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With the development of urbanization and the evolution of urban network systems, multimodal urban transport network (MUTN) systems play a vital role in improving network effects and operational efficiency. However, urban transport networks are easily affected by natural disasters and traffic incidents, which can lead to significant human and economic losses. Accordingly, it is vital to be able to assess the resilience of transport networks in the face of various disruptions. This study, therefore, utilizes complex network theory to analyze the resilience of multimodal urban transport networks, with the resilience accessed based on topological indices. The MUTN in Beijing is selected as a case study for simulation analysis. Based on the road network and subway network, a model MUTN is established, and the Monte Carlo method is used to simulate random attacks. The results show that the MUTN in Beijing has good resilience against disruptions. This study guides the evaluation of the overall resilience of multimodal urban transport networks and will be useful for transportation planners and decision-makers in dealing with emergencies and natural disasters in the future. [ABSTRACT FROM AUTHOR]

Various policies on electric vehicles (EVs) have been implemented in Beijing to promote EV diffusion, but previous research has rarely considered the indirect network effects between EVs and EV charging infrastructures (EVCIs), and has not explored the impact of EVCI-related incentives on EV diffusion. This study aims to enrich the literature by incorporating EVCI development and indirect network effects in EV diffusion. A system dynamic model is proposed to systematically analyse the interactions among consumer, EV manufacturer and EVCI operator and simulate EV diffusion and ECVI investment in Beijing. Multiple scenarios are adopted to investigate the joint effects of the policy mix on EV diffusion. The results indicate that the oil-to-electricity subsidy promoted EVs and EVCIs to a greater extent than the license plate control policy. The EVCI maintenance policy outperformed EVCI construction and production subsidies on EV diffusion. Synergistic effects exist when the oil-to-electricity subsidy is mixed with EVCI-related incentives. This study provides implications for policymakers to promote EV diffusion with innovative and efficient policy instruments. [ABSTRACT FROM AUTHOR]

• Solar photovoltaic and energy storage are introduced and analyzed. • Bus battery replacement and fleet-depot matching problem is jointly optimized. • Customized solution approaches are presented to solve the nonlinear models. • A case study is designed to demonstrate the proposed models and solution approaches. Battery capacity degradation in battery electric buses (BEBs) poses a significant operational challenge for transit agencies. This study presents a sustainable battery scheduling and echelon utilization framework considering battery capacity fading and charging infrastructure integrated with solar photovoltaic (PV) and energy storage systems. The framework aims to minimize the sum of bus charging, battery replacement, and carbon emission costs during the BEB lifespan. We first present a power battery replacement problem for a single bus fleet, then extend it to a joint power battery replacement and fleet-depot matching problem. Finally, we propose a power battery replacement and fleet-depot problem by introducing solar PV and energy storage systems. Dynamic programming, Lagrange relaxation, and a two-step approach are adopted to solve the three problems. A case study involving six bus depots in Beijing demonstrates that optimal battery replacement schedules can significantly lower charging costs. Moreover, integrating solar PV and energy storage is shown to considerably reduce both charging costs and carbon emissions. [ABSTRACT FROM AUTHOR]

With the tide of electrifying urban transportation systems by introducing electric vehicles, the differences between fuel vehicles and electric vehicles in driving styles and strategies to achieve eco-driving have become a burden for efficient operations of urban transportation systems. Most of the previous energy management strategies have sought to achieve system optimisation at a single-vehicle or multi-vehicles level, and failed to consider the vehicle-to-vehicle and vehicle-to-infrastructure effects in a global optimisation manner. Furthermore, as a typical human-in-the-loop cyber–physical system, the mobility behaviours of road users undoubtedly play a vital role in the cooperative and green operations of urban transportation systems. Yet little research has dedicated to develop means to incentivise energy-saving behaviours in transportation systems. Hence, in this paper, we propose a unifying queueing and neural network model to calculate the time and energy efficient course of actions and routes for different types of road users within an urban road network in a real time manner. The lower-level queueing model captures the interactive dynamics of road users and solves the optimal flow ratio at each intersection while the upper-level neural network model further customises desired routes for different types of road users. In addition, an incentive mechanism is proposed to encourage road users to follow the optimal actions via publishing various types of reward-gaining tasks. A case study in a designated area of Beijing shows that the use of the bi-level optimisation algorithm can reduce the average travel time by approximately 20% and decrease the energy consumption by 10% in comparison with the realistic trip data. • An overall energy management framework for urban road networks is built in detail. • A heterogeneous queueing network model is utilised to derive eco-driving strategies. • Random Neural Network is applied to search desired routes for heterogeneous users. • SUMO-based simulation experiment is conducted to validate the performance. • The framework improves time and energy efficiency at the cost of monetary incentives. [ABSTRACT FROM AUTHOR]

• We quantitatively explore the impacts of COVID-19 on the usage of bike sharing. • A novel method is proposed to estimate trip distances and trajectories of bike sharing. • Complex network theory is employed to explore the transformation of user behaviors. • COVID-19 impacts the user behaviors and environmental benefits of bike sharing significantly. The COVID-19 pandemic spreads rapidly around the world, and has given rise to huge impacts on all aspects of human society. This study utilizes big data techniques to analyze the impacts of COVID-19 on the user behaviors and environmental benefits of bike sharing. In this study, a novel method is proposed to calculate the trip distances and trajectories via a python package OSMnx so as to accurately estimate the environmental benefits of bike sharing. In addition, we employ the topological indices arising from complex network theory to quantitatively analyze the transformation of user behavior pattern of bike sharing during the COVID-19 pandemic. The results show that this pandemic has impacted the user behaviors and environmental benefits of bike sharing in Beijing significantly. During the pandemic, the estimated reductions of energy consumption and emissions on 6th Feb decreased to approximately 1 in 17 of those on a normal day, and the environmental benefits at most recovered to 70% of those in normal days. The impacts of COVID-19 on the environmental benefits in different districts are different. Furthermore, the decline of average strength and strength distribution obeying exponential distribution but with different slope rates suggests that people are less likely to take bike sharing to the places where were popular before. The pandemic has also increased the average trip time of bike sharing. Our research may facilitate the understanding of the impacts of COVID-19 pandemic on our society and environment, and also provide clues to adapt to this unprecedented pandemic so as to respond to similar events in the future. [ABSTRACT FROM AUTHOR]

As an environmentally friendly and convenient transport mode, bike-sharing has been fast developed in major cities around the world. More bike-sharing firms have been formed and intensified market competition. The government has stipulated regulations to intervene in bike-sharing firm operations and to maintain orderly competition. This paper establishes an integrated economic model to examine duopoly bike-sharing firm competition in both prices and the number of deployed bikes. Specifically, more deployed bikes in the urban area reduce the time cost for the users to find an available bike (i.e., the search cost), but they can also impose a negative social externality on the urban management. Two government policies to control the number of deployed bikes have been examined and benchmarked. One is the limit on the total number of the deployed bikes (i.e., the supply-side regulation), and the other is the restriction to allow the users to only register or use certain firms' services (i.e., the demand-side regulation of single-homing). Our model is also further calibrated by using the bike-sharing market data of Beijing for policy implications. It is found that, when the negative social externality of bike deployment is sufficiently small, the government should allow users' multi-homing (i.e., registration on both firms' platforms). However, when the negative social externality is high, the government should strictly control the number of bikes through both the supply-side and demand-side restrictions. Our analyses also suggest that it is never socially optimal to only adopt the demand-side restriction (i.e., forcing the single-homing). That is, the supply-side policy is more socially preferred to correct the negative social externality in most cases. When two bike-sharing firms offer quite heterogeneous services (i.e., low service substitutability), it is more socially optimal to only implement the supply-side policy. • Dockless bike-sharing firms compete in both price and number of deployed bikes. • The government adopts two alternative policies to control the number of deployed bikes. • With small negative externality, the supply-side regulation to limit deployed bikes is preferred. • It is never socially optimal to only adopt demand-side regulation by imposing single-homing. • The analytical model is also calibrated with real-world market data of Beijing for more insights. [ABSTRACT FROM AUTHOR]