Conflict analytics through the vehicle safety space in mixed traffic flows using UAV image sequences

Chen, Albert Y.; Chiu, Yen-Lin; Hsieh, Ming-Che; Lin, Po-Wei; Angah, Ohay

doi:10.1016/j.trc.2020.102744

Cited by 39 publications

(7 citation statements)

References 48 publications

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“…Figure 8 shows the impact of vehicle speed and position on MTPA during the process of driving out of a long tunnel. It can be seen that the variation range of the MTPA value is (15)(16)(17)(18)(19)(20) in stage 3, suddenly increases to (25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) in stage 4, and decreases to (5-15) in stage 5. Comparing the height of the MTPA in the three sub-graphs, it can be found that the MTPA value has experienced a significant increase from stage 3 to stage 4, and then a drastic decrease from stage 4 to stage 5 when the vehicle leaves the long tunnel.…”

Section: E Effect Of Speed and Position On Mtpamentioning

confidence: 96%

“…rough the image data collected by UAV at urban intersections, the SVM classifier could extract vehicle type, position, speed, and trajectory characteristics, thus realizing the detection of safe space between vehicles [33]. Support Vector Regression (SVR) is often used in traffic flow prediction and traffic safety analysis.…”

Section: Application Of the Support Vector Machine Model Inmentioning

confidence: 99%

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Traffic Safety Optimization Strategy of Mountainous Highway Tunnel Based on GA-SVR Visual Load Model

Shang

Bao

et al. 2022

Journal of Advanced Transportation

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Improving the driving safety of mountainous highway tunnels has become an urgent problem in China, while the existing literature pays more attention to the safety of urban tunnels. From the perspective of visual load, this paper built a GA-SVR model to analyze the influences of speed, design brightness, measured brightness, and position in the Gaogu long tunnel. The results show the following: firstly, the changes of MTPA in the long mountainous highway tunnels can be divided into five stages, which is different from the three-stage division of urban tunnels; secondly, the influencing degree of factors was varied in different stages: the position factor mattered most in stages 1, 2, 4, and 5, while the design brightness had the greatest impact in stage 3; thirdly, the driver’s psychological pressure was greatest on the entrance and exit section of the tunnel; lastly, the increased length of mountainous highway tunnels and the long-term enclosed driving environment made the psychological load of drivers intensified. Therefore, it was necessary for the mountainous highway tunnels to consider more accurate gradual lighting design in the 200-m sections after the entrance and before the exit, meanwhile enhancing traffic safety management and protections in the middle of the tunnel.

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Section: E Effect Of Speed and Position On Mtpamentioning

confidence: 96%

Section: Application Of the Support Vector Machine Model Inmentioning

confidence: 99%

Traffic Safety Optimization Strategy of Mountainous Highway Tunnel Based on GA-SVR Visual Load Model

Shang

Bao

et al. 2022

Journal of Advanced Transportation

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“…In most traffic conflict analysis, the predetermined pre-crash conditions are typically used as prerequisites for parameter calibration [32,33]. On the basis of this assumption, this section will describe the injury mechanism from the perspective of pre-crash analysis and derive several indicators in a prior formulation to measure the potential risk.…”

Section: Theorymentioning

confidence: 99%

Injury Risk Assessment and Interpretation for Roadway Crashes Based on Pre-Crash Indicators and Machine Learning Methods

et al. 2023

Applied Sciences

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Pre-crash injury risk (IR) assessment is essential for guiding efforts toward active vehicle safety. This work aims to conduct crash severity assessment using pre-crash information and establish the intrinsic mechanism of IR with proper interpretation methods. The impulse–momentum theory is used to propose novel a priori formulations of several severity indicators, including velocity change (ΔV), energy equivalent speed (EES), crash momentum index (CMI), and crash severity index (CSI). Six IR models based on different machine learning methods were applied to a fusion dataset containing 24,082 vehicle-level samples. Prediction results indicate that the pre-crash indicators (PCIs) are more influential than the commonly used basic crash information because the average accuracy of six models can be improved by 14.35% after utilizing PCIs. Furthermore, the features’ importance and their marginal effects are interpreted based on parameter estimation, Shapley additive explanation value, and partial dependence. The ΔV, EES, and CMI are identified as the determinant indicators of the potential IR, and their partial distributions are significantly influenced by the crash type and impact position. Based on partial dependence probabilities, the study establishes decision thresholds for PCIs for each severity category for different impact positions, which can serve as a useful reference for developing targeted safety strategies. These results suggest that the proposed method can effectively improve pre-crash IR assessment, which can be readily transferred to safety-related modeling in an active traffic management system.

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“…Collision data reports are unreliable because they are biased toward more damaging and harmful collisions (Tageldin et al 2015;Ismail et al 2009). As such, state-of-the-art traffic safety research has gravitated toward the proactive TCT approach (Chen et al 2020;Fu et al 2019). Although the collection of conflict indicators can be done through manual observations, recent research focused on the automated collection of traffic-conflict data using computer vision techniques (Arun et al 2021a, b, c).…”

Section: Computer Vision and Traffic Conflict Techniquesmentioning

confidence: 99%

Effect of Redesigning Public Shared Space Amid the COVID-19 Pandemic on Physical Distancing and Traffic Safety

Gouda

Fan

Luc

et al. 2021

J. Transp. Eng., Part A: Systems

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The concept of redesigning public spaces to encourage physical distancing amid the COVID-19 pandemic is being tested around the world. In Canada, municipalities are reallocating underutilized road lanes for active modes of transportation, such as walking and cycling. We evaluated the usage and benefit of these shared spaces to ensure redesign efforts are optimally allocated. We analyzed two sets of closedcircuit television (CCTV) footage before and after the change, covering April 7-13, 2020, at two locations using automated computer vision techniques. We detected and recorded physical distancing violations, traffic safety risks such as midblock crossing, speeds, and traffic conflicts, and generated trajectory maps of all road users. It was found that the redesign was utilized effectively by road users and improved physical distancing compliance without compromising traffic safety. The proposed framework also provides an innovative tool to automatically gather, extract, share, and analyze real-world data to improve response to the COVID-19 pandemic as well as future outbreaks of contagious diseases.

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Conflict analytics through the vehicle safety space in mixed traffic flows using UAV image sequences

Cited by 39 publications

References 48 publications

Traffic Safety Optimization Strategy of Mountainous Highway Tunnel Based on GA-SVR Visual Load Model

Traffic Safety Optimization Strategy of Mountainous Highway Tunnel Based on GA-SVR Visual Load Model

Injury Risk Assessment and Interpretation for Roadway Crashes Based on Pre-Crash Indicators and Machine Learning Methods

Effect of Redesigning Public Shared Space Amid the COVID-19 Pandemic on Physical Distancing and Traffic Safety

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