A5085202444- Transportation Planning and Optimization
- Transportation and Mobility Innovations
- Human Mobility and Location-Based Analysis
- Traffic Prediction and Management Techniques
- Traffic control and management
- Green IT and Sustainability
- Behavioral Health and Interventions
- Opportunistic and Delay-Tolerant Networks
- Impact of Technology on Adolescents
- Experimental Behavioral Economics Studies
- Digital Mental Health Interventions
- Simulation Techniques and Applications
- Urban Transport and Accessibility
- Electric Vehicles and Infrastructure
Uber AI (United States)
2019-2020
University of California, Berkeley
2017-2019
Lawrence Berkeley National Laboratory
2019
Activity-based travel demand models are becoming essential tools used in transportation planning and regional development scenario evaluation. They describe itineraries of individual travelers, namely, what activities they participating in, when perform these activities, how choose to the activity locales. However, data collection for activity-based is performed through surveys that infrequent, expensive, reflect changes with significant delays. Thanks ubiquitous cell phone data, we see an...
Background: Automatically tracking mental well-being could facilitate personalization of treatments for mood disorders such as depression and bipolar disorder. Smartphones present a novel ubiquitous opportunity to track individuals' behavior may be useful inferring automatically monitoring well-being. Objective: The aim this study was assess the extent which activity sleep with smartphone can used Methods: A cohort 106 individuals recruited install an app on their that would daily surveys...
The current trend toward urbanization and adoption of flexible innovative mobility technologies will have complex difficult-to-predict effects on urban transportation systems. Comprehensive methodological frameworks that account for the increasingly uncertain future state landscape do not yet exist. Furthermore, few approaches enabled massive ingestion data in planning tools capable offering flexibility scenario-based design. This article introduces Berkeley Integrated System Transportation...
Agent-based modeling in transportation problems requires detailed information on each of the agents that represent population region a study. To extend agent-based with social influence, connected synthetic both features and its networks need to be simulated. However, either traditional manually-collected household survey data (ACS) or recent large-scale passively-collected Call Detail Records (CDR) alone lacks features. This work proposes an algorithmic procedure makes use as well digital...
This article introduces BISTRO, a new open source transportation planning decision support system that uses an agent-based simulation and optimization approach to anticipate develop adaptive plans for possible technological disruptions growth scenarios. The framework was evaluated in the context of machine learning competition hosted within Uber Technologies, Inc., which over 400 engineers data scientists participated. For purposes this competition, benchmark model, based on city Sioux...
MATSim (Multi-Agent Transport Simulation Toolkit) is an open source large-scale agent-based transportation planning project applied to various areas like road transport, public freight regional evacuation, etc. BEAM (Behavior, Energy, Autonomy, and Mobility) framework extends enable powerful scalable analysis of urban systems. The agents from the simulation exhibit 'mode choice' behavior based on multinomial logit model. In our study, we consider eight mode choices viz. bike, car, walk, ride...