Map Matching for Fixed Sensor Data Based on Utility Theory

Urban traffic situation information is the basis of effective traffic guidance and traffic control. In order to promote communication and practical applications of traffic information, then can provide full, accurate, real time and sufficient traffic information service to public, government and correlative enterprises, in this paper, the authors proposed the dynamic traffic information collection and processing methods of intelligent transportation systems (ITS) common information platform (CIP) based on floating car technology. According to the characteristics of the ITS CIP data collection, they were required large scale, accurate and dynamic data collecting. A complete set of dynamic traffic information processing methods based on floating car technology were studied, including road data storage by two queues, error data elimination by taximeter, map matching by region dividing method and boundary rectangle, and traffic condition calculation by circle queues and A* algorithm. The application of the methods in the ITS CIP of Guangzhou City proved that the methods were quite effective and valuable to be popularized.

With the increasing availability of GPS-equipped mobile devices, location-based services have become an integral part of everyday life. Among one of the initial steps of positioning data management, map matching aims to reduce the uncertainty in a trajectory by matching the GPS points to the road network on a digital map. Most existing work has focused on estimating the likelihood of a candidate route based on the GPS observations, while neglecting to model the probability of a route choice from the perspective of drivers. In this work, we propose a novel feature-based map matching algorithm that estimates the cost of a candidate route based on both GPS observations and human factors. To take human factors into consideration is highly important, especially when dealing with low sampling rate data where most of the movement details are lost. Additionally, we simultaneously analyze a subsequence of coherent GPS points by utilizing a new segment-based probabilistic map matching strategy, which is less susceptible to the noisiness of the positioning data. We have evaluated both the offline and the online versions of our proposed approach on a public large-scale GPS dataset, which consists of 100 trajectories distributed all over the world. The experimental results show that our method is robust to sparse data with large sampling intervals (e.g., 60s ∼ 300s) and challenging track features (e.g., u-turns and loops). Measurements including map matching accuracy and system efficiency have been thoroughly evaluated and discussed. Compared with two state-of-the-art map matching algorithms, our method substantially reduces the route mismatch error by 6.4% ∼ 32.3% (either offline or online with the window size set to 360s), with a slight increase in terms of the processing time. The experimental results show that our proposed method obtains the state-of-the-art map matching results in all the different combinations of sampling rates and challenging features.

In Modern Era, many researchers are working to upgrade real time position tracking systems. Tracking systems fully depend on an appropriate road network and reliability of tracking vehicle data. The map matching concept provides intersection links between road segments. Map matching performance depends on GPS trajectory, but, it is unreliable due to weak GPS signalling issues such as atmospheric delay, multipath signalling, and ionospheric delay in the Indian mass. GPS Aided GEO Augmentation System (GAGAN) provides reliable signals services for correct positioning finding in the Indian mass. This work proposed map matching algorithm that computing closest point on the road network and also addresses the challenge of real time position mapping including position techniques.

GPS-based Vehicle Probe (GVP) has become an important floating car source in Intelligent Transportation System (ITS) for its efficiency and accuracy. We develop a real time GVP traffic estimation system in Taiwan by collecting the GPS data of commercial fleet and taxi. After filtering out some improper data, the retained GPS data are processed by an incremental map matching method which maps the GPS trajectories to real road map concerning of latency and accuracy. Then, road speed information can be produced by Speed Estimation module. To supply traffic information for public, road speed information are summarized and broadcast through a traffic information website. The experiments show that our system can reach good map matching accuracy and traffic information accuracy. Therefore, reasonable traffic information of freeways and urban areas can be sensed efficiently.

Map matching is a widely used technology for mapping tracks to road networks. Typically, tracks are recorded using publicly available Global Navigation Satellite Systems, and road networks are derived from the publicly available OpenStreetMap project. The challenge lies in resolving the discrepancies between the spatial location of the tracks and the underlying road network of the map. Map matching is a combination of defined models, algorithms, and metrics for resolving these differences that result from measurement and map errors. The goal is to find routes within the road network that best represent the given tracks. These matches allow further analysis since they are freed from the noise of the original track, they accurately overlap with the road network, and they are corrected for impossible detours and gaps that were present in the original track. Given the ongoing need for map matching in mobility research, in this work, we present a novel map matching method based on Markov decision processes with Reinforcement Learning algorithms. We introduce the new Candidate Adoption feature, which allows our model to dynamically resolve outliers and noise clusters. We also incorporate an improved Trajectory Simplification preprocessing algorithm for further improving our performance. In addition, we introduce a new map matching metric that evaluates direction changes in the routes, which effectively reduces detours and round trips in the results. We provide our map matching implementation as Open Source Software (OSS) and compare our new approach with multiple existing OSS solutions on several public data sets. Our novel method is more robust to noise and outliers than existing methods and it outperforms them in terms of accuracy and computational speed.

The rush for personalized user information, triggered by the daily generation of a staggering amount of geospatial data from multitude platforms, is leading to an erosion of users' location privacy. To ensure the privacy of moving objects on road networks, most existing works do not enforce a strict constrain that the anonymized or perturbed geospatial points should lie on the road segments. Thus, rendering the results unrealistic. In addition, humans armed with GPS-enabled devices have proven to be effective sensors which can be beneficial to traffic monitoring and other crowd sourcing services. However, they are discouraged to participate due to privacy concerns. Based on these drawbacks, we make a case for fusing privacy to map matching in road networks. In this paper, we propose a novel privacy preserving map matching technique that utilizes hidden Markov model, tangent distance and geometric properties of road segments. Our technique harnesses location privacy by first performing map matching. Then based on a defined set of a user's sensitive locations, we introduce a new cost function and employ it to determine a minimum cost alternate private route in our shortest path problem. We demonstrate using the Microsoft Seattle real dataset the effectiveness of our technique and show that it provides realistic privacy in road networks.

As the existing traffic information system based on the fixed traffic information center needs high cost for construct, and the other traffic information system based on inter vehicle communication pays more attention to safe driving information but pays less attention to traffic congestion information, especially for the traffic congestion information in the road network. This paper studies and puts forward a pattern for automatic traffic information service which realizes the function of automatic collection and dynamic transmission of traffic information in the road network on the basis of VANET, and with no need for fixed traffic information center. According to this pattern, we build the models for automatic collecting, integrating and processing, transmitting of traffic information, and automatic generating and dynamic updating of traffic information which covers the whole road network. Finally, in this paper, we also carry out traffic simulation for these models, analyze and evaluate rationality of these models and transmission effect.

Complex road networks, inaccurate GPS receiver output, low sampling rate and many other associated issues pose real challenges for map matching process. Genetic algorithms have recently been trialed for rendering GPS fix on digital maps. This manuscript introduces an improvised genetic algorithm named as post-processing genetic-inspired map matching (GiMM) algorithm. The proposed GiMM intends to mitigate inherent challenges associated with originally proposed genetic algorithm for map matching. The fitness function used by GiMM makes use of Bucket Dijkstra’s and fast dynamic time wrapping (FDTW) algorithms to render GPS information on digital maps. Bucket Dijkstra’s suggests the shortest path available in between two points, and FDTW is responsible for comparing two data series. Unlike traditional genetic algorithm for map matching, GiMM was evaluated on sparse as well as dense GPS data. The performance of the GiMM algorithm was evaluated in real time using OpenStreetMap data and GPS dataset mapped onto a road network of 82 km. GiMM uses population size, generation count, accuracy and execution time as input parameters. A maximum accuracy of 99.4% with root-mean-square error 0.06 was observed, whereas a minimum of 60% accuracy was observed at 0.47 root-mean-square error. Number of iterations and population size were concluded to be the most influential parameters for the performance of genetic algorithms for map matching.

With the rapid development of intelligent transportation systems (ITS) and location-based services (LBS), it is more important to match the original trajectory sequence generated by users/vehicles to the actual road network. Most of the existing online map matching algorithms are based on the idea of local processing, or require richer data input and more mathematical models to ensure matching accuracy. This paper presents a simple and effective map matching method, called self-adjusting online map matching (AOMM). The algorithm is developed based on hidden Markov model (HMM). Considering the topological and geometric properties of the road network, the emission probability and transition probability calculation formulas of HMM are defined. And three adjustment strategies are provided to deal with trajectory noise points, dense trajectory points, and offset trajectory points. The algorithm only needs latitude and longitude information of trajectory points, and can match point by point. Experimental results on open trajectory data show that the algorithm has high matching accuracy and low output delay, and can meet the requirements of general online map matching tasks.

GPS (Global Positioning System) has become an integral part of a vehicle system which provides speed, time, direction etc besides the navigation data. Speed is one of the primary attributes of vehicle accident. Many lives could have been saved if emergency service could receive accident information timely. This paper proposes to detect an accident from the map matched position of a vehicle by utilizing the GPS speed data and map matching algorithm and send accident location to an Alert Service Center. The GPS provides speed and position in every 0.1 second. The position data will be used in the map matching algorithm to locate the vehicle on the road. The present speed will be compared with the previous speed in every 0.1 second through a Microcontroller Unit. Whenever the speed will be falling below the safe calculated threshold speed, the system will generate an accident situation. It will check the vehicle location from map matching module and generate an accident situation if the vehicle is found outside the road network. This will reduce the false accident detection drastically. The map matched accident location is then sent by utilizing the GSM network. The proposed system will save many accident victims with timely rescue.

Accurately maintaining digital street maps is labor-intensive. To address this challenge, much work has studied automatically processing geospatial data sources such as GPS trajectories and satellite images to reduce the cost of maintaining digital maps. An end-to-end map update system would first process geospatial data sources to extract insights, and second leverage those insights to update and improve the map. However, prior work largely focuses on the first step of this pipeline: these map extraction methods infer road networks from scratch given geospatial data sources (in effect creating entirely new maps), but do not address the second step of leveraging this extracted information to update the existing digital map data. In this paper, we first explain why current map extraction techniques yield low accuracy when extended to update existing maps. We then propose a novel method that leverages the progression of satellite imagery over time to substantially improve accuracy. Our approach first compares satellite images captured at different times to identify portions of the physical road network that have visibly changed, and then updates the existing map accordingly. We show that our change-based approach reduces map update error rates four-fold.

This paper introduces the its-car navigation system prototype we developed under the context of Singapore. In the system, integrated GPS/DR is used for continuous vehicle positioning. A digital road network map database is built for the whole Singapore. The database is designed to support map matching, route determination, and route guidance functions. The prototype system demonstrates GPS positioning of the vehicle in real-time, tracking the vehicle onto a digital map with fix updates every one or two seconds. With the map matched vehicle location on the map, voice and/or text guidance is provide to the driver. The vehicle position can also be transferred to control center through communication data link for the purpose of fleet management.

This paper introduces the in-car navigation system prototype development and test results under the context of Singapore. In the system, integrated GPS/DR is used for continuous vehicle positioning. A digital road network map database is built for the whole Singapore. The database is designed to support map matching, route determination, and route guidance functions. The prototype system demonstrates GPS positioning of the vehicle in real-time, tracking the vehicle onto a digital map with fix updates every one or two seconds. With the map matched vehicle location on the map, voice and/or text guidance is provide to the driver. The vehicle position can also be transferred to control centre through communication data link for the purpose of fleet management.

Map matching is the process of finding a match for each GPS point in a vehicle's trajectory to roads on a digital map. Extensive research has been conducted during the last years yielding many algorithms based on different approaches. One of the challenges that face those algorithms is the interruption of GPS signals that occurs specially in dense urban environments. In these cases on-board sensors like odometers and accelerometers can be used temporarily for positioning, however due to the poor accuracy of these methods, the quality of map matching decreases significantly. In this paper, we propose a method that improves the quality of map matching when GPS signals are not available. This method is based on a particle filter using heading and velocity measurement. We evaluate this method through its integration with an existing topological map matching algorithm. We compare the performances when this algorithm is used alone and when associated with the particle filter.

In order to improve offline map matching accuracy of uncertain GPS trajectories, a map matching algorithm based on conditional random fields (CRF) and route preference mining is proposed. In this algorithm, road offset distance and the temporal-spatial relationship between the sampling points are used as features of GPS trajectory in a CRF model, which integrates the temporal-spatial context information flexibly. The driver route preference is also used to bolster the temporal-spatial context when a low GPS sampling rate impairs the resolving power of temporal-spatial context in CRF, allowing the map matching accuracy of uncertain GPS trajectories to get improved significantly. The experimental results show that our proposed algorithm is more accurate than existing methods, especially in the case of a low-sampling-rate.