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• A public transit accessibility under heat exposure model is studied with time-dependent consideration. • A column generation method to urban rail-bus-walk service network integration is devised. • Experiments in Beijing show the impact of heat exposure on public transit accessibility. The evolution of urban transportation systems is increasingly driven by the integration of rail, bus, and walking pathways, forming a cohesive service network. This urban rail-bus-walk service network integration is pivotal in addressing urban challenges and ensuring seamless mobility. Nevertheless, the effectiveness of such integrated systems depends on their accessibility and the willingness of individuals to take them as their primary transportation mode. In the context of climate change, extreme heat events pose an increasing threat to passenger comfort and health, which in turn, affects individuals' decisions to use public transit. Additionally, walking to transit stops or waiting for transportation becomes more challenging during such extreme heat conditions. This paper tackles the challenge of comprehensively addressing the evolving issues related to public transit accessibility in response to rising temperatures, an area of research that has received limited attention until now. This paper introduced an approach aimed at optimizing the urban rail-bus-walk service network with a focus on both accessibility and heat exposure considerations. By calculating the transportation accessibility level under varying temperature conditions and identifying its vulnerability to high temperatures, this paper presents a methodology to enhance the transit service. Such improvement involves not only adjustments to the integration network but also considering of cooling facilities, such as shaded routes and shelters. Column generation is applied to solve this problem and the optimization result demonstrates improvement of accessibility under the extreme heat condition. [ABSTRACT FROM AUTHOR]

Shared Autonomous Electric Vehicles (SAEVs) are pivotal for future transportation, offering both promise and challenges upon integration with the power grid. This symbiosis augments power system flexibility, stability and reliability through Vehicle-to-Grid (V2G) services, and optimize transportation efficiency. However, it amplifies the demand for robust charging infrastructure and electricity power during peak periods. This paper proposes a framework employing a sequential receding horizon optimization approach to manage SAEV mobility and charging dynamics. Focused on maximizing transportation service quality while ensuring power grid stability, the model accommodates dynamic trip requests and electricity generation, utilizing a rolling horizon algorithm. Notably, the study explores the potential of SAEVs in fortifying the integration of renewable energy resources (RES) into the power grid. Our research strives to equip policymakers and system planners with a robust tool for crafting efficient and sustainable future urban transportation and energy systems. • A novel modeling and optimization framework for SAEV-PowerGrid coupled system. • Applied a rolling horizon algorithm to solve the problem with realistic sequential and dynamic input. • Case study applications for downtown Beijing. • Revealed SAEV's significant role in integrating renewable energy resources into the powergrid. [ABSTRACT FROM AUTHOR]

Integrating emerging shared mobility with traditional fixed-line public transport is a promising solution to the mismatch between supply and demand in urban transportation systems. The advent of modular vehicles (MVs) provides opportunities for more flexible and seamless intermodal transit. The MVs, which have been implemented, are comprised of automated modular units (MUs), and can dynamically change the number of MUs comprising them at different times and stops. However, this innovative intermodal urban transit brings with it a new level of dynamism and uncertainty. In this paper, we study the problem of jointly optimizing the timetable and the vehicle schedule within an intermodal urban transit network utilizing MVs within the context of distributionally robust optimization (DRO), which allows MVs to dynamically (de)couple at each stop and permits flexible circulations of MUs across different transportation modes. We propose a DRO formulation to explore the trade-off between operators and passengers, with the objective of minimizing the worst-case expectation of the weighted sum of passengers' and operating costs. Furthermore, to address the computational intractability of the proposed DRO model, we design a discrepancy-based ambiguity set to reformulate it into a mixed-integer linear programming model. In order to obtain high-quality solutionss of realistic instances, we develop a customized decomposition-based algorithm. Extensive numerical experiments demonstrate the effectiveness of the proposed approach. The computational results of real-world case studies based on the operational data of Beijing Bus Line illustrate that the proposed integrated timetabling and vehicle scheduling method reduces the expected value of passengers' and operating costs by about 6% in comparison with the practical timetable and fixed-capacity vehicles typically used in the Beijing bus system. • We address the timetabling and vehicle scheduling problem concerning modular units. • We formulate a distributionally robust model for the investigated problem. • We develop an algorithm combining ALNS and GUROBI to generate high-quality solutions. • We verify the effectiveness of our proposed approaches by numerical experiments. [ABSTRACT FROM AUTHOR]

• A multitask learning model for short-term inflow prediction of multi-traffic modes is proposed. • The adaptive matrix and attention mechanism are used to extract the spatiotemporal features of multi-traffic modes. • We analyze the interaction mechanism in the multi-traffic modes system. • The research highlights the significance of jointly predicting the inflow of multi-traffic modes. Network-wide short-term inflow prediction is important in efficiently managing the urban transportation system. Nowadays, all kinds of traffic modes gradually become interconnected and form a complex multi-traffic modes system, while extensive studies focus on the single-traffic mode and ignore the correlations among different traffic modes. There exist some challenges for short-term inflow prediction of multi-traffic modes: (1) the interaction mechanism among multi-traffic modes is difficult to learn and few studies explore the mechanism, (2) the data of multi-traffic modes are usually heterogenous due to the different spatial units of different traffic modes, and (3) it is challenging to extract the complex and dynamic features of the multi-traffic modes and most existing methods apply static spatiotemporal correlations among multi-traffic modes, while the genuine correlations among different traffic modes might be missing. To tackle these challenges, this study proposed a multitask-learning-based model called M ulti M ode-former (M2-former) with the encoder-decoder structure for network-wide short-term inflow prediction of the multi-traffic modes system. Specifically, the encoder is designed to learn and capture the complex and dynamic spatiotemporal correlations of multi-traffic modes, and the decoder is designed to extract the features of the target traffic mode and share knowledge among multi-traffic modes. Extensive experiments are conducted based on the real-world multi-traffic modes system data of Beijing, China. Results prove the superiority of the M2-former. In addition, the spatial and temporal information interaction mechanisms among multi-traffic modes are also explored. This paper can provide a reliable method and critical insights for the management and understanding of a multi-traffic modes system. [ABSTRACT FROM AUTHOR]

This paper proposes a hierarchical Bayesian network model (BNM) to quantitatively evaluate the resilience of urban transportation systems. Based on systemic thinking and taking a sustainability perspective, we investigate the long-term resilience of the road transportation systems in four cities in China from 1998 to 2017, namely Beijing, Tianjin, Shanghai, and Chongqing, respectively. The model takes into account various factors collected from multi-source data platforms involved in stages of design, construction, operation, management, and innovation in road transportation systems. We test the model with the forward inference, sensitivity analysis, and backward inference. The result shows that the overall resilience scores of all four cities' transportation systems are within a moderate range with values between 49% to 59%. Although they all have an ever-increasing economic level, the levels of transportation resilience in Beijing and Tianjin decrease first and then gradually increase in a long run, which indicates a strong multi-dimensional, dynamic, and non-linear characteristic in resilience-economic coupling effect. Additionally, the results obtained from the sensitivity analysis and backward inference suggest that decision makers should pay more attention to the capabilities of quickly rebuilding and making changes to cope with future disturbances. As an exploratory study, this study clarifies the concepts of long-term multi-dimensional resilience and specific hazard-related resilience and provides an effective decision-support tool for stakeholders when building resilient infrastructure. • A novel BNM is proposed to evaluate multi-faceted transportation resilience. • Forward inference, sensitivity tests, and backward inference are conducted. • Long-term transportation resilience is highly dynamic in the tested cities. • The coupling effects of resilience with economic level is not always linear. • Abilities to rebuild and adapt through reconfiguration are of importance. [ABSTRACT FROM AUTHOR]