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Smartphone Based Incentive Framework for Dynamic Network Level Traffic Congestion Management
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2022-08-24
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[PDF-2.31 MB]
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Corporate Contributors:Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE) ; University of Florida Transportation Institute ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program
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Edition:Final Report 1/15/2020 to 8/24/2022
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Abstract:This study proposes to develop smartphone-based frameworks to develop/utilize real-time incentives (monetary, value-based, travel-related credits, information etc.) to influence drivers’ en route routing decisions to manage network-level system performance in congested dynamic traffic networks. The framework consists of: (i) analytical model and algorithm, (ii) driving simulator-based experiments to analyze drivers’ responses to the incentives, and (iii) a smartphone-based app. The analytical model and the algorithm determine the characteristics of the specific incentives to provide or utilize in real-time, including how, where, when, type and amount. The driving simulator-based experiments elicit contextual driver responses to the specific incentives provided in real time, which are used to understand driver behavior in this context, and to finetune the analytical model to be consistent with driver behavior/responses. The smartphone-based app is developed to populate incentives in real-time and identify incentives available en route to the specific driver using the app during his/her origin-destination trip. Accordingly, this study composes of two tasks. Task 1 of this study investigates the role of demand management techniques in generating system level benefits such as reduction in congestion or pollution. Task 2 of this study aims to alleviate traffic congestion by exploiting a novel information provision strategy. Specifically, it takes advantage of the information gaps between individuals and the central planner (CP) and developed a correlated equilibrium routing mechanism (CeRM), which suggests priorities to individual vehicles’ route choices and drives their route choices to an equilibrium with a systematically optimal traffic condition while still satisfying individuals’ selfish nature. Overall, the output of the two research tasks together will help understand how different types of incentives can be used to alleviate traffic congestion using smart phones/on-board smart devices. The completion of this study will help develop more efficient traffic congestion management tools.
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