Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (4): 1040-1046.doi: 10.13229/j.cnki.jdxbgxb.20221426

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Identification of road riding quality based on shared bike trajectory data

Ying HU1,2(),Chun-fu SHAO1,Shu-ling WANG2,Xi JIANG3,Hai-rui SUN4   

  1. 1.Key Laboratory of Integrated Transportation Big Data Application Technology for Transportation Industry，Beijing Jiaotong University，Beijing 100044，China
    2.Beijing Transport Institute，Beijing 100073，China
    3.School of Traffic and Transportation，Beijing Jiaotong University，Beijing 100044，China
    4.Beijing Best Transport Tech Co. ，Ltd. ，Beijing 100044，China
  • Received:2022-11-10 Online:2023-04-01 Published:2023-04-20

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Abstract:

Aiming at the challenges of trajectory data processing and application brought about by the large error of shared bike riding positioning data and the complexity of bicycle riding behavior， the method of data preprocessing， road network matching and rapid identification of road riding quality is studied. Based on the Hidden Markov Model （HMM）， an improved shared bike riding trajectory data matching method is proposed. Based on the density clustering algorithm， the identification of the parking point set of the trajectory is realized， the characteristics of the parking point set and the regularity of the positioning data are used to realize the calculation of the key parameters in line with the noise characteristics of shared bikes， and the optimal matching algorithm flow between bicycle trajectory data and road network is given. Using the matched trajectory data， a rapid identification method of bicycle traffic riding quality based on outlier test is proposed， and a case analysis is carried out in a certain area of Beijing. The accuracy of the method was verified by this method to be 87.7%.

Key words: transportation planning and management, share bike, trajectory data, road network matching, riding quality, outlier test

CLC Number: 

  • U491.2

Fig.1

Map display of shared bike trajectory point"

Fig.2

Map display of shared bike riding trajectory"

Fig.3

Track point matching road section"

Fig.4

Lattice for Road network matching of sharedbike trajectory point"

Fig.5

Clustered unordered track points during riding"

Fig.6

Each of trajectory points in “stop point”area"

Fig.7

Histogram of travel speed accessibility rate"

Fig.8

Histogram of riding space comfort rate"

Fig.9

Results of riding abnormal road section"

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