At the Future Mobility Lab, we investigate the potential and effects of innovations and new mobility forms on transportation systems and urban areas, focusing specifically on large-scale, complex systems, transport-environmental policies, automated mobility solutions, and equity. To achieve this, we develop and use state-of-the-art methodologies in behavioral modeling, simulation, data collection, and analytical
Latest Publication
We examined the energy implications of automated on-demand transportation, including vehicle propulsion, onboard sensing and computing, offboard data processing, and changes in travel behavior. We combined agent-based demand modeling with dynamic fleet management to simulate RoboTaxi and automated shuttle services under different demand levels and operating strategies in the Tel Aviv metropolitan area. Using data from automated vehicle manufacturers, we estimated the energy consumed by the supporting automation systems per kilometer traveled. Our results show that total energy demand depends not only on fleet size and travel demand, but also on technological configurations, service design, and user preferences. The findings reveal substantial hidden energy costs associated with automation, which may offset some of the benefits of electrification. They also show that prioritizing user convenience can limit ride-sharing and reduce potential energy savings.
We explored the complex interplay between urban activity patterns and traffic dynamics by introducing a novel, decoupled framework for integrating behavioral models with mesoscopic traffic assignments. Rather than relying on rigid, pre-integrated systems, our approach allows for a flexible "dialogue" between demand and supply, identifying a unique equilibrium where trip frequencies and travel times stabilize. By applying this methodology to a mid-sized city, we demonstrate that a balanced state between human behavior and infrastructure capacity can be reached through strategic perturbations, effectively bridging the gap between how people plan their days and how traffic actually flows. The results reveal a highly consistent distribution of urban movement, suggesting that this iterative approach can serve as a robust tool for planners seeking to simulate and synchronize the pulse of a city’s transportation network.