GPS‐data‐driven dynamic destination prediction for on‐demand one‐way carsharing system

Wang, Lei; Zhong, Yugao; Ma, Wanjing

doi:10.1049/iet-its.2018.5250

Cited by 15 publications

(9 citation statements)

References 26 publications

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“…Advances in navigation and location acquisition technologies such as global positioning system (GPS), global system for mobile communications (GSM) or wireless networks, together with the development of location‐based services, enable the smart devices to produce a massive amount of spatiotemporal data [1]. The use of this massive GPS data produced by location acquisition technologies is an important part of smart city and intelligent transportation system, and produced many opportunities for researchers in different application domains, such as traffic congestion estimation [2, 3], travel route planning [4], behaviour analysis [5], transportation monitoring [3, 6], point of interest recommendation [7], inference of taxi status [8], identifying travel trips and activities [9] or destination prediction [10, 11] etc. Transportation mode inference is the fundamental key research problem in the transportation domain, which aims to identify the sequence of transportation modes in a trip like a walk or bus from the trajectory data generated by the users through multiple sensors including GPS, GSM or accelerometer sensors etc.…”

Section: Introductionmentioning

confidence: 99%

Convolutional LSTM based transportation mode learning from raw GPS trajectories

Nawaz

Huang

Wang

et al. 2020

IET Intelligent Transport Systems

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Section: Introductionmentioning

confidence: 99%

Convolutional LSTM based transportation mode learning from raw GPS trajectories

Nawaz

Huang

Wang

et al. 2020

IET Intelligent Transport Systems

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“…Experiments show that conv-LSTM outperforms LSTM on capturing spatiotemporal correlations. Wang et al [31] proposed a Global Positioning System (GPS)-data-driven approach to solving car sharing system demand forecasting problems. Historical vehicle GPS data can match the user's current trajectory and infer its possible destination.…”

Section: Literature Reviewmentioning

confidence: 99%

Demand Management of Station-Based Car Sharing System Based on Deep Learning Forecasting

Zhong

et al. 2020

Journal of Advanced Transportation

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Metropolitan development has motivated car sharing into an attractive type of car leasing with the help of information technologies. In this paper, we propose a new approach based on deep learning techniques to assess the operation of a station-based car sharing system. First, we analyse the pick-up and drop-off operations of the station-based car sharing system, capturing the operational features of car sharing service and the behaviours of vehicle use from a temporal perspective. Then, we introduced an analytical system to detect the system operation concerning the spontaneous deviations derived from user demands from service provisions. We employed Long Short-Term Memory (LSTM) structure to forecast short-term future vehicle uses. An experimental case based on real-world data is reported to demonstrate the effectiveness of this approach. The results prove that the proposed structure generates high-quality predictions and the operation status derived from user demands.

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“…Characteristics of several methods can be seen in the table 9. [11], [15], [16], [17] [10], [11], [12], [13], [14], [15], [16] [15], [16], [17], [18], [19] Accuracy After the method was chosen, the rest of our research will continue with previous research, find something that is a contribution from research comparison of several studies with research on the validation of the enumeration process in a survey can be seen in table 10. Table 10 shows comparison of several study in decision-making with GPS data to analyze human behavior.…”

Section: The Data Used In Decision-making With Gps Datamentioning

confidence: 99%

Decision-Making Framework for Validation of Data Collection Process in a Survey with GPS Data

Faeni¹,

Hidayat²

2020

Proceedings of the Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 20

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This study uses Systematic Literature Review (SLR) to understand about research trends, methods, and data used in decision-making with GPS data. After reviewing 27 chosen journals and proceedings, this study concludes that there are several methods used to decide with GPS data such as decision tree, random forest, neural network, and support vector machine (SVM). Health, environment, Transportation, and agriculture are several fields of business that used GPS data to make a decision. The data analyzed consist of position, time, speed, track, and distance. Based on SLR result, we propose a method to validate the data collection process of a survey using three methods (SVM, decision tree and random forest) will be used to analyze the position, time, track and personal data of the surveyor.

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GPS‐data‐driven dynamic destination prediction for on‐demand one‐way carsharing system

Cited by 15 publications

References 26 publications

Convolutional LSTM based transportation mode learning from raw GPS trajectories

Convolutional LSTM based transportation mode learning from raw GPS trajectories

Demand Management of Station-Based Car Sharing System Based on Deep Learning Forecasting

Decision-Making Framework for Validation of Data Collection Process in a Survey with GPS Data

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