Bike-sharing systems (BSSs) are deployed in over a thousand cities worldwide and play an important role in many urban transportation systems. BSSs alleviate congestion, reduce pollution and promote physical exercise. It is essential to explore the spatiotemporal patterns of bike-sharing demand, as well as the factors that influence these patterns, in order to optimise system operational efficiency. In this study, an optimised geo-temporal graph is constructed using trip data from Moby Bikes, a dockless BSS operator. The process of optimising the graph unveiled prime locations for erecting new stations during future expansions of the BSS. The Louvain algorithm, a community detection technique, is employed to uncover usage patterns at different levels of temporal granularity. The community detection results reveal largely self-contained sub-networks that exhibit similar usage patterns at their respective levels of temporal granularity. Overall, this study reinforces that BSSs are intrinsically spatiotemporal systems, with community presence driven by spatiotemporal dynamics. These findings may aid operators in improving redistribution efficiency.

We study an optimal investment problem that arises in the context of the vehicle-sharing system. Given a set of locations to build stations, we need to determine i) the sequence of stations to be built and the number of vehicles to acquire in order to obtain the target state where all stations are built, and ii) the number of vehicles to acquire and their allocation in order to maximize the total profit returned by operating the system when some or all stations are open. The profitability associated with operating open stations, measured over a specific time period, is represented as a linear optimization problem applied to a collection of open stations. With operating capital, the owner of the system can open new stations. This property introduces a set-dependent aspect to the duration required for opening a new station, and the optimal investment problem can be viewed as a variant of the Traveling Salesman Problem (TSP) with set-dependent cost. We propose an A* search algorithm to address this particular variant of the TSP. Computational experiments highlight the benefits of the proposed algorithm in comparison to the widely recognized Dijkstra algorithm and propose future research to explore new possibilities and applications for both exact and approximate A* algorithms.

Abstract: We provide a comprehensive reply to the comment written by Stefan Boettcher [arXiv:2210.00623] Conversely, we highlight the broader algorithmic development underlying our original work, and (within our original framework) provide additional numerical results showing sizable improvements over our original data, thereby refuting the comment's original performance statements. Furthermore, it has already been shown that physics-inspired graph neural networks (PI-GNNs) can outperform greedy algorithms, in particular on hard, dense instances. We also argue that the internal (parallel) anatomy of graph neural networks is very different from the (sequential) nature of greedy algorithms, and (based on their usage at the scale of real-world social networks) point out that graph neural networks have demonstrated their potential for superior scalability compared to existing heuristics such as extremal optimization. Finally, we conclude highlighting the conceptual novelty of our work and outline some potential extensions.

The electrification of shared mobility has become popular across the globe. Many cities have their new shared e-mobility systems deployed, with continuously expanding coverage from central areas to the city edges. A key challenge in the operation of these systems is fleet rebalancing, i.e., how EVs should be repositioned to better satisfy future demand. This is particularly challenging in the context of expanding systems, because i) the range of the EVs is limited while charging time is typically long, which constrain the viable rebalancing operations; and ii) the EV stations in the system are dynamically changing, i.e., the legitimate targets for rebalancing operations can vary over time. We tackle these challenges by first investigating rich sets of data collected from a real-world shared e-mobility system for one year, analyzing the operation model, usage patterns and expansion dynamics of this new mobility mode. With the learned knowledge we design a high-fidelity simulator, which is able to abstract key operation details of EV sharing at fine granularity. Then we model the rebalancing task for shared e-mobility systems under continuous expansion as a Multi-Agent Reinforcement Learning (MARL) problem, which directly takes the range and charging properties of the EVs into account. We further propose a novel policy optimization approach with action cascading, which is able to cope with the expansion dynamics and solve the formulated MARL. We evaluate the proposed approach extensively, and experimental results show that our approach outperforms the state-of-the-art, offering significant performance gain in both satisfied demand and net revenue. A promising trend of future mobility is electric and shared. For instance, Figure 1 shows the spatial distribution of station occupancy rate (ratio and ride-sharing services [10], [11], and the de facto solution of parked vehicles to the total available space) in a real-world is to rebalance the fleet during operation.

Two Chinese astronauts went on a spacewalk Saturday from a new space station that is due to be completed later this year. Cai Xuzhe and Chen Dong's installed pumps, a handle to open the hatch door from outside in an emergency, and a foot-stop to fix an astronaut's feet to a robotic arm, state media said. China is building its own space station after being excluded by the U.S. from the International Space Station because its military runs the country's space program. American officials see a host of strategic challenges from China's space ambitions, in an echo of the U.S.-Soviet rivalry that prompted the race to the moon in the 1960s. The latest spacewalk was the second during a six-month mission that will oversee the completion of the space station.

Two astronauts on Sunday made the first spacewalk outside China's new orbital station to set up cameras and other equipment using a 50-foot-long robotic arm. Liu Boming and Tang Hongbo were shown by state TV climbing out of the airlock as Earth rolled past below them. The third crew member, commander Nie Haisheng, stayed inside. Liu and Tang spent nearly seven hours outside the station, the Chinese space agency said. The astronauts arrived June 17 for a three-month mission aboard China's third orbital station, part of an ambitious space program that landed a robot rover on Mars in May.

The Foundation for the Defense of Democracies issues an alarming report about Beijing's expanding tentacles in international agencies; Eric Shawn has the Fox News exclusive. Two astronauts on Sunday made the first spacewalk outside China's new orbital station to set up cameras and other equipment using a 15-meter-long (50-foot-long) robotic arm. Liu Boming and Tang Hongbo were shown by state TV climbing out of the airlock as Earth rolled past below them. The third crew member, commander Nie Haisheng, stayed inside. Liu and Tang spent nearly seven hours outside the station, the Chinese space agency said.

Bike sharing has become one of the major choices of transportation for residents in metropolitan cities worldwide. A station-based bike sharing system is usually operated in the way that a user picks up a bike from one station, and drops it off at another. Bike stations are, however, not static, as the bike stations are often reconfigured to accommodate changing demands or city urbanization over time. One of the key operations is to evaluate candidate locations and install new stations to expand the bike sharing station network. Conventional practices have been studied to predict existing station usage, while evaluating new stations is highly challenging due to the lack of the historical bike usage. To fill this gap, in this work we propose a novel and efficient bike station-level prediction algorithm called AtCoR, which can predict the bike usage at both existing and new stations (candidate locations during reconfiguration). In order to address the lack of historical data issues, virtual historical usage of new stations is generated according to their correlations with the surrounding existing stations, for AtCoR model initialization. We have designed novel station-centered heatmaps which characterize for each target station centered at the heatmap the trend that riders travel between it and the station's neighboring regions, enabling the model to capture the learnable features of the bike station network. The captured features are further applied to the prediction of bike usage for new stations. Our extensive experiment study on more than 23 million trips from three major bike sharing systems in US, including New York City, Chicago and Los Angeles, shows that AtCoR outperforms baselines and state-of-art models in prediction of both existing and future stations.

East Japan Railway Co. unveiled Monday autonomous disinfection and mobility robots at its recently opened high-tech station in Tokyo, as it aims to introduce them by March 2025. The cleaning robot, which was developed by Nippon Signal Co. and Cyberdyne Inc., sanitizes handrails, benches and other parts of Takanawa Gateway Station by spraying disinfectant. The artificial-intelligence equipped robot, Clinabo CL02, uses three-dimensional cameras and sensors to avoid obstacles. JR East said it is considering using the robot and other disinfection robots to be introduced later to sanitize the inside of train cars in the future. In another demonstration, a robot that looks like a Yamanote Line train car served coffee in a conference room at the station. Other robots that carry luggage, food and drinks, as well as personal mobility vehicles for transporting people inside and around the station are also being operated on a trial basis as part of a project showcasing the area around the new station.

East Japan Railway Co. on Monday unveiled autonomous disinfection and mobility robots at its recently opened high-tech station in Tokyo, as it aims to introduce them by March 2025. The cleaning robot, which was developed by Nippon Signal Co. and Cyberdyne Inc., sanitizes handrails, benches and other parts of Takanawa Gateway Station by spraying disinfectant. The artificial-intelligence equipped robot, Clinabo CL02, uses three-dimensional cameras and sensors to avoid obstacles. JR East said it is considering using the robot, and other disinfection robots to be introduced later, to sanitize the inside of train cars in the future. In another demonstration, a robot that looks like a Yamanote Line train car served coffee in a conference room at the station.