Smart Cities Center

This paper explores the addressable market for Urban Air Mobility (UAM) as a multi-modal alternative in a community. To justify public investment, UAM must serve urban mobility by carrying a significant portion of urban traffic. We develop a traffic demand analysis method to estimate the maximum number of people that can benefit from UAM, for a given use case, in a metropolitan region. We apply our method to about three hundred thousand cross-bay commute trips in the San Francisco Bay Area. We estimate the commuter demand shift to UAM under two criteria of flexibility to travel time...

Energy consumption in the transportation sector accounts for 28.8% of the total value among all the industry sectors in the United States, reaching 28.2 quadrillion btu in 2017. Having an accurate evaluation of the vehicle fuel and energy consumption values is a challenging task due to numerous implicit influential factors, such as the variety of powertrain configurations, time-varying traffic and congestion patterns, and emerging new technologies, such as regenerative braking. In this paper, we propose to present a data-driven computational framework to evaluate the energy impact on the...

Transportation systems are becoming increasingly complex with the evolution of emerging technologies, including deeper connectivity and automation, which will require more advanced control mechanisms for efficient operation (in terms of energy, mobility, and productivity). Stakeholders, including government agencies, industry, and local populations, all have an interest in efficient outcomes, yet there are few tools for developing a holistic understanding of urban dynamics. Simulating large-scale, high-fidelity transportation systems can help, but remains a challenging task, due to the...

Large-scale highway traffic forecasting approaches are critical for intelligent transportation systems. Recently, deep- learning-based traffic forecasting methods have emerged as promising approaches for a wide range of traffic forecasting tasks. These methods are specific to a given traffic network, however, and consequently they cannot be used for forecasting traffic on an unseen traffic network. Previous work has identified diffusion convolutional recurrent neural networks, (DCRNN), as a state-of- the-art method for highway traffic forecasting. It models the complex spatial and temporal...