Dynamic traffic bottlenecks identification based on congestion diffusion model by influence maximization in metro‐city scales-Reference-Cited by-同舟云学术

Dynamic traffic bottlenecks identification based on congestion diffusion model by influence maximization in metro‐city scales

Published:2020-10-09 Issue:6 Volume:33 Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat Pract Exper

Author:

Zhao Baoxin¹^ORCID,Xu Cheng‐Zhong²,Liu Siyuan³,Zhao Juanjuan⁴,Li Li⁴

Affiliation:

1. Shenzhen College of Advanced Technology University of Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences Shenzhen China

2. State Key Lab of IoTSC, Faculty of Science Technology University of Macau Macau China

3. Smeal College of Business Pennsylvania State University State College Pennsylvania USA

4. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.5790

Reference46 articles.

1. Understanding congested travel in urban areas;Çolak S;Nature Commun,2016

2. Cooperative Evaluation of the Cause of Urban Traffic Congestion via Connected Vehicles

3. GuzmanW YoungL PeszynskiK. Addressing the cause of the problem and not its symptom: Road congestion at railway stations. Paper presented at: Proceedings of the 40th Australasian Transport Research Forum (ATRF 2018);2018; Darwin Northern Territory Australia.

4. AvniO KaplanY. A method and system to identify congestion root cause and recommend possible mitigation measures based on cellular data and related applications thereof; October 25 2018 US Patent App. 15/769 068.

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Network-Based, Data-Driven Methodology for Identifying and Ranking Freight Bottlenecks;Data Science for Transportation;2024-09-09

2. A Network-Based, Data-Driven Methodology for Identifying and Ranking Freight Bottlenecks;2023

3. Influence blocking maximization on networks: Models, methods and applications;Physics Reports;2022-09

4. Identification, cost evaluation, and prioritization of urban traffic congestions and their origin;Scientific Reports;2022-07-29