The objective of this Handbook is to provide a comprehensive resource for selecting, designing, installing, and maintaining traffic sensors for signalized intersections and freeways. It is intended for use by traffic engineers and technicians having responsibility for traffic sensors, whether in-roadway or over-roadway sensors. These two families of sensors have different characteristics and thus corresponding advantages and disadvantages that are discussed throughout the Handbook. Topics covered include sensor technology, applications, in-roadway sensor design, installation techniques, and maintenance. The sensor technology chapter discusses the operation and uses of inductive loop detectors, magnetic sensors and detectors, video image processors, microwave radar sensors, laser radars, passive infrared and passive acoustic array sensors, and ultrasonic sensors, plus combinations of sensor technologies. The sensor application topics address safety, operational performance, multimodal issues, and physical and economic factors that the practitioner should consider. Appendixes include research, background papers, and implementation guidance. The information contained in this Handbook is based on the latest research available on treatments and best practices in use by the surveyed jurisdictions. References are provided for the student, practitioner, researcher, or decision maker who wishes to learn more about a particular subject.

Traffic Detector Handbook: Third Edition - Volume II

A long-standing problem in traffic engineering is to optimize the flow of vehicles through a given road network. Improving the timing of the traffic signals at intersections in the network is generally the most powerful and cost-effective means of achieving this goal. However, because of the many complex aspects of a traffic system—human behavioral considerations, vehicle flow interactions within the network, weather effects, traffic accidents, long-term (e.g. seasonal) variation, etc.—it has been notoriously difficult to determine the optimal signal timing. This is especially the case on a system-wide (multiple intersection) basis. Much of this difficulty has stemmed from the need to build extremely complex models of the traffic dynamics as a component of the control strategy. This paper presents a fundamentally different approach for optimal signal timing that eliminates the need for such complex models. The approach is based on a neural network (or other function approximator) serving as the basis for the control law, with the weight estimation occurring in closed-loop mode via the simultaneous perturbation stochastic approximation (SPSA) algorithm. The neural network function uses current traffic information to solve the current (instantaneous) traffic problem on a system-wide basis through an optimal signal timing strategy. The approach is illustrated by a realistic simulation of a nine-intersection network within the central business district of Manhattan, New York.

Traffic-responsive signal timing for system-wide traffic control

Due to the many complex aspects of a traffic system, it has been difficult to determine the optimal signal timing. Much of this difficulty has stemmed from the need to build extremely complex models of the traffic dynamics as a component of the control strategy. This paper presents a fundamentally different approach for optimal signal timing that eliminates the need for such complex models. The approach is based on a neural network serving as the basis for the control law, with the weight estimation occurring in closed-loop mode via the simultaneous perturbation stochastic approximation (SPSA) algorithm. Since the SPSA algorithm requires only loss function measurements, there is no system-wide model required for the weight estimation.

Scenarios play an important role throughout the information system development process. Scenarios are partial descriptions of system and environment behavior arising in restricted situations . They are instrumental to the following activities: describing and clarifying the relevant properties of the application domain, uncovering system requirements, evaluating design alternatives, and validating designs. This paper will describe these roles in the context of an example and explain how computer-based tools can support the use of scenarios throughout the development process. The thesis of this paper is based on experience with three such computer-based tools.

Utilizing Scenarios in the Software Development Process

Traffic represents one of the largest sources of primary air pollutants in urban areas. As a consequence, numerous abatement strategies are being pursued to decrease the ambient concentrations of a wide range of pollutants. A mutual characteristic of most of these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emissions inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for a wide range of vehicle types. The majority of inventories are compiled using ‘passive’ data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. Current trends are towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this paper, a methodology for estimating emissions from mobile sources using real-time data is described. This methodology is used to calculate emissions of sulphur dioxide (SO2), oxides of nitrogen (NOx), carbon monoxide (CO), volatile organic compounds (VOC), particulate matter less than 10 μm aerodynamic diameter (PM10), 1,3-butadiene (C4H6) and benzene (C6H6) at a test junction in Dublin. Traffic data, which are required on a street-by-street basis, is obtained from induction loops and closed circuit televisions (CCTV) as well as statistical data. The observed traffic data are compared to simulated data from a travel demand model. As a test case, an emissions inventory is compiled for a heavily trafficked signalized junction in an urban environment using the measured data. In order that the model may be validated, the predicted emissions are employed in a dispersion model along with local meteorological conditions and site geometry. The resultant pollutant concentrations are compared to average ambient kerbside conditions measured simultaneously with on-line air quality monitoring equipment.

Development of an emissions inventory model for mobile sources

The Los Angeles Automated Traffic Surveillance and Control (ATSAC) System is described, emphasizing those features that provide a powerful and flexible capability to manage traffic. Many traffic detectors are used to collect data used for critical intersection control, traffic responsive control in networks, real-time surveillance of traffic conditions using color-graphic monitors, real-time evaluation of system performance, and automated generation of traffic signal timing plans. Closed-circuit television is used to provide additional information regarding the cause of congestion. Manual control of intersections from the control center is used to correct nonrecurring congestion. The hierarchical system architecture consists of a very fast supervisor computer networked with the control console, color-graphic monitors, and area computers that communicate with up to 400 intersections and 1600 detectors each. An evaluation of system performance concluded that stops were reduced by 35%, intersection delay by 20%, travel time by 13%, fuel consumption by 12.5%, and air emissions by 10%. The benefit/cost was 9.8:1, and the system paid for itself in less than one year. >

E. Rowe

Papers

The Los-Angeles Automated Traffic Surveillance and Control (ATSAC) System