18-12-2012, 04:05 PM
Sensors for Unplanned Roadway Events--Simulation and Evaluation
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ABSTRACT
The purpose of this research was to demonstrate the feasibility of using sensor networks in traffic monitoring applications, specifically a rapidly deployable network of traffic sensors (NOTS) for short-term monitoring and data collection. A sensor network is an array of sensors attached to small computer nodes that have communications capabilities via wireless network. Our application problem is heavy duty truck data: vehicle classification and reidentification, particularly under slow or varying speed conditions. An experimental sensor, the IST Blade sensor, is essentially a portable inductive loop sensor that provides high resolution data. We used the Blade sensor for our initial experiments. We conducted a field experiment on the USC campus in order to collect data for development of classification algorithms. Our results are encouraging; classification accuracy is comparable to that of other recent research efforts. Once we have developed acceptable classification algorithms, two directions are apparent for future research: use of multiple sensors with the goal of improving classification results, and the use of vehicle signatures to allow re-identification of vehicles across multiple sensors.
INTRODUCTION
An emerging and rapidly developing field in computer science is sensor networks – many sensors attached to small, low cost computer nodes that have communications capability via wireless network. Sensor networks seek to exploit advances in computing power, battery power, and wireless communications to develop highly accurate sensing systems. The purpose of this research was to demonstrate the feasibility of using sensor networks in traffic monitoring applications, specifically a rapidly deployable network of traffic sensors (NOTS) for short-term monitoring and data collection. Our application problem is heavy duty truck data: vehicle classification and reidentification.
Background and Justification of Research
There is an ongoing need for traffic data to validate and calibrate regional and local transportation models. Regional models are used to test hypotheses regarding human travel behavior, transportation and land use interactions, and the effectiveness of alternative investments or pricing policies.1 Local transportation models are used to evaluate changes in economic activity or transportation system characteristics at a more disaggregate level. Traffic management policies, congestion reduction strategies and impacts of new development (such as housing or commercial centers) are some examples of local transportation model applications (Hansen, et al, 1993; Banister and Berechman, 2000; Transportation Research Board, 1995; Transportation Research Board 2002).
Freight flows are of growing interest within metropolitan areas, due to their recent rapid increase. As the impact of commodity flows has increased, government planners and system operators have a greater demand for commodity flow information and for better methods to track, analyze, and monitor these flows as they impact transportation networks and nodes. Demand for better information and analysis tools is particularly strong at the metropolitan level, because access to disaggregate data is limited and analysis tools are not yet well developed (Gordon and Pan, 2001; Sivakumar and Bhat, 2002).
The lack of data on truck traffic is particularly problematic. We have surprisingly little information on the characteristics of truck traffic and its distribution across space and time. State highway transportation departments have “weigh-in-motion” (WIM) stations at key locations on the interstate highway systems that provide truck traffic data, but there are relatively few such stations (there are only 104 stations in all of California), mainly due to their high installation and maintenance costs. In order to meet federal reporting requirements on heavy duty vehicles, additional WIM-type sensors are emplaced at other locations on the state highway system. Data on the arterial system is almost non-existent, because there is no easy way to collect such data.
Research Overview
Our first task was to review the state-of-the-art in current sensor systems. Existing emplaced systems (such as inductive loops and video cameras) provide reasonably accurate vehicle counts, speed and density estimations. Using inductive loops for vehicle re-identification is under research. . The state of the art in portable traffic monitoring is far less developed. Deployable systems tend to be large and expensive, or have limited functionality, and often require data to be downloaded and analyzed after-the-fact. For vehicle classification and queuing studies, human counting is still the method of choice; human counting is labor intensive and can only be done when personal safety can be assured. In addition, portable sensors today are often not tied in to central traffic management systems because of the cost of communication (the per-installation charges of a wired connection make regular telephone lines impossible, and the pay-per-bit cost makes cellular data connections undesirable). We also consulted with leading California researchers at the UC Institute of Transportation Studies and PATH in order to learn about current research in progress.
LITERATURE REVIEW
Various sensors have been produced to collect traffic information such as volume, occupancy, speed, vehicle classification, vehicle re-identification on freeways, arterials, ramps, parking lot, etc. The data collected are widely used in planning, research, and as the source for real-time traffic information for Intelligent Transportation Systems (ITS). Sensors can be roughly divided into two categories: emplaced and portable. Emplaced sensors are directly installed on road infrastructure and detect traffic information continuously. Once anchored, emplaced sensors are usually not removed. Portable sensors are used for short-term data collection and are moved from place to place. The use of portable sensor also typically requires lane closures, as with emplaced sensors, but the period of traffic disruption is shorter. There is no absolute boundary between these two kinds of sensors. Inductive loops sensors are known as typical emplaced sensors, but “blade” sensors, which basically are also made of inductive loops, are portable. Piezoelectric sensors, which are used flexibly from place to place, can also be used in permanently installed Weight-in-motion (WIM) systems. Another example is magnetic sensors. They can be cut into the pavement surface, but pavement cuts are not always needed.
[attachment=44245]
ABSTRACT
The purpose of this research was to demonstrate the feasibility of using sensor networks in traffic monitoring applications, specifically a rapidly deployable network of traffic sensors (NOTS) for short-term monitoring and data collection. A sensor network is an array of sensors attached to small computer nodes that have communications capabilities via wireless network. Our application problem is heavy duty truck data: vehicle classification and reidentification, particularly under slow or varying speed conditions. An experimental sensor, the IST Blade sensor, is essentially a portable inductive loop sensor that provides high resolution data. We used the Blade sensor for our initial experiments. We conducted a field experiment on the USC campus in order to collect data for development of classification algorithms. Our results are encouraging; classification accuracy is comparable to that of other recent research efforts. Once we have developed acceptable classification algorithms, two directions are apparent for future research: use of multiple sensors with the goal of improving classification results, and the use of vehicle signatures to allow re-identification of vehicles across multiple sensors.
INTRODUCTION
An emerging and rapidly developing field in computer science is sensor networks – many sensors attached to small, low cost computer nodes that have communications capability via wireless network. Sensor networks seek to exploit advances in computing power, battery power, and wireless communications to develop highly accurate sensing systems. The purpose of this research was to demonstrate the feasibility of using sensor networks in traffic monitoring applications, specifically a rapidly deployable network of traffic sensors (NOTS) for short-term monitoring and data collection. Our application problem is heavy duty truck data: vehicle classification and reidentification.
Background and Justification of Research
There is an ongoing need for traffic data to validate and calibrate regional and local transportation models. Regional models are used to test hypotheses regarding human travel behavior, transportation and land use interactions, and the effectiveness of alternative investments or pricing policies.1 Local transportation models are used to evaluate changes in economic activity or transportation system characteristics at a more disaggregate level. Traffic management policies, congestion reduction strategies and impacts of new development (such as housing or commercial centers) are some examples of local transportation model applications (Hansen, et al, 1993; Banister and Berechman, 2000; Transportation Research Board, 1995; Transportation Research Board 2002).
Freight flows are of growing interest within metropolitan areas, due to their recent rapid increase. As the impact of commodity flows has increased, government planners and system operators have a greater demand for commodity flow information and for better methods to track, analyze, and monitor these flows as they impact transportation networks and nodes. Demand for better information and analysis tools is particularly strong at the metropolitan level, because access to disaggregate data is limited and analysis tools are not yet well developed (Gordon and Pan, 2001; Sivakumar and Bhat, 2002).
The lack of data on truck traffic is particularly problematic. We have surprisingly little information on the characteristics of truck traffic and its distribution across space and time. State highway transportation departments have “weigh-in-motion” (WIM) stations at key locations on the interstate highway systems that provide truck traffic data, but there are relatively few such stations (there are only 104 stations in all of California), mainly due to their high installation and maintenance costs. In order to meet federal reporting requirements on heavy duty vehicles, additional WIM-type sensors are emplaced at other locations on the state highway system. Data on the arterial system is almost non-existent, because there is no easy way to collect such data.
Research Overview
Our first task was to review the state-of-the-art in current sensor systems. Existing emplaced systems (such as inductive loops and video cameras) provide reasonably accurate vehicle counts, speed and density estimations. Using inductive loops for vehicle re-identification is under research. . The state of the art in portable traffic monitoring is far less developed. Deployable systems tend to be large and expensive, or have limited functionality, and often require data to be downloaded and analyzed after-the-fact. For vehicle classification and queuing studies, human counting is still the method of choice; human counting is labor intensive and can only be done when personal safety can be assured. In addition, portable sensors today are often not tied in to central traffic management systems because of the cost of communication (the per-installation charges of a wired connection make regular telephone lines impossible, and the pay-per-bit cost makes cellular data connections undesirable). We also consulted with leading California researchers at the UC Institute of Transportation Studies and PATH in order to learn about current research in progress.
LITERATURE REVIEW
Various sensors have been produced to collect traffic information such as volume, occupancy, speed, vehicle classification, vehicle re-identification on freeways, arterials, ramps, parking lot, etc. The data collected are widely used in planning, research, and as the source for real-time traffic information for Intelligent Transportation Systems (ITS). Sensors can be roughly divided into two categories: emplaced and portable. Emplaced sensors are directly installed on road infrastructure and detect traffic information continuously. Once anchored, emplaced sensors are usually not removed. Portable sensors are used for short-term data collection and are moved from place to place. The use of portable sensor also typically requires lane closures, as with emplaced sensors, but the period of traffic disruption is shorter. There is no absolute boundary between these two kinds of sensors. Inductive loops sensors are known as typical emplaced sensors, but “blade” sensors, which basically are also made of inductive loops, are portable. Piezoelectric sensors, which are used flexibly from place to place, can also be used in permanently installed Weight-in-motion (WIM) systems. Another example is magnetic sensors. They can be cut into the pavement surface, but pavement cuts are not always needed.