Understanding vehicular routing behavior with location-based service data
MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, 200240, Shanghai, China
2 Department of Civil & Environmental Engineering, MIT, 02139, Cambridge, MA, USA
3 Department of City and Regional Planning, University of California, Berkeley, 94720, Berkeley, CA, USA
4 Energy Analysis & Environmental Impacts Division, Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA
5 Centre for Advanced Spatial Analysis, University College London, WC1E 6BT, London, UK
6 Department of Computer Science, University of Exeter, EX4 4NR, Exeter, UK
7 Department of Civil and Environmental Engineering, University of California, Berkeley, 94720, Berkeley, CA, USA
Accepted: 14 February 2021
Published online: 26 February 2021
Properly extracting patterns of individual mobility with high resolution data sources such as the one extracted from smartphone applications offers important opportunities. Potential opportunities not offered by call detailed records (CDRs), which offer resolutions triangulated from antennas, are route choices, travel modes detection and close encounters. Nowadays, there is not a standard and large scale data set collected over long periods that allows us to characterize these. In this work we thoroughly examine the use of data from smartphone applications, also referred to as location-based services (LBS) data, to extract and understand the vehicular route choice behavior. Taking the Dallas-Fort Worth metroplex as an example, we first extract the vehicular trips with simple rules and reconstruct the origin-destination matrix by coupling the extracted vehicular trips of the active LBS users and the United States census data. We then present a method to derive the commonly used routes by individuals from the LBS traces with varying sample rate intervals. We further inspect the relation between the number of routes and the trip characteristics, including the departure time, trip length and travel time. Specifically, we consider the travel time index and buffer index for the LBS users taking different number of routes. Empirical results demonstrate that during the peak hours, travelers tend to reduce the impact of traffic congestion by taking alternative routes. Overall, the proposed data analysis framework is cost-effective to treat sparse data generated from the use of smartphones to inform routing behavior. The potential in practice is to inform demand management strategies, by targeting individual users while generating large scale estimates of congestion mitigation.
Key words: Routing behavior / Route detection / Location-based services / Origin-destination matrix / Traffic congestion
© The Author(s) 2021
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