Abstract

Off-ramp areas are considered the critical sections of urban expressways where the exiting vehicles and straight-through vehicles merge. Therefore, lane-change behaviors frequently occur at the upstream of the urban expressway off-ramp, which lead to high chance of traffic crashes. This study looks at the risk of lane-change behaviors in the multilane urban expressway off-ramp areas. First, lane-change process information of exit vehicles in urban expressway off-ramp area was extracted from the Shanghai Naturalistic Driving Study (SH-NDS) database. Second, for each lane-change movements of exit vehicles, a risk evaluation indicator (risk perception, RP) was adopted to quantify the lane-change risk. Based on the RP, the study proposed a four-rank risk classification criterion using K-means clustering to define the risk rank of each lane-change movement. Finally, a lane-change risk rank classification model was developed for traffic in the off-ramp areas of multilane expressways using four distinctive influencing factors. Four influencing factors, namely, traffic congestion level, demand lane change times, lane-change direction, and relative distance between vehicle and exit, were used to describe the traffic flow characteristics and exiting lane-change route for the modeling purpose. The risk model was developed using two support vector machine models, which were based on the partial binary tree structure and the directed acyclic graph structure, respectively. The results showed that the overall accuracy of the partial binary tree structure classifier was 65.71 % and the average AUC value was 0.9004, both of which shows a better performance of the partial binary tree structure classifier, compared with the directed acyclic graph structure classifier.

References

1.
Duan
L.
, “
Effects of Operation Mode on Traffic Safety in Multi-Lane Freeway Diverge Segment
” (in Chinese) (PhD diss.,
Southeast University
,
2015
).
2.
Wang
B.
,
Gao
L.
, and
Juan
Z.
, “
Analysis of Lane Changing Conflict Based on TTA in Expressway Weaving Area
” (in Chinese),
Journal of System Simulation
30
, no. 
9
(September
2018
):
3306
3311
. https://doi.org/10.16182/j.issn1004731x.joss.201809010
3.
Park
H.
,
Oh
C.
,
Moon
J.
, and
Kim
S.
, “
Development of a Lane Change Risk Index Using Vehicle Trajectory Data
,”
Accident Analysis & Prevention
110
(January
2018
):
1
8
. https://doi.org/10.1016/j.aap.2017.10.015
4.
Toledo
T.
and
Zohar
D.
, “
Modeling Duration of Lane Changes
,”
Transportation Research Record: Journal of the Transportation Research Board
1999
, no.
1
(January
2007
):
71
78
.
5.
Fu
T.
,
Zangenehpour
S.
,
St-Aubin
P.
,
Fu
L.
, and
Miranda-Moreno
L. F.
, “
Using Microscopic Video Data Measures for Driver Behavior Analysis during Adverse Winter Weather: Opportunities and Challenges
,”
Journal of Modern Transportation
23
, no. 
2
(June
2015
):
81
92
. https://doi.org/10.1007/s40534-015-0073-3
6.
Noh
S.
and
An
K.
, “
Risk Assessment for Automatic Lane Change Maneuvers on Highways
” (paper presentation, 2017 IEEE International Conference on Robotics and Automation, Singapore, May 29–June 3,
2017
).
7.
Mori
M.
,
Miyajima
C.
,
Hirayama
T.
,
Kitaoka
N.
, and
Takeda
K.
, “
Integrated Modeling of Driver Gaze and Vehicle Operation Behavior to Estimate Risk Level During Lane Changes
” (paper presentation, 16th International IEEE Conference on Intelligent Transportation Systems, The Hague, the Netherlands, October 6–9,
2013
).
8.
Jiang
X. J.
, “
Lane-Change Risk Evaluation of Freeway Diverging Area Considering Visual Characteristics
” (in Chinese) (master’s thesis,
Nanjing University of Science and Technology
,
2017
).
9.
Dingus
T. A.
,
Klauer
S. G.
,
Neale
V. L.
,
Petersen
A.
,
Lee
S. E.
,
Sudweeks
J. D.
,
Perez
M. A.
, et al.,
The 100-Car Naturalistic Driving Study, Phase II - Results of the 100-Car Field Experiment, DOT HS 810 593
(
Washington, DC
:
National Highway Traffic Safety Administration
,
2006
).
10.
Uchida
N.
,
Kawakoshi
M.
,
Tagawa
T.
, and
Mochida
T.
, “
An Investigation of Factors Contributing to Major Crash Types in Japan Based on Naturalistic Driving Data
,”
IATSS Research
34
, no. 
1
(July
2010
):
22
30
. https://doi.org/10.1016/j.iatssr.2010.07.002
11.
Barnard
Y.
,
Utesch
F.
,
van Nes
N.
,
Eenink
R.
, and
Baumann
M.
, “
The Study Design of UDRIVE: The Naturalistic Driving Study across Europe for Cars, Trucks and Scooters
,”
European Transport Research Review
8
, no. 
2
(June
2016
): 14. https://doi.org/10.1007/s12544-016-0202-z
12.
Wang
X. S.
and
Li
Y.
, “
Characteristics Analysis of Lane Changing Behavior Based on the Naturalistic Driving Data
” (in Chinese),
Journal of Transport Information and Safety
34
, no. 
1
(February
2016
):
17
22
.
13.
Zhang
L.
,
Chen
C.
,
Zhang
J.
,
Fang
S.
, and
Guo
J.
, “
Modeling Lane-Changing Behavior in Freeway Off-Ramp Areas Using Naturalistic Driving Data
” (in Chinese),
Journal of Tongji University (Natural Science)
46
, no. 
3
(March
2018
):
318
325
, 333. https://doi.org/10.11908/j.issn.0253-374x.2018.03.006
14.
Yang
X.-F.
,
Zhou
Y.
, and
Fu
Q.
, “
A Lane-Changing and Off-Ramp Model of Vehicles on Enclosed Road Considering Driving Psychology
” (in Chinese),
Journal of Highway and Transportation Research and Development
32
, no. 
7
(September
2015
):
112
119
.
15.
Polus
A.
and
Mattar-Habib
C.
, “
New Consistency Model for Rural Highways and Its Relationship to Safety
,”
Journal of Transportation Engineering
130
, no. 
3
(May
2004
):
286
293
. https://doi.org/10.1061/(ASCE)0733-947X(2004)130:3(286)
16.
Garber
N. J.
and
Ehrhart
A. A.
,
The Effect of Speed, Flow and Geometric Characteristics on Crash Rates for Different Types of Virginia Highways, Final Report VTRC 00-R15
(
Charlottesville, VA
:
Virginia Transportation Research Council
,
2000
).
17.
Kockelman
K. M.
and
Kweon
Y.-J.
, “
Driver Injury Severity: An Application of Ordered Probit Models
,”
Accident Analysis & Prevention
34
, no. 
3
(May
2002
):
313
321
. https://doi.org/10.1016/S0001-4575(01)00028-8
18.
Ma
Z.
,
Chen
Y.
, and
Zhang
L.
, “
Influence Factors of Accident Severity for Urban Road
” (in Chinese),
Journal of Chongqing Jiaotong University (Natural Science)
33
, no. 
1
(February
2014
):
111
114
.
19.
Xie
Y.
,
Zhang
Y.
, and
Liang
F.
, “
Crash Injury Severity Analysis Using Bayesian Ordered Probit Models
,”
Journal of Transportation Engineering
135
, no. 
1
(January
2009
):
18
25
. https://doi.org/10.1061/(ASCE)0733-947X(2009)135:1(18)
20.
Guo
F.
,
Klauer
S. G.
,
Hankey
J. M.
, and
Dingus
T. A.
, “
Near Crashes as Crash Surrogate for Naturalistic Driving Studies
,”
Transportation Research Record: Journal of the Transportation Research Board
2147
, no. 
1
(January
2010
):
66
74
. https://doi.org/10.3141/2147-09
21.
Abdel-Aty
M.
and
Keller
J.
, “
Exploring the Overall and Specific Crash Severity Levels at Signalized Intersections
,”
Accident Analysis & Prevention
37
, no. 
3
(May
2005
):
417
425
. https://doi.org/10.1016/j.aap.2004.11.002
22.
Davis
G. A.
,
Hourdos
J.
,
Xiong
H.
, and
Chatterjee
I.
, “
Outline for a Causal Model of Traffic Conflicts and Crashes
,”
Accident Analysis & Prevention
43
, no. 
6
(November
2011
):
1907
1919
. https://doi.org/10.1016/j.aap.2011.05.001
23.
Fu
T.
,
Miranda-Moreno
L.
, and
Saunier
N.
, “
A Novel Framework to Evaluate Pedestrian Safety at Non-Signalized Locations
,”
Accident Analysis & Prevention
111
(February
2018
):
23
33
. https://doi.org/10.1016/j.aap.2017.11.015
24.
Wang
J.
,
Kong
Y.
, and
Fu
T.
, “
Expressway Crash Risk Prediction Using Back Propagation Neural Network: A Brief Investigation on Safety Resilience
,”
Accident Analysis & Prevention
124
(March
2019
):
180
192
. https://doi.org/10.1016/j.aap.2019.01.007
25.
Milton
J.
and
Mannering
F.
, “
The Relationship among Highway Geometrics, Traffic-Related Elements and Motor-Vehicle Accident Frequencies
,”
Transportation
25
, no. 
4
(November
1998
):
395
413
. https://doi.org/10.1023/A:1005095725001
26.
Dissanayake
S.
and
Lu
J.
, “
Analysis of Severity of Young Driver Crashes: Sequential Binary Logistic Regression Modeling
,”
Transportation Research Record: Journal of the Transportation Research Board
1784
, no. 
1
(January
2002
):
108
114
. https://doi.org/10.3141/1784-14
27.
Hassan
H. M.
and
Abdel-Aty
M. A.
, “
Predicting Reduced Visibility Related Crashes on Freeways Using Real-Time Traffic Flow Data
,”
Journal of Safety Research
45
(June
2013
):
29
36
. https://doi.org/10.1016/j.jsr.2012.12.004
28.
Xu
C.
,
Wang
Y.
,
Liu
P.
,
Wang
W.
, and
Bao
J.
, “
Quantitative Risk Assessment of Freeway Crash Casualty Using High-Resolution Traffic Data
,”
Reliability Engineering & System Safety
169
(January
2018
):
299
311
. https://doi.org/10.1016/j.ress.2017.09.005
29.
Ouyang
Y.
,
Shankar
V.
, and
Yamamoto
T.
, “
Modeling the Simultaneity in Injury Causation in Multivehicle Collisions
,”
Transportation Research Record: Journal of the Transportation Research Board
1784
, no. 
1
(January
2002
):
143
152
. https://doi.org/10.3141/1784-18
30.
Khattak
A. J.
and
Cassidy
G.
,
Factors that Influence Multivehicle Rear-End Crashes: Analysis of Crash Propagation and Injury Severity: Seed Grant Final Report
(
Knoxville, TN
:
University of Tennessee
,
1999
).
31.
Abdel-Aty
M. A.
and
Abdelwahab
H. T.
, “
Predicting Injury Severity Levels in Traffic Crashes: A Modeling Comparison
,”
Journal of Transportation Engineering
130
, no. 
2
(March
2004
):
204
210
. https://doi.org/10.1061/(asce)0733-947x(2004)130:2(204)
32.
Kondoh
T.
,
Yamamura
T.
,
Titazaki
S.
,
Kuge
N.
, and
Boer
E. R.
, “
Identification of Visual Cues and Quantification of Drivers’ Perception of Proximity Risk to the Lead Vehicle in Car-Following Situations
,”
Journal of Mechanical Systems for Transportation and Logistics
1
, no. 
2
(April
2008
):
170
180
. https://doi.org/10.1299/jmtl.1.170
33.
He
H.
,
Bai
Y.
,
Garcia
E. A.
, and
Li
S.
, “
ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning
” (paper presentation, IEEE International Joint Conference on Neural Networks, Hong Kong, June 1–8,
2008
).
34.
Hsu
C.-W.
and
Lin
C.-J.
, “
A Comparison of Methods for Multiclass Support Vector Machines
,”
IEEE Transactions on Neural Networks
13
, no. 
2
(March
2002
):
415
425
. https://doi.org/10.1109/72.991427
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