Understanding vehicular routing behavior with location-based service data

Yanyan Xu,Marta C. González,Riccardo Di Clemente

doi:10.1140/epjds/s13688-021-00267-w

Abstract

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.

Highlights

With the growing population in cities and the restructuring of urban economies and societies, a fundamental task of transportation planners and engineers is to effectively move people and goods [1]
We explore the location-based services (LBS) data, which refer to the collection of the check-in or trajectory data generated by a set of smartphone applications
This paper aims at analyzing the LBS for urban scale mobility demand, with focus in gaining insights on their use to extract routing behavior

Summary

Introduction

With the growing population in cities and the restructuring of urban economies and societies, a fundamental task of transportation planners and engineers is to effectively move people and goods [1]. A general solution to the lack of complete information is leveraging traffic assignment models, such as the user equilibrium assignment, dynamic traffic assignment, and the multi-agent approach, to assign each traveler to specific routes [15] These models assume that travelers choose their routes with the intention of minimizing travel costs, such as travel distance or time. The choice of route is simultaneously affected by multiple factors, and the route choice behavior of people follows the bounded rationality principle [16] This means that travelers can neither find the optimal routes because of the lack of accurate information about the traffic conditions, nor willing to spend much effort to obtain the optimized decision from complicated situations [17]. We attempt similar analysis with the additional challenge imposed by data with less temporal accuracy and much lower frequency of collection, but much more pervasive than the vehicular trajectory data

Objectives

Methods

Findings

Conclusion