Tracking traffic congestion and accidents using social media data

Tracking traffic congestion and accidents using social media data: A case study of Shanghai

Chang, Haoliang (City University of Hong Kong)
Li, L. (TU Delft Air Transport & Operations; City University of Hong Kong)
Huang, Jianxiang (The University of Hong Kong)
Zhang, Qingpeng (City University of Hong Kong)
Chin, Kwai Sang (City University of Hong Kong)

2022

Traffic congestion and accidents take a toll on commuters' daily experiences and society. Locating the venues prone to congestion and accidents and capturing their perception by public members is invaluable for transport policy-makers. However, few previous methods consider user perception toward the accidents and congestion in finding and profiling the accident- and congestion-prone areas, leaving decision-makers unaware of the subsequent behavior responses and priorities of retrofitting measures. This study develops a framework to identify and characterize the accident- and congestion-prone areas heatedly discussed on social media. First, we use natural language processing and deep learning to detect the accident- and congestion-relevant Chinese microblogs posted on Sina Weibo, a Chinese social media platform. Then a modified Kernel Density Estimation method considering the sentiment of microblogs is employed to find the accident- and congestion-prone regions. The results show that the 'congestion-prone areas' discussed on social media are mainly distributed throughout the historical urban core and the Northwest of Pudong New Area, in reasonably good agreements with actual congestion records. In contrast, the 'accident-prone areas' are primarily found in locations with severe accidents. Finally, the above venues are characterized in spatio-temporal and semantic aspects to understand the nature of the incidents and assess the priority level for mitigation measures. The outcomes can provide a reference for traffic authorities to inform resource allocation and prioritize mitigation measures in future traffic management.

Geographic information science
Kernel density estimation
Natural language processing
Social media data
Traffic accident
Traffic congestion

http://resolver.tudelft.nl/uuid:8176ef00-566e-4e21-99eb-7fbfa678e68d

https://doi.org/10.1016/j.aap.2022.106618

2023-07-01

0001-4575

Accident Analysis & Prevention, 169

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2022 Haoliang Chang, L. Li, Jianxiang Huang, Qingpeng Zhang, Kwai Sang Chin