intelligent urban traffic control system assingment 2
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Intelligent Urban Traffic Control System
KKKA6423
Assignment -2-
Supervisor
Prof. Dr. Riza Atiq Abdullah OK
Prepared by: Sarah hazim P 65407
rasha salah ahmed P64799
6. April .2013
Introduction
Urban traffic control (UTC) systems are a specialist form of traffic
management which integrate and co-ordinate traffic signal control over a
wide area in order to control traffic flows on the road network. Integration
and co-ordination between adjacent traffic signals involves designing a plan
based on the occurrence and duration of individual signal aspects and the
time offsets between them and introducing a system to link the signals
together electronically. A traffic responsive signal control system is a means
of adjusting the traffic signal settings (cycles, green splits and offsets),
which optimize a given objective function, such as minimizing travel time or
stops, in real-time based upon estimates of traffic conditions. There are
many different UTC systems in operation around the world, but they can
provide the basis for an extended control system, generally termed Urban
Traffic Management and Control (UTMC).
UTC systems can be used to obtain better traffic performance from a road
network by reducing delays to vehicles and the number of times they have to
stop. UTC systems also can be used to balance capacity in a network, to
attract or deter traffic from particular routes or areas, to give priority to
specific categories of vehicles such as public transport or to arrange for
queuing to take place in suitable parts of the network.
Demand impacts usually reduce travel time, but reduced travel times and
good network performance may increase road capacity. This may cause a
shift in demand towards car use. UTC systems may not make a positive
contribution to all policy objectives.
Split, Cycle and Offset Optimization Technique (SCOOT):
The Transport Research Laboratory (TRL) in collaboration with UK traffic
systems suppliers developed the SCOOT (Split Cycle Offset Optimization
Technique) urban traffic control system. SCOOT is now co-owned by Peek
Traffic Ltd, TRL Ltd and Siemens Traffic Controls Ltd. Early systems were
tested in the late 1970’s in Glasgow. The development of SCOOT for
general use was carried out in Coventry with the first commercial system
being installed in Maidstone in 1980. SCOOT is now used in over 170 towns
and cities in the UK and overseas.
SCOOT is a fully adaptive traffic control system which uses data from
vehicle detectors and optimizes traffic signal settings to reduce vehicle
delays and stops. There are a number of basic philosophies which lead to the
development of SCOOT. One of these was to provide a fast response to
changes in traffic conditions to enable SCOOT to respond to variations in
traffic demand on a cycle-by-cycle basis. SCOOT responds rapidly to
changes in traffic, but not so rapidly that it is unstable; it avoids large
fluctuations in control behavior as a result of temporary changes in traffic
patterns.
SCOOT not only reduces delay and congestion but also contains other traffic
management facilities. For example, in 1995 a new facility was introduced
to integrate active priority to buses [link to bus priority] with the common
SCOOT UTC system. The system is designed to allow buses to be detected
either by selective vehicle detectors or by an automatic vehicle location
(AVL) system.
Impacts on demand
SCOOT is in use worldwide and has been shown to give significant benefits
over the best fixed time operation. The effectiveness of the SCOOT strategy
has been assessed by major trials in five cities (Wood, 1993; SCOOT
website). The results from the trials are summarized in the table below.
Comparisons of the benefits of SCOOT, against good fixed time plans,
showed reductions in delays to vehicles of average 27% at Foleshill Road in
Coventry - a radial network in Coventry with long link lengths. In Worcester
the use of SCOOT rather than fixed time UTC showed considerable saving
which was estimated to be 83,000 vehicle hours or 357,000 per annum at
1985 prices. The replacement of isolated signal control in Worcester by
SCOOT was also estimated to save 180,000 vehicle hours per annum or
750,000 per annum. In Southampton, economic benefit, excluding accident
and fire damage savings, amounted to approximately 140,000 per annum at
1984 prices for the Ports wood/St. Denys area alone.
Research by Bell (1986) suggests that SCOOT is likely to achieve an extra
3% reduction in delay for every year that a fixed-time plan "ages". Further,
the effects of incidents have been excluded from many of the survey results
to ensure statistical validity. Since SCOOT is designed to adapt
automatically to compensate for ageing and incident effects, it is reasonable
to expect that, in many practical situations, SCOOT will achieve savings in
delay of 20% or more.
In 1993 a SCOOT demonstration project in Toronto showed an average
reduction in journey time of 8% and vehicle delays of 17% over the existing
fixed time plans. During weekday evenings and Saturdays, vehicle delays
were reduced by 21% and 34%. In unusual conditions following a baseball
game, delays were reduced by 61%, demonstrating SCOOT's ability to react
to unusual events. (Siemens Automotive, 1995)
In Sao Paulo in 1997 a survey showed that SCOOT reduced vehicle delays
by an average of 20% in one area tested and 38% in another over the
existing TRANSYT fixed time plans. It was estimated that financial benefits
to Sao Paulo as a result of these delay reductions would amount to
approximately $1.5 US million per year. (Mazzamatti et al, 1998)
Impacts on supply
Field trials of bus priority using SCOOT survey were carried out in areas of
Camden Town and Edgeware Road in London in 1996. The Camden
network consisted of 11 nodes and 28 links. The Edgeware Road site was a
linear network consisting of 8 nodes and 2 pelican crossings. The bus routes
were surveyed for the periods 7:00 - 12:00 and 14:00 - 19:00. The results
show that greater benefits can be obtained where there is lower saturation
level. (Bretherton et al.1996).
Figure (1): The flow of information in SCOOT based UTC system.
UTOPIA (Urban Traffic Optimization by Integrated Automation) / SPOT
(System for Priority and Optimization of Traffic):
Is designed and developed by FIAT Research Centre, ITAL TEL and
MIZAR Automation in Turin, Italy. The objective of the system is to
improve both private and public transport efficiency. The system has been
fully operational since 1985 on a network of about forty signalized junctions
in the central area of Turin. The area also contains a tram line and control of
the trams is integrated within UTOPIA/SPOT (Wood, 1993).
UTOPIA/SPOT is now used in several cities in Italy and also in the
Netherlands, USA, Norway, Finland and Denmark.
The system uses a hierarchical-decentralized control strategy, involving
intelligent local controllers to communicate with other signal controllers as
well as with a central computer. Central to the philosophy of the
UTOPIA/SPOT system is the provision of priority to selected public
transport vehicles at signalized junctions and improvements in mobility for
private vehicles, subject to any delays necessary to accommodate priority
vehicles (Wood, 1993). The French PRODYN system and the German
MOTION system have some similarities to SPOT, but have not been used
outside their counties (Kronborg and Davidsson, 2000).
Impacts on demand
The improvements attributed to UTOPIA in Turin have been calculated a
previous traffic responsive control strategy rather than against a fixed time
system. Benefits of implementing UTOPIA were shown to give an increase
in private traffic speed of 9.5% in 1985 and 15.9% in 1986, following
system tuning. In peak times the speed increases were 35%. Public transport
vehicles, which were given absolute priority, showed a speed increase of
19.9% in 1985 (Wood, 1993).
SPOT was introduced in Scandinavia in the early 1990 (Kronborg and
Davidsson, 2000). In Oslo, Norway, SPOT started to be operated in four
intersections with high priority to public transport in 1996. Only traffic
parallel with the tram routes was evaluated and had good results (15%
reduction in travel time).
Impacts on supply
UTOPIA/SPOT has been explicitly designed with public transport vehicle
priority in mind (Wood, 1993). Buses and LRT vehicles are given absolutely
priority at junctions, subject to the accuracy in forecasting their arrival time.
In Turin LRT are given higher priority than buses because they have more
passengers but extra priority can be assigned on a vehicle by vehicle basis if
required.
Total benefits of UTOPIA-SPOT:
UTOPIA-SPOT offers the network manager the following benefits:
- Keeps the flow going
- Manages timely public transport
- Fully adaptive, adjusts to the traffic situation
- Realizes strategic traffic policy objectives.
- Dynamic priority levels for public transport vehicles.
- Tuned and tested in lab situation before installation on-site.
- Open communication infrastructure.
Gaps and weaknesses
Many papers or reports on UTC systems evaluated only the impact on
efficiency such as reduction in journey time, delay and stops compared with
previous types of system. However, reducing travel times can increase road
capacity, and increasing capacity over a significant area may cause a shift in
demand towards car use and increase car traffic volume. The potential for
the benefits of UTC systems to be eroded by induced traffic needs to be
borne in mind. Relatively little information is available on environmental or
safety benefits.
Suffolk County Accessible Transportation (SCATs):
Suffolk County Transit is the provider of bus services in Suffolk County,
New York on Long Island in the United States and is an agency of the
Suffolk County government. It was founded in 1980 as a county-run
oversight and funding agency for a group of private contract operators which
had previously provided such services on their own. While the physical
maintenance and operation of the buses are provided by these providers,
other matters ranging from bus purchases to route and schedule planning to
fare rules are set by Suffolk Transit itself.
Though serving the entirety of Suffolk County, the one exception is in
Huntington, located in the northwestern part of the county, where that town's
private operator declined to join Suffolk Transit. Instead, Huntington took
over that town's system which became Huntington Area Rapid Transit, or
HART. Most of HART's routes do connect to both Suffolk Transit and
Nassau Inter-County Express and one can transfer between HART and
Suffolk Transit fairly easily. In addition, the village of Patchogue has its
own local bus service
Suffolk County Accessible Transportation (SCAT) is Suffolk Transit's
federally-mandated paratransit service for ADA-eligible passengers with
disabilities. SCAT service is available Monday through Friday, 6:00 AM to
8:30 PM and Saturday, 7:00 AM to 8:30 PM. The fare is $3.00.
Fare:
The current Suffolk County Transit base fare for most one-way local bus
travel is $2.00. For seniors and the disabled, the base fare is $0.75;
personal care attendants (PCA) may ride for free when traveling with
seniors or the disabled. Students with school-issued identification pay a
reduced fare of $1.25. Children under five years of age are free, with a limit
of three children for every adult. On routes S92 and 10C, the base fare is
$2.25[4]
Fare payment is conducted with the use of coins or paper currency, and
must be exact. Bus transfers cost an additional $0.25, and must be requested
and paid for upon boarding the bus. These transfers are valid for two hours
after issue and can be used on Suffolk County Transit connecting routes, or
to Nassau Inter-County Express (NICE) connecting routes with a special
transfer request slip (transfers to NICE require payment of a "step-up" fare.
Intelligent Traffic Adaptive Control Area (ITACA)
The ITACA adaptive traffic control system its application to traffic control
in the Spanish cities of Madrid and Barcelona. ITACA offers real-time
response to current and future traffic flow demands, and brings 'intelligence'
to fixed-time pattern control approaches. It incorporates: (1) an adaptive
system, which is used to evolve the best plan at each junction; and (2) an
expert system, which can use all the adaptive system's data and predictions
to obtain a global solution for the total traffic plan. This solution is
communicated to the adaptive system by a sophisticated use of importance
(weight) factors. The adaptive system has cycle, split, and offset optimizers,
and uses profiles to update the road network model. The model's
components include: (1) queue lengths; (2) congestion indicator; (3) load;
and (4) saturation flow modifier. The expert system is an optional part of
ITACA, which uses the model's current network data and its rules to adjust
the weights of each traffic movement. Its most obvious use is to avoid
secondary congestion, the blocking of junction exits by downstream queues.
It is expected that congestion strategies will develop differently for each
network, and depend strongly on users. Any number of overlapping
concurrent strategies can be implemented. For the covering abstract see
IRRD 877920.
ITACA recommendation:
From the exchange of knowledge with the project partners and the
discussions at ITACA meetings in Brabant some recommendations were
formulated to incorporate in a road map to Sustainable Mobility in Noord
Brabant:
Transition from value-added chain to a value-added network;
Stimulate and promote E-biking for shorter distances and develop
and promote sharing vehicles.
Make sustainable road transport an integrated part of city investment
plans
A market approach for mobility (supply and demand) combined with
government’s responsibility for market regulation
Experience, convenience, comfort, and personal safety should
become the first principles for development en innovation.
Max band: Max band is a bandwidth optimization program that calculates
signal timing plans on arterials and triangular networks. MAXBAND
produce cycle lengths offset speeds and phased sequences to maximize a
weighted sum of bandwidths. The primary advantage of MAXBAND is the
freedom to provide a range for the cycle time and speed. The lack of
incorporated bus flows and limited field tests are disadvantages of
MAXBAND.
Now day’s microcomputers are as commonly available as the electronic
calculators of the 70s and, while more expensive than calculators, are easily
within the economic reach virtually; to every transportation professional in
most locations throughout the world.
Developers of computerized traffic tools, such as the U.S. Department of
Transportation and some state Departments of Highways and Transportation,
universities and private organizations have promulgated a substantial suite of
software tools for every phase of transportation planning and engineering in
the past decades. The Federal Highway Administration (FHWA) and Federal
Transit Administration (FTA) have even set up microcomputer software
distribution and support centers to help get the products to users.
Currently for example, the Center for Microcomputers in Transportation
(McTrans), lists over 475 software tools in these functional areas:
Construction management;
Highway design, pavements, bridge design and hydraulics;
Maintenance;
Safety and accident records;
Surveying;
Traffic engineering;
Transit; and
Urban transportation planning.
RONDOn (Rolling horizon based Dynamic Optimization of signal
control):
Is a newly developed real-time traffic adaptive signal control system that
aims to reduce the response delay against the sudden changes of traffic flow.
RONDO project started in 1998. Since then continuous enhancements to
RONDO have been undertaken. Now, RONDO is challenging the new
problems, which are to promote traffic safety and to protect the
environments with keeping traffic efficiency. In this paper, the latest
additional functions are introduced to solve these problems. A plan to install
the pilot system at the beginning of 2001 is described.
Application
Rondo uses a feedback loop to govern the behavior of traffic in the network
core. It manages the flows that originate and terminate between various PoPs
(Points of Presence) in the network by directing these flows into the multiple
pathways that are created using MPLS Label Switched Paths. These LSPs
serve as conduits through the network that are unaffected by the local
optimization strategy of shortest path routing. Rather, Rondo optimizes
performance based on global traffic considerations in the network.
System Components
Rondo is composed of the major parts shown in Figure 2 above.
In the remainder of this paper, we will describe each element with emphasis
on the data collection subsystem and the analysis engine.
Physical Network
The experimental network is a set of 10 MPLS-enabled counters and Inter
connections patterned after a much-scaleddown representation of a major
service provider’s network backbone as depicted on their web site. We note
that the provider has 2500 PoPs worldwide so our model has only rough
equivalence to reality. However, even with only ten routers, our network
exhibits complex and often fascinating behaviors. Routers are connected
with 10-megabit links, which makes possible the creation of realistic
overload conditions. Each router models a PoP (Point of Presence) on the
network where customer nodes are attached. In Rondo, each node attached to
a PoP is a PC that sends and receives packets.
The network uses a combination of Cisco® 3620 and 3640 series routers.
The release of Cisco’s IOS (Internet Operating System) available on our
routers allows only destination - based selection of MPLS tunnels. -Cisco is
a registered trademark of Cisco Systems, Inc. Upgrades will ultimately allow
selection of the tunnels based on other parameters in the IP packet.
Programmable Load Generators and Loading Strategy
We use a collection of PCs programmed to generate time-varying loads
similar to those expected in an operational network. Background network
traffic on the network is constant in time and is generated by commercially
available packet generators. Loads are carefully crafted to cause a buildup of
congestion that does not have an overall steady state solution, and are
designed to stress the given physical topology.
Data-Collection System
The data-collection system uses a variety of devices and techniques to
monitor the conditions in the network. These include both active and passive
methodologies that capture such characteristics as throughput, loss, delay
and jitter. Data collection, a key part of Rondo, uses an extensible
architecture to provide rapid processing of data under time constraints for its
collection, reduction and transmission. Data flow from the network probes
through the collection system to the analysis engine with little latency and to
archival storage at a lower priority. Data are retained in a database system
for other applications such as service-level management that do not require
rapid data processing. We describe this part of the system in detail below.
Data Model and Database
Rondo uses the database for a variety of classes of information including
physical and logical network topology, configuration information and
archived measurement data. The algorithms, displays and other components
are driven by the information described by this model, and as such, the
organization of this model is crucial to the effectiveness of Rondo. The
model, which is important for other applications, is realized in a relational
database. The most important function of the database is to hold the state of
the network topology, which changes as the system reroutes LSPs to
alleviate congestion. The analysis and reroute engine periodically updates
the topology as the network is reconfigured.
Analysis and Rerouting Engine
This element of the system contains techniques for detecting congestion in a
network and altering the existing traffic flows to eliminate an overload
condition. The engine is designed to focus on more than link utilization,
which is the most basic metric of network performance. Utilization indicates
the level of activity between network elements and is often viewed as a
measure of network congestion. This view is too simple when one considers
the classes of traffic that flow over an IP network. High utilization of a link
is one form of congestion, but others might include excessive delay, jitter or
high packet loss, all of which could happen at relatively low levels of link
utilization. These are measures of congestion that seriously affect proposed
services in next-generation IP networks, including voice and video. The
engine is designed use any measurable quantity as an indication of a network
problem that needs correction.
MPLS Configuration and Control
Rondo relies on MPLS to form explicit paths through the core network.
Explicit paths allow precise control over the placement of traffic flows
within the routed domain of Rondo. All traffic in Rondo flows through
explicitly routed MPLS tunnels, which specify each node along a path from
the ingress to egress routers. The network configuration is initially optimal
in the sense that all tunnels travel via the shortest path in the network. Once
established, packets enter the MPLS tunnels as a function of their destination
address and are delivered to the egress router.
Rondo thus uses MPLS as a mechanism for packet forwarding that is not
directly aware of quality of service. Mixing packets with different levels of
quality of service in an LSP is possible though but limits the effectiveness of
available controls. Once the initial explicit paths are established, the analysis
and reroute engine operates to reroute packets through a path established by
a new MPLS tunnel, which may no longer be the shortest path. This action
currently takes place via IOS commands that are issued from the controller.
When MPLS traffic-engineering MIBs become available, the controller will
use SNMP to establish the new routes.
System Operation
The analysis and rerouting engine is in overall control of the system. The
engine communicates with the data collection system to establish a schedule
of network measurements. As the data collection system takes each
measurement, it notifies the analysis and rerouting engine of the presence of
new data. The engine combines the new data with the current system
configuration and previous data to decide on the appropriateness of rerouting
an MPLS tunnel. If a move is appropriate, the analysis engine reconfigures
the network through the LSP configuration control and updates the network
state in the database.
As we discuss in the following, the route of the new MPLS tunnel does not
necessarily preserve overall network optimality. Rather our goal is to reroute
traffic as quickly as possible to minimize the congestion at the expense of
achieving a theoretical optimum over the whole network. Global
optimization might imply moving many or even all the routes in the
network. The strategy in Rondo is to move from one to a few MPLS tunnels
over a period of a few minutes with minimal disruption to network traffic.
Conclusions
The particular techniques proposed are experimental and not yet
mainstream, especially when proposed for such a large, on-line, application.
The pro-active and re-active nature of agents can be a helpful paradigm in
intelligent traffic management and control. Further (real-life) tests on a
control strategy, based on intelligent and autonomous agents, are necessary
to provide appropriate evidence for operational use as relatively little is
known about the global behavior of these intelligent agent systems when
they are scaled up to deal with more realistic problems.
As this research is still ongoing we hope, in the end, to demonstrate that an
integrated dynamic urban traffic control system based on agent technology
can adapt and respond to real world traffic conditions in real-time. A
working prototype of such a system should give appropriate evidence on the
usability of AI agent based control systems.
Signal control systems that have the capability of optimizing and adjusting
the traffic light settings are able to improve the vehicular throughput and
minimize delay through appropriate response to changes in demand patterns.
With the introduction of two un-coupled loops, whether agent technology is
used or not, a different theory of traffic control can be met.
Artificial agents are a metaphor to be used for theoretical and
implementation purposes. Primarily results indicate that given an automated
control strategy implemented in the traffic signaling devices we can get a
system that makes better use of the capacity of the intersection. It has been
shown that control systems based on agent technology can adapt and
respond to changing conditions in real-time and in the meantime making
better use of the infrastructure.
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