Edited by André de Palma, Robin Lindsey, Emile Quinet and Roger Vickerman
Abdul Rawoof Pinjari and Chandra R. Bhat INTRODUCTION The primary focus of transportation planning, until the past three decades or so, was to meet long-term mobility needs by providing adequate transportation infrastructure supply. In such a supply-oriented planning process, the main role of travel demand models was to predict aggregate travel demand for long-term socio-economic scenarios, transport capacity characteristics and land-use configurations. Over the past three decades, however, because of escalating capital costs of new infrastructure and increasing concerns regarding traffic congestion and air-quality deterioration, the supply-oriented focus of transportation planning has expanded to include the objective of addressing accessibility needs and problems by managing travel demand within the available transportation supply. Consequently, there has been an increasing interest in travel demand management strategies, such as congestion pricing, that attempt to change transport service characteristics to influence individual travel behavior and control aggregate travel demand. The interest in analyzing the potential of travel demand management policies to manage travel demand, in turn, has led to a shift in the focus of travel demand modeling from the statistical prediction of aggregate-level long-term travel demand to understanding disaggregate-level (that is individual-level) behavioral responses to short-term demand management policies such as ridesharing incentives, congestion pricing and employer-based demand management schemes (alternate work schedules, telecommuting, and so forth). Individuals respond in complex ways to such changes in travel conditions. The limitation of the traditionally used statistically oriented trip-based travel modeling approach in capturing these complex individual responses has resulted in the development of behaviorally...
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