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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.12
- 수록면
- 585 - 603 (19page)
- DOI
- 10.7848/ksgpc.2023.41.6.585
이용수
초록· 키워드
오류제보하기
As urban areas have been undergoing accelerated development, there has also been increasing interest in solving problems in mobility. Experts have identified multimodal transportation as a key concept in addressing this problem by providing transportation options. In providing LBS (Location-based Services), spatial datasets, particularly network data, are essential in representing the navigable spaces for each mode of transportation. However, previous studies on spatial data modeling of multimodal transportation have omitted the spaces that exist between the spaces represented by such network data. Additionally, the integration of these datasets for providing such services is still faced with numerous problems, such as lack of specification, the variety in data formats, potential difficulties in data conversion, or computational burdens due to reliance on geometric properties. This paper proposes a method for integrating network datasets to represent the connectivity of various navigable spaces for supporting multimodal transportation based on topological relationships, described in a spatial data model expressed in UML (Unified Modeling Language). Moreover, using sample data, we demonstrate the model"s potential for representing seamless travel across transportation modes in a routing experiment. This paper presents a topological relationship-based method to integrate existing network data representing multimodal transportation despite the differences in data format or standard.
목차
Abstract
1. Introduction
2. Related Literature
3. Developing an Integrated Network Data Model for Multimodal Transportation
4. Experimental Implementation
5. Conclusions and Recommendations
References
참고문헌 (54)
참고문헌 신청
Aleta, A., Meloni, S., and Moreno, Y. (2017), A Multilayer perspective for the analysis of urban transportation systems, Scientific Reports, Vol. 7, No. 44359. pp. 1–9.https://doi.org/10.1038/srep44359
Anciaes, P.R. and Jones, P. (2018), Estimating preferences for different types of pedestrian crossing facilities, Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 52, pp. 222–237./https://doi.org/10.1016/j.trf.2017.11.025
Angelidou, M. (2017), The role of smart city characteristics in the plans of fifteen cities, Journal of Urban Technology, Vol. 24, No. 4, pp. 3–28.https://doi.org/10.1080/10630732.2017.1348880
BBBike (2022), BBBike Extract Service, Help BBBike extracts, Berlin https://extract.bbbike.org/extract.html (last date accessed: October 16, 2022).
Buehler, R. and Hamre, A. (2015), The multimodal majority? Driving, walking, cycling, and public transportation use among American adults, Transportation, Vol. 42, No. 6.pp. 1081–1101.https://doi.org/10.1007/s11116-014-9556-z
Anciaes, P.R. and Jones, P. (2018), Estimating preferences for different types of pedestrian crossing facilities, Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 52, pp. 222–237./https://doi.org/10.1016/j.trf.2017.11.025
Angelidou, M. (2017), The role of smart city characteristics in the plans of fifteen cities, Journal of Urban Technology, Vol. 24, No. 4, pp. 3–28.https://doi.org/10.1080/10630732.2017.1348880
BBBike (2022), BBBike Extract Service, Help BBBike extracts, Berlin https://extract.bbbike.org/extract.html (last date accessed: October 16, 2022).
Buehler, R. and Hamre, A. (2015), The multimodal majority? Driving, walking, cycling, and public transportation use among American adults, Transportation, Vol. 42, No. 6.pp. 1081–1101.https://doi.org/10.1007/s11116-014-9556-z
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