Accessibility and Energy Consumption Evaluation under Different Strategies of Mobility On-Demand Deployment

We present a novel framework for the analysis of the impact of smart mobility and new vehicle technologies on the future of urban mobility. To capture the variability in urban form and behaviors, we gather data from 331 cities and apply exploratory factor analysis to obtain 9 factors on which we cluster the cities. 13 initial typologies are discovered and further confirmed and described via a novel latent class choice model. From these typologies, we generate prototype cities
via hierarchical proportional fitting for population syntheses and assignment algorithms to initial36 ize parameters for a state-of-the-art activity-based model. Finally, we employ a set of strategies which we evaluate against each other for key performance measures. We consider shared and non38 shared smart mobility on-demand with or without mass transit. We also consider vehicle restriction strategies, specifically the ban of gasoline powered internal combustion engine vehicles. We use an integrated agent- based urban simulator, SimMobility, to simulate these strategies across the scenario sample. The resulting futures are then evaluated for activity-based accessibility, energy consumption and network performance measures. This work is novel in the academic literature by combining personal accessibility metrics to energy consumption when simulating urban mobility.