Model Based Vehicle Extraction and Tracking for Road Traffic Control

Engineering Applications of Artificial Intelligence

2014

This paper introduces a self-organizing traffic signal system for an urban road network. The key elements of this system are agents that control traffic signals at intersections. Each agent uses an interval microscopic traffic model to predict effects of its possible control actions in a short time horizon. The executed control action is selected on the basis of predicted delay intervals. Since the prediction results are represented by intervals, the agents can recognize and suspend those control actions, whose positive effect on the performance of traffic control is uncertain. Evaluation of the proposed traffic control system was performed in a simulation environment. The simulation experiments have shown that the proposed approach results in an improved performance, particularly for non-uniform traffic streams.

Section: Related Work and Contributionmentioning

confidence: 99%

A self-organizing system for urban traffic control based on predictive interval microscopic model

Engineering Applications of Artificial Intelligence

2014

“…This new technology creates a huge opportunity to extend the road-side sensor infrastructure of the existing traffic control and monitoring systems (Lee and Gerla, 2010). A major drawback of the current-generation systems is a limited access to the traffic data that are usually registered only in few fixed positions (Płaczek and Staniek, 2007). The coverage of these sensing platforms is narrow due to high installation and maintenance costs.…”

Section: Introductionmentioning

confidence: 99%

Selective data collection in vehicular networks for traffic control applications

Transportation Research Part C: Emerging Technologies

2012

Vehicular sensor network (VSN) is an emerging technology, which combines wireless communication offered by vehicular ad hoc networks (VANET) with sensing devices installed in vehicles. VSN creates a huge opportunity to extend the road-side sensor infrastructure of existing traffic control systems. The efficient use of the wireless communication medium is one of the basic issues in VSN applications development. This paper introduces a novel method of selective data collection for traffic control applications, which provides a significant reduction in data amounts transmitted through VSN. The underlying idea is to detect the necessity of data transfers on the basis of uncertainty determination of the traffic control decisions. According to the proposed approach, sensor data are transmitted from vehicles to the control node only at selected time moments. Data collected in VSN are processed using on-line traffic simulation technique, which enables traffic flow prediction, performance evaluation of control strategies and uncertainty estimation. If precision of the resulting information is insufficient, the optimal control strategy cannot be derived without ambiguity. As a result the control decision becomes uncertain and it is a signal informing that new traffic data from VSN are necessary to provide more precise prediction and to reduce the uncertainty of decision. The proposed method can be applied in traffic control systems of different types e.g. traffic signals, variable speed limits, and dynamic route guidance. The effectiveness of this method is illustrated in an experimental study on traffic control at signalised intersection.

“…The traffic model also has to provide interfaces for many different sources of traffic data that have become available recently (e.g. vision-based monitoring systems [11] and vehicular sensor networks [7]). To facilitate the on-line simulation, the traffic model has to be adjusted in order to maintain consistency between simulated and measured traffic.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Road Traffic Control Using a Fuzzy Cellular Model

Lecture Notes in Computer Science

2011

Abstract. In this paper a method is proposed for performance evaluation of road traffic control systems. The method is designed to be implemented in an on-line simulation environment, which enables optimisation of adaptive traffic control strategies. Performance measures are computed using a fuzzy cellular traffic model, formulated as a hybrid system combining cellular automata and fuzzy calculus. Experimental results show that the introduced method allows the performance to be evaluated using imprecise traffic measurements. Moreover, the fuzzy definitions of performance measures are convenient for uncertainty determination in traffic control decisions.