Personalised Intelligent Transportation Recommendation System Based On Big Data

Transportation is a strategic,leading,basic and service industry in the national economy.Building “China's strength in transportation” is the general goal of China's transportation development in the future.With the opening of the mobile Internet era,every user has become a contributor to traffic information.Users use smart phones to plan routes,call cars online,search for destinations,etc.A large amount of location-based data is generated by these devices and applications every day,including online orders,trajectory information,map query data,and geo-tagged check-in data.These super large-scale multi-source data are processed and fused in the cloud to generate the whole time traffic information of the city without blind spots.In the face of such a huge amount of data,it is necessary to continuously upgrade,improve and innovate the intelligent transportation decision-making system,analyze these data with machine learning and deep learning methods to help intelligent travel,thereby reducing traffic congestion and improving the level of urban road management.In this paper,based on the in-depth analysis of the existing large-scale traffic forecasting tasks,the concept of "Personalized Intelligent Transportation Recommendation System" is proposed.The idea of "personalized recommendation" is introduced into the intelligent transportation system to design a more intelligent transportation system.In view of the above analysis,this paper relies on the National Science Fund of China for Excellent Young Scholars "Multi-modal Transportation Network Optimization and Management"(71822007)and Southeast University Excellent Doctoral Dissertation Cultivation Fund(YBPY1927),and takes the geographical location recommendation,multi-mode travel mode recommendation and online car-hailing dispatching unit recommendation as the research background,using deep learning,reinforcement learning and other technologies to provide method and theoretical basis for the design of personalized intelligent transportation recommendation system.The main research contents of this paper are divided into the following aspects:First of all,the core ideas of the existing recommendation methods are all around user behavior,so the representation method of user behavior is studied.Aiming at the big data of travel behavior,a graph embedding learning method based on user behavior is proposed.By encoding the user's personalized travel behavior in a continuous vector space,it is used as the input of the supervised learning model to improve the performance of the supervised learning model.Secondly,in addition to the design of the recommendation system from an individual perspective,for the recommendation of online car-hailing dispatching units,it is necessary to analyze the traffic status from a macro perspective,and make a global perception of taxi demand and driver distribution.Aiming at spatio-temporal data,a traffic state prediction method oriented to spatio-temporal data is designed,including two different attention blocks to capture personalized spatial and temporal information,and fully explore the potential spatio-temporal patterns of traffic data.The model is explained from the perspective of physical interpretation,and the influence of neural network architecture on the prediction accuracy of spatio-temporal data is discussed.Thirdly,the traditional recommendation algorithms have certain limitations,which may give users a large number of homogenized recommendation results.The root cause of this problem is that users' preferences are not considered,that is,personalized user behavior.This problem is particularly prominent in the application of intelligent transportation.This paper points out that the prediction of traveler's destination is essentially a recommendation of geographic location.Inspired by the recommendation system,a two-stage destination prediction framework is designed.A personalized candidate set is generated based on the user's historical behavior,which can be regarded as rough sorting,and then the candidate destinations are sorted by feature engineering.Fourthly,accurate recommendations for multi-modal transportation modes can promote the development of intelligent transportation systems,help shorten travel time and alleviate traffic congestion.In the problem of multi-modal transportation travel mode recommendation,different users have different preferences for travel modes under different backgrounds.In this paper,according to the application scenarios of multi-modal travel mode recommendation,the system feature engineering design is carried out from the perspectives of users,travel mode,geographical location and time.In order to better study the co-occurrence in data,we construct a bipartite graph for O-D pairs and user-OD pairs in historical data,and then use graph embedding technology to convert the nodes in the graph into feature vectors.In view of the inconsistency between the evaluation index and the loss function in this study,we propose a post-processing algorithm to deal with the inconsistency between the prediction result and the evaluation index.Lastly,how to maintain a balance between increasing demand and limited supply is the core issue for the operation of online car-hailing platforms.In this paper,the essence of the recommendation of the online car-hailing dispatching unit is discussed,and it is pointed out that it is essentially a load balancing problem.Referring to the idea of recommendation system and load balancing,we design the overall framework of the algorithm,reinforcement learning returns the sorted recommendation action list,and then matches the scheduling request and action in the form of Round-Robin.Through reasonable dispatching unit recommendation,the balance between supply and demand can be achieved,thereby further reducing the waiting time of passengers and increasing the driver's revenue per unit time.