Data

Assessing Area Under the Curve as an Alternative to Latent Growth Curve Modeling for Repeated Measures Zero-Inflated Poisson Data: A Simulation Study

Rodriguez, Daniel
2023
Researchers interested in the assessment of substance use trajectories, and predictors of change, have several data analysis options. These include, among others, generalized estimating equations and latent growth curve modeling. One difficulty in the assessment of substance use, however, is the nature of the variables studied. Although counting instances of use (e.g., the number of cigarettes smoked per day) would seem to be the best option, such data present difficulties in that the distribution of these variables is not likely normal. Count variables often follow a Poisson distribution,...

Valuation of Public Bus Electrification with Open Data

Vijay, Upadhi
Woo, Soomin
Moura, Scott
Jain, Akshat
Rodriguez, David
Gambacorta, Sergio
Ferrara, Giuseppe
Lanuzza, Luigi
Zulberti, Christian
Mellekas, Erika
Papa, Carlo
2022

This research provides a novel framework to estimate the economic, environmental, and social values of electrifying public transit buses, for cities across the world, based on open-source data. Electric buses are a compelling candidate to replace diesel buses for the environmental and social benefits. However, the state-of-art models to evaluate the value of bus electrification are limited in applicability because they require granular and bespoke data on bus operation that can be difficult to procure. Our valuation tool uses General Transit Feed Specification, a standard data format used...

Robust Fault Diagnosis of Uncertain One-dimensional Wave Equations

Dey, Satadru
Moura, Scott
2018

Unlike its Ordinary Differential Equation (ODE) counterpart, fault diagnosis of Partial Differential Equations (PDE) has received limited attention in existing literature. The main difficulty in PDE fault diagnosis arises from the spatio-temporal evolution of the faults, as opposed to temporal-only fault dynamics in ODE systems. In this work, we develop a fault diagnosis scheme for one-dimensional wave equations. A key aspect of this fault diagnosis scheme is to distinguish the effect of uncertainties from faults. The scheme consists of a PDE observer whose output error is treated as a...

Planning for Electric Vehicles Coupled with Urban Mobility

Xu, Yanyan
Çolak, Serdar
Kara, Emre C.
Moura, Scott
González, Marta C.
2018

The rising adoption of plug-in electric vehicles (PEVs) leads to the alignment of their electricity and their mobility demands. Therefore, transportation and power infrastructures are becoming increasingly interdependent. In this work, we uncover patterns of PEV mobility by integrating for the first time two unique data sets: (i) mobile phone activity of 1.39 million Bay Area residents and (ii) charging activity of PEVs in 580,000 sessions obtained in the same region. We present a method to estimate individual mobility of PEV drivers at fine temporal and spatial resolution integrating...

Dual Hopfield Methods for Large-Scale Mixed-Integer Programming

Travacca, Bertrand
Moura, Scott
2018

We present a novel heuristic first order method for large-scale mixed-integer programs, more specifically we focus on mixed-integer quadratically constrained quadratic programs. Our method builds on Lagrangian relaxation techniques and Hopfield Neural Networks. For illustration, we apply this method to an economic load dispatch problem and compare with two convex approximation techniques.

Data-Driven Chance-Constrained Regulation Capacity Offering for Distributed Energy Resources

Zhang, Hongcai
Hu, Zechun
Munsing, Eric
Moura, Scott
Song, Yonghua
2019

This paper studies the behavior of a strategic aggregator offering regulation capacity on behalf of a group of distributed energy resources (DERs, e.g., plug-in electric vehicles) in a power market. Our objective is to maximize the aggregator's revenue while controlling the risk of penalties due to poor service delivery. To achieve this goal, we propose data-driven risk-averse strategies to effectively handle uncertainties in: 1) the DER parameters (e.g., load demands and flexibilities) and 2) subhourly regulation signals (to the accuracy of every few seconds). We design both the day-ahead...

Computer Programs for Traffic Operations

Alexander Skabardonis
Lu, Xiao-Yun
Berkeley University of California
California Department of Transportation
2025

The objective of this project was to develop recommendations toward a statewide policy of congestion responsive freeway ramp metering operation. The research is performed in two phases. In phase 1, alternative ramp metering activation strategies were evaluated through simulation modeling on a real-world freeway test site. In phase 2, "before" and "after" field data will be collected and analyzed on freeway test sites that have implemented congestion responsive ramp metering activation. This report describes the research performed in phase 1 of the project. A section of the US-101 freeway...

Assessment of Traffic Simulation Models : Final Report

Alexander Skabardonis
University of California, Berkeley
Washington State Department of Transportation
1999

This report describes a study which focused on the selection and application of traffic simulation models. The models were evaluated for: capabilities and features, input data requirements, output options, relationship with traditional planning and operational analysis tools, and modeling effort and costs.

Application of Simulation to Evaluate the Operation of Major Freeway Weaving Sections

Alexander Skabardonis
Cassidy, M
May, A D
Cohen, S
1989

This paper describes the findings from the application of the INTRAS microscopic simulation model to evaluate the traffic performance at major freeway weaving sections. The work performed is part of an ongoing research project to develop improved weaving analysis procedures that are particularly applicable to California conditions. The INTRAS model was modified to predict the speeds of weaving and nonweaving vehicles and applied on eight major freeway weaving sections for a range of traffic conditions at each site. Good agreement was obtained between the measured and predicted values....

A Spatial Queuing Model for the Emergency Vehicle Districting and Location Problem

Geroliminis, Nikolas
Karlaftis, Matthew G
Alexander Skabardonis
Transportation Research Part B: Methodological
2009

Emergency response systems in urban areas should be located to ensure adequate coverage and rapid response time. The authors develop a model for locating emergency vehicles on urban networks considering both spatial and temporal demand characteristics such as the probability that a server is not available when required. The authors also consider that service rates are not identical but may vary among servers and are dependent upon incident characteristics; corresponding districting and dispatching problems are also integrated in the location model. The model is applied using real data for...