This article estimated a multinomial logit model to identify the factors determining the severity of pedestrian-vehicle crashes in South Korea. Our results showed that relative to minor crashes, fatal and serious crashes were associated with collisions involving heavy vehicles; drivers who were drunk, male or under the age of 65; pedestrians who were over the age of 65 or female; and pedestrians who were hit in the middle of the road, on high speed roads, in inclement weather conditions, at night, on road links, in tunnels, on bridges, or on wider roads.

a b s t r a c tThis paper presents a Variable Speed Limit (VSL) control algorithm for simultaneously maximizing the mobility, safety and environmental benefit in a Connected Vehicle environment. Development of Connected Vehicle (CV)/Autonomous Vehicle (AV) technology has the potential to provide essential data at the microscopic level to provide a better understanding of real-time driver behavior. This paper investigated a VSL control algorithm using a microscopic approach by focusing on individual driver's behavior (e.g., acceleration and deceleration) through the use of Model Predictive Control (MPC) approach. A multi-objective optimization function was formulated with the aim of finding a balanced trade-off among mobility, safety and sustainability. A microscopic traffic flow prediction model was used to calculate Total Travel Time (TTT); a surrogate safety measure Time To Collision (TTC) was used to measure instantaneous safety; and, a microscopic fuel consumption model (VT-Micro) was used to measure the environmental impact. Real-time driver's compliance to the posted speed limit was used to adjust the optimal speed limit values. A sensitivity analysis was conducted to compare the performance of the developed approach for different weights in the objective function and for two different percentages of CV. The results showed that with 100% penetration rate, the developed VSL approach outperformed the uncontrolled scenario consistently, resulting in up to 20% of total travel time reductions, 6-11% of safety improvements and 5-16% reduction in fuel consumptions. Our findings revealed that the scenario which optimized for safety alone, resulted in more optimum improvements as compared to the multi-criteria optimization. Thus, one can argue that in case of 100% penetration rates of CVs, optimizing for safety alone is enough to achieve simultaneous and optimum improvements in all measures. However, mixed results were obtained in case of lower % penetration rate which showed higher collision risk when optimizing for only mobility or fuel consumption. This indicates that with such % penetration rate, multi-criteria optimization is crucial to realize optimum and balanced benefits for the examined measures.

No abstract

The aim of this paper is to develop insight into the potential of reinforcement learning (RL) agents and distributed reinforcement learning agents in the domain of transportation and traffic engineering and specifically in Intelligent Transport Systems (ITS). This paper provides a crystallized, comprehensive overview of the concept of RL and presents related successful applications in the field of traffic control and transportation engineering. It is divided into two parts: the first part provides a thorough overview of RL and its related methods and the second part reviews most recent applications of RL algorithms to the field of transportation engineering. Finally, it identifies many open research subjects in transportation in which the use of RL seems to be promising.Key words: reinforcement learning, machine learning, traffic control, artificial intelligence, intelligent transportation systems.

No abstract