Speaker
Description
In the era of depopulation and aging, public transportation demand is undergoing profound changes. Urban planning strategies, such as the compact city model, have been proposed to address these challenges by concentrating population and services into denser urban areas (OECD, 2012). While these strategies aim to enhance sustainability and livability, they also alter the spatial distribution of residents, leading to shifts in public transportation demand. These dynamic changes underscore the need for more efficient and intelligent tools to support transportation planning. To address this need, this study develops a digital simulation tool that integrates high-precision mobility data with extensive open spatial datasets, enabling planners to evaluate and predict the costs and impacts of public transportation strategies quickly and affordably.
The tool addresses transportation challenges under depopulation and aging trends through two scenarios. The first scenario evaluates public transportation demand in compact urban centers, where population density is increasing due to compact city policies. We hypothesize that increasing bus service frequency encourages sustainable travel behavior and improves public transportation efficiency. Using GTFS data accessed via API, the tool enables users to edit bus timetables to simulate additional services. To estimate passenger demand, the tool integrates a Pseudo People Flow model (Kashiyama et al., 2024), particularly the activity generation and transportation mode choice module. This approach provides actionable insights into the effectiveness of increasing bus frequency in urban centers.
The second scenario addresses transportation challenges in local areas experiencing population decline and aging. In such areas, traditional bus services may become inefficient, and flexible on-demand transportation solutions offer a practical alternative (Fielbaum, Tirachini and Alonso-Mora, 2024). This scenario assumes that certain bus services can be reduced and replaced with on-demand transport options to better meet passenger demand, particularly benefiting older adults with limited mobility. Using the synthetic human mobility dataset, the tool identifies affected passengers and reallocates demand to the proposed on-demand system. Simulations compare revenue and cost outcomes between the current bus system and on-demand services, while customizable parameters allow for refined calculations tailored to local conditions.
Implemented as a web application, the tool can be used nationwide across Japan. Preliminary user tests demonstrate its potential to support public transportation planning by providing actionable insights into the financial and operational impacts. This research represents an innovative application of digital technologies in spatial planning, addressing the dual challenges of depopulation and aging populations. By combining high-precision mobility data with customizable simulation tools, the study contributes to the development of efficient, sustainable, and inclusive urban mobility systems. Future work will focus on expanding the tool’s capabilities and incorporating additional scenarios to enhance its applicability to diverse urban and regional contexts.
References
Fielbaum, A., Tirachini, A. and Alonso-Mora, J. (2024) Improving public transportation via line-based integration of on-demand ridepooling. Transport Research Part A: Policy and Practice, 190, 104289.
Kashiyama, T., Pang, Y., Shibuya, Y., Yabe, T. and Sekimoto, Y. (2024) Nationwide synthetic human mobility dataset construction from limited travel surveys and open data. Computer‐Aided Civil and Infrastructure Engineering, 39(21), pp. 3337-3353
OECD (2012) Compact City Policies: A Comparative Assessment, OECD Green Growth Studies. Paris: OECD Publishing. Available at: https://doi.org/10.1787/9789264167865-en
| Keywords | Mobility digital twin; Public transportation planning; Urban simulation; Depopulation |
|---|---|
| Best Congress Paper Award | Yes |
