1 A METHODOLOGY FOR TRAFFIC SIGNAL CONTROL BASED ON LOGIC PROGRAMMING Giovanni FeliciIstituto di Analisi dei Sistemi ed Informatica (IASI-CNR), Consiglio Nazionale delle Ricerche Giovanni RinaldiIstituto di Analisi dei Sistemi ed Informatica (IASI-CNR), Consiglio Nazionale delle Ricerche Antonio SforzaDipartimento di Informatica e Sistemistica, Università degli studi di Napoli Federico II Klaus TruemperDepartment 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 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:

4 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:

5 Adaptive The control decisions depend on the state of the current traffic. Traffic detection and decision making are performed in real time Better use of the available resources Reactions to fluctuations in traffic flow Based on a 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 expertise Extremely flexible Readily 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 hardware No fixed-charge installation Modularity Reliability

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 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

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 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

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

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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