A trajectory map matching algorithm via exploring the average directional features of continuous windows

2021 ◽  
pp. 1-16
Author(s):  
Xiaohan Wang ◽  
Pei Wang ◽  
Weilong Chen ◽  
Wangwu Hu ◽  
Long Yang
Keyword(s):  
Road Network ◽  
Transition Probability ◽  
Location Based Services ◽  
Original Position ◽  
Map Matching ◽  
Trajectory Data ◽  
Matching Accuracy ◽  
Data Set ◽  
Matching Algorithm ◽  
Low Efficiency

Many location-based services require a pre-processing step of map matching. Due to the error of the original position data and the complexity of the road network, the matching algorithm will have matching errors when the complex road network is implemented, which is therefore challenging. Aiming at the problems of low matching accuracy and low efficiency of existing algorithms at Y-shaped intersections and roundabouts, this paper proposes a space-time-based continuous window average direction feature trajectory map matching algorithm (STDA-matching). Specifically, the algorithm not only adaptively generates road network topology data, but also obtains more accurate road network relationships. Based on this, the transition probability is calculated by using the average direction feature of the continuous window of the track points to improve the matching accuracy of the algorithm. Secondly, the algorithm simplifies the trajectory by clustering the GPS trajectory data aggregation points to improve the matching efficiency of the algorithm. Finally, we use a real and effective data set to compare the algorithm with the two existing algorithms. Experimental results show that our algorithm is effective.

scholarly journals Map-Matching Using Hidden Markov Model and Path Choice Preferences under Sparse Trajectory

2021 ◽  
Vol 13 (22) ◽  
pp. 12820
Author(s):  
Zhengang Xiong ◽  
Bin Li ◽  
Dongmei Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Road Network ◽  
Hidden Markov ◽  
Transition Probability ◽  
Shortest Paths ◽  
Low Frequency ◽  
Map Matching ◽  
Trajectory Data ◽  
Matching Accuracy

In the field of map matching, algorithms using topological relationships of road networks along with other data are normally suitable for high frequency trajectory data. However, for low frequency trajectory data, the above methods may cause problems of low matching accuracy. In addition, most past studies only use information from the road network and trajectory, without considering the traveler’s path choice preferences. In order to address the above-mentioned issue, we propose a new map matching method that combines the widely used Hidden Markov Model (HMM) with the path choice preference of decision makers. When calculating transition probability in the HMM, in addition to shortest paths and road network topology relationships, the choice preferences of travelers are also taken into account. The proposed algorithm is tested using sparse and noisy trajectory data with four different sampling intervals, while compared the results with the two underlying algorithms. The results show that our algorithm can improve the matching accuracy, especially for higher frequency locating trajectory. Importantly, the method takes into account the route choice preferences while correcting deviating trajectory points to the corresponding road segments, making the assumptions more reasonable. The case-study is in the city of Beijing, China.

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

2017 ◽  
Vol 2645 (1) ◽  
pp. 67-75 ◽  
Author(s):  
Lei Zhu ◽  
Jacob R. Holden ◽  
Jeffrey D. Gonder
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Longest Common Subsequence ◽  
Gps Data ◽  
Map Matching ◽  
Trajectory Data ◽  
Data Set ◽  
Matching Algorithm ◽  
Gps Trajectory ◽  
Trajectory Segmentation

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.

scholarly journals Map Matching Algorithm: Trajectory and Sequential Map Analysis on Road Network

2018 ◽  
Vol 5 (16) ◽  
pp. 155999
Author(s):  
Kanta Sharma ◽  
Ramesh Poonia ◽  
Raghvendra Kumar ◽  
Surendra Sunda ◽  
Dac-Nhuong Le
Keyword(s):  
Road Network ◽  
Map Matching ◽  
Matching Algorithm ◽  
Map Analysis

scholarly journals HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction

2018 ◽  
Author(s):  
Dejiang Kong ◽  
Fei Wu
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Location Based Services ◽  
Location Prediction ◽  
Trajectory Data ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Long Time ◽  
Long Short Term Memory

The widely use of positioning technology has made mining the movements of people feasible and plenty of trajectory data have been accumulated. How to efficiently leverage these data for location prediction has become an increasingly popular research topic as it is fundamental to location-based services (LBS). The existing methods often focus either on long time (days or months) visit prediction (i.e., the recommendation of point of interest) or on real time location prediction (i.e., trajectory prediction). In this paper, we are interested in the location prediction problem in a weak real time condition and aim to predict users' movement in next minutes or hours. We propose a Spatial-Temporal Long-Short Term Memory (ST-LSTM) model which naturally combines spatial-temporal influence into LSTM to mitigate the problem of data sparsity. Further, we employ a hierarchical extension of the proposed ST-LSTM (HST-LSTM) in an encoder-decoder manner which models the contextual historic visit information in order to boost the prediction performance. The proposed HST-LSTM is evaluated on a real world trajectory data set and the experimental results demonstrate the effectiveness of the proposed model.

Map-matching approach based on link factor and hidden Markov model

2021 ◽  
pp. 1-17
Author(s):  
Haiyan Zhang ◽  
Yonglong Luo ◽  
Qingying Yu ◽  
Xiaoyao Zheng ◽  
Xuejing Li
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Road Network ◽  
Hidden Markov ◽  
Transition Probability ◽  
Map Matching ◽  
The Road ◽  
Positioning Errors ◽  
Road Section ◽  
Gps Devices

An accurate map matching is an essential but difficult step in mapping raw float car trajectories onto a digital road network. This task is challenging because of the unavoidable positioning errors of GPS devices and the complexity of the road network structure. Aiming to address these problems, in this study, we focus on three improvements over the existing hidden Markov model: (i) The direction feature between the current and historical points is used for calculating the observation probability; (ii) With regard to the reachable cost between the current road section and the destination, we overcome the shortcoming of feature rarefaction when calculating the transition probability with low sampling rates; (iii) The directional similarity shows a good performance in complex intersection environments. The experimental results verify that the proposed algorithm can reduce the error rate in intersection matching and is suitable for GPS devices with low sampling rates.

scholarly journals A LANDMARK MATCHING ALGORITHM FOR THE GEOSTATIONARY SATELLITE IMAGES BASED ON MULTI-LEVEL GRIDS

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 569-574 ◽  
Author(s):  
S. Y. Hou ◽  
Z. Y. Qin ◽  
L. Niu ◽  
W. G. Zhang ◽  
W. T. Ai
Keyword(s):  
Cloud Cover ◽  
Satellite Image ◽  
Geostationary Satellite ◽  
Layer By Layer ◽  
Matching Accuracy ◽  
Data Set ◽  
Matching Algorithm ◽  
Fourth Level ◽  
Multi Level ◽  
Landmark Matching

Abstract. The resolution of geostationary satellite image is not high and the image is covered with clouds. At present, when the extracted feature points are unstable, there are some problems, such as low matching accuracy or even matching failure. In this paper, a landmark matching algorithm is proposed to directly establish the multi-level grids for the image coastline and the coastline template. Through the similarity measure of the multi-level grids, the landmark matching is realized layer by layer. First of all, we've finished cloud detection, establishment of landmark data set, and extraction of image coastline. Then we design and implement the landmark matching algorithm based on multi-level grids. Finally, through analysis from different levels of landmarks and different proportion of cloud cover, the advantages and applicable conditions of this algorithm are given. The experimental results show that: 1) with the increase of cloud cover, the correct rate of landmark matching decreases, but the decrease is small. It shows that the matching algorithm in this paper is stable. Correct matching rate could always be stable at about 75 percent in the fourth level. 2) when the proportion of cloud cover is less than 20 percent, the higher the matching level, the higher the matching accuracy. When the cloud cover is more than 20 percent, the matching accuracy in the fourth level is the highest. This algorithm provides a stable method for the landmark matching of geostationary satellite image.

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