ITS Berkeley

Solar Phased Arrays-based Wireless Power for Commercial Aviation Decarbonization

Claudel, Christian
Wang, Tianyi
Xu, Yiming
Byeon, Jiseop
Jiao, Junfeng
Mohammadi, Javad
Kockelman, Kara
Alexandre Bayen
2026

Decarbonizing aviation remains challenging because energy-dense jet fuels dominate beyond short-range operations, while batteries impose severe range and payload penalties. Here we evaluate a new infrastructure pathway in which utility-scale solar farms equipped with solar phased arrays wirelessly beam microwave power to hybrid-electric aircraft during cruise. Integrating 143,152 U.S. flight trajectories, 5,712 solar farms and wireless power transfer models, we quantify the spatial, temporal, and operational potential of this concept at continental scale. We find that benefits are highly...

Solar Phased Arrays-based Wireless Power Transfer for Commercial Airlines can Reduce Energy Costs and Carbon Emissions in the United States

Wang, Tianyi
Xu, Yiming
Byeon, Jiseop
Jiao, Junfeng
Mohammadi, Javad
Kockelman, Kara
Claudel, Christian
Alexandre Bayen
2026

Decarbonizing aviation remains challenging because energy-dense jet fuels dominate beyond short-range operations, while batteries impose severe range and payload penalties. Here we evaluate a new infrastructure pathway in which utility-scale solar farms equipped with solar phased arrays wirelessly beam microwave power to hybrid-electric aircraft during cruise. Integrating 143,152 U.S. flight trajectories, 5,712 solar farms and wireless power transfer models, we quantify the spatial, temporal, and operational potential of this concept at continental scale. We find that benefits are highly...

Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore

Jiang, Shan
Ferreira, Joseph
Marta Gonzalez
2017

In this study, with Singapore as an example, we demonstrate how we can use mobile phone call detail record (CDR) data, which contains millions of anonymous users, to extract individual mobility networks comparable to the activity-based approach. Such an approach is widely used in the transportation planning practice to develop urban micro simulations of individual daily activities and travel; yet it depends highly on detailed travel survey data to capture individual activity-based behavior. We provide an innovative data mining framework that synthesizes the state-of-the-art techniques in...

Dynamic Lane Allocation in UAM Corridors for Efficient Multimodal Door-to-Door Mobility

Park, Jung Ho
Kam, Jordan
Bulusu, Vishwanath
Alexandre Bayen
Sengupta, Raja
2026

This article presents dynamic directional lane allocation in urban air mobility (UAM) corridors as a discrete-time mixed-integer linear program (MILP). This formulation activates, deactivates, and reverses lane direction as bi-directional airspace demand evolves. We model demand from disaggregate ground travel data by decomposing each trip into a multi-modal sequence with first-, middle-, and last-mile legs and routing the UAM-served middle-mile segment through a vertiport-side dispatch model. We use the San Francisco Bay Area as a case study by placing a multi-region spanning corridor...

Extended Discrete Choice Models: Integrated Framework, Flexible Error Structures, and Latent Variables

Joan Walker
2001

Discrete choice methods model a decision-maker’s choice among a set of mutually exclusive and collectively exhaustive alternatives. They are used in a variety of disciplines (transportation, economics, psychology, public policy, etc.) in order to inform policy and marketing decisions and to better understand and test hypotheses of behavior. This dissertation is concerned with the enhancement of discrete choice methods.

Urban Magnetism Through the Lens of Geo-tagged Photography

Paldino, Silvia
Bojic, Iva
Sobolevsky, Stanislav
Ratti, Carlo
Marta Gonzalez
2015

There is an increasing trend of people leaving digital traces through social media. This reality opens new horizons for urban studies. With this kind of data, researchers and urban planners can detect many aspects of how people live in cities and can also suggest how to transform cities into more efficient and smarter places to live in. In particular, their digital trails can be used to investigate tastes of individuals, and what attracts them to live in a particular city or to spend their vacation there. In this paper we propose an unconventional way to study how people experience the...

Toward Temporal Realism in City-Scale Crisis Response Simulation using LLM Agents

Zhang, Anping
Tan, Yang
Tang, Yuanbo
Tang, Huaze
Ye, Qiuhua
Marta Gonzalez
Li, Yang
2026

Human collective participation is rarely steady in time: it is bursty, with short episodes of intense activity separated by long quiet intervals. In crisis response and community mobilization, predicting when people act matters as much as predicting whether they act. Such settings are increasingly modeled with LLM-based social simulators, yet these simulators are validated on whether each action is individually plausible, not on whether actions are timed as in reality. Their temporal realism, the degree to which simulated activity reproduces the bursty, heavy-tailed timing of real human...

Personalized Routing for Multitudes in Smart Cities

De Domenico, Manlio
Lima, Antonio
Arenas, Alex
Marta Gonzalez
2015

Human mobility in a city represents a fascinating complex system that combines social interactions, daily constraints and random explorations. New collections of data that capture human mobility not only help us to understand their underlying patterns but also to design intelligent systems. Bringing us the opportunity to reduce traffic and to develop other applications that make cities more adaptable to human needs. In this paper, we propose an adaptive routing strategy which accounts for individual constraints to recommend personalized routes and, at the same time, for constraints imposed...

Using Large Scale GPS Data to Reveal EV Driver Activity Patterns Beyond Charging Sessions

Clark, Callie
Driscoll, Anne
Ren, Xiyuan
Salah, Salsabil
Marta Gonzalez
Chow, Joseph Y. J.
Yabe, Takahiro
2026

Accurate insights into electric vehicle (EV) driver behavior are essential for long-term infrastructure planning, grid management, and understanding downstream economic impacts, yet individual level data on EV mobility remains limited. Here, we develop a scalable framework to infer EV ownership and charging behavior from passively collected, high-resolution mobility traces covering over 760,000 drivers across four major U.S. metropolitan areas. We identify likely EV drivers based on distinctive visitation patterns to charging stations and gas stations, frequency of visits, and daily travel...