Original Article
Abstract
References
Information
Purpose: The purpose of this study is to explain the pivotal role of the travel forecasting process in urban transportation planning. This study emphasizes the use of travel forecasting models to anticipate future traffic. Method: This study examines the methodology used in urban travel demand modeling within transportation planning, specifically focusing on the Urban Transportation Modeling System (UTMS). UTMS is designed to predict various aspects of urban transportation, including quantities, temporal patterns, origin-destination pairs, modal preferences, and optimal routes in metropolitan areas. By analyzing UTMS and its operational framework, this research aims to enhance an understanding of contemporary urban travel demand modeling practices and their implications for transportation planning and urban mobility management. Result: The result of this study provides a nuanced understanding of travel dynamics, emphasizing the influence of variables such as average income, household size, and vehicle ownership on travel patterns. Furthermore, the attraction model highlights specific areas of significance, elucidating the role of retail locations, non-retail areas, and other locales in shaping the observed dynamics of transportation. Conclusion: The study methodically addressed urban travel dynamics in a four-ward area, employing a comprehensive modeling approach involving trip generation, attraction, distribution, modal split, and assignment. The findings, such as the prevalence of motorbikes as the primary mode of transportation and the impact of adjusted traffic patterns on reduced travel times, offer valuable insights for urban planners and policymakers in optimizing transportation networks. These insights can inform strategic decisions to enhance efficiency and sustainability in urban mobility planning.
- Ahmed, B. (2012). "The traditional four steps transportation modeling using simplified transport network: A case study of Dhaka City, Bangladesh." International Journal of Advance Science,Engineering,Technology & Research, Vol. 1, No. 1, Article #03.
- Aminzadeh, B., Levinson, D. (2020). Transportation Impacts of Automated Vehicles. In Autonomous Vehicles and Future Mobility, Elsevier, Netherlands, pp. 101-119.
- Chakirov, A., Susilo, Y., Kottenhoff, K. (2020). "Intermodal connectivity and passenger travel behavior: A systematic review." Transport Reviews, Vol. 40, No. 2, pp. 235-262.
- Curtis, C. (2015). The Congestion Myth: Living with Traffic Jams. Reaktion Books, London, United Kingdom
- Hensher, D.A., Mulley, C., Ho, C. (2017). "What drives bus rapid transit system patronage? A comparative analysis of metropolitan cities with different levels of bus rapid transit system development." Transport Policy, Vol. 54, pp. 43-52.
- Mahmassani, H.S. (2018). "Smart mobility: Connecting everyone and everything." Transportation Research Part A: Policy and Practice, Vol. 115, pp. 4-5.
- Zhang, J., Gao, Z., Wang, D.Z.W., Chen, Z. (2017). "Traffic congestion management in smart cities: Challenges, methodologies, and opportunities." IEEE Communications Magazine, Vol. 55, No. 1, pp. 106-112.
- Publisher :The Korean Society of Disaster Information
- Publisher(Ko) :한국재난정보학회
- Journal Title :Journal of the Society of Disaster Information
- Journal Title(Ko) :한국재난정보학회논문집
- Volume : 20
- No :2
- Pages :257-269
- DOI :https://doi.org/10.15683/kosdi.2024.6.30.257


Journal of the Society of Disaster Information






