Transportation Modelling
Gordon Neal
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.
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 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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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