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.

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.

Complex real time systems need databases to support concurrent data access and provide well defined interfaces between software modules. However, conventional database systems and prior real time database systems do not provide the performance or predictability needed by high speed, hard real time applications. The authors designed, implemented, and evaluated an object oriented database system called MDARTS (Multiprocessor Database Architecture for Real Time Systems). MDARTS avoids the client server overhead of most prior real time database systems and object oriented, real time systems by moving transaction execution into application tasks. By eliminating these sources of overhead and focusing on basic data management services for control systems (data sharing, serializable transactions, and multiprocessor support), the MDARTS prototype provides hard real time transaction times approximately three orders of magnitude faster than prior real time database systems. MDARTS ensures bounded locking delay by disabling preemption when a transaction is waiting for a lock, and hence, allows for the estimation of worst case transaction execution times. Another contribution of MDARTS is that it supports explicit declarations of real time requirements and semantic constraints within application code. The MDARTS library examines these declarations at application initialization time and attempts to construct objects that are compatible with the requirements. Besides local shared memory transactions with hard real time response time guarantees, MDARTS also supports remote transactions that use remote procedure calls for data access with less stringent timing constraints. The MDARTS prototype is implemented in C++ and it runs on VME based multiprocessors and Sun workstations.

Database schemata often experience considerable changes during the development and initial use phases of database systems for advanced applications such as manufacturing automation and computer-aided design. An automated schema evolution system can significantly reduce the amount of effort and potential errors related to schema changes. Although schema evolution for nonreal-time databases was the subject of previous research, its impact on real-time database systems remains unexplored. These advanced applications typically utilize object-oriented data models to handle complex data types. However, there exists no agreed-upon real-time object-oriented data model that can be used as a foundation to define a schema-evolution framework. Therefore, the authors first design a conceptual real-time object-oriented data model, called Real-time Object Model with Performance Polymorphism (ROMPP). It captures the key characteristics of real-time applications-namely, timing constraints and performance polymorphism-by utilizing specialization-dimension and letter-class hierarchy constructs, respectively. They then re-evaluate previous (nonreal-time) schema evolution support in the context of real-time databases. This results in modifications to the semantics of schema changes and to the needs of schema change resolution rules and schema invariants. Furthermore, they expand the schema change framework with new constructs-including new schema change operators, new resolution rules, and new invariants-necessary for handling the real-time characteristics of ROMPP.