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Computers ind. Engng Vol. 29, No. 1-4, pp. 205-209, 1995 Copyright © 1995 Elsevier Science Ltd

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AutoCAR - A Decision Support Tool for Freeway Traffic Controllers

John Murray & Yili Liu

Dept. of Industrial & Operations Engineering University of Michigan, Ann Arbor, M148109

Abstract

An important part of road traffic operations is the ability to locate and resolve congestion- causing traffic incidents as quickly as possible. The provision of appropriate decision support tools which help maintain this form of situation awareness is therefore mandated. This paper describes the overall architecture and design of one such tool. AutoCAR (Automated Congestion Analysis & Report) is an object-oriented, rule-based expert system which incorporates some concepts from catastrophe theory to help differentiate between demand-based and incident-based traffic congestion.

Introduction

The increas ing need for advanced

transportation management and information

facilities has attracted significant research

attention in North America in recent years

[ 1 ]. Much of the work is being carried out in

the context of Intelligent Transportation

Systems (ITS), and a strategic plan for

research into intelligent vehicle-highway

systems has been approved by the US federal

government [2].

Advanced traffic management systems

(ATMS) present some types of problems

which are not adequately addressed by most

of the common research models of control

room activity. In the first place, information

about events in the target system (i.e. the

road or freeway network) is typically

distributed among several different agencies

in addition to the freeway operators, such as

police, transit operators, etc. Furthermore,

control centers have very little actual

physical or authoritative control over the

drivers of vehicles in the system; most

influence is thus informational and hortatory

in nature[3]. In contrast to this, typical

control center research work usually relies

upon centralized access to all pertinent

information and generally assumes a domain

populated by relat ively skilled and

understanding individuals such as plant floor

technicians, pilots, or astronauts[4].

The University of Michigan, is cooperating

with MITS (Mich igan In t e l l i gen t

Transportation Systems) to explore various

means of enhancing traffic operations in the

Detroit metropolitan area. The MITS control

center currently handles traffic on about 40

miles of freeway; this is expected to expand

to over 200 miles within the next several

years. It therefore becomes increasingly

necessary to aid center operators in focusing

on the critical issues by automating some of

the more routine traffic management tasks.

205

The integration of multiple information

sources is of significant interest for the needs

206

~mln -pavement & ~ _ _

eterins system I sensors , , , /

Q Direct CCTV ~-- surveillance

C - ,o,,.. ) Construction Dispatcher Reports Information

17th International Conference on Computers and Industrial Engineering

techniques. Reliable identification of • ~ r l a l surveillance~ cellular c a l l s | unanticipated congestion can be used to help

J automate the transmission of relevant traffic

status messages using var ious ITS -(Emergency ) technologies. In this way, useful and up-to- Patrol Reports

J date information can be passed quickly to

drivers with little interaction required by

traffic control center operations staff.

I I Chang eablel ] Traffic [ ATIS [ Message [ Operations I

Service I Sign [ Support I Providers I System I

Figure 1. Typical Traffic Information Flow

of advanced traffic operations [5,6]. The

design of a comprehensive set of decision

support tools should take into account

disparate sources of information and be

amenable to incorporating them at some

point in the lifecycle. Figure 1 shows an

example context in which such tools need to

be used.

The focus of the present paper is the design

of one such tool - an object-oriented, rule-

based expert system entitled AutoCAR

(Automated Congestion Analysis & Report).

We describe the operational context and the

general architecture of the tool, and discuss

some aspects of potential enhancements to it.

Operational Environment

The motivat ion behind our work on

AutoCAR was to examine how some

characteristics of freeway traffic flow may be

recognized using intell igent systems

The main source of numeric traffic data is

often an in-pavement inductive loop sensing

system. In most areas, these are located

around one-third to one-half mile spacing on

the freeway; however, for extended regional

coverage, they may be separated by up to two

or three miles. The processed data from this

system is typically updated for control center

use about every 15 to 30 seconds. In

addition, the views of critical portions of the

network from a set of CCTV surveillance

cameras may be brought up on control center

monitors.

The lower level of inductive loop coverage in

a very extensive traffic network can result in

much longer delays before unanticipated

congestion would be recognized directly

from the incoming numeric data. Even with

blanket CCTV coverage and adequate

visibility, manual scanning of several

hundred video monitors may not be very

practical. Figure 1 shows several other

sources of information which are of

assistance to the traffic operator in this

environment. These are typically other

distinct organizations such as police

authorities, transit operators, and commercial

traffic information services.

Each of these entities has partial data directly

available about the domain so that, from a

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system modeling point of view, the overall

real-time knowledge about the environment

is essentially distributed among several

different entities. Furthermore, the format

and reliability of incoming information may

greatly vary. The implication is that the

available data should be used to maintain a

generalized model of the freeway network by

the appropriate pre-processing, as shown in

Figure 2. The state of the model can then be

assessed a variety of heuristics, which

permits all relevant sources to be taken into

account. We now discuss each of these

components in turn.

Incoming Data from various sources

Information Pre-processing I

y

_ Reasoning_ Heuristics

Inference Engine

I Conclusions

Figure 2. Model Maintenance and Reasoning

Domain Model of Freeway Network

Our initial requirement in selecting a

software development tool was to find one

which would support an integrated approach

to the various components rather than expend

Computers and Industrial Engineering 207

resources wastefully by trying to force-fit

disjoint tools into a coupled architecture. A

tool was required which would support a

fairly close coupling between an object-based

model of the target environment with an

appropriate knowledge representation of the

human operators' typical heuristics. This

form of integrated modeling of human-

machine environments is of particular

research interest in the computer human

factors field.

It was anticipated that a basic expert system

tool using a forward-chaining inference

engine architecture would be useful to codify

a traffic center operator's typical work

processes. However, we also needed some

flexibility in handling various other forms of

knowledge representations, as well as some

typical traffic patterns and signatures. For

example, some form of procedural reasoning

would provide us with better logical control

over the handling of 'sequences of traffic

events', which is essentially a dynamic

domain. These considerations suggested that

the CLIPS reasoning system from NASA [7]

would be a good candidate for use in this

environment.

The basic building block of the domain

model is the Segment object, which is an

abstract class representing a stretch of one-

directional roadway in a freeway network. A

Segment typically corresponds to the stretch

monitored by one inductive loop station, but

this is not a requirement. Components within

the Segment object are used to hold most of

the dynamic characteristics of that stretch of

roadway, such as current average speed and

flow rates. The topology of the network is

208 17th International Conference on

defined using linkage between segments; the

links are defined in four Input and Output

abstract classes which also inherit from the

Segment class. The concrete objects which

are instantiated for each stretch of road then

receive the appropriate type (merge, split,

etc.) from combinations of the Inputs and

Outputs. Figure 3 outlines this structure.

[Segment [

I 2"input I l ~ 1 2-output I 'T -t 1-outp,,t 1-input

I L i i

Figure 3. Network Object Inheritance

Traffic Congestion Algorithm

There is an extensive body of research work

which is focused on algorithms to identify the

presence of traffic congestion. For our

purposes, it was important to select one

which would (i) maximize utilization of the

numeric data available to us, (ii) provide

solid opportunities for extension to include

additional information sources, and (iii)

integrate well with our overall modeling

strategy. Some traffic flow research which

applies the mathematical approach of

catastrophe theory suggested that it might be

Computers and Industrial Engineering

a fruitful basis for use in our environment [8].

The method has been used off-line with data

collected from the Queen Elizabeth Way in

Ontario. When compared to other incident

detection algorithms, the approach has been

shown to perform quite well, both in

accuracy of detection rate and reduction in

false alarms [9].

The main advantage of adopting this

approach from our point of view is its use of

temporal changes in data from a single

monitoring station to identify operations in

the congested and uncongested regimes. This

permits us to characterize conditions within

each monitored road segment independently

of the state of information concerning

neighboring segments. With each new

reading, the single station algorithm updates

its estimate of what constitutes 'minimum

uncongested flow' during normal operations.

When that threshold is crossed, congestion is

signaled and remains so until a minimum

period of uncongested flow is again

observed.

The other advantage provided by catastrophe

theory is that it helps formalize the

discontinuities typically found in the

relationships among the vehicle speed, flow,

and occupancy variables. More traditional

methods of traffic analysis use models which

assume continuous variables only. In such

models, a drastic change in one parameter

value can only occur by 'forcing' the others

through their maximum capacity values.

Thus, a sudden reduction in vehicle speed

'must' be accompanied by a rise and fall in

flow rate through its maximum value if the

model is to be accepted as valid. This

17th International Conference on Computers and Industrial Engineering

behavior pattern for traffic variables is

typically noticeable only when congestion

has built up because of excessive demand on

the freeway network.

On the other hand, empirical data typically

associated with accidents and other events

reveals a dramatic shift from one operating

regime to another in a strongly discontinuous

fashion. At the onset of incident-based

conges t ion , a sharp drop in speed is

accompanied by a sudden rise in occupancy,

while the typical flow rate is not markedly

affected. (It may be noted that the process is

reversible in a mathemat ica l sense; an

equivalent regime shift can often be observed

again during the dissipation phase when the

cause o f the bo t t l eneck is cleared.

Intuitively, one can understand the real-world

behavior; an incident 'suddenly' occurs, and

is often just as suddenly cleared.)

By adopting this approach, we gain an

opportunity to develop rule-based heuristics

which focus on the discontinuity aspects of

the traffic parameters to differentiate between

incident-based congestion and that caused by

excessive demand.

Conclusion

The design of knowledge systems sometimes

tries to force a structure onto what is

essentially incomplete, vague, and possibly

inconsistent knowledge. This is part of the

just i f icat ion for bounding the domain of

discourse. However , in the real world,

people more or less manage to 'muddle along'

regardless. An information modeling process

which tries to identify traffic conditions

solely from some observed numeric data may

209

not really reflect the actual process which a

human operator uses. Many traffic incident

detection systems are ill-suited to being

adapted to incorporate other sources of

in fo rmat ion typ ica l ly used in t raf f ic

operations environments. The architecture of

AutoCAR is suff ic ient ly adaptable and

extensible to encompass additional inputs and

operating heuristics from a variety of sources.

Acknowledgment

The authors would like to acknowledge the

support provided for this work by grant from

the US Federal Highway Administration to

the ITS Research Center of Excellence at the

University of Michigan.

References

[1] Saxton (1991). Special Issue on Intelligent Vehicle Highway Systems (IVHS), IEEE Transactions on Vehicular Technology, L. Saxton editor, February 1991.

[2] IVHS America (1992). Strategic Plan for Intelligent Vehicle Highway Systems (IVHS) in the United States, US Dept. of Transportation 1992.

[3] Murray J, & Liu Y, "Towards a Distributed Intelligent Agent Architecture for Human-Machine Systems in Hortatory Operations", IEEE Trans. on Systems, Man, and Cybernetics, (in review).

[4] Kearney, M, "The Evolution of the Mission Control Center.", Proceedings of the IEEE 75:399- 416 Mar '87

[5] Morris J & Marber J, "Virginia Traffic Management". ITE Journal, Vol 62, July 1992.

[6] Pierce Vet al. "The San Antonio Advanced Traffic Management System", ITE Journal, June Vol 64. 1994.

[7] NASA, "CLIPS Version 6.0 Reference Manual", Software Technology Branch, Johnson Space Center, 1993.

[8] Persaud B & Hall F, "Catastrophe Theory and Patterns in 30-second Freeway Traffic Data", Transportation Research A, Vol 23A No 2, 1989.

[9] Forl~es G, "Identifying Incident Congestion", ITE Journal, June 1992.