Understanding the spatial dynamics of bike-sharing usage is critical for effective urban planning and mobility resource management. In this study, we propose an interpretable deep learning approach to uncover spatial relationships embedded in bike-sharing activities. Specifically, we develop a spatially-adapted SHapley Additive exPlanations (SHAP)-based method to quantify the spatial dependencies between locations in bike-sharing activities and apply it to interpret the predictions of a bike-sharing model. Extensive experiments upon Citi Bike data from New York City in December 2023 reveal that spatial influence does not strictly follow geographic proximity and is anisotropic. Additionally, non-member users exhibit weaker spatial dependencies in their bike usage behavior, resulting in lower short-term predictability compared to member users. Our studies shed deep insights into the spatial dynamics of bike-sharing systems and provide guidance for more effective service deployment and system design.

Accurate prediction of citywide crowd activity levels (CALs), i.e., the numbers of participants of citywide crowd activities under different venue categories at certain time and locations, is essential for the city management, the personal service applications, and the entrepreneurs in commercial strategic planning. Existing studies have not thoroughly taken into account the complex spatial and temporal interactions among different categories of CALs and their extreme occurrences, leading to lowered adaptivity and accuracy of their models. To address above concerns, we have proposed IE-CALP , a novel spatio-temporal I nteractive attention-based and E xtreme-aware model for C rowd A ctivity L evel P rediction. The tasks of IE-CALP consist of (a) forecasting the spatial distributions of various CALs at different city regions (spatial CALs), and (b) predicting the number of participants per category of the CALs (categorical CALs). To realize above, we have designed a novel spatial CAL-POI interaction-attentive learning component in IE-CALP to model the spatial interactions across different CAL categories, as well as those among the spatial urban regions and CALs. In addition, IE-CALP incorporate the multi-level trends (e.g., daily and weekly levels of temporal granularity) of CALs through a multi-level temporal feature learning component. Furthermore, to enhance the model adaptivity to extreme CALs (e.g., during extreme urban events or weather conditions), we further take into account the extreme value theory and model the impacts of historical CALs upon the occurrences of extreme CALs. Extensive experiments upon a total of 738,715 CAL records and 246,660 POIs in New York City (NYC), Los Angeles (LA), and Tokyo have further validated the accuracy, adaptivity, and effectiveness of IE-CALP ’s interaction-attentive and extreme-aware CAL predictions.

Thanks to advances in the urban big data, the bike sharing, especially station-based bike sharing, has emerged as the important first-/last-mile connectivities in many smart cities. Bike station network (BSN) expansion recommendation, i.e., recommending placement locations of new stations, is essential for satisfying local mobility demands, enhancing the BSN service quality, and may significantly affect the resource fairness and accessibility of different communities in the neighborhood. Furthermore, the dynamic and complex urban mobility environments make the station placement highly challenging to satisfy the mobility needs.

The occurrence of special contexts or events (e.g., extreme weather conditions, festival events, other urban anomalies) can significantly influence the movement patterns of urban mobility (e.g., human crowds, transportation systems). Accurate mobility modeling and prediction under the occurrences of such anomaly events is therefore imperative for city management and urban resource allocation. In this study, we propose EALGAP, a novel Extreme-Aware Local-Global Attention urban mobility Prediction model. Specifically, EALGAP models the spatio-temporal global and local impacts of mobility in different regions and time steps for mobility prediction at various city regions. EALGAP takes into account the global impacts by extracting the overall or regular spatial dependencies and temporal patterns of mobility systems for different regions. We have designed a temporally-varying normalization and data-driven technique to quantify the extreme degrees, i.e., how significantly the extreme events have impacted the local mobility trend, of the patterns within different regions and time steps. We have conducted ex-tensive experimental studies upon four different mobility datasets (over 13 million trips in total) harvested from two metropolitan cities in U.S. with anomalous natural or social events (e.g., hurricane events, other extreme weather conditions, and the Federal holidays). Our results have demonstrated the accuracy, effectiveness, and extreme-awareness of our proposed EALGAP with more than 44.12% error reduction on average compared with other state-of-the-art approaches.

Urban dockless e-scooter sharing (DES) has become a popular Web-of-Things (WoT) service and widely adopted globally. Despite its early commercial success, conventional mobility demand and supply prediction based on machine learning and subsequent redistribution may favor advantaged socio-economic communities and tourist regions, at the expense of reducing mobility accessibility and resource allocation for historically disadvantaged communities. To address this unfairness, we propose a socially-Equitable Interactive Graph information fusion-based mobility flow prediction system for Dockless E-scooter Sharing (EIGDES). By considering city regions as nodes connected by trips, EIGDES learns and captures the complex interactions across spatial and temporal graph features through a novel interactive graph information dissemination and fusion structure. We further design a novel model learning objective with metrics that capture both the mobility distributions and the socio-economic factors, ensuring spatial fairness in the communities’ resource accessibility and their experienced DES prediction accuracy. Through its integration with the optimization regularizer, EIGDES jointly learns the DES flow patterns and socio-economic factors, and returns socially-equitable flow predictions. Our in-depth experimental study upon more than 2,122,270 DES trips from three metropolitan cities in North America has demonstrated EIGDES’s effectiveness in accurate prediction of DES flow patterns with substantial reduction of mobility unfairness.

Bike sharing service (BSS) networks have been proliferating all over the globe thanks to their success as the first/last-mile connectivity inside a smart city. Their (re)configuration — i.e., station (re)placement and dock resizing — has thus become increasingly important for BSS providers and smart city planners. Instead of using conventional labor-intensive manual surveys, we propose a novel information fusion framework calledCBikesthat (re)configures the BSS network by jointly fusing crowdsourced station suggestions from online websites and the usage history of bike stations. Using comprehensive real data analyses, we identify and exploit important global trip patterns to (re)configure the BSS network while mitigating the local biases of individual feedbacks. Specifically, crowdsourced feedbacks, station usage, cost and other constraints are fused into a joint optimization of BSS network configuration. We also model the spatial distributions of station usage to account for and estimate the unexplored regions without historical usage information. We further design a semidefinite programming transformation to solve the bike station (re)placement problem efficiently and effectively. Our extensive data analytics and evaluation have shownCBikes’ effectiveness and accuracy in (re)placing stations and resizing docks based on three large BSS systems (with$>$900 stations) in Chicago, Twin Cities (Minneapolis–Saint Paul), and Los Angeles.

As an alternative means of convenient and smart transportation, mobility-on-demand (MOD), typified by online ride-sharing and connected taxicabs, has been rapidly growing and spreading worldwide. The large volume of complex traffic and the uncertainty of market supplies/demands have made it essential for many MOD service providers toproactivelydispatch vehicles towards ride-seekers. To meet this need effectively, we proposeSTRide, an MOD coordination learning mechanism reinforced spatio-temporally with capsules. We formalize the adaptive coordination of vehicles into a reinforcement learning framework.STRideincorporates spatial and temporal distributions of supplies (vehicles) and demands (ride requests), customers’ preferences and other external factors. A novel spatio-temporal capsule neural network is designed to predict the provider’s rewards based on MOD network states, vehicles and their dispatch actions. This way, the MOD platform adapts itself to the supply-demand dynamics with the best potential rewards. We have conducted extensive data analytics and experimental evaluation with five large-scale datasets ($\sim$27 million rides from Uber, NYC/Chicago Taxis, Didi and Car2Go).STRideis shown to outperform state-of-the-arts, substantially reducing request-rejection rate and passenger waiting time, and also increasing the service provider’s profits.

Dockless E-scooter Sharing (DES) has become a popular means of last-mile commute for many smart cities. As e-scooters are getting deployed dynamically and flexibly across city regions that expand and/or shrink, accurate prediction of the e-scooter distribution given the reconfigured regions becomes essential for city planning. We presentGCScoot, a novel flow distribution prediction approach for reconfiguring urban DES systems. Based on real-world datasets with reconfiguration, we analyze e-scooter distribution features and flow dynamics for the data-driven designs. We propose a novel spatio-temporal graph capsule neural network withinGCScootto predict future dockless e-scooter flows given the reconfigured regions.GCScootpre-processes historical spatial e-scooter distributions into flow graph structures, where discretized city regions are considered as nodes and inter-region flows as edges. To facilitate initial training, we cluster the regions and generate virtual data for new deployment regions based on their peers in the same cluster. Given above designs, the region-to-region correlations embedded within the temporal flow graphs are captured via the multi-graph capsule convolutional neural network which accurately predicts the DES flows. Extensive studies upon four e-scooter datasets (total$>$3.4 million rides) in four populous US cities have corroborated accuracy and effectiveness ofGCScootin predicting the e-scooter distributions.

Taking into account the availability of the historical GPS trajectories of drivers, given a new GPS trajectory, Driver mobility fingerprint (DMF) identification aims at (i) determining whether a generated trajectory belongs to a potential driver, and (ii) detecting if a trajectory is likely anomalous based on a driver's historical data. Prior studies often consider hand-crafted feature engineering techniques to extract DMFs while contextual factors like weather and points-of-interest (POIs) are hardly accounted for, which might not achieve satisfactory identification results. To address above, we propose RM-Drive, a novel framework based on reinforced feature extraction and multi-resolution learning. Specifically, we first employ spatio-temporal inverse reinforcement learning (ST-IRL) to extract DMFs from historical trajectories. Then, we generate trajectory embeddings by fusing the extracted DMFs and the contextual factors using the multi-resolution trajectory embedding network (MTE-Net). Our proposed MTE-Net consists of multi-resolution convolutional neural network (MR-CNN), which enables the model to learn the multi-resolution features of the DMFs. Finally, we leverage the trajectory embeddings for the driver classification and anomaly detection. We have conducted extensive evaluation studies upon RM-Drive with two real-world datasets, and our results demonstrate the performance improvements from the state-of-the-art of driver classification and anomaly detection respectively by 21% and 11% on average based on several evaluation metrics, including accuracy, precision, and recall, etc.

As a healthy, efficient and green alternative to motorized urban travel, bike sharing has been increasingly popular, leading to wide deployment and use of bikes instead of cars. Accurate bike-flow prediction at the individual station level is essential for bike sharing service. Due to the spatial and temporal complexities of traffic networks and the lack of data-driven design for bike stations, existing methods cannot predict the fine-grained bike flows to/from each station.

Thanks to recent progresses in mobile payment, IoT, electric motors, batteries and location-based services, Dockless E-scooter Sharing (DES) has become a popular means of last-mile commute for a growing number of (smart) cities. As e-scooters are getting deployed dynamically and flexibly across city regions that expand and/or shrink, with subsequent social, commercial and environmental evaluation, accurate prediction of the distribution of e-scooters given reconfigured regions becomes essential for the city planners and service providers.

As an alternative means of convenient and smart transportation, mobility-on-demand (MOD), typified by online ride-sharing and connected taxicabs, has been rapidly growing and spreading worldwide. The large volume of complex traffic and the uncertainty of market supplies/demands have made it essential for many MOD service providers to proactively dispatch vehicles towards ride-seekers.

Pricing in mobility-on-demand (MOD) networks, such as Uber, Lyft, and connected taxicabs, is done adaptively by leveraging the price responsiveness of drivers (supplies) and passengers (demands) to achieve such goals as maximizing drivers’ incomes, improving riders’ experience, and sustaining platform operation. Existing pricing policies only respond to short-term demand fluctuations without accurate trip forecast and spatial demand-supply balancing, thus mismatching drivers to riders and resulting in loss of profit. We propose CAPrice, a novel adaptive pricing scheme for urban MOD networks. It uses a new spatio-temporal deep capsule network (STCapsNet) that accurately predicts ride demands and driver supplies with vectorized neuron capsules while accounting for comprehensive spatio-temporal and external factors. Given accurate perception of zone-to-zone traffic flows in a city, CAPrice formulates a joint optimization problem by considering spatial equilibrium to balance the platform, providing drivers and riders/passengers with proactive pricing “signals.” We have conducted an extensive experimental evaluation upon over 4.0× 10 8 MOD trips (Uber, Didi Chuxing, and connected taxicabs) in New York City, Beijing, and Chengdu, validating the accuracy, effectiveness, and profitability (often 20% ride prediction accuracy and 30% profit improvements over the state-of-the-arts) of CAPrice in managing urban MOD networks.