Search

Abstract: This research presents a disaggregated modeling approach for investigating the link between winter road collision occurrence, weather, road surface conditions, traffic exposure, temporal trends and site-specific effects. This approach is unique as it allows for quantification of the safety effects of different winter road maintenance activities at an operational level. Different collision frequency models are calibrated using hourly data collected from 31 different highway routes across Ontario, Canada. It is found that factors such as visibility, precipitation intensity, air temperature, wind speed, exposure, month of the winter season, and storm hour have statistically significant effects on winter road safety. Most importantly, road surface conditions are identified as one of the major contributing factors, representing the first contribution showing the empirical relationship between safety and road surface conditions at such a disaggregate level. The applicability of the modeling framework is demonstrated using several examples, such as quantification of the benefits of alternative maintenance operations and evaluation of the effects of different service standards using safety as a performance measure. [Copyright &y& Elsevier]

Abstract: This research presents a modeling approach to investigate the association of the accident frequency during a snow storm event with road surface conditions, visibility and other influencing factors controlling for traffic exposure. The results have the premise to be applied for evaluating different maintenance strategies using safety as a performance measure. As part of this approach, this research introduces a road surface condition index as a surrogate measure of the commonly used friction measure to capture different road surface conditions. Data from various data sources, such as weather, road condition observations, traffic counts and accidents, are integrated and used to test three event-based models including the Negative Binomial model, the generalized NB model and the zero inflated NB model. These models are compared for their capability to explain differences in accident frequencies between individual snow storms. It was found that the generalized NB model best fits the data, and is most capable of capturing heterogeneity other than excess zeros. Among the main results, it was found that the road surface condition index was statistically significant influencing the accident occurrence. This research is the first showing the empirical relationship between safety and road surface conditions at a disaggregate level (event-based), making it feasible to quantify the safety benefits of alternative maintenance goals and methods. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a methodology to estimate and/or update SPF parameters under limited data conditions. [•] An extensive simulation exercise is developed to demonstrate the robustness of the suggested method. [•] Results clearly show the appropriateness of the presented technique. [•] This study contributes to unification of SPF updating process and understanding of comparative aspects of a large number of priors. [ABSTRACT FROM AUTHOR]

• An integrated framework is proposed to predict lane-changing risk. • Driving intentions are recognized using LSTM neural network. • LGBM algorithm achieves a higher lane-changing risk prediction accuracy. • Feature importance analysis is conducted using LGBM classifier. Proactive lane-changing (LC) risk prediction can assist driver's LC decision-making to ensure driving safety. However, most previous studies on LC risk prediction did not consider the driver's intention recognition, which made it difficult to guarantee the timeliness and practicability of LC risk prediction. Moreover, the difference in driving risks and its influencing factors between LC to left lane (LCL) and LC to right lane (LCR) have rarely been investigated. To bridge the above research gaps, this study proposes a proactive LC risk prediction framework which integrates the LC intention recognition module and LC risk prediction module. The Long Short-term Memory (LSTM) neural network with time-series input was employed to recognize the driver's LC intention. The Light Gradient Boosting Machine (LGBM) algorithm was then applied to predict the LC risk. Feature importance analysis was lastly conducted to obtain the key features that affect the LC risk. The highD trajectory dataset was used for framework validation. Results show that the recognition accuracy of the driver's LCL, LCR and lane-keeping (LK) intentions based on the proposed LSTM model are 97%, 96% and 97%, respectively. Meanwhile, the LGBM algorithm outperforms other machine learning algorithms in LC risk prediction. The results from feature importance analysis show that the interaction characteristics of the LC vehicle and its preceding vehicle in the current lane have the greatest impact on the LC risk. The proposed framework could potentially be implemented in advanced driver-assistance system (ADAS) or autonomous driving system for improved driving safety. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we aim to identify the different factors that influence injury severity of highway vehicle occupants, in particular drivers, involved in a vehicle-train collision at highway-railway grade crossings. The commonly used approach to modeling vehicle occupant injury severity is the traditional ordered response model that assumes the effect of various exogenous factors on injury severity to be constant across all accidents. The current research effort attempts to address this issue by applying an innovative latent segmentation based ordered logit model to evaluate the effects of various factors on the injury severity of vehicle drivers. In this model, the highway-railway crossings are assigned probabilistically to different segments based on their attributes with a separate injury severity component for each segment. The validity and strength of the formulated collision consequence model is tested using the US Federal Railroad Administration database which includes inventory data of all the railroad crossings in the US and collision data at these highway railway crossings from 1997 to 2006. The model estimation results clearly highlight the existence of risk segmentation within the affected grade crossing population by the presence of active warning devices, presence of permanent structure near the crossing and roadway type. The key factors influencing injury severity include driver age, time of the accident, presence of snow and/or rain, vehicle role in the crash and motorist action prior to the crash. [Copyright &y& Elsevier]