Road Network Matching Research Articles

Using Probe Counts to Provide High-Resolution Detector Data for a Microscopic Traffic Simulation

Microscopic traffic simulations have become increasingly important for research targeting connected vehicles. They are especially appreciated for enabling investigations targeting large areas, which would be practically impossible or too expensive in the real world. However, such large-scale simulation scenarios often lack validation with real-world measurements since these data are often not available. To overcome this issue, this work integrates probe counts from floating car data as reference counts to model a large-scale microscopic traffic scenario with high-resolution detector data. To integrate the frequent probe counts, a road network matching is required. Thus, a novel road network matching method based on a decision tree classifier is proposed. The classifier automatically adjusts its cosine similarity and Hausdorff distance-based similarity metrics to match the network’s requirements. The approach performs well with an F1-score of 95.6%. However, post-processing steps are required to produce a sufficiently consistent detector dataset for the subsequent traffic simulation. The finally modeled traffic shows a good agreement of 95.1%. with upscaled probe counts and no unrealistic traffic jams, teleports, or collisions in the simulation. We conclude that probe counts can lead to consistent traffic simulations and, especially with increasing and consistent penetration rates in the future, help to accurately model large-scale microscopic traffic simulations.

An iterative framework with active learning to match segments in road networks

ABSTRACT Road network matching that detects arc-to-arc relations is a crucial prerequisite for the update of road data. The increasing complexity of multi-source and multi-scale road network data challenges the existing methods on accuracy and efficiency. This paper focuses on the interactive-based probabilistic relaxation approach. It is difficult to obtain satisfactory results by using completely automatic matching algorithm in some complicated road networks such as multi-lane carriageways. We try to improve the matching accuracy by combining optimization matching model with manual interaction. The method uses the module of active learning to construct unlabeled sample pool from preliminary matching of probabilistic relaxation, and then selects the arcs with the highest uncertainty by query function. The selected road is then handed over to humans to determine its arc-to-arc relations in the other road network. Finally, the matching parameters are automatically adjusted according to the user’s feedback information, so as to realize the dynamic optimization of the model. Our interaction method is efficient as it only needs to specify few arc-to-arc mappings and others can be amended automatically. Our experimental results reveal that active learning can substantially improve the performance of standard probabilistic relaxation algorithms in road network matching.

Optimal road matching by relaxation to min-cost network flow

• Proposed an optimization model to achieve the global optimal road network matching; • Proposed a multivariate matching relation adaptive min-cost network flow method; • Constraints-based iterative relaxation is imposed to reduce matching ambiguities. Road matching is the prerequisite for integrating different data to obtain a complete, correct road network. The growth of multi-source data calls for the advancement of matching methods. There are many methods of road matching, ranging from traditional methods using threshold or weighted judgment to methods based on statistical and optimization models. This article provides an optimal matching method by relaxation to min-cost network flow. The matching problem is considered an optimization problem, i.e., finding a plan to match features in two datasets to minimize the total difference. The optimization-based matching method can overcome traditional method’s suboptimal nature. Referencing more road information than the other methods make our method superior in its flexibility and applicability to different matching relationships. Additionally, the iterative relaxation by angle, flow, and shortest path-based constraints is introduced to reduce ambiguous matching. We demonstrate the model in real-world experiments. Our method achieves a better result than the other four methods: it has a more stable performance for matching pairs of more complicated correspondence relations and easily confused situations. The average matching precision, recall, and f-score were 0.985 in two test sites.

Fast Aerial Image Geolocalization Using the Projective-Invariant Contour Feature

We address the problem of aerial image geolocalization over an area as large as a whole city through road network matching, which is modeled as a 2D point set registration problem under the 2D projective transformation and solved in a two-stage manner. In the first stage, all the potential transformations aligning the query road point set to the reference road point set are found by local point feature matching. A local geometric feature, called the Projective-Invariant Contour Feature (PICF), which consists of a road intersection and the closest points to it in each direction, is specifically designed. We prove that the proposed PICF is equivariant under the 2D projective transformation group. We then encode the PICF with a projective-invariant descriptor to enable the fast search of potential correspondences. The bad correspondences are then removed by a geometric consistency check with the graph-cut algorithm effectively. In the second stage, a flexible strategy is developed to recover the homography transformation with all the PICF correspondences with the Random Sample Consensus (RANSAC) method or to recover the transformation with only one correspondence and then refine it with the local-to-global Iterative Closest Point (ICP) algorithm when only a few correspondences exist. The strategy makes our method efficient to deal with both scenes where roads are sparse and scenes where roads are dense. The refined transformations are then verified with alignment accuracy to determine whether they are accepted as correct. Experimental results show that our method runs faster and greatly improves the recall compared with the state-of-the-art methods.

A Hierarchical Matching Method for Vectorial Road Networks Using Delaunay Triangulation

The primary objective of vectorial road network matching is to identify homonymous roads from two different data sources. Previous methods usually focus on matching road networks with the same coordinate system but rarely with different or unknown coordinate systems, which may lead to nontrivial and nonsystematic deviations (e.g., rotation angle) between homonymous objects. To fill this gap, this study proposes a novel hierarchical road network matching method based on Delaunay triangulation (DTRM). First, the entire urban road network is divided into three levels (L1, L2, L3) by using the principle of stroke. Then, the triangular meshes are constructed from L2, and the minimum matching unit (MMU) in the triangular mesh is used instead of the traditional “node-arc” unit to measure the similarity for the matching of L2. Lastly, a hierarchical matching solution integrating the probabilistic relaxation method and MMU similarity is yielded to identify the matching relationships of the three-level road network. Experiments conducted in Wuhan, China, and Auckland, New Zealand, show that the MMU similarity metrics can effectively calculate the similarity value with different rotation angles, and DTRM has higher precision than the benchmark probability-relaxation-matching method (PRM) and can correctly identify the most matching-relationships with an average accuracy of 89.63%. This study provides a matching framework for road networks with different or even unknown coordinate systems and contributes to the integration and updating of urban road networks.

Combined Matching Approach of Road Networks Under Different Scales Considering Constraints of Cartographic Generalization

Identifying corresponding objects from geospatial databases at different levels of detail is crucial, especially in multi-scale road network matching, which is the prerequisite of data conflation, updating and quality assessment. `Stroke' has a considerable effect on automatic road network generalization, and is essential in the road network matching process. In road network generalization, topological relationships may change, and some roads may be deleted. In this paper, we propose a combined stroke-based matching approach of road networks considering the constraints of cartographic generalization for road networks under different scales. In the entire stroke matching, we utilize the modified Hausdorff distance for geometric similarity. We consider the topological differences in the structural similarity calculation and propose a new weight calculation method. Partial stroke matching can further identify the corresponding roads with changes and updates in different scales. We also propose a method of roundabout detection and matching. The proposed approach could not only match road networks with a small scale difference, but also road networks with a large scale difference. And it can successfully identify the M:N, M:1, 1:1, 1/M:1/N, and 1/M:1 matching relationships. The effectiveness of the proposed approach is verified by experimental results.

Incremental Updating Information Extraction and Topology Conflict Detection Method for Updating Road Network

Abstract. In recent years, Located Based Service (LBS) has become one of the hotspots on the application of geographic information to the government and the public. Providing a good performance map to the users is an important part of the location service system. Compared with the traditional web spatial information service system, service system of map information also put forward higher requirements of real time and veracity. The problem of updating electronic map in real-time cannot be effectively solved, hindering the updating of multi-scale electronic map and restricting the development of location service market. As a very important branch for the updating data field of geographic information science, incremental update has become a hot issue in the current international GIS research field. The extraction of incremental information includes two key technologies, namely road network matching and road network selection. After incremental updating roads were inserted into the database, coordinating topological conflicts caused by these inserted roads was the mainly task. However, the accuracy and algorithm efficiency of each technology need further improvement to provide the users with the latest and the most accurate basic geographic data. As the "skeleton" element of a map, the road network rapidly changes, carrying out more urgent requires for the incremental road update. Aiming at key technology problems of the updated road network, the paper focused on the following three parts:(1) To address the problems that the traditional probabilistic relaxation method only adopted geometric constraints as one of road matching criterions and could not respond to M: N matching pattern, an improved probabilistic relaxation method was proposed from the combined views of local optimization and global one, integrating geometric indicators with topology ones to achieve an effect with local optimization, as well as identifying M: N matching pattern by inserting virtual nodes to achieve a globally optimal effect. Then the matching strategies and corresponding implement algorisms were designed for different matching patterns. The case test showed that the overall matching accuracy of each evaluation indictor reached over 90%, increasing by 6%–12%; the evaluation indicators on both spatial and attribute properties increased by 4%–6%; the proper buffer threshold could be defined as twice the average value of the closest distances from all nodes in the candidate matching dataset.(2) Aiming at the low accuracy and irrational structural selection caused by only using linear pattern or areal pattern, an improved method was proposed combining linear method as well as areal ones. The proposed method improves the Stroke generation algorithm of linear pattern using OLS model taking the overall information from the roads to be connected. Meanwhile, it partitioned road network by weighted Voronoi diagrams. The roads were selected under the process of calculating the importance of Stroke and its road density threshold. The test demonstrated that the improved method added the accuracy of Stroke generation results, and the results of road selection could maintain the overall characteristics of the road network as well as its spatial distribution information. Compared with method only using one pattern, the combined method had a better accuracy of road selection.(3) According to the characteristics of detection topology for an updated road network, this paper proposed a topology conflict detection algorithm considering the incremental update of multi-scale network. The algorithm designed K-level topological neighbourhood to identify incremental neighbourhood road segments, built a topological refinement model based on geometric metrics, proposed checking rules based on comprehensive operation operator, and detected the conflict topology by using improved topological distance. The experimental results showed that 1) the accuracy and recall of the proposed method were more than 90%; 2) considering conflict topology caused by the generalization, the accuracy increased by 29.2%; 3) the average path length of a road network could be used as the reference of the best K-value in the K order incremental neighbourhood method.

A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture

The road-network matching method is an effective tool for map integration, fusion, and update. Due to the complexity of road networks in the real world, matching methods often contain a series of complicated processes to identify homonymous roads and deal with their intricate relationship. However, traditional road-network matching algorithms, which are mainly central processing unit (CPU)-based approaches, may have performance bottleneck problems when facing big data. We developed a particle-swarm optimization (PSO)-based parallel road-network matching method on graphics-processing unit (GPU). Based on the characteristics of the two main stages (similarity computation and matching-relationship identification), data-partition and task-partition strategies were utilized, respectively, to fully use GPU threads. Experiments were conducted on datasets with 14 different scales. Results indicate that the parallel PSO-based matching algorithm (PSOM) could correctly identify most matching relationships with an average accuracy of 84.44%, which was at the same level as the accuracy of a benchmark—the probability-relaxation-matching (PRM) method. The PSOM approach significantly reduced the road-network matching time in dealing with large amounts of data in comparison with the PRM method. This paper provides a common parallel algorithm framework for road-network matching algorithms and contributes to integration and update of large-scale road-networks.

An approach to geometry-based dynamic location referencing

IntroductionAn important requirement for knowledge infrastructures in smart cities is the continuous updating of location-based information. Protocols for dynamic location referencing like e.g. OpenLR or AGORA-C tackle the problem of accurately matching locations between dissimilar digital maps. They are map-agnostic and aim at limiting the amount of descriptive data to reduce bandwidth. However, there are applications for which the weaker requirement of map-independence is completely adequate, and for some there are even no restrictions in bandwidth (e.g. in the EC-funded project ROSATTE, and in the DLR projects MobiLind and KeepMoving), and with relaxed constraints it is possible to learn from methods in similar areas like road network matching and map conflation, in order to achieve a more accurate solution. Following this path, this paper presents a map-independent approach developed in the ongoing DLR project I.MoVe, which can be combined with a bandwidth-efficient dynamic location referencing method like e.g. OpenLR to target applications with bandwidth restrictions.MethodsThe proposed new approach works line-oriented and is guided by a measure of geometric dissimilarity. It is a top-down approach, recursively splitting up the source route into parts, thereby following a divide-and-conquer strategy to reduce the problem until it can be solved trivially.ResultsIt is currently capable of mapping closed line locations (i.e. circular routes, representing either the boundaries of areas or the tours of e.g. delivery trucks) from a TeleAtlas map to a NAVTEQ map on-the-fly with a success rate of 97.5% (OpenLR: only 82.5%), and also capable of mapping short line locations (i.e. linear routes) on-the-fly between the same maps, with a success rate of 99.7% (OpenLR: 91.9%ConclusionIn conclusion, the new approach to match linear or circular routes between two dissimilar maps is highly accurate and map-independent, but access to both involved maps is required. The approach can also be combined with a bandwidth-efficient dynamic location referencing method like e.g. OpenLR to obtain a compact format before the descriptive data is transmitted.

Automatic and Accurate Conflation of Different Road-Network Vector Data towards Multi-Modal Navigation

With the rapid improvement of geospatial data acquisition and processing techniques, a variety of geospatial databases from public or private organizations have become available. Quite often, one dataset may be superior to other datasets in one, but not all aspects. In Germany, for instance, there were three major road network vector data, viz. Tele Atlas (which is now “TOMTOM”), NAVTEQ (which is now “here”), and ATKIS. However, none of them was qualified for the purpose of multi-modal navigation (e.g., driving + walking): Tele Atlas and NAVTEQ consist of comprehensive routing-relevant information, but many pedestrian ways are missing; ATKIS covers more pedestrian areas but the road objects are not fully attributed. To satisfy the requirements of multi-modal navigation, an automatic approach has been proposed to conflate different road networks together, which involves five routines: (a) road-network matching between datasets; (b) identification of the pedestrian ways; (c) geometric transformation to eliminate geometric inconsistency; (d) topologic remodeling of the conflated road network; and (e) error checking and correction. The proposed approach demonstrates high performance in a number of large test areas and therefore has been successfully utilized for the real-world data production in the whole region of Germany. As a result, the conflated road network allows the multi-modal navigation of “driving + walking”.

Road Network Matching Method Based on Particle Swarm Optimization Algorithm

Combined the global optimization ability of particle swarm algorithm and memory capacity of tabu algorithm, this paper proposed an automatic vector road network matching method based on the combination of particle swarm opti- mization and tabu strategy. Firstly, the similarity between node entities is evaluated by means of geometric and topologi- cal characteristics. Then, the basic principle of global optimization of particle swarm optimization is introduced and road matching model based on particle swarm optimization algorithm is designed. Meanwhile, the tabu search algorithm is joined, by using the ability of tabu strategy which expanded the search of the neighborhood. The algorithm fully reflects the climbing feature of tabu strategy, in order to find the global optimal solution of the matching relationship of road network entities. Three different forms of road network data of Wuhan are selected to test our method, the result indicates that the matching method based on the combination of particle swarm optimization and tabu strategy is effective and fea- sible, which can provide a new idea to solve the matching problem.

Matching Road Network Combining Hierarchical Strokes and Probabilistic Relaxation Method

Aiming at the complex multisource road network matching modes, this paper proposed a road network match- ing method based on hierarchical stable strokes. The progress of the matching method adopts recursive method and gives consideration to both global consistency and local similarity of homonymous road features. One layer of hierarchical strokes generating from road spatial structure are selected at a match and the matched parts are used as a stable reference for the next layer. The detailed matching is implemented by an iterative probability relaxation method. In the course of coarse matching three similarity indicators, including the geometry shape similarity of strokes, the relative position of nodes and strokes and the topological structure similarity of nodes, are introduced into the initial matching probability calculation. After that, an iterative calculation is carried out for updating matching probability. Finally, a self-designed se- lecting principle is applied to trace the exact matching results. Matching road centerline from different producers of the same geographical area shows that our method finds 1:1, 1:N and M:N matching modes and gets a satisfactory matching result, which means hierarchical stable reference and the stroke structure can provide an effective way to identify different matching relations and avoid the non-rigid deviation of the homonymous features.

COMPARISON OF POINT MATCHING TECHNIQUES FOR ROAD NETWORK MATCHING

Abstract. Map conflation investigates the unique identification of geographical entities across different maps depicting the same geographic region. It involves a matching process which aims to find commonalities between geographic features. A specific subdomain of conflation called Road Network Matching establishes correspondences between road networks of different maps on multiple layers of abstraction, ranging from elementary point locations to high-level structures such as road segments or even subgraphs derived from the induced graph of a road network. The process of identifying points located on different maps by means of geometrical, topological and semantical information is called point matching. This paper provides an overview of various techniques for point matching, which is a fundamental requirement for subsequent matching steps focusing on complex high-level entities in geospatial networks. Common point matching approaches as well as certain combinations of these are described, classified and evaluated. Furthermore, a novel similarity metric called the Exact Angular Index is introduced, which considers both topological and geometrical aspects. The results offer a basis for further research on a bottom-up matching process for complex map features, which must rely upon findings derived from suitable point matching algorithms. In the context of Road Network Matching, reliable point matches provide an immediate starting point for finding matches between line segments describing the geometry and topology of road networks, which may in turn be used for performing a structural high-level matching on the network level.

Stroke Oriented Road Network Macrostructure Matching Algorithm

Aimed at the complex geometry calculation and uncertainty in spatial data matching, a new algorithm termed “stroke oriented macrostructure matching algorithm” was put forward in this paper. Taking road network as example, all strokes were picked up at first. Then all strokes were divided into types based on the length criteria. Finally, strokes of different types were matched respectively via a macrostructure combined matching algorithm so as to establish the matching relationships. Examples illustrate that this algorithm has more advantages in road network matching. For example, it adopts macro matching architecture, and its matching result is more accuracy etc.

A road-network matching approach guided by ‘structure’

With the increasing availability of diverse geospatial databases over more than two decades, the road-network matching has become an issue of growing interest. To a certain extent, a road network can be regarded as a unit characterized by various re-occurring structures, such as dual carriageways (parallel lines), roundabouts, narrow passages, navigation stubbles and slip roads around cloverleaf junctions. As the various road structures take on different geometric or topologic characteristics, it is hardly possible to efficiently match all of them using the same criteria or methods. To circumvent this problem, a new matching approach guided by ‘structure’ is proposed in this article, which is composed of three consecutive processes: category recognition, process modelling and process execution. The structure-guided matching approach is able to classify the road network into several structure categories and then assign to each matching category a specific matching strategy with proper parameters or criteria, which allows a context-related analysis and thus considerably improves the matching performance in terms of matching rate and accuracy.