Transportation forecasting

Transportation forecasting is the process of estimating the number of vehicles or travelers that will use a specific transportation facility in the future. A forecast estimates, for instance, the number of vehicles on a planned freeway or bridge, the ridership on a railway line, the number of passengers patronizing an airport, or the number of ships calling on a seaport. Traffic forecasting begins with the collection of data on current traffic. Together with data on population, employment, trip rates, travel costs, etc., traffic data are used to develop a traffic demand model. Feeding data on future population, employment, etc. into the model results in output for future traffic, typically estimated for each segment of the transportation infrastructure in question, e.g., each roadway segment or each railway station.

Traffic forecasts are used for several key purposes in transportation policy, planning, and engineering: to calculate the capacity of infrastructure, e.g., how many lanes a bridge should have; to estimate the financial and social viability of projects, e.g., using cost-benefit analysis and social impact analysis; and to calculate environmental impacts, e.g., air pollution and noise.

Four-step models

Within the rational planning framework, transportation forecasts have traditionally followed the sequential four-step model or urban transportation planning (UTP) procedure, first implemented on mainframe computers in the 1950s at the Detroit Area Transportation Study and Chicago Area Transportation Study (CATS).

Land use forecasting sets the stage for the process. Typically, forecasts are made for the region as a whole, e.g., of population growth. Such forecasts provide control totals for the local land use analysis. Typically, the region is divided into zones and by trend or regression analysis, the population and employment are determined for each.

The four steps of the classical urban transportation planning system model are:
* Trip generation determines the frequency of origins or destinations of trips in each zone by trip purpose, as a function of land uses and household demographics, and other socio-economic factors.
* Trip distribution matches origins with destinations, often using a gravity model function, equivalent to an entropy maximizing model. Older models include the fratar model.
* Mode choice computes the proportion of trips between each origin and destination that use a particular transportation mode. This model is often of the logit form, developed by Nobel Prize winner Daniel McFadden.
* Route assignment allocates trips between an origin and destination by a particular mode to a route. Often (for highway route assignment) Wardrop's principle of user equilibrium is applied (equivalent to a Nash equilibrium), wherein each traveler chooses the shortest (travel time) path, subject to every other driver doing the same. The difficulty is that travel times are a function of demand, while demand is a function of travel time, the so-called bi-level problem. Another approach is to use the Stackelberg competition model, where users ("followers") respond to the actions of a "leader", in this case for example a traffic manager. This leader anticipates on the response of the followers.

After the classical model, evaluative decision criteria are applied. A typical criterion is cost-benefit analysis. Such analysis might be applied after the network assignment model identifies needed capacity: is such capacity worthwhile? In addition to identifying the forecasting and decision steps as additional steps in the process, it is important to note that forecasting and decision-making permeate each step in the UTP process. Planning deals with the future, and it is forecasting dependent.

Activity-based models

An example of an activity-based approach is the New York Metropolitan Transportation Council's Best Practice Model. [ [ NYMTC BPM Model] ]

Precursor steps

Although not identified as steps in the UTP process, a lot of data gathering is involved in the UTP analysis process. Census and land use data are obtained, and there are home interview surveys. Home interview surveys, land use data, and special trip attraction surveys provide the information on which the UTP analysis tools are exercised.

Data collection, management, and processing; model estimation; and use of models to yield plans are much used techniques in the UTP process. In the early days, census data was augmented that with data collection methods that had been developed by the Bureau of Public Roads (a predecessor of the Federal Highway Administration): traffic counting procedures, cordon “where are you coming from and where are you going” counts, and home interview techniques. Protocols for coding networks and the notion of analysis or traffic zones emerged at the CATS.

Model estimation used existing techniques, and plans were developed using whatever models had been developed in a study. The main difference between today and yesterday is the development of some analytic resources specific to transportation planning, in addition to the BPR data acquisition techniques used in the early days.


The sequential and aggregate nature of transportation forecasting has come under much criticism. While improvements have been made, in particular giving an activity-base to travel demand, much remains to be done. In the 1990s, most federal investment in model research went to the Transims project at Los Alamos National Laboratory, giving physicists a crack at the problem. While the use of supercomputers and the detailed simulations may be an improvement on practice, they have yet to be shown to be better (more accurate) than conventional models. A commercial version was spun off to IBM, [ [ Transims ] ] and an open source version is also being actively maintained as TRANSIMS Open-Source. [ [ TRANSIMS Open-Source - Home ] ]


Accurate traffic forecasts are critical to arriving at the right capacity for transportation infrastructure, that is, for building infrastructure that is neither too large or too small to meet the demand. Accurate traffic forecasts are also critical to obtaining valid results from the cost-benefit analyses, environmental impact assessments, and social impact studies that typically form the basis for decisions on whether to build new transportation infrastructure or not.

To date there has been little research into this area. However, a peer-reviewed study of a large number of traffic forecasts found that a significant number of forecasts are inaccurate.cite web | url = | last = Flyvbjerg, Holm, and Buhl | title = Inaccuracy in Traffic Forecasts | publisher = Transport Reviews | work = Vol. 26, No. 1, 1–24 |date=January 2006 ] In particular:
* for nine out of ten railway projects the study found that passenger forecasts were overestimated, with an average overestimate of 106%,
* for half of all road projects, including bridges and tunnels, the study found that the difference between actual and forecast traffic was more than 20%, while for 25% of road projects the difference was more than 40%.

Measured over decades, a scheme can have such a large turnover that even a small percentage change in the projected traffic can indicate a significant positive or negative economic effect. For road schemes, the study noted that changes in land use and difficulties in estimating journeys are often blamed for these errors. Sensitivity analysis is normally carried out to understand the performance of the scheme if the parameters change; but subsequent official policy decisions can also have a major effect, such as changes to the area plan, discount rates and values of time. For rail schemes, inaccuracies are often blamed on uncertain measurement of passenger journeys and deliberately slanted forecasts by over-optimistic promoters.

See also

* Air traffic control
* Optimism bias
* Reference class forecasting
* Road traffic control



* [ Michael Meyer, Eric J Miller. Urban Transportation Planning, McGraw-Hill, 2nd edition, 2000.]
* Ascott, Elizabeth. 2006. Benefit Cost Analysis of Wonderworld Drive Overpass in San Marcos, Texas. Applied Research Project. Texas State University.

External links

* [ Transportation Systems Analysis Model] - TSAM is a nationwide transportation planning model to forecast intercity travel behavior in the United States.

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