Improving the throughput of transportation networks with a time-optimization routing strategy

ABSTRACT

Traffic congestion, a common and complicated phenomenon in urban transportation systems, is attracting increasing attention from researchers in Geographical Information Science (GIS) and other fields. In this study, we illustrate a general mechanism that reveals the relationship between travel time and dynamic traffic conditions. We measure a vehicle’s travel time to its destination along any path, where the travel time is calculated based on the path length and on the real-time traffic volume and transport capacity of each road segment on the path. On the basis of this measurement, we present a simple dynamic routing strategy that allows each vehicle to dynamically choose the path to its destination while imposing the minimum travel time. The application of our routing algorithm to the Chengdu street network, Barabási–Albert scale-free network and Erdös–Rényi random network shows that the proposed strategy remarkably improves network throughput and balances traffic load distribution. Our findings suggest that mining the time mechanism of network transport is important to explore efficient time-optimization routing algorithms to enhance the transport capacity of urban street networks and other kinds of networks.

KEYWORDS:

• Traffic flow
• routing algorithm
• travel time
• network throughput
• GIS

View correction statement:

Erratum

Acknowledgments

This work was supported by the National Natural Science Foundation of China (NSFC) (Grant Nos. 41401434, 41602355, 41671432), the Scientific Research Foundation of the Education Department of Sichuan Province of China (Grant No. 16ZB0107), the Scientific Innovation Team of Remote Sensing Science and Technology of Chengdu University of Technology (Grant No. KYTD201501), the Youth Fund of Chengdu University of Technology (Grant No. 2016QJ02), the National Basic Surveying and Mapping Technology Project (Grant No. 2017KJ0303), and the Sichuan Provincial Bureau on Surveying and Mapping Project (Grant No. 2017ZC05).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (NSFC) [Grant Nos. 41401434, 41602355 and 41671432], the Scientific Research Foundation of the Education Department of Sichuan Province of China [Grant No. 16ZB0107], the Scientific Innovation Team of Remote Sensing Science and Technology of Chengdu University of Technology [Grant No. KYTD201501], the Youth Fund of Chengdu University of Technology [Grant No. 2016QJ02], the National Basic Surveying and Mapping Technology Project [Grant No. 2017KJ0303] and the Sichuan Provincial Bureau on Surveying and Mapping Project [Grant No. 2017ZC05].

Notes on contributors

Gang Liu

Gang Liu is an Associate Professor in the College of Earth Sciences at Chengdu University of Technology, Chengdu, China. E-mail: [email protected]. His research interests include geographical information systems, urban transportation planning, geospatial analysis and complex networks.

Wen Long

Wen Long is a graduate student in the College of Earth Sciences at Chengdu University of Technology, Chengdu, China. E-mail: [email protected]. Her research interests include urban transportation planning and complex networks.

Jingchao Wang

Jingchao Wang a graduate student in the College of Tourism and Urban-Rural Planning at Chengdu University of Technology, Chengdu, China. E-mail: [email protected]. His research interests include urban transportation planning.

Peichao Gao

Peichao Gao is a PhD student in the Department of Land Surveying and Geo-Informatics at The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. E-mail: [email protected]. His research interests include network analysis and geospatial analysis.

Jing He

Jing He is a Lecturer in the College of Earth Sciences at Chengdu University of Technology, Chengdu, China. E-mail: [email protected]. His research interests include remote sensing, spatial network modeling and geospatial visualization.

Zhiyong Luo

Zhiyong Luo a Lecturer in the College of Earth Sciences at Chengdu University of Technology, Chengdu, China. E-mail: [email protected]. His research interests include big data, geospatial analysis and cartography.

Lian Li

Lian Li is an Engineer in the Sixth Topographic Survey Team at National Administration of Surveying, Mapping and Geoinformation, Chengdu, China. E-mail: [email protected]. His research interests include geographical information systems and spatial network modeling.

Yongshu Li

Yongshu Li is a Professor in the Faculty of Geosciences and Environmental Engineering at Southwest Jiaotong University, Chengdu, China. E-mail: [email protected]. His research interests include geographical information systems, transportation and geospatial analysis.