Map Matching for Urban High-Sampling-Frequency GPS Trajectories

Liu, Minshi; Ling, Zhenhao; Ge, Junlian; Long, Yi; Che, Weitao

doi:10.3390/ijgi9010031

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…Similar to previous studies, the observation probability model refers only to the current candidate point and the observation point [24]. In this paper, observation probability is determined by distance, bearing deviation and speed constraint rules between observation and candidate road segments.…”

Section: Observation Probabilitymentioning

confidence: 93%

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

Xiong

2021

Sustainability

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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.

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Section: Observation Probabilitymentioning

confidence: 93%

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

Xiong

2021

Sustainability

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“…Zhang et al [8] proposed a map matching system based on the evaluation of similar trajectories. Brakatsoulas et al [9], Kong and Yang [10], Hashemi and Karimi [11], and Liu et al [12] have proposed different improvements for map matching based on road filters and road trajectories. In this study, OpenStreetMap (OSM) was used as the reference for road data.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing Prediction Model of Travel Times of the Logistics Fleets of Large Convenience Store Chains Using Machine Learning

2023

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Convenience store chains are many people’s top choice for dining and leisure and have logistics procedures that involve each store receiving multiple deliveries because of the varying delivery periods and suitable temperatures for different goods. The estimated arrival time for each delivery has a huge impact on the route arrangement and convenience store preparation for dispatchers to schedule future deliveries. This study collected global positioning system travel data from a fleet of one of the top convenience store chains in Taiwan between April 2021 and March 2022 and proposed machine learning to establish a model to predict travel times. For unavailable data, we proposed the nonlinear regression equation to fill in the missing GPS data. Moreover, the study used the data between April 2022 and September 2022 with mean absolute percentage error to validate the prediction effects exceeding 97%. Therefore, the proposed model based on historical data and the machine learning algorithm in this study can help logistics fleets estimate accurate travel times for their scheduling of future delivery tasks and arranging routes.

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“…As noted above, there are many approaches to resolve position error correction [21,22,26,27,32], but there are few studies on efficiency improvements for map matching methods in complex road networks and for large truck fleet management. In this paper, efficiency improvement is of vital concern for practical large truck fleet management.…”

Section: Map Matching Algorithmsmentioning

confidence: 99%

Development of Multilayer-Based Map Matching to Enhance Performance in Large Truck Fleet Dispatching

Chou

Hoang

et al. 2021

IJGI

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Spatial information technology has been widely used for vehicles in general and for fleet management. Many studies have focused on improving vehicle positioning accuracy, although few studies have focused on efficiency improvements for managing large truck fleets in the context of the current complex network of roads. Therefore, this paper proposes a multilayer-based map matching algorithm with different spatial data structures to deal rapidly with large amounts of coordinate data. Using the dimension reduction technique, the geodesic coordinates can be transformed into plane coordinates. This study provides multiple layer grouping combinations to deal with complex road networks. We integrated these techniques and employed a puncture method to process the geometric computation with spatial data-mining approaches. We constructed a spatial division index and combined this with the puncture method, which improves the efficiency of the system and can enhance data retrieval efficiency for large truck fleet dispatching. This paper also used a multilayer-based map matching algorithm with raster data structures. Comparing the results revealed that the look-up table method offers the best outcome. The proposed multilayer-based map matching algorithm using the look-up table method is suited to obtaining competitive performance in identifying efficiency improvements for large truck fleet dispatching.

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Map Matching for Urban High-Sampling-Frequency GPS Trajectories

Cited by 12 publications

References 28 publications

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

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

Developing Prediction Model of Travel Times of the Logistics Fleets of Large Convenience Store Chains Using Machine Learning

Development of Multilayer-Based Map Matching to Enhance Performance in Large Truck Fleet Dispatching

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