ACCIDENT MANAGEMENT
USING WIRELESS NETWORKS:
Estimating
Incident Related Congestion on Freeways
Based
on Incident Severity
By:
Avi Kripalani
William Scherer
A Research Project Report
For the Center for ITS Implementation Research (ITS)
A U.S. DOT University Transportation Center
Avi Kripalani and William Scherer
Department of Systems and Information Engineering
University of Virginia
Center for Transportation Studies at the
University of Virginia produces outstanding transportation professionals,
innovative research results and provides important public service. The Center
for Transportation Studies is committed to academic excellence,
multi-disciplinary research and to developing state-of-the-art facilities.
Through a partnership with the Virginia Department of Transportation’s (VDOT)
Research Council (VTRC), CTS faculty hold joint appointments, VTRC research
scientists teach specialized courses, and graduate student work is supported
through a Graduate Research Assistantship Program. CTS receives substantial
financial support from two federal University Transportation Center Grants: the
Mid-Atlantic Universities Transportation Center (MAUTC), and through the
National ITS Implementation Research Center (ITS Center). Other related
research activities of the faculty include funding through FHWA, NSF, US
Department of Transportation, VDOT, other governmental agencies and private
companies.
Disclaimer: The contents of this report
reflect the views of the authors, who are responsible for the facts and the
accuracy of the information presented herein.
This document is disseminated under the sponsorship of the Department of
Transportation, University Transportation Centers Program, in the interest
of information exchange. The U.S.
Government assumes no liability for the contents or use thereof.
1. Report No.
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2. Government Accession No.
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3. Recipient’s Catalog No.
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4. Title and Subtitle
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5. Report Date
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ACCIDENT
MANAGEMENT USING WIRELESS NETWORKS: ESTIMATING INCIDENT RELATED CONGESTION ON
FREEWAYS BASED ON INCIDENT SEVERITY
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July 2007
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6. Performing Organization
Code
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7. Author(s)
Avi Kripalani, William
Scherer
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8. Performing Organization
Report No.
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9. Performing Organization
and Address
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10. Work Unit No. (TRAIS)
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Center for Transportation
Studies
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University of Virginia
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11. Contract or Grant No.
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PO Box 400742
Charlottesville, VA
22904-7472
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12. Sponsoring Agencies'
Name and Address
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13. Type of Report and
Period Covered
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Office of University
Programs, Research and Special Programs Administration
US Department of
Transportation
400 Seventh Street, SW
Washington DC 20590-0001
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Final Report
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14. Sponsoring Agency Code
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15. Supplementary Notes
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16. Abstract
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The effects of traffic incidents on metropolitan
freeways extend beyond causing congestion and delays. Immediate impacts include decreased
productivity, increased pollution and reduced safety on highways. State and local governments spend billions
of dollars annually on construction projects and Intelligent Transportation
Systems (ITS) in an effort to curb the adverse consequences of incidents and
incident related delays.
Effective identification and response on highways is
one key to reducing the costs associated with traffic incidents. Within the context of a prototype incident
identification and response system developed by the University of Virginia’s
Systems Technology Integration Laboratory (STIL), this research aims to
develop a statistical approach to modeling congestion associated with freeway
incidents. The ability to predict
congestion will provide more information and greater situational awareness to
emergency responders and traffic managers, and will allow travelers to make
more informed route selection decisions.
By combining data from multiple sources, it is
possible to match incident severity estimates for freeway incidents with
associated traffic flow counts at the time of the accident. Four metrics for freeway congestion were
derived from the traffic flow data, and these metrics were then modeled as
functions of the incident severity estimates. The results of multiple linear regression analysis showed that
quantifiable relationships exist between the congestion metrics and incident
severity data such as the number of vehicles involved in an incident as well
as the number of serious injuries reported at the scene.
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17 Key Words
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18. Distribution Statement
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Incidents, congestion, data
integration, VII
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No restrictions. This
document is available to the public.
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Table of Contents
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Abstract
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2
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Introduction
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3
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Problem Statement
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3
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Intelligent Highway System
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6
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Literature Review
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9
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Freeway
Incident Management
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9
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Event Data
Recorders
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12
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Injury
Prediction
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14
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Freeway
Concepts
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15
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Traffic
Prediction
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Injury
Severity
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20
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Methodology
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21
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Data
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State Data
System
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Virginia
Transportation Research Council (VTRC)
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Data
Integration
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Traffic
Flow Data
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Metrics
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25
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Modeling
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27
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Assumptions
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28
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Study
Limitations
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28
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Analysis of
Complete Set
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29
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Reduced
Data Set
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41
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Classification
Trees
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41
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Results
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43
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Accident
Volume Model
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43
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Maximum
Difference Model
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46
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Vehicle
Hours Lost Model
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48
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Percent
Vehicle Hours Lost Model
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50
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Conclusions
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53
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Contributions
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53
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Future Work
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55
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References
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57
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Abstract
The effects of
traffic incidents on metropolitan freeways extend beyond causing congestion and
delays. Immediate impacts include
decreased productivity, increased pollution and reduced safety on
highways. State and local governments
spend billions of dollars annually on construction projects and Intelligent
Transportation Systems (ITS) in an effort to curb the adverse consequences of
incidents and incident related delays.
Effective
identification and response on highways is one key to reducing the costs
associated with traffic incidents.
Within the context of a prototype incident identification and response
system developed by the University of Virginia’s Systems Technology Integration
Laboratory (STIL), this research aims to develop a statistical approach to
modeling congestion associated with freeway incidents. The ability to predict congestion will
provide more information and greater situational awareness to emergency
responders and traffic managers, and will allow travelers to make more informed
route selection decisions.
By combining data
from multiple sources, it is possible to match incident severity estimates for
freeway incidents with associated traffic flow counts at the time of the
accident. Four metrics for freeway
congestion were derived from the traffic flow data, and these metrics were then
modeled as functions of the incident severity estimates. The results of multiple linear regression
analysis showed that quantifiable relationships exist between the congestion
metrics and incident severity data such as the number of vehicles involved in
an incident as well as the number of serious injuries reported at the
scene.
Introduction
Problem Statement
Situational
awareness in the critical early stages of a traffic incident is the key to
alleviating a great deal of the congestion that plagues America’s major
metropolitan regions (VDOT). Being able
to detect, respond to and clear an incident faster and more efficiently can
reduce congestion related delays from traffic accidents by up to 45% according
to the Virginia Department of Transportation (VDOT). Within the scope of a comprehensive incident detection and
response system, this research aims to model freeway traffic flow metrics by
using measures for incident severity.
Developed by the
University of Virginia’s Systems Technology Integration Laboratory (STIL), the
Intelligent Highway System (IHS) aims to automate and streamline the process of
incident detection and response. By
leveraging the increased sensing and communications capabilities of motor
vehicles, the IHS is able to automatically detect car accidents and instantly
broadcast pertinent data to appropriate authorities in order to begin the
process of response.
One of the goals
of the IHS is to be able to predict the traffic implications of an
incident. An incident is any event that
temporarily reduces roadway capacity, such as accidents, debris, disabled
vehicles, and hazardous material spills (HCM).
Because this work is part of a larger context of an emergency response
system, this study will focus on accidents only.
It is intuitively
clear that the congestion due to an incident that occupies two of three freeway
lanes will cause more congestion than an incident that occupies only one (Smith
and Qin 362). What is not clear is the
extent to which congestion due to an incident is also a function of the
severity of the incident. Severity can
be defined either as the damage done to the vehicles themselves, or as the
degree of injury sustained by the passengers involved in the accident. This research aims to model the relationships
between incident severity and the congestion associated with that
incident. Measures of incident severity
include vehicle damage, the number of vehicles involved, the number of injuries
and the severity of the injuries, among others.
A widely accepted
metric for measuring the effect of incidents on traffic flow is capacity
reduction (Smith and Qin). Capacity is
defined by the Transportation Research Board as the maximum hourly rate at
which persons or vehicles can reasonably be expected to transverse a point or
uniform section of a lane or roadway during a given time period under
prevailing roadway, traffic, and control conditions (TRB). In order to truly measure the capacity
reduction on a roadway, the roadway of interest must be at its capacity before
an incident occurs. For freeways, this
capacity is estimated to be 2200 vehicles per hour per lane (HCM). When the capacity of a roadway is reduced
below the demand on that roadway, perhaps in the event of an accident, the
volume counts decrease for the duration of incidents. This is a reflection of the decreased speed at which drivers are
able to move along the roadway. The
data set used for this research was limited in size and contained only a small
percentage of incidents where flow on the roadway was at or near capacity
before an incident, therefore alternative metrics for quantifying the effects
of an incident were developed.
The obvious
benefits of increased situational awareness are those of better route guidance
and more efficient and accurate re-routing of traffic via Variable Message
Signs on highways (Hobeika). The
information could also be disseminated over the internet to allow travelers to
make more informed decisions regarding their route selection based on predicted
traffic delays.
As described in
the Literature Review, it is apparent that much study has been done regarding
the prediction of traffic conditions following incidents. Significant research has also been conducted
to predict accident severity based on pertinent data. In the past, however, these two issues have been treated as
separate problems and not addressed together in one body of work. There has been little or no research to
bridge the gap between two areas of study that intuitively seem to be
connected. This research aims to close
that gap and determine whether including incident severity estimates is
valuable in the modeling of freeway congestion.
Intelligent Highway System (IHS)
The broader
context for the proposed prediction method is the Intelligent Highway System
(IHS). Developed as a prototype system,
the IHS aims to automate and streamline the process of incident identification
and response. The system leverages the
increased sensing and communications capabilities of new vehicles. With the addition of acceleration sensors, a
Global Positioning System (GPS) receiver and a modem connected to the cellular
network, a vehicle can become a source of useful data at the point of incident.
The
data that can be sent from the vehicle is limited only by the sensing
capabilities of that vehicle. In the
near future, automobiles will be equipped with a wide range of sensors
including seat belt sensors, video cameras and biometric sensors to determine
the condition of the passengers with ever increasing accuracy.
When
an incident occurs, and the accelerometers experience a g-force above a
pre-determined threshold, the vehicle’s computer recognizes an accident
event. This process is similar to
airbag control processes available in automobiles today (German). The computer then broadcasts relevant data
including GPS position and accelerometer outputs to an Emergency Center using
Hypercast, a Java based communications protocol. The Emergency Center can be thought of as an information broker
that broadcasts appropriate messages to appropriate players in the event of an
accident, also using Hypercast. The
Hypercast protocol’s flexibility in terms of enabling message transmission
between wired and wireless users is well documented (Hunter et. al), and was
the logical protocol choice for the application development.
One
of the players that receive data from the Emergency Center is the Crash
Simulation Lab. The Crash Lab runs a
finite element simulation, with the accelerometer data as input, to estimate
the severity of the injuries sustained by the passengers in the crash. The Crash lab then broadcasts the severity
estimates back to the Emergency Center and to area hospitals as well as the
Traffic Analysis Center. The Traffic
Analysis Center is where congestion metrics would be estimated. Currently, the predictive models that exist
do not use severity measures to estimate metrics for congestion
The
IHS is similar to automatic accident notification systems currently available
in vehicles manufactured by General Motors, among others, as a service
available through the manufacturer.
Incident response centers that are a part of the service are
automatically notified in the event of air bag deployment in a subscribing
vehicle, at which point emergency responders can be notified by service
staff. The IHS, however, is a fully
automated, distributed system, that can be modified and upgraded rapidly and
easily without any disruption to the system.
Individual localities can implement an IHS for a given jurisdiction and
stand alone from similar national or even state wide systems. The diagram below shows the flow of
information in the event of an incident.
Figure 1 Information Flow over the HIS
Literature
Review
Freeway Incident Management
When attempting to
improve the large scale system that includes the freeways of a metropolitan
area and its supporting emergency response agencies, it is important to address
certain issues relating to incident identification, response and management in
the current system. The proposed work
aims to model metrics for congestion in terms of incident severity, but the
interaction between the type of response and the capacity reduction/duration of
an incident must be noted. It is also
necessary to examine the emergency response perspective and traffic management
perspective and note their similarities and differences in the event of an
accident on a major freeway. Effective
freeway management can also lead to fewer incidents. According to the Federal Highway Administration’s Traffic
Management Handbook, 13 percent of all peak period crashes were a direct result
of a previous incident (FHWA).
There
is an interaction between the scale of emergency response to a situation and
the congestion as well as the duration of an incident. It can be argued that with all other factors
being equal, an incident with a larger emergency response will cause greater
congestion and result in longer delays than an incident with a smaller response
(Saunders). Additionally, an incident
with inadequate response will have a longer duration, or time to clear, than an
incident with appropriate response because of the lag in arrival of the right
equipment and personnel (Nathanail)..
Therefore, it is in the best interest of motorists that an appropriate
response to an incident is deployed.
The decisions regarding the type of response to an emergency can be made
based on better information with the implementation of a system such as the IHS
that would provide information from the scene such as injury severity and data
that could allow for immediate traffic analysis.
There
are two perspectives of an incident that have potentially competing
objectives. The Emergency response
perspective has the goal of minimizing the time it takes to respond to an
incident with the appropriate equipment and personnel, and the traffic
management perspective has the goal of restoring the normal traffic flow on the
affected roadway (Zograforos, 536).
These somewhat competing objectives have an effect on the scale of the
response and the time it takes to clear an incident, and therefore play a role
in determining the congestion associated with an incident.
The motivation for
improvements in response to freeway incidents is clear when we realize that approximately
650,000 Americans suffer serious injuries in vehicle crashes every year, yet
these injuries occur in less than one percent of all motor vehicle crashes
(Champion). Thus, it is important to be
able to identify the crashes that have the potential for causing life
threatening injuries. By accurately
identifying the small percentage of accidents that may cause serious injuries,
better resource allocation can be achieved, increasing the likelihood of timely
response. It has been established that
the first 60 minutes following an incident, or the “golden hour”, determines
whether a patient will live or die (Champion).
Therefore, quantifiable benefits can be achieved by reducing response
time.
There are three
key stages that make up the response time of a vehicular accident (Evanco):
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Accident notification time, or the time between the
crash and emergency medical service (EMS) notification. This includes detection time
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The time between EMS notification and EMS arrival at
the scene of the crash
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The time between EMS arrival at the scene and arrival
at the hospital
In a statistical
model based study, Evanco et. al . found that the average notification time was
5.2 minutes and an average of 43.8 deaths per year per state in the year
1990. The study examined the effects of
variables such as Vehicle Miles Traveled (VMT), Mean Vehicle Speed (MVS) and
alcohol consumption. The conclusions of
the work emphasize the significant impact of reducing the notification time after
an accident, noting that a reduction in the average notification time 5.2
minutes to 3 minute would result in an 11% reduction in fatalities, and a
reduction to 2 minutes would result in a 15% reduction in fatalities
(Evanco). An automatic notification
system such as The Intelligent Highway System (IHS) could conceivably reduce
the notification time a