A Survey of Moving Objects kNN Query
in Road Network Environment
Wei Jiang
1 , Guanyu Li
1(&) , Jingmin An
2 , Yunhao Sun
1 ,
Heng Chen
1,3 , and Xinying Chen
1,4
1 Faculty of Information Science and Technology, Dalian Maritime University,
Dalian, China
rabitlee@163.com
2 Faculty of Computer and Software, Dalian Neusoft University of Information,
Dalian, China
3 School of Software Technology, Dalian University of Foreign Languages,
Dalian, China
4 School of Software Technology, Dalian Jiao Tong University, Dalian, China
Abstract. With the widespread application of the global positioning service
(LBS) technology in the road network environment, the k-nearest neighbor
query problem of moving objects in the road network has become a research hot
spot for many scholars. This paper introduces the research status of k-nearest
neighbor query of moving objects in the road network, analyzes and summarizes
the research results of scholars at home and abroad, and finally analyzes the
challenges of k-nearest neighbor queries for moving objects on the road network
in the future.
Keywords: Road network environment Á Moving object Á k-nearest neighbor
query
1 Introduction
With the rapid development of mobile communication and spatial positioning technology, the location-based services (LBS) have been promoted. The research of
k-nearest neighbor query technology for moving objects in road networks has very
important social price and commercial value [1, 2]. The k-nearest neighbor query is one
of the common queries in the location service, which refers to finding the k target points
of interest (PoI) of the nearest location to the query requester, for example, finding the
nearest k taxis or gas stations from the query requester.
In practical applications, such as sharing the renter, according to the query results of
the k satisfying conditions returned by the query requester according to the request, the
system determines whether the different query requesters have the same riding route,
thereby determining whether to give them a carpool. Aiming at the demand of more
and more users, the k-nearest neighbor trajectory query processing method of moving
objects in the road network needs to be solved urgently, which has important practical
significance.
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 629–636, 2020.
https://doi.org/10.1007/978-981-15-0187-6_75
in Road Network Environment
Wei Jiang
1 , Guanyu Li
1(&) , Jingmin An
2 , Yunhao Sun
1 ,
Heng Chen
1,3 , and Xinying Chen
1,4
1 Faculty of Information Science and Technology, Dalian Maritime University,
Dalian, China
rabitlee@163.com
2 Faculty of Computer and Software, Dalian Neusoft University of Information,
Dalian, China
3 School of Software Technology, Dalian University of Foreign Languages,
Dalian, China
4 School of Software Technology, Dalian Jiao Tong University, Dalian, China
Abstract. With the widespread application of the global positioning service
(LBS) technology in the road network environment, the k-nearest neighbor
query problem of moving objects in the road network has become a research hot
spot for many scholars. This paper introduces the research status of k-nearest
neighbor query of moving objects in the road network, analyzes and summarizes
the research results of scholars at home and abroad, and finally analyzes the
challenges of k-nearest neighbor queries for moving objects on the road network
in the future.
Keywords: Road network environment Á Moving object Á k-nearest neighbor
query
1 Introduction
With the rapid development of mobile communication and spatial positioning technology, the location-based services (LBS) have been promoted. The research of
k-nearest neighbor query technology for moving objects in road networks has very
important social price and commercial value [1, 2]. The k-nearest neighbor query is one
of the common queries in the location service, which refers to finding the k target points
of interest (PoI) of the nearest location to the query requester, for example, finding the
nearest k taxis or gas stations from the query requester.
In practical applications, such as sharing the renter, according to the query results of
the k satisfying conditions returned by the query requester according to the request, the
system determines whether the different query requesters have the same riding route,
thereby determining whether to give them a carpool. Aiming at the demand of more
and more users, the k-nearest neighbor trajectory query processing method of moving
objects in the road network needs to be solved urgently, which has important practical
significance.
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 629–636, 2020.
https://doi.org/10.1007/978-981-15-0187-6_75