Microscopic Simulation Analysis of Connected and Autonomous Cars and Trucks at a Freeway Merge Area

This study recommends strategies to reduce the delay time and increase comfortability of Autonomous Car (AC)-Autonomous Truck (AT) mixed traffic. Several scenarios were tested to evaluate the effects of Adaptive Cruise Control (ACC)/Cooperative Adaptive Cruise Control (CACC) on Autonomous Vehicles’ (AVs) delay time, merging time and comfortability using sensitivity analysis. Also, the simultaneous effects of different percentages of ATs and various time gaps on the delay time were analyzed. The study was conducted on a 5.25 km-long freeway including a merge area using AIMSUN. It was found that 1) increasing the sensitivity to speed and distance errors was not an appropriate strategy since not only it did not reduce the delay and merging times, but it also decreased the comfortability, 2) shorter time gaps between AVs and between platoons decreased the delay and merging times, but it decreased the comfortability of AVs. Hence, there is a trade-off between reduction in delay time and driver’s comfort in shorter time gaps, 3) Maximum platoon size did not affect the delay and merging times significantly, while it increased the comfortability. Thus, a higher maximum platoon size can be effective, 4) cooperative lane-changing (CLC) led to the highest increase in speed of AVs in the on-ramp and the merge sections. However, CLC caused less comfortability, and 5) as the percentages of ATs increased, longer time gaps could be adopted for AVs so that the delay time reduced more significantly. In general, shorter time gaps and CLC decreased the delay and merging times, while both decreased the comfortability. And, higher maximum platoon size did not affect the delay and merging times, while increased comfortability. Thus, it is recommended to develop more advanced AV control strategies with the balance between the delay and comfortability based on the time gaps, platoon size and CLC

A genetic programming system with an epigenetic mechanism for traffic signal control

Traffic congestion is an increasing problem in most cities around the world. It impacts businesses as well as commuters, small cities and large ones in developing as well as developed economies. One approach to decrease urban traffic congestion is to optimize the traffic signal behaviour in order to be adaptive to changes in the traffic conditions. From the perspective of intelligent transportation systems, this optimization problem is called the traffic signal control problem and is considered a large combinatorial problem with high complexity and uncertainty. A novel approach to the traffic signal control problem is proposed in this thesis. The approach includes a new mechanism for Genetic Programming inspired by Epigenetics. Epigenetic mechanisms play an important role in biological processes such as phenotype differentiation, memory consolidation within generations and environmentally induced epigenetic modification of behaviour. These properties lead us to consider the implementation of epigenetic mechanisms as a way to improve the performance of Evolutionary Algorithms in solution to real-world problems with dynamic environmental changes, such as the traffic control signal problem. The epigenetic mechanism proposed was evaluated in four traffic scenarios with different properties and traffic conditions using two microscopic simulators. The results of these experiments indicate that Genetic Programming was able to generate competitive actuated traffic signal controllers for all the scenarios tested. Furthermore, the use of the epigenetic mechanism improved the performance of Genetic Programming in all the scenarios. The evolved controllers adapt to modifications in the traffic density and require less monitoring and less human interaction than other solutions because they dynamically adjust the signal behaviour depending on the local traffic conditions at each intersection