Revealing Recurrent Urban Congestion Evolution Patterns with Taxi Trajectories

Shi An,Jian Wang,Haiqiang Yang

doi:10.3390/ijgi7040128

Shi An, Jian Wang + Show 1 more

Open Access

https://doi.org/10.3390/ijgi7040128

Copy DOI

Abstract

Urban congestion can be classified into two types: Recurrent Congestion (RC) and Non-Recurrent Congestion (NRC). RC is more regular than NRC, having fixed and long-standing patterns. Mining urban recurrent congestion evolution patterns can assist with congestion cause analysis and the creation of alleviating strategies. Most existing methods for analyzing urban congestion patterns are based on traditional traffic detector data, which is inflexible and expensive. Additionally, prior research primarily focused on the microscopic model, which simulated congestion propagation based on theoretical models and hypothetical networks. As such, most previous models and methods are difficult to apply to real case scenarios. Therefore, we investigated recurrent congestion patterns by mining historical taxi trajectory data that were collected in Harbin, China. A three-step method is proposed to reveal urban recurrent congestion evolution patterns. Firstly, a grid-based congestion detection method is presented by calculating the change in taxi global positioning system (GPS) trajectory patterns. Secondly, a customized cluster algorithm is applied to measure the recurrent congestion area. Finally, a series of indicators are proposed to reflect RC evolution patterns. A case study was competed in the Harbin urban area to evaluate the main methods. Finally, RC cause analysis and alleviating strategy are discussed. The results study are expected to provide a better understanding of urban RC evolution patterns.

Highlights

Recurrent congestion (RC) and Non-Recurrent Congestion (NRC) are two typical types of congestion occurring in urban areas [1]
With the adoption of location-based services (LBS), increasing amounts of multiple-source locating information can be obtained in a city
Using taxi global positioning system (GPS) trajectory data to study urban RC patterns is reasonable and valid, and the method can be widely applied at a lower cost in comparison with methods based on traditional traffic detectors

Summary

Introduction

Recurrent congestion (RC) and Non-Recurrent Congestion (NRC) are two typical types of congestion occurring in urban areas [1]. RC is usually caused by insufficient traffic capacity, excess travel demand, and poor signal control [2,3], to name a few. This type of congestion is regular with typical fixed features, such as having the same generating period, similar spatio-temporal influence scope, duration, etc. With a sampling frequency of 30 s, almost 30 million GPS records are obtained every 24 h for managing and operating the taxis. These locating data can reflect the traffic state of the urban environment. Using taxi GPS trajectory data to study urban RC patterns is reasonable and valid, and the method can be widely applied at a lower cost in comparison with methods based on traditional traffic detectors

Objectives

Methods

Discussion

Conclusion