GPS and Map matching based vehicle accident detection system

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

Map Matching Algorithm for Real-Time Data Processing of Non-route GPS Data in Seoul

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

In this research, a passenger-centric passenger information system is proposed, which uses crowd sourcing and mobile phones so that passengers are not only information consumers but are also providers of information to the system. Passengers can allow the system to track their location through their smart phones when they are traveling on public transportation; this will compensate for the lack of a vehicle tracking system in public transportation, particularly in nonurban areas. Map-matching (MM) algorithms integrate location data (i.e.,latitude and longitude) obtained from positioning sensors (in this case, mobile GPS) with a digital geographic information system (GIS) road map. Any map-matching algorithm first identifies the road link on which a vehicle is traveling and then determines the vehicle's location on that road segment. In the proposed information system, at a given point of time, a number of vehicle locations (latitude and longitude) are received from passengers traveling on a bus. In order to provide a precise vehicle location at a given point of time, a novel map-matching algorithm using fuzzy logic, which integrates multiple vehicle locations (obtained from passenger's smart phones) with a GIS road map, has been developed. The developed map-matching algorithm was tested using real-world data collected on four different bus routes in Aberdeenshire, Scotland, and also GPS data collected in and around Nottingham, United Kingdom. It was identified that the developed MM algorithm is efficient and capable of supporting the proposed passenger information system. (C) 2013 American Society of Civil Engineers.

Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS) data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. This paper introduces a novel map-matching algorithm based on a hidden Markov model (HMM) for GPS positioning and mobile phone positioning with a low sampling rate. The HMM is a statistical model well known for providing solutions to temporal recognition applications such as text and speech recognition. In this work, the hidden Markov chain model was built to establish a map-matching process, using the geometric data, the topologies matrix of road links in road network and refined quad-tree data structure. HMM-based map-matching exploits the Viterbi algorithm to find the optimized road link sequence. The sequence consists of hidden states in the HMM model. The HMM-based map-matching algorithm is validated on a vehicle trajectory using GPS and mobile phone data. The results show a significant improvement in mobile phone positioning and high and low sampling of GPS data.

Nowadays, traffic accidents are one of the main reasons of fatalities in the world, many lives can be saved by reducing the time between the occurrence of an accident and the arrival of first help at its location. In research literature, numerous accident detection and notification systems were proposed to solve this problem. Including built-in unit, black-box, and mobile phone-based solutions. Our research has been devoted to design a black-box system that integrates Global System for Mobile Communication (GSM) and Global Positioning System (GPS) technologies to locate and send a Minimum Set of Data (MSD) containing important information about the accident collected using different sensors such as the accident type, longitude and latitude, time and speed of the vehicle to the emergency services (Hospitals, civil protection, and police). This system is developed based on the Raspberry Pi 3 in order to provide both low cost and high system performances.

Locating devices on the road network is crucial for any location-based system. Accuracy of map matching algorithms may highly affect the accuracy of any location-based service. This paper includes an empirical review of five major map matching algorithms for locating a device on a digital road network. A standard dataset was used to simulate the working of map matching algorithms. After ascertaining the accuracy of map matching algorithms, it was tested on a real road network. Six different routes varying from 0.6 kilometers to 32 kilometers, covering a total distance of 82.2 kilometers were included in the experiment. Performance of map matching algorithms was evaluated on a total of 2094 road nodes with 1271070 Global Positioning System (GPS) points on the basis of matched, unmatched nodes with root mean square error. It was concluded that Hidden-Markov Model based map matching algorithms has reasonably good accuracy (96% using global data and 89% using Indian dataset) and execution time in comparison to geometric, topological, Kalman filter and Frechet distance based algorithms.

GPS (global position system) trajectories collected by urban cars depict the citizens’ daily trips and reflect the traffic situation in city areas. The process called map matching is to match the GPS point sequence to the corresponding road which is the fundamental step for further travel pattern mining and transport situation analysis. The existing research based on the incremental map matching applies only to GPS trajectories of high-sampling-rate (0 to 30 s). However most actually collected GPS trajectories are with a low-sampling-rate (more than 2 min) for saving the collection and transmission costs. In this paper, we proposed an incremental map matching algorithm based on weighted shortest path, called WSI-matching. By matching single GPS point to its candidate road and filling the missing path between two GPS points, it improves the overall matching accuracy with a relatively low time complexity compared to the traditional global matching algorithm. Experiment results show that our WSI-matching algorithm present obvious advantages over traditional incremental algorithms and global algorithms in terms of both matching accuracy and running time, and it adapts to either high-sampling-rate trajectories or low-sampling-rate trajectories.

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.

The global positioning system (GPS) has become the most extensively used positioning and navigation tool in the world. Applications of GPS abound in surveying, mapping, transportation, agriculture, military planning, GIS, and the geosciences. However, the positional and elevation accuracy of any given GPS location is prone to error, due to a number of factors. The applications of Global Positioning System (GPS) positioning is more and more popular, especially the intelligent navigation system which relies on GPS and Dead Reckoning technology is developing quickly for future huge market in China. In this paper a practical combined positioning model of GPS/DR/MM is put forward, which integrates GPS, Gyro, Vehicle Speed Sensor (VSS) and digital navigation maps to provide accurate and real-time position for intelligent navigation system. This model is designed for automotive navigation system making use of Kalman filter to improve position and map matching veracity by means of filtering raw GPS and DR signals, and then map-matching technology is used to provide map coordinates for map displaying. In practical examples, for illustrating the validity of the model, several experiments and their results of integrated GPS/DR positioning in intelligent navigation system will be shown for the conclusion that Kalman Filter based GPS/DR integrating position approach is necessary, feasible and efficient for intelligent navigation application. Certainly, this combined positioning model, similar to other model, can not resolve all situation issues. Finally, some suggestions are given for further improving integrated GPS/DR/MM application.

In this paper, a novel approach for improving vehicular positioning is presented. This method is based on the cooperation of the vehicles by communicating their measured information about their position. This method consists of two steps. In the first step, we introduce our cooperative map matching method. This map matching method uses the V2V communication in a vehicular ad hoc network (VANET) to exchange global positioning system (GPS) information between vehicles. Having a precise road map, vehicles can apply the road constraints of other vehicles in their own map matching process and acquire a significant improvement in their positioning. After that, we have proposed the concept of a dynamic base station DGPS (DDGPS), which is used by vehicles in the second step to generate and broadcast the GPS pseudorange corrections that can be used by newly arrived vehicles to improve their positioning. The DDGPS is a decentralized cooperative method that aims to improve the GPS positioning by estimating and compensating the common error in GPS pseudorange measurements. It can be seen as an extension of DGPS where the base stations are not necessarily static with an exact known position. In the DDGPS method, the pseudorange corrections are estimated based on the receiver's belief on its positioning and its uncertainty and then broadcasted to other GPS receivers. The performance of the proposed algorithm has been verified with simulations in several realistic scenarios.

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

Precise navigation is a vital need for many modern vehicular applications. The global positioning system (GPS) cannot provide continuous navigation information in urban areas. The widely used inertial navigation system (INS) can provide full vehicle state at high rates. However, the accuracy diverges quickly in low cost microelectromechanical systems (MEMS) based INS due to bias, drift, noise, and other errors. These errors can be corrected in a stationary state. But detecting stationary state is a challenging task. A novel stationary state detection technique from the variation of acceleration, heading, and pitch and roll of an attitude heading reference system (AHRS) built from the inertial measurement unit (IMU) sensors is proposed. Besides, the map matching (MM) algorithm detects the intersections where the vehicle is likely to stop. Combining these two results, the stationary state is detected with a smaller timing window of 3 s. A longer timing window of 5 s is used when the stationary state is detected only from the AHRS. The experimental results show that the stationary state is correctly identified and the position error is reduced to 90% and outperforms previously reported work. The proposed algorithm would help to reduce INS errors and enhance the performance of the navigation system.

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.

Prediction of accurate vehicle location is essential for various (Intelligent Transport System) ITS applications. Existing vehicle location systems are work on different position sensor and smart computing devices to execute map matching algorithm. There exist necessary needs for enhancing the performance of receivers in terms of position towards the error characteristics of (Global Position System) GPS signals. Simple map matching algorithm faces difficulties in testing on road trial. The proposed algorithm and approach overcome many of the drawbacks of existing methods and is capable of achieving higher accuracy, precise navigation in critical conditions for high carrier frequency signaling on road network.