A review of road safety evaluation methods based on driving behavior

Du, Zijun; Deng, Min; Lyu, Nengchao; Wang, Yugang

doi:10.1016/j.jtte.2023.07.005

Cited by 3 publications

(2 citation statements)

References 112 publications

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“…In recent years, traffic safety evaluation has emerged as a pivotal research focus, with the aim of identifying potential safety hazards and proposing targeted enhancements to elevate road traffic safety. This focus underscores the critical importance of proactive measures in mitigating risks and ensuring safer travel for all road users [5,6]. Two primary methods for traffic safety evaluation are methods based on statistical models and methods based on conflicts [7].…”

Section: Surrogate Safety Measuresmentioning

confidence: 99%

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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: Surrogate Safety Measuresmentioning

confidence: 99%

“…This enables the interpretation of the temporal proximity of conflicts to indicate the probability of a potential conflict, even in the absence of an actual collision [21]. The calculation of TTR is governed by Equation (5).…”

Section: Potential Conflict Risk Indexmentioning

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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Exploring speeding behavior using naturalistic car driving data from smartphones

Kontaxi,

Tzoutzoulis,

Ziakopoulos

et al. 2023

Journal of Traffic and Transportation Engineering (English Edit

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Mapping urban mobility using vehicle telematics to understand driving behaviour

Xiang,

Ghaffarpasand,

Pope

2024

Sci Rep

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Telematics data, primarily collected from on-board vehicle devices (OBDs), has been utilised in this study to generate a thorough understanding of driving behaviour. The urban case study area is the large metropolitan region of the West Midlands, UK, but the approach is generalizable and translatable to other global urban regions. The new approach of GeoSpatial and Temporal Mapping of Urban Mobility (GeoSTMUM) is used to convert telematics data into driving metrics, including the relative time the vehicle fleet spends idling, cruising, accelerating, and decelerating. The telematics data is also used to parameterize driving volatility and aggressiveness, which are key factors within road safety, which is a global issue. Two approaches to defining aggressive driving are applied and assessed, they are vehicle jerk (the second derivative of vehicle speed), and the profile of speed versus acceleration/deceleration. The telematics-based approach has a very high spatial resolution (15–150 m) and temporal resolution (2 h), which can be used to develop more accurate driving cycles. The approach allows for the determination of road segments with the highest potential for aggressive driving and highlights where additional safety measures could beneficially be adopted. Results highlight the strong correlation between vehicle road occupancy and aggressive driving.

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A review of road safety evaluation methods based on driving behavior

Cited by 3 publications

References 112 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 speeding behavior using naturalistic car driving data from smartphones

Mapping urban mobility using vehicle telematics to understand driving behaviour

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