Transportation Research Part C: Emerging Technologies
Current map-matching algorithms for transport applications: State-of-the art and future research directions
Introduction
A range of intelligent transport system (ITS) applications and services such as route guidance, fleet management, road user charging, accident and emergency response, bus arrival information, and other location based services (LBS) require location information. For instance, buses equipped with a navigation system can determine their locations and send the information back to a control centre enabling bus operators to predict the arrival of buses at bus stops and hence improve the service level of public transport systems. The horizontal positioning accuracy for such ITS applications is in the range of 1 m to 40 m (95%, i.e. at the 2σ level), with relatively high requirements on integrity, continuity and system availability.
In the last few years, the Global Positioning System (GPS) has established itself as a major positioning technology for providing location data for ITS applications. Zito et al. (1995) provide a good overview of the use of GPS as a tool for intelligent vehicle-highway systems. Deduced Reckoning (commonly referred to as ‘Dead’ Reckoning or DR) sensors consisting of an odometer and a gyroscope are routinely used to bridge any gaps in GPS positioning (Kubrak et al., 2006). This information is then used with spatial road network data to determine the spatial reference of vehicle location via a process known as map matching.
Map-matching algorithms use inputs generated from positioning technologies (such as GPS or GPS integrated with DR) and supplement this with data from a high resolution spatial road network map to provide an enhanced positioning output. The general purpose of a map-matching algorithm is to identify the correct road segment on which the vehicle is travelling and to determine the vehicle location on that segment (Greenfeld, 2002, Quddus et al., 2003). Map-matching not only enables the physical location of the vehicle to be identified but also improves the positioning accuracy if good spatial road network data are available (Ochieng et al., 2004). This means that the determination of a vehicle location on a particular road identified by a map-matching algorithm depends to a large extent on the quality of the spatial road map used with the algorithm. A poor quality road map could lead to a large error in map-matched solutions.
A map-matching algorithm can be developed generically for all applications or for a specific application. For example, Taylor et al. (2006) developed a map-matching algorithm referred to as Odometer Map Matched GPS (OMMGPS) applicable to services where the most likely path or route is known in advance. In this paper, only generic map-matching algorithms are reviewed. A map-matching algorithm can also be developed for real-time applications or for those where post-processing is sufficient. For instance, Marchal et al. (2005) developed an efficient post-processing map-matching method for large GPS data. In the review presented in this paper, only real-time map-matching algorithms are considered as most ITS services require a map-matching algorithm that can be implemented in real-time.
It is essential that the map-matching algorithm used in any navigation module meet the specified requirements set for that particular service. Although the performance of a map-matching algorithm depends on the characteristics of input data (Chen et al., 2005), the technique used in the algorithm can enhance overall performance. For instance, the performance of a map-matching algorithm based on fuzzy logic theory may be better than that of an algorithm based on the topological analysis of spatial road network data if all else are equal. There are at least 35 map-matching algorithms produced and published in the literature during the period 1989–2006, most of which are recent reflecting the growth in the need for ITS services. The positioning accuracy and quality offered by these algorithms has also improved over the years. This is mainly due to the use of advanced techniques in the algorithms such as Kalman filtering, fuzzy logic, and belief theory, and the improvement in the performance of positioning sensors and the quality and quantity of spatial road network data.
Another important operational consideration is the sampling frequency. Although most ATT services (navigation and road guidance, distance-based road pricing, etc.) require a sample frequency of 1 Hz, some ATT services (such as bus arrival information at bus stops) only require a sample frequency of 0.3 Hz or lower. This can obviously influence the design of an optimal map-matching algorithm, as the performance of some navigation sensors vary, for example with speed. This aspect of the performance of map-matching is measured in part by the required navigation performance parameter of continuity.
Different algorithms, however, have different strengths and weaknesses. Some algorithms may perform very well within suburban areas but may not be appropriate for urban areas and vice versa. A review of the literature suggests that existing map-matching algorithms are not capable of satisfying the requirements of all ITS applications and services. For instance, bus priority at junctions requires a 2-D positioning accuracy of 5 m (95%) with integrity. None of the existing algorithms can meet this positioning requirement, especially, within dense urban areas. This implies that apart from other elements including input data sources, further improvements to map-matching algorithms are essential. To accomplish this, it is necessary to identify the constraints and limitations of existing map-matching algorithms for further research. Therefore, the objectives of this paper are to perform an in-depth literature review of existing map-matching algorithms and then to uncover the constraints and limitations of these algorithms. In addition to this, the paper also recommends ideas for future research to overcome these limitations. The potential impacts of the European Geostationary Overlay Service (EGNOS) and the forthcoming Galileo system on the performance of map-matching algorithms are highlighted also. It is important to emphasise that this paper is intended to serve as a key reference for future research and development of map-matching algorithms by bringing together existing knowledge and defining future research directions.
The remainder of the paper is structured as follows. First, an in-depth literature review of map-matching algorithms is presented, followed by a presentation of the performance of some existing map-matching algorithms. The next section describes the constraints and limitations of existing map-matching algorithms. This is followed by a discussion of the potential impacts of Galileo and EGNOS on the performance of map matching algorithms. Conclusions summarise the key constraints and limitations of existing algorithms and provide some thoughts on future research directions.
Section snippets
Literature review
As stated above, map-matching algorithms are used to determine the location of a vehicle on a road. Most of the formulated algorithms utilise navigation data from GPS (or GPS integrated with DR sensors) and digital spatial road network data. One of the common assumptions in the literature on map-matching is that the vehicle is essentially constrained to a finite network of roads. While this assumption is valid for most vehicles under most operating conditions, problems may be encountered for
Constraints and limitations
Some of the map-matching algorithms discussed in the previous sections possess the capability to support the navigation module of many ITS applications and services. For example, a positioning accuracy up to 5.5 m (95%) is achievable within suburban areas using some algorithms. Among these algorithms, the fuzzy logic map-matching algorithm provides the best performance both in urban and suburban areas. However, as reviewed in the previous sections, there are a number of issues that hinder the
Impacts of Galileo and EGNOS
The Galileo System will be an independent, global, European-controlled, satellite-based navigation system that will provide a number of services to users equipped with Galileo-compatible receivers. Galileo will also provide a number of navigation and search and rescue (SAR) services globally (ESA, 2002). The most relevant services to surface transport are: the Open Service (OS) which will provide positioning, navigation and timing services, free of charge, for mass market navigation
Conclusions
The navigation function of an intelligent transport system can be supported by a map-matching algorithm that integrates positioning data with spatial road network data. This paper has presented an in-depth literature review of map-matching algorithms. A number of different techniques are used in the map-matching processes such as simple search techniques (e.g., point-to-point matching, point-to-curve matching) and complex ones including the applications of probability theory, fuzzy logic
References (59)
- et al.
Map accuracy and location expression in transportation reality and prospects
Transportation Research C
(2000) - et al.
GPS accuracy estimation using map-matching techniques: Applied to vehicle positioning and odometer calibration
Computers, Environments, and Urban Systems
(2006) - et al.
Some map-matching algorithms for personal navigation assistants
Transportation Research Part C
(2000) - et al.
Global positioning systems in the time domain: How useful a tool for intelligent vehicle-highway systems?
Transportation Research C: Emerging Technologies
(1995) - et al.
Efficient worst-case data structures for range searching
Acta Inf.
(1980) - Bernstein, D., Kornhauser, A., 1996. An introduction to map-matching for personal navigation assistants....
- et al.
Simple map-matching algorithm applied to intelligent winter maintenance vehicle data
Transportation Research Record
(2005) - et al.
Location-based spatial data management in navigation systems
IEEE Symposium on Intelligent Vehicle
(2002) - Chen, W., YU, M., LI, Z.-L., Chen, Y.-Q., 2003. Integrated vehicle navigation system for urban applications. In:...
- et al.
Effects of sensor errors on the performance of map-matching
Journal of Navigation
(2005)
Autonomous vehicle positioning with GPS in urban canyon environments
IEEE Transactions on Robotics and Automation
A generalization of Bayesian inference
Journal of the Royal Statistical Society, Series B
A Road-matching method for precise vehicle localization using Kalman filtering and belief theory
Autonomous Robots
A hybrid map-matching algorithm based on fuzzy comprehensive Judgment
IEEE Proceedings on Intelligent Transportation Systems
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
A map-matching method for GPS based real-time vehicle location
Journal of Navigation
A map-matching method with the innovation of the Kalman filtering
IEICE Trans. Fund. Electron. Comm. Comput. Sci.
Understanding GPS – Principles and Applications
Adaptive fuzzy-network based C-measure map-matching algorithm for car navigation system
IEEE Transactions on industrial electronics
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