The Application of an Integrated Behavioral Activity-Travel Simulation Model for Pricing Policy Analysis

The Application of an Integrated Behavioral Activity-Travel Simulation Model for Pricing Policy Analysis

Karthik C. Konduri (University of Connecticut, USA), Ram M. Pendyala (Arizona State University, USA), Daehyun You (Arizona State University, USA), Yi-Chang Chiu (The University of Arizona, USA), Mark Hickman (University of Queensland, Australia), Hyunsoo Noh (University of Arizona, USA), Paul Waddell (University of California – Berkeley, USA), Liming Wang (Portland State University, USA) and Brian Gardner (U.S. Department of Transportation, USA)
Copyright: © 2014 |Pages: 17
DOI: 10.4018/978-1-4666-4920-0.ch005
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This chapter demonstrates the feasibility of applying an integrated microsimulation model of activity-travel demand and dynamic traffic assignment for analyzing the impact of pricing policies on traveler activity-travel choices. The model system is based on a dynamic integration framework wherein the activity-travel simulator and the dynamic traffic assignment model communicate with one another along the continuous time axis so that trips are routed and simulated on the network as and when they are generated. This framework is applied to the analysis of a system-wide pricing policy for a small case study site to demonstrate how the model responds to various levels of pricing. Case study results show that trip lengths, travel time expenditures, and vehicle miles of travel are affected to a greater degree than activity-trip rates and activity durations as a result of pricing policies. Measures of change output by the model are found to be consistent with elasticity estimates reported in the literature.
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In an era of rising concerns about traffic congestion, energy sustainability, greenhouse gas emissions that potentially contribute to global climate change, and limited financial resources, many jurisdictions around the world are contemplating the implementation of pricing policies and strategies to address the growing demands for transport infrastructure. Some of the pricing policies that are being considered include system-wide mileage based fees that may vary by time of day and location, gas taxes, auto ownership/registration fees, parking pricing, corridor-based tolls that may vary by time of day and vehicle occupancy, high-occupancy toll (HOT) lanes, variable pricing that is demand responsive and ensures free flow conditions at all times, and area- or cordon-based pricing limited to certain subareas such as a central business district. There is considerable interest in the implementation of pricing policies for two reasons; first, pricing policies are viewed as a mechanism to manage travel demand and better distribute the demand for travel over time and space thus easing congestion on the network, and second, pricing policies are viewed as revenue generation mechanisms to raise much needed resources to invest in the transport infrastructure (e.g., Hensher and Puckett, 2007; Kockelman and Kalmanje, 2005).

The widespread interest in pricing policies has resulted in considerable attention being directed to modeling the impact of pricing policies on travel demand and network dynamics in a behaviorally realistic way. Several studies have been undertaken in the recent past with a view to developing frameworks and operational model systems capable of accurately forecasting the impacts of pricing strategies (e.g., Zhang, Mahmassani, & Vovsha, 2011). Challenges in accurately reflecting the impacts of pricing strategies on travel demand stem from the complex interactions and adjustments involved in the formation and execution of activity-travel schedules in time-sensitive networks. A pricing policy may impact the generation of activities, the manner in which activities (and therefore, trips) are chained together, destination choice, mode choice, time of day choice, route choice, task allocation among household members, and solo and joint activity-travel engagement by household members. All of these activity-travel choices are intertwined in complex ways that are not yet completely understood. As a result, modeling changes in activity-travel behavior in response to a pricing policy, while accounting for the myriad time-space interactions and constraints that shape activity-travel patterns, remains a complex and challenging task (Pendyala, 2005). Although emerging tour-based models (Vovsha and Bradley, 2006) attempt to reflect the impacts of pricing policies by carrying logsum terms (representing accessibility measures) through a series of nests in a nested logit model system, such models often lack the treatment of time as a continuous entity which is critical to accurately reflecting changes in activity-travel demand in the presence of time-space prism constraints. Moreover, there has been limited progress thus far in the integration of continuous-time activity-travel behavior models with dynamic network models, which is needed to fully encapsulate the range of activity-travel modifications that a pricing policy may bring about. Focusing the modeling effort largely on the demand side or the network side, without a tight coupling between the two enterprises, raises the risk of not being able to adequately capture travel behavior impacts in a holistic fashion.

With a view to contributing towards modeling pricing in the planning process, this chapter presents an integrated modeling framework of demand and supply that is enhanced to reflect the impacts of pricing policies on activity-travel behavior and network dynamics in a prism-constrained behavioral paradigm. The enhancements to the modeling framework are intended to help make an integrated demand-supply model responsive to pricing policies even if some or all of the individual choice models that comprise the demand model do not include price or cost as explanatory variables. Although the framework and the extensions to it can be applied in the context of any type of pricing policy, the application in this chapter focuses on a system-wide pricing strategy such as a mileage based fee, VMT (vehicle miles traveled) tax, or enhanced fuel tax. The chapter includes discussions on how the framework can be easily applied in the context of parking pricing strategies, subarea/cordon pricing, or corridor-based tolling or pricing strategies – some or all of which may vary by time of day.

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