A Bus-Scheduling Method Based on Multi-Sensor Data Fusion and Payment Authenticity Verification

It is of great significance to ensure public transportation management capabilities by improving urban transport services. One method solve the problems related quality data submitted for funding as well accuracy and transparency supervision review processes; moreover, public-transportation-service systems a viable such problems. With technological advancements application new technologies automatic driving multiple payment, it has gradually become difficult user-data verification systems, based on original single bus payment method, cater these technologies. Diversified complex methods have highlighted need methods. Firstly, in this paper, we constructed Origin–Destination (OD) model bus-passenger flows using real-time transmission passenger-multiple-payment data, on-board-video passenger flow detection vehicle positioning data. On basis, waybill other intelligent wait stations were integrated, so establish travel chain theory matching temporal spatial distribution model. Then, an OD analysis public-transport could be carried out, with detailed vehicle, station line-passenger flow, characteristics excavated. according means-end theory, spatiotemporal was obtained carry out perform refinement station, flow. Thereby, explored. Subsequently, payment-authenticity-verification models established data-validity assessment, video-data analysis, passenger-flow estimation, early warnings order determine authenticity Lastly, multi-sensor fusion models, combined use autonomous buses, test promoted. That is, taking scheduling scenario, research tested verified historical The results showed that accurately predicted had positive role efficiency payment-data-authenticity verification. can enhance service transportation.