Analyzing travel behavior in Hanoi using Support Vector Machine
| Autor: | Hai-Bang Ly, Binh Thai Pham, Thi My Thanh Truong, Dongwoo Lee, Sybil Derrible |
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| Rok vydání: | 2023 |
| Předmět: |
FOS: Social and economic geography
Computer science business.industry Geography Planning and Development Transportation 90507 Transport Engineering Machine learning computer.software_genre 90599 Civil Engineering not elsewhere classified Support vector machine FOS: Psychology Travel behavior 120506 Transport Planning Artificial intelligence Travel mode 170203 Knowledge Representation and Machine Learning business computer FOS: Civil engineering Interview survey |
| DOI: | 10.25417/uic.21990434.v1 |
| Popis: | This study investigates travel decisions (i.e., travel mode and destination) in Hanoi (Vietnam) using Support Vector Machine (SVM). First, a travel interview survey was conducted and 311 responses were collected across Hanoi. Second, a SVM model was trained to predict travel decisions and compared with a multinomial logit (MNL) model (as a benchmark). Third, the most important variables that affect travel decisions were ranked and discussed. The results show that SVM achieves an accuracy of 76.1% (compared to 72.9% for MNL). Moreover, proposed parking charge, household income, trip mode, and trip cost are found to be the most important variables. In contrast, trip purpose, gender, and occupation are found to negatively affect the model. Overall, low travel cost and low motorcycle parking charge, especially for commuters and shoppers, make people less willing to switch to more sustainable modes such as public and active transport. |
| Databáze: | OpenAIRE |
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