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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 ScienceUniversity of Texas at Dallas

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Outline of presentation

• Logic programming for traffic control

• The application

• Performance Evaluation

• Detectors Data

• Floating Probe Car

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• 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.

FACTS about Traffic Control:

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• 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

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

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Adaptive

The control decisionsdepend on the state of the current traffic. Traffic detection anddecision making areperformed in real time

Better use of the available resourcesReactions to fluctuations in traffic flow

Based ona Logic Model

The state of the traffic, the decision variables, and the control strategies are expressed in first order logic

Easy to understand Can reproduce human expertiseExtremely flexibleReadily modeled by traffic engineer

Main features of Control System

Decentralized

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

Low cost hardwareNo fixed-charge installationModularityReliability

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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

The decisions are represented by logic variables associated with transitions between the phases

The control strategy is represented by a set of logic statements that connect traffic and decision variables using the Leibniz Syntax

Traffic Variables

Decision Variables

Control Strategy

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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

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A Simulated Session

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Network of Workstation Unix

C standard language with X11 graphic libraries

Distributed computation over more workstations for real time simulation

Built-in Leibniz interface

Network design

Control strategy design

Visual test

Performance analysis

Logic algorithm compilation

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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)

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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

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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

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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

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LOGIC CONTROL VS. FIXED TIME: PERCENTUAL SAVINGS

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10,30-16 11-12 12,01-13 13,01-14 14,01-15 15,.01-16

Experiments

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LOGIC CONTROL VS. DYNAMIC: PERCENTUAL SAVINGS

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Floating Probe Car

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Floating Probe Car

• 14 paths around the intersection

• round trip time

• average speed

• fuel consumption

• emission of HC and CO

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AFRAGOLA

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PATHS 1, 2, 4, 14

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PATHS 6, 7, 9, 10

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PATHS 3, 5, 8, 11

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PATHS 12, 13

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POINTS MAPPED ON THE GIS – GPS ERROS

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POINTS MAPPED ON THE GIS –ERRORS CORRECTION

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POINTS MAPPED ON THE GIS – CORRECTED PATHS

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Floating Probe Car

SEGMENT DYNAMIC LOGIC SAVINGS1 53,33 64,38 20,7%2 97,56 43,17 -55,8%3 50,58 48,50 -4,1%4 74,00 82,00 10,8%

average 68,87 59,51 -13,6%

TIME ON SEGMENTS

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Floating Probe Car

DYN LOG SAV DYN LOG SAV DYN LOG SAV1 12,51 14,51 16% 2,59 3,04 17% 43,22 53,23 23%2 17,42 11,99 -31% 3,73 2,44 -34% 68,54 40,05 -42%3 12,97 12,21 -6% 2,65 2,49 -6% 44,76 41,09 -8%4 14,67 15,71 7% 3,07 3,25 6% 54,07 57,94 7%

average 14,39 13,60 -5% 3,01 2,81 -7% 52,65 48,07 -9%

COSEGMENT FUEL HC

SEGMENT DYNAMIC LOGIC SAVINGS1 9,69 12,26 26,5%2 16,78 5,67 -66,2%3 6,45 4,00 -38,0%4 11,58 13,69 18,2%

average 11,13 8,90 -20,0%

STOPS ON SEGMENTS (< 10kmh)