Incremental route inference from low-sampling GPS data: An opportunistic approach to online map matching

Luo, Linbo; Hou, Xiangting; Cai, Wentong; Guo, Bin

doi:10.1016/j.ins.2019.10.060

Cited by 30 publications

(9 citation statements)

References 31 publications

(62 reference statements)

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“…The Wi-Fi probe technology has a relatively concentrated detection range than GPS data [21] and fast detection speed. So short-time passenger route restoration and monitoring can be realized.…”

Section: Related Workmentioning

confidence: 99%

Spatio-temporal trajectory estimation based on incomplete Wi-Fi probe data in urban rail transit network

Jiang

Sun

et al. 2021

Knowledge-Based Systems

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This study presents a methodology for estimating passenger’s spatio-temporal trajectory with personalization and timeliness by using incomplete Wi-Fi probe data in urban rail transit network. Unlike the automatic fare collection data that only records passenger’s entries and exits, the Wi-Fi probe data can capture more detailed passenger movements, such as riding a train or waiting on a platform. However, the estimation of spatio-temporal trajectories remains as a challenging task because a few unfavorable situations could result into deficient data. To address this problem, we first describe the Wi-Fi probe data and summarize their common defects. Then, the n -gram method is developed to infer missing spatio-temporal location information. Next, an estimation algorithm is designed to generate feasible spatio-temporal trajectories for each individual passenger by integrating multiple data sources, i.e., urban rail transit network topology, Wi-Fi probe data, train schedules, etc. This proposed method is tested on both simulated data in blind experiments and real-world data from a complex urban rail transit network. The results of case study show that 93% of passengers’ unique physical routes can be estimated. Then, for 80% of passengers, the number of feasible spatio-temporal trajectories can be reduced to one or two. Potential applications of the trajectory estimation approach are also identified.

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Section: Related Workmentioning

confidence: 99%

Spatio-temporal trajectory estimation based on incomplete Wi-Fi probe data in urban rail transit network

Jiang

Sun

et al. 2021

Knowledge-Based Systems

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“…To ensure the extraction accuracy, the trajectory data must be pre-processed first, i.e., the trajectory correction. Existing correction methods (e.g., Hidden Markov Model-based [27], incremental route-based [28] and location sequence-based [29] methods, etc.) usually rely on existing map data and require multiple iterations, which is less efficient if long sequences of floating vehicle trajectory data are encountered.…”

Section: B Applications Of Floating Vehicle Trajectory Data In Remote Sensingmentioning

confidence: 99%

Road Extraction Using a Dual Attention Dilated-LinkNet Based on Satellite Images and Floating Vehicle Trajectory Data

Gao¹,

Wang²,

Wang³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Automatic extraction of road from multi-source remote sensing data has always been a challenging task. Factors such as shadow occlusion and multi-source data alignment errors prevent current deep learning-based road extraction methods from acquiring road features with high complementarity, redundancy, and crossover. Unlike previous works that capture contexts by multi-scale feature fusion, we propose a dual attention dilated-LinkNet (DAD-LinkNet) to adaptively integrate local road features with their global dependencies by joint using satellite image and floating vehicle trajectory data. Firstly, a joint leastsquares feature matching-based floating vehicle trajectory correction model is used to correct the floating vehicle trajectory; then a convolutional network model DAD-LinkNet based on a dual-attention mechanism is proposed, and road features are extracted from the channel domain and spatial domain of the target image in turn by constructing a dual-attention module in the dilated convolutional layer and adopting a cascade connection; a weighted hyperparameter loss function is used as the loss function of the model; finally, the road extraction is completed based on the proposed DAD-LinkNet model. Experiments on three datasets show that the proposed DAD-LinkNet model outperforms the state-of-the-art methods in terms of accuracy and connectivity.

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“…However, the HMM-based map-matching algorithms always have a delay between GPS observation points and path inference. Luo et al [30] proposed an online map-matching method based on chance matching, which gradually inferred driving path from GPS with low-sampling rate and low output delay. This algorithm is different from HMM.…”

Section: Advanced Map-matching Algorithmsmentioning

confidence: 99%

Deep learning enabled vehicle trajectory map‐matching method with advanced spatial–temporal analysis

Liu

Fang

Tong

et al. 2020

IET intell. transp. syst

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Global positioning system (GPS) trajectory map matching projects GPS coordinates to the road network. Most existing algorithms focus on the geometric and topological relationships of the road network, while did not make full use of the historical road network information and floating car data. In this study, the authors proposed a deep learning enabled vehicle trajectory map-matching method with advanced spatial-temporal analysis (DST-MM). The algorithm mainly focused on the following three aspects: (i) analyse the spatial relevancy from the prospective of geometric analysis, topology analysis and intersection analysis; (ii) to make full use of the historical and real-time data, a deep learning model was conducted to extract the road network and vehicle trajectory features and (iii) establish a speed prediction model and nest it in the temporal analysis structure. It narrows down the path search range through establishing the dynamic candidate graph. Experimental results show that the proposed DST-MM algorithm outperforms the existing algorithms in terms of matching accuracy for low-sampling frequencies GPS data, especially in the central urban area.

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Incremental route inference from low-sampling GPS data: An opportunistic approach to online map matching

Cited by 30 publications

References 31 publications

Spatio-temporal trajectory estimation based on incomplete Wi-Fi probe data in urban rail transit network

Spatio-temporal trajectory estimation based on incomplete Wi-Fi probe data in urban rail transit network

Road Extraction Using a Dual Attention Dilated-LinkNet Based on Satellite Images and Floating Vehicle Trajectory Data

Deep learning enabled vehicle trajectory map‐matching method with advanced spatial–temporal analysis

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