Research About Taxi Cruising Route Based On GPS Big Data

With the rapid development of economic and urbanization construction,people’s travel needs have increased accordingly,and the taxis has become an important part of urban public transportation.However,due to the uneven distribution of passenger sources,the phenomenon of "no-load cruise" of taxis is more serious,which reduces the operating efficiency of taxis and wastes urban resources.The thesis takes Xi’an taxi as a research object,with the support of GPS big data,it considers the dynamic passenger success rate during driving,and establishes a passenger-tracking path recommendation model based on the psychology of taxi drivers.The law of the surrounding passenger sources of taxis that enter into the no-load state can help drivers make route decisions.This model can alleviate road traffic pressure and reduce waste of resources.In order to further develop the path of taxi drivers as a special audience Recommended software provides a systematic approach.In this paper,a taxi GPS database is established and managed uniformly,the preprocessing,secondary processing,and coordinate conversion are performed.Map matching is performed using the implicit Markov algorithm,and the trajectory data is associated with the road network data.Calculate and extract the data information of taxi getting on and off,analyzing the taxi operation characteristics.The DBSCAN algorithm is used to perform a cluster analysis on the passenger points of the taxi,a nuclear density analysis is performed on the OD data of the rail transit,and a visual processing is performed to lay the foundation for subsequent calculations.Different from the traditional route recommendation model which aims at the least time consuming or the shortest route,the paper proposes a new recommendation method based on the research on the calculation of the route of the recommended driver to the passenger hotspot.While the journey is short,the goal is to improve the success rate of the passenger during the journey.Considering that the driver wants to successfully carry passengers as soon as possible,instead of having to successfully carry passengers at hot spots,the model has improved the Dijkstra algorithm.Based on the time spent on the road and the probability of carrying passengers on the road,the weight value in the algorithm is calculated Get the recommended path to each passenger hotspot,and then comprehensively quantify the link evaluation,time cost,orbital station radiation intensity of each path,and the regional saturation of the hotspot to get the final recommended path.Finally,this paper combines the urban structure characteristics of Xi’an with a case study of the GPS data of approximately 11,400 taxis in Xi’an,analyzing the operating characteristics of taxis in Xi’an,and calculating the passenger search path for the selected drop-off point,combining with the urban functional area The actual conditions such as the structure and the residents’ travel laws have verified the effectiveness of the model and algorithm,and can provide theoretical references for the calculation of passengerseeking paths for various types of vehicles,such as taxis and network-reserved vehicles,as well as taxis and even urban traffic operation management People assist in making decisions.