Modelling the Relationship Between Post Encroachment Time and Signal Timings Using UAV Video data Zubayer Islam Ph.D.

2025-05-06 0 0 1.17MB 16 页 10玖币
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Modelling the Relationship Between Post Encroachment Time and Signal Timings Using
UAV Video data
Zubayer Islam, Ph.D.
Post-Doctoral Scholar
Department of Civil, Environmental and Construction Engineering
University of Central Florida, Orlando, FL 32816, USA
Email: zubayer_islam@knights.ucf.edu
Mohamed Abdel-Aty Ph.D., P.E.
Pegasus Professor and Chair
Department of Civil, Environmental and Construction Engineering
University of Central Florida, Orlando, FL 32816, USA
Tel: (407)823-4535
Email: M.Aty@ucf.edu
Amrita Goswamy, Ph.D.
Post-Doctoral Scholar
Department of Civil, Environmental and Construction Engineering
University of Central Florida, Orlando, FL 32816, USA
Email: amrita.goswamy@ucf.edu
Amr Abdelraouf, Ph.D.
Post-Doctoral Scholar
Department of Civil, Environmental and Construction Engineering
University of Central Florida, Orlando, FL 32816, USA
Email: amr.abdelraouf@Knights.ucf.edu
Ou Zheng
Department of Civil, Environmental and Construction Engineering
University of Central Florida, Orlando, FL 32816, USA
Email: ouzheng1993@Knights.ucf.edu
Islam, Abdel-Aty, Goswamy and Abdelraouf
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ABSTRACT
Intersection safety often relies on the correct modelling of signal phasing and timing parameters. A slight
increase in yellow time or red time can have significant impact on the rear end crashes or conflicts. This
paper aims to identify the relationship between surrogate safety measures and signal phasing. Unmanned
Aerial Vehicle (UAV) video data has been used to study an intersection. Post Encroachment Time (PET)
between vehicles was calculated from the video data as well as speed, heading and relevant signal timing
parameters such as all red time, red clearance time, yellow time, etc. Random Parameter Ordered Logit
Model was used to model the relationship between PET and these signal timing parameters. Overall, the
results showed that yellow time and red clearance time is positively related to PETs. The model was also
able to idendity certain signal phases that could be a potential safety hazard and would need to be retimed
by considering the PETs. The odds ratios from the models also indicates that increasing the yellow and red
clearance times by one second can improve the PET levels by 16% and 3% respectively.
Keywords: UAV video data, Post Encroachment Time, Signal Timing, All-Red Time, Red Clearance
Time, Yellow Time
Islam, Abdel-Aty, Goswamy and Abdelraouf
3
INTRODUCTION
Traffic analysis from a safety point of view has largely relied on crash data. Various statistical
methods and machine learning methods have been implemented to understand proactive natures of crash
enabling real time prediction of these events. Countermeasures have been developed based on accident data
as well. However, crash data can be rare events and thereare notable shortcomings of these types of data
such as incorrect reasoning, subjectivism, inaccurate data, etc. (1, 2). Moreover, the specific reasoning to a
crash can often be factors other than roadway characteristics and traffic features which cannot be modelled
using the prediction algorithms in the literature. On the other hand, conflict events are more common and
therefore, can help better to understand design flaws of roadway as well as traffic conditions that impacts
conflicts. Several previous studies have definitively proven conflict analysis as an alternative to crash
analysis with similar results (2-5). Several metrics has thus been developed to measure conflict such as
Time-to-collision (TTC) (6), time exposed TTC (TET), time integrated TTC (TIT), time-to-lane crossing
(TLC) (7), Post encroach time (PET), gap time (GT), encoding time (ET), and time advantage (TAdv) (8),
etc. The surrogate safety measures are usually dependent on exact localization of road users. For
example, to calculate TTC, initial location and velocity would be needed. This requires precise GPS
locations. An effective way to study an intersection would be with the help of an Unmanned Aerial Vehicle
(UAV) that can be then used to extract accurate trajectories at the centimeter level. These are a better
alternative than roadside cameras which have distortion of localization at camera edges. UAVs are also
known for easy maneuvering, flexibility, and low cost. UAVs have become an emerging video analysis
solution at the transportation level in the recent years. It is often augmented with radar and infrared cameras
that can provide a bird’s eye view of an intersection including the approaches. In this study, an intersection
was analyed with respect to PET from the data available through UAV. The signal timing at that instant
was also captured. The purpose of this study was to analyze the interaction of safety events and relate it to
the signal states.
LITERATURE REVIEW
Traffic safety at intersections has been shown to be dependent on signal timing at that intersection. For
example, altering signal phases can better or worsen intersection safety (9). Several studies have found that
there is a direct relation between signal timings and crashes. After any retiming of signals, a crash reduction
factor is also estimated but few studies have also reported that there were no significant relationships (10).
Guo, Wang (11) showed that adaptive intersections experienced fewer crashes than isolate ones. The study
was extensive and included over 170 intersections in Florida, USA but the results were based on signal
timing sheets only since real traffic data was not available. Midenet, Saunier (12) evaluated signal safety
by measuring the exposure to lateral collisions using video feed. Approach level data from traffic detectors
including speed, volume was found to be associated with significant crash risk (13). It was also reported in
this study that longer green time for left turn, higher green ratio can improve the safety at intersections. The
main limitation of all the studies is that crash events are usually rare and therefore, these studies would only
rely on the spatial relationship between crash events and traffic parameters. It has been shown in several
studies that the temporal relationship need to be included as well since traffic parameters and signal timing
would vary largely throughout the day and even across days (14-16). Moreover, there are notable
shortcomings of these types of police reported crash data such as incorrect reasoning, subjectivism,
inaccurate data, etc. (1, 2). Additionally, there is the moral dilemma of waiting for fatalities to happen
before taking an appropriate countermeasure making it a reactive approach. Crash events are also rare, and
it takes a long time to study a location or conduct a before-after study. Surrogate safety measures provide
an alternate and proactive methodology that does not require much time and solves the moral dilemma to a
great extent. Several studies have also shown that it can significantly correlate to crashes and can mostly
be used as an alternative (2-5, 17).
Using surrogate safety measures for signal timing was first proposed by Stevanovic, Stevanovic
(18). The study proposed the integration of optimization and surrogate safety measure assessment at the
microscopic level considering both the safety and efficiency. Network wide optimization was also studied
Islam, Abdel-Aty, Goswamy and Abdelraouf
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in recent time (19). This work also incorporates simulation and surrogate safety measures to find optimal
solution using a model callibrated from real-world data. The influence of signal phasing on the safety and
traffic smootheness was also stuided (20, 21). It was also shown that optimization of the left turn waiting
zones would improve capacity without degrading traffic flow (22) while Lin and Huang (23) improved both
at signal coordination level across multiple intersections. All the studies have relied on simulation softwares
such as VISSIM to model traffic signals and safety. While some studies calibrate the models based on real
traffic flow, the ground data can be siginificantly different than the simulation. This work addresses this
research gap and uses real-world data from UAVs to evaluate signal timing based on Post Encroachment
Time (PET). The main objective of this work was to evaluate the impact of all-red time, red clearance time,
red time, yellow time and green time on the surrogate safety measures based on real-world data. These can
also help relevant authorities to understand intersection traffic with respect to PET and gain insight whether
the signal timing need optimization or not. Moreover, the odds ratio was also calculated to show that one
second increase of yellow and red clearance time will help to increase the PET level thereby improving the
safety condition of the intersection.
DATA PREPARATION
Trajectory Data
The vehicle trajectories provided by the CitySim dataset (24) were utilized to identify, process, and
analyze PET conflicts in this study. The CitySim dataset is composed of top-view drone-video-based
vehicle trajectories. The authors identified vehicle trajectories using mask-RCNN and subsequently
extracted and exported rotation-aware bounding boxes. The dataset contains vehicle trajectories sampled at
30 frames per second. For each trajectory point, the dataset provides four bounding box positions, speed,
and heading. In this work, the University@Alafaya intersection location was selected for development,
evaluation, and analysis. The intersection geometry is illustrated in Figure 1. It is a signalized intersection
between Alafaya Trail (9 lanes) and University Boulevard (9 lanes). The utilized trajectories were extracted
from a video recorded on a weekday between 5:40 PM and 6:40 PM (afternoon peak). A total of 4871
vehicles passed through the intersection during that period of time. The different phases are also shown in
Figure 1. There are three through lanes for each of the phases 2,4,6 and 8 while two left turning lanes for
phases 1,3,5 and 7. The approach 4 does not have any exclusice right turn lanes while the other through
phases all have an exclusive right turn lane.
Figure 1. Study intersection location showing the different phases
UCF-SST CitySim Dataset
University @ Alafaya
摘要：

ModellingtheRelationshipBetweenPostEncroachmentTimeandSignalTimingsUsingUAVVideodataZubayerIslam,Ph.D.Post-DoctoralScholarDepartmentofCivil,EnvironmentalandConstructionEngineeringUniversityofCentralFlorida,Orlando,FL32816,USAEmail:zubayer_islam@knights.ucf.eduMohamedAbdel-AtyPh.D.,P.E.PegasusProfess...

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