This paper develops a vehicle scheduling method for the electric bus (EB) route considering stochastic volatilities in trip travel time and energy consumption. First, a model for estimating the trip energy consumption is proposed based on field‐collected data, and the probability distribution function of trip energy consumption considering the stochastic volatility is determined. Second, we propose the charging strategy to recharge buses during their idle times. The impacts of stochastic volatilities on the departure time, the idle time, the battery state of charge, and the energy consumption of each trip are analyzed. Third, an optimization model is built with the objectives of minimizing the expectation of delays in trip departure times, the summation of energy consumption expectations, and bus procurement costs. Finally, a real bus route is taken as an example to validate the proposed method. Results show that reasonable idle times can be generated by optimizing the scheduling plan, and it is helpful to stop the accumulation of stochastic volatilities. Collaboratively optimizing vehicle scheduling and charging plans can reduce the EB fleet and delay times while meeting the route operation needs.

Purpose: The purpose of this study is to develop an optimization method for charging plans with the implementation of time-of-day (TOD) electricity tariff, to reduce electricity bill. Design/methodology/approach: Two optimization models for charging plans respectively with fixed and stochastic trip travel times are developed, to minimize the electricity costs of daily operation of an electric bus. The charging time is taken as the optimization variable. The TOD electricity tariff is considered, and the energy consumption model is developed based on real operation data. An optimal charging plan provides charging times at bus idle times in operation hours during the whole day (charging time is 0 if the bus is not get charged at idle time) which ensure the regular operation of every trip served by this bus. Findings: The electricity costs of the bus route can be reduced by applying the optimal charging plans. Originality/value: This paper produces a viable option for transit agencies to reduce their operation costs.

Accurate prediction of bus arrival time at the stop line is a vital element to the bus signal priority system, but most previous approaches focused on predicting bus arrival times at next bus stops only. This paper develops a travel time prediction model to predict bus arrival time on the basis of global positioning system (GPS) data. Bus travel time from the detected location to stop line is divided into three parts: travel time from present point to the end of anterior queue, waiting time for the green light, and time for discharging anterior queue vehicles. Case studies were conducted in real-life signalized intersections to evaluate the performance of the model. Results showed that the presented model provided acceptable prediction accuracy. In addition, by considering the log interval of GPS data and prediction error, a determination method of the optimal decision-making zone when using sequential GPS data was developed.

The prevailing reinforcement-learning-based traffic signal control methods are typically staging-optimizable or duration-optimizable, depending on the action spaces. In this paper, we use hybrid proximal policy optimization to synchronously optimize the stage specification and green interval duration. Under reformulated traffic demands, the intrinsic imperfections of (implementing optimization in) discrete or continuous action spaces are revealed. By comparison, hybrid action space offers a unified search space, in which our proposed method is able to better balance the trade-off between frequent switching and unsaturated release. Experiments in both single-agent and multi-agent scenarios are given to demonstrate that the proposed method reduces queue length and delay by an average of 12.72% and 11.89%, compared to the state-of-the-art RL methods. Furthermore, by calculating the Gini coefficients of right-of-way, we reveal that the proposed method does not harm fairness while improving efficiency.

The emission of greenhouse gases from public transportation has aroused extensive public attention in recent years. Electric buses have the advantage of zero emission, which could prevent the further deterioration of environmental problems. Since 2018, the number of electric buses has exceeded that of traditional buses. Thus, it is an inevitable trend for the sustainable development of the automobile industry to replace traditional fuel buses, and developing electric buses is an important measure to relieve traffic congestion. Furthermore, the bus scheduling has a significant impact on passenger travel times and operating costs. It is common that passenger demand at different stops is uneven in a public transportation system. Since applying all-stop scheduling only cannot match the passenger demand of some stops with bus resources, this paper proposes an integrated all-stop and short-turning service for electric buses, reducing the influence of uneven ridership on load factor to enhance transit attractiveness. Simultaneously, considering the time-of-use pricing strategy used by the power sector, the combinational charging strategy of daytime and overnight is proposed to reduce electricity costs. Finally, the branch-and-price algorithm is adopted to solve this problem. Compared with all-stop scheduling, the results demonstrate a reduction of 13.5% in total time cost under the combinational scheduling.