This study addresses integrated bus maintenance and vehicle scheduling in a day-to-day dynamic operation setting to ensure that regular preventative maintenance and newly identified predictive maintenance needs are handled promptly. The problem is formulated as a mixed-integer linear programming aiming to minimize operational costs and risks over a day-to-day rolling planning horizon. A two-stage decomposition (TSD) method is proposed to solve this challenging problem. In the first stage, bus maintenance is scheduled on a daily basis. The second stage then optimizes specific bus maintenance and vehicle schedules within each day. With such a problem decomposition, the computational complexity is significantly reduced as the within-day problems become independent. Computational experiments conducted on a real-world bus line demonstrate the effectiveness and superiority of the TSD method. Compared to a one-step solution method and an algorithm in a similar existing study, the proposed TSD method consumes far less computation time, while delivering high-quality solutions. Moreover, the TSD solution method can be further greatly accelerated if the within-day problems of the second stage are solved in parallel with different computers. The computational results also highlight the benefits of the proposed two-stage optimization approach in enhancing operational cost-efficiency and bus resource utilization.

Train timetabling plays a major role in railway planning processes, serving as a link between service providers and commuters to ensure reliable service delivery. However, mathematical optimization application to expansive subway systems is uncertain due to challenges in coordinating multiple lines, the necessity for integration with passenger demand, and multi-modal coordination. This study comprehensively reviews three main sub-stages of timetabling research areas: nominal, robust, and rescheduling problems. We synthesized the relevant studies considering the mathematical modeling of designed strategies, the incorporated stakeholders’ views, the characteristics of solution methods, and the extent of their practical use. Recent efforts to enhance subway system resiliency have had a limited focus on applying artificial intelligence methodologies to subway train timetabling, particularly emphasizing rescheduling problems. In the future, the research community is expected to broaden the application of artificial intelligence to encompass various aspects of subway train timetabling, incorporating four suggested considerations. These include coordination between multiple traffic modes, risk control, deep learning and mathematical optimization, and carbon peaking and neutrality goals to achieve standardized net-zero carbon emission objectives. These considerations represent a strategic pathway towards the next generation of subway train control systems. To conclude, the study aims to introduce theoretical aspects and research gaps in subway timetabling for the dedicated subway network and to provide future research directions.

The integrated ride-sourcing mode, developed by third-party integrators, is a feasible solution to market fragmentation because it integrates travel demand and vehicle supply. However, intense competition between platforms reduces the efficiency of the dispatching process. To tackle this issue, a two-stage dispatching framework is proposed, utilizing a partially observable Markov decision process (POMDP) to model the dispatching problem as a mixed cooperative-competitive reinforcement learning task. Within this framework, the Multi-Graph Hierarchical Multi-Head Attention-Deep Deterministic Policy Gradient (MGHMHA-DDPG) algorithm is proposed to determine the generalized values of driver-passenger pairs. A combinatorial optimization model is then formulated to identify the dispatching scheme that maximizes these values. Furthermore, the MGHMHA-DDPG algorithm incorporates a multi-graph convolutional module, a hierarchical multi-head attention module, and a gated recurrent module to model the global supply-demand distribution, the cooperation potential of vehicles, and the hidden features of the temporal dimension, respectively. Experiments using Beijing-based data demonstrate that the MGHMHA-DDPG algorithm outperforms benchmark methods in terms of market revenues and order response rates. This indicates that the MGHMHA-DDPG algorithm effectively mitigates dispatching conflicts between platforms and enhances overall market efficiency.

Shared autonomous electric mobility has attracted significant interest in recent years due to its potential to save energy consumption, enhance mobility accessibility, reduce air pollution, mitigate traffic congestion, etc. Although providing convenient, low-cost, and environmentally-friendly mobility, there are still some roadblocks to achieve efficient shared autonomous electric mobility, e.g., how to enable the accessibility of shared autonomous electric vehicles in time. To overcome these roadblocks, in this article, we designSafari, an efficientSharedAutonomous electric vehicleFleet mAnagement system with jointRepositioning and chargIng based on dynamic deadlines to improve both user experience and operating profits. OurSafariconsiders not only the highly dynamic user demand forvehicle repositioning(i.e., where to relocate) but also many practical factors like the time-varying charging pricing forcharging scheduling(i.e., where to charge). To perform the two tasks efficiently, inSafari, we design a dynamic deadline-based deep reinforcement learning algorithm, which generates dynamic deadlines via usage prediction combined with an error compensation mechanism to adaptively learn the optimal decisions for satisfying highly dynamic and unbalanced user demand in real time. More importantly, we implement and evaluate theSafarisystem with 10-month real-world shared electric vehicle data, and the extensive experimental results show that ourSafariachieves 100% of accessibility and effectively reduces 26.2% of charging costs and reduces 31.8% of vehicle movements for energy saving with a small runtime overhead at the same time. Furthermore, the results also showSafarihas a great potential to achieve efficient and accessible shared autonomous electric mobility during its long-term expansion and evolution process.

Reassignment strategies are of great significance to improve the dispatching efficiency of the ride-sourcing market by reassigning drivers and passengers. However, due to the focus on the feasibility of the reassignment strategy in the short period, previous studies ignore possible reassignment opportunities in the future and inevitably make short-sighted reassignment decisions. To fully exploit the effect of the reassignment strategy, this study proposes a two-stage reassignment framework, which integrates a reinforcement learning algorithm and the bilateral matching reassignment model. The Markov decision process is adopted to dynamically model the reassignment problem. In the framework, the reinforcement learning algorithm is utilized to first learn the randomness and dynamics of travel patterns from historical data and select vehicles participating in the reassignment process. Then, the bilateral matching reassignment model formulates the matching relationship after reassignment for passengers (drivers). Furthermore, for the cases where reassignment may increase individual matching distance, a personalized bilateral matching reassignment model is developed to avoid that. Experiments based on real data in Beijing found that learning passenger travel patterns and adjusting vehicle reassignment moments can greatly improve the passenger experience and reduce driving costs. The results also suggest that the efficiency of the reassignment strategy is influenced by the supply and demand conditions of the ride-sourcing system. This justifies that the framework can be applied to optimize the dispatching process, reduce carbon emissions, and build an eco-friendly travel system.

Electric carsharing, i.e., electric vehicle sharing, as an emerging mobility-on-demand service, has been proliferating worldwide recently. Though providing convenient, low-cost, and environmentally-friendly mobility, there are also some potential roadblocks in electric carsharing services due to existing inefficient fleet management strategies, which relocate the vehicles using predefined periodic schedules without self-adapting to the highly dynamic user demand, and many practical factors like time-variant charging pricing also have not been fully considered. To remedy these problems, in this paper, we design Record, an effective fleet management system with joint Repositioning and Charging for electric carsharing based on dynamic deadlines to improve its operating profits and also satisfy users' real-time pickup and return demand. Record considers not only the highly dynamic user demand for vehicle repositioning (i.e., where to relocate) but also the time-varying charging pricing for charging scheduling (i.e., where to charge). To perform the two tasks efficiently, in Record, we design a dynamic deadline-based distributed deep reinforcement learning algorithm, which generates dynamic deadlines via usage prediction combined with an error compensation mechanism to adaptively search and learn the optimal locations for satisfying highly dynamic and unbalanced user demand in real time. We implement and evaluate the Record system with 10-month real-world electric carsharing data, and the extensive experimental results show that our Record effectively reduces 25.8% of charging costs and reduces 30.2% of vehicle movements by workers, and it also satisfies user demand and achieves a small runtime overhead at the same time.

We are witnessing a rapid growth of electrified vehicles due to the ever-increasing concerns on urban air quality and energy security. Compared to other types of electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to their high owning and operating costs, long charging time, and the uneven spatial distribution of charging facilities. Moreover, the highly dynamic environment factors such as unpredictable traffic congestion, different passenger demands, and even the changing weather can significantly affect electric bus charging efficiency and potentially hinder the further promotion of large-scale electric bus fleets. To address these issues, in this article, we first analyze a real-world dataset including massive data from 16,359 electric buses, 1,400 bus lines, and 5,562 bus stops. Then, we investigate the electric bus network to understand its operating and charging patterns, and further verify the necessity and feasibility of a real-time charging scheduling. With such understanding, we design busCharging , a pricing-aware real-time charging scheduling system based on Markov Decision Process to reduce the overall charging and operating costs for city-scale electric bus fleets, taking the time-variant electricity pricing into account. To show the effectiveness of busCharging , we implement it with the real-world data from Shenzhen, which includes GPS data of electric buses, the metadata of all bus lines and bus stops, combined with data of 376 charging stations for electric buses. The evaluation results show that busCharging dramatically reduces the charging cost by 23.7% and 12.8% of electricity usage simultaneously. Finally, we design a scheduling-based charging station expansion strategy to verify our busCharging is also effective during the charging station expansion process.

Due to the ever-growing concerns over air pollution and energy security, more and more cities have started to replace their conventional taxi fleets with electric ones. Even though environmentally friendly, the rapid promotion of electric taxis raises problems to both taxi drivers and governments, e.g., prolonged waiting/charging time, unbalanced utilization of charging infrastructures, and inadequate taxi supply due to the long charging time. In this article, we conduct the first longitudinal measurement study to understand the long-term evolution of mobility and charging patterns by utilizing 5-year data from one of the largest electric taxi networks in the world, i.e., the Shenzhen electric taxi network in China. In particular, (1) we first perform an electric taxi contextualization about their operation and charging activities; (2) then we design a generic charging event extraction algorithm based on GPS data and charging station data, and (3) based on the contextualization and extracted charging activities, we perform a comprehensive measurement study called ePat to explore the evolution of the electric taxi network from the mobility and charging perspectives. Our ePat is based on 4.8 TB taxi GPS data, 240 GB taxi transaction data, and metadata from 117 charging stations, during an evolution process from 427 electric taxis in 2013 to 13,178 in 2018. Moreover, ePat also explores the impacts of various contexts and benefits during the evolution process. Our ePat as a comprehensive measurement of the electric taxi network mobility and charging evolution has the potential to advance the understanding of the evolution patterns of electric taxi networks and pave the way for analyzing future shared autonomous vehicles.

In urban rail transit systems, energy conservation is a challenging problem due to the rising environmental and social issues. The existing literature on this topic usually ignores time-variant characteristics of passenger demand at each station. Based on the real-world time-dependent smart-card automated fare collection data, this paper develops a bi-objective timetable optimization model to minimize the total passenger waiting time and the pure energy consumption. In the model formulation, the total passenger waiting time is subjected to the train capacity in the oversaturated condition, and the pure energy consumption is represented by the difference between the traction energy consumption and the regenerative energy within a given period. Numerical examples based on the real-world data from Beijing Yizhuang metro line are conducted. The results indicate that the developed model can improve passenger service and reduce energy consumption efficiently in comparisons with the timetable used currently.

Advanced information is increasingly being used as an external intervention tool to positively influence system performance. In many traffic assignment problems, the proportion of travellers that reroute is assumed to be constant (static rerouting behavior), whereas the number of travellers that modify their routes will change dynamically with the cost difference (dynamic rerouting behavior). In this paper, dynamic rerouting behavior is considered in day-to-day traffic assignment models to capture travellers' reactions to advanced information. The properties of a dynamic rerouting weight function are studied using survey data. Our goal is to better understand the dynamic evolution of network flow. In the model, the rerouting weight varies dynamically with the cost difference between travellers' estimated and expected costs. The linear stability of the equilibrium is analyzed. Both theoretical analyses and numerical simulations indicated that dynamic rerouting behavior increases the stability domain and decreases the parameter sensitivity. Additionally, the dynamic evolution of the cost and flow near the stability boundary is studied. The results show that the dynamic rerouting behavior helps to improve the convergence speed and dampen the oscillations in the evolution process. This paper explains the influence of dynamic rerouting choice behavior on the evolution patterns of transportation networks and provides guidance for network design and management.