The economic development of cities is closely connected to their transport infrastructure and systems. Implementing effective transport policies is important to enhance the efficiency of the exiting transportation system. An important question is the degree to which these policy effects align with the initial objectives of these policies over time. Monitoring and understanding the longitudinal effects of transport policies is critical for effective decision-making.

This paper presents a methodology to assess the effect of the length of passing zone on the operation and safety of two-lane rural highways based on the probability and the rate of passing maneuvers ending in a no-passing zone. The methodology was applied using observed passing maneuver data collected with tripod-mounted camcorders at passing zones in Uganda. Findings show that the rate at which passing maneuvers end in a no-passing zone increases with traffic volume and unequal distribution of traffic in the two directions, absolute vertical grade, and percent of heavy vehicles in the subject direction. Additionally, the probability of passing maneuvers ending in a no-passing zone reaches 0.50 when the remaining sight distance from the beginning of the passing zone is 245. m for passenger cars or short trucks (2-3 axles), and 300. m for long trucks (4-7 axles) as the passed vehicles. These results suggest policy changes in design and marking of passing zones to enhance safety and operation of two-lane rural highways.

The main goal of in-vehicle technologies and co-operative services is to reduce congestion and increase traffic safety. This is achieved by alerting drivers on risky traffic conditions ahead of them and by exchanging traffic and safety related information for the particular road segment with nearby vehicles. Road capacity, level of service, safety, and air pollution are impacted to a large extent by car-following behavior of drivers. Car-following behavior is an essential component of micro-simulation models. This paper investigates the impact of an infrastructure-to-vehicle (I2V) co-operative system on drivers' car-following behavior. Test drivers in this experiment drove an instrumented vehicle with and without the system. Collected trajectory data of the subject vehicle and the vehicle in front, as well as socio-demographic characteristics of the test drivers were used to estimate car-following models capturing their driving behavior with and without the I2V system. The results show that the co-operative system harmonized the behavior of drivers and reduced the range of acceleration and deceleration differences among them. The observed impact of the system was largest on the older group of drivers.

In-vehicle technologies and cooperative services are attracting a lot of attention for their potential to deal with congestion problems and improve traffic safety. This paper aims to investigate the impact of infrastructure-to-vehicle cooperative systems, case of COOPERS, at the aggregate level, on traffic performance. A factorial experiment is designed with two factors: traffic demand and penetration of the system with three levels each. In total, nine scenarios are investigated. To replicate driving behavior with and without the system, speed distributions from a simulator experiment are used. A motorway section of 4 km is built in VISSIM simulation software. Indicators such as speed, density, delays and travel times are chosen to evaluate and compare the motorway performance with and without the system. The results show that drivers driving with the system activated are more aware and alert to near future traffic conditions compared to driving without the system. Driving with the system activated is characterized by smoother and longer speed decelerations when approaching critical incident/accident events. The results show as well that the factors investigated significantly impact the motorway performance. Congestion reduces the impact of the system whereas higher penetration levels improve traffic operation on the motorway. Future research directions can include (1) investigating the impact of the system at the micro level such as lane changing or car-following behaviors; (2) levels of compliance with the system, which is an important aspect as well.

In-vehicle technologies and co-operative services have potential to ease congestion problems and improve traffic safety. This paper investigates the impact of infrastructure-to-vehicle co-operative systems, case of CO-OPerative SystEms for Intelligent Road Safety (COOPERS), on driver behavior. Thirty-five test drivers drove an instrumented vehicle, twice, with and without the system. Data related to driving behavior, physiological measurements, and user acceptance was collected. A macro-level approach was used to evaluate the potential impact of such systems on driver behavior and traffic safety. The results in terms of speeds, following gaps, and physiological measurements indicate a positive impact. Furthermore, drivers' opinions show that the system is in general acceptable and useful.

Car-following behavior, which describes the behavior of a vehicle while following the vehicle in front of it, has a significant impact on traffic performance, safety, and air pollution. In addition, car-following is an essential component of micro-simulation models. Over the last decade the use of microscopic simulation models as a tool for investigating traffic systems, ITS applications, and emission impacts, is becoming increasingly popular. The paper presents a flexible framework for modeling car-following behavior that relaxes some limitations and assumptions of the most commonly used car following models. The proposed approach recognizes different regimes in driving such as car-following, free-flow, emergency stopping, and incorporates different decisions in each regime, such as acceleration, deceleration, and do-nothing depending on the situation. A case study using NGSIM vehicle trajectory data is used to illustrate the proposed model structure. Statistical tests suggest that the model performs better than previous models.