Measuring the Impact of Motivations on Travelers’ Strategic Decisions in Different Traffic Conditions: Data Collection, Analysis and Modeling Open Access
AbstractMeasuring the Impact of Motivations on Travelers’ Strategic Decisions in Different Traffic Conditions: Data Collection, Analysis and ModelingContinued growth in travel demand and the corresponding congestion occurrence accentuate the need of Active Transportation and Demand Management (ATDM) that results in predictive rather than reactive congestion mitigation strategies (applications) to reduce demand and thus improve the performance of different surface transportation facilities. Through either deploying a new ATDM application or to amend existing ones, there should be an effort to “optimize” the efficiency of such applications by considering the travelers’ behavioral responsiveness. Earlier research work mainly utilized simulations and field experiments to suggest different improvements associated mainly with pricing and/or information provisions to travelers; however, few of such studies considered the dynamics of travelers’ motivations as an essential component in the performance of a given ATDM application. In other words, how travelers’ motivations change as a function of the ATDM applications’ characteristics remains largely unexplored.Occupying such a niche, this study examines the theory of planned behavior (TPB) to analyze travelers’ motivational patterns leading to their mode choices via an elaborate survey experiment; develops an online virtual environment to capture the dynamics of travelers’ motivations in association with different travel circumstances; measures how travelers’ motivations are associated with different mode choices (i.e., ride-alone personal vehicle and light rail transit) when facing different surrounding environments (i.e., inclement weather and incident conditions); quantifies the resistance to or the inertia associated with a given mode (change); and consequently offers guidelines towards more efficient Active Transportation and Demand Management applications that consider travelers’ motivations.The accomplishment of this study concludes the following findings:1. TPB is feasible in predicting/explaining travelers’ mode choices, that is, travelers’ mode choices are primarily determined by intentions (motivations). Income and age are two additional characteristics that influence mode choices.2. Travelers’ reasoned choices are mainly attitude-oriented. Different attitudinal aspects (e.g., reliability, economy, and convenience) are accentuated or compromised along with the changes of travel conditions and ATDM strategies.3. The levels of inertia vary between individuals and population groups. A sufficient incentive (introduced by travel conditions and/or ATDM strategies) can lead to the overcoming of inertia
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