Map Matching Research Articles

Landslide susceptibility mapping and similar case matching based on case library: a case study of Xinjing landslide, China

Landslide susceptibility mapping (LSM) predicts potential landslide risks based on historical landslides, playing a crucial role in landslide prevention and control. This study proposes a novel approach for LSM in open-pit mines, leveraging a comprehensive landslide case database. This method addresses the limitations of traditional LSM approaches, such as sensitivity to historical landslide records, low-quality inventories, and limited dataset size. By expanding the training set and incorporating case-based reasoning (CB-LSM) using Long Short-Term Memory (LSTM) for machine learning, the model demonstrated a 10% improvement in predictive accuracy compared to statistical models, showing strong applicability even in areas with low-quality landslide inventories. Additionally, this study enhances the practical utility of LSM by introducing a similar case matching method, employing Fuzzy C-Means (FCM) clustering, which links high-risk areas identified by LSM with specific landslide prevention measures. The proposed approach not only improves the accuracy of landslide predictions but also strengthens the connection between LSM results and effective landslide prevention strategies. Given the higher risks associated with landslides in open-pit mines, the proposed method significantly lowers the threshold for applying LSM in these environments, offering a valuable tool for safeguarding personnel and property.

How to visualize the spatial uncertainty of landmark representations in maps?

Landmark representations in maps play an important role in spatial tasks such as self-localization, spatial orientation, and navigation. By matching landmarks to their map representations, people can identify their current location and orientation within a map. However, spatial inaccuracies of landmark representations like these of pictograms in maps have been found to affect the ability to successfully match spatial environments to their map representations. These map matching difficulties could negatively affect the performance in spatial tasks. Due to the increasing number of data sources used in modern map services, for example provided by volunteers, map data quality has become more heterogeneous and uncertain. Therefore, solutions for counteracting the negative effects of spatially inaccurate landmark representations on map matching are required. Here, we report two studies that investigate whether visualizing spatial landmark uncertainty can improve map matching. Although suggestions for visualizing uncertainty exist, there is no empirical evidence whether they can intuitively communicate spatial uncertainty of landmark representations. If map users are made aware of potential spatial inaccuracies in a map, they may observe small or moderate spatial inaccuracies of landmark representations but still be able to match these to the represented landmarks. Our findings support this assumption. Using pictogram size, transparency, or circular uncertainty areas around pictograms to communicate spatial uncertainty increased the likelihood of a perceived match between 3D environments or 360° images and a corresponding map representation when landmark representations were spatially inaccurate. Furthermore, our findings indicate that, in addition to landmarks, people use spatial reference points such as road segments for map matching. Thus, if sufficient other spatial reference points are available both in the environment and its map representation, negative effects of spatially inaccurate landmark representations on map matching may be less severe.

Positive connotations of map-matching based on sub-city districts for trajectory data analytics

We propose a new data pre-processing method, sub-district-based map matching (SDBMM), which involves projecting a trajectory onto sub-city districts (SCDs), geographical areas on the map with irregular boundaries. Thus, this method differs significantly from traditional map-matching processes, which match trajectory data points to the nearest or most probable road segments. With SDBMM, a moving object is represented through gradual transitions through a set of SCDs instead of ‘blinking’ at the road segments rapidly. This change in information granularity yields more presentation efficiency. As SCDs are meaningful partitions of a city based on urban planning or travel behaviours, SDBMM also underlies a new ground for post hoc analytics. In the first application, we perform SDBMM for a large taxi-trajectory dataset in a large city. This case verifies SDBMM with sufficient massive data and brings valuable knowledge to several parties (i.e. taxi drivers, service operators, and the government) in managing the taxi transport mode in the city and policy-making. We apply SDBMM in a second application of anti-drug investigation and find that SCDs with pathway entrances/exits across the mountain ranges are usually the hot traces of drug transactions. These practical applications may foster greater confidence in future utilisations of SDBMM.

Map matching on low sampling rate trajectories through deep inverse reinforcement learning and multi-intention modeling

Analyzing freight vehicle movements using GPS trajectory data presents challenges due to environmental conditions and hardware limitations impacting data accuracy. Map matching, the process of aligning GPS signals with road networks, facilitates accurate route reconstruction. However, existing methods have limitations, particularly with low and ultra-low sampling rates. They often assume the shortest path between points, overlook historical data insights and neglect diverse driving behaviors, which may not align with real-world scenarios where shortest paths are not always optimal and different drivers exhibit varied behaviors. These limitations affect existing methods’ reliability, especially when we face low sampling rate trajectories. In this study, we propose multi-intention deep inverse reinforcement learning for map matching (MIDIRL) to address these challenges. MIDIRL integrates deep neural networks and multi-intention capturing mechanisms with inverse reinforcement learning to model complex driving preferences from historical trajectories, improving map matching accuracy, especially in ultra-low-frequency trajectories. Our experiments on real-world datasets demonstrate MIDIRL’s improved accuracy and efficiency of map matching compared to previous methods, even with limited training data.

Deep learning‐based location prediction in VANET

AbstractIn recent years, Vehicular Ad‐hoc Network (VANET) has become an essential component of intelligent transportation systems that, along with the previous systems such as traffic condition, accident alert, automatic parking, and cruise control, use the communication of vehicle to vehicle and vehicle to the roadside unit to facilitate road transportation. Several challenges hinder efforts to improve traffic conditions and reduce traffic fatalities through VANET. A critical challenge is achieving highly accurate and reliable vehicle localization within the VANET. Additionally, the frequent unavailability of Global Positioning System (GPS), particularly in tunnels and parking lots, presents another significant obstacle. Traditional methods like Dead Reckoning offer low accuracy and reliability due to accumulating errors. Similarly, GPS positioning, map matching with mobile phone location services, and other existing solutions struggle with accuracy and economic feasibility. In this article, two Kalman filter approaches are used based on signal statistical information and the other learning‐based networks, including traditional neural network, deep neural network and LSTM (long short‐term memory) to locate the car. The prediction error of car position with root mean square measures. The squared error and distance prediction error are evaluated. It is shown that in terms of prediction time and processing time of vehicle localization, all the vehicle localization methods are efficient in terms of response time for localization, and Kalman filter methods, traditional neural network and deep neural network are faster than LSTM method. Also, in terms of localization error, Kalman filter works better than learning‐based methods, and in learning‐based methods, both deep neural network and LSTM methods perform better than traditional neural network in terms of localization error.

Uncertainty‐aware LiDAR‐based localization for outdoor mobile robots

AbstractAccurate and robust localization is essential for autonomous mobile robots. Map matching based on Light Detection and Ranging (LiDAR) sensors has been widely adopted to estimate the global location of robots. However, map‐matching performance can be degraded when the environment changes or when sufficient features are unavailable. Indiscriminately incorporating inaccurate map‐matching poses for localization can significantly decrease the reliability of pose estimation. This paper aims to develop a robust LiDAR‐based localization method based on map matching. We focus on determining appropriate weights that are computed from the uncertainty of map‐matching poses. The uncertainty of map‐matching poses is estimated by the probability distribution over the poses. We exploit the normal distribution transform map to derive the probability distribution. A factor graph is employed to combine the map‐matching pose, LiDAR‐inertial odometry, and global navigation satellite system information. Experimental verification was successfully conducted outdoors on the university campus in three different scenarios, each involving changing or dynamic environments. We compared the performance of the proposed method with three LiDAR‐based localization methods. The experimental results show that robust localization performances can be achieved even when map‐matching poses are inaccurate in various outdoor environments. The experimental video can be found at https://youtu.be/L6p8gwxn4ak.

Enhancing Map Matching Accuracy Using Backtracking Technique

Map matching integrates positioning data with road network information to accurately identify a vehicle exact location on a specific road. Modern location-based services heavily rely on accurate map matching techniques to match observed trajectories with road networks. This paper presents a novel map matching approach that leverages topological information and integrates the backtracking technique to enhance the accuracy and robustness of map matching. By considering the logical relationships between road segments, our method achieves excellent results in scenarios with complex road geometries and challenging urban environments. We integrate the backtracking technique to address noise and ambiguity in trajectory data, resulting in improved precision and a reduced likelihood of false matches. Extensive experimentation using real-world trajectory datasets in Beijing validates the effectiveness of our approach, highlighting its superiority over traditional methods. The algorithm accuracy achieved as high as 99.92%. The results of the evaluation demonstrate that the proposed approach exhibits superior performance compared to earlier map-matching algorithms in both simple and complex road networks. Additionally, it can be effectively implemented on GPS datasets with different time intervals. In addition to the obvious map matching applications of our technology, it has the potential to be adapted across domains, with implications in transportation, urban planning, and location-based services. This paper not only presents a substantial advancement in map matching but also opens new avenues for location-based analyses enhancing and services across various domains.

Symmetry-Enhanced Fuzzy Logic Analysis in Parallel and Cross-Road Scenarios: Optimizing Direction and Distance Weights for Map Matching

This study addresses the challenges of setting segmentation points in the membership function and determining appropriate weights for different types of information within a fuzzy logic algorithm for map matching. We use linear fitting to derive an empirical formula for setting segmentation points for the information membership function. Furthermore, we evaluate the effects of various weights for direction and distance information in parallel and cross-road scenarios. The research identified the optimal distance that achieves the highest matching accuracy and provided insights into how the weights of connection, direction, and distance information affect this accuracy. The simulations confirmed the critical importance of precise segmentation point settings and weight determinations in enhancing the accuracy of fuzzy logic algorithms for map matching. The results underscore the potency of our tailored parameter-setting strategy and contribute to knowledge of symmetry, offering practical insights for implementing fuzzy logic in map matching with a particular emphasis on the principle of symmetry in algorithm design and information processing.

Next track point prediction using a flexible strategy of subgraph learning on road networks

Accurately predicting the next track point of vehicle travel is crucial for various Intelligent Transportation System (ITS) applications, such as travel behavior studies, traffic control, and traffic congestion monitoring. Recent works on trajectory prediction follow a paradigm that first represents the raw trajectory and subsequently makes predictions based on that representation. Currently, trajectory representation methods tend to project trajectory points to road networks by map matching and represent trajectories based on the representation of matched roads. However, precisely matching trajectories to roads is a challenge in ITS, as the matching precision is greatly affected by the quality of the trajectory. Meanwhile, since it is difficult to discern whether trajectory matching results are accurate or confounded, how to effectively utilize this type of uncertain geographic context information is also a challenge, which is defined as the Uncertain Geographic Context Problem (UGCoP) in geographic information science. Therefore, we propose a flexible strategy of subgraph learning, referred to as SLM, for predicting the next track point of vehicles. Specifically, a subgraph generation module is first proposed to extract topology contextual information of the roads around historical trajectory points. Secondly, a subgraph learning module is designed to learn rich spatial and temporal features from generated subgraphs. Finally, the extracted spatiotemporal features will be fed into a prediction module to predict the next track points of vehicles on road networks. Our model enables the effective utilization of uncertain geographic context information of trajectories on road networks while avoiding the error brought by map matching. Extensive experiments based on trajectory datasets in two different cities confirm the effectiveness of our approach.

Neural network for automatic ICD-EGMs matching in pace-mapping and ablation of ventricular arrhythmias

Abstract Background Mapping of the ventricular arrhythmias (VA) is often not possible due to the non-inducibility of the electrophysiological study (EPS). Since twelve-lead electrocardiograms (ECG) of the clinical ventricular tachycardia (VT) are also often not available, therefore stored implantable cardioverter-defibrillator (ICD) electrograms (EGMs) of the clinical VT are often the only documentation. The pace mapping matching to the stored ICD-EGM template of the clinical VT can help to identify the VT exit site. Automatic objective computing of similarity scores between EGMs is impossible because ICD programmer devices do not have any interface allowing the export of digital EGMs. Therefore, images can only be accessed for similarity by subjective eyeballing. The MatcherNet Siamese neural network takes a pair of EGM images and predicts their similarity. Purpose This study aimed to assess the feasibility of the automatic ICD-EGMs matching by MatcherNet neural network for pace mapping of VT/premature ventricular contraction (PVC) exit site. Methods Consecutive patients undergoing VT ablation or ablation of ventricular fibrillation, triggered by PVC were included in the study, if the clinical VT/PVC was not inducible during EPS but ICD-EGMs of clinical VT/PVC were stored in the device. Electro-anatomical mapping was performed to reveal substrates (low-voltage areas, abnormal potentials, DEEP-potentials, conduction crowding). Subsequently, the low-output pacing from various locations tagged on the 3D map of LV was performed, and the paced ICD-EGM images were stored from the ICD programmer device for offline analysis and calculation of the similarity. RF ablation was conducted based on the substrate map findings, including the area of the most remarkable similarity between paced ICD-EGMs and clinical ICD-EGM templates assessed by eyeballing. The ICD-EGMs similarity scores (Manhattan similarity and Pearson correlation) were predicted by MatcherNet for each pacing site offline and transferred to the 3D map for the assessment of similarity distribution (Figure 1). Results We included 7 patients with VA episodes registered and stored by their ICD. The clinical characteristics are presented in Table1. In all patients, we were able to find the sites of highest ICD-EGM similarity to the clinical template with gradual decrementing of the similarity score at the increasingly distant pacing areas. The site of the highest similarity was collocated with VT substrate and ablation areas in all patients. Abrupt change of the similarity score at the adjacent pacing sites was detected in the 2 cases, indicating the possible protected VT isthmus. After ablation procedure only one patient with DCM experienced VA recurrence. Conclusion This technology opens new possibilities for the utilization of ICD-EGMs during the pace mapping of ventricular tachycardia, especially in the cases of non-inducibility during electrophysiological study.Figure 1Table 1

A Novel Automatic Map Matching Method Based on Hybrid Computing Framework of Hidden Markov Model and Conditional Random Field

In recent years, automatic map matching has received great technical progress. However, when it comes to vague matching situations such as improper vocabulary use, there still lack reliable solutions. To handle the current gap, this paper proposes a novel automatic map matching method based on the hybrid computing framework of hidden Markov model (HMM) and conditional random field. First, the data filtering is completed by performing second-order transformation towards automatic matching conditions of HMM. Then, the data classification is completed using automatic data classification based on the conditional random field. After that, a hybrid computing framework with spatial elements and layer selection is built to generate map matching results. Finally, some simulation experiments are conducted for evaluation. For one thing, the trend of matching accuracy changes under specified conditions is basically the same as that of nonspecified conditions. The maximum difference in matching calculation values is about 3 times. However, once the vocabulary continues to increase, the difference in matching results between the two narrows to 10–20%. For the other thing, the matching accuracy of a specified state is higher than that of sending a specified state. While nonspecified fuzzy matching accuracy is about 3 times higher and nonspecified precision matching accuracy is about 50% higher.

Traffic trajectory data analysis technology based on HMM model map matching algorithm.

The rapid growth of traffic trajectory data and the development of positioning technology have driven the demand for its analysis. However, in the current application scenarios, there are some problems such as the deviation between positioning data and real roads and low accuracy of existing trajectory data traffic prediction models. Therefore, a map matching algorithm based on hidden Markov models is proposed in this study. The algorithm starts from the global route, selects K nearest candidate paths, and identifies candidate points through the candidate paths. It uses changes in speed, angle, and other information to generate a state transition matrix that match trajectory points to the actual route. When processing trajectory data in the experiment, K = 5 is selected as the optimal value, the algorithm takes 51 ms and the accuracy is 95.3%. The algorithm performed well in a variety of road conditions, especially in parallel and mixed road sections, with an accuracy rate of more than 96%. Although the time loss of this algorithm is slightly increased compared with the traditional algorithm, its accuracy is stable. Under different road conditions, the accuracy of the algorithm is 98.3%, 97.5%, 94.8% and 96%, respectively. The accuracy of the algorithm based on traditional hidden Markov models is 95.9%, 95.7%, 95.4% and 94.6%, respectively. It can be seen that the accuracy of the algorithm designed has higher precision. The experiment proves that the map matching algorithms based on hidden Markov models is superior to other algorithms in terms of matching accuracy. This study makes the processing of traffic trajectory data more accurate.

A New Trajectory Reduction Method for Mobile Devices Operating Both Online and Offline

AbstractHighly accurate location data have become essential in nearly all contemporary global applications, including but not limited to route planning, processing traffic data, identifying common routes, map matching, and enhancing agricultural productivity. However, the abundance of unnecessary and redundant data leads to various challenges, especially concerning storage, processing, and transmission. Despite the existence of numerous studies aimed at addressing these GNSS data management challenges, the reduction problem is either partially resolved, or enhancements are made to existing solutions in nearly all of them. In this study, a novel reduction method is introduced, offering both a high reduction rate and accuracy, suitable for operation on mobile devices in both offline and online modes. The proposed method uses windowing with reference points during the decision phase to decrease the number of points. By utilizing the angle and its threshold between the decision point and reference points, we achieved a method characterized by low algorithmic complexity and a high reduction rate, suitable for online operation on mobile devices. Experiments and comparisons revealed that the proposed method had a 91.01% reduction in GNSS data which is 7.73% lower, a 5.8744e$$-$$ - 04 RMSE error which is 2e+7 times better, and a 14.54 ms running time which is 25% faster than RDP algorithm. The results indicate that incorporating the proposed method into current methodologies could be beneficial, particularly in scenarios where real-time, high-precision location data are essential.

Correction to ‘Open source map matching with Markov decision processes: A new method and a detailed benchmark with existing approaches’

Correction to ‘Open source map matching with Markov decision processes: A new method and a detailed benchmark with existing approaches’

A Map-Aided Fast Initialization Method for the Magnetic Positioning of Vehicles

Magnetic positioning is a promising technique for vehicles in global navigation satellite system (GNSS)-denied scenarios. In general, a fixed-length magnetic sequence is required to provide an initial positioning result, which means that users need to wait a relatively long distance. To minimize this initialization distance, a map-aided fast initialization method, including magnetic database construction and magnetic positioning, is proposed in this paper. For magnetic database construction, a multisource fused database is established using a precise and effective strategy in which the positions of reference points (RPs) and the diverse information of paths are obtained from the map and the magnetic field is calculated using data collected during driving. For magnetic positioning, we innovatively propose a coarse–fine combination method that improves the positioning accuracy within a short distance. In the coarse map matching stage, by detecting the vehicle’s motion and utilizing the topological relationships between paths, the search range is precisely narrowed. In the fine magnetic localization stage, an improved mean absolute difference (MAD) metric and a derivative metric are combined to form a joint matching criterion to determine the positioning result. The experimental results illustrate the importance of each module in the proposed method, which improves the precision of the database up to 80% and significantly shortens the initialization distance up to 50%.

Autonomous navigation and adaptive path planning in dynamic greenhouse environments utilizing improved LeGO‐LOAM and OpenPlanner algorithms

AbstractThe autonomous navigation of greenhouse robots depends on precise mapping, accurate localization information and a robust path planning strategy. However, the complex agricultural environment introduces significant challenges to robot perception and path planning. In this study, a hardware system designed exclusively for greenhouse agricultural environments is presented, employing multi‐sensor fusion to diminish the interference of complex environmental conditions. Furthermore, a robust autonomous navigation framework based on the improved lightweight and ground optimized lidar odometry and mapping (LeGO‐LOAM) and OpenPlanner has been proposed. In the perception phase, a relocalization module is integrated into the LeGO‐LOAM framework. Comprising two key steps—map matching and filtering optimization, it ensures a more precise pose relocalization. During the path planning process, ground structure and plant density are considered in our Enhanced OpenPlanner. Additionally, a hysteresis strategy is introduced to enhance the stability of system state transitions. The performance of the navigation system in this paper was evaluated in several complex greenhouse environments. The integration of the relocalization module significantly decreases the absolute pose error (APE) in the perception process, resulting in more accurate pose estimation and relocalization information. In our experiments, the APE was reduced by at least 24.42%. Moreover, our enhanced OpenPlanner exhibits the capability to plan safer trajectories and achieve more stable state transitions in the experiments. The results underscore the safety and robustness of our proposed approach, highlighting its promising application prospects in autonomous navigation for agricultural robots.

Retrieving Similar Trajectories from Cellular Data of Multiple Carriers at City Scale

Retrieving similar trajectories aims to search for the trajectories that are close to a query trajectory in spatio-temporal domain from a large trajectory dataset. This is critical for a variety of applications, like transportation planning and mobility analysis. Unlike previous studies that perform similar trajectory retrieval on fine-grained GPS data or single cellular carrier, we investigate the feasibility of finding similar trajectories from cellular data of multiple carriers, which provide more comprehensive coverage of population and space. To handle the issues of spatial bias of cellular data from multiple carriers, coarse spatial granularity, and irregular sparse temporal sampling, we develop a holistic system cellSim . Specifically, to avoid the issue of spatial bias, we first propose a novel map matching approach, which transforms the cell tower sequences from multiple carriers to routes on a unified road map. Then, to address the issue of temporal sparse sampling, we generate multiple routes with different confidences to increase the probability of finding truly similar trajectories. Finally, a new trajectory similarity measure is developed for similar trajectory search by calculating the similarities between the irregularly-sampled trajectories. Extensive experiments on a large-scale cellular dataset from two carriers and real-world 1,701 km query trajectories reveal that cellSim provides state-of-the-art performance for similar trajectory retrieval.

Map matching queries on realistic input graphs under the Fréchet distance

Map matching is a common preprocessing step for analysing vehicle trajectories. In the theory community, the most popular approach for map matching is to compute a path on the road network that is the most spatially similar to the trajectory, where spatial similarity is measured using the Fréchet distance. A shortcoming of existing map matching algorithms under the Fréchet distance is that every time a trajectory is matched, the entire road network needs to be reprocessed from scratch. An open problem is whether one can preprocess the road network into a data structure, so that map matching queries can be answered in sublinear time. In this paper, we investigate map matching queries under the Fréchet distance. We provide a negative result for geometric planar graphs. We show that, unless SETH fails, there is no data structure that can be constructed in polynomial time that answers map matching queries in O (( pq ) 1 − δ ) query time for any δ > 0, where p and q are the complexities of the geometric planar graph and the query trajectory, respectively. We provide a positive result for realistic input graphs, which we regard as the main result of this paper. We show that for c -packed graphs, one can construct a data structure of \(\tilde{O}(cp) \) size that can answer (1 + ε)-approximate map matching queries in \(\tilde{O}(c^4 q \log ^4 p) \) time, where \(\tilde{O}(\cdot) \) hides lower-order factors and dependence on ε.

Trajectory Data-Driven Network Representation for Traffic State Prediction using Deep Learning

In this study, we propose a trajectory data-driven network representation method, specifically leveraging directional statistics. This approach allows us to extract major intersections and define links from observed trajectories, thereby mitigating the reliance on existing network data and map matching. We apply Graph Convolutional Networks and Long-Short Term Memory models to the trajectory data-driven network representation, suggesting the potential for fast and accurate traffic state prediction. The results imply significant reduction in computational complexity while demonstrating promising prediction accuracy. Our proposed method offers a valuable approach for analyzing and modeling transportation networks using real-world trajectory data, providing insights into traffic patterns and facilitating the exploration of more efficient traffic management strategies.

GraphMM: Graph-Based Vehicular Map Matching by Leveraging Trajectory and Road Correlations

Map matching of sparse vehicle trajectories is a fundamental problem in location-based services, such as traffic flow analysis and vehicle routing. Existing literature mainly relies on sequence-to-sequence (Seq2Seq) models to capture the intra-trajectory correlation of an input trajectory and to sequentially predict the matched road segments. Due to the limited expressive capability of sequential models, these methods fall short of extracting inter-trajectory and trajectory-road correlations as well as correlation between road segments. We present <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GraphMM</b> , a graph-based approach that explicitly utilizes all aforementioned correlations. Our model exploits the graph nature of map matching and incorporates graph neural networks and conditional models to leverage both road and trajectory graph topology, while manages to align road segments and trajectories in latent space. We formally analyze the expressive power of our model in capturing various correlations and propose efficient algorithms for model training and inference. In particular, our optimization techniques dramatically reduce the computational complexity, making our model feasible on datasets with thousands of road segments. Extensive experiments show that our model significantly enhances prediction accuracy, while improving training and inference efficiency by up to an order of magnitude over both the industrial implementation of the hidden Markov model and state-of-the-art Seq2Seq-based methods.