How many crashes can connected vehicle and automated vehicle technologies prevent: A meta-analysis

Wang, Ling; Zhong, Hao; Ma, Wanjing; Abdel-Aty, Mohamed; Park, Juneyoung

doi:10.1016/j.aap.2019.105299

Cited by 105 publications

(53 citation statements)

References 46 publications

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“…By combining the trajectories of vehicles, the conflicts between pedestrians/bikes and vehicles could be explored extensively. Moreover, the proposed method could be integrated into connected vehicle systems to provide information to drivers (33)(34)(35).…”

Section: Discussionmentioning

confidence: 99%

Automated Safety Diagnosis Based on Unmanned Aerial Vehicle Video and Deep Learning Algorithm

Abdel-Aty

Zheng

et al. 2020

Transportation Research Record: Journal of the Transportation R

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This paper presents an automated traffic safety diagnostics solution named “Automated Roadway Conflict Identification System” (ARCIS) that uses deep learning techniques to process traffic videos collected by unmanned aerial vehicle (UAV). Mask region convolutional neural network (R-CNN) is employed to improve detection of vehicles in UAV videos. The detected vehicles are tracked by a channel and spatial reliability tracking algorithm, and vehicle trajectories are generated based on the tracking algorithm. Missing vehicles can be identified and tracked by identifying stationary vehicles and comparing intersect of union (IOU) between the detection results and the tracking results. Rotated bounding rectangles based on the pixel-to-pixel manner masks that are generated by mask R-CNN detection are introduced to obtain precise vehicle size and location data. Based on the vehicle trajectories, post-encroachment time (PET) is calculated for each conflict event at the pixel level. By comparing the PET values and the threshold, conflicts with the corresponding pixels in which the conflicts happened can be reported. Various conflict types: rear-end, head on, sideswipe, and angle, can also be determined. A case study at a typical signalized intersection is presented; the results indicate that the proposed framework could significantly improve the accuracy of the output data. Moreover, safety diagnostics for the studied intersection are conducted by calculating the PET values for each conflict event. It is expected that the proposed detection and tracking method with UAVs could help diagnose road safety problems efficiently and appropriate countermeasures could then be proposed.

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Section: Discussionmentioning

confidence: 99%

Automated Safety Diagnosis Based on Unmanned Aerial Vehicle Video and Deep Learning Algorithm

Abdel-Aty

Zheng

et al. 2020

Transportation Research Record: Journal of the Transportation R

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“…For future work, the choice of distribution types can be expanded when analysing the travel time distribution, since the multimode distribution can provide a better fitting performance under some traffic conditions [34][35][36][37]. is paper mainly examined the difference of rainy weather's impact on TTR under different conditions; thus, the difference of other adverse weather should also be investigated if the sample size for each category is sufficient.…”

Section: Discussionmentioning

confidence: 99%

Examining the Impact of Adverse Weather on Travel Time Reliability of Urban Corridors in Shanghai

Zou

Zhu

Xie

et al. 2020

Journal of Advanced Transportation

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Travel time reliability (TTR) is widely used to evaluate transportation system performance. Adverse weather condition is an important factor for affecting TTR, which can cause traffic congestions and crashes. Considering the traffic characteristics under different traffic conditions, it is necessary to explore the impact of adverse weather on TTR under different conditions. This study conducted an empirical travel time analysis using traffic data and weather data collected on Yanan corridor in Shanghai. The travel time distributions were analysed under different roadway types, weather, and time of day. Four typical scenarios (i.e., peak hours and off-peak hours on elevated expressway, peak hours and off-peak hours on arterial road) were considered in the TTR analysis. Four measures were calculated to evaluate the impact of adverse weather on TTR. The results indicated that the lognormal distribution is preferred for describing the travel time data. Compared with off-peak hours, the impact of adverse weather is more significant for peak hours. The travel time variability, buffer time index, misery index, and frequency of congestion increased by an average of 29%, 19%, 22%, and 63%, respectively, under the adverse weather condition. The findings in this study are useful for transportation management agencies to design traffic control strategies when adverse weather occurs.

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“…By extracting the traffic crash records involving the pre-crash behavior of non-motorists from the National Automotive Sampling System—General Estimates System (NASS-GES), Dong et al [ 9 ] examined the risk factors influencing the injury severities of non-motorists (including pedestrians and cyclists) at mid-block crossing, and the role of the non-motorists’ pre-crash behavior in their resulting injury severity outcomes was also explored based on a mixed logit model. Besides, some typical studies [ 17 , 18 , 19 ] relating to the traffic crash severity also provided useful references to the study of pedestrian injury severity.…”

Section: Literature Reviewmentioning

confidence: 99%

Examining Injury Severity of Pedestrians in Vehicle–Pedestrian Crashes at Mid-Blocks Using Path Analysis

Peng

Chen

2020

IJERPH

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Walking is a sustainable mode of transport which has well established health and environmental benefits. Unfortunately, hundreds of thousands of pedestrians lose their lives each year over the world due to involvement in road traffic crashes, and mid-blocks witness a significant portion of pedestrian fatalities. This study examined the direct and indirect effects of various contributing factors on the pedestrian injury severity in vehicle–pedestrian crashes at mid-blocks. Data of vehicle–pedestrian crashes during 2002–2009 were extracted from the NASS-GES, with pre-crash behaviors and injury severity included. The SEM path analysis method was applied to uncover the inter-relationships between the pedestrian injury severity and various explanatory variables. Both the direct and indirect effects of these explanatory variables on the pedestrian injury severity were calculated based on the marginal effects in the multinomial and ordered logit models. The results indicate some variables including number of road lanes and the age of pedestrian have indirect impacts on the injury severity through influencing the pre-crash behaviors. Although most indirect effects are relatively small compared with the direct effects, the results in this study still provide some valuable information to improve the overall understanding of pedestrian injury severity at mid-blocks.

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How many crashes can connected vehicle and automated vehicle technologies prevent: A meta-analysis

Cited by 105 publications

References 46 publications

Automated Safety Diagnosis Based on Unmanned Aerial Vehicle Video and Deep Learning Algorithm

Automated Safety Diagnosis Based on Unmanned Aerial Vehicle Video and Deep Learning Algorithm

Examining the Impact of Adverse Weather on Travel Time Reliability of Urban Corridors in Shanghai

Examining Injury Severity of Pedestrians in Vehicle–Pedestrian Crashes at Mid-Blocks Using Path Analysis

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