Understanding network travel time reliability with on-demand ride service data

Abstract

Travel time reliability is of increasing importance for travelers, shippers, and transportation managers because traffic congestion has become worse in major urban areas in recent years. To better evaluate the urban network-wide travel time reliability, five indices based on the emerging on-demand ride service data are proposed: network free flow time rate (NFFTR), network travel time rate (NTTR), network planning time rate (NPTR), network buffer time rate (NBTR), and network buffer time rate index (NBTRI). These indices take into account the probability distribution of the travel time rate (i.e., travel time spent for the unit distance, in min/km) of each origin-destination (OD) pair in the road network. We use real-world data extracted from DiDi-Chuxing, which is the largest on-demand ride service platform in China. For demonstrative purposes, the network-wide travel time reliability of Beijing is analyzed in detail from two dimensions of time and space. The results show that the road network is more unreliable in AM/PM peaks than other time periods, and the most reliable time period is the early morning. Additionally, we can find that the central region is more unreliable than other regions of the city based on the spatial analysis results. The proposed network travel time reliability indices provide insights for the comprehensive evaluation of the road network traffic dynamics and day-to-day travel time variations.

Graphical abstract

Keywords

network travel time reliability / on-demand ride services / travel time rate / OD

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Acknowledgments

This research is financially supported by Zhejiang Provincial Natural Science Foundation of China [Grant No. LR17E080002], Key Laboratory of Road & Traffic Engineering of the Ministry of Education [Grant No. TJDDZHCX004], National Natural Science Foundation of China [Grant Nos. 51508505, 71771198, 51338008], and Fundamental Research Funds for the Central Universities [Grant No. 2017QNA4025]. The authors are grateful to DiDi Chuxing (www.xiaojukeji.com) for providing us some sample data.

RIGHTS & PERMISSIONS

2017 The Author(s) 2017. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0