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Shared electric vehicles (SEVs) have emerged as a promising solution to contribute to sustainable urban mobility. However, ensuring the efficient operation and effective battery management of SEV systems remains a complex challenge. This challenge stems from factors such as slow plug-in charging, the potential role of SEVs in balancing grid load pressure, and the optimization of SEV operations to ensure their economic viability. To tackle these challenges, this paper introduces an integrated strategy for optimizing various aspects of SEV systems, encompassing strategies like Vehicle-to-Grid (V2G), Battery-to-Grid (B2G), and battery swapping. This approach is built on a space-time-energy network model that facilitates the optimization of battery charging and discharging scheduling, SEV operations like relocations and battery swapping, battery swapping station selection and the number of batteries. The objective of this approach is to maximize profits while addressing operational constraints and the complexities of energy management within SEV systems. Given the substantial complexity that arises with large-problem scales, the paper introduces a column generation-based heuristic algorithm. Extensive experimental validation is conducted, including sensitivity analysis on different charging speeds and fleet sizes. The results illuminate the impact of varying charging rates and fleet sizes on performance indicators. Notably, it is observed that battery swapping is particularly effective as an auxiliary charging method when the number of vehicles is limited. Conversely, in scenarios with a large fleet, the necessity for battery swapping diminishes. Moreover, results show the effectiveness of V2G and B2G technologies in grid load balancing.

Teleoperated or remote-controlled driving complements automated driving and acts as transitional technology toward full automation. An economic advantage of teleoperated driving in logistics operations lies in managing fleets with fewer teleoperators compared to vehicles with in-vehicle drivers. This alleviates growing truck driver shortage problems in the logistics industry and saves costs. However, a trade-off exists between the teleoperator-to-vehicle ratio and the service level of teleoperation. This study designs a simulation framework to explore this trade-off generating multiple performance indicators as proxies for teleoperation service level. By applying the framework, we identify factors influencing the trade-off and optimal teleoperator-to-vehicle ratios under different scenarios. Our case study on road freight tours in The Netherlands reveals that for any operational setting, a teleoperation-to-vehicle ratio below one can manage all freight truck tours without delay, while one represents the current situation. The minimum teleoperator-to-vehicle ratio for zero-delay operations is never above 0.6, implying a minimum of 40% teleoperation labor cost saving. For operations where a small delay is allowed, teleoperator-to-vehicle ratios as low as 0.4 are shown to be feasible, which indicates potential savings of up to 60%. This confirms great promise for a positive business case for the teleoperated driving as a service.

Electric mobility hubs (eHUBS) are locations where multiple shared electric modes including electric cars and e-bikes are available. To assess their potential to reduce private car use, it is important to investigate to what extent people would switch to eHUBS modes after their introduction. Moreover, people may adapt their behaviour differently depending on their current travel mode. This study is based on stated preference data collected in Amsterdam. We analysed the data using mixed logit models. We found users of different modes not only have a varied general preference for different shared modes, but also have different sensitivity for attributes such as travel time and cost. Compared to car users, public transport users are more likely to switch towards the eHUBS modes. People who bike and walk have strong inertia, but the percentage choosing eHUBS modes doubles when the trip distance is longer (5 or 10 km).

This study proposes a hybrid deep-learning-metaheuristic framework with a bi-level architecture for road network design problems (NDPs). We train a graph neural network (GNN) to approximate the solution of the user equilibrium (UE) traffic assignment problem and use inferences made by the trained model to calculate fitness function evaluations of a genetic algorithm (GA) to approximate solutions for NDPs. Using three test networks, two NDP variants and an exact solver as benchmark, we show that on average, our proposed framework can provide solutions within 1.5% gap of the best results in less than 0.5% of the time used by the exact solution procedure. Our framework can be utilized within an expert system for infrastructure planning to determine the best infrastructure planning and management decisions under different scenarios. Given the flexibility of the framework, it can easily be adapted to many other decision problems that can be modeled as bi-level problems on graphs. Moreover, we foreseen interesting future research directions, thus we also put forward a brief research agenda for this topic. The key observation from our research that can shape future research is that the fitness function evaluation time using the inferences made by the GNN model was in the order of milliseconds, which points to an opportunity and a need for novel heuristics that 1) can cope well with noisy fitness function values provided by deep learning models, and 2) can use the significantly enlarged efficiency of the evaluation step to explore the search space effectively (rather than efficiently). This opens a new avenue for a modern class of metaheuristics that are crafted for use with AI-powered predictors., Comment: Two case studies added, intro, discussion and conclusion extended, details added to method and experiments, typos fixed, title revised, references added

Ride-sourcing drivers as individual service suppliers can freely adopt their own relocation strategies including waiting, cruising freely, or following the platform recommendations. These decisions substantially impact the balance between supply and demand, and consequently affect system performance. We conducted a stated choice experiment to study the searching behaviour of ride-sourcing drivers and examine novel policies. A unique dataset of 576 ride-sourcing drivers working in the US was collected and a choice modelling approach was used to estimate the effects of multiple existing and hypothetical attributes. The results suggest that relocation strategies of ride-sourcing drivers considerably vary between different groups of drivers. Surge pricing significantly stimulates drivers to head towards the designated areas. However, the distance between the location of drivers and surge or high-demand areas demotivates them to follow the platform repositioning recommendations. We discuss the implications of our findings for various platform policies on real-time information sharing and platform repositioning guidance.

The performance of ride-sourcing services such as Uber and Lyft is determined by the collective choices of individual drivers who are not only chauffeurs but private fleet providers. In such a context, ride-sourcing drivers are free to decide whether to accept or decline ride requests assigned by the ride-hailing platform. Drivers' ride acceptance behaviour can significantly influence system performance in terms of riders' waiting time (associated with the level of service), drivers' occupation rate and idle time (related to drivers' income), and platform revenue and reputation. Hence, it is of great importance to identify the underlying determinants of the ride acceptance behaviour of drivers. To this end, we collected a unique dataset from ride-sourcing drivers working in the United States and the Netherlands through a cross-sectional stated preference experiment designed based upon disparate information conveyed to the respondents. Using a choice modelling approach, we estimated the effects of various existing and hypothetical attributes influencing the ride acceptance choice. Employment status, experience level with the platform, and working shift are found to be the key individual-specific determinants. Part-time and beginning drivers who work on midweek days (Monday-Thursday) have a higher tendency to accept ride offers. Results also reveal that pickup time, which is the travel time between the driver's location and the rider's waiting spot, has a negative impact on ride acceptance. Moreover, the findings suggest that a guaranteed tip (i.e., the minimum amount of tip that is indicated upfront by the prospective rider, a feature that is currently not available) and an additional income due to surge pricing are valued noticeably higher than trip fare. The provided insights can be used to develop customised matching and pricing strategies to improve system efficiency., Comment: 25 pages, 4 figures