Transportation mode choice behavior with recommender systems: A case study on Beijing
Understanding and predicting mode choice behavior in urban areas is an ongoing challenge, with several factors identified in past studies, e.g. built-environment, household statistics, trip properties, and many models being developed, e.g., regression and nested logit models. Existing research studi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-09-01
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| Series: | Transportation Research Interdisciplinary Perspectives |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590198221001147 |
| _version_ | 1818576584240529408 |
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| author | Xiaoqian Sun Sebastian Wandelt |
| author_facet | Xiaoqian Sun Sebastian Wandelt |
| author_sort | Xiaoqian Sun |
| collection | DOAJ |
| description | Understanding and predicting mode choice behavior in urban areas is an ongoing challenge, with several factors identified in past studies, e.g. built-environment, household statistics, trip properties, and many models being developed, e.g., regression and nested logit models. Existing research studies are predominantly designed around stated preferences surveys on small subsets of a population. The massive use of smartphones and route recommendation systems, however, offers the possibility of interacting with users, opening the potential to better understand and influence mode choice behavior, compared to sole offline analysis.This study explores the ability to predict travelers’ mode choice behavior in Beijing based on a collection of 300,000 recommended transportation alternatives from Baidu. The unique context of Beijing, with its enormous congestion and excessive penetration of smart phones, provides a unique view on actual transportation mode choice at a large scale; and behavioral changes induced by mobile communication technologies. We use machine learning techniques to identify the effects of driving variables, including transportation mode accessibility, weather conditions, alternative trip costs, and time of day. We find robust evidence supporting the observation that users preferably select the first-ranked alternative provided by the route recommendation system. This observation should be exploited further by transportation policy-makers to guide users towards greener and environmental-friendly transport modes. |
| first_indexed | 2024-12-16T06:16:20Z |
| format | Article |
| id | doaj.art-b4240f940c284af693e1137965991b83 |
| institution | Directory Open Access Journal |
| issn | 2590-1982 |
| language | English |
| last_indexed | 2024-12-16T06:16:20Z |
| publishDate | 2021-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transportation Research Interdisciplinary Perspectives |
| spelling | doaj.art-b4240f940c284af693e1137965991b832022-12-21T22:41:16ZengElsevierTransportation Research Interdisciplinary Perspectives2590-19822021-09-0111100408Transportation mode choice behavior with recommender systems: A case study on BeijingXiaoqian Sun0Sebastian Wandelt1School of Electronic and Information Engineering, Beihang University, 100191 Beijing, China; National Engineering Laboratory for Integrated Transportation Big Data, 100191 Beijing, ChinaSchool of Electronic and Information Engineering, Beihang University, 100191 Beijing, China; Corresponding author.Understanding and predicting mode choice behavior in urban areas is an ongoing challenge, with several factors identified in past studies, e.g. built-environment, household statistics, trip properties, and many models being developed, e.g., regression and nested logit models. Existing research studies are predominantly designed around stated preferences surveys on small subsets of a population. The massive use of smartphones and route recommendation systems, however, offers the possibility of interacting with users, opening the potential to better understand and influence mode choice behavior, compared to sole offline analysis.This study explores the ability to predict travelers’ mode choice behavior in Beijing based on a collection of 300,000 recommended transportation alternatives from Baidu. The unique context of Beijing, with its enormous congestion and excessive penetration of smart phones, provides a unique view on actual transportation mode choice at a large scale; and behavioral changes induced by mobile communication technologies. We use machine learning techniques to identify the effects of driving variables, including transportation mode accessibility, weather conditions, alternative trip costs, and time of day. We find robust evidence supporting the observation that users preferably select the first-ranked alternative provided by the route recommendation system. This observation should be exploited further by transportation policy-makers to guide users towards greener and environmental-friendly transport modes.http://www.sciencedirect.com/science/article/pii/S2590198221001147Travel mode choice behaviorRecommendation systemsMachine learning |
| spellingShingle | Xiaoqian Sun Sebastian Wandelt Transportation mode choice behavior with recommender systems: A case study on Beijing Transportation Research Interdisciplinary Perspectives Travel mode choice behavior Recommendation systems Machine learning |
| title | Transportation mode choice behavior with recommender systems: A case study on Beijing |
| title_full | Transportation mode choice behavior with recommender systems: A case study on Beijing |
| title_fullStr | Transportation mode choice behavior with recommender systems: A case study on Beijing |
| title_full_unstemmed | Transportation mode choice behavior with recommender systems: A case study on Beijing |
| title_short | Transportation mode choice behavior with recommender systems: A case study on Beijing |
| title_sort | transportation mode choice behavior with recommender systems a case study on beijing |
| topic | Travel mode choice behavior Recommendation systems Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2590198221001147 |
| work_keys_str_mv | AT xiaoqiansun transportationmodechoicebehaviorwithrecommendersystemsacasestudyonbeijing AT sebastianwandelt transportationmodechoicebehaviorwithrecommendersystemsacasestudyonbeijing |