Quantification of the impact of traffic incidents on speed reduction: A causal inference based approach
Journal article, 2021

This paper designs a systemic framework to quantify speed reduction induced by traffic incidents using a causal inference framework. The results can provide a reference to traffic managers for evaluating incident severities, thus take proper control measures after the incident in order not to underestimate or overestimate the negative impact. A two-phase scheme is proposed, including impacted region determination and speed reduction quantification. We first propose a Frame Region (FR) method, based on the shockwave propagation, to determine the spatiotemporal impacted region (SIR) using speed map. It is worth-noting that we design a statistical experiment to prove the rationality of congestion threshold selection. Secondly, we introduce a causal inference method for identifying the matched freeway segments. The traffic condition of finally matched freeway segments can be served as non-incident traffic condition of the incident occurred location, which contributes to quantifying the incident impact on speed reduction. We further demonstrate the proposed method in a case study by taking advantage of an incident record and related real freeway speed data in China. An interesting observation is that, along with the freeway segments away from the incident location, the congestion duration time of different freeway segments firstly rises and then decreases. The case study also illustrates the impact of incident on speed lasts almost 3 h and the congestion caused by the incident spreads 11 km, while the average causal effect of incident on all the impacted freeway segments is 42.3 km/h.

Traffic incident

Spatiotemporal impact

Causal inference

Speed map

Author

Danni Cao

Beijing Jiaotong University

Jianjun Wu

Beijing Jiaotong University

Xianlei Dong

Shandong Normal University

Huijun Sun

Beijing Jiaotong University

Xiaobo Qu

Chalmers, Architecture and Civil Engineering, GeoEngineering

Zhenzhen Yang

Beijing Jiaotong University

Accident Analysis and Prevention

0001-4575 (ISSN)

Vol. 157 106163

Subject Categories

Transport Systems and Logistics

Infrastructure Engineering

Other Civil Engineering

DOI

10.1016/j.aap.2021.106163

PubMed

33989872

More information

Latest update

6/22/2021