10-03-2012, 05:02 PM
GIS AND GPS
[attachment=18195]
1 ABSTRACT
The fundamental principle of intelligent transportation systems is to match the complexity of travel demands with advanced supply-side analysis, evaluation,management, and control strategies. A fundamental limitation is the lack of basic knowledge of travel demands at the network level. Modeling and sensor technology is primarily limited to aggregate parameters or micro-simulations based on aggregate distributions of behavior. Global Positioning Systems (GPS) are one of several available technologies which allow individual vehicle trajectories to be recorded and analyzed. Potential applications of GPS which are relevant to the ATMS Testbed are implementation in probe vehicles to deliver real-time performance data to complement loop and other sensor data and implementation in vehicles from sampled households to record route choice behavior. A flexible GPS-based data collection unit has been designed which incorporates GPS, data logging capabilities, two-way wireless communications, and a user interface in an embedded system which eliminates (or minimizes) driver interaction. The design and initial implementation tests in the ATMS Testbed are presented herein. This research is continued in PATH Task Order 4120; the final report of that project will present final system design, implementation, and field test results. Key Words: In-vehicle data logger, GPS, CDPD, TRACER45
2 OVERVIEW
Despite recent advances in Intelligent Transportation System (ITS) technologies, the potential for improving transportation system performance is curtailed by our limited understanding of the relationships between transportation system performance and characteristics of travel demand. While our models of supply tell us what we do under various traffic scenarios, there is little that we may confidently implement due to a fundamental lack of understanding of individual travel demand and route utilization. Any attempt to implement traffic management strategies requires assuming a demand pattern, one that cannot be subsequently modified based on the implemented supply modification. Even the real-time application of simple Advanced Transportation Management and Information Systems (ATMIS) strategies such as modification of signal timings is severely limited by the inability to generate real-time origin/destination (OD) matrices, let alone demand matrices that reflect modifications directly resulting from the implemented strategies.The difficulty in obtaining reliable and accurate travel behavior data is one of the primary reasons individual travel behavior and route selection is so poorly understood.
2.2 Project Status
This project was jointly supported by PATH MOU 3006 (and subsequently, PATH Task Order 4120) and the California Advanced ATMIS Testbed associated with the Institute of Transportation Studies (ITS) at the University of California, Irvine. The capabilities of the ATMIS Testbed that contributed to the project included the provision of historical and real-time traffic flow conditions from traffic sensors deployed throughout the Orange County, California freeway and arterial network and laboratory platforms for data collection and analysis applications. This project was designed as a two phase study. The first phase of the project was concerned with demonstrating the feasibility of the GPS technologies for various data collection applications. The second phase will extend the basic proof-of-concept field tests from the first phase and focus on route choice studies. The Phase 1 (MOU 3006) project objectives are as follows:
2.3 Report Organization
This report summarizes work to-date with GPS-based in-vehicle data collection for both traffic operations and travel behavior. Section 2 provides an overview of the research literature associated with GPS applications in transportation. Section 3 provides an overview of the TRACER system. Section 4 documents some of the initial experience using the EDCU and provides a summary of initial tests of system functionality. Comprehensive evaluation results for probe vehicle and route choice studies will be presented in the final report for the continuation project (TO 4120). Section 5 presents the technical design parameters for the extensible data collection unit (EDCU) unit itself. Section 6 provides an overview of the TRACERMap GIS developed to analyze the data from the EDCU. Finally, Section 7 provides a summary and describes the continuation work for PATH Task Order 4120.4
3 LITERATURE REVIEW
In-vehicle travel time data collection techniques that have emerged with the development of advanced ITS technologies include cellular phone tracking, automatic vehicle identification (AVI), and automatic vehicle location (AVL). GPS technology, which is among the most recently applied of the AVL technologies, has become a commonly used data collection tool because of its ability to automatically collect the speed and spatial coordinates of vehicles at regular time intervals (e.g. one second). Applications of GPS for real-time in-vehicle tracking include (1) probe vehicle surveillance, (2) congestion management, (3) historical database generation, (4) fleet management, and (5) travel diary surveys. Also presented are reviews of related research topics including (6) GPS and map matching, (7) user interaction concerns, and (8) the integration of GPS in web-based travel surveys. Urvei
3.1 GPS Applications in Probe Vehicle S llance
Since the 1920s, transportation professionals have been using the floating car technique to collect travel time information. Traditionally, this technique used a manual method to collect travel time data. This method required the driver to operate the test vehicle while a passenger recorded elapsed time information at predefined checkpoints using a pen and paper, audio tape recorders, or portable computers. Although the manual method is advantageous in that it requires minimal equipment costs and a low skill level, the fact that it is very labor intensive is a significant drawback.
[attachment=18195]
1 ABSTRACT
The fundamental principle of intelligent transportation systems is to match the complexity of travel demands with advanced supply-side analysis, evaluation,management, and control strategies. A fundamental limitation is the lack of basic knowledge of travel demands at the network level. Modeling and sensor technology is primarily limited to aggregate parameters or micro-simulations based on aggregate distributions of behavior. Global Positioning Systems (GPS) are one of several available technologies which allow individual vehicle trajectories to be recorded and analyzed. Potential applications of GPS which are relevant to the ATMS Testbed are implementation in probe vehicles to deliver real-time performance data to complement loop and other sensor data and implementation in vehicles from sampled households to record route choice behavior. A flexible GPS-based data collection unit has been designed which incorporates GPS, data logging capabilities, two-way wireless communications, and a user interface in an embedded system which eliminates (or minimizes) driver interaction. The design and initial implementation tests in the ATMS Testbed are presented herein. This research is continued in PATH Task Order 4120; the final report of that project will present final system design, implementation, and field test results. Key Words: In-vehicle data logger, GPS, CDPD, TRACER45
2 OVERVIEW
Despite recent advances in Intelligent Transportation System (ITS) technologies, the potential for improving transportation system performance is curtailed by our limited understanding of the relationships between transportation system performance and characteristics of travel demand. While our models of supply tell us what we do under various traffic scenarios, there is little that we may confidently implement due to a fundamental lack of understanding of individual travel demand and route utilization. Any attempt to implement traffic management strategies requires assuming a demand pattern, one that cannot be subsequently modified based on the implemented supply modification. Even the real-time application of simple Advanced Transportation Management and Information Systems (ATMIS) strategies such as modification of signal timings is severely limited by the inability to generate real-time origin/destination (OD) matrices, let alone demand matrices that reflect modifications directly resulting from the implemented strategies.The difficulty in obtaining reliable and accurate travel behavior data is one of the primary reasons individual travel behavior and route selection is so poorly understood.
2.2 Project Status
This project was jointly supported by PATH MOU 3006 (and subsequently, PATH Task Order 4120) and the California Advanced ATMIS Testbed associated with the Institute of Transportation Studies (ITS) at the University of California, Irvine. The capabilities of the ATMIS Testbed that contributed to the project included the provision of historical and real-time traffic flow conditions from traffic sensors deployed throughout the Orange County, California freeway and arterial network and laboratory platforms for data collection and analysis applications. This project was designed as a two phase study. The first phase of the project was concerned with demonstrating the feasibility of the GPS technologies for various data collection applications. The second phase will extend the basic proof-of-concept field tests from the first phase and focus on route choice studies. The Phase 1 (MOU 3006) project objectives are as follows:
2.3 Report Organization
This report summarizes work to-date with GPS-based in-vehicle data collection for both traffic operations and travel behavior. Section 2 provides an overview of the research literature associated with GPS applications in transportation. Section 3 provides an overview of the TRACER system. Section 4 documents some of the initial experience using the EDCU and provides a summary of initial tests of system functionality. Comprehensive evaluation results for probe vehicle and route choice studies will be presented in the final report for the continuation project (TO 4120). Section 5 presents the technical design parameters for the extensible data collection unit (EDCU) unit itself. Section 6 provides an overview of the TRACERMap GIS developed to analyze the data from the EDCU. Finally, Section 7 provides a summary and describes the continuation work for PATH Task Order 4120.4
3 LITERATURE REVIEW
In-vehicle travel time data collection techniques that have emerged with the development of advanced ITS technologies include cellular phone tracking, automatic vehicle identification (AVI), and automatic vehicle location (AVL). GPS technology, which is among the most recently applied of the AVL technologies, has become a commonly used data collection tool because of its ability to automatically collect the speed and spatial coordinates of vehicles at regular time intervals (e.g. one second). Applications of GPS for real-time in-vehicle tracking include (1) probe vehicle surveillance, (2) congestion management, (3) historical database generation, (4) fleet management, and (5) travel diary surveys. Also presented are reviews of related research topics including (6) GPS and map matching, (7) user interaction concerns, and (8) the integration of GPS in web-based travel surveys. Urvei
3.1 GPS Applications in Probe Vehicle S llance
Since the 1920s, transportation professionals have been using the floating car technique to collect travel time information. Traditionally, this technique used a manual method to collect travel time data. This method required the driver to operate the test vehicle while a passenger recorded elapsed time information at predefined checkpoints using a pen and paper, audio tape recorders, or portable computers. Although the manual method is advantageous in that it requires minimal equipment costs and a low skill level, the fact that it is very labor intensive is a significant drawback.