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• 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]

• This research developed a segment‐level derailment prediction model using Canadian derailment inventory data. • The research used a negative binomial modelling technique to predict the number of derailment for each railway segment. • The maximum daily train traffic, maximum train speed, segment length, and number of stations are used to estimate the derailment frequency. Train related accidents, particularly derailments, can lead to severe consequences especially when they involve injuries or fatalities or when they involve hazardous materials that might result in environmental impacts. Whereas numerous road safety studies have suggested appropriate approaches to predicting vehicle-to-vehicle collisions, very few railway safety studies have considered predicting the number of derailments on rail tracks in North America. In addition, the existing few rail safety assessment and derailment prediction models have often been constrained by aggregated data limiting the safety assessments by, for example, failing to consider segment-level characteristics. This paper focused on the development of an integrated database for the development of a segment-level derailment prediction model for Canada's rail network. The primary objective of this paper is to report how challenges in the data integration process were overcome and also to develop a network screening tool to identify segments with high derailment risk in Canada's rail network. Negative binomial regression and the Empirical Bayes technique were used to estimate the predicted number of derailments on Canada's rail network at the segment level. A network screening process was then successfully applied to identify key segments of safety concern: the top ten segments of concern accounted for approximately 1% of the rail network allowing decision makers to focus their derailment mitigation efforts on a manageable part of Canada's vast rail network. The data processing approach and analysis in this study have strong implications for advancing research on rail safety in North America. [ABSTRACT FROM AUTHOR]

• Channelized right turns have been widely implemented to reduce traffic delay. • The impact channelized right turns on pedestrian safety has not been addressed. • The performance of different right-turn designs on pedestrian safety is compared. • Surrogate safety and behavioral measures from multiple facets of safety were used. • Channelized right turns increase pedestrian risks from different safety dimensions. Channelized right turns or slip lanes have been widely implemented as an effective countermeasure of reducing traffic delay and number of conflicts between vehicles at signalized intersections. However, only a few studies have investigated the impact of channelized right turns (in left-band driving countries) on pedestrian safety. Channelized right turns may increase the risks for pedestrians since they bring pedestrian-vehicle interactions in a fully non-signalized environment. Furthermore, the increased turning radius at channelized lanes can lead to higher vehicle speeds. This paper investigates the impact of channelized right turns on pedestrian safety based on surrogate safety and behavior measures. Video data were collected from twelve signalized intersections in the city of Zunyi, China, involving three main types of right-turn designs: 1) non-channelized right-only lanes, 2) non-channelized right-through lanes, and 3) channelized right-turn lanes. Different measures are used, including interaction and behavior measures based on a recent-proposed Distance-Velocity model, the PET measurement, speed measurements, and observations of failures in interactions (pedestrian retreats and evasive maneuvers from pedestrians or vehicles). Results indicate that the design of channelized right-turn lane increases pedestrian risks at signalized intersections from different dimensions of safety. The impact of the nighttime condition on pedestrian safety was also compared. Pedestrians are safer at nighttime at non-channelized locations, while the impact of nighttime conditions on pedestrian safety at channelized intersections was not ascertained. Consequently, cities should be cautious to install channelized intersections as a safety countermeasure. Treatments are needed to improve pedestrian safety if channelized right turns are implemented. [ABSTRACT FROM AUTHOR]