Shaowei Yu

To provide data support for developing fixed-route DRT based on FRT to reduce operating costs inside base routes in urban-rural traffic corridors, this paper estimated running time and energy losses due to unproductive stops at bus stations in urban-rural traffic corridors. Firstly, 14 urban-rural bus routes without ticket sellers in Xi’an are selected to demonstrate the universality of unproductive stops at bus stations. Secondly, a model for estimating running time and energy losses based on the VT-CPFM model is developed. Finally, running time and energy losses due to unproductive stops in two representative urban-rural traffic corridors are estimated. Estimated results show that the average running time loss ratios of different rounds in Routes 332, 333, 335, 338 and G1 range from 8.30% to 17.52% and that average fuel loss ratios range from 9.16% to 13.30%. In addition, the monetary loss in energy consumption of Route G1 in 2019 is estimated to be up to 193213 yuan. This study proves that unproductive stops at bus stations generally exist in urban-rural bus routes and can result in significant running time and energy losses and that developing fixed-route DRT based on FRT leveraging V2I with mobile APP in representative urban-rural traffic corridors is very necessary, which is expected to reduce energy consumption and running time.

The conventional bus propagation process has two main shortcomings: one is bus bunching, the other is extra energy consumption by idling at signalized intersection and unexpected speed variation along the route. To overcome these problems simultaneously, an extended bus propagation model and an anti-bunching control are proposed. To extend the time-based bus propagation model, we employ finite state machine and intelligent driver model to establish a spatial-temporal based bus propagation model accounting for bus dynamic motion and passenger swapping behavior between bunching buses. To mitigate bus bunching and improve bus fuel economy in a cyber-traffic environment, an anticipated average speed planning is employed to improve headway regularity and reduce the chance of encountering red light, and then model predictive control accounting for state and control constraints is used to generate a smooth speed trajectory for connected bus to follow the commands given by anticipated average speed planning, which will in turn ensure that all the connected buses traverse signalized intersection and approach downstream stop in ecological driving behaviors. Numerical simulations show that the proposed model can imitate passenger swapping behavior when bus bunching occurs, and the anti-bunching control is suitable to mitigate bus bunching and guide connected bus to traverse signalized intersection and reach downstream stop with fewer delays.

This paper focuses on the effects of vehicle gap changes on fuel and emission performance of the simulated traffic flow in the adaptive cruise control (ACC) strategy. Firstly, the close correlation of vehicle gap changes and the host car's behaviors was explored with the measured car-following data. Secondly, the correlation between the host car's velocity and vehicle gap changes with different memory steps was also explored to develop the nth car’s optimal velocity function. Thirdly, a microscopic traffic simulation program was created for analyzing the traffic flow evolution process and approximately estimating the fuel consumptions and exhaust emissions. As a result, it was seen that vehicle gap changes with memory significantly affect fuel economy and emission performance of the simulated traffic flow in the ACC strategy, which can result in low fuel consumptions and exhaust emissions. This study is an incremental step forward for designing the control strategy of the ACC system.

To avoid unnecessary fuel loss in signalized intersections, this paper presents a dynamic speed strategy considering two successive intersections during green phase in real time. It is assumed that the vehicle can receive signal phase and timing (SPaT) and position information via vehicle-to-infrastructure and vehicle-to-vehicle communications by dedicated short range communications (DSRC). To improve the optimization speed of the strategy and implement related constraints, an approximate speed profile optimization strategy is simplified and presented. When a vehicle approaches the communication range of DSRC and receives the information, optimization strategy minimizes the fuel consumption through repeated iterations until the vehicle passes the second intersection. The results indicate that fuel savings can reach up to 69%, which is significantly beneficial for a connected vehicle to pass two successive intersections if it can follow the speed advisors strictly.