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Journal Article

Map-matching in complex urban road networks

TL;DR: An improved probabilistic Map Matching (MM) algorithm to reconcile inaccurate locational data with inaccurate digital road network data and an optimal estimation technique to determine the vehicle position on the link has been developed and is described.
Abstract: Global Navigation Satellite Systems (GNSS) such as GPS and digital road maps can be used for land vehicle navigation systems. However, GPS requires a level of augmentation with other navigation sensors and systems such as Dead Reckoning (DR) devices, in order to achieve the required navigation performance (RNP) in some areas such as urban canyons, streets with dense tree cover, and tunnels. One of the common solutions is to integrate GPS with DR by employing a Kalman Filter (Zhao et al., 2003). The integrated navigation systems usually rely on various types of sensors. Even with very good sensor calibration and sensor fusion technologies, inaccuracies in the positioning sensors are often inevitable. There are also errors associated with spatial road network data. This paper develops an improved probabilistic Map Matching (MM) algorithm to reconcile inaccurate locational data with inaccurate digital road network data. The basic characteristics of the algorithm take into account the error sources associated with the positioning sensors, the historical trajectory of the vehicle, topological information on the road network (e.g., connectivity and orientation of links), and the heading and speed information of the vehicle. This then enables a precise identification of the correct link on which the vehicle is travelling. An optimal estimation technique to determine the vehicle position on the link has also been developed and is described. Positioning data was obtained from a comprehensive field test carried out in Central London. The algorithm was tested on a complex urban road network with a high resolution digital road map. The performance of the algorithm was found to be very good for different traffic maneuvers and a significant improvement over using just an integrated GPS/DR solution.
Citations
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Journal ArticleDOI
Yu Zheng1
TL;DR: A systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics, and introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors.
Abstract: The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.

1,289 citations


Cites background or methods from "Map-matching in complex urban road ..."

  • ...According to the additional information used, map matching algorithms can be categorized into four groups: geometric [36], topological [22][118], probabilistic [75] [83] [86] and other advanced techniques [63][73][126]....

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  • ...To deal with noisy and low-sampling rate trajectories, probabilistic algorithms [Ochieng et al. 2004; Pink and Hummel 2008; Quddus et al. 2006] make explicit provisions for GPS noise and consider multiple possible paths through the road network to find the best one....

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  • ...…algorithms can be categorized into four groups: geometric [Greenfeld 2002], topological [Chen et al. 2003; Yin and Wolfson 2004], probabilistic [Ochieng et al. 2004; Pink and Hummel 2008; Quddus et al. 2006], and other advanced techniques [Lou et al. 2009; Newson and Krumm 2009; Yuan et al.…...

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  • ...To deal with noisy and lowsampling rate trajectories, probabilistic algorithms [75][83][86] make explicit provisions for GPS noise and consider multiple possible paths through the road network to find the best one....

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Journal ArticleDOI
TL;DR: The constraints and limitations of existing map matching algorithms are uncovered by an in-depth literature review and some ideas for monitoring the integrity of map-matching algorithms are presented.
Abstract: 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.

799 citations


Cites background from "Map-matching in complex urban road ..."

  • ...Ochieng et al. (2004) developed an enhanced probabilistic map-matching algorithm....

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  • ...…algorithms in the literature can be categorised into four groups: geometric (Bernstein and Kornhauser, 1996), topological (White et al., 2000; Joshi, 2001; Greenfeld, 2002; Chen et al., 2003; Quddus et al., 2003), probabilistic (Zhao, 1997; Ochieng et al., 2004), and other advanced…...

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  • ...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....

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  • ...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)....

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  • ...…1996), topological (White et al., 2000; Joshi, 2001; Greenfeld, 2002; Chen et al., 2003; Quddus et al., 2003), probabilistic (Zhao, 1997; Ochieng et al., 2004), and other advanced techniques (El Najjar and Bonnifait, 2005; Pyo et al., 2001; Yang et al., 2003; Jagadeesh et al., 2004;…...

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Journal ArticleDOI
TL;DR: A statistical model for urban road network travel time estimation using vehicle trajectories obtained from low frequency GPS probes as observations and the potential of using sparse probe vehicle data for monitoring the performance of the urban transport system is highlighted.
Abstract: The paper presents a statistical model for urban road network travel time estimation using vehicle trajectories obtained from low frequency GPS probes as observations, where the vehicles typically cover multiple network links between reports. The network model separates trip travel times into link travel times and intersection delays and allows correlation between travel times on different network links based on a spatial moving average (SMA) structure. The observation model presents a way to estimate the parameters of the network model, including the correlation structure, through low frequency sampling of vehicle traces. Link-specific effects are combined with link attributes (speed limit, functional class, etc.) and trip conditions (day of week, season, weather, etc.) as explanatory variables. The approach captures the underlying factors behind spatial and temporal variations in speeds, which is useful for traffic management, planning and forecasting. The model is estimated using maximum likelihood. The model is applied in a case study for the network of Stockholm, Sweden. Link attributes and trip conditions (including recent snowfall) have significant effects on travel times and there is significant positive correlation between segments. The case study highlights the potential of using sparse probe vehicle data for monitoring the performance of the urban transport system.

381 citations


Cites background or methods from "Map-matching in complex urban road ..."

  • ...obabilistic graphical model in which the path variables pt are unobserved. Depending on the specifics of the transition models, ˇ^ ( xjp )and , probabilistic inference has been done with Kalman filters [28], [29], the forward algorithm or the Viterbi algorithm [4], [5], or particle filters [17]. Hidden Markov Model representations, however, suffer from the selection bias problem, first noted in the labeli...

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  • ...undred meters, with very few intersections). In this context, there is usually a dominant path that starts from a well-defined point, and Bayesian filters accurately reconstruct paths from observations [28], [38], [17]. When sampling rates are lower and observed points are further apart, however, a large number of paths are possible between two points. Researchers have recently focused on efficiently ide...

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  • ...rithms cannot readily cope with ambiguous observations [24], and were soon expanded into probabilistic frameworks. A number of implementations were explored: particle filters [29], [17], Kalman filters [28], Hidden Markov Models [4], and less mainstream approaches based on Fuzzy Logic and Belief Theory. Two types of information are missing in a sequence of GPS readings: the exact location of the vehicle...

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Proceedings ArticleDOI
23 May 2010
TL;DR: This work proposes an Interactive Voting-based Map Matching (IVMM) algorithm that does not only consider the spatial and temporal information of a GPS trajectory but also devise a voting-based strategy to model the weighted mutual influences between GPS points.
Abstract: Matching a raw GPS trajectory to roads on a digital map is often referred to as the Map Matching problem. However, the occurrence of the low-sampling-rate trajectories (e.g. one point per 2 minutes) has brought lots of challenges to existing map matching algorithms. To address this problem, we propose an Interactive Voting-based Map Matching (IVMM) algorithm based on the following three insights: 1) The position context of a GPS point as well as the topological information of road networks, 2) the mutual influence between GPS points (i.e., the matching result of a point references the positions of its neighbors; in turn, when matching its neighbors, the position of this point will also be referenced), and 3) the strength of the mutual influence weighted by the distance between GPS points (i.e., the farther distance is the weaker influence exists). In this approach, we do not only consider the spatial and temporal information of a GPS trajectory but also devise a voting-based strategy to model the weighted mutual influences between GPS points. We evaluate our IVMM algorithm based on a user labeled real trajectory dataset. As a result, the IVMM algorithm outperforms the related method (ST-Matching algorithm).

315 citations


Cites methods from "Map-matching in complex urban road ..."

  • ...According to the information of input GPS tracking data used, existing methods can be categorized into four groups: geometric [12], topological [13, 14], probabilistic [15] and other advanced techniques [16, 17, 19]....

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  • ...A probabilistic map matching algorithm is developed in [15]....

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Journal ArticleDOI
TL;DR: The basic concept and pipeline of traffic data visualization is introduced, an overview of related data processing techniques is provided, and existing methods for depicting the temporal, spatial, numerical, and categorical properties of Traffic data are summarized.
Abstract: Data-driven intelligent transportation systems utilize data resources generated within intelligent systems to improve the performance of transportation systems and provide convenient and reliable services. Traffic data refer to datasets generated and collected on moving vehicles and objects. Data visualization is an efficient means to represent distributions and structures of datasets and reveal hidden patterns in the data. This paper introduces the basic concept and pipeline of traffic data visualization, provides an overview of related data processing techniques, and summarizes existing methods for depicting the temporal, spatial, numerical, and categorical properties of traffic data.

243 citations

References
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Book
01 Jan 1974
TL;DR: This is the first book on the optimal estimation that places its major emphasis on practical applications, treating the subject more from an engineering than a mathematical orientation, and the theory and practice of optimal estimation is presented.
Abstract: This is the first book on the optimal estimation that places its major emphasis on practical applications, treating the subject more from an engineering than a mathematical orientation. Even so, theoretical and mathematical concepts are introduced and developed sufficiently to make the book a self-contained source of instruction for readers without prior knowledge of the basic principles of the field. The work is the product of the technical staff of the The Analytic Sciences Corporation (TASC), an organization whose success has resulted largely from its applications of optimal estimation techniques to a wide variety of real situations involving large-scale systemsArthur Gelb writes in the Foreword that "It is our intent throughout to provide a simple and interesting picture of the central issues underlying modern estimation theory and practice. Heuristic, rather than theoretically elegant, arguments are used extensively, with emphasis on physical insights and key questions of practical importance."Numerous illustrative examples, many based on actual applications, have been interspersed throughout the text to lead the student to a concrete understanding of the theoretical material. The inclusion of problems with "built-in" answers at the end of each of the nine chapters further enhances the self-study potential of the text.After a brief historical prelude, the book introduces the mathematics underlying random process theory and state-space characterization of linear dynamic systems. The theory and practice of optimal estimation is them presented, including filtering, smoothing, and prediction. Both linear and non-linear systems, and continuous- and discrete-time cases, are covered in considerable detail. New results are described concerning the application of covariance analysis to non-linear systems and the connection between observers and optimal estimators. The final chapters treat such practical and often pivotal issues as suboptimal structure, and computer loading considerations.This book is an outgrowth of a course given by TASC at a number of US Government facilities. Virtually all of the members of the TASC technical staff have, at one time and in one way or another, contributed to the material contained in the work

6,015 citations

Journal ArticleDOI
01 Apr 1976

1,524 citations


"Map-matching in complex urban road ..." refers background in this paper

  • ...Since there is no more information available about the estimate of e , according to Gelb (1979) the optimal estimate is simply the linear function of the measurements in the form e...

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  • ...Since there is no more information available about the estimate of e , according to Gelb (1979) the optimal estimate is simply the linear function of the measurements in the form e 2ω 1 ρσ 2ω 2121 )( σωω =E 1 ρ 1 2 2211ˆ ekeke += (8) where and still need to be specified and are independent of e ....

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Proceedings ArticleDOI
01 Jul 1980
TL;DR: A new algorithm for solving the hidden surface (or line) problem is described, to more rapidly generate realistic images of 3-D scenes composed of polygons, and the development of theoretical foundations in the area are presented.
Abstract: This paper describes a new algorithm for solving the hidden surface (or line) problem, to more rapidly generate realistic images of 3-D scenes composed of polygons, and presents the development of theoretical foundations in the area as well as additional related algorithms. As in many applications the environment to be displayed consists of polygons many of whose relative geometric relations are static, we attempt to capitalize on this by preprocessing the environment's database so as to decrease the run-time computations required to generate a scene. This preprocessing is based on generating a “binary space partitioning” tree whose in order traversal of visibility priority at run-time will produce a linear order, dependent upon the viewing position, on (parts of) the polygons, which can then be used to easily solve the hidden surface problem. In the application where the entire environment is static with only the viewing-position changing, as is common in simulation, the results presented will be sufficient to solve completely the hidden surface problem.

861 citations


"Map-matching in complex urban road ..." refers background in this paper

  • ...A number of data structures and algorithms exist to identify the closest node (or shape point) from a given point in a network (e.g., Bentley and Maurer, 1980; Fuchs et al., 1980)....

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Journal ArticleDOI
TL;DR: This paper considers map matching algorithms that can be used to reconcile inaccurate locational data with an inaccurate map/network.
Abstract: Third-generation personal navigation assistants (PNAs) (i.e., those that provide a map, the user's current location, and directions) must be able to reconcile the user's location with the underlying map. This process is known as map matching. Most existing research has focused on map matching when both the user's location and the map are known with a high degree of accuracy. However, there are many situations in which this is unlikely to be the case. Hence, this paper considers map matching algorithms that can be used to reconcile inaccurate locational data with an inaccurate map/network.

647 citations


"Map-matching in complex urban road ..." refers background in this paper

  • ...This is also known as curve-to-curve matching (Bernstein and Kornhauser, 1996; White et al., 2000)....

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  • ...Another geometric MM approach is point-tocurve matching (e.g., Bernstein and Kornhauser, 1996; White et al., 2000; Taylor et al. 2001)....

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01 Jan 2002
TL;DR: A topologically based matching procedure that was tested with low quality GPS data and the performance of the algorithms was found to produce outstanding results.
Abstract: GPS based navigation and route guidance systems are becoming increasingly popular among bus operators, fleet managers and travelers. To provide this functionality, one has to have a GPS receiver, a digital map of the traveled network and software that can associate (match) the user's position with a location on the digital map. Matching the user's location has to be done even when the GPS location and the underlying digital map have inaccuracies and errors. There are several approaches for solving this map matching task. Some only match the user's location to the nearest street node while others are able to locate the user at specific location on the traveled street segment. In this paper a topologically based matching procedure is presented. The procedure was tested with low quality GPS data to assess its robustness. The performance of the algorithms was found to produce outstanding results

494 citations


"Map-matching in complex urban road ..." refers background or result in this paper

  • ...2 Note that digital spatial data specifically for transportation applications are beginning to emerge Id T is to identify the actual link among the candidate links (Greenfeld, 2002; Quddus et al. 2003)....

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  • ...However, Greenfeld (2002) described that the orthogonal projection of the position fixes on the arc are different from the actual location of the vehicle on the arc....

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  • ...Greenfeld (2002) reviews several approaches for solving the map matching problem and proposes a weighted topological algorithm....

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  • ...Greenfeld (2002) also suggests that additional research is required to verify the accurate performance of the algorithm and to make an accurate position determination on a given road segment....

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  • ...This approach is quite sensitive to outliers and depends on point-to-point matching, sometimes giving unexpected results (Greenfeld 2002)....

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