{"title":"活动特征与个体特征对交通方式选择的影响分析——以匈牙利布达佩斯市为例","authors":"Wissam Qassim Al-Salih, D. Esztergár-Kiss","doi":"10.1145/3512576.3512671","DOIUrl":null,"url":null,"abstract":"Dealing with the present congestion and creating sustainable transport systems has been the greatest challenge of urban transport planning. As transportation activities affect environmental quality, knowledge, why travellers prefer specific modes can help a region do judicious transportation planning. The travellers' daily trip and mode choice process have mainly involved three types of choice: mode choice, time choice, and route choice. In this paper, we focus on studying the impact of the activity characteristics that include activity purpose and the characteristics of the individuals, such as gender, age and income, and average distance of the trip, on the transport mode choice for completing activities of travellers. The aim is to use statistical analysis to investigate the impact of these variables on transport mode choice applying the decision tree model, which provides a set of easy-to-interpret decision rules necessary to make appropriate decisions. The data processing in this study is based on actual observations of travel behaviour in Budapest. Our analysis's findings indicate that income, distance, and activity purpose are the most significant factors in the decisions related to mode choice. Also, this study provides promising insights for developing activity chain modelling.","PeriodicalId":278114,"journal":{"name":"Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Analysis of the Impact of Activity Characteristics and Individual Characteristics on the Transport Mode Choice: A case Study of Budapest city, Hungary\",\"authors\":\"Wissam Qassim Al-Salih, D. Esztergár-Kiss\",\"doi\":\"10.1145/3512576.3512671\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Dealing with the present congestion and creating sustainable transport systems has been the greatest challenge of urban transport planning. As transportation activities affect environmental quality, knowledge, why travellers prefer specific modes can help a region do judicious transportation planning. The travellers' daily trip and mode choice process have mainly involved three types of choice: mode choice, time choice, and route choice. In this paper, we focus on studying the impact of the activity characteristics that include activity purpose and the characteristics of the individuals, such as gender, age and income, and average distance of the trip, on the transport mode choice for completing activities of travellers. The aim is to use statistical analysis to investigate the impact of these variables on transport mode choice applying the decision tree model, which provides a set of easy-to-interpret decision rules necessary to make appropriate decisions. The data processing in this study is based on actual observations of travel behaviour in Budapest. Our analysis's findings indicate that income, distance, and activity purpose are the most significant factors in the decisions related to mode choice. Also, this study provides promising insights for developing activity chain modelling.\",\"PeriodicalId\":278114,\"journal\":{\"name\":\"Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3512576.3512671\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3512576.3512671","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Analysis of the Impact of Activity Characteristics and Individual Characteristics on the Transport Mode Choice: A case Study of Budapest city, Hungary
Dealing with the present congestion and creating sustainable transport systems has been the greatest challenge of urban transport planning. As transportation activities affect environmental quality, knowledge, why travellers prefer specific modes can help a region do judicious transportation planning. The travellers' daily trip and mode choice process have mainly involved three types of choice: mode choice, time choice, and route choice. In this paper, we focus on studying the impact of the activity characteristics that include activity purpose and the characteristics of the individuals, such as gender, age and income, and average distance of the trip, on the transport mode choice for completing activities of travellers. The aim is to use statistical analysis to investigate the impact of these variables on transport mode choice applying the decision tree model, which provides a set of easy-to-interpret decision rules necessary to make appropriate decisions. The data processing in this study is based on actual observations of travel behaviour in Budapest. Our analysis's findings indicate that income, distance, and activity purpose are the most significant factors in the decisions related to mode choice. Also, this study provides promising insights for developing activity chain modelling.