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Transportation Modelling: Challenges & Solutions

What is transportation modelling? How do you create a transportation model and how are they used? What are the so-called “four steps” of transportation modelling? What is activity-or agent-based modelling? We answer the most important questions concerning the topic of transportation modelling.

Today, city and traffic planners must balance between various competing demands. The transition towards a more sustainable mobility ecosystem is more urgent than ever in order to meet challenges like climate change and growing urbanization and shape livable environments. At the same time, the demand for mobility and easily accessible means of transport is growing steadily. Everyone expects safe, accessible, fast, and comfortable transportation. Planners are therefore tasked with coming up with reliable transport solutions that are affordable, efficient, and equitable.

In transport planning and the development of advanced mobility systems, forecasting travel behavior and demand for travel plays a crucial role. Only if you can estimate how and where people will be traveling in the coming years, you can make the right decisions for a future mobility system. Traffic flow modelling and simulation enables planners to understand the current issues in their transportation system, identify opportunities and forecast and measure effects of development planning. It serves as the base to make sound decisions and set the right framework for the future of transportation.

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What is transportation modelling?

A transport model is a detailed digital replica of the complex real-world transport and land use system. It represents the numerous complex travel choices people make, their movement patterns and thus level of demand for travel, as well as the transport system network capacities.

Transportation modelling is not limited to car traffic, it’s multimodal. All modes of transport and their interactions can be modelled. This includes bicycles, pedestrians, public transport, new micro-mobility modes, and even air traffic. Transportation models are a kind of digital playground to assess the impact of different transportation and land use options and to identify how the transport system is likely to perform in the future. Transportation modelling is thus a powerful tool to for reliable urban and transport what-if analysis and scenario planning.

What is the purpose of a transport model?

Transport models are the foundation of transportation and traffic planning. Transportation systems involve many components and stakeholders, each with their own perspective and interests. Further, transportation is closely linked to many other aspects of society. Therefore, transportation planning is not usually about finding the ‘one optimal solution’, but considering a range of possible measures, policies and external conditions and then suggest suitable actions for political or commercial decision making.  This is called “what-if” analysis, or scenario analysis.

Transportation modelling tools enable the modelling experts to quickly develop different scenarios for a transport network and test them under a range of assumed future demographic or economic conditions.

The question of where people will live and work in the future and how and where they will travel is crucial for planning infrastructure and transport services and for creating a future-proven mobility system. Travel demand models represent all transport-relevant decision processes that make people move. Within a model, future scenarios for population growth, land use, transport networks and mobility behavior can be built to assess the impact of these changes. This enables planners to determine whether a new highway lane is needed, how the public transportation network should be expanded to meet demand, where locations for new bus terminals or logistics hubs should be sited, or how people's mobility behavior will change with autonomous vehicles.

Transportation modelling enables planners to

  • Develop advanced and future-proofed transport strategies and solutions.
  • Conduct traffic analyses and forecasts
  • Plan public transport services
  • Setting the framework to adapt to new mobility services such as autonomous driving

What are use cases of transport modelling?

Traffic models are used for a wide variety of applications. Here are some examples:

Transportation masterplans & Infrastructure planning

Cities and transportation agencies today face the challenge of creating a mobility infrastructure that satisfies all needs. Not only in terms of efficient movement of people and goods but also concerning planning goals such as safety and sustainability.  Transport modelling helps to plan and design new infrastructure while taking future developments into account and making them easily adaptable to changing demographic, economic or spatial conditions.

Transportation modelling supports:

  • Planning & design of new infrastructures
  • Long term development of transportation infrastructure according to demographic projections and land use development
  • Accessibility to different services and by various modes


Public Transport & Rail planning

How can the public transport network be expanded? Where does a new bus line make sense, where are new stops needed? Which frequency serves the demand and creates an attractive offer? Transport modelling provides a detailed representation of all modes of public transport such as bus, tram, underground, taxi, rail, and train. It allows planners to design reliable transit services which optimally serve passengers needs and allow efficient operations.

Transport models support:

  • Development of lines & timetables for future years (1, 5, 10 years)
  • Fleet planning (long term vehicle procurement), Vehicle allocation
  • Planning for operation of electric buses
  • Planning of services
  • Subnetwork tendering in Public Transit Agencies
  • Allocation of revenues & subsidies to operators by agency
  • Evaluation of fare structures
  • Analysis of passenger counts
  • Rider equity analysis


Development of transportation policies and regulations

Transportation models provide an important basis for defining framework conditions and regulations in transportation policy. For example, in the introduction of low emission zones or other traffic regulations, or as a basis for efficient traffic management.


New mobility planning

The future of mobility is gearing towards electric and autonomous vehicles.  In addition, ride and vehicle sharing are increasingly important. Transport planners must adapt to these new mobility services and make the necessary changes to serve their community’s needs. How can the charging infrastructure for e-mobility be strategically planned? What impact will autonomous vehicles have on traffic flow and road capacity? How can on-demand and sharing services be planned in such a way that they enrich existing public transport services? Transportation modelling is indispensable for setting the right course for a future-oriented mobility ecosystem.


What is the workflow to develop, maintain and apply a transport model

For strategic transportation planning there is a relatively clear distinction between mode development and model application. Model development is the process to set up a base model which reproduces the mobility in the planning regions at a given time (the base year). This model is built from various data sources, which all should relate to the base year. By adjusting various parameters and inputs, the model is calibrated to match traffic counts and various survey data (such as vehicle counts, public transit passenger boardings, trip distance distributions), which are also collected for the base model. Due to these data and calibration requirements, the base year of the model will often be an earlier year than the actual year when the model is developed.

Once calibrated and approved, this base (year) model can then be used in many different applications to develop projects and test scenarios. This model may then be handed to different agencies or consultants for their project studies.

As the transport system evolves over time, also the base model needs to be maintained and updated in order to remain representative for the model region. Bigger updates to the model will usually also require a recalibration. The frequency of such updates depends on the scope of mobility changes in the region and the project timeline and budget.

What are macroscopic, mesoscopic and microscopic models?

The three terms refer to the level of detail in which, in particular, road traffic is modelled. In macroscopic models, traffic is modelled with a flow model similar to fluids, generating outputs e.g. as fractional volumes on links and turns. Macroscopic models can be used to assess traffic in large scale networks, at the expense of simulation detail. In contrast, microscopic simulation models provide a detailed simulation of individual vehicles, with their acceleration, deceleration, and precise movements along links and through intersections. The output of microscopic models are therefore detailed trajectories of individual vehicles. The higher computational requirements render them less applicable to large scale networks.  Mesoscopic models (or simulation-based assignment models) combine aspects of both models, by simulating traffic in large scale networks through a simplified vehicle movement models which omit aspects like acceleration or deceleration. Mesoscopic models provide enough detail for assessment of traffic management measures etc., while still being applicable to large scale networks.

What methods are used for transportation modelling?

Depending on the required level of detail and accuracy, the forecast period, available input data, resources and know how, different mathematical approaches can be used. Historically, an aggregate methodology referred to as the 4-step-model, or trip-based model, has been most used. Recently, more detailed disaggregate approaches referred to as activity-based models or agent-based models (ABM) have been implemented in many locations. Both approaches, and some other model types are explained in the following:

The classical 4-step travel demand modelling process

The 4-step-process is an established methodology for urban, regional and national travel demand modelling. The aggregate planning transportation model compromises four steps related to travel choices.

1. Trip generation –  how many trips are generated?

The first step in the four-step transportation planning process deals with the question of how many trips originate in or are destined for a particular travel analysis zone (TAZ).  TAZs are neighborhoods in the model area and serve as the source or destination for trips. TAZs are also coded with land use data like the number of households and employment for understanding travel demand.  The trips generated are related to different trip purposes, for example, work, shopping, or leisure. The production and attraction of trips are driven by so-called trip rates, averages based on the number of people in households or the number of vehicles available.

Sometimes, dedicated TAZs are introduced to the model to represent facilities such as airports or large factories which feature special trip production and attraction characteristics. 

The output of the trip generation step is a set of production and attraction values associated with each zone.


2. Trip distribution – where do trips go?

Destination choice is the second component of four-step transportation planning. The trip distribution step matches trip origins with destination. This is done by weighing the attractiveness of the potential destination and the effort required to get there such as road distance, travel time, and toll/cost.

The result is that the original demand of a TAZ is split across several destination zones. Depending on the segmentation of the model, multiple distribution matrices may be generated, for example by trip purpose or household income.


3. Mode Choice – what travel mode is used for each trip?

In the third step, trips between the TAZs are allocated to different transportation modes. Which mode of transport people are using depends on their preferences and aspects of their household or person such as car ownership. Other factors such as travel time, cost, parking availability, and number of transfers for transit have an additional influence on the modal split. These variables and parameters are typically incorporated into a logit model to calculate the split of demand across the modes.

As an output of the mode choice procedure, the trip matrices from the distribution step are further refined into trip matrices per mode.

4. Trip Assignment – what is the route of each trip?

In the assignment step, the trips between an origin and destination by a particular mode are ‘assigned’ to a specific path. This means that the trip matrices from the prior steps are used as an input to assign route flows to the actual transportation network. Traffic volumes for road segments (or links) and transit vehicle loads are generated, and often analyzed as a result

There are different network assignment procedures for different types of transport modes:

Road traffic assignment

For road-based traffic by cars, heavy goods vehicles, etc., which are constrained by road capacity, iterative equilibrium network assignment procedures are applied. The distribution of traffic to different routes in these procedures is driven by the observation that the actual travel speeds on roads decrease with the amount of traffic on the road in relation to the capacity (the saturation level). This is expressed by volume-delay-functions (or capacity-restraint-functions). With increasing traffic load and decreasing travel speeds on primary roads, road users shift to secondary, faster routes.

The assignment procedures iteratively shift fractions of travel demand between different routes, until all routes allocated to each pair of origin-destination zones experience the same (or very similar) travel time (or generalized cost). This balancing is done for all pairs at once, converging to a network-wide equilibrium state, called the Wardrop equilibrium. Due to the vast number of routes considered, the equilibrium is never met exactly. A gap measure is used to indicate the level of convergence reached in the assignment process. Good convergence of the base model is essential for transportation planning because, with bad convergence, it is impossible to distinguish scenario effects from random assignment artifacts/noise in later model applications.

Public Transport assignment

The process of assigning public transport trips works differently from road assignments.  Public transit networks consist of distinct transit lines with specific service frequencies and possible stop waiting times. The transit network can only be entered at specific stops, therefore access and egress– usually by walking – are required. For a given origin-destination zone pair,  there may not be a  direct connection and so one or multiple transfers may be required. Furthermore, transit fares are considered in the route choice as well.

There are different factors that influence the journey experience, such as travel time, number of transfers, waiting times, or access and egress time. Within a public transit assignment, these factors are considered in a choice model.

Various trip connection alternatives are derived from the public transit network and the timetable. Trips are then distributed to these alternatives based on traveler preferences and the resulting public transit network line, stop, and vehicle boardings and volumes available for analysis.

Active modes / Bike assignment

Although active transport modes such as bikes often use the same road infrastructure as cars, the route choice of cyclists is influenced by other factors then of drivers.  Travel speeds of bicycles are not due to the capacity effect like travel speeds of cars. Instead, cyclists are more sensitive to features such as slopes, paving, traffic lights, striping, etc. Therefore, choice models reflecting these aspects are applied for assigning active mode travel to different route alternatives.

Disaggregate Activity-based / Agent-based models (ABM)

In contrast to aggregate trip-based models, disaggregate models model people and/or households individually and often with more precise home and activity locations. Since individual data is not available for the entire population, a ‘synthetic population’ of households and persons is generated from statistical data and distributions of key variables such as household income and person age.

The general choice model structure applied in ABM is very flexible. The person and household attributes attached to each individual, as well as their previous decisions, can be considered in each subsequent choice step. This allows a more realistic representation of their mobility in terms of travel during the simulation day as household context variables (e.g. family car usage), long-term decisions (e.g. car ownership), and tour variables (e.g. drove to work) can be used to more precisely estimate travel decisions.  Understanding of time and space is typically more precision as well which makes estimating tolled/priced travel and active mode travel more accurate.

ABM models generate daily activity plans covering each person’s relevant activities along with their location, timing, mode, and route (in some cases).  This synthetic travel diary provides much higher spatial and temporal resolution of model outputs for analysis. As the results are generated as individual tours, trips, and activities, they are easier for non-experts to understand than the traditional fractional traffic flows generated by aggregate models. On the other hand, setting up and calibrating ABM models is more complex than aggregate models.

Other modelling methods

Aggregate tour-based / activity-based models

While maintaining the spatial aggregation and segmentation of the classical 4-step models, aggregate tour-based models consider tours of individuals spanning multiple activities at different locations. They incorporate some aspects of ABM models and some aspects of the traditional aggregate models.

Incremental / pivot-point models

Pivot-point models are similar to simple growth models that relate growth to a relevant variable (such as change in ticket price) in that they estimate changes in travel demand from changes in travel cost. But instead of simply applying a fixed elasticity related to one or a few selected variables, they typically reflect the complex choices travelers take in an incremental logit model. As this approach allows to consider various variables influencing travel choices, it has been widely adopted in transportation planning, e.g. for project appraisal. Some national guidelines (e.g. the UK TAG guideline, (Transport, 2022)) for project appraisal provide detailed instructions and model parameters for the workflow.

Multimodal modelling

For many travelers, mobility is not limited to using a single mode of transportation for a trip.  Instead, they may take their private car to reach a park & ride facility, use public transit to get to the city center and then continue their journey by e-scooter to reach their destination. Other examples are car sharing or ride sharing systems, which are operated by cars, but to the user they appear much like a public transit mode. For multimodal modelling these special requirements and framework conditions need to be taken into account.

How is freight transport and commercial traffic considered?

Freight transport, as well as commercial and service activities, generate a large share of the overall traffic volume. Due to the large variety of operations involved, and the heterogeneity and complexity of logistics chains, modelling commercial and freight transportation is less standardized. The availability of input data and the calibration of models restrains a wide application of these models. Many smaller scale models (for urban areas) only roughly assess commercial and freight traffic. More complex, bespoke models are often built on the national scale for assessing freight transport based on internal and external supply chains.

How are aggregated transportation models structured?

Spatial model structure - What are transport analysis zones (TAZ)

The core principle of aggregate models is the spatial dissection of the study area into travel analysis zones (TAZ). These zones seamlessly cover the study area and are often associated with statistical units (communities, census tracts, …) where statistical data are collected. Most inputs and outputs are aggregated to these TAZ and can be prepared and analyzed with GIS tools. Since TAZs are the core units of the model computations, many outputs such as network level-of-service indicators (called skims) (travel time, cost, number of transfers in public transport, etc.) and travel demand flows (person trips, etc.) between the TAZ are generated in form of square matrices.  The rows are the origin zones and the columns are the destination zones.

Population segmentation

People have different travel patterns depending on their life situation and other aspects. An employee for example covers different road routes than a student or retiree. People in urban areas have different travel options than in rural areas. In 4-step-models, this is reflected by segmentizing the total population of the TAZ into different groups or ‘demand segments’. The number of segments and the characteristics considered for the segmentation (e.g. age, employment status, car availability) depend on model scope, budget, and data availability. For each segment, individual parameters for trip characteristics by purpose, mode preference, value-of-time etc. can then be applied in the modelling steps.

Consideration of time – temporal scope

Most travel demand models are used for strategic considerations. The planning horizon is in the range of years. In this context, it is often sufficient to consider average daily traffic volumes. Thus, many strategic models are designed to model traffic volumes and trips per average day. As travel patterns tend to have a high degree of temporal clustering, and because transportation infrastructure needs to be designed to meet peak demands, separate models for peak hour traffic estimates are often created. If models are used for more operational studies, e.g. of traffic management strategies, then a higher temporal resolution of demand, e.g. to hourly values, may be required.



How are transportation models built?

What datasets are used for building transportation models?

For setting up transportation models, existing datasets are processed and combined wherever possible. In general, several different types of input data are required to build a model.

Transportation networks and supply data

Transportation networks form the base for any transportation model. Road networks can be extracted from navigation databases or from GIS datasets. Some processing may be required to provide essential attributes specific to transportation models, such as capacity. Public transit networks and timetables are often available in common formats such as GTFS from scheduling systems. Other networks, e.g. for cycling, sea transport or air traffic may be available from GIS datasets or other online sources. Although not required for most calculations, its best if the modal networks can be merged into a single multi-modal network, e.g. by mapping bus lines to the road network. This enables multimodal analysis and consideration of interactions between modes.

Land use, demographic, and economic data

Land use data, demographic variables (population, age, employment, income, …) and other data (workplaces, school places, …) are needed to assess the origins and destination of travel demand. Much of this data is available from census data and land use monitoring in databases or GIS formats. For aggregated models, this information is usually condensed to the TAZ level, while ABM models preserve the individual activity locations, household locations and synthetic population.

Travel behaviour parameters

Most calculations in the transportation modelling process – regardless of the model type – are based on parameters describing human travel behavior. These parameters can be estimated from household travel diary surveys or estimated by statistical methods from other datasets such as mobile phone data.

Observed control data

Although they are not needed for the model calculation itself, control datasets are important for the model calibration process. These can be observed traffic volumes from manual or automated vehicle counts, transit passenger counts from automated count systems or ticketing systems, or similar. Observed distributions of actual trip distances, travel times etc. are also useful for calibrating demand models and often come from household travel surveys or Big Data.


Defining the model sequence

The model sequence describes the sequence of calculations, data processing steps and output generation which is executed to ‘run’ the model or to assess a scenario. Depending on the software used for modelling, the sequence, along with the calculation parameters, may be defined through a graphical user interface or through a scripting / programming language.  In many cases, travel demand models implement feedback loops, so sub-parts of the model sequence will be executed several times during a single model run. Complex models or specific calculations may even launch external software tools for additional components.

Model calibration

To be used as a planning tool, transportation models must achieve an accurate replication of travel patterns in the base year. Only thereby can future scenarios be assessed in a meaningful way. To achieve this, model results are compared to observed data, usually at least vehicle counts, public transport passenger counts and/or trip distance and travel time distributions. Based on these comparisons, model parameters and other aspects are adjusted until the calibration requirements are met (e.g., max +/- 5% deviation at 85% of the count locations). Calibration is often a time-consuming process, requiring expert knowledge and experience. A number of automated procedures can be applied to automatically adjust model components to match observed counts, e.g. by adding constants to utility functions or by automated a posteriori matrix adjustment. While speeding up the calibration process, these methods can have issues regarding model expressiveness and can lead to model overfitting.

How are transportation models analyzed?

What outputs and results are generated by transportation models?

Transport models provide various results on different levels of detail and segmentation. The most frequently used outputs are traffic volumes for different modes of transport on the links. These provide direct insights into local traffic impacts, e.g. on noise and emission levels. For public transit planning, comparable outputs are volumes on transit lines or individual services, or boarding, alighting and transferring at individual stops. The assignments generate full paths from trip origin to trip destination through the network, along with the respective volumes, allowing detailed analysis of which travelers use each link. This is very helpful, e.g. when planning roadworks and bypasses.

For more comprehensive scenario assessments, a multitude of global KPIs can be provided for the full network, or for subsections like certain relations or destinations. For studies regarding decarbonization, this can be metrics such as total mileage or congestion length- For transit studies KPIs like total ridership, average travel time or number of transfers, estimated fare revenues, operating cost are provided. The various network skim matrices generated during the model computation provide valuable insights on issues related to accessibility and connectivity.

What tools can be used for analyzing transport model outputs?

Many of the outputs produced by transportation models can easily be analyzed in tables, charts, or GIS maps. For visualizing link volumes, 2D or 3D bar maps are a popular rendering.

The richness and structure of outputs from transportation models allows for much more sophisticated analysis. Only a few of the many available tools can be described in the following:

  • One of the most important tools is the ‘flow bundle’ or ‘select link’ analysis. This extracts all the paths traversing selected network elements. Analysts can asses which travelers will be affected by changes to these elements.
  • Isochrone calculations provide insights to accessibility based on detailed information on travel times and network connectivity contained in a transport model.
  • The information captured in network skim matrices and trip demand matrices can be visualized and aggregated to identify demand flows and overall travel patterns.
  • The time profiles of activities at selected locations can be investigated, e.g. charging of electric vehicles at charging facilities or the presence of visitors at a shopping center.
  • Crowding profiles and transfer locations of transit lines or signalization coordination in green bands can be analyzed in specialized time-space diagrams.


How are transportation models connected to other plannings and applications?

Mobility is at the heart of human activity. That’s why transportation planning never stands alone.   It is linked to many other urban planning activities and provides invaluable inputs to the respective processes and tools. Some examples are:

Air quality assessment and climate change

Road traffic is responsible for a large share of carbon emissions and other pollutants affecting air quality in cities. Many transportation planning project therefore aim to reduce those emissions, e.g. by introducing Low Emission Zones in city centers. Transportation models compute the traffic volumes resulting from such measures, while also indicating unwanted side effects like overall increases in trip lengths. The combination with emission models enables planners for example to assess different fleet compositions of electric and combustion engines.

Noise evaluations

Similar to air quality, traffic volumes computed by transportation models can also feed into noise emission models. Based on detailed models of the built-up environment etc., planners can assess the noise exposure and possible reductions within dedicated tools.

Accessibility and equity

Access to different types of services like health care, education, or groceries is strongly dependent on transportation – while not all mobility alternatives are equally suitable for all people. The detailed representation of all modes and the segmentation of transportation models for different population types allow concise planning for equity in service provisioning.

Public Transit, Rail and Ride Sharing Fleet planning

The procurement of vehicles for public transit, rail services or ride sharing offers requires huge financial investments and has long lead times. Therefore, implementing or adjusting such services is usually not a short-term matter but requires proper advance planning. In particular, the conversion of gasoline bus fleets to e-bus fleets requires careful assessment of the different operational concepts (overnight charging, opportunity charging etc.) and the needed charging infrastructure. Transportation models can be used to evaluate the fleet of vehicles required to operate a planned service or to assess how different types of vehicles will perform in the planned service.

Roadworks planning

When infrastructure needs to be maintained or replaced, or other roadworks affect the available road capacity, transportation models help to assess the relocation of traffic flows and to ensure sufficient capacity and smooth operation on the alternate routes.

Land use & energy grids

When zoning systems and land use of a city or region are designed, transportation systems need to be adjusted. Transportation models play a major role to support this process. With the shift towards electric mobility, other parts of the urban infrastructure such as the electric grid also may need adjustment. Transportation models provide key insights for helping to dimension these assets.

Road safety

Road safety plays a major role in providing livable cities. Transportation models can help in identifying and analyzing accident hot spots and designing network alternatives for mitigation.

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