Eco-Driving Systems for Connected Automated Vehicles: Multi-Objective Trajectory Optimization

This study aims to leverage advances in connected automated vehicle (CAV) technology to design an eco-driving and platooning system that can improve the fuel and operational efficiency of vehicles during freeway driving. Following a two-stage control logic, the proposed algorithm optimizes CAVs’ trajectories with three objectives: travel time minimization, fuel consumption minimization, and traffic safety improvement. The first stage, designed for CAV trajectory planning, is carried out with two optimization models. The second stage, for real-time control purposes, is developed to ensure the operational safety of CAVs. Based on extensive numerical simulations, the results have confirmed the effectiveness of the proposed framework both in mitigating freeway congestion and in reducing vehicles’ fuel consumption

Development of Model-based Transit Signal Priority Control for local Arterials

AbstractThis paper presents a transit signal priority (TSP) model designed to benefit both bus riders and passenger-car users. Most of conventional priority methods are applied at the isolated intersection. However, this kind of control strategies may failed to reduce the travel time since the prioritized buses have to stop at the downstream intersections. Therefore, along the line of headway-based research, this study intends to develop a new TSP control approach with the concerns of bus passenger delay on the entire arterial. Moreover, a basic method for queue length estimation is presented to evaluate the impacts of TSP control on passenger cars. The control objective is to minimize bus passenger waiting time at the downstream bus stop, simultaneously ensuring the total person delay of entire intersection is not increased. Using the microscopic simulation, the proposed strategy has shown its benefits in reducing bus passenger waiting time and total intersection delay