Exploring the Potential of Social Media Data in Interpreting Traffic Congestion: A Case Study of Jiangsu Freeways

Zhao Liu; Shanglu He; Fan Ding; Huachun Tan; Yingshun Liu

doi:10.1061/9780784484869.147

Exploring the Potential of Social Media Data in Interpreting Traffic Congestion: A Case Study of Jiangsu Freeways

Zhao Liu
, Shanglu He^*
, Fan Ding
, Huachun Tan
, Yingshun Liu

^*Corresponding author for this work

Southeast University, Nanjing
Nanjing University of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Social network platforms provide more open data interfaces for the public, which could be a source for various applications, especially for mining traffic jams. This study aims to identify the congestion-prone areas of Jiangsu freeways via Sina Weibo and further analyze the characteristics of the areas. Specifically, this study first obtained congestion-related microblogs from Sina Weibo using a web crawler, then a series of data preprocessing methods were applied to optimize the information. Second, the document frequency-based method was adopted to figure out the congestion-prone areas. Afterward, the characteristics of the areas were explored by the Apriori-based algorithm and the co-occurrence network diagram. Finally, the experiment results were testified with the ground truth from the authority “Jiangsu Freeway 96777.” It indicated that the identified congestion-prone areas fit well with those from the ground truth. Therefore, the proposed methods provide an auxiliary way to interpret traffic congestion for traffic operators.

Original language	English
Title of host publication	CICTP 2023
Subtitle of host publication	Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals
Editors	Yanyan Chen, Jianming Ma, Guohui Zhang, Haizhong Wang, Lijun Sun, Zhengbing He
Publisher	American Society of Civil Engineers (ASCE)
Pages	1535-1546
Number of pages	12
ISBN (Electronic)	9780784484869
DOIs	https://doi.org/10.1061/9780784484869.147
Publication status	Published - 2023
Externally published	Yes
Event	23rd COTA International Conference of Transportation Professionals: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation, CICTP 2023 - Beijing, China Duration: 14 Jul 2023 → 17 Jul 2023

Publication series

Name	CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals

Conference

Conference	23rd COTA International Conference of Transportation Professionals: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation, CICTP 2023
Country/Territory	China
City	Beijing
Period	14/07/23 → 17/07/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Access to Document

10.1061/9780784484869.147

Cite this

Liu, Z., He, S., Ding, F., Tan, H., & Liu, Y. (2023). Exploring the Potential of Social Media Data in Interpreting Traffic Congestion: A Case Study of Jiangsu Freeways. In Y. Chen, J. Ma, G. Zhang, H. Wang, L. Sun, & Z. He (Eds.), CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals (pp. 1535-1546). (CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals). American Society of Civil Engineers (ASCE). https://doi.org/10.1061/9780784484869.147

Liu, Zhao ; He, Shanglu ; Ding, Fan et al. / Exploring the Potential of Social Media Data in Interpreting Traffic Congestion : A Case Study of Jiangsu Freeways. CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals. editor / Yanyan Chen ; Jianming Ma ; Guohui Zhang ; Haizhong Wang ; Lijun Sun ; Zhengbing He. American Society of Civil Engineers (ASCE), 2023. pp. 1535-1546 (CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals).

@inproceedings{7065b86b1fe14a13a0a8a6074c9aae2f,

title = "Exploring the Potential of Social Media Data in Interpreting Traffic Congestion: A Case Study of Jiangsu Freeways",

abstract = "Social network platforms provide more open data interfaces for the public, which could be a source for various applications, especially for mining traffic jams. This study aims to identify the congestion-prone areas of Jiangsu freeways via Sina Weibo and further analyze the characteristics of the areas. Specifically, this study first obtained congestion-related microblogs from Sina Weibo using a web crawler, then a series of data preprocessing methods were applied to optimize the information. Second, the document frequency-based method was adopted to figure out the congestion-prone areas. Afterward, the characteristics of the areas were explored by the Apriori-based algorithm and the co-occurrence network diagram. Finally, the experiment results were testified with the ground truth from the authority “Jiangsu Freeway 96777.” It indicated that the identified congestion-prone areas fit well with those from the ground truth. Therefore, the proposed methods provide an auxiliary way to interpret traffic congestion for traffic operators.",

author = "Zhao Liu and Shanglu He and Fan Ding and Huachun Tan and Yingshun Liu",

note = "Publisher Copyright: {\textcopyright} ASCE.; 23rd COTA International Conference of Transportation Professionals: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation, CICTP 2023 ; Conference date: 14-07-2023 Through 17-07-2023",

year = "2023",

doi = "10.1061/9780784484869.147",

language = "English",

series = "CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals",

publisher = "American Society of Civil Engineers (ASCE)",

pages = "1535--1546",

editor = "Yanyan Chen and Jianming Ma and Guohui Zhang and Haizhong Wang and Lijun Sun and Zhengbing He",

booktitle = "CICTP 2023",

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}

Liu, Z, He, S, Ding, F, Tan, H & Liu, Y 2023, Exploring the Potential of Social Media Data in Interpreting Traffic Congestion: A Case Study of Jiangsu Freeways. in Y Chen, J Ma, G Zhang, H Wang, L Sun & Z He (eds), CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals. CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals, American Society of Civil Engineers (ASCE), pp. 1535-1546, 23rd COTA International Conference of Transportation Professionals: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation, CICTP 2023, Beijing, China, 14/07/23. https://doi.org/10.1061/9780784484869.147

Exploring the Potential of Social Media Data in Interpreting Traffic Congestion: A Case Study of Jiangsu Freeways. / Liu, Zhao; He, Shanglu; Ding, Fan et al.
CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals. ed. / Yanyan Chen; Jianming Ma; Guohui Zhang; Haizhong Wang; Lijun Sun; Zhengbing He. American Society of Civil Engineers (ASCE), 2023. p. 1535-1546 (CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Exploring the Potential of Social Media Data in Interpreting Traffic Congestion

T2 - 23rd COTA International Conference of Transportation Professionals: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation, CICTP 2023

AU - Liu, Zhao

AU - He, Shanglu

AU - Ding, Fan

AU - Tan, Huachun

AU - Liu, Yingshun

N1 - Publisher Copyright: © ASCE.

PY - 2023

Y1 - 2023

N2 - Social network platforms provide more open data interfaces for the public, which could be a source for various applications, especially for mining traffic jams. This study aims to identify the congestion-prone areas of Jiangsu freeways via Sina Weibo and further analyze the characteristics of the areas. Specifically, this study first obtained congestion-related microblogs from Sina Weibo using a web crawler, then a series of data preprocessing methods were applied to optimize the information. Second, the document frequency-based method was adopted to figure out the congestion-prone areas. Afterward, the characteristics of the areas were explored by the Apriori-based algorithm and the co-occurrence network diagram. Finally, the experiment results were testified with the ground truth from the authority “Jiangsu Freeway 96777.” It indicated that the identified congestion-prone areas fit well with those from the ground truth. Therefore, the proposed methods provide an auxiliary way to interpret traffic congestion for traffic operators.

AB - Social network platforms provide more open data interfaces for the public, which could be a source for various applications, especially for mining traffic jams. This study aims to identify the congestion-prone areas of Jiangsu freeways via Sina Weibo and further analyze the characteristics of the areas. Specifically, this study first obtained congestion-related microblogs from Sina Weibo using a web crawler, then a series of data preprocessing methods were applied to optimize the information. Second, the document frequency-based method was adopted to figure out the congestion-prone areas. Afterward, the characteristics of the areas were explored by the Apriori-based algorithm and the co-occurrence network diagram. Finally, the experiment results were testified with the ground truth from the authority “Jiangsu Freeway 96777.” It indicated that the identified congestion-prone areas fit well with those from the ground truth. Therefore, the proposed methods provide an auxiliary way to interpret traffic congestion for traffic operators.

UR - https://www.scopus.com/pages/publications/85174068304

U2 - 10.1061/9780784484869.147

DO - 10.1061/9780784484869.147

M3 - Conference contribution

AN - SCOPUS:85174068304

T3 - CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals

SP - 1535

EP - 1546

BT - CICTP 2023

A2 - Chen, Yanyan

A2 - Ma, Jianming

A2 - Zhang, Guohui

A2 - Wang, Haizhong

A2 - Sun, Lijun

A2 - He, Zhengbing

PB - American Society of Civil Engineers (ASCE)

Y2 - 14 July 2023 through 17 July 2023

ER -

Liu Z, He S, Ding F, Tan H, Liu Y. Exploring the Potential of Social Media Data in Interpreting Traffic Congestion: A Case Study of Jiangsu Freeways. In Chen Y, Ma J, Zhang G, Wang H, Sun L, He Z, editors, CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals. American Society of Civil Engineers (ASCE). 2023. p. 1535-1546. (CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals). doi: 10.1061/9780784484869.147

Exploring the Potential of Social Media Data in Interpreting Traffic Congestion: A Case Study of Jiangsu Freeways

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