

At the work-end of work trips, the number of trips generated might be a function as below:

{\displaystyle T_{w}=f(jobs(area\ of\ space\ by\ type,\ occupancy\ rate))\,\!}

Work-End Trips are sometimes functions of:

  • Area of Workspace
  • Occupancy Rate
  • Other Job-Related Elements

Shop-end [ edit | edit source ]

Similarly shopping trips depend on a number of factors:

{\displaystyle \,\!T_{s}=f(number\ of\ retail\ workers,\ type\ of\ retail,\ area,\ location,\ competition)}

Shop-End Trips are sometimes functions of:

  • Number of Retail Workers
  • Type of Retail Available
  • Area of Retail Available
  • Competition
  • Other Retail-Related Elements

Input Data [ edit | edit source ]

A forecasting activity conducted by planners or economists, such as one based on the concept of economic base analysis, provides aggregate measures of population and activity growth. Land use forecasting distributes forecast changes in activities across traffic zones.

Estimating Models [ edit | edit source ]

Which is more accurate: the data or the average? The problem with averages (or aggregates) is that every individual’s trip-making pattern is different.

To estimate trip generation at the home end, a cross-classification model can be used. This is basically constructing a table where the rows and columns have different attributes, and each cell in the table shows a predicted number of trips, this is generally derived directly from data.

In the example cross-classification model: The dependent variable is trips per person. The independent variables are dwelling type (single or multiple family), household size (1, 2, 3, 4, or 5+ persons per household), and person age.

The figure below shows a typical example of how trips vary by age in both single-family and multi-family residence types.

height=150px

The figure below shows a moving average.

height=150px

Non-home-end [ edit | edit source ]

The trip generation rates for both “work” and “other” trip ends can be developed using Ordinary Least Squares (OLS) regression (a statistical technique for fitting curves to minimize the sum of squared errors (the difference between predicted and actual value) relating trips to employment by type and population characteristics.

{\displaystyle E_{off}\,\!}

A typical form of the equation can be expressed as:

{\displaystyle T_{D,k}=a_{1}E_{off,k}+a_{2}E_{oth,k}+a_{3}E_{ret,k}\,\!}

Normalization [ edit | edit source ]

For each trip purpose (e.g. home to work trips), the number of trips originating at home must equal the number of trips destined for work. Two distinct models may give two results. There are several techniques for dealing with this problem. One can either assume one model is correct and adjust the other, or split the difference.

It is necessary to ensure that the total number of trip origins equals the total number of trip destinations, since each trip interchange by definition must have two trip ends.

The rates developed for the home end are assumed to be most accurate,

The basic equation for normalization:

{\displaystyle T'_{D,j}=T_{D,j}{\frac {\sum \limits _{i=1}^{I}{T_{O,i}}}{\sum \limits _{j=1}^{J}{T_{D,j}}}}\,\!}

Sample Problems [ edit | edit source ]

  • Problem ( Solution )

Variables [ edit | edit source ]

{\displaystyle T_{O},i}

Abbreviations [ edit | edit source ]

  • H2W - Home to work
  • W2H - Work to home
  • W2O - Work to other
  • O2W - Other to work
  • H2O - Home to other
  • O2H - Other to home
  • O2O - Other to other
  • HBO - Home based other (includes H2O, O2H)
  • HBW - Home based work (H2W, W2H)
  • NHB - Non-home based (O2W, W2O, O2O)

External Exercises [ edit | edit source ]

Use the ADAM software at the STREET website and try Assignment #1 to learn how changes in analysis zone characteristics generate additional trips on the network.

Additional Problems [ edit | edit source ]

  • Additional Problems

End Notes [ edit | edit source ]

Further reading [ edit | edit source ].

  • Trip Generation article on wikipedia

Videos [ edit | edit source ]

  • Trip Generation
  • Normalization

References [ edit | edit source ]

trip engineering definition

  • Book:Fundamentals of Transportation

Navigation menu

Planning Tank

Trip generation

What is trip generation .

A trip is usually defined in transport modeling as a single journey made by an individual between two points by a specified mode of travel and for a defined purpose. Trips are often considered as productions of a particular land-use and attracted to other specified land-uses. The number of trips arises in unit time, usually for a specified zonal land use , is called the trip generation rate.

How to estimate trip generation ?

Trip generation is estimated in three ways:

(i) traditionally by linear and multiple regression

(ii) by aggregating the trip generating capability of a household or car and aggregating the total according to the distribution of each selected category in the zones, and

(iii) by household classification method through a catalogue of the characteristic mean trip rates for specific types of household.

The attraction points are identified as trip generated by work, and other purpose visits. By assigning suitable values to the independent variables of the regression equations forecasts can be made of the future trip ends for zones by either method.

Trip Generation

Trip distribution :Trip generation estimates the number and types of trips originating and terminating in zones. Trip distribution is the process of computing the number of trips between one zone and all other. A trip matrix is drawn up with the sums of rows indicating the total number of trips originating in zone i and the sums of columns the total number of destinations  attracted to zone j.

Each cell in the matrix indicates the number of trips that go from each origin zone to each destination zone. The trips on the diagonal are intra-zonal trips, trips that originate and end in the same zone. The balancing equation is implemented in a series of steps that include modeling the number of trips originating in each cases, adding in trips originating from outside the study area(external trips), and statistically balancing the origins and destinations.

This is done in the trip generation stage. But, it is essential that the step should have been completed for the trip distribution to be implemented. Two trip distribution matrices need to be distinguished. The first is the observed distribution. This is the actual number of trips that are observed traveling between each origin zone and each destination zone. It is calculated by simply enumerating the number of trips by each origin-destination combination. It is also called trip-link. The second distribution is a model of the trip distribution matrix, called the predicted distribution.

Generally trips should be distributed over the area proportionally to the attractiveness of activities and inversely proportional to the travel resistances between areas. It is assumed that the trips between zones will be by the most direct or cheapest routes and, taking each zone in turn, a minimum path is traced out to all other zones to form a minimum path tree. The trip distribution is a model of travel between zones-trips or links. The modeled trip distribution can then be compared to the actual distribution to see whether the model produces a reasonable approximation.

Read about:  Zoning of Land for OD Survey , Traffic Volume Count , Origin Destination Survey Methods

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trip engineering definition

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3.6: 3-6 Route Choice

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  • Page ID 48085

  • David Levinson et al.
  • Associate Professor (Engineering) via Wikipedia

Route assignment , route choice , or traffic assignment concerns the selection of routes (alternative called paths) between origins and destinations in transportation networks. It is the fourth step in the conventional transportation forecasting model, following Trip Generation, Destination Choice, and Mode Choice. The zonal interchange analysis of trip distribution provides origin-destination trip tables. Mode choice analysis tells which travelers will use which mode. To determine facility needs and costs and benefits, we need to know the number of travelers on each route and link of the network (a route is simply a chain of links between an origin and destination). We need to undertake traffic (or trip) assignment. Suppose there is a network of highways and transit systems and a proposed addition. We first want to know the present pattern of travel times and flows and then what would happen if the addition were made.

Link Performance Function

The cost that a driver imposes on others is called the marginal cost. However, when making decisions, a driver only faces his own cost (the average cost) and ignores any costs imposed on others (the marginal cost).

  • \[AverageCost=\dfrac{S_T}{Q}\]
  • \[MarginalCost=\dfrac{\delta S_T}{\delta Q}\]

where \(S_T\) is the total cost, and \(Q\) is the flow.

BPR Link Performance Function

Suppose we are considering a highway network. For each link there is a function stating the relationship between resistance and volume of traffic. The Bureau of Public Roads (BPR) developed a link (arc) congestion (or volume-delay, or link performance) function, which we will term S a (Q a )

\[S_a(Q_a)=t_a(1+0.15\dfrac ({Q_a}{c_a})^4)\]

t a = free-flow travel time on link a per unit of time

Q a = flow (or volume) of traffic on link a per unit of time (somewhat more accurately: flow attempting to use link a )

c a = capacity of link a per unit of time

S a (Q a ) is the average travel time for a vehicle on link a

There are other congestion functions. The CATS has long used a function different from that used by the BPR, but there seems to be little difference between results when the CATS and BPR functions are compared.

Can Flow Exceed Capacity?

On a link, the capacity is thought of as “outflow.” Demand is inflow.

If inflow > outflow for a period of time, there is queueing (and delay).

For Example, for a 1 hour period, if 2100 cars arrive and 2000 depart, 100 are still there. The link performance function tries to represent that phenomenon in a simple way.

Wardrop's Principles of Equilibrium

User Equilibrium

Each user acts to minimize his/her own cost, subject to every other user doing the same. Travel times are equal on all used routes and lower than on any unused route.

  • System optimal

Each user acts to minimize the total travel time on the system.

Price of Anarchy

The reason we have congestion is that people are selfish. The cost of that selfishness (when people behave according to their own interest rather than society's) is the price of anarchy .

The ratio of system-wide travel time under User Equilibrium and System Optimal conditions.

For a two-link network with linear link performance functions (latency functions), Price of Anarchy is < 4/3.

Is this too much? Should something be done, or is 33% waste acceptable? [The loss may be larger/smaller in other cases, under different assumptions, etc.]

Conservation of Flow

An important factor in road assignment is the conservation of flow. This means that the number of vehicles entering the intersection (link segment) equals the number of vehicles exiting the intersection for a given period of time (except for sources and sinks).

Similarly, the number of vehicles entering the back of the link equals the number exiting the front (over a long period of time).

Auto assignment

Long-standing techniques.

The above examples are adequate for a problem of two links, however real networks are much more complicated. The problem of estimating how many users are on each route is long standing. Planners started looking hard at it as freeways and expressways (motorways) began to be developed. The freeway offered a superior level of service over the local street system and diverted traffic from the local system. At first, diversion was the technique. Ratios of travel time were used, tempered by considerations of costs, comfort, and level of service.

The Chicago Area Transportation Study (CATS) researchers developed diversion curves for freeways versus local streets. There was much work in California also, for California had early experiences with freeway planning. In addition to work of a diversion sort, the CATS attacked some technical problems that arise when one works with complex networks. One result was the Moore algorithm for finding shortest paths on networks.

The issue the diversion approach didn’t handle was the feedback from the quantity of traffic on links and routes. If a lot of vehicles try to use a facility, the facility becomes congested and travel time increases. Absent some way to consider feedback, early planning studies (actually, most in the period 1960-1975) ignored feedback. They used the Moore algorithm to determine shortest paths and assigned all traffic to shortest paths. That’s called all or nothing assignment because either all of the traffic from i to j moves along a route or it does not.

The all-or-nothing or shortest path assignment is not trivial from a technical-computational view. Each traffic zone is connected to n - 1 zones, so there are numerous paths to be considered. In addition, we are ultimately interested in traffic on links. A link may be a part of several paths, and traffic along paths has to be summed link by link.

An argument can be made favoring the all-or-nothing approach. It goes this way: The planning study is to support investments so that a good level of service is available on all links. Using the travel times associated with the planned level of service, calculations indicate how traffic will flow once improvements are in place. Knowing the quantities of traffic on links, the capacity to be supplied to meet the desired level of service can be calculated.

Heuristic procedures

To take account of the affect of traffic loading on travel times and traffic equilibria, several heuristic calculation procedures were developed. One heuristic proceeds incrementally. The traffic to be assigned is divided into parts (usually 4). Assign the first part of the traffic. Compute new travel times and assign the next part of the traffic. The last step is repeated until all the traffic is assigned. The CATS used a variation on this; it assigned row by row in the O-D table.

The heuristic included in the FHWA collection of computer programs proceeds another way.

  • Step 0: Start by loading all traffic using an all or nothing procedure.
  • Step 1: Compute the resulting travel times and reassign traffic.
  • Step 2: Now, begin to reassign using weights. Compute the weighted travel times in the previous two loadings and use those for the next assignment. The latest iteration gets a weight of 0.25 and the previous gets a weight of 0.75.
  • Step 3. Continue.

These procedures seem to work “pretty well,” but they are not exact.

Frank-Wolfe algorithm

Dafermos (1968) applied the Frank-Wolfe algorithm (1956, Florian 1976), which can be used to deal with the traffic equilibrium problem.

Equilibrium Assignment

To assign traffic to paths and links we have to have rules, and there are the well-known Wardrop equilibrium (1952) conditions. The essence of these is that travelers will strive to find the shortest (least resistance) path from origin to destination, and network equilibrium occurs when no traveler can decrease travel effort by shifting to a new path. These are termed user optimal conditions, for no user will gain from changing travel paths once the system is in equilibrium.

The user optimum equilibrium can be found by solving the following nonlinear programming problem

\[min \displaystyle \sum_{a} \displaystyle\int\limits_{0}^{v_a}S_a(Q_a)\, dx\]

subject to:

\[Q_a=\displaystyle\sum_{i}\displaystyle\sum_{j}\displaystyle\sum_{r}\alpha_{ij}^{ar}Q_{ij}^r\]

\[sum_{r}Q_{ij}^r=Q_{ij}\]

\[Q_a\ge 0, Q_{ij}^r\ge 0\]

where \(Q_{ij}^r\) is the number of vehicles on path r from origin i to destination j . So constraint (2) says that all travel must take place: i = 1 ... n; j = 1 ... n

\(\alpha_{ij}^{ar}\)= 1 if link a is on path r from i to j ; zero otherwise.

So constraint (1) sums traffic on each link. There is a constraint for each link on the network. Constraint (3) assures no negative traffic.

Transit assignment

There are also methods that have been developed to assign passengers to transit vehicles. In an effort to increase the accuracy of transit assignment estimates, a number of assumptions are generally made. Examples of these include the following:

  • All transit trips are run on a set and predefined schedule that is known or readily available to the users.
  • There is a fixed capacity associated with the transit service (car/trolley/bus capacity).

trip engineering definition

Solve for the flows on Links a and b in the Simple Network of two parallel links just shown if the link performance function on link a :

\(S_a=5+2*Q_a\)

and the function on link b :

\(S_b=10+Q_b\)

where total flow between the origin and destination is 1000 trips.

Time (Cost) is equal on all used routes so \(S_a=S_b\)

And we have Conservation of flow so, \(Q_a+Q_b=Q_o=Q_d=1000\)

\(5+2*(1000-Q_b)=10+Q_b\)

\(1995=3Q_b\)

\(Q_b=665;Q_a=335\)

An example from Eash, Janson, and Boyce (1979) will illustrate the solution to the nonlinear program problem. There are two links from node 1 to node 2, and there is a resistance function for each link (see Figure 1). Areas under the curves in Figure 2 correspond to the integration from 0 to a in equation 1, they sum to 220,674. Note that the function for link b is plotted in the reverse direction.

\(S_a=15(1+0.15(\dfrac{Q_a}{1000})^4)\)

\(S_b=20(1+0.15(\dfrac{Q_a}{3000})^4)\)

\(Q_a+Q_b=8000\)

Show graphically the equilibrium result.

trip engineering definition

At equilibrium there are 2,152 vehicles on link a and 5,847 on link b . Travel time is the same on each route: about 63.

Figure 3 illustrates an allocation of vehicles that is not consistent with the equilibrium solution. The curves are unchanged, but with the new allocation of vehicles to routes the shaded area has to be included in the solution, so the Figure 3 solution is larger than the solution in Figure 2 by the area of the shaded area.

Assume the traffic flow from Milwaukee to Chicago, is 15000 vehicles per hour. The flow is divided between two parallel facilities, a freeway and an arterial. Flow on the freeway is denoted \(Q_f\), and flow on the two-lane arterial is denoted \(Q_a\).

The travel time (in minutes) on the freeway (\(C_f\)) is given by:

\(C_f=10+Q_f/1500\)

\(C_a=15+Q_a/1000\)

Apply Wardrop's User Equilibrium Principle, and determine the flow and travel time on both routes.

The travel times are set equal to one another

\(C_f=C_a\)

\(10+Q_f/1500=15+Q_a/1000\)

The total traffic flow is equal to 15000

\(Q_f+Q_a=15000\)

\(Q_a=15000-Q_f\)

\(10+Q_f/1500=15+(15000-Q_f)/1000\)

Solve for \(Q_f\)

\(Q_f=60000/5=12000\)

\(Q_a=15000-Q_f=3000\)

Thought Questions

  • How can we get drivers to consider their marginal cost?
  • Alternatively: How can we get drivers to behave in a “System Optimal” way?

Sample Problems

Given a flow of six (6) units from origin “o” to destination “r”. Flow on each route ab is designated with Qab in the Time Function. Apply Wardrop's Network Equilibrium Principle (Users Equalize Travel Times on all used routes)

A. What is the flow and travel time on each link? (complete the table below) for Network A

Link Attributes

B. What is the system optimal assignment?

C. What is the Price of Anarchy?

What is the flow and travel time on each link? Complete the table below for Network A:

These four links are really 2 links O-P-R and O-Q-R, because by conservation of flow Qop = Qpr and Qoq = Qqr.

By Wardrop's Equilibrium Principle, the travel time (cost) on each used route must be equal. Therefore \(C_{opr}=C_{oqr}\)

OR \(25+6*Q_{opr}=20+7*Q_{oqr}\)

\(5+6*Q_{opr}=7*Q_{oqr}\)

\(Q_{oqr}=5/7+6*Q_{opr}/7\)

By the conservation of flow principle

\(Q_{oqr}+Q_{opr}=6\)

\(Q_{opr}=6-Q_{oqr}\)

By substitution

\Q_{oqr}=5/7+6/7(6-Q_{oqr})=41/7-6*Q_{oqr}/7\)

\(13*Q_{oqr}=41\)

\(Q_{oqr}=41/13=3.15\)

\(Q_{opr}=2.84\)

\(42.01=25+6(2.84)\)

\(42.05=20+7(3.15)\)

Check (within rounding error)

or expanding back to the original table:

User Equilibrium: Total Delay = 42.01 * 6 = 252.06

What is the system optimal assignment?

Conservation of Flow:

\(Q_{opr}+Q_{oqr}=6\)

\(TotalDelay=Q_{opr}(25+6*Q_{oqr})+Q_{oqr}(20+7*Q_{oqr})\)

\(25Q_{opr}+6Q_{opr}^2+(6_Q_{opr})(20+7(6-Q_{opr}))\)

\(25Q_{opr}+6Q_{opr}^2+(6_Q_{opr})(62-7Q_{opr}))\)

\(25Q_{opr}+6Q_{opr}^2+372-62Q_{opr}-42Q_{opr}+7Q_{opr}^2\)

\(13Q_{opr}^2-79Q_{opr}+372\)

Analytic Solution requires minimizing total delay

\(\deltaC/\deltaQ=26Q_{opr}-79=0\)

\(Q_{opr}=79/26-3.04\)

\(Q_{oqr}=6-Q_{opr}=2.96\)

And we can compute the SO travel times on each path

\(C_{opr,SO}=25+6*3.04=43.24\)

\(C_{opr,SO}=20+7*2.96=40.72\)

Note that unlike the UE solution, \(C_{opr,SO}\g C_{oqr,SO}\)

Total Delay = 3.04(25+ 6*3.04) + 2.96(20+7*2.96) = 131.45+120.53= 251.98

Note: one could also use software such as a "Solver" algorithm to find this solution.

What is the Price of Anarchy?

User Equilibrium: Total Delay =252.06 System Optimal: Total Delay = 251.98

Price of Anarchy = 252.06/251.98 = 1.0003 < 4/3

The Marcytown - Rivertown corridor was served by 3 bridges, according to the attached map. The bridge over the River on the route directly connecting Marcytown and Citytown collapsed, leaving two alternatives, via Donkeytown and a direct. Assume the travel time functions Cij in minutes, Qij in vehicles/hour, on the five links routes are as given.

Marcytown - Rivertown Cmr = 5 + Qmr/1000

Marcytown - Citytown (prior to collapse) Cmc = 5 + Qmc/1000

Marcytown - Citytown (after collapse) Cmr = ∞

Citytown - Rivertown Ccr = 1 + Qcr/500

Marcytown - Donkeytown Cmd = 7 + Qmd/500

Donkeytown - Rivertown Cdr = 9 + Qdr/1000

Also assume there are 10000 vehicles per hour that want to make the trip. If travelers behave according to Wardrops user equilibrium principle.

A) Prior to the collapse, how many vehicles used each route?

Route A (Marcytown-Rivertown) = Ca = 5 + Qa/1000

Route B (Marcytown-Citytown-Rivertown) = Cb = 5 + Qb/1000 + 1 + Qb/500 = 6 + 3Qb/1000

Route C (Marcytown-Donkeytown-Rivertown)= Cc = 7 + Qc/500 + 9 + Qc/1000 = 16 + 3Qc/1000

At equilibrium the travel time on all three used routes will be the same: Ca = Cb = Cc

We also know that Qa + Qb + Qc = 10000

Solving the above set of equations will provide the following results:

Qa = 8467;Qb = 2267;Qc = −867

We know that flow cannot be negative. By looking at the travel time equations we can see a pattern.

Even with a flow of 0 vehicles the travel time on route C(16 minutes) is higher than A or B. This indicates that vehicles will choose route A or B and we can ignore Route C.

Solving the following equations:

Route A (Marcytown-Rivertown) = Ca = 5 + Qa /1000

Route B (Marcytown-Citytown-Rivertown) = Cb = 6 + 3Qb /1000

Qa + Qb = 10000

We can the following values:

Qa = 7750; Qb = 2250; Qc = 0

B) After the collapse, how many vehicles used each route?

We now have only two routes, route A and C since Route B is no longer possible. We could solve the following equations:

Route C (Marcytown- Donkeytown-Rivertown) = Cc = 16 + 3Qc /1000

Qa+ Qc= 10000

But we know from above table that Route C is going to be more expensive in terms of travel time even with zero vehicles using that route. We can therefore assume that Route A is the only option and allocate all the 10,000 vehicles to Route A.

If we actually solve the problem using the above set of equations, you will get the following results:

Qa = 10250; Qc = -250

which again indicates that route C is not an option since flow cannot be negative.

C) After the collapse, public officials want to reduce inefficiencies in the system, how many vehicles would have to be shifted between routes? What is the “price of anarchy” in this case?

TotalDelayUE =(15)(10,000)=150,000

System Optimal

TotalDelaySO =(Qa)(5+Qa/1000)+(Qc)(16+3Qc/1000)

Using Qa + Qc = 10,000

TotalDelaySO =(Qa2)/250−71Qa+460000

Minimize total delay ∂((Qa2)/250 − 71Qa + 460000)/∂Qa = 0

Qa/125−7 → Qa = 8875 Qc = 1125 Ca = 13,875 Cc = 19,375

TotalDelaySO =144938

Price of Anarchy = 150,000/144,938 = 1.035

  • \(C_T\) - total cost
  • \(C_k\) - travel cost on link \(k\)
  • \(Q_k\) - flow (volume) on link \(k\)

Abbreviations

  • VDF - Volume Delay Function
  • LPF - Link Performance Function
  • BPR - Bureau of Public Roads
  • UE - User Equilbrium
  • SO - System Optimal
  • DTA - Dynamic Traffic Assignment
  • DUE - Deterministic User Equilibrium
  • SUE - Stochastic User Equilibrium
  • AC - Average Cost
  • MC - Marginal Cost
  • Route assignment, route choice, auto assignment
  • Volume-delay function, link performance function
  • User equilibrium
  • Conservation of flow
  • Average cost
  • Marginal cost

External Exercises

Use the ADAM software at the STREET website and try Assignment #3 to learn how changes in network characteristics impact route choice.

Additional Questions

1. If trip distribution depends on travel times, and travel times depend on the trip table (resulting from trip distribution) that is assigned to the road network, how do we solve this problem (conceptually)?

2. Do drivers behave in a system optimal or a user optimal way? How can you get them to move from one to the other.

3. Identify a mechanism that can ensure the system optimal outcome is achieved in route assignment, rather than the user equilibrium. Why would we want such an outcome? What are the drawbacks to the mechanism you identified?

4. Assume the flow from Dakotopolis to New Fargo, is 5300 vehicles per hour. The flow is divided between two parallel facilities, a freeway and an arterial. Flow on the freeway is denoted \(Q_f\), and flow on the two-lane arterial is denoted \(Q_r\). The travel time on the freeway \(C_f\) is given by:

\(C_f=5+Q_f/1000\)

The travel time on the arterial (Cr) is given by

\(C_r=7+Q_r/500\)

(a) Apply Wardrop's User Equilibrium Principle, and determine the flow and travel time on both routes from Dakotopolis to New Fargo.

(b) Solve for the System Optimal Solution and determine the flow and travel time on both routes.

5. Given a flow of 10,000 vehicles from origin to destination traveling on three parallel routes. Flow on each route A, B, or C is designated with \(Q_a\), \(Q_b\), \(Q_c\) in the Time Function Respectively. Apply Wardrop's Network Equilibrium Principle (Users Equalize Travel Times on all used routes), and determine the flow on each route.

\(T_A=500+20Q_A\)

\(T_B=1000+10Q_B\)

\(T_C=2000+30Q_C\)

  • How does average cost differ from marginal cost?
  • How do System Optimal and User Equilibrium travel time differ?
  • Why do we want people to behave in an SO way?
  • How can you get people to behave in an SO way?
  • Who was John Glen Wardrop?
  • What are Wardrop’s Two Principles?
  • What does conservation of flow require in route assignment?
  • Can Variable Message Signs be used to encourage System Optimal behavior?
  • What is freeflow travel time?
  • If a problem has more than two routes, where does the extra equation come from?
  • How can you determine if a route is unused?
  • What is the difference between capacity and flow
  • Draw a typical volume-delay function for a deterministic, static user equilibrium assignment.
  • Can Q be negative?
  • What is route assignment?
  • Is it important that the output travel times from route choice be consistent with the input travel times for destination choice and mode choice? Why?

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Part III: Travel Demand Modeling

11 Chapter 11: Second Step of Four Step Modeling (Trip Distribution)

This chapter describes the second step of the four-step travel demand modeling or trip distribution. It focuses on the procedure that distributes the trips generated from or attracted to each zone in the study area. In this step, the trip distribution input is the trip generation step’s output and the interzonal transportation costs. Based on the concepts of the gravity model, the trip flows between each pair of zones can be calculated as an OD matrix. The chapter discusses essential concepts and techniques, such as growth factors and calibration methods.

Learning Objectives

Student Learning Outcomes

  • Explain trip distribution and how to relate it to the first step (trip generation) results.
  • Summarize the factors that determine the level of attractiveness of zones in a travel demand model.
  • Summarize and compare different methods for trip distribution estimation within FSM.
  • Complete the trip distribution step by balancing total trip productions and attractions after the trip distribution step.

Prep/quiz/assessments

  • What factors affect the attractiveness of the zones in trip distribution, and what input data is needed to measure such attractiveness?
  • What are the advantages and disadvantages of the three trip distribution methods (gravity model, intervening opportunities, and Fratar model)?
  • What are the friction factor and K-factor in trip distribution, and how do they help to calibrate model results?
  • How should we balance trip attraction and production after performing trip distribution? Explain.

11.1 Introduction

In this chapter, we will discuss trip distribution as the second step of FSM. We estimated trip generation and attractions for a study area from the first step of FSM. In the second step, we use these results as input for the trip distribution estimations. The outputs from the second step are also called O/D pairs (Tij) and show us the number of trips between zone  I  (origin) and zone  J (destination) (Levine, 2010). In other words, trip distribution translates the findings of the first step into an extensive matrix of origins and destinations in terms of TAZs. It identifies each pair’s travel impedance (such as travel time or cost). Figure 11.1 shows what the input and outputs of this step of the model are, using impedance functions.

trip attractions and productions for each trip purposes that becomes trip distribution (matrix) for each trip purpose.

The main question this step tries to answer is what portion of trips produced in or attracted to a zone would go to each of the other zones?

In terms of methodology, we use several basic methods for the trip distribution step, such as the gravity model, growth factor models, and intervening opportunities. However, the gravity model is the most common one, based on the rationales described in this chapter.

Before delving into the methods for estimating trip distribution, it is essential to explore some of the basic and principal components of trip distribution steps. We must note that these components are independent of the framework or the methodology, and we must prepare them regardless of the method we adopt for trip distribution estimation.

As mentioned, trip distribution is the second step of FSM, through which we appropriate trip productions to all other zones. The results would form a matrix presenting the number of intrazonal and interzonal trips in a single table (Lima et al., 2011).

The level of attractiveness of a zone depends on several factors (Cesario, 1973):

  • Uniqueness : This factor indicates how unique a service or employment center is and thus attracts more trips regardless of distance.
  • Distance:  the distance between two zones plays an impedance role, meaning that the further the two zones are from each other, the fewer trips will be distributed between them.
  • Closeness  to other services: we assume that wherever is more approximate to other attractive services will attract more trips within an urban area.
  • Urban or rural area : We assume that whether the zone is urban or rural, the attraction rate for the zone would be different when controlling for other factors.

In addition to the attractiveness factors of the destination, the emissivity of the origin is also a determining factor, usually represented by population, employment, or income (Cesario, 1973).

With a general understanding of the factors affecting trip distribution from origin and destination, we can now proceed with an introduction to methodology.

11.2 Gravity Model

As we discussed, the most common method appropriate for trip distribution is the gravity model. Gravity models are easy to understand yet very accurate, and they can also accommodate different factors such as population, employment, socio-demographics, and transportation systems. That said, almost all U.S. Departments of Transportation widely use gravity models. In contrast, the growth factor model needs additional data about trip distribution in the base year and an estimate of the number of future trips in each zone, which is only sometimes available (Meyer, 2016).

The foundation of this model is that the number of trips between two zones is directly related to the total number of trip attractions in the destination and is inversely proportional to a function cost represented by travel time or cost needed to travel between two zones  (Council, 2006). The formula gets its name from Newton’s law of gravity, which states that the attractiveness between two bodies is related to their mass (positively) and also to the distance between them (negatively) (Verlinde, 2011). In our case, the masses are trip generation and attraction and the time distance traveled or travel cost. While using the gravity model is straightforward, the major challenge is finding the best value for the impedance factor. This value is very contextual and varies in different conditions.

Equation (1) shows the fundamental equation of trip distribution:

Trips between TAZ1 and TAZ2=Trips prodduced in TAZ1*(Attractiveness of TAZ2 /Attractiveness of all TAZs   (1)

As equation (1) shows, the total trips between zones are equal to the products of the trips produced in a zone, a ratio of the attractiveness of the destination zone, and the total attractiveness of all zones. We can represent the gravity model in several different ways. Remodifying equation (1), the gravity model can be rewritten as:

Trips ij =Productions i *(Attractions j *FF ij *k ij /∑Attractions j *FF ij *k ij )    (2)

Where Trips ij is the number of trips between zone i and zone j , Prouctionsi is trip production in zone i , Attractionsj is total trips attracted to zone j , FF ij is the friction factor (travel impedance) between i and j , and K ij are the socio-economic factors of zones i and j . These values will be elaborated later in this chapter.

From the above equations, the mathematical format of gravity model can be seen in equation (3):

T_ij=P_i\ [(A_j\ F_ij\ K_ij)/(\sum_l\ A_j\ F_ij\ K_ij\ )]

T ij = number of trips that are produced in zone i and attracted to zone j

P i = total number of trips produced in zone i

A j = number of trips attracted to zone j

F ij = a value which is an inverse function of travel time

K ij = socioeconomic adjustment factor for interchange ij

As you know, we determine Pi and Aj values through the trip generation process. As shown in Chapter 10, the sum of all productions and attractions should be equal (PE, 2017). Numerous studies have indicated that people value their travel time differently for different trip purposes (like work trips vs. recreational trips) (Hansen, 1962; Allen, 1984; Thill & Kim, 2005). Accordingly, it is rational to compute the gravity model for each trip purpose with different friction factors (Meyer, 2016).

11.2.1 Friction Factor

The friction factor or impedance factor is a value that can be varied for different trip purposes because, with the FSM model, we assume that travel behavior depends on trip purpose. Impedance captures the elements of the spatial separation of two zones, represented as travel time or travel cost. Friction factors can be estimated using different measures. A simple measure of friction factor is the travel time between the zones. Another method is to adopt an exponential formula in which the friction factor is 1/exp(m × tij). Gamma distributions  with scaling factors can also be employed to estimate distribution (Cambridge Systematics, 2010; Meyer, 2016).

The impedance factor reflects the difficulty of traveling between two zones. The friction factor is higher for easier accessibility between two zones and would be zero if no individual is willing to travel between two zones. In the process of friction factor estimation, there is also a calibration step. For calibration, trip generation and attraction values are distributed between O-D pairs using the gravity model. Next, we compare the number of trips with a particular amount of time to the results of the O-D survey (observed data). If the numbers do not match, we must perform the calibration to adjust the friction factor. In the case of using travel time for the impedance factor, we can represent the relationship between the friction factor and time in the form t­­­­­­­-1, t–2, e– t (Ashford & Covault, 1969). We estimate the friction factor for the entire analysis area. However, such an assumption is limiting in that impedances like travel costs or time can affect different households differently. For instance, in a city that implements toll on a specific highway, low-income people may be unable to allocate their limited resources to pay the price. On the other hand, those able and willing to pay will use the road. We can also specify friction factors for different trip purposes. The figure below (Figure 11.2) shows the function of the friction factor appropriate to the time and for different trip purposes.

This figure shows the curve of impedance function calibrated for each trip purpose.

In very general terms, a friction factor F ij that is an inverse function of travel impedance W ij is used in trip distribution to plug in the travelers’ willingness to travel between zone i and zone j .

F ij =1/W ij

11.2.2 K-Factor

In travel demand modeling, several socio-economic factors influence travel behavior and demand for different purposes. As shown in Chapter 10, the most cardinal factors in travel demand modeling can be income, auto ownership, multimodal system availability, age, or job type (Pan et al., 2020). The k-factor developed and plugged into the gravity model represents variation in socio-economic factors and helps adjust interzonal trips accordingly. For example, a  blue-collar employee  working in a low-income suburb may exhibit different travel behaviors (in terms of mode choice and frequency of travel) compared to a  white-collar employee  working in the CBD with a higher payment. The K-factor is determined and plugged into the gravity formula to accommodate such differences.

Some neighborhoods offer housing and jobs to workers of a certain income level. People who work in chain restaurants have completely different incomes than those who work in headquarters in the CBD. In a country like the U.S., these groups are likely to live far from each other. Also, as we discussed, people of different income levels or social statuses may respond differently to travel impedances like travel time or cost. We can determine K values in the calibration process by comparing the estimated results and observed data for the base year (Tawfik & Rakha, 2012). K numeric value will be above one if the socio-economic factors contribute to more travel and below one if otherwise (Meyer, 2016). Figure 11.3 shows the mean number of trips for different age groups (K-factor) and various trip purposes. Accordingly, calculating friction factors and K-factors for different purposes and socio-economic groups yields a better fit to the data.

number of trips by age for 4 trip purposes (work, shopping, family and social) for three years (1990, 2001, 2009).

11.2.3 Example 1

Let a small area have three zones (TAZs). Table 11.1 shows the trip generation results for each zone, and Table 11.2 shows the travel time for each pair of zones. Additionally, the friction factor is also given in this example as a function of travel time in Table 11.3. The intrazonal travel time for zone 1 is larger than those of most other inter-zone times because of the geographical characteristics of the zone and lack of access within the area. Using this information, please calculate the number of trips for each pair of zones.

For calculating trip distribution between these three zones, we use the trip generation and attraction table computed in the first step of the FSM model as input data and then use the gravity model for calculation. Table 11.1, 11.2, and 11.3  represent the trip generated and attracted for each zone, travel time between each pair of zones, and friction factor derived from the travel time.

Now with this information, we can start the calculation process. First, we have to estimate the attractiveness of each zone using the equation (1)

For example, for zone 1 we have:

Attractiveness1= 210*26=5460

Attractiveness2= 210*35=7350

Attractiveness3= 350*35=12250

Now, we use the pivotal formula of the gravity model (equation 2). Accordingly, we have (K-factor set to 1):

T_{1-1}=220\times\frac{210\times26}{(210\times26) (270\times41) (350\times52)}

The result of the calculation is summarized in Table 11.4:

However, our calculations’ results do not match the already existing and observed data. The mentioned mismatch is why calibration and balancing of the matrix are needed. In other words, we must perform more than one iteration of the model to generate more accurate results. For performing a double or triple iteration, we use a formula discussed at the end of this chapter (example adapted from: Garber & Hoel, 2018).

11.3 Growth Factor Model

After successfully calibrating and validating the data we have estimated, we can also apply the gravity model to forecast future travel behavior or travel pattern in our study area. Using the change in land-use data, socioeconomic data, or any other changes in the whole system, we can predict future trip distributions. We can calculate Trip distribution from the O-D table for either base or forecasting year when the friction factor and K-factor data are unavailable or unsatisfactorily calibrated. Depending on historical trends and data, growth factor models are limited if an observed O-D table is unavailable. Similar to the trip generation step, growth factor models cannot incorporate updated travel time as the change in travel time between zones can highly affect travel patterns (Qsim, 2016).

One of the most common mathematical formulas of the growth factor model is the Fratar method, shown in equation (4). Through his method, the future distribution of trips from one zone is equal to the present distribution multiplied by the growth factor of the destination zone between now and the forecasting year (Heanue & Pyers, 1966). The formula to calculate future trip values is shown in equation (4):

T_{ij}=\left(t_iG_i\right)\frac{t_{ij}G_j}{\sum_{x}\hairsp\hairsp t_{ix}G_x}

T ij =number of trips estimated from zone  to zone t i  =present trip generation in zone G x =growth factor of zone T i  =future trip generation in zone t ix =number of trips between zone  and other zones t ij =present trips between zone  and zone G j =growth factor of zone

The following section will discuss an example illustrating the application of the Fratar method.

11.3.1 Example 2

In this example, let our study area consist of four TAZs, and Table 11.5 is showing the current trip distributions. Assuming that the growth rate for each TAZ is as Table 11.6 shows, calculate the number of trips between each two TAZs in the future year.

For this problem, we should use the Fratar Method. During this method, it is also very important that we will have two estimations for each pair. These values should be averaged, and the new value would be the final T ij .

Based on the formula, calculations are as follows:

T_ij =(t_i G_i ) (t_ij G_j)/(∑_x t_ix G_x )

Based on the calculations, the first iteration of the method will yield the following table:

As table (11.7) illustrates, we estimate the future trip rates between zones using the Fratar formula. However, the problem is that the estimated total number of trips generated in each zone is not equal to the actual trip generation. Thus, a second iteration is needed here. In the second iteration, the new O-D matrix serves as the input, based on which we calculate new growth ratios. We expect a trip generation to occur in five years, and the trip generation is estimated in the preceding calculation.As an exercise, please conduct as many iterations as needed to bring the estimated and actual trip generations into a close alignment.  

11.3.2 Example 3

In a hypothetical area, we are interested to know how many trips from a university campus that generated about 2,000 trips per day are attracted by three different shopping malls at various distances from the campus. In Figure 11.4, the hypothetical area, the number of trips generated by campus, and the total number of trips attracted for each zone are presented:

This figure shows the trip generator and the three possible destination with their travel time.

  •  socioeconomic adj. Factor K=1.0
  •  Calibration factor C=2.0

As the first step, we need to calculate the friction factor for each pair of zones based on travel time (t). Given is the following formula with which we calculate friction factor:

F 1j =t ij ^(-2)

Next, using the friction factor, we use the gravity model to calculate the relative attractiveness of each zone. In Table 11.8 , you can see how calculations are being carried out for each zone.

Next, with having relative attractiveness of each zone (or probability of attracting trips), we plug in the trip generation rate for the campus (6,000) to finally estimate the number of trips attracted from the campus to each zone. Figure 11.5 shows the final results.

This figure shows the results of example 3 graphically.

11.4 Model Calibration and Validation

Model validation is an integral part of all simulation and modeling procedures. One of the most essential steps in FSM modeling is developing a procedure to calibrate its final outputs (predictions) with actual and observed data. To do this, usually, model parameters are adjusted so that the observed data and estimations have fewer mismatches (Meyer, 2016). After such adjustments, the model with calibrated parameters can help in simulation and future scenario analyses.

After completing the trip distribution step, we compare model calibration and adjustment results in each category (i.e., by trip purpose) with recorded real-world trips from the O-D survey. If the two values are not identical, we reassign model parameters like FF or K-factors, and re-run the gravity model. The process continues until the observed data and estimations are very close (ratio between 0.9 and 1.1).

The following example shows a process of the trip distribution step with calibration.

11.4.1 Example 4

In this example, we elaborate the procedure of calibration. The starting point in this procedure is to identify the inputs of the model, which are the outcomes of the trip generation process. The results from the surveys and actual trip data, travel time between each pair of zones (friction factor), and socioeconomic conditions between each pair are shown in the following tables (11.10, 11.11, and 11.12 ).

For the friction factor, as we discussed in previous sections, there are several formulas such as  negative exponential or inverse power function that can be used for calculating friction factors from the impeding factors like travel cost or time.

In the next step, we use the gravity formula and plug in the inputs for estimating the number of trips between each pair of zones. Table 11.14 shows the relative attractiveness of each zone, and Table 11.15 shows the number of trips between each pair of zones.

Now, by looking to the last table we can see that the total number of trips produced is exactly matching to the results of the trip generation table. But, the total attractions and actual data have a mismatch. In the next step, we apply the calibration methods in order to make our final results more accurate.

In the first iteration of calibration, we have to generate a value called column factor, which is the result of dividing actual data attraction by estimated attractions. Then we apply this number for each pair in the same column.

In Table 11.15, we can observe that the sum of attractions is now the same as the actual data, but the sum of generation amounts is now different from actual data generation. In this step, we perform another iteration, the same as the first iteration but instead of column factor, we plug in row factor value, which is the result of dividing actual data trip generation by estimated generation.

The third iteration is needed because the sum of attraction is different with the actual data once more, and we have to generate another column factor. The results are shown in Table 11.17.

Based on the third iteration results, we see the attractions are now accurate and trip generations have very insignificant differences with actual data. At this point, we can stop the calibration. However, the procedure can continue to calibrate results to decrease the difference as much as possible. The sensitivity of the calibration, or in other words, the threshold for the row and column factors, can be adjusted by the modeler.

  • Uniqueness is a quantity defined for a TAZ that indicates how unique that zone or trip attraction center is.

Gamma distribution is a probability distribution that is used for converting travel times into impedance functions

Blue-collar employee is a worker who usually performs manual and low-skill duties for their work.

White-collar employee is a worker who is high-skill and performs professional, or administrative work.

Key Takeaways

In this chapter, we covered:

  • What trip distribution is and the factors that determine attractiveness of zones for travel demand.
  • Different modeling frameworks appropriate for trip distribution and their mathematical formulation.
  • What advantages and disadvantages of different methods and assumptions in trip distribution are.
  • How to perform a trip distribution manually using simplified transportation networks.

Allen, B. (1984). Trip distribution using composite impedance. Transportation Research Record , 944 , 118–127.

Seggerman, KE. (2010). Increasing the integration of TDM into the land use and development process. Fairfax County (Virginia) Department of Transportation, May. Department of Transportation.

Cesario, F. J. (1973). A generalized trip distribution model. Regional Science Journal , 13 (2), 1973-08

Council, A. T. (2006). National guidelines for transport system management in Australia 2006 .  Australia Transportation Council. https://www.atap.gov.au/sites/default/files/National_Guidelines_Volume_1.pdf

Garber, N. J., & Hoel, L. A. (2018).  Traffic and highway engineering . Cengage Learning.

Hansen, W. G. (1962). Evaluation of gravity model trip distribution procedures . Highway Research Board Bulletin, 347 . https://onlinepubs.trb.org/Onlinepubs/hrbbulletin/347/347-007.pdf

Ned Levine (2015).  CrimeStat : A spatial statistics program for the analysis of crime incident locations (v 4.02). Ned Levine & Associates, Houston, Texas, and the National Institute of Justice, Washington, D.C. August.

Lima & Associates. (2011). Lincoln travel demand model . Lincoln Metropolitan Planning Organization. (2011). https://www.lincoln.ne.gov/files/sharedassets/public/planning/mpo/projects-amp-reports/tdm11.pdf

Meyer, M. D. (2016). Transportation planning handbook . John Wiley & Sons.

NHI. (2005). Introduction to Urban Travel Demand Forecasting . In National Highway Administration (Ed.), Introduction to Urban Travel Demand Forecasting. American University. . National Highway Institute : Search for Courses (dot.gov)

Pan, Q., Jin, Z., & Liu, X. (2020). Measuring the effects of job competition and matching on employment accessibility. Transportation Research Part D: Transport and Environment , 87 , 102535. https://doi.org/10.1016/j.trd.2020.102535

PE Lindeburg, M. R. (2017). PPI FE civil review – A comprehensive FE civil review manual (First edition). PPI, a Kaplan Company.

Qasim, G. (2015). Travel demand modeling: AL-Amarah city as a case study . [Unpublished Doctoral dissertation , the Engineering College University of Baghdad]

Tawfik, A. M., & Rakha, H. A. (2012). Human aspects of route choice behavior: Incorporating perceptions, learning trends, latent classes, and personality traits in the modeling of driver heterogeneity in route choice behavior . Virginia Tech Transportation Institute . Blacksburg, Virginia   https://vtechworks.lib.vt.edu/handle/10919/55070

Thill, J.-C., & Kim, M. (2005). Trip making, induced travel demand, and accessibility. Journal of Geographical Systems , 7 (2), 229–248. https://doi.org/10.1007/s10109-005-0158-3

Verlinde, E. (2011). On the origin of gravity and the laws of Newton. Journal of High Energy Physics , 2011 (4), 1–27. https://link.springer.com/content/pdf/10.1007/JHEP04(2011)029.pdf

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1 1.1 Background In 1978, the Transportation Research Board (TRB) published NCHRP Report 187: Quick-Response Urban Travel Estimation Techniques and Transferable Parameters (Sosslau et al., 1978). This report described default parameters, factors, and manual techniques for doing planning analysis. The report and its default data were used widely by the transportation planning profession for almost 20 years. In 1998, drawing on several newer data sources, including the 1990 Census and Nation- wide Personal Transportation Survey, an update to NCHRP Report 187 was published in the form of NCHRP Report 365: Travel Estimation Techniques for Urban Planning (Martin and McGuckin, 1998). Since NCHRP Report 365 was published, significant changes have occurred affecting the complexity, scope, and context of transportation planning. Transportation planning tools have evolved and proliferated, enabling improved and more flexible analyses to support decisions. The demands on trans- portation planning have expanded into special populations and broader issues (e.g., safety, congestion, pricing, air quality, environment, climate change, and freight). In addition, the default data and parameters in NCHRP Report 365 need to be updated to reflect the planning requirements of today and the next 10 years. The objective of this report is to revise and update NCHRP Report 365 to reflect current travel characteristics and to pro- vide guidance on travel demand forecasting procedures and their application for solving common transportation problems. It is written for “modeling practitioners,” who are the public agency and private-sector planners with responsibility for devel- oping, overseeing the development of, evaluating, validating, and implementing travel demand models. This updated report includes the optional use of default parameters and appropriate references to other more sophisticated techniques. The report is intended to allow practitioners to use travel demand fore- casting methods to address the full range of transportation planning issues (e.g., environmental, air quality, freight, multimodal, and other critical concerns). One of the features of this report is the provision of trans- ferable parameters for use when locally specific data are not available for use in model estimation. The parameters pre- sented in this report are also useful to practitioners who are modeling urban areas that have local data but wish to check the reasonableness of model parameters estimated from such data. Additionally, key travel measures, such as average travel times by trip purpose, are provided for use in checking model results. Both the transferable parameters and the travel measures come from two main sources: the 2009 National Household Travel Survey (NHTS) and a database of model documentation for 69 metropolitan planning organizations (MPOs) assembled for the development of this report. There are two primary ways in which planners can make use of this information: 1. Using transferable parameters in the development of travel model components when local data suitable for model development are insufficient or unavailable; and 2. Checking the reasonableness of model outputs. This report is written at a time of exciting change in the field of travel demand forecasting. The four-step modeling process that has been the paradigm for decades is no longer the only approach used in urban area modeling. Tour- and activity-based models have been and are being developed in several urban areas, including a sizable percentage of the largest areas in the United States. This change has the potential to significantly improve the accuracy and analytical capability of travel demand models. At the same time, the four-step process will continue to be used for many years, especially in the smaller- and medium- sized urban areas for which this report will remain a valuable resource. With that in mind, this report provides information on parameters and modeling techniques consistent with the C h a p t e r 1 Introduction

2four-step process and Chapter 4, which contains the key information on parameters and techniques, is organized con- sistent with the four-step approach. Chapter 6 of this report presents information relevant to advanced modeling practices, including activity-based models and traffic simulation. This report is organized as follows: • Chapter 1—Introduction; • Chapter 2—Planning Applications Context; • Chapter 3—Data Needed for Modeling; • Chapter 4—Model Components: – Vehicle Availability, – Trip Generation, – Trip Distribution, – External Travel, – Mode Choice, – Automobile Occupancy, – Time-of-Day, – Freight/Truck Modeling, – Highway Assignment, and – Transit Assignment; • Chapter 5—Model Validation and Reasonableness Checking; • Chapter 6—Emerging Modeling Practices; and • Chapter 7—Case Studies. This report is not intended to be a comprehensive primer for persons developing a travel model. For more complete information on model development, readers may wish to consult the following sources: • “Introduction to Urban Travel Demand Forecasting” (Federal Highway Administration, 2008); • “Introduction to Travel Demand Forecasting Self- Instructional CD-ROM” (Federal Highway Administra- tion, 2002); • NCHRP Report 365: Travel Estimation Techniques for Urban Planning (Martin and McGuckin, 1998); • An Introduction to Urban Travel Demand Forecasting— A Self-Instructional Text (Federal Highway Administration and Urban Mass Transit Administration, 1977); • FSUTMS Comprehensive Modeling Online Training Workshop (http://www.fsutmsonline.net/online_training/ index.html#w1l3e3); and • Modeling Transport (Ortuzar and Willumsen, 2001). 1.2 Travel Demand Forecasting: Trends and Issues While there are other methods used to estimate travel demand in urban areas, travel demand forecasting and mod- eling remain important tools in the analysis of transportation plans, projects, and policies. Modeling results are useful to those making transportation decisions (and analysts assisting in the decision-making process) in system and facility design and operations and to those developing transportation policy. NCHRP Report 365 (Martin and McGuckin, 1998) pro- vides a brief history of travel demand forecasting through its publication year of 1998; notably, the evolution of the use of models from the evaluation of long-range plans and major transportation investments to a variety of ongoing, every- day transportation planning analyses. Since the publication of NCHRP Report 365, several areas have experienced rapid advances in travel modeling: • The four-step modeling process has seen a number of enhancements. These include the more widespread incor- poration of time-of-day modeling into what had been a process for modeling entire average weekdays; common use of supplementary model steps, such as vehicle availability models; the inclusion of nonmotorized travel in models; and enhancements to procedures for the four main model components (e.g., the use of logit destination choice models for trip distribution). • Data collection techniques have advanced, particularly in the use of new technology such as global positioning systems (GPS) as well as improvements to procedures for performing household travel and transit rider surveys and traffic counts. • A new generation of travel demand modeling software has been developed, which not only takes advantage of modern computing environments but also includes, to various degrees, integration with geographic information systems (GIS). • There has been an increased use of integrated land use- transportation models, in contrast to the use of static land use allocation models. • Tour- and activity-based modeling has been introduced and implemented. • Increasingly, travel demand models have been more directly integrated with traffic simulation models. Most travel demand modeling software vendors have developed traffic simulation packages. At the same time, new transportation planning require- ments have contributed to a number of new uses for models, including: • The analysis of a variety of road pricing options, including toll roads, high-occupancy toll (HOT) lanes, cordon pricing, and congestion pricing that varies by time of day; • The Federal Transit Administration’s (FTA’s) user benefits measure for the Section 5309 New Starts program of transit projects, which has led to an increased awareness of model properties that can inadvertently affect ridership forecasts;

3 • The evaluation of alternative land use patterns and their effects on travel demand; and • The need to evaluate (1) the impacts of climate change on transportation supply and demand, (2) the effects of travel on climate and the environment, and (3) energy and air quality impacts. These types of analyses are in addition to several traditional types of analyses for which travel models are still regularly used: • Development of long-range transportation plans; • Highway and transit project evaluation; • Air quality conformity (recently including greenhouse gas emissions analysis); and • Site impact studies for developments. 1.3 Overview of the Four-Step Travel Modeling Process The methods presented in this report follow the conven- tional sequential process for estimating transportation demand that is often called the “four-step” process: • Step 1—Trip Generation (discussed in Section 4.4), • Step 2—Trip Distribution (discussed in Section 4.5), • Step 3—Mode Choice (discussed in Section 4.7), and • Step 4—Assignment (discussed in Sections 4.11 and 4.12). There are other components commonly included in the four-step process, as shown in Figure 1.1 and described in the following paragraphs. The serial nature of the process is not meant to imply that the decisions made by travelers are actually made sequentially rather than simultaneously, nor that the decisions are made in exactly the order implied by the four-step process. For example, the decision of the destination for the trip may follow or be made simultaneously with the choice of mode. Nor is the four-step process meant to imply that the decisions for each trip are made independently of the decisions for other trips. For example, the choice of a mode for a given trip may depend on the choice of mode in the preceding trip. In four-step travel models, the unit of travel is the “trip,” defined as a person or vehicle traveling from an origin to a destination with no intermediate stops. Since people traveling for different reasons behave differently, four-step models segment trips by trip purpose. The number and definition of trip purposes in a model depend on the types of information the model needs to provide for planning analyses, the char- acteristics of the region being modeled, and the availability of data with which to obtain model parameters and the inputs to the model. The minimum number of trip purposes in most models is three: home-based work, home-based nonwork, and nonhome based. In this report, these three trip purposes are referred to as the “classic three” purposes. The purpose of trip generation is to estimate the num- ber of trips of each type that begin or end in each location, based on the amount of activity in an analysis area. In most models, trips are aggregated to a specific unit of geography (e.g., a traffic analysis zone). The estimated number of daily trips will be in the flow unit that is used by the model, which is usually one of the following: vehicle trips; person trips in motorized modes (auto and transit); or person trips by all modes, including both motorized and nonmotorized (walking, bicycling) modes. Trip generation models require some explanatory variables that are related to trip-making behavior and some functions that estimate the number of trips based on these explanatory variables. Typical variables include the number of households classified by characteristics such as number of persons, number of workers, vehicle availability, income level, and employment by type. The output of trip generation is trip productions and attractions by traffic analysis zone and by purpose. Trip distribution addresses the question of how many trips travel between units of geography (e.g., traffic analysis zones). In effect, it links the trip productions and attractions from the trip generation step. Trip distribution requires explanatory variables that are related to the cost (including time) of travel between zones, as well as the amount of trip-making activity in both the origin zone and the destination zone. The outputs of trip distribution are production-attraction zonal trip tables by purpose. Models of external travel estimate the trips that originate or are destined outside the model’s geographic region (the model area). These models include elements of trip generation and distribution, and so the outputs are trip tables represent- ing external travel. Mode choice is the third step in the four-step process. In this step, the trips in the tables output by the trip distri- bution step are split into trips by travel mode. The mode definitions vary depending on the types of transportation options offered in the model’s geographic region and the types of planning analyses required, but they can be generally grouped into auto mobile, transit, and nonmotorized modes. Transit modes may be defined by access mode (walk, auto) and/or by service type (local bus, express bus, heavy rail, light rail, commuter rail, etc.). Nonmotorized modes, which are not yet included in some models, especially in smaller urban areas, include walking and bicycling. Auto modes are often defined by occupancy levels (drive alone, shared ride with two occupants, etc.). When auto modes are not modeled separately, automobile occupancy factors are used to convert the auto person trips to vehicle trips prior to assignment. The outputs of the mode choice process include person trip tables by mode and purpose and auto vehicle trip tables.

4Time-of-day modeling is used to divide the daily trips into trips for various time periods, such as morning and afternoon peak periods, mid-day, and evening. This division may occur at any point between trip generation and trip assignment. Most four-step models that include the time-of-day step use fixed factors applied to daily trips by purpose, although more sophisticated time-of-day choice models are sometimes used. While the four-step process focuses on personal travel, commercial vehicle/freight travel is a significant component of travel in most urban areas and must also be considered in the model. While simple factoring methods applied to per- sonal travel trip tables are sometimes used, a better approach is to model such travel separately, creating truck/commercial vehicle trip tables. The final step in the four-step process is trip assignment. This step consists of separate highway and transit assignment processes. The highway assignment process routes vehicle trips from the origin-destination trip tables onto paths along Forecast Year Highway Network Forecast Year Transit Network Forecast Year Socioeconomic DataTrip Generation Model Internal Productions and Attractions by Purpose Trip Distribution Model Mode Choice Model Person and Vehicle Trip Tables by Purpose/Time Period Time of Day Model Person and Vehicle Trip Tables by Mode/Purpose/Time Period Highway Assignment CHECK: Input and output times consistent? Transit Assignment Highway Volumes/ Times by Time Period Transit Volumes/ Times by Time Period Input Data Model Output Model Component Decision Feedback Loop Yes No Truck Trip Generation and Distribution Models Production/Attraction Person Trip Tables by Purpose Truck Vehicle Trip Tables by Purpose Truck Time of Day Model Truck Vehicle Trip Tables by Time Period External Trip Generation and Distribution Models External Vehicle Trip Tables by Time Period Figure 1.1. Four-step modeling process.

5 the highway network, resulting in traffic volumes on network links by time of day and, perhaps, vehicle type. Speed and travel time estimates, which reflect the levels of congestion indicated by link volumes, are also output. The transit assignment process routes trips from the transit trip tables onto individual transit routes and links, resulting in transit line volumes and station/ stop boardings and alightings. Because of the simplification associated with and the resul- tant error introduced by the sequential process, there is some- times “feedback” introduced into the process, as indicated by the upward arrows in Figure 1.1 (Travel Model Improvement Program, 2009). Feedback of travel times is often required, particularly in congested areas (usually these are larger urban areas), where the levels of congestion, especially for forecast scenarios, may be unknown at the beginning of the process. An iterative process using output travel times is used to rerun the input steps until a convergence is reached between input and output times. Because simple iteration (using travel time outputs from one iteration directly as inputs into the next iteration) may not converge quickly (or at all), averaging of results among iterations is often employed. Alternative approaches include the method of successive averages, constant weights applied to each iteration, and the Evans algorithm (Evans, 1976). Although there are a few different methods for implement- ing the iterative feedback process, they do not employ param- eters that are transferable, and so feedback methods are not discussed in this report. However, analysts should be aware that many of the analysis procedures discussed in the report that use travel times as inputs (for example, trip distribution and mode choice) are affected by changes in travel times that may result from the use of feedback methods. 1.4 Summary of Techniques and Parameters Chapter 4 presents information on (1) the analytical tech- niques used in the various components of conventional travel demand models and (2) parameters for these mod- els obtained from typical models around the United States and from the 2009 NHTS. These parameters can be used by analysts for urban areas without sufficient local data to use in estimating model parameters and for areas that have already developed model parameters for reasonableness checking. While it is preferable to use model parameters that are based on local data, this may be impossible due to data or other resource limitations. In such cases, it is common practice to transfer parameters from other applicable models or data sets. Chapter 4 presents parameters that may be used in these cases, along with information about how these parameters can be used, and their limitations. 1.5 Model Validation and Reasonableness Checking Another important use of the information in this report will be for model validation and reasonableness checking. There are other recent sources for information on how the general process of model validation can be done. Chapter 5 provides basic guidance on model validation and reasonable- ness checking, with a specific focus on how to use the informa- tion in the report, particularly the information in Chapter 4. It is not intended to duplicate other reference material on validation but, rather, provide an overview on validation consistent with the other sources. 1.6 Advanced Travel Analysis Procedures The techniques and parameters discussed in this report focus on conventional modeling procedures (the four-step process). However, there have been many recent advances in travel modeling methods, and some urban areas, especially larger areas, have started to use more advanced approaches to modeling. Chapter 6 introduces concepts of advanced model- ing procedures, such as activity-based models, dynamic traffic assignment models, and traffic simulation models. It is not intended to provide comprehensive documentation of these advanced models but rather to describe how they work and how they differ from the conventional models discussed in the rest of the report. 1.7 Case Study Applications One of the valuable features in NCHRP Report 365 was the inclusion of a case study to illustrate the application of the parameters and techniques contained in it. In this report, two case studies are presented to illustrate the use of the information in two contexts: one for a smaller urban area and one for a larger urban area with a multimodal travel model. These case studies are presented in Chapter 7. 1.8 Glossary of Terms Used in This Report MPO—Metropolitan Planning Organization, the federally designated entity for transportation planning in an urban area. In most areas, the MPO is responsible for maintaining and running the travel model, although in some places, other agencies, such as the state department of transportation, may have that responsibility. In this report, the term “MPO” is sometimes used to refer to the agency responsible for the model, although it is recognized that, in some areas, this agency is not officially the MPO.

6Model area—The area covered by the travel demand model being referred to. Often, but not always, this is the area under the jurisdiction of the MPO. The boundary of the model area is referred to as the cordon. Trips that cross the cordon are called external trips; modeling of external trips is discussed in Section 4.6. Person trip—A one-way trip made by a person by any mode from an origin to a destination, usually assumed to be without stops. In many models, person trips are the units used in all model steps through mode choice. Person trips are the usual units in transit assignment, but person trips are converted to vehicle trips for highway assignment. Trip attraction—In four-step models, the trip end of a home-based trip that occurs at the nonhome location, or the destination end of a nonhome-based trip. Trip production—In four-step models, the trip end of a home-based trip that occurs at the home, or the origin end of a nonhome-based trip. Vehicle trip—A trip made by a motorized vehicle from an origin to a destination, usually assumed to be without stops. It may be associated with a more-than-one-person trip (for example, in a carpool). Vehicle trips are the usual units in highway assignment, sometimes categorized by the number of passengers per vehicle. In some models, vehicle trips are used as the units of travel throughout the modeling process. Motorized and nonmotorized trips—Motorized trips are the subset of person trips that are made by auto or transit, as opposed to walking or bicycling trips, which are referred to as nonmotorized trips. In-vehicle time—The total time on a person trip that is spent in a vehicle. For auto trips, this is the time spent in the auto and does not include walk access/egress time. For transit trips, this is the time spent in the transit vehicle and does not include walk access/egress time, wait time, or time spent transferring between vehicles. Usually, transit auto access/ egress time is considered in-vehicle time. Out-of-vehicle time—The total time on a person trip that is not spent in a vehicle. For auto trips, this is usually the walk access/egress time. For transit trips, this is the walk access/ egress time, wait time, and time spent transferring between vehicles. In some models, components of out-of-vehicle time are considered separately, while in others, a single out-of- vehicle time variable is used.

TRB’s National Cooperative Highway Research Program (NCHRP) Report 716: Travel Demand Forecasting: Parameters and Techniques provides guidelines on travel demand forecasting procedures and their application for helping to solve common transportation problems.

The report presents a range of approaches that are designed to allow users to determine the level of detail and sophistication in selecting modeling and analysis techniques based on their situations. The report addresses techniques, optional use of default parameters, and includes references to other more sophisticated techniques.

Errata: Table C.4, Coefficients for Four U.S. Logit Vehicle Availability Models in the print and electronic versions of the publications of NCHRP Report 716 should be replaced with the revised Table C.4 .

NCHRP Report 716 is an update to NCHRP Report 365 : Travel Estimation Techniques for Urban Planning .

In January 2014 TRB released NCHRP Report 735 : Long-Distance and Rural Travel Transferable Parameters for Statewide Travel Forecasting Models , which supplements NCHRP Report 716.

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https://nap.nationalacademies.org/catalog/27432/critical-issues-in-transportation-for-2024-and-beyond

TRID the TRIS and ITRD database

Trip Generation Updates: Pass-by and Diverted Trips

The "Trip Generation Handbook, Second Edition: An ITE Recommended Practice" was published by the Institute of Transportation Engineers (ITE) in June 2004. The purpose of the handbook was to “provide instruction and guidance in the proper use of data presented in Trip Generation, and to provide information on supplemental issues of importance in estimating trip generation for development sites.” In 2012, ITE established an expert review panel to relook at the Handbook’s Second Edition and determine the appropriate content, format and organization for the Third Edition. This paper describes the recommended revisions for Chapter 5, Pass-by, Primary and Diverted Linked Trips of the handbook. These revisions included updating definitions, graphics, tables and example problems. In addition, a greater emphasis was put on the proper use of diverted trips.

  • Record URL: http://www.ite.org/library/conference/compendium13/pdf/CB13C1002.pdf

trip engineering definition

  • Abstract reprinted with permission from the Institute of Transportation Engineers.

Institute of Transportation Engineers (ITE)

  • Tripi, Eric J
  • ITE Technical Conference and Exhibit
  • Location: San Diego CA
  • Date: 2013-3-3 to 2013-3-6
  • Publication Date: 2013
  • Media Type: Digital/other
  • Features: Figures; Tables;
  • Pagination: 7p
  • Monograph Title: ITE 2013 Technical Conference and Exhibit

Subject/Index Terms

  • TRT Terms: Data collection ; Data quality ; Handbooks ; Traffic data ; Trip generation ; Trip purpose
  • Identifier Terms: Trip Generation Handbook, Second Edition: An ITE Recommended Practice
  • Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

  • Accession Number: 01492065
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Sep 3 2013 12:30PM

2 crew members die during ‘incident’ on Holland America cruise ship

  • Associated Press

FORT LAUDERDALE, Fla. — Two crew members on a Holland America cruise ship died during an “incident” in the ship’s engineering space, the cruise line said.

The unidentified crew members died Friday while the Florida-based Nieuw Amsterdam was at Half Moon Cay in the Bahamas, Holland America said in a statement.

Authorities were notified and the cause of the accident is being investigated, the cruise line said. Crew members were being offered counseling.

“All of us at Holland America Line are deeply saddened by this incident and our thoughts and prayers are with our team members’ families at this difficult time,” the statement said. “The safety, security and welfare of all guests and crew are the company’s absolute priority.”

The cruise line did not offer any further details about the crew members, nor which agency was handling the investigation. The ship set sail out of Fort Lauderdale on March 16 for a seven-night trip.

The Associated Press is an independent, not-for-profit news cooperative headquartered in New York City. Our teams in over 100 countries tell the world's stories, from breaking news to investigative reporting.

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Kwik Trip bought 151 acres in Dane County for a new distribution center, near future Buc-ee's

trip engineering definition

Wisconsin's fan-favorite gas station and convenience store chain Kwik Trip is expanding again.

Earlier this week, Kwik Trip completed a transaction to purchase 151 acres of empty farmland in the village of DeForest in northern Dane County, DeForest village administrator Bill Chang said. The land, in the southwest quadrant of the intersection between Highways 51 and 19, was purchased for $14.1 million, the Wisconsin State Journal reported .

Sixty acres will be developed into a satellite distribution center and fleet services center for Kwik Trip, Chang said. These facilities will serve about 350 Kwik Trip stores in southern Wisconsin. The distribution center and the fleet services center are each expected to bring 200 jobs to DeForest, Chang said, for a total of 400.

"We're excited to see such a strong company come to DeForest," he continued.

The remaining 91 acres will be set aside for future development, Chang added. The DeForest distribution center will be Kwik Trip's first outside of La Crosse , where the company is headquartered. Currently, more than 80% of the products sold in Kwik Trip stores are supplied by the company's La Crosse-based food production and distribution operations.

Chang said groundbreaking on the DeForest site will likely occur this spring or summer, but the project won't be complete for at least a year and a half. The village will have a lot to do to prepare for the distribution center's arrival.

"There will be improvements that will be necessary along the highways to ensure that (Kwik Trip's) traffic is addressed ... including intersection improvements and widening of the local road leading to the site," Chang said. "In the site itself, there will need to be an internal road network."

When reached for comment, Kwik Trip said it's "still doing (its) due diligence on the property," and does not have any specific details to share at this time.

DeForest's future Kwik Trip distribution center will be less than 10 minutes from Buc-ee's

Kwik Trip isn't the only hugely popular gas station and convenience store chain making a mark on DeForest in the coming years. The Texas-based chain Buc-ee's plans to build one of Wisconsin's largest gas stations in the village, northwest of the Interstate 39/90/94 and Highway V interchange.

Though the Kwik Trip distribution center site is less than 10 minutes from the proposed Buc-ee's site, Chang said neither business's expansion plans will be affected by the other.

Kwik Trip continues to expand throughout Wisconsin

The DeForest project is part of a $151 million business expansion Kwik Trip announced late last year that is expected to create more than 500 jobs by 2027.

In La Crosse, this has included a completed expansion of Kwik Trip's burrito production line, a new 40-foot production line for Dunkers and cake doughnuts, a cooler expansion in the dairy and additional packaging automation in the sweets bakery.

Kwik Trip is also renovating an office building in Onalaska, which will house training, accounting and operations support, including a 24/7 helpline for the stores.

Kwik Trip operates over 800 stores in the Midwest, including more than 500 in Wisconsin. It plans to continue adding about 50 new stores every year.

Journal Sentinel reporter Karl Ebert contributed to this report.

More: How a DIY business strategy led to Kwik Trip's explosive growth. 11.5 million people visit the stores every week.

More: Self-checkout stations are coming to Kwik Trips across Wisconsin

Baltimore bridge collapse wasn't first major accident for giant container ship Dali

Propulsion failed on the cargo ship that struck the Francis Key Bridge in Baltimore early Tuesday as it was leaving port, causing it to collapse into the frigid Patapsco River. Its crew warned Maryland officials of a possible collision because they had lost control.

“The vessel notified MD Department of Transportation (MDOT) that they had lost control of the vessel” and a collision with the bridge “was possible,” according to an unclassified Department of Homeland Security report. “The vessel struck the bridge causing a complete collapse.”

An official speaking on condition of anonymity confirmed to USA TODAY that the DHS’ Cybersecurity and Infrastructure Security Agency is working with federal, state, and local officials “to understand the potential impacts of this morning’s collapse of the Francis Scott Key Bridge.”

Clay Diamond, executive director, American Pilots’ Association, told USA TODAY power issues are not unusual on cargo ships, which are so large they cannot easily course correct.

“It’s likely that virtually every pilot in the country has experienced a power loss of some kind (but) it generally is momentary,” Diamond said. “This was a complete blackout of all the power on the ship, so that’s unusual. Of course this happened at the worst possible location.” 

The ship in Tuesday's crash, Dali, was involved in at least one prior accident when it collided with a shipping pier in Belgium.

That 2016 incident occurred as the Dali was leaving port in Antwerp and struck a loading pier made of stone, causing damage to the ship’s stern, according to VesselFinder.com, a site that tracks ships across the world. An investigation determined a mistake made by the ship’s master and pilot was to blame.

No one was injured in that crash, although the ship required repair and a full inspection before being returned to service. The pier – or berth – was also seriously damaged and had to be closed.

VesselFinder reports that the Dali was chartered by Maersk, the same company chartering it during the Baltimore harbor incident.

The 9-year-old container ship had passed previous inspections during its time at sea, but during one such inspection in June at the Port of San Antonio in Chile, officials discovered a deficiency with its "propulsion and auxiliary machinery (gauges, thermometers, etc)," according to the Tokyo MOU, an intergovernmental maritime authority in the Asia-Pacific region.

The report provided no other information about the deficiency except to note that it was not serious enough to remove the ship from service.

Follow here for live updates: Baltimore's Key Bridge collapses after ship strike; construction crew missing: Live Updates

Why did Dali crash into the Baltimore bridge?

Officials said Tuesday they’re investigating the collision, including whether systems on board lost electricity early Tuesday morning, which could be related to mechanical failure, according to a U.S. official who was not authorized to speak publicly.

Accidents at sea, known as marine casualties, are not uncommon, the source told USA TODAY. However, “allisions,” in which a moving object strikes a stationary one with catastrophic results, are far less common. The investigation of the power loss aboard the Dali, a Singapore-flagged vessel, will be a high priority.

In a video posted to social media, lights on the Dali shut off, then turned back on, then shut off again before the ship struck a support pier on the bridge.

Numerous cargo and cruise ships have lost power over the years.

The International Convention for the Safety of Life at Sea requires all international vessels to have two independent sources of electricity, both of which should be able to maintain the ship's seaworthiness on their own, according to a safety study about power failures on ships , citing the International Convention for the Safety of Life at Sea.

The Dali's emergency generator was likely responsible for the lights coming back on after the initial blackout, Diamond said.

“There was still some steerage left when they initially lost power,” he said. “We’ve been told the ship never recovered propulsion. The emergency generator is a diesel itself – so if you light off the generator, that’s also going to put off a puff of exhaust.”

Under maritime law, all foreign flagged vessels must be piloted into state ports by a state licensed pilot so the Dali's pilot is licensed by Association of Maryland Pilots .

Diamond described the incident based on information from the Maryland agency that licensed the pilot aboard the ship. His organization represents that group and all other state piloting agencies in the US.

“The pilot was directing navigation of the ship as it happened,” he said. “He asked the captain to get the engines back online. They weren’t able to do that, so the pilot took all the action he could. He tried to steer, to keep the ship in the channel. He also dropped the ship’s anchor to slow the ship and guide the direction.

“Neither one was enough. The ship never did regain its engine power.”

How big is the Dali ship?

The Dali is a 984-foot container vessel built in 2015 by Hyundai Heavy Industries in South Korea. With a cruising speed of about 22 knots – roughly 25 mph. It has traveled the world carrying goods from port to port.

The ship, constructed of high-strength steel, has one engine and one propeller, according to MarineTraffic.com.

The Dali arrived in Baltimore on Sunday from the Port of Norfolk in Virginia. Before that, it had been in New York and came through the Panama Canal.

It remains at the scene of the collapse as authorities investigate.

Who owns and operates the Dali?

It is owned by the Singapore-based Grace Ocean Pte Ltd but managed by Synergy Marine Group, also based in Singapore. It was carrying Maersk customers’ cargo, according to a statement from the shipping company.

“We are deeply concerned by this incident and are closely monitoring the situation,” Maersk said in the statement. 

Synergy, which describes itself as a leading ship manager with more than 600 vessels under its guidance, issued a statement on its website acknowledging the incident and reporting no injuries among its crew and no pollution in the water. There were two pilots on board and 22 crew members in all, according to Synergy, all of them from India.

USA TODAY reached out to Synergy on Tuesday, but the company did not immediately return a call seeking comment.

Contributing: Josh Susong

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National Security Adviser Makes Covert Trip to Kyiv

Jake Sullivan met with President Volodymyr Zelensky of Ukraine and his senior officials as additional U.S. aid continued to languish in the House.

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Jake Sullivan, in a black suit and a red tie, speaks into a microphone while seated in front of American and Ukrainian flags. Next to him is Andriy Yermak, the head of the presidential office of Ukraine, who is wearing green military-style clothes.

By Zolan Kanno-Youngs

Reporting from Phoenix

President Biden’s top national security official made a secret trip to Kyiv on Wednesday, as Ukrainian soldiers holding off Russian troops are running out of munitions and U.S. aid remains stalled in congressional gridlock.

Jake Sullivan, the national security adviser, met with President Volodymyr Zelensky of Ukraine and his senior officials “to reaffirm the United States’ unwavering commitment to Ukraine in its self-defense against Russia’s brutal invasion,” said a national security spokeswoman, Adrienne Watson. “He stressed the urgent need for the U.S. House of Representatives to pass the national security supplemental to meet Ukraine’s critical battlefield needs.”

The covert trip showed the rising sense of urgency in the White House to pressure Congress to pass billions of dollars of aid for Ukraine, a financial package that the Biden administration says the country needs to defend itself against Russia.

The White House has tried, so far unsuccessfully, to push House Republicans to support a $60 billion emergency spending plan for weapons for Ukraine and to bolster armament production in the United States.

With that funding held back and future U.S. aid in limbo, the administration last week sent Ukraine a $300 million package that included air defense interceptors, artillery rounds, armor systems and an older version of the Army’s longer-range missile systems known as ATACMS. But that package is most likely going to hold off Russia for only a matter of weeks, U.S. officials have said.

“Ukrainian troops have fought bravely, are fighting bravely throughout this war,” Mr. Sullivan said when the package was announced, “but they are now forced to ration their ammunition under pressure on multiple fronts.”

Mr. Sullivan’s visit came one day after Defense Secretary Lloyd J. Austin III met with other backers of Ukraine in Germany to strategize on how to maintain military support for Kyiv.

“Ukraine’s battle remains one of the great causes of our time,” Mr. Austin said.

Zolan Kanno-Youngs is a White House correspondent, covering President Biden and his administration. More about Zolan Kanno-Youngs

Our Coverage of the War in Ukraine

News and Analysis

Russian missiles streaked into Kyiv  in the biggest assault on the Ukrainian capital in weeks, injuring several people and damaging several buildings.

Jake Sullivan, President Biden’s top national security official, made a secret trip to Kyiv to meet with President Volodymyr Zelensky and reaffirm the United States’ unwavering commitment to Ukraine.

Under pressure to come up with billions of dollars to support Ukraine’s military, the E.U. said that it had devised a legal way to use frozen Russian assets  to help arm Ukraine.

Symbolism or Strategy?: Ukrainians say that defending places with little strategic value is worth the cost in casualties and weapons , because the attacking Russians pay an even higher price. American officials aren’t so sure.

Elaborate Tales: As the Ukraine war grinds on, the Kremlin has created increasingly complex fabrications online  to discredit Ukraine’s leader, Volodymyr Zelensky, and undermine the country’s support in the West.

Targeting Russia’s Oil Industry: With its army short of ammunition and troops to break the deadlock on the battlefield, Kyiv has increasingly taken the fight beyond the Ukrainian border, attacking oil infrastructure deep in Russian territory .

How We Verify Our Reporting

Our team of visual journalists analyzes satellite images, photographs , videos and radio transmissions  to independently confirm troop movements and other details.

We monitor and authenticate reports on social media, corroborating these with eyewitness accounts and interviews. Read more about our reporting efforts .

COMMENTS

  1. 3.4: Trip Generation

    3.4: Trip Generation. Trip Generation is the first step in the conventional four-step transportation forecasting process (followed by Destination Choice, Mode Choice, and Route Choice), widely used for forecasting travel demands. It predicts the number of trips originating in or destined for a particular traffic analysis zone.

  2. How to Determine Trip Generation Types

    Pass-By and Diverted Number of Trips. Use either local data or ITE data to determine a percentage of the reduced trip generation that is pass-by or diverted. Similar to the ITE Trip Generation data, both pass-by and diverted trip percentages are available by average rate or an equation for many land uses. Use this percentage to calculate the ...

  3. What is Trip Generation

    Trip Generation - Yarger Engineering, Inc. 317-475-1100 Email Us. ... There are software packages that can do this, but Yarger Engineering, Inc. uses a combination of spreadsheets to avoid the "black box" results that can come from a travel demand model and to be able to do detailed modifications when necessary. The result is a forecast ...

  4. Chapter 10: First Step of Four Step Modeling (Trip Generation

    Trip: A trip is defined as a one-way person movement by a mechanized mode of transport. A trio generally has two trip ends. ... Seminar and research methods in civil engineering research program, University of Philippines Diliman. doi: 10.13140/2.1.2171.7126. Ben-Akiva, M.E., Bowman, J.L. (1998). Activity based travel demand model systems.

  5. Fundamentals of Transportation/Trip Generation

    Trip Generation is the first step in the conventional four-step transportation forecasting process (followed by Destination Choice, Mode Choice, and Route Choice), widely used for forecasting travel demands.It predicts the number of trips originating in or destined for a particular traffic analysis zone. Every trip has two ends, and we need to know where both of them are. The first part is ...

  6. Trip and Parking Generation

    Trip and Parking Generation. Click here to access information on Trip Generation. Parking Generation, 6th Edition - October 2023. The ITE Parking Generation Manual, 6th Edition is an educational tool for transportation professionals, zoning boards and others who are interested in estimating parking demand of a proposed development.The Parking Generation web app—ITEParkGen allows electronic ...

  7. Trip generation

    Trip generation is the first step in the conventional four-step transportation forecasting process used for forecasting travel demands. It predicts the number of trips originating in or destined for a particular traffic analysis zone (TAZ). Trip generation analysis focuses on residences and residential trip generation is thought of as a function of the social and economic attributes of households.

  8. Other Resources

    The recommended procedure for estimating internal trip capture and trip generation for a mixed-use development is a series of nine steps: Step 1: Determine whether methodology is appropriate for study site. Step 2: Estimate person trip generation for individual on-site land uses. Step 3: Estimate proximity between on-site land use pairs.

  9. Trip Generation Manual, 9th Edition, Volumes 1, 2 and 3

    With this edition, "Trip Generation Handbook, 2nd Edition: An ITE Recommended Practice" (Publication Number RP-028B) is being included as part of Volume 1. Volumes 2 and 3, Data, provide information that is based on trip generation studies submitted voluntarily to ITE by public agencies, developers, consulting firms and associations.

  10. Trip Generation Handbook, Second Edition: An ITE Recommended Practice

    This handbook has two purposes. The first one is to provide instruction and guidance in the proper use of data presented in Trip Generation, 7th Edition. The second is to provide additional information and data on supplemental issues of importance in estimating trip generation for development sites. Chapter 2 provides guidance in the selection ...

  11. TRIP GENERATION. FIFTH EDITION

    TRIP GENERATION. FIFTH EDITION. The purpose of this report is to provide a single document on trip generation for all land uses for which data have been provided to the Institute of Transportation Engineers. This fifth edition contains considerably more data than previous editions. Data from more than 1,000 new studies have been added to the ...

  12. Trip Generation Analysis

    Virtually all of the trip attraction models use employment and an identifier of location as independent variables. (p. 110) Early trip generation models were commonly developed by regression analysis because of its power and simplicity. The independent variables in such models were usually zonal averages of the various factors of influence.

  13. Trip generation in Transport Planning

    Trip generation is estimated in three ways: (i) traditionally by linear and multiple regression. (ii) by aggregating the trip generating capability of a household or car and aggregating the total according to the distribution of each selected category in the zones, and. (iii) by household classification method through a catalogue of the ...

  14. 3.6: 3-6 Route Choice

    Link Performance Function. The cost that a driver imposes on others is called the marginal cost. However, when making decisions, a driver only faces his own cost (the average cost) and ignores any costs imposed on others (the marginal cost). AverageCost = ST Q. A v e r a g e C o s t = S T Q (3.6.1) MarginalCost = δST δQ.

  15. Introduction to Transportation Modeling: Travel Demand Modeling and

    Initial development models for trip generation, distribution, and diversion emerged in the 1950s, leading to the application of the four-step travel demand modeling (FSM) approach in a transportation study in the Chicago area. This model was primarily highway-oriented, aiming to compare new facility development and improved traffic engineering.

  16. Chapter 11: Second Step of Four Step Modeling (Trip Distribution

    11 Chapter 11: Second Step of Four Step Modeling (Trip Distribution) . Abstract. This chapter describes the second step of the four-step travel demand modeling or trip distribution. It focuses on the procedure that distributes the trips generated from or attracted to each zone in the study area.

  17. Traffic Engineering

    Traffic Engineering. Traffic Engineering is the subdiscipline of transportation engineering that addresses the planning, design and operation of streets and highways, their networks, adjacent land uses and interaction with other modes of transportation and their terminals. ITE provides a wide variety of tools and training materials that address ...

  18. Chapter 1

    Since people traveling for different reasons behave differently, four-step models segment trips by trip purpose. The number and definition of trip purposes in a model depend on the types of information the model needs to provide for planning analyses, the char- acteristics of the region being modeled, and the availability of data with which to ...

  19. Round-trip engineering

    Round-trip engineering (RTE) in the context of model-driven architecture is a functionality of software development tools that synchronizes two or more related software artifacts, such as, source code, models, configuration files, documentation, etc. between each other. The need for round-trip engineering arises when the same information is present in multiple artifacts and when an ...

  20. What does trip mean?

    Nov 7, 2012. #2. Trip generally refers to an unplanned, uncontrolled, "emergency" shutdown of a machine or process or piece of equipment. For example, if an excessive high vibration is detected on a piece of rotating equipment, it's customary to "trip" that piece of equipment by immediately interrupting or shutting off the flow of energy to the ...

  21. A Definition of Round-trip Engineering

    A detailed definition of Round-trip Engineering in which views are transparent is provided, to enable studies on the requirements on views, view transformations and view op erations and how these relate to each other. This paper provides a definition of Round-trip Engineering ( RE) and introduces RE-systems. Where previous definitions of RE are abstract in the sense that they treat views as ...

  22. Trip Generation Updates: Pass-by and Diverted Trips

    This paper describes the recommended revisions for Chapter 5, Pass-by, Primary and Diverted Linked Trips of the handbook. These revisions included updating definitions, graphics, tables and example problems. In addition, a greater emphasis was put on the proper use of diverted trips. Abstract reprinted with permission from the Institute of ...

  23. A Systematic Comparison of Roundtrip Software Engineering Approaches

    Sendall[14] states that "round-trip engineering is a challenging task that will become an important facilitator for many approaches to Model-Driven Software Development." Round-trip engineering involves the synchronization and maintenance of the model, allowing software engineers to move freely between different representations.

  24. 2 crew members die during 'incident' on Holland America cruise ship

    A Holland America cruise ship is shown in Victoria, Canada on Saturday, April 9, 2022. Two crew members on a Holland America cruise ship died during an "incident" in the ship's engineering ...

  25. New Kwik Trip distribution center planned for 151 acres in Dane County

    DeForest's future Kwik Trip distribution center will be less than 10 minutes from Buc-ee's. Kwik Trip isn't the only hugely popular gas station and convenience store chain making a mark on ...

  26. Dali ship that caused Baltimore bridge collapse was in prior accident

    The ship in Tuesday's crash, Dali, was involved in at least one prior accident when it collided with a shipping pier in Belgium. That 2016 incident occurred as the Dali was leaving port in Antwerp ...

  27. Jake Sullivan Makes Covert Trip to Ukraine

    The covert trip showed the rising sense of urgency in the White House to pressure Congress to pass billions of dollars of aid for Ukraine, a financial package that the Biden administration says ...