Investigating factors influencing drivers' speed selection behavior under reduced visibility conditions

Zhang, Weihua; Hu, Zhe; Feng, Zhongxiang; Ma, Changxi; Wang, Kun; Zhang, Xuxin

doi:10.1080/15389588.2018.1453134

Cited by 18 publications

(11 citation statements)

References 17 publications

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“…In this study, a mixed logit model (MXL) is used to predict the probability of the three driverinjury severity outcomes at highway-rail grade crossing accidents reported by police to estimate discrete driver-injury severity outcomes [32,33],…”

Section: Mixed Logit Modelmentioning

confidence: 99%

The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

Hao

Xiang

et al. 2018

Journal of Advanced Transportation

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116

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The effect of aggressive driving behavior on driver's injury severity is analyzed by considering a comprehensive set of variables at highway-rail grade crossings in the US. In doing so, we are able to use a mixed logit modelling approach; the study explores the determinants of driver-injury severity with and without aggressive driving behaviors at highway-rail grade crossings. Significant differences exist between drivers' injury severity with and without aggressive driving behaviors at highway-rail grade crossings. The level of injury for younger male drivers increases a lot if they are with aggressive driving behavior. In addition, driving during peak-hour is found to be a statistically significant predictor of high level injury severity with aggressive driving behavior. Moreover, environmental factors are also found to be statistically significant. The increased level of injury severity accidents happened for drivers with aggressive driving behavior in the morning peak (6-9 am), and the probability of fatality increases in both snow and fog condition. Driving in open space area is also found to be a significant factor of high level injury severity with aggressive driving behaviors. Bad weather conditions are found to increase the probability of drivers' high level injury severity for drivers with aggressive driving behaviors.

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Section: Mixed Logit Modelmentioning

confidence: 99%

The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

Hao

Xiang

et al. 2018

Journal of Advanced Transportation

Self Cite

116

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“…To conform to the model logic, according to the MI of Model 1, the corresponding path was increased from large to small, and the modified Model 2 was obtained. The fitting results are shown in Table 5, which indicate that the fitting degree of Model 2 is acceptable (Zhang et al, 2018;Wang et al, 2019). The drivers' SA influential factors path relationship is shown in Figure 4 for Models 1 and 2.…”

Section: Performance Of Different Modelsmentioning

confidence: 83%

“…While existing research as to the role of gender in promoting SA is mixed, with some studies finding that gender did not have a significant impact on SA in various driving scenarios (Heenan et al, 2014), some studies have found that gender could result in significant differences in driving behavior (Machin and Sankey, 2008;Zhang et al, 2018;Wang et al, 2019). This factor requires further investigation.…”

Section: Proposed Methodology Factors Affecting Situation Awarenessmentioning

confidence: 97%

Structural Equation Modeling of Drivers’ Situation Awareness Considering Road and Driver Factors

Yang

Chen

et al. 2020

Front. Psychol.

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Driver's situation awareness (SA) is one of the key elements that affect driving decisionmaking and driving behavior. SA is influenced by many factors, and previous studies have focused only on individual factors. This study presents a comprehensive study to explore the path relationships and influence mechanism between SA and all influential factors, including road characteristics, driver characteristics and states, distracting elements, and cognitive ability. A structural equation model that relates SA to its influential factors is developed. A total of 324 valid questionnaires were collected to analyze and identify the relationships between the factors. The results show that the preceding influential factors have significant effects on SA, which is consistent with previous research. Based on path coefficients, positive effects were: cognitive abilities (0.500), driver state (0.360), age (0.277), driving experience (0.198), and gender (0.156). Negative effects were: distracting elements (−0.253) and road characteristics (−0.213). The results of this comprehensive study provide a valuable reference for the development of driver training programs and driving regulations.

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“…The corresponding odds ratios are 2.57 during dark hours with street lighting, and 4.61 during dusk or dawn conditions. Since speed selection behavior of drivers in reduced visibility changes with their varying driving ability, the vehicle's speed is not always within a safe range, which contributes to the increment in the fatality risk [41].…”

Section: Basic Modelmentioning

confidence: 99%

Analyzing Pedestrian Fatality Risk in Accidents at Mid-Blocks

Chen¹,

Ma²,

Chen³

2019

JTTs

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Objective: Pedestrian safety is considered as one of the greatest concerns, especially for developing countries. In the year of 2015, about 48% pedestrian accidents with 56% fatalities occurred at mid-blocks in Beijing. Since the high frequency and fatality risk, this study focused on pedestrian accidents taking place at mid-blocks and aimed at identifying significant factors. Methods: Based on total 10,948 crash records, a binary logit model was established to explore the impact of various factors on the probability of pedestrian's death. Furthermore, first-degree interaction effects were introduced into the basic model. The Hosmer-Lemeshow goodness-of-fit test was used to assess the model performance. Odds ratio was calculated for categorical variables to compare significant accident conditions with the conference level. Variables within consideration in this study included weather, area type, road type, speed limit, pedestrian location, lighting condition, vehicle type, pedestrian gender and pedestrian age. Results: The calibration results of the model show that the increased fatality chances of an accident at mid-blocks are associated with normal weather, rural area, two-way divided road, crossing elsewhere in carriageway, darkness (especially for no street lighting), light vehicle, large vehicle and male pedestrian. With road speed limit increasing by 10 km/h, the probability of death accordingly increases by 46%. Older victims have higher chances of being killed in a crash. Moreover, three interaction effects are found significant: rural area and two-way divided, rural area and crossing elsewhere as well as speed limit and pedestrian age. Conclusions: This study has analyzed police accident data and identified factors significant to the death probability of pedestrians in accidents occurred at mid-blocks. Recommendations and improving measures were proposed correspondingly. Behaviors of different road users at mid-blocks should be taken into account in the future research.

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Investigating factors influencing drivers' speed selection behavior under reduced visibility conditions

Cited by 18 publications

References 17 publications

The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

Structural Equation Modeling of Drivers’ Situation Awareness Considering Road and Driver Factors

Analyzing Pedestrian Fatality Risk in Accidents at Mid-Blocks

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