Data

Planning for Electric Vehicles Coupled with Urban Mobility

Xu, Yanyan
Çolak, Serdar
Kara, Emre C.
Moura, Scott J.
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...

Robust Fault Diagnosis of Uncertain One-dimensional Wave Equations

Dey, Satadru
Moura, Scott J.
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...

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 J.
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...

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...

Optimal Sampling Strategies for Statistical Models with Discrete Dependent Variables

Daganzo, Carlos F.
1980

The object of this paper is to improve the cost-effectiveness of data gathering procedures for models with discrete dependent variables. It is assumed throughout the paper that the true value of the parameter vector is approximately known and that, with that information, one must select a statistically optimal number of observations from different population subgroups to refine the accuracy of the estimate. It is shown that the problem can be reduced to a small mathematical program whose objective function can be written after a few preliminary algebraic manipulations. For binary choice...

Multinomial Probit with Time-Series Data: Unifying State Dependence and Serial Correlation Models

Daganzo, Carlos F.
Sheffi, Y.
1982

This paper develops a general method for treating discrete data sets containing individuals that have made more than one choice under varying stimuli. The multinomial probit model is shown to possess properties that make it very attractive for this application, as with it, it is possible to develop an estimation process that uses all the information in the data, and is both relatively inexpensive and consistent with utility maximization. The method, which is a generalization of Heckman's binary model, can include taste variations and more than two alternatives.

Extrapolating One-Week Automobile Usage Data to Longer Time Periods

Horowitz, Abraham D.
1983

This study illustrates a statistical procedure that can be used to estimate the fraction of a given population experiencing a “rare” event during a long time period, given a few days of observation. In an automobile usage context, the rare event could be the occurrence of an automobile occupancy of four or more persons and/or a travel distance of 100 miles or more on any given day. The technique, which can be important for the design of durable goods, is illustrated with four numerical examples.

Extrapolating Automobile Usage Data to Long Time Periods

Horowitz, Abraham D.
Daganzo, Carlos F.
1986

This study illustrates a statistical procedure that can be used to estimate the fraction of a given population experiencing a “rare” event during a long time period, given a few days of observation. In an automobile usage context, the rare event could be the occurrence of an automobile occupancy of four or more persons and/or a travel distance of 100 miles or more on any given day. The technique, which can be important for the design of durable goods, is illustrated with four numerical examples.

Bounding the VRP Distance Before Knowing the Location of Points

Daganzo, Carlos F.
1991

This note presents upper bounds for the minimum distance needed to visit n points in a unit circle, with a vehicle fleet based at its center and allowed to visit a maximum of q points per vehicle tour. The paper shows that the minimum distance can never exceed: [2n/q]+ + pi q. If points are randomly and uniformly distributed, and travel can only take place on a ring-radial network, the paper also proves that for q = 0(n**beta), 0 less than beta less than 1/2, the average minimum distance does not exceed: [4n/3q] + 0.82(pi n)**1/2 + 0(q). For the Euclidean metric, it is claimed that a...