congestion scenario-based vehicle classification detection...
TRANSCRIPT
Congestion Scenario-Based Vehicle Classification
Detection Models Based on Traffic Flow
Characteristics and Observed Event Data
Symposium Celebrating 50 Years of
Traffic Flow Theory
Traffic Flow Theory and Characteristics Committee (AHB45)
2014 Summer Meeting - August 11-13, 2014 - Portland, Oregon
Heng Wei, PhD, PE
Associate Professor, Director, ART-Engines Transportation Research Lab, University of Cincinnati
Qingyi Ai, PhD
Acadis-US, Inc.
Zhixia Li, PhD Traffic Operations and Safety (TOPS) Laboratory, University of Wisconsin-Madison
Haizhong Wang, PhD School of Civil & Construction Engineering, Oregon State University
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Motivation
• Dual-loop detector• Dual-loop detector data is widely used as a data
source for permanent traffic count because it is
reliable and less costly.
• As a vehicle enters or leaves the loop, the electronic
unit will send a pulse to the controller. This pulse is
recorded and indicates that a vehicle is detected
• Information obtained from dual-loop detectors
includes timestamp, vehicle count and occupancy.
Vehicle speed and vehicle length can be calculated
from the loop information.
Background & Problem Statement
• Problem in vehicle classification
detection
• Under non-free flow, the difference
between on-times on two loops is often
large. The existing dual-loop length-
based vehicle classification model
produces many errors under congested
traffic conditions, especially under stop-
and-go traffic flow condition.
• The errors may be contributed by the
complex characteristics of traffic flows
under congestion; but quantification of
such contributing factors remains
unclear.
• The algorithm of screening dual-loop
detector data may remove those data
points which actually are good.
Layout of a Dual-loop Detector on Highway
3
Traffic Flow Models Literature Review
• Greenshield (1935) first proposed the traffic stream theory for relating flow rate, speed, and density.
Greenberg (1959) and Underwood (1961) etc. revised the model of speed and density to fit a curve.
• Kerner et al. (1994/2010) defined traffic flows in 3 categories: free flow, synchronized flow, and stop-and-go
flow (Figure below). The free flow has high travel speed and low traffic volume and density. The congested
traffic flow is further classified into synchronized flow (S) and wide moving jam (J). The synchronized flow
has relative low speed and high volume and density. A wide moving jam is a moving jam that maintains the
mean velocity of the downstream front of the jam as the jam propagates.
• The synchronized traffic is also described as the traffic oscillation by other researchers (Bertini and Leal,
2005, Zielke et al., 2008; Ahn and Cassidy, 2007; Daganzo, 2002, etc). • Treiber M. and Kesting A. (2011)
studied the convective instability
in congested flow according to
the stability significantly different
sets of traffic patterns (Blandin
et al., 2013).
• It is necessary to determine
what traffic variables and
thresholds of the selected traffic
variables will be used to
describe the traffic phases and
identify the transitions between
them.
4
Motivation Objectives and Methods
• Objectives• Identify traffic related factors which may effect the
accuracy of vehicle length estimation.
• Develop a heuristic framework to identify different trafficstates which will ensure the use of suitable models under aspecific traffic state.
• Develop vehicle classification models under congestedtraffic flows.
• Methods• Both ground-truth vehicle trajectory and simultaneous
loop event data are used to characterize the impact ofcongested traffic on length-based vehicle classification.
• Eight scenarios are synthesized to define the vehicles’stopping locations over two single loops of the dual-loopstation.
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Case Study Study Site and Data Collection
• Study site: the I-70/71 in Columbus and I-71 at Williams Ave Cincinnati in Cincinnati
• Video Data Collection: 26 hours video data for 3 days at in Columbus; 8 hours video data
for 2 days in Cincinnati.
• Ground-truth vehicle trajectory data was extracted from VEVID.
• Concurrent event dual-loop data obtained from the TMC at ODOT.
• GPS data is collected using a probe car equipped with a GPS data logger.
I-71 in Cincinnati, Ohio
I-70/71 in Columbus, Ohio
Camera Location
Dual-loop detectors
Study Site
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Modeling Traffic Conditions on Speed & Density
• Speed and density are applied to depict the empirical double Z-characteristic
shape for the phase transitions between two different phases.
• To simplify the procedure of the traffic condition identification, the F→S transition
was merged into free flow phase and S→J transition into synchronized phase.
• Density can be estimated from the loop data by the following Equation if the
average vehicle length of the traffic flow for varying time of a day could be
predetermined based on the historical traffic data.
Ki = density of the traffic flow
(vpkmpl) for time period i of a
day;
Occ = loop occupancy
measurement (%);
Lv = average vehicle length (m);
Leff = effective detector length
(m).
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Modeling Traffic Conditions on Speed & Density
K = flow density, vehicles/km/lane; u = speed, km/h; FF = freeflow period; SF = synchronized flow; TJ
= traffic jam phase; SU = special or unreasonable case; t = a short period of time (5 minutes in this
study); 𝑣(𝑡) = the average speed in time interval t, km/h; 𝑣(𝑡 + 1) = the average speed in the
successive time interval t+1, km/h;var(v) = the variation of all vehicles’ speed during time interval t; Δv
= predefined threshold of spot speed difference in successive time intervals, km/h; v* = predefined
threshold of the speed variance range in successive time intervals, (km/h)2; 𝑜𝑐𝑐 𝑡 = the average
occupancy during time interval t; and 𝑜𝑐𝑐 𝑡 + 1 = the average occupancy in the successive time
interval t+1; Δocc = the predefined occupancy bandwidth during the time interval t; and occ* = the
maximum average occupancy during the time interval t.
Based on the statistical analysis of the collected dual-loop data set in this study, Δv is determined as
10 mph and v* is determined as 49 mph2 (or the standard deviation is 7 mph).
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ModelingS
cen
ari
os o
f V
eh
icle
Sto
pp
ing
Lo
cati
on
on
Lo
op
s u
nd
er
Co
ng
esti
on
Scenario 1 2 3 4 5 6 7 8
Percentage of
Samples67.3% 9.7% 12.1% 4.6% 4.2% 0.9% 0.7% 0.5%
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Modeling Length-Based Vehicle Classification
• Vehicle Classification Model under
Synchronized Traffic (VC-Sync
model) for Scenarios 1-3:
• Assumptions: constant acceleration or
deceleration over the detection area
Where, Lv = length of the detected vehicle (ft);
Ls = length of each single loop within the dual-loop
(ft);
vo = speed of the vehicle entering the upstream
loop (M loop) (ft/s);
a = vehicle acceleration (ft/s2); and
D = distance between two loops (ft); t = t3-t1;
OnT1, = t2-t1; and OnT2 = t4-t3 .
2
0 1 1
1( )
2v sL v OnT a OnT L
02
D a tv
t
1 2
2 2
2 1 1 2
2 ( )
( ) ( ) ( )
OnT OnTDa
t OnT OnT OnT OnT t
• Vehicle Classification Model under Stop-
and-go Traffic for Scenario 4: Stop-on-
Both-Loop model (SBL model):
;
Where, Lv = length of vehicle (ft);
Ls = length of each single loop within the dual-loop (ft);
tdec = time period from a vehicle entering M loop to its stop (s);
tacc = time period from a vehicle starting to move to leaving M
loop (s);
a = the average acceleration rate of vehicles when they start
to move under stop-and-go traffic (ft/s2);
ts = time period for a vehicle stopping on both loops (s);
vmin = the minimum speed which can maintain a vehicle
running without stop (ft/s);
f1, f2, and f3 = adjusting factors for different vehicle types (in
this study, f1= f2= f3=1);
D, t, t2, t3, OnT1, and OnT2 are the same as defined
previously.
2
1 2
1 1
2v dec acc sL f t D f a t L
t
1dec acc st t OnT t 2
2 3 3
min
accs
tt t t f
v
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Modeling Algorithm for Identifying Traffic Conditions
yes
Congested Traffic
OnT1>ts1, and OnT2<ts1
Scenario 2
VC-Sync model
OnT1<ts1, and OnT2>ts1
Scenario 3
VC-Sync model
OnT1>ts1, OnT2>ts1, t3-t1<ts2,
and t4-t2<ts2
Scenario 4
SBL model
Note: 1. ts1 is the threshold of OnT1 and OnT2, and ts2 is the threshold of timestamp differences; t1, t2, t3,
t4, OnT1, and OnT2 are the same as defined previously.
2. In this study, ts1 and ts2 are determined as 4.1s and 3.0s, respectively.
OnT1<ts1, and OnT2<ts1
Scenario 1
VC-Sync model
Under congested traffic, the following algorithm is proposed to
identify a vehicle’s stopping status (Scenarios 1-4):
Flowchart for Identifying Vehicle Stopping Status
no
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Testing Compare with Existing Applied Model
0
20
40
60
80
100
120
140
160
0 20 40 60 80 100 120 140 160 180 200
Veh
icle
Len
gth
(ft
)
Vehicle No.
Synchronized Traffic
Ground-Truth Vehicle Length
Existing Model
VC-Sync Model
Vehicle
Classification
Model under
Synchronized
Traffic (VC-Sync
model) vs. the
Existing Model
0
50
100
150
200
250
300
350
0 20 40 60 80 100 120 140 160 180
Veh
icle
Len
gth
(ft
)
Vehicle No.
Stop-and-go Traffic
Ground-Truth Vehicle Length
Existing Model
VC-Stog Model
Vehicle Classification Model
under Stop-and-Go Traffic (VC-
Stog model) vs. Existing Model
Error:
Existing model: 235%
VC-Stog model: 17.1%
Error:
Existing model: 33.5%
VC-Sync model: 6.7%
Note: 3-bin scheme standard
adopted by ODOT is used: small
vehicles (length ≤ 8.5 m), medium
vehicle (8.5 m <length< 14.0 m),
and large vehicle (length ≥ 14.0 m).
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Conclusions
• The existing vehicle classification model has been proven to
produce many errors, both under synchronized and stop-and-
go traffic.
• VC-Sync model has been developed to model under
synchronized traffic conditions.
• Relative error: 33.5% 6.7%
• VC-Stog model has been developed to model under stop-
and-go traffic conditions.
• Relative error: 235% 17.1%
• VEVID plays an important role in extracting ground-truth data.
• Traffic stream models are well applied in vehicle classification
modeling problems.
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Thank you very much!
Acknowledgement
• The study is supported by an Ohio
Transportation Consortium (OTC)
grant.
• Dr. Benjamin Coifman at the Ohio
State University who provided the
event dual-loop data.
• Fellows in the ART Engine Lab
provide great help in VEVID
update and data collection.
…Questions?