Dynamic scheduling method for customized buses and car pooling based on passenger appointments

Abstract

The invention provides a dynamic scheduling method for customized buses and car pooling based on passenger appointments. Adding modifying and deleting orders are completed on a cell phone App, and an order service queue is established on a central platform for orders of all clients based on departure time. Based on the strategy of first come first serve, each order on the front of a queue is processed gradually. A possible path between starting points is calculated with shortest distance algorithm and constraint examination object functions by taking the position information of onboard GPS terminals and all order messages assigned to a customized bus before into consideration. The cost for each customized bus to complete the order is calculated, and the bus with the lowest cost is chosen to complete the order based on the greedy principle. The accessing passenger set of the customized bus is updated. The process is repeated to assign all passenger appointment to buses. Reliable technical support is provided for dynamic scheduling customized buses and car pooling based on passenger appointments.

Description

Technical field: [0001] The invention relates to the technical field of public transport informationization, in particular to a method for dynamic dispatching of customized shared public transport vehicles based on passenger reservations. Background technique: [0002] Because the conventional customized bus (taxi, special car) ride mode is that passengers take a car alone, according to the time and space distribution of travel needs of urban residents, the phenomenon that the empty-load rate of customized buses is often high and residents are "difficult to find a car", resulting in The great waste of vehicle resources is an important problem that needs to be solved in the operation and management of customized public transport in cities in my country. Carpooling and / or multiple passengers needing carpooling is one of the effective ways to solve the above problems. Use information technologies such as mobile phone APP, Internet of Things and wireless communication to dynamica...

AI Technical Summary

Problems solved by technology

However, the research on the scheduling algorithm of the intelligent bus customization scheduling system is still insufficient. It is difficult to meet the needs of a large number of carpool passengers in the complex road network, and the overall optimization cannot be achieved. A taxi sharing model suitable for the travel distribution characteristics of large and medium-sized cities in China. For the taxi operation route, the passenger demand determined by each node is solved by a decision tree. For the temporary passenger demand during the operation process, the adjusted route is calculated by a dynamic specification algorithm. Feasibility of insertion to achieve overall dynamic planning and scheduling; Ma Mengqi through the SP questionnaire survey of passengers' willingness to share a ride, the logistic modeling of the probability of passengers agreeing to share a ride, the dynamic shortest path exploration method, and a new method of cost calculation, To realize the shared travel of passengers with roughly the same travel route; Wu Fang, with the goal of maximizing the income of shared taxi drivers, passenger service rate and minimizing passenger costs, established the optimal taxi route selection and dispatching optimization model. An improved particle swarm optimization algorithm was designed to solve the problem; Cheng Jie, based on the information of different types of taxis and passenger groups, jointly considered the problem of route and rate optimization, and established a dynamic model of many-to-many with the interests of travelers and drivers as the optimization goal. Taxi carpooling model; Qin Yunmei applied for a patent "Taxi carpooling cluster optimization system based on complex road network and its optimization method", the main solution is to establish a cluster optimization matching model, and then run the algorithm to obtain the ratio of taxi detours , the shortest trip, the maximum matching and the cluster optimization scheme of the constraint conditions of carpooling

[0004] It can be seen from the above that the existing carpool scheduling algorithm customized by intelligent public transport mainly focuses on the generation algorithm of the carpool vehicle scheduling plan for static demand, and generally uses intelligent algorithms such as dynamic programming, genetic algorithm, and particle swarm optimization, but it cannot solve the problem of large-scale carpool vehicles. There is a certain gap between the dispatching problem and the actual application; the customized bus sharing vehicle of information environment technology is aimed at dynamically adding, modifying and deleting orders, and there is an urgent need for a dynamic scheduling method for sharing vehicles with dynamic needs. There are few existing studies, especially It is the lack of research to consider the influence of passengers' origin, destination, waiting time and on-board time on the dispatching results.

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