| Author Name | Affiliation | Postcode | | Wen Zheng | School of Transportation, Southeast University, Nanjing 211189, China | 211189 | | Wenquan Li* | School of Transportation, Southeast University, Nanjing 211189, China | 211189 | | Qian Chen | School of Transportation, Southeast University, Nanjing 211189, China | 211189 | | Yan Zheng | School of Transportation, Southeast University, Nanjing 211189, China | 211189 | | Chenhao Zhang | School of Transportation, Southeast University, Nanjing 211189, China | 211189 |
|
| Abstract: |
| Determining trip purpose is an important link to explore travel rules. In this paper, we take automobile users in urban areas as the research object, combine unsupervised learning and supervised learning methods to analyze their travel characteristics, and focus on the classification and prediction of automobile users’ trip purposes. However, previous studies on trip purposes mainly focused on questionnaires and GPS data, which cannot well reflect the characteristics of automobile travel. In order to avoid the multi-day behavior variability and unobservable heterogeneity of individual characteristics ignored in traditional traffic questionnaires, traffic monitoring data from the Northern District of Qingdao are used, and the K-means clustering method is applied to estimate the trip purposes of automobile users. Then, Adaptive Boosting (AdaBoost) and Random Forest (RF) methods are used to classify and predict trip purposes. Finally, the result shows: (1) the purpose of automobile users can be mainly divided into four clusters, which include Commuting travel, Taxi travel, Flexible life demand travel in daytime and Evening entertainment and leisure shopping, respectively; (2) the Random Forest method performs significantly better than AdaBoost in trip purpose prediction for higher accuracy; (3) the average prediction accuracy of Random Forest under hyper-parameters optimization reaches 96.25%, which proves the feasibility and rationality of the above clustering results. |
| Key words: Trip purpose, Automobile users, Traffic monitoring data, K-means clustering, AdaBoost, Random Forest |
| DOI：10.11916/j.issn.1005-9113.2022099 |
| Clc Number:U491.1 + 22 |
| Fund: |
