Support vector machine and back propagation neutral network approaches for trip mode prediction using mobile phone data

Zhou, Zhuping; Yang, Jiwei; Qi, Yong; Cai, Yifei

doi:10.1049/iet-its.2018.5203

Cited by 17 publications

(10 citation statements)

References 15 publications

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“…Given that many individuals are unable to access all bene ts of transportation services due to their limited mobility and the low-mobility population is large in China, some researchers started focusing on low-mobility individuals (LMIs, e.g., older adults, low-income individuals, and individuals with disabilities) in various elds, such as policy development [1], tra c data collection [2], and modeling mode choice [3].…”

Section: Introductionmentioning

confidence: 99%

Transit Route Network Design for Low-Mobility Individuals Using a Hybrid Metaheuristic Approach

Tao

Ren

Yang

2020

Journal of Advanced Transportation

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This paper follows the previous effort of authors and builds the model of transit route network design for low-mobility individuals, proposing an appropriate solution methodology. Firstly, a desired objective, whose priority is to meet transit demands of low-mobility individuals followed by those of general public, is presented to minimize the weighted sum of direct traveler, transfer, and unsatisfied demand costs. Then, a hybrid metaheuristic approach based on ant colony and genetic algorithms is formulated to solve the proposed model in accordance with current conditions (i.e., existing routes that may need to undergo configuration adjustments to different degrees). Finally, the case study of Wenling is presented to highlight the performance and benefits of the proposed model and solution methodology.

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Section: Introductionmentioning

confidence: 99%

Transit Route Network Design for Low-Mobility Individuals Using a Hybrid Metaheuristic Approach

Tao

Ren

Yang

2020

Journal of Advanced Transportation

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“…[38], [51], [52], [54] Investigating urban activity destinations and human travel patterns to monitor the concentration of people.…”

Section: High Accuracymentioning

confidence: 99%

Urban sensing based on mobile phone data: approaches, applications, and challenges

Ghahramani

Zhou

Wang

2020

IEEE/CAA J. Autom. Sinica

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Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications. The ability to accurately and extensively monitor and analyze these data is necessary. Much concern in mobile data analysis is related to human beings and their behaviours. Due to the potential value that lies behind these massive data, there have been different proposed approaches for understanding corresponding patterns. To that end, monitoring people's interactions, whether counting them at fixed locations or tracking them by generating origindestination matrices is crucial. The former can be used to determine the utilization of assets like roads and city attractions. The latter is valuable when planning transport infrastructure. Such insights allow a government to predict the adoption of new roads, new public transport routes, modification of existing infrastructure, and detection of congestion zones, resulting in more efficient designs and improvement. Smartphone data exploration can help research in various fields, e.g., urban planning, transportation, health care, and business marketing. It can also help organizations in decision making, policy implementation, monitoring and evaluation at all levels. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data. We classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.

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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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Support vector machine and back propagation neutral network approaches for trip mode prediction using mobile phone data

Cited by 17 publications

References 15 publications

Transit Route Network Design for Low-Mobility Individuals Using a Hybrid Metaheuristic Approach

Transit Route Network Design for Low-Mobility Individuals Using a Hybrid Metaheuristic Approach

Urban sensing based on mobile phone data: approaches, applications, and challenges

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

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