Map Matching Algorithm Research Articles

Research on parallelized real-time map matching algorithm for massive GPS data

In construction of smart city, numerous vehicles’ trajectory data are produced by Global Positioning System (GPS) to track their real time location. When these GPS data are processed by map matching, results can be used to support a large number of ITS applications such as real time road condition calculation, inspection of traffic event and emergency treatment. However, as the fast explosive growth of monitored vehicle number, massive GPS data proposes overwhelming challenges for map matching. Consequently, traditional map matching algorithms can hardly satisfy high demands for matching speed and accuracy. Therefore, a real time map matching algorithm for numerous GPS data is proposed to guarantee high matching accuracy and matching efficiency. Meanwhile, it can meet demands of GPS data processing required by the monitor of numerous vehicles within the city. Main contributions of the method are: (1) A Kalman filter based correcting algorithm is proposed to improve the matching accuracy of the traditional topological algorithm on the complicated road sections such as intersections and parallel roads. (2) Based on the Spark streaming framework, the serial map-matching algorithm is converted into a parallelized map-matching algorithm, which significantly improves the processing efficiency of the map matching. (3) A gridding method being applicable to the parallelized algorithm was proposed by the paper. The GPS data in the same grid were allocated to the same computing unit to improve the efficiency of the parallelized computation. Experimental results show that the matching accuracy of the algorithm demonstrated by the paper is increased by 10%; the matching efficiency is 25% higher than same amount of stand-alone computers. A cluster of 15 computers that operates the proposed algorithm is capable for the real time map matching for GPS data produced by 800 thousand vehicles, which can effectively and extensively support the lastingly increased demand for processing numerous GPS data.

Accurate Real-time Map Matching for Challenging Environments

We present the SnapNet system, which provides accurate real-time map matching for cellular-based trajectory traces. Such traces are characterized by input locations that are far from the actual road segment, errors on the order of kilometers, back-and-forth transitions, and highly sparse input data. SnapNet applies a series of filters to handle the noisy locations and an interpolation stage to address the data sparseness. At the core of SnapNet is a novel incremental HMM algorithm that combines digital map hints in the estimation process and a number of heuristics to reduce the noise and provide real-time estimations. Evaluation of SnapNet using actual traces from different cities covering more than 400 km shows that it can achieve a precision and recall of more than 90% under noisy coarse-grained input location estimates. This maps to over 97% and 34% enhancement in precision and recall, respectively, when compared to the traditional HMM map-matching algorithms. Moreover, SnapNet has a latency of 0.58 ms per location estimate.

Tight integration of kinematic precise point positioning and digital map for land vehicle localisation

This paper proposes a method for tight integration of kinematic precise point positioning (PPP) and digital map. The proposed method starts with PPP to estimate the vehicle position. PPP uses Global Positioning System observables, and precise orbit and clock products and additional error modelling to estimate the navigation solution. The estimated navigation solution is used as the input to the fuzzy logic based map matching (MM) algorithm. The proposed MM improves the kinematic PPP by constraining the vehicle location on the road, which provides the map-matched position and the road link azimuth as the feedback to the Kalman filter of PPP. The results indicate that a tight integration of digital map and kinematic PPP outperforms a stand-alone PPP and improves the horizontal root-mean-square error (RMSE) by 56.5% and 60.7% for the open sky and the harsh environment, respectively. Moreover, the float ambiguities can converge faster using this method.

A novel algorithm of low sampling rate GPS trajectories on map-matching

Map-matching is the process of matching the GPS locus to the road network on the digital map. However, due to the most existing map-matching algorithms that are based on high sampling rate, when the sampling interval is increased, the correct rate of the algorithm will be greatly reduced. Based on this, this paper proposed a new algorithm of map-matching for low sampling rate GPS trajectories. The algorithm gave full consideration to the road network of the geometric structure and topological structure and the mutual influence between adjacent points (time, speed information) by calculating the probability of each trajectory point of candidate points to determine matching results. At the end of this paper, we use the data of Beijing UCAR Inc.’s car in a case study. This case demonstrates: For low sampling rate matching track points in the complex road, the algorithm has a good uptime, and an exact match was found.

An offline mobile application for automatic evacuation guiding in outdoor environments

When a large-scale disaster occurs, evacuees have to move to safe places speedily, under highly stressful situations. For this purpose, an automatic evacuation guiding scheme based on unconscious cooperation between evacuees and their mobile nodes has been proposed and its performance, e.g., average/maximum evacuation times, has been well studied through simulation experiments. In this paper, we focus on the mobile application, which plays the key role of the automatic evacuation guiding scheme. The mobile application should be designed to operate even under offline environments because it may be disconnected from the network due to highly damaged communication infrastructures. We first design such an offline mobile application with required functions, i.e., positioning, estimation of evacuees’ situations, process of road network data, and presentation of useful information. Since positioning data obtained by Global Positioning System (GPS) may include errors, we also propose a scheme to estimate the evacuees’ situations, e.g., current position on road network and encounter with blocked road segments, by applying map matching algorithms, which try to identify a correct position and a road segment of an evacuee from erroneous positioning data. We further implement the offline application on an actual mobile phone. Through experiments, we evaluate the fundamental characteristics of the application, i.e., GPS positioning accuracy, processing time, and continuous operating time. Furthermore, we show the effectiveness of the proposed scheme in terms of estimation accuracy of an actual evacuation route, and that of blocked road segments.

Map-Matching Poor-Quality GPS Data in Urban Environments: The pgMapMatch Package

Global Positioning System (GPS) data have become ubiquitous in many areas of transportation planning and academic research. The usefulness of GPS data often depends on the points being matched to the underlying street network – a process known as “map matching.” Formally, the map-matching problem is to identify the true path, consisting of a sequence of street edges (i.e. blocks, or segments between intersections), given a sequence of GPS coordinates. Several algorithms have been developed to map-match GPS data. However, there are three major shortcomings of existing approaches. First, some algorithms assume that the user takes the fastest route, and does not circle or deviate. This is an inappropriate assumption in contexts where drivers circle in search of a parking space or have a personal preference for a circuitous route. Second, published algorithms often leave the implementation to the user, and do not provide code or a software implementation. Third, when map-matching large datasets where the matches cannot be individually inspected, published algorithms do not provide a way to evaluate the likelihood that the match is correct. This paper presents a new map-matching algorithm that is designed for use with poor-quality, heterogeneous GPS traces in urban environments, where drivers may circle for parking and GPS quality may be affected by underground parking, tall buildings and similar obstacles. The paper is accompanied by open-source Python code that is designed to work with PostGIS spatial databases. In a test dataset that includes many poor-quality traces, our new algorithm accurately matched 87% of traces, compared to 67% in a widely available alternative.

Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data

With the development of smartphones and portable GPS devices, large-scale, high-resolution GPS data can be collected. Map matching is a critical step in studying vehicle driving activity and recognizing network traffic conditions from the data. A new trajectory segmentation map-matching algorithm is proposed to deal accurately and efficiently with large-scale, high-resolution GPS trajectory data. The new algorithm separated the GPS trajectory into segments. It found the shortest path for each segment in a scientific manner and ultimately generated a best-matched path for the entire trajectory. The similarity of a trajectory segment and its matched path is described by a similarity score system based on the longest common subsequence. The numerical experiment indicated that the proposed map-matching algorithm was very promising in relation to accuracy and computational efficiency. Large-scale data set applications verified that the proposed method is robust and capable of dealing with real-world, large-scale GPS data in a computationally efficient and accurate manner.

A Survey of Selected Indoor Positioning Methods for Smartphones

This paper provides an overview of the most significant existing methods for indoor positioning on a contemporary smartphone. The approaches include Wi-Fi and Bluetooth based positioning, magnetic field fingerprinting, map aided navigation using building floor plans, and aiding from self-contained sensors. Wi-Fi and Bluetooth based positioning methods considered in this survey are fingerprint approaches that determine a user's position using a database of radio signal strength measurements that were collected earlier at known locations. Magnetic field fingerprinting can be used in an information fusion algorithm to improve positioning. The map-matching algorithms include application of wall constraints, topological indoor maps, and building geometry for heading correction. Finally, methods for step counting, step length and direction estimation, orientation tracking, motion classification, transit mode detection, and floor change detection in multi-storey buildings are discussed.

데이터 누적을 이용한 반사도 지역 지도 생성과 반사도 지도 기반 정밀 차량 위치 추정

For the safe driving of autonomous vehicles, accurate position estimation is required. Generally, position error must be less than 1m because of lane keeping. However, GPS positioning error is more than 1m. Therefore, we must correct this error and a map matching algorithm is generally used. Especially, road marking intensity map have been used in many studies. In previous work, 3D LIDAR with many vertical layers was used to generate a local intensity map. Because it can be obtained sufficient longitudinal information for map matching. However, it is expensive and sufficient road marking information cannot be obtained in rush hour situations. In this paper, we propose a localization algorithm using an accumulated intensity local map. An accumulated intensity local map can be generated with sufficient longitudinal information using 3D LIDAR with a few vertical layers. Using this algorithm, we can also obtain sufficient intensity information in rush hour situations. Thus, it is possible to increase the reliability of the map matching and get accurate position estimation result. In the experimental result, the lateral RMS position error is about 0.12m and the longitudinal RMS error is about 0.19m.

Benefits of Mobile Phone Technology for Personal Environmental Monitoring.

BackgroundTracking individuals in environmental epidemiological studies using novel mobile phone technologies can provide valuable information on geolocation and physical activity, which will improve our understanding of environmental exposures.ObjectiveThe objective of this study was to assess the performance of one of the least expensive mobile phones on the market to track people's travel-activity pattern.MethodsAdults living and working in Barcelona (72/162 bicycle commuters) carried simultaneously a mobile phone and a Global Positioning System (GPS) tracker and filled in a travel-activity diary (TAD) for 1 week (N=162). The CalFit app for mobile phones was used to log participants’ geographical location and physical activity. The geographical location data were assigned to different microenvironments (home, work or school, in transit, others) with a newly developed spatiotemporal map-matching algorithm. The tracking performance of the mobile phones was compared with that of the GPS trackers using chi-square test and Kruskal-Wallis rank sum test. The minute agreement across all microenvironments between the TAD and the algorithm was compared using the Gwet agreement coefficient (AC1).ResultsThe mobile phone acquired locations for 905 (29.2%) more trips reported in travel diaries than the GPS tracker (P<.001) and had a median accuracy of 25 m. Subjects spent on average 57.9%, 19.9%, 9.0%, and 13.2% of time at home, work, in transit, and other places, respectively, according to the TAD and 57.5%, 18.8%, 11.6%, and 12.1%, respectively, according to the map-matching algorithm. The overall minute agreement between both methods was high (AC1 .811, 95% CI .810-.812).ConclusionsThe use of mobile phones running the CalFit app provides better information on which microenvironments people spend their time in than previous approaches based only on GPS trackers. The improvements of mobile phone technology in microenvironment determination are because the mobile phones are faster at identifying first locations and capable of getting location in challenging environments thanks to the combination of assisted-GPS technology and network positioning systems. Moreover, collecting location information from mobile phones, which are already carried by individuals, allows monitoring more people with a cheaper and less burdensome method than deploying GPS trackers.

Implementation of generic algorithm in map-matching model

This paper proposed a novelty map-matching method based on multi-criteria genetic algorithm. In addition, the new ideas are introduced, such as incorporation of dynamic time wrapping method and dynamic programming technique. Dynamic time wrapping is used to evaluate the geometric similarity between recorded trajectory and observed route, while dynamic programming is used to accelerate the calculations of fitness functions in genetic algorithm. Model is tested on real data collected from the street network, which yielded good results with regard to accuracy and running time. Furthermore, it is shown that proposed model is capable to process the GPS data, recorded on dense street network with negligible amount of errors, which is the most challenging task for majority of map-matching algorithms. Therefore, the main impact of this research is observed through the development of better and more precise global map-matching algorithm. This is really important for expert and intelligent transport systems, which are based on input data collected by floating car data systems. This type of system relies on high quality post-processing map-matching model, which should provide accurate results in order to ensure that intelligent transport system makes more precise estimations and predictions of traffic conditions.

Developing a weighted-base map matching algorithm with the focus on modeling parameters and weights optimization

Developing a weighted-base map matching algorithm with the focus on modeling parameters and weights optimization

Road Map Inference: A Segmentation and Grouping Framework

We propose a new segmentation and grouping framework for road map inference from GPS traces. We first present a progressive Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm with an orientation constraint to partition the whole point set of the traces into clusters that represent road segments. A new point cluster grouping algorithm, according to the topological relationship and spatial proximity of the point clusters to recover the road network, is then developed. After generating the point clusters, the robust Locally-Weighted Scatterplot Smooth (Lowess) method is used to extract their centerlines. We then propose to build the topological relationship of the centerlines by a Hidden Markov Model (HMM)-based map matching algorithm; and to assess whether the spatial proximity between point clusters by assuming the distances from the points to the centerline comply with a Gaussian distribution. Finally, the point clusters are grouped according to their topological relationship and spatial proximity to form strokes for recovering the road map. Experimental results show that our algorithm is robust to noise and varied sampling rates. The generated road maps show high geometric accuracy.

A weight-based map-matching algorithm for vehicle navigation in complex urban networks

ABSTRACTA map-matching algorithm is an integral part of every navigation system and reconciles raw and inaccurate positional data (usually from a global positioning system [GPS]) with digital road network data. Since both performance (speed) and accuracy are equally important in real-time map-matching, an accurate and efficient map-matching algorithm is presented in this article. The proposed algorithm has three steps: initialization, same-segment, and next-segment. Distance between the GPS point and road segments, difference between the heading of the GPS point and direction of road segments, and difference between the direction of consecutive GPS points and direction of road segments are used to identify the best segment among candidates near intersections. In contrast to constant weights applied in existing algorithms, the weight of each criterion in this algorithm is dynamic. The weights of criteria are calculated for each GPS point based on its: (a) positional accuracy, (b) speed, and (c) traveled distance from previous GPS point. The algorithm considers a confidence level on the assigned segment to each GPS point, which is calculated based on the density and complexity of roads around the GPS point. The evaluation results indicate 95.34% correct segment identification and 92.19% correct segment assignment. The most important feature of our algorithm is that the high correct segment identification percentage achieved in urban areas is through a simple and efficient weight-based method that does not depend on any additional data or positioning sensors other than digital road network and GPS.

Map‐matching algorithm applied to bicycle global positioning system traces in Bologna

A novel map-matching algorithm is proposed, implemented and applied to global positioning system (GPS) traces which have been recorded by cyclists in Bologna, a medium-sized city in the North of Italy. The algorithm has been developed to match geo-referenced traces to a sequence of edges of a given road network model. Map-matching for bike trips is particularly challenging as cyclists often use footpath or parks which are not necessarily represented by the road network model. The matching algorithm should smartly tolerate the lack of network information. The algorithm should also be fast and capable of processing thousands of GPS traces in a reasonable time. The proposed probability-based method, which also exploits information on various network attributes, allows a reliable and fast map matching, even in dense street networks and with interrupted GPS data streams. In fact, one serious issue is to find a reliability measure which allows to verify the matched routes, without the knowledge of the real routes, as the available cyclist traces are anonymous. In addition to the reliability check, a sensitivity analyses with respect to the most relevant parameters has been conducted. The advantages of the proposed map-matching algorithm are quantified through a direct comparison with a topology-based map-matching algorithm from literature.

Novel RFID-Based Pose Estimation Using Single Stationary Antenna

In this paper, we propose a novel radio-frequency identification (RFID)-based methodology that can estimate the position and orientation of an object. Unlike other RFID-based localization systems, the proposed method employs a single nonsteered stationary antenna and three passive tags mounted on each object. Our scheme is based on power map matching algorithm that is capable in estimating the location and orientation of objects. Unlike conventional power map matching schemes, which create the map from actual measurements, the proposed power map is automatically generated based on a simple mathematical model. The application under consideration is based on two-dimensional (2-D) open spatial environment where the area of each object is no less than a quarter of a square meter with position estimation errors stretching to tens of centimeters. This work provides an alternative and relatively cheap approach for remotely estimating the pose of an object. As a proof of concept, we justify our scheme analytically and provide numerical and experimental data to substantiate the feasibility of our methodology.

Definition of an Enhanced Map-Matching Algorithm for Urban Environments with Poor GNSS Signal Quality.

Vehicle positioning is a key factor for numerous information and assistance applications that are included in vehicles and for which satellite positioning is mainly used. However, this positioning process can result in errors and lead to measurement uncertainties. These errors come mainly from two sources: errors and simplifications of digital maps and errors in locating the vehicle. From that inaccurate data, the task of assigning the vehicle’s location to a link on the digital map at every instant is carried out by map-matching algorithms. These algorithms have been developed to fulfil that need and attempt to amend these errors to offer the user a suitable positioning. In this research; an algorithm is developed that attempts to solve the errors in positioning when the Global Navigation Satellite System (GNSS) signal reception is frequently lost. The algorithm has been tested with satisfactory results in a complex urban environment of narrow streets and tall buildings where errors and signal reception losses of the GPS receiver are frequent.

Context-aware GPS integrity monitoring for intelligent transport systems

The integrity of positioning systems has become an increasingly important requirement for location-based intelligent transport systems (ITS), for example electronic toll collection (ETC), public transport operations and traffic control services. In ITS, satellite navigation systems, such as global positioning system (GPS), are used to provide real-time vehicle positioning information including details of longitude, latitude, direction and speed. Map matching algorithms are used to integrate the positioning information into the digital road map. However, the navigation systems used in ITS cannot provide the high quality positioning information required by most services, due to the various types of errors made in the map matching process and experienced by GPS sensors such as signal outage, and errors due to atmospheric effects and receiver measurement errors, all of which are difficult to measure. An error in the positioning information or map matching process might lead to the inaccurate determination of a vehicle location. This could have legal or economic consequences for ITS applications such as traffic law enforcement systems (e.g., speed fining). Such applications require integrity when measuring the vehicle position and speed information and in the map matching process when locating the vehicle on the correct road segment to avoid errors when charging drivers. Consequently, the integrity algorithm for the navigation system should include a guarantee that the systems do not produce misleading or faulty information as this may lead to significant errors in the ITS services provided. In this paper, a high integrity GPS monitoring algorithm based on the concept of context-awareness that can be applied with real time ITS services to integrate changes in the integrity status of the navigation system was developed. Results suggest that the new integrity algorithm can support various types of location-based ITS services (e.g., route guidance).

Route Choice of the Shortest Travel Time Based on Floating Car Data

Finding a route with shortest travel time according to the traffic condition can help travelers to make better route choice decisions. In this paper, the shortest travel time based on FCD (floating car data) which is used to assess overall traffic conditions is proposed. To better fit FCD and road map, a new map matching algorithm which fully considers distance factor, direction factor, and accessibility factor is designed to map all GPS (Global Positioning System) points to roads. A mixed graph structure is constructed and a route analysis algorithm of shortest travel time which considers the dynamic edge weight is designed. By comparing with other map matching algorithms, the proposed method has a higher accuracy. The comparison results show that the shortest travel time path is longer than the shortest distance path, but it costs less traveling time. The implementation of the route choice based on the shortest travel time method can be used to guide people’s travel by selecting the space-time dependent optimal path.

Using Probe Vehicles for Pavement Monitoring: Experimental Results from Tests Performed on a Road Network

Recently, IFSTTAR has taken an interest in the use of probe vehicles for the road monitoring of some characteristics such as the pavement longitudinal profile. After studying the feasibility of the method on a metrological angle, a measurement chain and an automated data processing tool have been developed. The process begins with the data collection using low-cost sensors embedded in each probe vehicle and ends with the presentation to the road managers of the profile quality indicator matched on a map. Thereafter, this innovative pavement monitoring method has been tested with the deployment of a dozen of probe vehicles on a road network of about one thousand kilometers long. This article aims to present, in a first step, the different tools developed by IFSTTAR (data collection systems, map-matching algorithms, database format, etc.) and then, in a second step, to comment the results coming from the application of the pavement monitoring method on the road test network (level of accuracy, technological and scientific limits, opinion of the road manager, etc.).