As a promising battery-sharing service, battery swapping has become one of the most important electric-scooter (called e-scooter) energy refueling mechanisms. However, by analyzing a battery swapping dataset of 108,574 e-scooter drivers and 41,358 batteries and surveying 224 of these drivers, we find that drivers are facing insufficient energy replenishment during the battery swapping process, i.e., swapping out an undercharged battery from the battery swapping station. To identify the root cause, we perform a data-driven analysis and reveal an imbalance in spatial and temporal swapping behavior patterns, which causes a significant decrease in battery charging time. Inspired by these findings, we design iSwap, a novel spatio-temporal battery swapping recommendation system, which improves drivers’ swapping experience by proactively coordinating spatio-temporal unbalanced behavior patterns. iSwap considers not only drivers’ individual temporal preference for swapping time recommendation (i.e., when to swap) but also the complex mutual influence among drivers for swapping station recommendation (i.e., where to swap). To perform the two tasks efficiently, we propose a swapping behavior-aware phase demand balancing schema and a submodularity-based near-optimal swapping station recommendation algorithm. Finally, through the evaluation, iSwap improves the probability of swapping out a fully charged battery by 42.1% and driver satisfaction by 16.8% compared to the ground truth.

With the popularity of Mobility-on-Demand (MOD) vehicles, a new market called MOD-Vehicular-Crowdsensing (MOVE-CS) was introduced for drivers to earn more by collecting road data. Unfortunately, MOVE-CS failed after two years of operation. To identify the root cause, we survey 581 drivers and reveal its simple incentive model based on blindly competitive rewards. This model brings most drivers few yields, resulting in their withdrawals. In contrast, a similar market termed MOD-Human-Crowdsensing (MOMAN-CS) remains successful thanks to a complex model based on exclusively customized rewards. Hence, we wonder whether MOVE-CS can be resurrected by learning from MOMAN-CS. Despite considerable similarity, we can hardly apply the incentive model of MOMAN-CS to MOVE-CS, since MOD drivers are also concerned with passenger missions that dominate their earnings. To this end, we analyze a large-scale dataset of 12,493 MOD vehicles, finding that drivers have explicit preference for short-term, immediate gains as well as implicit rationality in pursuit of long-term, stable profits. Therefore, we design a novel driver-oriented incentive mechanism for MOVE-CS, calledLSTAloc, at the heart of which lies a spatial-temporal differentiation-aware task allocation scheme empowered by submodular optimization. Applied to the dataset, our design would essentially benefit both the drivers and platform to incentivize MOD vehicular crowdsensing efficiently, thus possessing the potential to resurrect MOVE-CS.

Vehicles equipped with abundant sensors offer a promising way for large-scale, low-cost road data collection. To realize this potential, a well-designed vehicle scheduling scheme is essential for deploying the recruited drivers efficiently. Nevertheless, existing works fail to consider the marginal effect among drivers’ collections. Different from them, this article introduces a, to the best of our knowledge, new multiple-vehicle scheduling problem that jointly optimizes task allocation and vehicle trajectory planning to maximize the overall collection utility by accounting for the marginal effect in drivers’ data collections. However, solving this problem is non-trivial due to its involvement with multiple coupled NP-hard problems. To this end, we propose MeSched, a marginal effect-aware multiple-vehicle scheduling scheme designed for road data collection. Specifically, we first present a greedy-based auxiliary graph construction method to disentangle the initial problem into multiple independent single-vehicle scheduling subproblems. Furthermore, we build an approximate surrogate function that transforms each subproblem into a tractable form involving only a single variable. The theoretical analysis proves that MeSched can achieve a 1-(1/ e ) ¼ -approximation ratio in polynomial time. Comprehensive evaluations based on a real-world trajectory dataset of 12,493 vehicles demonstrate that MeSched can significantly improve the collection utility by 104.5% on average compared with four baselines.