Analysis of lane-changing conflict between cars and trucks at freeway merging sections using UAV video data

Lu, Yichen; Cheng, Kai; Zhang, Yue; Chen, Xinqiang; Zou, Yajie

doi:10.1080/19439962.2022.2147611

Cited by 8 publications

(2 citation statements)

References 46 publications

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“…The videos are shot in 4K resolution (4096 × 2160 pixels) at a frame rate of 25 Hz, totaling 3 h, 38 min, and 6 s of footage. In this analysis, the vehicles are categorized into two groups: cars and trucks, with the latter being defined as vehicles exceeding 6 m in length [46]. The vehicle trajectory processing, as outlined by Chen et al [47], encompasses three stages.…”

Section: The Outer Ring Expressway Datasetmentioning

confidence: 99%

A New Surrogate Safety Measure Considering Temporal–Spatial Proximity and Severity of Potential Collisions

Tang,

Lu,

Liao

et al. 2024

Applied Sciences

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Accurate identification and analysis of traffic conflicts through surrogate safety measures (SSMs) are crucial for safety evaluation in road systems. Existing SSMs for conflict identification and analysis mostly consider the temporal–spatial proximity of conflicts without taking into account the severity of potential collisions. This makes SSMs unsuitable for traffic safety evaluation in complex road environments. In order to address the shortcomings above, this study first introduces a new SSM called the Potential Conflict Risk Index (PCRI). To validate the effectiveness of PCRI, the inD dataset is adopted for conflict identification comparison between time-to-collision (TTC) and PCRI. Using PCRI, this study conducts a conflict analysis in the freeway merging areas based on the data from the Outer Ring Expressway Dataset (ORED), accounting for differences between cars and trucks. The comparative results between TTC and PCRI show that PCRI can provide a more comprehensive identification of conflicts and a more accurate identification of the moment with the highest conflict risk. The results of conflict analysis suggest that conflicts occur more frequently in situations involving trucks, and these conflicts commonly occur in closer proximity to the on-ramp at freeway merging areas. The findings from this study can improve the accuracy of conflict identification under different conflict patterns, enhancing the specificity of traffic safety measures and ultimately ensuring the safety of road systems.

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Section: The Outer Ring Expressway Datasetmentioning

confidence: 99%

A New Surrogate Safety Measure Considering Temporal–Spatial Proximity and Severity of Potential Collisions

Tang,

Lu,

Liao

et al. 2024

Applied Sciences

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“…Another widely used method to analyze driving behavior is by examining the trajectory of vehicles. Researchers have used onboard cameras or drones to record vehicle trajectories and explore crash risk factors combined with microscopic trafc-fow indicators [18]. Meanwhile, more publicly available datasets of naturalistic driving studies are used to assess driving risk [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Behavior-Driven Crash Risk Prediction Model: The Role of Onboard Navigation Data in Road Safety

Ma,

Lu,

Wong

2023

Journal of Advanced Transportation

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Driving behavior has frequently been overlooked in previous road traffic crash research. Hereby, abnormal (extreme) driving behavior data transmitted by the onboard navigation systems were collected for vehicles involved in traffic crashes, including sharp-lane-change, sharp-acceleration, and sudden-braking behaviors. Using these data in conjunction with expressway crash records, multiple classification learners were trained to establish a behavior-driven risk prediction model. To further investigate the influence of driving behavior on crash risk, partial dependence plots (PDPs) were applied. Regression analyses indicate that models have a stronger effect when derivative features such as frequency of specific deviant behavior, speed, and acceleration in the behavior process are included. The behavioral RUSBoost model surpasses other models, achieving an AUC prediction metric of 0.782 and outperforming traditional traffic-flow-driven machine learning models. PDP analysis demonstrates that the sudden-braking behavior is the leading contributory factor of expressway crashes, particularly when the acceleration exceeds 0.5 G. This study confirms the potential of predicting crash risks through augmenting behavior data from navigation software; the findings lay a foundation for countermeasures.

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A surrogate model-based approach for adaptive selection of the optimal traffic conflict prediction model

Wu,

Lee,

et al. 2024

Accident Analysis & Prevention

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Analysis of lane-changing conflict between cars and trucks at freeway merging sections using UAV video data

Cited by 8 publications

References 46 publications

A New Surrogate Safety Measure Considering Temporal–Spatial Proximity and Severity of Potential Collisions

A New Surrogate Safety Measure Considering Temporal–Spatial Proximity and Severity of Potential Collisions

Exploring the Behavior-Driven Crash Risk Prediction Model: The Role of Onboard Navigation Data in Road Safety

A surrogate model-based approach for adaptive selection of the optimal traffic conflict prediction model

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