Modeling

A Total Cost of Ownership Model for Low Temperature PEM Fuel Cells in Combined Heat and Power and Backup Power Applications

Max Wei
Tim Lipman
Ahmad Mayyas
Joshua Chien
Shuk Han Chan
David Gosselin
Hanna Breunig
Michael Stadler
Thomas McKone
Paul Beattie
Patricia Chong
Whitney Colella
Brian James
2014

A total cost of ownership model is described for low temperature proton exchange membrane stationary fuel cell systems for combined heat and power (CHP) applications from 1-250kW and backup power applications from 1-50kW. System designs and functional specifications for these two applications were developed across the range of system power levels. Bottom-up cost estimates were made for balance of plant costs, and detailed direct cost estimates for key fuel cell stack components were derived using design-for-manufacturing-and-assembly techniques. The development of high throughput,...

SMART Mobility. Advanced Fueling Infrastructure Capstone Report

John Smart
Zicheng Bi
Alicia Birky
Brennan Borlaug
Erin Burrell
Eleftheria Koutou
Dong-Yeon Lee
Tim Lipman
Andrew Meintz
Eric Miller
Ahmed Mohamed
Matthew Moniot
Amy Moore
Yutaka Motoaki
Zachary Needell
Omer Onar
Clement Rames
Nicholas Reincke
Mohammad Roni
Shawn Salisbury
Colin Sheppard
Danho Ange Lionel Toba
Victor Walker
Dustin Weigl
Eric Wood
Fei Xie
Yonggen Yi
Teng Zeng
Hongcai Zhang
Yan Zhou
Zhi Zhou
2020

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019 and included five research pillars: Connected and Automated Vehicles,...

Reducing Greenhouse Emissions and Fuel Consumption: Sustainable Approaches for Surface Transportation

Susan Shaheen
Tim Lipman
2007

Climate change is rapidly becoming known as a tangible issue that must be addressed to avoid major environmental consequences in the future. Recent change in public opinion has been caused by the physical signs of climate change–melting glaciers, rising sea levels, more severe storm and drought events, and hotter average global temperatures annually. Transportation is a major contributor of carbon dioxide (CO2) and other greenhouse gas emissions from human activity, accounting for approximately 14 percent of total anthropogenic emissions globally and about 27 percent in the U.S....

A Total Cost of Ownership Model for Solid Oxide Fuel Cells in Combined Heat and Power and Power-Only Applications

Roberto Scataglini
Ahmad Mayyas
Max Wei
Shuk Han Chan
Tim Lipman
David Gosselin
Anna D’ Alessio
Hanna Breunig
Whitney Colella
2015

A total cost of ownership model (TCO) is described for emerging applications in stationary fuel cell systems. Solid oxide fuel cell systems (SOFC) for use in combined heat and power (CHP) and poweronly applications from 1 to 250 kilowatts-electric (kWe1) are considered. The total cost of ownership framework expands the direct manufacturing cost modeling framework of other studies to include operational costs and life-cycle impact assessment of possible ancillary financial benefits during operation and at end-of-life. These include credits for reduced emissions of global warming gases such...

Reducing Annotation Cost in Vision Language Pedestrian Re Identification via Uncertainty Driven Sampling

Anderson, Michael
Daniel Rodriguez
Chen, Yi
2026

Scaling pedestrian re-identification for autonomous driving is limited by the cost of identity labeling across large camera networks. Inspired by CLIP-based uncertainty modal modeling, this paper proposes an active learning approach that selects labeling candidates using uncertainty in the joint vision–language embedding space. The method combines (i) uncertainty sampling for ambiguous matches, (ii) diversity sampling based on embedding coverage, and (iii) batch acquisition with redundancy control. Experiments are conducted on a large-scale dataset with 400,000 images and 50,000 identities...

Evaluating the Public Perception of a Feebate Policy in California Through the Estimation and Cross-Validation of an Ordinal Regression Model

Elliot Martin
Susan Shaheen
Tim Lipman
Madonna Camel
2014

Understanding the key factors influencing policy perception can be critical for informing the design of public policies. Feebates is a unique public policy that is meant to influence vehicle purchases. It presents buyers with a rebate for purchasing low-emission vehicles and a fee for purchasing high-emission vehicles. Because feebates directly impacts the consumer, understanding the dynamics of public perception, support, and opposition is important. This study explores the public perception of a potential feebate policy within California, and evaluates the robustness of an ordinal...

DeepTimeGeo: Trajectory Reconstruction From Sparse Data With Transformer

Cao, Shangqing
Wu, Jiaman
Kasliwal, Aparimit
Chen, Baoqi
Perona, Giuseppe
Marta Gonzalez
2026

The completion of sparse Location-Based Service (LBS) data for modeling urban-scale origin-destination (OD) flow is of great importance to transportation planning applications. Sparse trajectories lack realistic human mobility patterns. Only with completed trajectories one can derive urban-scale OD flow that resembles complete travel diaries as those gathered by surveys or actively collecting phone applications. We present DeepTimeGeo (DTG), a transformer encoder-only model that reconstructs complete trajectories from sparse LBS inputs. We adopt a rank-based representation of locations to...

Learning to Recommend in Unknown Games

Alanqary, Arwa
Baba, Zakaria
Wu, Manxi
Alexandre Bayen
2026

We study preference learning through recommendations in multi-agent game settings, where a moderator repeatedly interacts with agents whose utility functions are unknown. In each round, the moderator issues action recommendations and observes whether agents follow or deviate from them. We consider two canonical behavioral feedback models-best response and quantal response-and study how the information revealed by each model affects the learnability of agents' utilities. We show that under quantal-response feedback the game is learnable, up to a positive affine equivalence class, with...

Probabilistic Structure of Two-Lane Road Traffic

Carlos Daganzo
1975

In most predictive models for two-lane road traffic, it is assumed that platoons have no physical dimensions, thus restricting their applicability to light traffic where a platoon cannot be long enough to block the progression of the next one. In this paper a model that can be used for heavy traffic is presented. A queueing theory approach in which vehicles are allowed to have physical dimensions yields the platoon length distribution, the delays to fast vehicles, the headway process and the flow density diagram for both the space and time processes. Unlike in other models, the passing...

Multinomial Probit and Qualitative Choice: A Computationally Efficient Algorithm

Carlos Daganzo
Bouthelier, Fernando
Sheffi, Yosef
1977

Even though multinomial probit models have many attractive theoretical features and have been proposed for diverse choice problems (such as modal split and route choice in the transportation field), they have never been used in practice due to the lack of an adequate numerical technique for their application. The purpose of this paper is to introduce such a technique and to demonstrate the feasibility of forecasting with multinominal probit models. Our limited computational experience with the proposed numerical technique indicates that it is accurate, and can be efficiently applied to...