Growing populations and environmental issues are a burden for urban transport systems. Current research fails to offer multimodal integrated solutions maximizing time, cost, emissions, and satisfaction. We introduce the first optimization model integrating carpooling with micro-mobility for multi-leg routing in dynamic urban conditions (peak, weather, accidents). In synthetically generated data calibrated with real-world trends, our framework performs up to 25% shorter travel times, 30% reduced peak-hour emissions, and sub-second computation for 40-node networks over single-mode baselines. The model’s scenario-aware flexibility and policy-controllable weights ( to ) offer planners a scalable solution for sustainable mobility. The paper’s primary contribution is its integrated optimization framework integrating carpooling, micro-mobility, and multi-leg routing in dynamic urban conditions, an absent component in prior single-mode or static models. Our scenario-based analysis demonstrates up to 30% travel time and emissions reduction over stand-alone mobility solutions.
A User-Centered Theoretical Model for Future Urban Transit Systems
2025
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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