Next Article in Journal
Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders
Next Article in Special Issue
Stakeholder Specific Multi-Scale Spatial Representation of Urban Building-Stocks
Previous Article in Journal
Procedural Generation of Large-Scale Forests Using a Graph-Based Neutral Landscape Model
Previous Article in Special Issue
Extraction of Tourist Destinations and Comparative Analysis of Preferences Between Foreign Tourists and Domestic Tourists on the Basis of Geotagged Social Media Data
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2018, 7(4), 128; https://doi.org/10.3390/ijgi7040128

Revealing Recurrent Urban Congestion Evolution Patterns with Taxi Trajectories

School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China
*
Authors to whom correspondence should be addressed.
Received: 4 February 2018 / Revised: 10 March 2018 / Accepted: 17 March 2018 / Published: 21 March 2018
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
View Full-Text   |   Download PDF [5262 KB, uploaded 3 May 2018]   |  

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. View Full-Text
Keywords: recurrent congestion; congestion evolution patterns; GPS trajectory; cluster algorithm recurrent congestion; congestion evolution patterns; GPS trajectory; cluster algorithm
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

An, S.; Yang, H.; Wang, J. Revealing Recurrent Urban Congestion Evolution Patterns with Taxi Trajectories. ISPRS Int. J. Geo-Inf. 2018, 7, 128.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top