Map-matching algorithms integrate positioning data with spatial road network data (roadway centrelines) to identify the correct link on which a vehicle is travelling and to determine the location of a vehicle on a link. A map-matching algorithm could be used as a key component to improve the performance of systems that support the navigation function of intelligent transport systems (ITS). The required horizontal positioning accuracy of such ITS applications is in the range of 1 m to 40 m (95%) with relatively stringent requirements placed on integrity (quality), continuity and system availability. A number of map-matching algorithms have been developed by researchers around the world using different techniques such as topological analysis of spatial road network data, probabilistic theory, Kalman filter, fuzzy logic, and belief theory. The performances of these algorithms have improved over the years due to the application of advanced techniques in the map matching processes and improvements in the quality of both positioning and spatial road network data. However, these algorithms are not always capable of supporting ITS applications with high required navigation performance, especially in difficult and complex environments such as dense urban areas. This suggests that research should be directed at identifying any constraints and limitations of existing map matching algorithms as a prerequisite for the formulation of algorithm improvements. The objectives of this paper are thus to uncover the constraints and limitations by an in-depth literature review and to recommend ideas to address them. This paper also highlights the potential impacts of the forthcoming European Galileo system and the European Geostationary Overlay Service (EGNOS) on the performance of map matching algorithms. Although not addressed in detail, the paper also presents some ideas for monitoring the integrity of map-matching algorithms. The map-matching algorithms considered in this paper are generic and do not assume knowledge of ‘future’ information (i.e. based on either cost or time). Clearly, such data would result in relatively simple map-matching algorithms.

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Current map-matching algorithms for transport applications: State-of-the art and future research directions

Vehicle tracking data is an essential "raw" material for a broad range of applications such as traffic management and control, routing, and navigation. An important issue with this data is its accuracy. The method of sampling vehicular movement using GPS is affected by two error sources and consequently produces inaccurate trajectory data. To become useful, the data has to be related to the underlying road network by means of map matching algorithms. We present three such algorithms that consider especially the trajectory nature of the data rather than simply the current position as in the typical map-matching case. An incremental algorithm is proposed that matches consecutive portions of the trajectory to the road network, effectively trading accuracy for speed of computation. In contrast, the two global algorithms compare the entire trajectory to candidate paths in the road network. The algorithms are evaluated in terms of (i) their running time and (ii) the quality of their matching result. Two novel quality measures utilizing the Frechet distance are introduced and subsequently used in an experimental evaluation to assess the quality of matching real tracking data to a road network.

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On map-matching vehicle tracking data

In-car positioning and navigation has been a killer application for Global Positioning System (GPS) receivers, and a variety of electronics for consumers and professionals have been launched on a large scale. Positioning technologies based on stand-alone GPS receivers are vulnerable and, thus, have to be supported by additional information sources to obtain the desired accuracy, integrity, availability, and continuity of service. A survey of the information sources and information fusion technologies used in current in-car navigation systems is presented. The pros and cons of the four commonly used information sources, namely, 1) receivers for radio-based positioning using satellites, 2) vehicle motion sensors, 3) vehicle models, and 4) digital map information, are described. Common filters to combine the information from the various sources are discussed. The expansion of the number of satellites and the number of satellite systems, with their usage of available radio spectrum, is an enabler for further development, in combination with the rapid development of microelectromechanical inertial sensors and refined digital maps.

In-Car Positioning and Navigation Technologies—A Survey

This newly revised and greatly expanded edition of the popular Artech House book Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems offers you a current and comprehensive understanding of satellite navigation, inertial navigation, terrestrial radio navigation, dead reckoning, and environmental feature matching . It provides both an introduction to navigation systems and an in-depth treatment of INS/GNSS and multisensor integration. The second edition offers a wealth of added and updated material, including a brand new chapter on the principles of radio positioning and a chapter devoted to important applications in the field. Other updates include expanded treatments of map matching, image-based navigation, attitude determination, acoustic positioning, pedestrian navigation, advanced GNSS techniques, and several terrestrial and short-range radio positioning technologies. The book shows you how satellite, inertial, and other navigation technologies work, and focuses on processing chains and error sources. In addition, you get a clear introduction to coordinate frames, multi-frame kinematics, Earth models, gravity, Kalman filtering, and nonlinear filtering. Providing solutions to common integration problems, the book describes and compares different integration architectures, and explains how to model different error sources. You get a broad and penetrating overview of current technology and are brought up to speed with the latest developments in the field, including context-dependent and cooperative positioning. DVD Included: Features eleven appendices, interactive worked examples, basic GNSS and INS MATLAB simulation software, and problems and exercises to help you master the material.

Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Second Edition

This paper describes a map-matching algorithm designed to support the navigational functions of a real-time vehicle performance and emissions monitoring system currently under development, and other transport telematics applications. The algorithm is used together with the outputs of an extended Kalman filter formulation for the integration of GPS and dead reckoning data, and a spatial digital database of the road network, to provide continuous, accurate and reliable vehicle location on a given road segment. This is irrespective of the constraints of the operational environment, thus alleviating outage and accuracy problems associated with the use of stand-alone location sensors. The map-matching algorithm has been tested using real field data and has been found to be superior to existing algorithms, particularly in how it performs at road intersections.

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A general map matching algorithm for transport telematics applications

This paper describes the features of an extended Kalman filter algorithm designed to support the navigational function of a real-time vehicle performance and emissions monitoring system currently under development. The Kalman filter is used to process global positioning system (GPS) data enhanced with dead reckoning (DR) in an integrated mode, to provide continuous positioning in built-up areas. The dynamic model and filter algorithms are discussed in detail, followed by the findings based on computer simulations and a limited field trial carried out in the Greater London area. The results demonstrate that use of the extended Kalman filter algorithm enables the integrated system employing GPS and low cost DR devices to meet the required navigation performance of the device under development.

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An Extended Kalman Filter Algorithm for Integrating GPS and Low Cost Dead Reckoning System Data for Vehicle Performance and Emissions Monitoring

Integration of GPS and dead reckoning for real-time vehicle performance and emissions

Transport-related environmental problems continue to constitute a major challenge to policy makers at all levels. A key feature of these problems is that they arise from the interaction of human behavioral systems and physical systems. Thus, to improve our understanding of environmental and health problems associated with vehicle emissions it is necessary to combine data on both travel and traffic behavior with environmental data linked to the corresponding spatial and temporal variables. There are currently no such databases available. A new low-cost real-time device is currently under development utilizing the latest developments in environmental monitoring, navigation, communications, data mining and warehousing to capture spatio-temporally referenced data on vehicle and driver performance and the level of emissions and concentrations. Because of the need to acquire data in all environments, there are potential limitations in using a global satellite navigation system such as GPS to determine the spatial and temporal data in built-up areas. Therefore, an augmentation strategy involving differential GPS and new low-cost dead reckoning sensors utilizing micro-electro-mechanical systems technology has been explored. This paper presents a high-level description of the real-time vehicle performance and emissions monitoring system, and details the results of a study carried out to characterize the performance of stand-alone and augmented GPS, and assess whether the required navigation performance is achievable. The study characterized the performance of different types of stand-alone GPS receivers and GPS augmented with differential infrastructure and low-cost dead reckoning sensors. The performance indicators used were satellite visibility, coverage, accuracy and integrity. The results highlight the weaknesses and differences in performance, depending on the type of GPS receiver used and shows that, unlike GPS alone, an integrated system employing GPS and low-cost dead reckoning sensors is capable of meeting the required navigation performance in built-up areas. Furthermore, no significant difference in accuracy between stand-alone GPS and differential GPS has been seen.

Integration of GPS and dead reckoning for real-time vehicle performance and emissions monitoring

Problems posed by the environmental impact of transport are serious, growing and constitute a major challenge to policy makers at all levels. The current array of technological, institutional and planning tools available to deal with these problems are inadequate and need urgently to be upgraded. A key feature of these problems is that they arise from the interaction of human behavioural systems and physical systems. Thus, to improve our understanding of environmental and health problems associated with vehicle emissions, it is necessary to combine data on both travel and traffic behaviour with environmental data. This requires simultaneous data on travel, traffic and environmental variables. There are currently no such integrated databases available in the United Kingdom, and the inability to collect self-consistent traffic, travel and environmental data is a major impediment to the development of the necessary scientific underpinning for effective policy interventions. This paper presents a high level description of a real-time global positioning system based vehicle performance and emissions monitoring system currently under development in the UK, to contribute to the realisation of the data requirements highlighted above.

Lin Zhao

Papers

A general map matching algorithm for transport telematics applications

An Extended Kalman Filter Algorithm for Integrating GPS and Low Cost Dead Reckoning System Data for Vehicle Performance and Emissions Monitoring

Integration of GPS and dead reckoning for real-time vehicle performance and emissions

Integration of GPS and dead reckoning for real-time vehicle performance and emissions monitoring

Monitoring real-time vehicle performance and emissions