Data Mining Method of Sequential Patterns for Vehicle Trajectory Prediction in VANET
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Data Mining Method of Sequential Patterns for Vehicle Trajectory Prediction in VANET Hong Zhang1,2 · Li He3 Accepted: 29 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In order to provide the future direction of vehicle flow for travelers, it is necessary to predict the driving trajectory of vehicles running on the road in a certain segments. According to the characteristics of vehicle sequential pattern, the topology of vehicular ad hoc network (VANET) is linked to the real road vehicle movement trajectory. Some new definitions related to sequential patterns in VANET environment are proposed. Based on RSU and V2V schemes, a vehicle movement database is established, and sequential pattern data mining is carried out. Afterward, their communication overhead is evaluated. The support and confidence of the movement rules generated by vehicle routing patterns are calculated to extract the probability of frequent driving trajectories. Keywords Vehicle trajectory prediction · Vehicle routing sequence · Sequential patterns · Vehicular ad-hoc network · Data mining
1 Introduction The era of 5G is coming. With the gradual application of Internet of Vehicles and ‘Internet+’ technology, intelligence and networking have changed the transport mode of human beings. At the same time, people have higher requirements for travel efficiency, as well as safety and environmental issues. Internal and external factors continue to promote the technologies of Internet of Vehicles and draw widespread attention. In recent years, vehicle safety devices are becoming more advanced, while traffic accidents are still a serious problem. It is believed by many researchers that 60% of road accidents can be avoided by conveying the warning information to drivers in time before accidents [1]. Vehicular ad hoc networks (VANET) is an upgraded version of mobile ad hoc networks (MANET), and VANET involves the exchange of information between mobile vehicles. Vehicular communication in VANET is very important that can effectively improve traffic safety * Hong Zhang zh‑[email protected] 1
Transportation Institute of Inner Mongolia University, Hohhot 010070, Inner Mongolia, China
2
Inner Mongolia Engineering Research Center for Urban Transportation Data Science and Applications, Hohhot 010070, Inner Mongolia, China
3
Guiyang Transport Development Research Center, Guiyang 550000, China
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and comfort. It can be predicted that almost all vehicles will be equipped with on-board VANET communication equipment in the near future [2, 3]. The prediction of vehicles’ routes during driving is one of the new fields of study [4]. There are many advantages to obtaining the expected routes and destinations of vehicles. For example, congestion levels in certain regions can be predicted at a specific time of a day; police can use the information of the route and destination of a vehicle to pursue and capture an escaped prisoner and can effectively deploy roadblocks and backups. Seq
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