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The Future Mobility Research Lab
In future mobility lab we investigate the potential and impacts of innovations and New Mobility forms on transportation systems and urban environment with an explicit focus on large-scale complex systems, transport-environmental policies, future/automated mobility solutions, and equity. For this purpose, we develop and use state-of-the-art methodologies in behavioral models, simulation, data collection, and analytical tools.
Latest Publication
We explore the implications of automated mobility-on-demand (AMoD) services in dense, transit-oriented cities through a comprehensive simulation in the prototype city of Tel Aviv. Our high-fidelity study reveals that while AMoD initially contributes to increased congestion and vehicle travel, integration with public transit and measures to reduce car ownership effectively mitigate these impacts. The analysis emphasizes the importance of policies targeting decreased car ownership for more efficient energy consumption and reduced greenhouse gas emissions in urban mobility. Discover the nuanced effects of various AMoD implementation strategies on trip patterns and environmental sustainability.
The complexity of addressing disruptive trends and disaggregated management strategies in transportation has led to the increased adoption of Agent-Based and Activity-Based modeling. However, broad implementation is hindered by the computational demands of calibrating intricate behavioral parameters in Activity-Based models. This paper introduces a novel Bayesian Optimization approach, utilizing an improved Random Forest surrogate model, to automate the calibration process and overcome these challenges. The proposed method sets a benchmark by calibrating the largest set of parameters yet, employing a sequential model-based algorithm configuration theory. Tested in Tallinn, Estonia, with 477 behavioral parameters, the calibration process yields satisfactory results for major indicators, including an OD matrix average mismatch of 15.92 vehicles per day and a 4% error in the overall number of trips.
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