To find the passenger Origin-Destination (OD) flow with entry-only smartcard transaction data, the existing rule-based methods estimate the alighting and transfer location approximately, but the accuracy of these rule-based methods may be compromised. To increase the accuracy of the estimation, this paper proposes a learning-based method to estimate real-time OD flow on a bus network. This model is based on the Generative Adversarial Networks self-supervised learning framework, which can allow to learn high-accuracy estimation without using observed passenger alighting data. To show the effectiveness and efficiency of the proposed method, a case study is conducted on the real-world datasets in Tianjin, China, Brisbane, Australia, and Panjin, China. Using the entry-only data in the Tianjin case, results show the effectiveness to estimate accurate OD flow without alighting data. The sensitivity analysis on the proportion of data loss shows that when the loss rate is less than 40%, the proposed method is still capable to provide accurate outputs. Using the ground-truth OD flow data in the Brisbane case, results show the ability to obtain the realistic estimation from flawed OD flow. The robustness analysis results also show the sufficient accuracy for bus planning and operation under various data flaw levels. Using the onboard survey data in Panjin case, the proposed method’s capability of repairing the rule-based estimation is further verified.

Bus platooning on urban bus corridors can improve the efficiency of dedicated bus lanes and reduce energy consumption. However, due to the multiple routes operating on the bus corridor, bus platooning may induce congestion at the bus stops and disturb regular headway. To address these bus operational issues, this paper proposes a two-stage dynamic bus platooning model considering bus routes and provides safe and efficient trajectory planning for buses in each platoon. To solve the proposed model, a Particle Swarm Optimization embedded Dynamic Programming algorithm is customized to find the time of leaving and joining the platoon, and Broyden-Fletcher-Goldfarb-Shanno Sequential Quadratic Programming method is used to control the trajectories of bus platooning in real time. Using the SUMO simulator, a real-world case study is conducted to show the effectiveness of the proposed method over traditional methods. The results show that the fuel consumption is reduced by 18.23%, operational efficiency is improved by 8.15%, and the average passenger waiting time is decreased by 5.73%. Sensitivity analysis reveals the impact of the number of routes, fleet size and stop spacing on system performance. The proposed dynamic platooning method outperforms the CACC method across different fleet sizes. Results show that the desired fleet size is greater than 60 buses, the stop spacing is greater than 500 m, especially in large cities or long-distance bus systems.

the mobile vehicle-to-vehicle charging vehicles (MCV) for electric modular buses (EMB) have greatly advanced in recent years, where the MCV is an electric vehicle with an extra battery that can charge another electric vehicle on the move. In this study, we propose a mobile charging vehicle routing problem for EMBs, in which both MCVs and charging stations are used to charge electric modular buses. This study proposes a joint optimization model for EMBs and MCVs, where the CPLEX solver was used to solve the constructed case to verify the feasibility and validity of the proposed model, and the results show a 10.5% cost reduction compared to the traditional electric modular bus system. Meanwhile, the sensitivity test is designed to reveal the effects of the number of serviced trips on the performance of the electric modular bus system.

The bunching mitigation and eco-driving of buses have traditionally been treated separately in the literature and formulated in different frameworks. Although a large amount of research has been conducted aiming at providing eco-driving advisory to drivers, impacts of bus operation are not considered. Ignoring the constraints induced by buses could result in transit service irregularity. The present work aims to bridge this gap by formulating optimal control model of buses’ eco-speed and service regularity as a non-linear program. In this study, an integrated bus eco-driving speed and holding time controller is developed to obtain the optimal eco-driving advisory and operational schedule of bus fleet. A bi-level optimization model is formulated for the whole transit line with multiple signalized intersections. The proposed model is solved by a hybrid solution algorithm, which embeds the particle swarm optimization for the upper-level problem and the pseudo-spectral method for the lower-level problem. The proposed controller is tested on real-world cases with the Simulation of Urban MObility (SUMO), and is compared to non-control, speed control, and holding control methods. Simulation results show that the proposed controller reduces the headway deviation by 78.27% and reduces total fuel consumption by 23.95%.

This study proposes a robust route optimization model for electric Demand-Responsive Transit (e-DRT) services, where dispatched vehicles may deviate from the determined plan to serve real-time demands. In particular, online partial charging strategies are coordinated with flexible service schedules. To benefit the productivity of the e-DRT system, the route schedule and charging time are changed dynamically. A two-phase Adaptive Large Neighborhood Search (ALNS) -based heuristic is proposed to effectively solve the proposed problem. The baseline case and large-scale cases are presented to verify the effectiveness and accuracy of the proposed method. Comparisons between CPLEX and the proposed algorithm suggest that the proposed algorithm can considerably improve computational efficiency. A comparative analysis shows the proposed model takes 21% less total cost than the alternative non-robust model. Further, two sensitivity tests are designed to unveil the impacts of unmet real-time requests and the charging rate on the e-DRT’s performance.

This research proposes a TSPcut controller that enables connected and automated buses to cut through traffic to make TSP green light. The proposed controller overcomes the shortcomings of conventional TSP strategies and is able to: 1) overtake slowing moving vehicles in order to catch TSP green time; 2) decide the best time to pass the intersection; 3) considering the stochasticity of surrounding traffic; and 4) functional under partially connected and automated environment. It takes full advantage of connected vehicle technology by taking in real-time vehicle and infrastructure information as optimization input. The problem is formulated as an SMPC problem and is solved by a high-efficient dynamic programming algorithm. The nonlinear bicycle model is adopted as the system dynamics to realize CAV bus’s lane-changing and overtaking function. The stochasticity of surrounding traffic is considered as a probability distribution which is transformed into a linear chance constraint. Simulation evaluation is conduct to compare the TSPcut against NTSP, CTSP and BocTSP. Sensitive analysis is conducted for congestion levels. The evaluation results demonstrate that the TSPcut improves the bus delay reduction by 17.9%–49.1%, and the benefits are 3.5% to 16.1% greater than that of other TSP systems. The range is caused by different congestion levels. In addition. Further tests are conducted to analyze how CAV bus’s arrival time and the speed of background traffic influence the performance of the TSPcut.

This research proposes a Model Predictive Control (MPC) based path tracker controller. It is designed for maneuvering an autonomous driving vehicle to follow its desired trajectory smoothly and accurately. The proposed path tracker has the following features: i) formulated in the time and space mixed-domain to improved control accuracy ii) with consideration of vehicle dynamics; iii) with consideration of vehicle control delay. Simulation and field test results demonstrate that the maximum longitudinal speed error is 2.3km/h and the maximum lateral position error is 11cm. It is 27% smaller than that of the conventional path-trackers. Moreover, the average computation time of the proposed path-tracker is 12 milliseconds on a laptop equipped with an Intel i7-4710MQ CPU. It indicates that the proposed path tracker is ready for real-time implementation.

This study investigates the problem of roadside unit (RSU) location optimization for information propagation under stochastic traffic conditions. The goal of RSU location optimization is to promote multihop information propagation in the Internet of Vehicles which is the promising application of the Internet of Things in transportation. Considering the information propagation time is significantly affected by traffic density and traffic density is endowed with randomness, the problem is formulated as a two-stage mixed-integer nonlinear stochastic programming. The model aims to minimize the sum of the cost associated with RSU investment and the expectation of the penalty cost associated with the network information propagation time exceeding an acceptable threshold. In the first stage of the programming, the number and location of RSUs are determined when network-wide traffic density is not realized. In the second stage, given the RSU location schemes determined in the first stage and the realization of traffic density, the information propagation shortest paths are determined for all origin-destination pairs to minimize network information propagation time. A genetic algorithm (GA) integrated with the solution of a mixed-integer linear programming (GA-MILP) is proposed to solve the model. Numerical results indicate that the advantage of the proposed model in the reduced information propagation time per cost over the deterministic model can be up to 15.54%. Compared with the conventional GA, the GA-MILP has 10.01% higher computation efficiency. This further leads to a 14.73% lower objective value achieved by the GA-MILP when the number of iterations is 50.

This article presents a mathematical model to design a demand-responsive connector (DRC) bus operational network for improving the service quality and accessibility of public transportation systems. The proposed model features an integrated framework that simultaneously guides passengers to reach their nearest bus stops and routes buses to transport passengers at selected bus stops to connected stations of major transit systems. Passengers' preferred time for pick up is fully considered in the model. For this purpose, this study proposes a multi-objective mixed-integer linear programing model to effectively capture the interactions between users with their predefined service time windows and DRC bus network. This study further develops a three-stage heuristic to yield suboptimal solutions to the model in a reasonable time. Case study results demonstrate the effectiveness of the proposed model.

This research proposes a distributed Successive Platoon-Lane-Change (SuPLC) controller based on optimal control.