The Experts below are selected from a list of 216 Experts worldwide ranked by ideXlab platform
J.j. Henry - One of the best experts on this subject based on the ideXlab platform.
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Design by Petri nets of an Intersection signal Controller
Transportation Research Part C: Emerging Technologies, 1996Co-Authors: Jean-louis Gallego, Jean-loup Farges, J.j. HenryAbstract:Abstract Currently, traffic responsive control strategies of an isolated Intersection gather available information about traffic conditions and then, in a real-time context, determine a stage (set of simultaneous green signals) by optimizing an arbitrary criterion. This optimal control is then executed by the Intersection Controller, which transforms the stage in traffic signal colors. In this paper, an optimal signal control device for any generic Intersection is constructed just from the functioning rules: a timed color sequence for each signal, clearing times between conflicting signals and a maximum service delay for any user waiting in the Intersection. The design takes into account two constraints: the Controller must always respect the specified safety operating rules, and it must give the maximum freedom to the upper level. Petri nets formalism is a graphical and mathematical tool adapted to the modeling of the main features of discrete event systems; it can be used to translate specifications into operational systems. Each of the previous three safety rules can be translated in this formalism, furnishing an exact Intersection Controller, described by a Petri net. The application of the methodology to an actual Intersection is presented. It shows that the Intersection Controller description is fully achieved by Petri nets, providing both a model for any upper control level and automation for any real implementation.
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TRAFFIC MANAGEMENT. RTS ENGLISH ISSUE NUMBER 6. TURNING MOVEMENT RATIOS ESTIMATION USING INDUCTIVE LOOP DETECTORS AND INFORMATION FROM ROUTE GUIDANCE SYSTEMS
Recherche - Transports - Sécurité, 1991Co-Authors: A. Kessaci, J-l Farges, J.j. HenryAbstract:Intersection turning movement ratios are needed for traffic light control. The authors show that a 20 percent error in these parameters induces a 6 to 8 percent loss in the quality of service, depending on the control method. Thus there developed a turning movement ratio estimation method which uses loop sensors located upstream and downstream of the Intersection and stage information from the Intersection Controller. This method is based on Kalman filtering with equality and inequality constraints. Simulation results show that time varying parameters can be estimated in real time. Finally the authors show how information from a route guidance system can be integrated into the method.
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TURNING MOVEMENT RATIOS ESTIMATION USING INDUCTIVE LOOP DETECTORS AND INFORMATION FROM ROUTE GUIDANCE SYSTEMS
Recherche - Transports - Sécurité, 1991Co-Authors: A. Kessaci, J-l Farges, J.j. HenryAbstract:Intersection turning movement ratios are needed for traffic light control. This article shows that a 20% error in these parameters induces a 6 to 8% loss in the quality of service depending on the control method. Thus a turning movement ratio estimation method is developed which uses loop sensors located upstream and downstream of the Intersection and stage information from the Intersection Controller. This method is based on Kalman filtering with equality and inequality constraints. Simulation results show that time varying parameters can be estimated in real time. Finally it is shown how information from a route guidance system can be integrated into the method. For the French abstract see IRRD 125466.
Michela Milano - One of the best experts on this subject based on the ideXlab platform.
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AI*IA - Swarm-Based Controller for Traffic Lights Management
Lecture Notes in Computer Science, 2015Co-Authors: Federico Caselli, Alessio Bonfietti, Michela MilanoAbstract:This paper presents a Traffic Lights control system, inspired by Swarm intelligence methodologies, in which every Intersection Controller makes independent decisions to pursue common goals and is able to improve the global traffic performance. The solution is low cost and widely applicable to different urban scenarios. This work is developed within the COLOMBO european project. Control methods are divided into macroscopic and microscopic control levels: the former reacts to macroscopic key figures such as mean congestion length and mean traffic density and acts on the choice of the signal program or the development of the frame signal program; the latter includes changes at short notice based on changes in the traffic flow: they include methods for signal program adaptation and development. The developed system has been widely tested on synthetic benchmarks with promising results.
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DIVANet@MSWiM - Swarm-based traffic lights policy selection
Proceedings of the fourth ACM international symposium on Development and analysis of intelligent vehicular networks and applications - DIVANet '14, 2014Co-Authors: Riccardo Belletti, Alessio Bonfietti, Luca Foschini, Michela Milano, Daniel KrajzewiczAbstract:This paper presents a Traffic Lights control system, inspired by Swarm intelligence methodologies, in which every Intersection Controller makes independent decisions to pursue common goals and is able to improve global traffic performance. The solution is low cost and widely applicable to different urban scenarios. This work is developed within the COLOMBO European project. Control methods are divided into macroscopic and microscopic control level methods. The former react to macroscopic key figures such as mean congestion length and mean traffic density and acts on the signal program choice or the development of the frame signal program. The latter include changes at short notice based on changes in the traffic flow: they include methods for signal program adaptation and development. The developed system has been widely tested on synthetic benchmarks with promising results.
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ITSC - Urban Traffic Control system using self-organization
13th International IEEE Conference on Intelligent Transportation Systems, 2010Co-Authors: Gianfilippo Slager, Michela MilanoAbstract:This paper presents a Urban Traffic Control (UTC) system, inspired to techniques exploited by social insects to coordinate themselves and specialize their behaviour, without any centralized coordination or explicit communication. Local traffic is handled at an Intersection by a Controller, executing simple local reactive rule-based policies. Every Intersection Controller chooses on its own which policy to use, according to stimuli perceived from the environment through sensors, thus being influenced by other Controllers indirectly. The execution of simple reactive local policies lets an overall traffic control to emerge, with complex behaviours not defined a priori and unaware to the single Controller. Simulations demonstrate that the exploiting of emergent behaviours is desirable, as the system outperforms traditional traffic control methods. Dynamic specialization makes the system able to cope with traffic variation and to perform traffic shaping in a positive way.
Hossam Abdelgawad - One of the best experts on this subject based on the ideXlab platform.
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Multiagent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLIN-ATSC): Methodology and Large-Scale Application on Downtown Toronto
IEEE Transactions on Intelligent Transportation Systems, 2013Co-Authors: Samah El-tantawy, Baher Abdulhai, Hossam AbdelgawadAbstract:Population is steadily increasing worldwide, resulting in intractable traffic congestion in dense urban areas. Adaptive traffic signal control (ATSC) has shown strong potential to effectively alleviate urban traffic congestion by adjusting signal timing plans in real time in response to traffic fluctuations to achieve desirable objectives (e.g., minimize delay). Efficient and robust ATSC can be designed using a multiagent reinforcement learning (MARL) approach in which each Controller (agent) is responsible for the control of traffic lights around a single traffic junction. Applying MARL approaches to the ATSC problem is associated with a few challenges as agents typically react to changes in the environment at the individual level, but the overall behavior of all agents may not be optimal. This paper presents the development and evaluation of a novel system of multiagent reinforcement learning for integrated network of adaptive traffic signal Controllers (MARLIN-ATSC). MARLIN-ATSC offers two possible modes: 1) independent mode, where each Intersection Controller works independently of other agents; and 2) integrated mode, where each Controller coordinates signal control actions with neighboring Intersections. MARLIN-ATSC is tested on a large-scale simulated network of 59 Intersections in the lower downtown core of the City of Toronto, ON, Canada, for the morning rush hour. The results show unprecedented reduction in the average Intersection delay ranging from 27% in mode 1 to 39% in mode 2 at the network level and travel-time savings of 15% in mode 1 and 26% in mode 2, along the busiest routes in Downtown Toronto.
Behnam Torabi - One of the best experts on this subject based on the ideXlab platform.
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AAMAS - DALI: An Agent-Plug-In System to "Smartify" Conventional Traffic Control Systems
2020Co-Authors: Behnam Torabi, Rym Zalila-wenksternAbstract:The DALI system aims at making existing conventional traffic control systems autonomous and smart. We achieve this goal by plugging-in a software agent into each existing Intersection Controller which becomes "the brain" of the Controller. The agents analyze the traffic data, communicate with each other directly, and collaborate to execute a timing strategy that improves traffic flow. DALI was thoroughly tested through simulation, then deployed on three major Intersections in the City of Richardson (Dallas-Fort Worth metroplex), Texas. The data collected for a three week period shows that on average, DALI reduced delay by 40.12%.
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AAMAS - A Multi-Hop Agent-Based Traffic Signal Timing System for the City of Richardson
2018Co-Authors: Behnam Torabi, Rym Zalila-wenkstern, Robert SaylorAbstract:In this paper, we present a multi-agent Traffic Signal Timing system (TST) where Intersection Controller agents collaborate with one another across congested areas of the traffic network. The multi-hop agent-based traffic system is based on the TST of the City of Richardson, Texas, and is intended to be deployed with minimal changes to the infrastructure.
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PAAMS - MATISSE 3.0: A Large-Scale Multi-agent Simulation System for Intelligent Transportation Systems
Advances in Practical Applications of Agents Multi-Agent Systems and Complexity: The PAAMS Collection, 2018Co-Authors: Behnam Torabi, Mohammad Al-zinati, Rym Z. WenksternAbstract:In this demo we present MATISSE 3.0, a microscopic simulator for agent-based Intelligent Transportation Systems (ITS). MATISSE provides abstract classes for the definition of vehicle and Intersection-Controller agents as well as components for the concurrent 2D and 3D visualizations of the simulation. The control GUI allows the user to change various agent properties at run time. The OSM converter imports entire traffic networks from Open Street Map. We illustrate the use of MATISSE through the development of a simulation for the City of Richardson, Texas.
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ISC2 - A Self-Adaptive Collaborative Multi-Agent based Traffic Signal Timing System
2018 IEEE International Smart Cities Conference (ISC2), 2018Co-Authors: Behnam Torabi, Rym Z. Wenkstern, Robert SaylorAbstract:In this paper, we present DALI, a self-adaptive, collaborative multi-agent Traffic Signal Timing system (TST). Intersection Controller agents collaborate with one another and adapt their timing plans based on the traffic conditions. Reinforcement learning is used to optimize values for the various thresholds necessary to dynamically determine the scope of collaboration between the agents. DALI was implement in MATISSE 3.0, a large-scale agent-based micro-simulator. Experimental results show an improvement over traditional and reinforcement learning TSTs.
Conrad Foster - One of the best experts on this subject based on the ideXlab platform.
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Traffic-Light-Preemption Vehicle-Transponder Software Module
2005Co-Authors: Aaron Bachelder, Conrad FosterAbstract:A prototype wireless data-communication and control system automatically modifies the switching of traffic lights to give priority to emergency vehicles. The system, which was reported in several NASA Tech Briefs articles at earlier stages of development, includes a transponder on each emergency vehicle, a monitoring and control unit (an Intersection Controller) at each Intersection equipped with traffic lights, and a central monitoring subsystem. An essential component of the system is a software module executed by a microController in each transponder. This module integrates and broadcasts data on the position, velocity, acceleration, and emergency status of the vehicle. The position, velocity, and acceleration data are derived partly from the Global Positioning System, partly from deductive reckoning, and partly from a diagnostic computer aboard the vehicle. The software module also monitors similar broadcasts from other vehicles and from Intersection Controllers, informs the driver of which Intersections it controls, and generates visible and audible alerts to inform the driver of any other emergency vehicles that are close enough to create a potential hazard. The execution of the software module can be monitored remotely and the module can be upgraded remotely and, hence, automatically
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Intersection-Controller Software Module
2005Co-Authors: Aaron Bachelder, Conrad FosterAbstract:An important part of the emergency-vehicle traffic-light-preemption system summarized in the preceding article is a software module executed by a microController in each Intersection Controller. This module monitors the broadcasts from all nearby participating emergency vehicles and Intersections. It gathers the broadcast data pertaining to the positions and velocities of the vehicles and the timing of traffic and pedestrian lights and processes the data into predictions of the future positions of the vehicles. Analyzing the predictions by a combination of proximity tests, map-matching techniques, and statistical calculations designed to minimize the adverse effects of uncertainties in vehicle positions and headings, the module decides whether to preempt and issues the appropriate commands to the traffic lights, pedestrian lights, and electronic warning signs at the Intersection. The module also broadcasts its state to all nearby vehicles and Intersections. The module is designed to mitigate the effects of missing data and of unpredictable delays in the system. It has been intensively tested and refined so that it fails to warn in very few cases and issues very few false warnings.
