Purpose: Advanced driving assistance system (ADAS) has been applied in commercial vehicles. This paper aims to evaluate the influence factors of commercial vehicle drivers’ acceptance on ADAS and explore the characteristics of each key factors. Two most widely used functions, forward collision warning (FCW) and lane departure warning (LDW), were considered in this paper.Design/methodology/approach: A random forests algorithm was applied to evaluate the influence factors of commercial drivers’ acceptance. ADAS data of 24 commercial vehicles were recorded from 1 November to 21 December 2018, in Jiangsu province. Respond or not was set as dependent variables, while six influence factors were considered.Findings: The acceptance rate for FCW and LDW systems was 69.52% and 38.76%, respectively. The accuracy of random forests model for FCW and LDW systems is 0.816 and 0.820, respectively. For FCW system, vehicle speed, duration time and warning hour are three key factors. Drivers prefer to respond in a short duration during daytime and low vehicle speed. While for LDW system, duration time, vehicle speed and driver age are three key factors. Older drivers have higher respond probability under higher vehicle speed, and the respond time is longer than FCW system.Originality/value: Few research studies have focused on the attitudes of commercial vehicle drivers, though commercial vehicle accidents were proved to be more severe than passenger vehicles. The results of this study can help researchers to better understand the behavior of commercial vehicle drivers and make corresponding recommendations for ADAS of commercial vehicles.

The primary objective of this study was to develop a quantitative approach to estimate bus dwell time and time lost serving stop, which included acceleration and deceleration time, dead time, and time for serving boarding and alighting passengers. A polynomial model incorporating kinematics of a particle was derived for estimating bus acceleration and deceleration time. In addition, descriptive statistics methods were used to analyze dead time. A case study was conducted to show the applicability of the proposed model with data collected from the seven most common types of bus stops in China. R-square and Mean Absolute Percentage Error (MAPE) were calculated to be 0.8840 and 13.20% for non-peak and 0.8387 and 13.46% for peak, indicating the method was well-validated and could be practically used in China. Further research can be conducted to investigate the effects of different weather conditions and locations on the performance of the proposed method.

Pedestrians are the most vulnerable road users and are at risk of severe consequences when involved in traffic accidents. The purpose of this research is to determine the factors that have significant impacts on the increasing likelihood of pedestrians being seriously injured or killed when involved in a collision with a single vehicle at an intersection over a recent 6-year period. Both 2013–2015 General Estimates System (GES) and 2016–2018 Crash Report Sampling System (CRSS) crash data were used in the analysis. Logistic regression models for the two crash datasets showed that there were four common significant variables affecting pedestrians’ injury levels. The following pairwise comparisons of these common significant factors using the Wald chi-square statistic test showed similar log-odds with few exceptions, suggesting that these affecting factors share similar effects from 2013 through 2018. In both datasets, results showed that a high likelihood of pedestrians’ severe injuries was associated with pedestrians older than 25, dark lighting conditions, light trucks and buses, and vehicles’ straight maneuver. Furthermore, the GES data distinguished further factors imposing higher threats on pedestrians as being drivers’ 19–25 age group, speeding, pedestrians’ roadway crossings maneuvers, and rain conditions. Crashes that occurred at intersections with more than two lanes or during summertime had significantly higher odds of resulting in severe injuries for pedestrians than crashes at two-lane intersections or during wintertime, respectively, in the CRSS dataset. Results of this study contribute to a better understanding of the recent changes in pedestrian safety at intersections and potential countermeasure design suggestions.