RRCF: an abnormal pulse diagnosis factor for road abnormal hotspots detection
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ORIGINAL RESEARCH
RRCF: an abnormal pulse diagnosis factor for road abnormal hotspots detection Lingqiu Zeng1,2 · Guangyan He1 · Qingwen Han3,4 · Sheng Cheng3 · Lei Ye2,3 · XiaoChang Hu5 Received: 11 May 2019 / Accepted: 2 August 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract Road hotspots detection method is a key issue in the field of intelligent transportation research. Compared with normal hotspots caused by high traffic flow, abnormal hotspots, which are results of road accidents, perform an occurrence time random behavior and difficult to predict. Deducing from the pulse diagnosis method, in this paper, a region real-time congestion factor is constructed to realize road abnormal hotspots discovery. Taxi’s GPS data of Hangzhou City, China are employed to find abnormal pulse of road segment, while the relationship between proposed congestion factor and the real-time traffic data is discussed. Two accidental scenarios are built to verify the validity of the proposed method. The experiment results show that the proposed method performs well in real-time abnormal hotspot detection and analysis output could be useful in path planning and traffic management. Keywords Abnormal hotspots · Traffic analysis · Congestion · Taxi GPS
1 Introduction
* Qingwen Han [email protected] Lingqiu Zeng [email protected] Guangyan He [email protected] Sheng Cheng [email protected] Lei Ye [email protected] XiaoChang Hu [email protected] 1
College of Computer Science, ChongQing University, Chongqing 400044, China
2
Key Laboratory of Advanced Manufacture Technology for Automobile, PartsChongqing University of Technology, Ministry of Education, Chongqing 400054, China
3
School of Microelectronics and Communication Engineering, ChongQing University, Chongqing 400044, China
4
State Key Laboratory of Vehicle NVH and Safety Technology, Chongqing 401122, China
5
Chongqing Automotive Collaborative Innovation Center, ChongQing University, Chongqing 400044, China
In recent years, due to the rapidly increasing of road vehicles, traffic jams become a serious problem in past 20 years, which may be an obstruction of economy, environment and the living standards. As for driver, they are prefer to know the congestion sections which are on their way, so that they can choose a better way to avoid these congestion sections. Until now, most of the navigation systems have provided real-time road condition information to drivers to avoid congestion sections (Smart City Research Group 2017). However, as we known, the traffic congestions have duration times due to different reasons. According to their cause factor, we could divide road hotspots into two categories, which are normal hotspots (NHSP) and abnormal hotspots (AHSP). NHSP is caused by heavy road traffic flow, while AHSP is caused by road traffic accidents. In general, the occurrence of NHSP shows regularity with the change of daytime, while that of AHSP is almost random. AHSP is produced by road accidents, of which occurring time and hazard level a
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