By Edward A. Beimborn
Center for UrbanTransportation Studies
University of Wisconsin-Milwaukee<
This document has been incorporated into a book "Inside the Blackbox, Making Transportation Models Work for Livable Communities" It is availible from the Citizens for a Better Environment at 414-271-7280 or the Environmental Defense Fund (publication 99215S) at 202-387-3500 (public information office)
This primer is intended to explain the urban transportation modeling process works, the assumptions made and the steps used to forecast travel demand for urban transportation planning. This is done in order to help to understand the process and its implications and to help people to interpret and comment on its results. The primer is intended for use by local or regional planning commissioners, elected officials and interested citizens who have to react to transportation plans.
Transportation planning uses the term 'models' extensively. This term is used to refer to a series of mathematical equations that are used to represent how choices are made when people travel. Travel demand occurs as a result of thousands of individual travelers making individual decisions on how, where and when to travel. These decisions are affected by many factors such as family situations, characteristics of the person making the trip, and the choices (destination, route and mode) available for the trip. Mathematical relationships are used to represent (model) human behavior in making these choices. Models require a series of assumptions in order to work and are limited by the data available to make forecasts. The coefficients and parameters in the model are set (calibrated) to match existing data. Normally, these relationships are assumed to be valid and to remain constant in the future.
Travel demand modeling was first developed in the late 1950's as a means to do highway planning. As the need to look at other problems such as transit, land use issues and air quality analysis arose, the modeling process has been modified to add additional techniques to deal with these problems.
Why are Models Important?
Models are important because transportation plans and investments are based on what the models say about future travel. Models are used to estimate the number of trips that will be made on a transportation systems alternative at some future date. These estimates are the basis for transportation plans and are used in major investment analysis, environmental impact statements and in setting priorities for investments. Models are based upon assumptions of the way in which travel occurs. A clear understanding of the modeling process is important to help to understand transportation plans and their recommendations.
Models provide forecasts only for those factors and alternatives which are explicitly included in the equations of the models. If the models are not sensitive to certain polices or programs (i.e. policy sensitive), the models will not show the effect these policies. This could lead to a conclusion that such polices are ineffective. This would be wrong because the models were not capable of testing the policy. For example, travel forecasting models usually exclude pedestrian and bicycle trips. Plans that include bicycle or pedestrian system improvements will not show any impact from the modeling procedure if the models ignore these types of trips. However, It would not be correct to conclude that pedestrian or bicycle improvements are ineffective. The actual impact is unknown. Therefore it is critical that the assumptions used in the modeling process and the model limitations be explicitly stated and considered before decisions are made.
This primer is divided into two sections, an overview of transportation planning in general - the context in which transportation models are used and a more specific description of the actual models used to forecast future travel. Since the basic purpose of transportation planning is to answer questions about future travel, the primer will use a question and answer format to explain how transportation planning takes place.
Transportation modeling is used to develop information to help make decisions on the future development and management of transportation systems, especially in urban areas. It is used as part of an overall transportation planning process which involves a forecast of travel patterns 15 to 25 years into the future and an attempt to develop a future transportation system that will work effectively in the future. Transportation has significant effects on land use, mobility, economic development, environmental quality, government finance and the quality of life. Wise planning is needed to help create high quality transportation services at a reasonable cost with minimal environmental impact. Failure to plan can lead to severe traffic congestion, dangerous travel patterns, undesirable land use patterns, adverse environmental impact and wasteful use of money and resources. Significant transportation projects require a long lead time for their design and construction.
What is the legal basis for transportation planning?
Transportation planning is required in the United States as a condition to receive federal transportation funds for larger urban areas. Requirements for urban transportation planning were first enacted in legislation passed on 1962. These have been expanded and modified in subsequent legislation, most recently through the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA) and the Transportation Efficiency Act (TEA-21). ISTEA specifically listed 15 factors that must be considered in urban transportation planning. These factors have led to planning regulations that require planning agencies to deal with air quality issues, multimodal planning, better management of existing systems, expanded public input and financial analysis requirements. Generally they have led to a greater role for transportation planning in urban areas, especially with a need to consider a wider range of alternatives and consequences of transportation investment choices.
What organizations are required for transportation planning?
Transportation planning is a cooperative effort between different units of local, state and federal government with opportunities for citizen input and participation. In areas of over 50,000 population an agency is designated as a metropolitan planning organization (MPO) to conduct a regional planning project. This is usually an agency such as a council of governments or a regional planning commission. In some cases, especially in smaller urban areas it could be a state department of transportation or a city orcounty government. The MPO works cooperatively with local governments and units of state government, such as the departments of transportation and natural resources, in preparing plans. State and local government might also engage in transportation planning for specific issues that relate to their jurisdictions. Citizen participation is an important part of the planning process. This should begin early and continue throughout the process. This process is used to help elected officials make decisions for the future development of their transportation systems.
In rural areas or smaller urban areas transportation planning still occurs but there is it is a simpler process. There is less emphasis on dealing with congestion as a problem and consequently not as much need for travel forecasting models. Transportation planning tends to be for highways only. Priorities for highway improvements are usually set to deal with specific safety problems or to rehabilitate segments with poor pavements. Sometimes economic development potential of new highways is used as a basis for their construction. Highways are seen by some as a means of attracting new employment to an area. While this sometimes occurs, it needs to be carefully looked at to determine if it is new employment of merely a shift in employment from one area to another (for example the shift of retail business from a town center to the edge of a community when a bypass is built). Transit service in rural areas and small town is usually policy based, i.e. the elected body of a community decides that service will be provided at a certain level to serve specific user groups.
Transportation planning is a complex process that involves a basic sequence of steps. Several can take place at once and it is not unusual to repeat some of the steps several times. Travel demand models are used in the forecasting step of the process as the means to predict how well alternative plans perform in meeting goals. The basic steps in the transportation planning process are the following:
- Problem definition: This step identifies the key transportation, socio-economic and land use issues and problems facing the community. This step may also involve definition of the size of an area to be studied, determination of the scope of the study and the establishment of a committee structure to oversee the planning process.
- Define goals, objectives and criteria: A consensus should be developed by elected officials and citizens about the future of the community and its transportation system. Goals are developed for the quality of transportation service, environmental impacts and costs. Some of these will likely be in conflict. A good planning effort will identify the trade- offs between these factors among alternatives in a clear, concise way to help make decisions. Along with goals it is important to identify more specific objectives and criteria which can be used to specifically measure how well alternative plans perform in meeting the more general goals.
- Data collection: Data must be compiled about the present status of the transportation system and its use. This could include traffic data, transit ridership statistics, census information and interviews of households about their travel patterns. Data are also gathered on land use, development trends, environmental factors, and financial resources. This will assist in problem definition and in developing methods to forecast future travel patterns. Good data are essential to the planning process. The statement 'garbage in/garbage out' applies in transportation planning. Without good data, the results of the planning process have little real meaning and can lead to the wrong projects selected and a wrong direction for the region.
- Forecasts (Modeling): Data from existing travel is used to make forecasts of future travel using travel demand models. This requires forecasts of future land use and economic conditions as well as understanding of how people make travel choices. Forecasting requires large amounts of data and is done under many assumptions. The basics assumptions and procedures used for travel demand forecasting are set out in section II of the primer.
- Develop alternatives: Forecasts are used to determine the performance of alternative future land use and transportation systems. Alternatives normally include different land use and transportation systems and mixtures of highway and transit services and facilities. Since land use affects travel and travel affects land use, both must be considered.
- Evaluation: Results of forecasts are used to compare the performance of alternatives in meeting goals, objectives and criteria. This information may be extensively discussed by interested citizens, elected officials, different government agencies and the private sector. Ultimately decisions are made by appropriate elected or appointed groups for future transportation projects.
- Implementation plan: Once decisions are made, plans should be further developed and refined for implementation. This may include more detailed analysis for design and evaluation following the same ar process as above.
Regional plans should be formally adopted by local and state governments to guide their programs. The regional plan then provides a basis for the preparation of a Transportation Improvement Program (TIP) which lists the specific projects to be implemented for the next three to six years. Once that a regional transportation plan is complete further efforts are needed to refine and implement the plan. Corridor studies are undertaken to refine the location of major new facilities. This may involve a major investment study (MIS) which would include an environmental impact statement (EIS). Other studies that are needed may be the development of transit operational plans, preliminary engineering studies, and jurisdictional studies. These studies may be done by the MPO or by state or local government depending on the problem to be addressed. Regional transportation plans should be updated periodically, at least once every five to ten years.
Models are used in a sequence of steps to answer a series of questions about future travel patterns. The basic questions asked and the modeling step they involve are as follows:
1. What will our community look like in the future?
Population, Economic and Land Use ModelsBefore forecasts are made
of travel, it is necessary to develop forecast of future population ,economic
activity and land use. Transportation planning is directly linked to land use
planning. Trips are assumed to follow future land use patterns. If land use is
changed, there will be a change in travel patterns.
How many people will there be? (population forecasts)Future
population forecasts are based on assumptions about birth rates, death rates and
the rate of migration into or out of the study area. Current information about
the ages of the population is used to forecast ahead by the calculation of the
number of births, deaths and migrants added or leaving the region in each year
of the future. These rates are assumed to remain constant or to change in a
specified way. These rates have changed substantially over the past 30 years so
often several forecasts are made under different growth rate assumptions.
Population forecasts can be made by the planning agency itself or they can use
forecasts done by others such as a state agency.
What activities will people engage in? (economic forecasts)
Forecasts need to be made of future employment levels as these are the basis for forecasts of travel to work, school, shopping, etc. Economic forecasts are done in conjunction with the population forecasts since the two are highly interrelated. Employment grows because the population is growing, but migration rates into and out of the community depend upon the growth of the economy. Assumptions have to be made of the ability of a region to generate new basic employment and to hold onto its existing basic employment. Basic employment is that which exports good and service outside of the region. It is different from the non-basic or local sector of the economy which circulates the money brought into a region by the basic sector. Total employment is found by applying an economic multiplier to basic employment.
Population and economic growth has to be distributed to different locations in order to do travel forecasts. It is necessary to know where people will live, work, shop and go to school in the future to estimate of future trip making. Future allocation of land use may be based on past trends, assumptions about changes in trends or through a negotiation process among local officials. Land use plans are be developed to change existing trends if is felt that current trends will not continue or are undesirable.
The first step in a land use planning process is to establish specific land use goals and associated land use rates. Goals need to be set concerning preservation of open space, wetlands and environmental corridors as well as land use mixes and densities. Quantities of land required for various uses are established to meet projections of population and employment. Alternative plans can be developed to reflect different goals, land use policies and assumptions. For example, land use plans could be developed to continue current trends, to reduce low density urban development, or to concentrate development along major corridors or in satellite communities. Different assumptions could be made regarding the extent to which environmentally sensitive areas and prime agricultural land will be protected. It is important to understand that land use and transportation are highly interrelated. The typical process uses the land use plan to determine the transportation plan. However, transportation has a major effect on land use and should be considered when a land use plan is developed.
Once the quantities of land needed are known for the future, it must be allocated to specific locations. A regional allocation is important since local communities often overestimate their growth. Individual community zoning often allocates far more commercial and industrial land use than may be necessary when looked at from a regional perspective. Land use allocation can be done either through a judgement technique or through a modeling process. The judgement technique involves the allocation of growth in steps to smaller and smaller geographic areas considering past trends, availability of open land for future potential development and local plans and zoning ordinances. It is sometimes done with the use of an expert panel that includes local planners, developers, financiers and real estate brokers. An allocation is made following rules and guidelines as established from the land use goals.
A modeling approach to land use allocation can be used to determine the impact of transportation facilities on growth patterns. The locations of basic employment are set by hand and the model locates other employment and residential land use in relationship to the basic employment. Allocations are determined based on the availability of open land and upon the accessibility that is provided from a proposed transportation plan. The modeling process finds a balance between supply and demand for both land use and transportation. As such it indicates how land use change is driven by changes in the transportation system. This can be helpful in that it could indicate undesirable trends and/or suggest policies to avoid them. This approach is relatively new and has only been used in limited locations.
Common limitations and issues: some of the common limitations which could be of concern in the land use planning process are the following.
1. No feedback with transportation plans. Common practice is that land use plans are developed before transportation plans and assumed to not change as a result of the transportation improvements. This is especially common in the preparation of environmental impact statements and highway location studies.
2. Current development is fixed. Land use plans generally only deal with new growth on vacant land and assume that current development will be unchanged. Effects of redevelopment programs, changing use of neighborhoods and so forth are normally not considered.
3. Mixed use benefits are not considered. Land use patterns that facilitate walking and non automobile travel are not easily dealt with in the modelling process and generally not considered.
Travel Demand Models
The travel forecasting process is at the heart of urban transportation planning. Travel forecasting models are used to project future traffic and are the basis for the determination of the need for new road capacity, transit service changes and changes in land use policies and patterns. Travel demand modeling involves a series of mathematical models that attempt to simulate human behavior while traveling. The models are done in a sequence of steps that answer a series of questions about traveler decisions. Attempts are made to simulate all choices that travelers make in response to a given system of highways, transit and policies. Many assumptions need to be made about how people make decisions, the factors they consider and how they react a particular transportation alternative.
The travel simulation process follows trips as they begin at a trip generation zone, move through a network of links and nodes and end at a trip attracting zone. The simulation process is known as the four step process for the four basic models used. These are: trip generation, trip distribution, mode split and traffic assignments. These models are used to answer a series of questions as explained in the remainder of the primer. In addition the process used to represent urban areas and the use of model results will also be described.
How is the city represented for computer analysis? (Zone/Network system)
Travel simulations require that an urban area be represented as a
series of small geographic areas called travel analysis zones (TAZs). Zones are
characterized by their population, employment and other factors and are the
places where trips begin (trip producers) or end (trip attractors). Trip making
is first estimated at the household level and then aggregated to the zone level.
Trip making is assumed to begin at the center of activity in a zone (zone
centroid). Trips that are very short, that begin and end in a single zone
(intrazonal trips) are usually not directly included in the forecasts. This
limits the analysis of pedestrian and bicycle trips in the typical travel demand
modeling process since they tend to be short trips.
Zones can be as small as a single block but typically are 1/4 to one mile square in area. A planning study can easily use 500-2000 zones. A large number of zones will increase forecast accuracy but will require more data and computer processing time. Zones tend to be small in areas of high population and larger in more rural areas. Internal zones are those within the study area while external zones are those outside of the study area. The study area should be large enough so that nearly all (over 90%) of the trips begin and end within the study area.
The highway system and transit systems are represented as networks for computer analysis. Networks consist of links to represent highways segments or transit lines and nodes to represent intersections and other points on the network. Data for links includes travel times on the link, average speeds, capacity, and direction. Node data includes information about intersections and the location of the node (coordinates).
How many Trips will there be? (trip generation)The first step in
travel forecasting is trip generation. In this step information from land use,
population and economic forecasts are used to estimate how many person trips
will be made to and from each zone. This is done separately by trip purpose.
Trip purposes that can be used include: home based work trips (work trips that
begin or end at home), home based shopping trips, home based other trips, school
trips, non-home based trips (trips that neither begin nor end at home), truck
trips and taxi trips. Trip generation uses trip rates that are averages for
large segment of the study area. Trip productions are based on household
characteristics such as the number of people in the household and the number of
vehicles available. For example, a household with four people and two vehicles
may be assumed to produce 3.00 work trips per day. Trips per household are then
expanded to trips per zone. Trip attractions are typically based on the level of
employment in a zone. For example a zone could be assumed to attract 1.32 home
based work trips for every person employed in that zone. Trip generation is used
to calculate person trips. These are later adjusted in the mode split/auto
occupancy step to determine vehicle trips.
Common limitations and issues: some of the common limitations which could be of concern in trip generation are the following.
1. Independent decisions. Travel behavior is a complex process where often decisions of one household member are dependent on others in the household. For example, child care needs may affect how and when people travel to work. This interdependency for trip making is not considered.
2. Limited trip purposes. With no more than four to eight trip purposes, a simplified trip pattern results. All shopping trips are treated the same whether shopping is done for groceries or lumber. Home based "other" trip purposes cover a wide variety of purposes - medical, visit friends, banking, etc. which are influenced by a wider variety of factors than those used in the modeling process.
3. Limited variables. Trip making is found as a function of only a few variables such as auto ownership, household size and employment. Other factors such as the quality of transit service, ease of walking or bicycling, fuel prices, land use design and so forth are not typically included.
4. Combinations of trips (trip chaining) are ignored. Travelers may often combine a variety of purposes into a sequence of trips as the run errands and link together activities. This is called trip chaining and is a complex process. The modeling process treats such trip combinations in a very limited way. For example, non home based trips are calculated based only employment characteristics of zones and do not consider how members of a household coordinate their errands.
5. Feedback, cause and effect problems. Trip generation models sometimes calculate trips as a function of factors that in turn could depend on how many trips there are. For example shopping trip attractions are found as a function of retail employment, but it could also be argued that the number of retail employees at a shopping center will depend on how many people come there to shop. This 'chicken and egg' problem comes up frequently in travel forecasts and is difficult to avoid. Another example is that trip making depends on auto availability, but it could be also argued that the number of automobiles a household owns would depend upon how active they are in making trips.
How do the trip ends connect together? (Trip distribution)Trip
generation only finds the number of trips that begin or end at a particular
zone. These trip ends are linked together to form an origin-destination pattern
of trips through the process of trip distribution. Trip distribution is used to
represent the process of destination choice, i.e. "I need to go shopping but
where should I go to meet my shopping needs?". Trip distribution leads to a
large increase in the amount of data which needs to be dealt with.
Origin-destination tables are very large. For example a 1200 zone study area
would have a 1,440,000 possible trip combinations in its O-D table. Separate
tables are also done for each trip purpose.
The most commonly used procedure for trip distribution is the 'gravity model'. The gravity model takes the trips produced at one zone and distributes to other zones based on the size of the other zones (as measured by their trip attractions) and on the basis of the distance to other zones. A zone with a large number of trip attractions will receive a greater number of distributed trips than one with a small number of trip attractions. Distance to possible destinations is the other factor used in the gravity model. The number of trips to a given destination decreases with the distance to the destination (it is inversely proportional). The distance effect is found through a calibration process which tries to lead to a distribution of trips from the model similar to that found from field data.
'Distance' can be measured several ways. The simplest way this is done is to use auto travel times between zones as the measurement of distance. Other ways might be to use a combination of auto travel time and cost as the measurement of distance. Still another way is to use a combination of transit and auto times and costs (composite cost). This method involves using multiplying auto travel times and costs by a percentage and transit time/cost another percentage to get a composite time and cost of both modes. Because of calculation procedures, the model must be iterated a number of times in order to balance the trip numbers to match the trip productions and attractions found in trip generation.
Common limitations and issues: some of the common limitations which could be of concern in trip distribution are as follows:
1. Constant trip lengths: In order for the model to be used as a forecasting tool it must be assumed that the average lengths of trips that occur now will remain constant in the future. Since trip lengths are measured by travel time this means that improvements in the transportation system that reduce travel times are assumed to be balanced by a further separation of origins and destinations.
2. Use of automobile travel times only to represent 'distance'. The gravity model requires a measurement of the distance between zones. This is almost always based on automobile travel times rather than transit travel times and leads to a wider distribution of trips (they are spread out over a wider radius of places) than if transit times were used. This process limits the ability to represent travel patterns of households that locate on a transit route and travel to points along that route. This may be particularly important if a rail transit system is being analyzed.
3. Limited effect of socio-economic-cultural factors. The gravity model distributes trips only on the basis of size of the trip ends (trip productions, trip attractions) and travel times between the trip ends. Thus the model would predict a large number of trips between a high income residential area and a nearby low income employment area or between a spanish speaking neighborhood and a nearly non-spanish speaking neighborhood. The actual distribution of trips is affected by the nature of the people and activities that are involved and their socio - economic and cultural characteristics as well as the size and distance factors used in the model. For example such factors as: differences in income, crime conditions, and attractiveness of the route are not considered. Furthermore, groups of travelers might avoid some areas of the city and favor others based on socio-economic-cultural reasons. Adjustments are sometimes made in the model to account for such factors, but it is difficult since the effects of such factors on travel is difficult to quantify much less to predict how it would change over time.
4. Feedback problems: Travel times are needed to calculate trip distribution, however travel times depend upon the level of congestion on streets in the network. The level of congestion is not known during the trip distribution step since that is found in a later calculation. Normally what is done is that travel times are assumed and checked later. If the assumed values differ from the actual values, the model should be iterated a number of times to get the inputs and outputs of the model to balance.
How will people travel? (mode choice/auto occupancy analysis)
Mode choice is one of the most critical parts of the travel demand modeling process. It is the step where trips between a given origin and destination are split into trips using transit, trips by car pool or as automobile passengers and trips by automobile drivers. Calculations are conducted that compare the attractiveness of travel by different modes to determine their relative usage. All proposals to improve public transit or to change the ease of using the automobile are passed through the mode split/auto occupancy process as part of their assessment and evaluation. It is important to understand what factors are used and how the process is conducted in order to plan, design and implement new systems of transportation.
The most commonly used process for mode split is to use the 'Logit' model. This involves a comparison of the "disutility" of travel between two points for the different modes that are available. Disutility is a term used to represent a combination of the travel time, cost and convenience of a mode between an origin and a destination. It is found by placing multipliers (weights) on these factors and adding them together. Travel time is divided into two components: in-vehicle time to represent the time when a traveler is actually in a vehicle and out-of-vehicle time which includes time spent traveling which occurs outside of the vehicle (time to walk to and from transit stops or parking places, waiting time, transfer time). Out-of-vehicle time is used to represent "convenience" and is typically multiplied by a factor of 2.0 to 7.0 to give it greater importance in the calculations. This is because travelers do not like to wait or walk long distances to their destinations. The size of the multiplier will be different depending upon the purpose of the trip. This is because it has been found that people tend to be more willing to wait or walk longer distances for work trips than for shopping trips.
Travel cost is multiplied by a factor to represent the value that travelers place on time savings for a particular trip purpose. For transit trips, the cost of the trip is given as the average transit fare for that trip while for auto trips cost is found by adding the parking cost to the length of the trip as multiplied by a cost per mile. Auto cost is based on a "perceived" cost per mile (on the order of 5-7 cents/mile) which only includes fuel and oil costs and does not include ownership, insurance, maintenance and other fixed costs (total costs of automobile travel are 25-40 cents per mile). Travelers have been found to only consider the costs that vary with an individual trip rather than all costs when making mode choice decisions.
Disutility calculations may also contain a "mode bias factor" which is used to represent other characteristics or travel modes which may influence the choice of mode (such as a difference in privacy and comfort between transit and automobiles). The mode bias factor is used as a constant in the analysis and is found by attempt to fit the model to actual travel behavior data. Generally, the disutility equations do not recognize differences within travel modes. For example, a bus system and a rail system with the same time and cost characteristics will have the same disutility values. There are no special factors that allow for the difference in attractiveness of alternative technologies.
Once disutilities are known for the various mode choices between an origin and a destination, the trips are split among various modes based on the relative differences between disutilities. The logit equation is used in this step. A large advantage in disutility will mean a high percentage for that mode. Mode splits are calculated to match splits found from actual traveler data. Sometimes a fixed percentage is used for the minimum transit use (percent captive users) to represent travelers who have no automobile available or are unable to use an automobile for their trip.
Automobile trips must be converted from person trips to vehicle trips with an auto occupancy model. Mode split and auto occupancy analysis can be two separate steps or can be combined into a single step, depending on how a forecasting process is set up. In the simplest application a highway/transit split is made first which is followed by a split of automobile trips into auto driver and auto passenger trips. More complex analysis splits trips into multiple categories (single occupant auto, two person car pool, 3-5 person car pool, van pool, local bus, express bus, etc.). Auto occupancy analysis is often a highly simplified process which uses fixed auto occupancy rates for a given trip purpose or for given household size and auto ownership categories. This means that the forecasts of car pooling are insensitive to changes in the cost of travel, the cost of parking, the presence of special programs to promote car pooling such as may occur as a result of the clean air act.
Common limitations and issues: some of the common limitations which could be of concern in mode split are as follows:
1. Mode choice is only affected by time and cost characteristics. An important thing to understand about mode choice analysis is that shifts mode usage would only be predicted to occur only if there are changes in the characteristics of the modes, i.e. there must be a change in the in-vehicle time, out-of-vehicle time or cost of the automobile or transit for the model to predict changes in demand. Thus if one substitutes a light rail transit system for a bus system without changes in travel times or costs from the bus system, the model would not show any difference in demand. People are assumed to make travel choices based only on the factors in the model, factors not in the model will have no effect on results predicted by the models.
2. Omitted factors. Factors which are not included in the model such as crime, safety, security, etc. concerns have no effect. They are assumed to be included as a result of the calibration process. However, if an alternative has different characteristics for some of the omitted factors, no change will be predicted by the model. Such effects need to be factored in by hand and require considerable skill and assumptions.
3. Access times are simplified. No consideration is given to the ease of walking in a community and the characteristics of a waiting facility in the choice process. Strategies to improve local access to transit or the quality of a place to wait do not have an effect on the models.
4. Constant weights. The importance of time cost and convenience is assumed to remain constant for a given trip purpose. Trip purpose categories are very broad (i.e. 'shop', 'other'). Differences in the importance of time and cost within these categories are ignored.
What routes will be used? (traffic assignment)
Once trips have been split into highway and transit trips, the specific path that they use to travel from their origin to their destination must be found. These trips are then assigned to that path in the step called traffic assignment. Traffic assignment is the most time consuming and data intensive step in the process and is done differently for highway trips and transit trips. The process first involves the calculation of the shortest path from each origin to all destinations (usually the minimum time path is used). Trips for each O-D pair are then assigned to the links in the minimum path and the trips are added up for each link. The assigned trip volume is then compared to the capacity of the link to see if it is congested. If a link is congested the speed on the link needs to be reduced to result in a longer travel time on that link. Changes in travel times means that the shortest path may change. Hence the whole process is repeated several times (iterated) until there is an equilibrium between travel demand and travel supply. Trips on congested links will be shifted to uncongested links until this equilibrium, condition occurs. Traffic assignment is the most complex calculation in the travel modeling sequence and there are a variety of ways in which it is done to keep computer time to a minimum.
Transit trip assignment is done in a similar way to auto trip assignment except that transit headways are adjusted rather than travel times. Transit headways (minutes between vehicles) affect the capacity of a transit route. Short headways mean more frequent service and a greater number of vehicles. Normally short headways are assumed initially. Trips are assigned to vehicles and if the vehicles have low ridership, headways are increased to provide fewer vehicles and higher ridership per trip. This process is repeated until transit supply and demand are in balance.
It is important to understand the concept of equilibrium. If a highway or transit route is congested during the peak hour, its excess trips will shift to other routes, to other destinations to other modes or to other times of day. Increases in capacity will cause shifts back to the facility to reach a new equilibrium point. Furthermore it may also lead to additional trip making in the form of 'induced' trips. These would be trips that did not take place before the facility was expanded. The new equilibrium may mean that the congestion is reestablished on the facility.
Considerations of time of day are also important. Traffic assignment is typically done for peak hour travel while forecasts of trips are done on a daily basis. A ratio of peak hour travel to daily travel is needed to convert daily trips to peak hour travel (for example it may be assumed that ten percent of travel occurs in the peak hour). Numbers used for this step are very important in that a small change in the values assumed will make a considerable difference in the level of congestion forecasted on a network. Normally the modeling process does not deal with how traffic congestion dissipates over time.
Common limitations and issues: some of the common limitations which could be of concern in traffic assignment are as follows:
1. Intersection delay is ignored. Most traffic assignment procedures assume that delay occurs on the links rather than at intersections. This is a good assumption for through roads and freeways but not for highways with extensive signalized intersections. Intersections involve highly complex movements and signal systems. They are highly simplified in traffic assignment and the assignment process does not modify control systems in reaching an equilibrium. Use of sophisticated traffic signal systems, freeway ramp meters or enhanced network control of traffic cannot be easily analyzed with conventional traffic assignment procedures.
2. Travel only occurs on the network. It is assumed that all trips begin and end at a single point in a zone (the centroids) and occurs only on the links included in the network. Not all roads streets are included in the network nor all possible trip beginning and end points included. The zone/network system is a simplification of reality and excludes some travel, especially shorter trips. To get total travel, say for air pollution analysis, a certain percentage of off network travel must be added to assignment results.
3. Capacities are simplified. To determine the capacity of roadways and transit systems requires a complex process of calculations that consider many factors. In most travel forecasts this is greatly simplified. Capacity is found based only on the number of lanes of a roadway and its type (freeway or arterial). Most travel demand models used for large transportation planning studies do not consider other factors such as truck movement, highway geometry and other factors affecting capacity in their calculations.
4. Time of day variations. Traffic varies considerably throughout the day and during the week. The travel demand forecasts are made on a daily basis for a typical weekday and then converted to peak hour conditions. Daily trips are multiplied by a "hour adjustment factor", for example 10%, to convert them to peak hour trips. The number assumed for this factor is very critical. A small variation, say plus or minus one percent, will make a large difference in the level of congestion that would be forecast on a network.
5. Emphasis on peak hour travel. As described above, forecasts are done for the peak hour on a typical weekday. A forecast for the peak hour of the day does not provide any information on what is happening the other 23 hours of the day. The duration of congestion beyond the peak hour, i.e. peak spreading, is not determined. In addition travel forecasts are made for a 'average weekday'. Variation in travel by time of year or day of the week are usually not considered.
What are the effects of the travel?
Equilibrium traffic assignment results indicate the amount of travel to be expected on each link in the network at some future date with a given transportation system. Levels of congestion, travel times, speed of travel and vehicle miles of travel i.e. VMT are direct outputs from the modeling process. Link traffic volumes are also used to determine other effects of travel for plan evaluation. Some of the key effects are accidents, and estimates of air pollution emissions. Each of these effects needs to be estimated through further calculations. Typically these are done by applying accident or emission rates by highway type and by speed. Assumptions need to be made of the speed characteristics of travel for non-peak hours of the day and for variation in travel by time of the year.
How can models be improved?
Transportation models are being called upon to provide forecasts for a complex set of problems that in some cases can go beyond their capabilities and original purpose. Travel demand management, employer based trip reduction programs, pedestrian and bicycle programs and land use polices may not be handled well in the process. Transportation travel forecasting models uses packaged computer programs which have limitations on how easily they can be changed. In some cases the models can be modified to accommodate additional factors or procedures (quick fix) while in other cases major modifications are needed or a new software is required. The following are some potential modifications of the models that may help to improve their usefulness.