Synthesis of Traveler Choice Research: Improving Modeling Accuracy for Better Transportation Decisionmaking

Scope and Conceptual Framework

This chapter discusses the report’s conceptual framework and defines categories to organize its key elements. The framework’s core components are operational interventions, information dissemination, traveler choice dimensions, and network and non-network influencing factors.

Background

For some time, transportation planning focused on meeting mobility needs by providing adequate infrastructure. This supply-oriented planning neglected demand-oriented models in that the main purpose was to predict aggregate long-term demand for the strategic planning of the transport infrastructure, not necessarily taking full account of the impact of such plans on travel demand. The increasing costs of infrastructure and the spatial limitations in areas of high population density together with the externalities in these areas have changed supply-oriented planning to incorporate and manage demand. Based on this shift toward demand-oriented management, operational interventions have emerged, including congestion pricing, which changes the service characteristics to influence travel behavior, and dissemination of real-time information on the level of service. The interest in analyzing transport policies in terms of their impact has led to the use of disaggregate demand models, which seek to understand short-term effects of policies such as congestion pricing, telecommuting, and ride-sharing programs. The limitation of traditional trip-based travel models to capture the complex ways travelers respond to such policies has led to the development of behavior-oriented, activity-based models as well as the introduction of traveler response to current cost and service information.

Traditional trip-based static assignment models cannot cope with time-varying properties of traffic flow, which is essential in managing travel demand with timely and dynamic optimized interventions. The limitations of static assignment models and increasing computational capacities have improved the supply side toward dynamic traffic assignment models, which are time-dependent and able to model the buildup and dissipation of traffic congestion. Dynamic traffic assignment models are therefore able to accommodate the effects of intelligent transportation systems and system management interventions such as ramp meters, traffic lights, and congestion pricing.

Although progress has been made on the demand side and the supply side, each area has progressed rather independently of the other. Since travel behavior studies are related to many different fields, most models do not capture all direct and indirect influences on travel behavior or all feedback effects from behavior decisions on travel volumes and service (which would capture the interaction between the demand and supply models).

Scope Delineation

Traveler behavior research is a broad field. The synthesis presented in this report is not intended to be comprehensive of the whole field and all possible traveler choice dimensions, but rather, to focus on operational planning and management interventions influencing traveler behavior. The bounds at the operational management level have a more narrow scope than, for example, at a strategic level involving resource acquisition and network design. Nevertheless, long-term traveler decisions such as mode shifts, auto ownership, and location changes are of interest as activity assessments are becoming more realistic to model. On the intervention side, the focus is mainly on ATDM, a strategy to operate technologies in a proactive way to address potential problems before they occur. ATDM covers MTD and ICM, including Dynamic Mobility Applications. This synthesis also covers active traffic management on the supply side, since supply and demand management overlap in certain areas, such as information supply. Because most supply management interventions change the network’s level of service, the focus is on network factors influencing traveler behavior. However, demand management interventions change non-network factors as well. The bounds of focus for non-network factors influencing traveler behavior are broad and not clearly defined. Non-network factors range from weather, which is natural and easy to observe, to walkability, which can be designed for and is less straightforward to measure. Non-network factors are important, as they interact with network factors in decisionmaking and define the environment
and attractiveness of choice alternatives. Traveler and vehicle characteristics also influence traveler choices.

Taken together, these confounding forces and influences become difficult to separate. The comprehensive framework presented in this report attempts to conceptualize these person-network interactions over short-, medium-, and long-term time horizons. The framework seeks to capture the anticipated and actual effects of operational interventions on the supply and demand sides.

Conceptual framework

Transport planning aims to describe, understand, and model the choices made by households and individuals during the execution of their daily lives, including the more or less frequent journeys outside their daily activity space.^(1,2) Behavioral demand models feed supply and network models to assign traffic to the infrastructure. These models are in turn used to evaluate and optimize changes to the transport system undertaken by the owners of its various components, including reductions or expansions of road capacity through interventions, where demand management and supply management are involved, as well as policy changes. Travelers make decisions based on the characteristics of the system and their own perceptions. For example, as new information becomes available, travelers adjust their perception and adapt their travel behavior. Factors of the system and en route decisions must be considered. Travelers also decide where they want to live, where their workplace is, and whether to own a car or buy monthly transit or toll passes. Travelers must decide how often and where their everyday and less frequent journeys take them, which mode of transport to use (if multimodal trip alternatives are available), when to start trips, and what route to take. In decisionmaking, bounded rationality plays a substantial role: people make rational decisions based on a limited amount of knowledge and assessment capacity, not necessarily fully informed or fully rational choices.

Operational interventions, supply and demand management, traveler choice dimensions, and factors affecting user response (either endogenous or exogenous) interact with each other, as summarized in the conceptual framework in figure 1.

Figure1. Chart. Conceptual Framework.

Household and individual behavior-change dimensions can be categorized on the basis of the time frame over which they take place, and hence, the level of analysis where a particular decision or group of decisions must be considered, as follows:

·        Short-term, trip-level decisions take place within a day as well as from day to day. Trip-level decisions can be categorized further into the following types:⁽³⁾

o   Pre-trip (strategic) high-level traveler choices take place before departure (i.e., trip-making decisions).

o   En-route (tactical) high-level traveler choices take place during the trip (i.e., route modification).

·        Medium-term decisions involve behavioral patterns such as activity chain planning and adjustments that take place over a longer period than hours or days.

·        Long-term, lifestyle, and mobility decisions affecting vehicle holdings and location choices take place over weeks, months, and years.

Operational intervention programs can be categorized by the interventions or controls with which they seek to improve system operations and performance by influencing underlying traveler choices.⁽⁴⁾ They can target the supply side by modifying the network with traffic and infrastructure access controls (e.g., ramp metering), which affect behavior through the level
of service as an influence factor. Alternatively, operational interventions may affect demand directly with pricing (e.g., congestion pricing). Demand and supply management overlap as information supply (e.g., variable message signs and earlier traveler time dissemination) targets both demand and supply indirectly through demand response.

In addition to demand management, which influences household and individual behavior directly, there are further influencing factors, which can be divided into categories, as follows:

·        Traveler.

o   Traveler and household characteristics that affect traveler behavior.

o   Vehicle characteristics that affect traveler behavior (e.g., type, dynamics).

·        System.

o   Network characteristics (e.g., connectivity, length of route, and roadway types) and segment elements that define roadway and transit path characteristics
(e.g., ride quality, lanes, and frequency).

o   Environment, events, states, or features of the network that affect traveler behavior but do not originate from system control strategies (e.g., weather, walking paths, and other characteristics of transit service besides route configuration, such as headways).

It is important to note that behavior choices are denoted as behavior changes, as they are better represented as the outcome of an adjustment process of a current choice rather than as the outcome of a choice process that does not recognize one’s current state. Also, the arrows in figure 1 not only show the possible mappings of an explanatory variable on a possible outcome but also represent the perception of attributes and characteristics by the user in question.