Quantification of the impact of traffic incidents on speed reduction: A causal inference based approach. (July 2021)

• Record Type: Journal Article
• Authors: Cao, Danni; Wu, Jianjun; Dong, Xianlei; Sun, Huijun; Qu, Xiaobo; Yang, Zhenzhen
• Is Part Of: Accident analysis and prevention. Volume 157(2021)
• Publication Date: 2021-07
• Subjects: Traffic incident -- Spatiotemporal impact -- Speed map -- Causal inference; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2021.106163
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Firstly, from the image recognition perspective, we propose a method called FR based on the propagation characteristic of traffic congestion to determine the spatiotemporal impacted region (SIR). Secondly we apply causal inference to quantify the impact on speed reductions induced by the incident. Thirdly we provide a proof method to illustrate the rationality of the congestion threshold in FR using statistical hypothesis testing. The proposed method can avoid subjective judgment when we determine the SIR for a certain incident. Abstract: 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.