Map Matching Algorithm Research Articles

Efficient Trajectory Clustering with Road Network Constraints Based on Spatiotemporal Buffering

The analysis of individuals’ movement behaviors is an important area of research in geographic information sciences, with broad applications in smart mobility and transportation systems. Recent advances in information and communication technologies have enabled the collection of vast amounts of mobility data for investigating movement behaviors using trajectory data mining techniques. Trajectory clustering is one commonly used method, but most existing methods require a complete similarity matrix to quantify the similarities among users’ trajectories in the dataset. This creates a significant computational overhead for large datasets with many user trajectories. To address this complexity, an efficient clustering-based method for network constraint trajectories is proposed, which can help with transportation planning and reduce traffic congestion on roads. The proposed algorithm is based on spatiotemporal buffering and overlapping operations and involves the following steps: (i) Trajectory preprocessing, which uses an efficient map-matching algorithm to match trajectory points to the road network. (ii) Trajectory segmentation, where a Compressed Linear Reference (CLR) technique is used to convert the discrete 3D trajectories to 2D CLR space. (iii) Spatiotemporal proximity analysis, which calculates a partial similarity matrix using the Longest Common Subsequence similarity indicator in CLR space. (iv) Trajectory clustering, which uses density-based and hierarchical clustering approaches to cluster the trajectories. To verify the proposed clustering-based method, a case study is carried out using real trajectories from the GeoLife project of Microsoft Research Asia. The case study results demonstrate the effectiveness and efficiency of the proposed method compared with other state-of-the-art clustering-based methods.

A Map-Matching Algorithm With Extraction of Multi-Group Information for Low-Frequency Data

The growing use of probe vehicles generates a huge number of global navigation satellite systems (GNSS) data. Limited by satellite positioning technology, further improving the accuracy of map matching (MM) is challenging work, especially for low-frequency trajectories. When matching a trajectory, the ego vehicle’s spatial-temporal information of the present trip is most useful with the least amount of data. In addition, there is a large number of other data, e.g., other vehicles’ state and past prediction results, but it is hard to extract useful information for matching maps and inferring paths. Most of the MM studies have used only the ego vehicle’s data and ignored other vehicles’ data. Based on those, this article designs a new MM method to make full use of “big data.” We first sort all the data into four groups according to their spatial and temporal distance from the present matching probe, which allows us to sort for their usefulness. Then we design three different methods to extract valuable information (scores) from them: a score for speed and bearing, one for historical usage, and another for traffic state using a spectral graph Markov neural network. Finally, we use a modified top-K shortest-path method to search the candidate paths within an ellipse region and then use the fused score to infer the path (projected location). We test the proposed method against baseline algorithms using a real-world dataset in China. The results show that all scoring methods can enhance MM accuracy. Furthermore, our method outperforms the others, especially when the GNSS probing frequency is ≤ 0.01 Hz.

VIS-MM: a novel map-matching algorithm with semantic fusion from vehicle-borne images

Conventional map-matching (MM) algorithms take blind eyes to the complexity in realistic traffic conditions and hence present significant limitations in distinguishing the detailed driving paths of vehicles within complex urban road networks. The popularity of vehicle-borne cameras and advances in image recognition technologies provide an opportunity to remedy the gap through integrating vehicle-borne image semantic information with MM algorithms. Following this logic, this article proposes a novel MM algorithm with semantic fusion from vehicle-borne images (VIS-MM) suited to the parallel road scenes. First, a multipath output algorithm is developed using the hidden Markov model to obtain candidate paths. Second, image recognition techniques are employed to extract vehicle-borne image semantics. Finally, the entropy weight method is performed to determine the most promising driving path among the candidate paths. The experimental results show that semantic fusion from vehicle-borne images contributes to a significant improvement of accuracy from 66.18% to 99.88% against the parallel road scenes. The proposed map-matching algorithm can be applied into the fields of unmanned autonomous navigation and crowdsourcing updating of high-definition maps.

Graph Supported Mode Detection within Mobile Phone Data Trajectories

Mobile phone data (MPD) has been used in various studies to analyze human travel behavior in time and space. While the pure number of available trips is promising, data quality and the information density of each individual trajectory is relatively low. Travel mode detection of MPD-trajectories is a challenging task, since geographic location data is noisy and the events within a trajectory are irregular (event-based instead of time discrete). In this paper, we present a method to identify travel modes typically observed in urban areas, such as walking, bicycle, tram, bus, and car. The method requires concise network graphs for each mode. Annotated GPS trajectories were collected from volunteers as ground truth to train and validate various machine learning algorithms. The cleaned trajectories of the MPD are segmented into individual trips, which are mapped on the network graphs using a map-matching algorithm. Various features, such as trip distance and travel speed, were analyzed to identify the most suitable features for classifying the available modes with a random forest (RF) and a support-vector machine algorithm. With the RF algorithm, about 80% of all trips were associated to the correct mode. Since the total dataset was only comprised of about 600 trips, which then needed to be split into an evaluation dataset and a training dataset, we suspect the accuracy of the method will increase with more data. Based on the results, this work presents a proof of concept for determining the travel mode of MPD-trajectories.

An Interval Type-2 Fuzzy Logic-Based Map Matching Algorithm for Airport Ground Movements

Airports and their related operations have become the major bottlenecks to the entire air traffic management system, raising predictability, safety, and environmental concerns. One of the underpinning techniques for digital and sustainable air transport is airport ground movement optimization. Currently, real ground movement data is made freely available for the majority of aircraft at many airports. However, the recorded data is not accurate enough due to measurement errors and general uncertainties. In this article, we aim to develop a new interval type-2 fuzzy logic-based map matching algorithm, which can match each raw data point to the correct airport segment. To this aim, we first specifically design a set of interval type-2 Sugeno fuzzy rules and their associated rule weights, as well as the model output, based on preliminary experiments and sensitivity tests. Then, the fuzzy membership functions are fine-tuned by a particle swarm optimization algorithm. Moreover, an extra checking step using the available data is further integrated to improve map matching accuracy. Using the real-world aircraft movement data at Hong Kong airport, we compared the developed algorithm with other well known map matching algorithms. Experimental results show that the designed interval type-2 fuzzy rules have the potential to handle map matching uncertainties, and the extra checking step can effectively improve map matching accuracy. The proposed algorithm is demonstrated to be robust and achieve the best map matching accuracy of over 96% without compromising the run time.

A novel map matching algorithm for real-time location using low frequency floating trajectory data

The continuous enhancement of technologies and modern well-equipped infrastructures are necessary for easy life. Road accident and missing vehicle ratio are very challenging in preventing misshapenness because these are continually increasing due to traffic hazards. The single way to protect human life from such type of conditions that is more reliable navigation services such as correct location tracking of vehicles on the road network. The real-time location tracking methods fully depends on the map matching algorithms, which also compute a reliable path on the road network. A smart vehicle can provide more reliable tracking services during or before any misshaping using proposed map matching algorithm. This work contributes to ensure correct location for necessary action during misshaping, alert accident zone and communicate messages without wasting valuable time. The proposed approach is validated on the real tracking data and is compared against poor GPS service.

IoT Based Condition Monitoring for Railway Track Fault Detection in Smart Cities

The railways have the busiest and biggest networks in the world and play a key role in driving the economic growth of any country. The railways still follow the traditional method of manual inspection which is very time-consuming and not economical. The proposed work demonstrates maintenance i.e. condition monitoring based fault detection. In this method, the system will measure accelerations at the train bogie and can find railway track longitudinal profile, and rail condition from the response provided by inertial sensors mounted on the in-service train. The vertical and lateral accelerations of railway bogies are used to evaluate faults in railway tracks. A tachometer system and a map-matching algorithm are used to pinpoint the location of faults on tracks. This system will communicate these vibration messages wirelessly to a cloud service, which processes the data, and using knowledge of sensor location and vibration history the determination of the track condition is performed on a regular basis. This data is then conveyed to infrastructure managers in the form of alerts, thereby facilitating a condition-based maintenance approach equipped with IoT. The system can easily indicate the weak locations in the track that can be further analyzed in detail by the diagnostician. Thus, the system aids in tracking maintenance strategies and will reduce downtime. This condition-based maintenance system provides smart detection of faults as compared with traditional methods.

A Filtered-Marine Map-Based Matching Method for Gravity-Aided Navigation of Underwater Vehicles

Gravity-aided inertial navigation system (INS) has been widely applied in the application of autonomous underwater vehicle. Due to the uneven distribution of the gravitational field, the matching performance varies unsteadily when underwater vehicles pass through real marine environment. In this article, a filtered marine map-matching method based on sliding window iterated closest contour point (ICCP) algorithm is proposed. The filtered marine-map records the information of the contour line, which is extracted from the gravity anomaly value. The trajectory can be represented by different vectors between the contour lines corresponding to the sampled gravity points. In addition, a model-free matching area selection method is proposed. A multilayer support vector classifier is established by hierarchical classification method. The genetic algorithm is adopted to optimize the penalty factor and kernel function parameters. The marine experimental results indicate that the proposed matching area selection method can fully excavate the gravity information and provide directional guidance. The proposed filtered marine map-matching algorithm effectively improved the matching accuracy for underwater vehicles.

An improved hidden Markov model-based map matching algorithm considering candidate point grouping and trajectory connectivity

ABSTRACT The hidden Markov model-based map matching algorithm (HMM-MM) is an effective method for online vehicle navigation and offline trajectory position correction. Common HMM-MMs are susceptible to the influence of adjacent road segment endpoints and similar parallel roads, because the multi-index probability model may ignore some indexes when the probability of other indexes is high. This makes the map-matching result not meet the assumption that vehicles always travel the shortest or optimal path, and it cannot guarantee that the trajectory points can match to the nearest position of the maximum likelihood road segment, resulting in poor accuracy. In this paper, an IHMM-MM is proposed. IHMM-MM (1) modifies the definition of transition probability and no longer takes the straight-line distance between trajectory points as the reference for the shortest path length between candidate point pairs. (2) supplements the definition of observation probability and introduces the point-line relation function to screen and group candidate points. (3) adds additional logic outside the HMM probability model to consider the trajectory connectivity and fill in the key trajectory points where the vehicles travel. Experiments show that the IHMM-MM can effectively improve the sampling frequency of trajectory data and has better performance in complex urban road environments.

Map-Matching Error Identification in the Absence of Ground Truth

Map-matching of trajectory data has widespread applications in vehicle tracking, traffic flow analysis, route planning, and intelligent transportation systems. Map-matching algorithms snap a set of trajectory points observed by a satellite navigation system to the most likely route segments of a map. However, due to the unavoidable errors in the recorded trajectory points and the incomplete map data, map-matching algorithms may match points to incorrect segments, leading to map-matching errors. Identification of these map-matching errors in the absence of ground truth can only be achieved by visual inspection and reasoning. Thus, the identification of map-matching errors without ground truth is a time-consuming and mundane task. Although research has focused on improving map-matching algorithms, to our knowledge no attempts have been made to automatically classify and identify the residual map-matching errors. In this work, we propose the first method to automatically identify map-matching errors in the absence of ground truth, i.e., only using the recorded trajectory points and the map-matched route. We have evaluated our method on a public dataset and observed an average accuracy of 91% in automatically identifying map-matching errors, thus helping analysts to significantly reduce manual effort for map-matching quality assurance.

High-Frequency Trajectory Map Matching Algorithm Based on Road Network Topology

Accurately mapping the raw global position system (GPS) trajectories to the road network is the basis for studying the application of trajectory data. This study proposes a novel off-line map matching algorithm based on road network topology, to address the problems of low execution efficiency and poor matching accuracy of selective look-ahead map matching (SLAMM) algorithm. First, the noise points of the trajectory data are removed by data preprocessing. Second, the algorithm searches for critical samples in the trajectory data and segments the data accordingly. Then, the adjacent road segments around the transition node corresponding to the critical sample are selected as candidate arcs. Finally, the segmented trajectory data are matched to the road network by constructing an error ellipse. The algorithm fully considers the topology of the road network and the characteristics of high-frequency trajectory data. The experimental results, using Beijing trajectory data to perform matching on an actual road network environment, show that the proposed algorithm is more efficient and robust than other map matching algorithms for high-frequency trajectories.

LCR-SMM: Large Convergence Region Semantic Map Matching Through Expectation Maximization

The matching and fusion of local maps generated by multiple robots can greatly enhance the performance of relative localization and collaborative mapping. Currently, existing semantic matching methods are partly based on classical iterative closet point, which typically fails in cases with large initial errors. What’s more, current semantic matching algorithms have high computation complexity in optimizing the transformation matrix. To address the challenge of large initial errors and low matching efficiency, this article proposes a novel large convergence region semantic map matching algorithm. The key novelty of this work is the designing of the initial transformation optimization algorithm and the probabilistic registration model to increase the convergence region. To reduce the initial error before the iteration process, the initial transformation matrix is optimized by estimating the credibility of the data association. At the same time, a factor reflecting the uncertainty of the initial error is calculated and introduced to the formulation of the probabilistic registration model, thereby accelerating the convergence process. The proposed algorithm is performed on public datasets and compared with existing methods, demonstrating the significant improvement in terms of matching accuracy and robustness.

Distribution-Based Weights Estimation for Map Matching Algorithms

With the development of mobile computing, map matching algorithms are utilized to recover vehicles’ traveling routes. Existing map matching algorithms usually infer the traveling routes based on a driving cost model that considers various traveling features and driving preferences (referred to as weights). In general, the weights are estimated either empirically or through data-driven approaches. For empirical setting, the weights are determined based on field studies and must be reestimated when the driving scenarios are changed. Alternatively, in data-driven approaches, the weights are determined from the analysis of historical routes of vehicles. However, estimation bias may be introduced due to the selection of data sources. To decrease the estimation bias, this article proposes a distribution-based weights estimation method. Specifically, the weights are estimated by inferring their distributions through Bayesian inference based on the driver traces collected in a city scale. Next, a sampling-based strategy is adopted to determine the weights values from their distributions. Experimental results show that the proposed approach achieves a higher map matching accuracy than the existing approaches, and it is applicable for both linear and nonlinear driving cost models used in map matching algorithms. Moreover, the estimated weights can help to understand driver preferences in different traveling cases.

Evaluation of map‐matching algorithms for smartphone‐based active travel data

Global Positioning System (GPS) data on walking and cycling trips can generate useful insights for transportation systems but require substantial processing. One of the key GPS data processing steps is “map-matching”, or inference of the sequence of network links traversed during travel. The objective of this research is to evaluate the accuracy of existing map-matching algorithms for GPS data on active travel. A method to flag erroneous map-matching results without requiring ground-truth data and improvements for active travel data are also proposed. Six map-matching algorithms are applied to a sample of 63 trajectories, stratified on network density and average heading change, extracted from a large set of real-world trips from metropolitan Vancouver, Canada. Results show that the best performing method is PgMapMatch, which can be further improved by adjustments to link costs and allowing wrong-way travel. Two other algorithms have similarly accurate routes (70–90% accuracy, depending on the measure), but fail to generate routes for about a third of trips. The proposed error detection measure can be used (without ground truth data) to flag matched routes requiring visual inspection, with a recommendation to look for: Wrong-way travel, missing links in the network data, and parallel facilities on the same street.

OBD-Data-Assisted Cost-Based Map-Matching Algorithm for Low-Sampled Telematics Data in Urban Environments

A myriad of connected vehicles collects large-scale telematics data throughout cities, enabling data-based infrastructure planning. To truly benefit from this emerging technology, it is important to integrate pervasive telematics data with map data to produce more tractable and readable information for traffic flows and safety. Map-matching algorithms enable the projection of noisy trajectory data onto map data as a means of integrating telematics data. However, map-matching poses challenges due to higher levels of positioning errors and complex road networks. The authors propose a novel map-matching algorithm that can fuse in-vehicle data with trajectory data to improve the efficiency and accuracy of the algorithm. The proposed algorithm combines the probabilistic and weight-based map-matching frameworks. The novelty of the proposed algorithm includes (i) an adaptive segment candidate search mechanism based on in-vehicle speed information, (ii) adaptive matching parameters to reflect the variations in the Global Positioning System (GPS) noise levels, (iii) a novel transition probability that uses in-vehicle speed data, and (iv) a backend data query system for the shortest routes. Map-matching results were validated based on ground-truth data collected using an in-vehicle sensing device developed by the authors, as well as comparing with a commonly-used off-the-shelf map-matching platform. The proposed algorithm is proven to be robust, with an accuracy of 97.45%, particularly where map data are denser and GPS noise is high.

Control Optimization of Scenic Spot Navigation System Based on Map Matching Algorithm

In order to solve the problem that the smart phone GPS positioning technology cannot guarantee the location accuracy in the scenic spot navigation system, a tourist spot matching method based on the improved HMM model was proposed. Firstly, the traditional linear road model is improved in electronic map design. Secondly, the concept of minimum peripheral error rectangle is proposed based on the theory of error ellipse. Then, combined with location data, road data, and scenic spot data, the improved HMM model is used to calculate the matching road. Finally, Matlab simulation experiment and comparison experiment are carried out. The results show that the accuracy of the map matching algorithm based on the improved HMM model can reach 94.5%, and the average accuracy can reach 93.1%, which is 3.6% higher than that of the traditional HMM model. The test results show that the proposed algorithm is correct and practical and has a good application prospect.

SeqPolar: Sequence Matching of Polarized LiDAR Map With HMM for Intelligent Vehicle Localization

3D LiDAR maps are playing a more and more important role in intelligent vehicle localization. In this paper, the proposed SeqPolar localization method consists of two parts. The first part is a novel node-based LiDAR map representation method, termed polarized LiDAR map (PLM), and the second is a map matching algorithm based on a second-order hidden Markov model (HMM2) for vehicle localization. Unlike existing 3D LiDAR map, which is usually constructed by accumulating 3D LiDAR clouds collected at different times, the proposed polarized LiDAR map is generated from a set of nodes. Each node consists of three elements: a polarized LiDAR image, scene features extracted from the polarized LiDAR image, and sensor pose. The polarized LiDAR image encodes 3D coordinates and reflectivities of the 3D LiDAR cloud using a multi-channel image format, a more concise, straightforward, and structured representation of the 3D LiDAR cloud. In the localization step, we propose an HMM2-based method to match a sequence of input polarized images within the map nodes and find the nearest node from the PLM. Afterward, the vehicle is readily localized from the matched map node with 3D registration. The proposed SeqPolar localization method has been validated with the actual field dataset and the public KITTI database. Experimental results demonstrate that the proposed HMM2-based matching method can achieve up to 98% accuracy to find the nearest node from PLM. Moreover, the SeqPolar localization method based on the pre-built PLM can achieve 30-centimeter localization accuracy in average on both test datasets.

Low-Frequency Trajectory Map Matching Method Based on Vehicle Heading Segmentation

Numerous Global Positioning System connected vehicles are collecting extensive data remotely in cities, enabling data-driven infrastructure planning. To truly benefit from this emerging technology, it is important to combine telematics and map data to make it easier to extract and mine useful information from the data. By performing map matching, data points that cannot be accurately located on the road network can be projected onto the correct road segment. As an important means of remote data processing, it has become an important pre-processing step in the field of data mining. However, due to the various errors of location devices and the complexity of road networks, map matching technology also faces great challenges. In order to improve the efficiency and accuracy of the map matching algorithm, this study proposes an offline method for low-frequency trajectory data map matching based on vehicle trajectory segmentation. First, the trajectory is segmented based on the vehicle’s travel direction. Then, the comprehensive probability of the corresponding road segment is calculated based on the spatial probability and the directional probability of each road segment around the location. Third, the k candidate matching paths under consideration are selected based on the comprehensive probability evaluation. Finally, the shortest path planning and the probability calculation of the different candidate paths are combined to find the optimal matching path. The experimental results on the real trajectory dataset in Shanghai and the road network environment show that the proposed algorithm has better accuracy, efficiency, and robustness than other algorithms.

Capturing Spatial Relationship Mapping Patterns between GPS Coordinates and Road Network Using Machine Learning and Partitioning Techniques

Map matching is a technique used to identify which path a vehicle is travelling on in a road map. Since it is a crucial fundamental step for a wide range of transportation applications, many map-matching algorithms have been devised ranging from simple geometric calculation methods to more sophisticated methods. However, the study of spatial relationship patterns between GPS coordinates and road segments mapped with map-matching has not received enough attention from researchers. This paper presents a framework, called Proxy Map Matching (PMM), to learn such patterns using machine learning techniques. However, we find that solely employing machine learning techniques on such data is not sufficient to capture the patterns. Solving this problem that way results in an inaccurate proxy model. In PMM, we construct several proxy map matchers and assign them to each group of data based on their spatial proximity, thereby achieving accuracy improvement. An experiment on real-world data shows that the framework achieves above 85% accuracy with integration of machine learning techniques, and outperforms the methods which solely employ machine learning techniques significantly. Moreover, the proposed proxy model can perform very fast matching. For 14,177 GPS coordinate pairs per second, we achieve 88.4% accuracy.

Glocal map-matching algorithm for high-frequency and large-scale GPS data

The global positioning system (GPS) trajectory data are extensively utilized in various fields, such as driving behavior analysis, vehicle navigation systems, and traffic management. GPS sensors installed in numerous driving recorders and smartphones facilitate data collection on a large-scale in a high-frequency manner. Therefore, map-matching algorithms are indispensable to identify the GPS trajectories on a road network. Although the local map-matching algorithm reduces computation time, it lacks sufficient accuracy. Conversely, the global map-matching algorithm enhances matching accuracy; however, the computations are time consuming in the case of large-scale data. Therefore, this study proposes a method to improve the accuracy of the local map-matching algorithm without affecting its efficiency. The proposed method first executes the incremental map-matching algorithm. It then identifies the mismatching links in the results based on the connectivity of the links. Finally, the shortest path algorithm and the longest common subsequence are used to correct these error links. An elderly driver’s driving recorder data were used to conduct the experiment to compare the proposed method with four state-of-the-art map-matching algorithms in terms of accuracy and efficiency. The experimental results indicate that the proposed method can significantly increase the accuracy and efficiency of the map-matching process when considering high-frequency and large-scale data. Particularly, compared with the two-benchmark global map-matching algorithms, the proposed method can reduce the error rate of map-matching by nearly half, only consuming 18% and 58% of the computation time of the two global algorithms, respectively.