A combined use of microscopic traffic simulation and extreme value methods for traffic safety evaluation

Wang, Chen; Xu, Chengcheng; Xia, Jingxin; Qian, Zhendong; Lu, Linjun

doi:10.1016/j.trc.2018.03.011

Cited by 124 publications

(46 citation statements)

References 21 publications

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“…Two signalized intersections in Fengxian District in Shanghai were selected in this study, one for testing and another for validation. At the initial stage of this study, video clips were collected by portable cameras, set up on roadside high-rise buildings [11]. This set-up provided enough view coverage to collect most data needed, including traffic conflicts, volume, and headway.…”

Section: Data Collectionmentioning

confidence: 99%

“…Conflicts were identified based on traditional TCT methods, by four trained observers. Those observers were early examined their intrareliability and interreliability, by watching other video clips [11]. A clear sign of evasive actions (e.g., braking, swerving) was used as the sign of conflicts.…”

Section: Data Collectionmentioning

confidence: 99%

“…In this study, a commercial microscopic simulation package VISSIM was used to develop simulation models for the two intersections. VISSIM has been extensively used in simulation studies [9][10][11][15][16][17], due to its high flexibility in simulating actual traffic conditions, especially microscopic driver behaviors [18,19]. Geometric data were collected based on aerial photographs captured by drone and field observations.…”

Section: Simulation Model Developmentmentioning

confidence: 99%

“…To do this, it is important to reflect actual vehicle interactions. Previous research found that by incorporating a safety MOP (e.g., conflict counts) for calibration, simulation models appeared to better reflect actual vehicle interactions than calibrating effective MOPs [7][8][9][10][11]. However, such calibration strategy requires additional calibration efforts of safety-related data collection.…”

Section: Introductionmentioning

confidence: 99%

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On the Effects of Various Measures of Performance Selections on Simulation Model Calibration Performance

Wang

2018

Journal of Advanced Transportation

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Objective. This paper examines the effects of various measures of performance (MOP) selections on simulation model calibration performance, in terms of reflecting actual traffic conditions and vehicle interactions. Method. Two intersections in Shanghai were selected for simulation model calibration, one for testing and another for validation. Three effective MOPs were utilized, including average travel time (i.e., time passing the intersection), average queue length, and vehicle headway distribution. The counts of three types of traffic conflicts (i.e., crossing, rear-end, and lane change) were used as safety MOPs. Those MOPs, as calibration objectives, were examined and compared. Results. The results of the testing site showed that different effective MOPs had their own advantages: average travel time appeared to be the best in reflecting lane change and rear-end conflicts while headway distribution performed the best consistency between simulated and actual crossing conflicts. Compared to the safety MOPs, average travel time and headway distribution still performed better, in terms of resulting in more similar simulated conflict metrics (e.g., TTC, PET) to actual ones. A multicriteria calibration strategy based on average travel time and headway distribution generally had better performances in reflecting actual traffic conditions and vehicle interactions than using any single effective or safety MOP. Similar results were found for the validation site. Conclusion. To simulate actual traffic conditions and vehicle interactions, multiple effective MOPs could be simultaneously considered for model calibration, instead of using safety MOPs.

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Section: Data Collectionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Simulation Model Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Effects of Various Measures of Performance Selections on Simulation Model Calibration Performance

Wang

2018

Journal of Advanced Transportation

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“…Sometimes, even deep-learning based vision algorithms did not perform well in those low-visibility conditions [12][13][14], due to relatively low image quality. us, some additional e orts were o en added to improve detection performance, such as image enhancement methods [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition

Wang

Dai

Zhou

et al. 2020

Journal of Advanced Transportation

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In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. First, Retinex image enhancement algorithm was introduced to improve the quality of images, collected under low-visibility conditions (e.g., heavy rainy days, foggy days and dark night with poor lights). Then, a Yolo v3 model was trained to detect multiple objects from images, including fallen pedestrians/cyclists, vehicle rollover, moving/stopped vehicles, moving/stopped cyclists/pedestrians, and so on. Then, a set of features were developed from the Yolo outputs, based on which a decision model was trained for crash detection. An experiment was conducted to validate the model framework. The results showed that the proposed framework achieved a high detection rate of 92.5%, with relatively low false alarm rate of 7.5%. There are some useful findings: (1) the proposed model outperformed empirical rule-based detection models; (2) image enhancement method can largely improve crash detection performance under low-visibility conditions; (3) the accuracy of object detection (e.g., bounding box prediction) can impact crash detection performance, especially for minor motor-vehicle crashes. Overall, the proposed framework can be considered as a promising tool for quick crash detection in mixed traffic flow environment under various visibility conditions. Some limitations are also discussed in the paper.

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Why do road traffic collision types repeat themselves? Look back before moving forward

Das

Shafie

Hamim

et al. 2021

Hum Ftrs & Erg Mfg Svc

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Fatalities due to road crashes are rising globally, with the situation being particularly severe in low‐income countries. Traditional investigation methods have reached a ceiling in terms of reducing road traffic fatalities, hence safety researchers have begun to explore systems‐based approaches. In this study, two bus‐pedestrian collisions occurring in Bangladesh and separated by a time span of 13 years have been analyzed using the Accimap methodology. Building on and going further than the Accimaps of the two collisions, the recommendations that have been proposed to reduce the recurrence of such collision types have been graphically represented in a combined Preventimap. This graphically displays the inter‐connectedness of the countermeasures. Further, the countermeasures have been mapped against six road safety E's, namely engineering, education, enforcement, economics, emergency response, and enablement. Comparing the countermeasures arising from each of the two collisions showed that some preventive strategies are unique to one collision while others are common to both collisions. With a significant proportion of the proposed recommendations being of similar types, we discuss the idea that had prompt and appropriate intervention measures been implemented at different levels of the system after the first collision, the probability of the occurrence of the second collision would have been significantly less. The same would apply to similar collisions in the future. We also discuss the benefits of the Preventimap representation and its potential uses in road safety policy and practice.

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A combined use of microscopic traffic simulation and extreme value methods for traffic safety evaluation

Cited by 124 publications

References 21 publications

On the Effects of Various Measures of Performance Selections on Simulation Model Calibration Performance

On the Effects of Various Measures of Performance Selections on Simulation Model Calibration Performance

A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition

Why do road traffic collision types repeat themselves? Look back before moving forward

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