United States Department of Transportation
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Impact of Smartphone Applications on Trip Routing [supporting datasets]
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2021-02-15
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[PDF-143.73 KB]
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Alternative Title:STRIDE Project A - Trip Routing Choices, Diversion Prediction, and Traffic Management with Decentralized Traveler Information Data
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Corporate Contributors:United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE) ; University of Florida Transportation Institute
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Abstract:The Georgia Institute of Technology-led research evaluated trip re-routing potential of route guidance apps, how drivers utilized the information provided, and the impact of traffic re-routing on roadway facility usage, congestion, and prevailing speeds. Major findings of this study included a variety of characteristics associated with navigation apps and no singular uniform usage of navigation apps for all users or roadway facilities, a difference in stated vs real behavior associated with navigation app usage and travel behavior. Additionally, this study reveals the many challenges associated with collecting personal location history data through face-to-face surveys and online. The Florida International University led research developed a direct method to estimate the diversion for individual incidents based on mainline detector data and incident data. The study found evidence that the diversion was constrained by the capacity of the signals at the off-ramps, indicating the need for special signal control plans during incidents to increase the capacity of the off-ramps and adjacent signals leading to the main parallel routes. Data analytic models were developed in the study, allowing the prediction of the diversion rate based on the incident severity, number of blocked lanes, time of the incident occurrence, and incident locations. Three different models were developed utilizing Linear Regression (LR), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). Among the developed models, the MLP model appeared to produce the best results. The models developed in this study can be used for prediction of diversion rate based on incident characteristics. The Jackson State University led research focused on the application of the traveler information data in congestion management. This part of the study used decentralized traveler information data to locate potential congestions to be applied with the gating control traffic management strategies to reduce traffic congestions in emergency events. Travel time reliability measures were applied to account for delays and identify significant traffic congestions for potential gate locations in evacuation zones. Performance of the gating control traffic management strategies were evaluated using a case study, with DTALite program, a simulation based Dynamic Traffic Assignment (DTA) tool. The traffic simulations in the case study for the evacuation network in Memphis, Tennessee configured with the gating control strategies using the decentralized traveler information data showed the effectiveness of the gating control traffic management strategies in managing evacuation traffic operations.
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