Smart Cities Center

Smart Cities and Control [Technical Activities]

Raja Sengupta
Amin, Saurabh
Annaswamy, Anuradha
Scott Moura
Bulusu, Vishwanath
2015

Presents an update on IEEE Control Systems Society Technical Activities Board activities.

A Traffic Demand Analysis Method for Urban Air Mobility

Bulusu, Vishwanath
Onat, Emin Burak
Raja Sengupta
Yedavalli, Pavan
Jane Macfarlane
2021

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...

Temporal Sampling Constraints for GeoSpatial Path Reconstruction in a Transportation Network

Jane Macfarlane
Xu, Bo
2017

In this paper, we address the problem of recovering traveled geospatial paths on a transportation network from time sampled location traces. Determining the proper sampling rate for path reconstruction has not traditionally been addressed ahead of the collection process. Instead various uncertainty models have been created and tuned to estimate possible geospatial paths from an existing set of location measurements. This paper suggests that the geospatial road density sets a fundamental constraint on the sampling frequency. The result shows that a sufficient sampling rate is determined by...

Data-Driven Energy Use Estimation in Large Scale Transportation Networks

Wang, Bin
Chan, Cy
Somasi, Divya
Jane Macfarlane
Rask, Eric
2019

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...

Method and Apparatus for Providing Semantic-Free Traffic Prediction

Pietrobon, Davide
Lewis, Andrew
Jane Macfarlane
2017

An approach is provided for semantic-free traffic prediction. The approach involves dividing a travel-speed data stream into a plurality of travel-speed patterns. The travel-speed data stream represents vehicle travel speeds occurring in a road network. The approach also involves representing each of the plurality of travel-speed patterns by a respective token. The respective token is selected from a dictionary of tokens representing a plurality of travel-speed templates determined from historical travel-speed data. The approach further involves matching a sequence of the respective...

Mobiliti: Scalable Transportation Simulation Using High-Performance Parallel Computing

Chan, Cy
Wang, Bin
Bachan, John
Jane Macfarlane
2018

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...

Method and Apparatus for Next Token Prediction Based on Previously Observed Tokens

Pietrobon, Davide
Lewis, Andrew
Jane Macfarlane
Berry, Robert
2018

An approach is provided for next token prediction based on previously observed tokens. The approach involves receiving an observed time series of tokens, wherein each of the tokens represents an observed data pattern. The approach also involves adding a most recent token from the observed time series of tokens into a variable token set. The approach further involves processing a historical token set to determine a historical token sequence comprising the variable token set followed by a next token. The approach further involves recursively adding a next most recent token from the...

When Apps Rule the Road: The Proliferation of Navigation Apps is Causing Traffic Chaos. It's time to Restore Order

Jane Macfarlane
2019

Miguel Street is a winding, narrow route through the Glen Park neighborhood of San Francisco. Until a few years ago, only those living along the road traveled it, and they understood its challenges well. Now it's packed with cars that use it as a shortcut from congested Mission Street to heavily traveled Market Street. Residents must struggle to get to their homes, and accidents are a daily occurrence. The problem began when smartphone apps like Waze, Apple Maps, and Google Maps came into widespread use, offering drivers real-time routing around traffic tie-ups. An estimated 1 billion...

Quasi-Dynamic Traffic Assignment using High Performance Computing

Chan, Cy
Kuncheria, Anu
Zhao, Bingyu
Cabannes, Theophile
Keimer, Alexander
Wang, Bin
Alexandre Bayen
Jane Macfarlane
2021

Traffic assignment methods are some of the key approaches used to model flow patterns that arise in transportation networks. Since static traffic assignment does not have a notion of time, it is not designed to represent temporal dynamics that arise as vehicles flow through the network and demand varies through the day. Dynamic traffic assignment methods attempt to resolve these issues, but require significant computational resources if modeling urban-scale regions (on the order of millions of links and vehicles) and often take days of compute time to complete. The focus of this work is...

Air Space Maps

Jane Macfarlane
Adachi, Jeffrey
Dannenbring, Aaron
2021

Drone space is defined according to a building model and a buffer space. At least one three-dimensional geometry is identified from the building model. The buffer space is calculated from the three-dimensional geometry. Coordinates for a drone air space are defined based on the buffer space. At least one path segment may be identified based on the coordinates for the drone air space, and the coordinates for drone air space are stored in a map database in association with the at least one path segment.