Revealing Recurrent Urban Congestion Evolution Patterns with Taxi Trajectories

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.

Mapping to cells: a map-independent approach for traffic congestion detection

“…As a result, the links and GPS points in the map are offset, and it is difficult for the map matching algorithm to match them together; secondly, urban transportation networks have a complex composition, which makes it difficult to find a suitable map matching algorithm. It is worth mentioning that 5,6 tries to divide the study area into 200-500 m cells, and then maps the GPS points to the cells to evaluate the traffic status of the area. Compared with map matching, mapping trajectory points to cells is computationally efficient.…”

A Method for Exploring and Analyzing Spatiotemporal Patterns of Traffic Congestion in Expressway Networks Based on Origin–Destination Data

“…However, existing studies pay less attention to the spatiotemporal patterns of traffic congestion for regional expressway networks. Instead, they mainly focus on the traffic congestion of urban roads and have developed various methods to identify [10][11][12][13][14][15][16], predict [17][18][19][20][21], and analyze [22][23][24][25][26][27][28][29] urban traffic congestion. Compared with urban traffic congestion, there are fewer studies that focus on traffic congestion in expressways.…”

Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning

“…Several researchers employed taxi GPS trajectory data to represent the traffic flow. For example, based on the taxi GPS trajectories, Shi [16] clarified the urban recurrent congestion evolution patterns, and Kan [17] detected the traffic congestion at a turn level. Liu [18] defined the congestion coefficient by utilizing taxi GPS trajectories and studied the traffic status in the morning and evening rush hours in Beijing.…”