Trajectory Compression under Network Constraints

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.

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.

Parallelization is the key method to speedup advanced driving assistance systems (ADAS) and autonomous vehicle applications especially using emerging multicore and manycore processors. Localization and map matching are among the fundamental parts of such applications. This paper presents a parallel implementation and performance analysis of particle filter based vehicle localization and map-matching algorithm both on a multicore processor using Open Multi-Processing (OpenMP) and on a graphics processing unit (GPU) using the Compute Unified Device Architecture (CUDA). In the proposed implementation, GPS and odometer data is fused with digital map information. Tests were performed on real data captured in vehicle environment comprising various speed and road conditions. Test results show that speedups up to 75 times can be achieved on parallel GPU implementation over sequential counterpart. As this speedup complies with real-time performance requirements, high computational cost of using map topology information with large number of particles in particle filter implementation is proven to be handled by emerging technologies. The effect of number of particles on error rate of localization and map matching is also investigated.

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

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.

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

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.

The process of map matching takes a sequence of possibly noisy GPS coordinates from a vehicle trace and estimates the actual road positions---a crucial first step needed by many GPS applications. There has been a plethora of methods for map matching published, but most of them are evaluated on low-noise datasets obtained from a planned route. And comparisons with other methods are very limited. Based on our previous unifying framework used to catalog different mathematical formulas in many published methods, we evaluate representative algorithms using the low-noise dataset from the GIS Cup 2012 and a high-noise dataset collected from Shanghai downtown. Our experiments reveal that global max-weight and global geometrical map matching methods are the most accurate, but each has its weaknesses. We therefore propose a new map matching algorithm that integrates Fréchet distance with global weight optimization, which is more accurate across all sampling intervals.

The computation speed and output latency of map matching are important considerations when processing location data, especially smartphone-generated noisy and sparse data, from a large number of users for real-time transportation applications. In this paper, we examine the factors affecting the efficiency of online map matching algorithms that are based on probabilistic sequence models such as Hidden Markov Models (HMM) and present several heuristic optimizations to improve their speed and latency. As shortest path computations account for most of the running time of probabilistic map matching algorithms, we propose a method for reducing the total number of such computations by pruning unlikely states in the probabilistic sequence model. Furthermore, we speed up the one-to-many shortest path computations by limiting the search space to an elliptical area that encompasses all the targeted destinations. We present a technique for reducing the latency of the Viterbi algorithm used to find the most likely state sequence in a HMM or a similar model. This technique enables the early output of partial state sequences based on an estimate of the probability of a state being part of the eventual most likely sequence. Experiments using real-world location data show that the heuristic optimizations significantly reduce the running time and output latency with negligible loss of accuracy.

In this paper, a map-matching (MM) algorithm which combines an estimated position with digital road data is proposed. The presented algorithm using a virtual track is appropriate for a MEMS-based pedestrian dead reckoning (PDR) system, which can be used in mobile devices. Most of the previous MM algorithms are for car navigation systems and GPS-based navigation system, so existing MM algorithms are not appropriate for the pure DR-based pedestrian navigation system. The biggest problem of previous MM algorithms is that they cannot determine the correct road segment (link) due to the DR characteristics. In DR-based navigation system, the current position is propagated from the previous estimated position. This means that the MM result can be placed on a wrong link when MM algorithm fails to decide the correct link at once. It is a critical problem. Previous algorithms never overcome this problem because they did not consider pure DR characteristics. The MM algorithm using the virtual track is proposed to overcome this problem with improved accuracy. Performance of the proposed MM algorithm was verified by experiments.

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.

For pt.I see ibid., vol.49, no.2, p.467-75 (2000). In this paper, applications of the map matching algorithm proposed in part I are presented. In particular, steady-state Kalman filters are proposed and applied for filtering the yaw rate and tachometer signals. In addition, experimental results, using a quartz yaw rate sensor and axle encoders aboard a freight train, are included to show the performance of the proposed map matching algorithm.

Map matching (MM), pins the drifting position data to the correct road link on which a vehicle is travelling, is a crucial step needed by many industrial or research ITS projects which rely on post-hoc analysis of trajectories. To address the unprecedented challenge of massive GPS data processing in urban transportation data center nowadays, this paper proposed an improved parallel topological map-matching algorithm that aims to achieve highest efficiency as well as guaranteed accuracy. The main contributions of this work include: I) a weighting scheme based on cost-effectiveness ratio to reduce candidate path set in low time cost, II) a novel leapfrog method to omit the redundant GPS points that are not needed in path determination, III) parallelized processing using Map Reduce paradigm. Experiment show that these improvements greatly reduced algorithm's running time when compare to the state of the art.

In this paper existing map matching algorithms are combined and modified such, that the resulting algorithm is suitable for the implementation on the graphics processing unit (GPU). The map matching algorithm implemented on GPU consists of a geometrical and topological processing step, which provides high accuracy with high efficiency at the same time. An important building block of the implementation is the parallelization of the R∗-tree search. An efficient implementation is achieved by high data parallelism and minimal divergence between execution blocks. The presented map matching algorithm performs better than available open source implementations.

Accuracy of independent GPS navigation systems is tied to the map-matching (MM) algorithm, which overcomes GPS error and traffic map error to map ve-hicle GPS tracks onto corresponding roads. This paper presents a map-matching process compatible with junction adjusting. An innovative idea is expressed in pa-per to improve the accuracy of intersection node at vector map by utilizing the large amounts of GPS trajectory data, which come from map-matching procedure. With the adaptively corrected digital map, the simulation result gave better map-matching effect, and showed the effectiveness of map-matching compatible with junction adjusting.