Nessun%20titolo%20diapositiva

1
A METHODOLOGY FOR TRAFFIC SIGNAL CONTROL BASED
ON LOGIC PROGRAMMING Giovanni Felici Istituto di
Analisi dei Sistemi ed Informatica (IASI-CNR),
Consiglio Nazionale delle Ricerche Giovanni
Rinaldi Istituto di Analisi dei Sistemi ed
Informatica (IASI-CNR), Consiglio Nazionale delle
Ricerche Antonio Sforza Dipartimento di
Informatica e Sistemistica, Università degli
studi di Napoli Federico II Klaus
Truemper Department of Computer
Science University of Texas at Dallas
2

• Outline of presentation
• Logic programming for traffic control
• The application
• Performance Evaluation
• Detectors Data
• Floating Probe Car

3
FACTS about Traffic Control

• Small adjustments of the length of the phases (?
  5 to10 secs) can produce consistent savings
• Signal synchronization can be driven by traffic
  in a decentralized fashion
• The control system must be able to adapt to
  irregular intersections
• The control system must learn as it works
• Traffic detection is crucial. There is a
  trade-off between quantity and quality of the
  information, and it is important to find the
  right balance for each intersection.

4
Research Project initially funded by Progetto
Finalizzato Trasporti 2 - CNR

• Istituto di Analisi dei Sistemi ed Informatica
  (IASI - CNR)
• University of Texas at Dallas, Computer Science
  Program
• Centro Studi sui Sistemi di Trasporto (CSST Roma)
• project started in 1993
• use of state of the art tools for Logic
  Programming and Logic Optimization (the Leibniz
  System)
• use of a visual traffic microsimulator to
  implement and test different control strategies
• control strategies developed by this tool have
  proved to generate consistent savings when
  compared with traditional traffic control systems

5
Main features of Control System
Each signal is controlled by an independent
control unit. No supervision is needed.
Neighboring units exchange a limited amount of
information

• Low cost hardware
• No fixed-charge installation
• Modularity
• Reliability

Decentralized
The state of the traffic, the decision variables,
and the control strategies are expressed in first
order logic
Based on a Logic Model

• Easy to understand
• Can reproduce human expertise
• Extremely flexible
• Readily modeled by traffic engineer

6
The state of traffic at the proximity of the
intersection is detected by a set of traffic
detectors and is translated into True/False
values of logic predicates
Traffic Variables
The decisions are represented by logic variables
associated with transitions between the phases
Decision Variables
The control strategy is represented by a set of
logic statements that connect traffic and
decision variables using the Leibniz Syntax
Control Strategy
7

• Visual Microsimulation
• Micro Traffic Simulator for urban networks
• Each car is simulated independently with
  car-following principles
• Each signal is simulated
• Several traffic generation patterns
• Traffic behaviour and effectiveness of logic
  strategies can be visually evaluated
• Statistics on performance indicators and traffic
  patterns can be collected

8
A Simulated Session
9
Network design
Visual test

• Network of Workstation Unix
• C standard language with X11 graphic libraries
• Distributed computation over more workstations
  for real time simulation
• Built-in Leibniz interface

Control strategy design
Logic algorithm compilation
Performance analysis
10
THE APPLICATION Afragola

• Partners
• IASI-CNR (Istituto di Analisi dei Sistemi ed
  Informatica)
• TechNapoli consortium
• Dipartimento di Informatica e Sistemistica,
  Università degli studi di Napoli Federico II
• ELASIS, Sistema Ricerca FIAT nel mezzogiorno
• CSST Napoli (Centro Studi sui Sistemi
  diTrasporto, FIAT)
• University of Texas at Dallas, Department of
  Computer Science
• Tecnosistem
• SelfSime (Signal Control Hardware)

11
Main characteristics of the installation

• Autoscope Camera detection system
• 5 presence counters and 3 queue counters for each
  approach (4)
• 2 cycles, one with 2 and one with 4 phases
• traffic detected is often noisy or not precise
  due to the position of the cameras also the
  topology of the intersection makes virtual loops
  fail at times
• The control system receives data from the
  detectors and produces the control decision
  (switch to next phase or stay in current phase)
  every 3 seconds
• The Logic Strategy
• 104 logic variables
• 185 logic statements
• max solution time below 0.05 second

12
Performances Evaluation

• 3 different control methods were tested on the
  same intersection
• fixed time where fixed cycle was obtained with
  TRANSYT
• dynamic adaptive system built-in in Selfsime
  signal hardware
• logic control
• Performances compared by
• data from detectors
• floating probe car

13
Evaluation Data from Detectors

• Indicator sum of occupancy figures of all queue
  counters
• comparisons are made for similar traffic
  conditions
• we consider comparisons of two methods only if
  experiments were run on the same day, same hour,
  and same incoming traffic (tolerance of approx.
  5)
• very good behaviour of logic control just by
  observation
• logic control is consistently better than fixed
  time and dynamic control

14
(No Transcript)
15
Floating Probe Car
16

• Floating Probe Car
• 14 paths around the intersection
• round trip time
• average speed
• fuel consumption
• emission of HC and CO

17
AFRAGOLA
18
PATHS 1, 2, 4, 14
19
PATHS 6, 7, 9, 10
20
PATHS 3, 5, 8, 11
21
PATHS 12, 13
22
POINTS MAPPED ON THE GIS GPS ERROS
23
POINTS MAPPED ON THE GIS ERRORS CORRECTION
24
POINTS MAPPED ON THE GIS CORRECTED PATHS
25
Floating Probe Car
26
Floating Probe Car