Citation: Zhang, Y.; Ji, Y.; Yu, J.
Estimation Method for Road Link
Travel Time Considering the
Heterogeneity of Driving Styles. Appl.
Sci. 2022, 12, 5017. https://doi.org/
10.3390/app12105017
Academic Editor: Nadia Giuffrida
Received: 17 March 2022
Accepted: 13 May 2022
Published: 16 May 2022
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Article
Estimation Method for Road Link Travel Time Considering the
Heterogeneity of Driving Styles
Yuhui Zhang
1,2
, Yanjie Ji
1,2,3,
* and Jiajie Yu
1,2
1
School of Transportation, Southeast University, Nanjing 211189, China; zhangyuhui@seu.edu.cn (Y.Z.);
jiajieyu@seu.edu.cn (J.Y.)
2
National Demonstration Center for Experimental Road and Traffic Engineering Education, Southeast
University, Nanjing 211189, China
3
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast
University, Nanjing 211189, China
* Correspondence: jiyanjie@seu.edu.cn; Tel.: +86-138-1399-6939
Abstract:
To solve the problem of low automatic number plate recognition (ANPR) data integrity
and low completion accuracy of incomplete traffic data, which affects the quality and utilization of
ANPR data, this paper proposed a model for estimating the travel time of the road link that considers
the heterogeneity of the driving styles. The travel time of historical road sections in the road network
was extracted from ANPR data. The driving crowd was clustered through density-based spatial
clustering of applications with noise (DBSCAN) based on the time slot, the number of trips, and the
travel time. To avoid the excessive data difference between different classes and the distortion of
the complement data, the Lagrange interpolation method was adopted to complement the missing
road link travel time within each cluster. Taking Ningbo city in China as an example, the travel time
completion accuracies of the proposed method and the direct interpolation method were compared.
The results show that the interpolation method considering the heterogeneity of driving styles is
more sufficient to increase the completion accuracy by 37.4% compared with the direct interpolation
manner. The comparison result verifies the effectiveness of the proposed method and can provide
more reliable data support for the construction of the transportation system.
Keywords:
automatic number plate recognition (ANPR); density-based spatial clustering of
applications with noise (DBSCAN); outliers supplement; travel time estimation (TTE); heterogeneity
of driving styles
1. Introduction
The continuous advancement of urbanization has caused many urban road transporta-
tion systems to face increasing congestion, which threatens the environment and transport
efficiency [
1
]. To address issues such as traffic congestion, understanding the traffic state is
critical at many levels of traffic management and traffic policy. With the rapid development
of computer science and the progress of traffic system sensors, the collection of massive
traffic data has shown its advantages in traffic decision-making [
2
]. ANPR is one of the deci-
sive components of intelligent transportation systems [
3
]. It is often used in traffic big data
analysis such as travel time estimation, OD (Origin–Destination) estimation, commuting
recognition, etc. The ANPR uses image processing technology to collect vehicle information
at the time of the shooting, such as shooting time, vehicle type, vehicle license plate, etc.,
which can obtain a large amount of traffic information [
4
]. There is much helpful traffic
information in these data, and some characteristics related to traffic flow can be obtained
through traffic data mining technology [
5
]. Thereby, traffic big data can be converted into
readable information for traffic information prediction and management control [
6
]. By
mining ANPR data, researchers can obtain the travel characteristics of travelers and judge
Appl. Sci. 2022, 12, 5017. https://doi.org/10.3390/app12105017 https://www.mdpi.com/journal/applsci
