Tracking traffic congestion and accidents using social media data : A case study of Shanghai
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Article number | 106618 |
Journal / Publication | Accident Analysis and Prevention |
Volume | 169 |
Online published | 26 Feb 2022 |
Publication status | Published - May 2022 |
Link(s)
Abstract
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.
Research Area(s)
- Geographic information science, Kernel density estimation, Natural language processing, Social media data, Traffic accident, Traffic congestion
Bibliographic Note
Citation Format(s)
In: Accident Analysis and Prevention, Vol. 169, 106618, 05.2022.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review