published journal article
Freeway ramp metering using artificial neural networks
Transportation Research Part C: Emerging Technologies
Publication Date
October 1, 1997
Area of Expertise: Unspecified
published journal article
In memoriam frank a. Haight 1919-2006 - obituary
TRANSPORTATION RESEARCH PART B: METHODOLOGICAL
Publication Date
January 1, 2007
Author(s)
Thomas Golob, Molly I. Haight
Suggested Citation
Thomas F. Golob and Molly I. Haight (2007) “In memoriam frank a. Haight 1919-2006 - obituary”, TRANSPORTATION RESEARCH PART B: METHODOLOGICAL, 41(1), pp. 1–3. Available at: 10.1016/j.trb.2006.07.001. published journal article
Analyzing building-height restrictions: Predicted impacts and welfare costs
Regional Science and Urban Economics
Publication Date
March 1, 2005
Author(s)
Alain Bertaud, Jan Brueckner
Abstract
This paper analyzes the impacts and the welfare costs of building-height restrictions. The theoretical analysis demonstrates that a height restriction causes a city to expand spatially, a consequence of lower densities near the center. The analysis also establishes that the consumer welfare cost generated by the height restriction can be measured by the increase in commuting cost for the household living at the edge of the city. Simulation results for a stylized urban area show that this welfare cost is approximately 2% of household income. Illustrative calculations for Bangalore, India, where a height restriction is in force, suggest a similar welfare cost, which lies in the 1.5-4.5% range. (c) 2005 Elsevier B.V. All rights reserved.
Suggested Citation
Alain Bertaud and Jan K. Brueckner (2005) “Analyzing building-height restrictions: Predicted impacts and welfare costs”, Regional Science and Urban Economics, 35(2), pp. 109–125. Available at: 10.1016/j.regsciurbeco.2004.02.004. published journal article
Harnessing cross-border resources to confront climate change
Environmental Science & Policy
Publication Date
September 1, 2018
Author(s)
Octavio Aburto-Oropeza, Andrew F. Johnson, Mickey Agha, Edith B. Allen, Michael F. Allen, Jesús Arellano González, Diego M. Arenas Moreno, Rodrigo Beas-Luna, Scott Butterfield, Gabriel Caetano, Jennifer E. Caselle, Gamaliel Castañeda Gaytán, Max C.N. Castorani, Linh Anh Cat, Kyle Cavanaugh, Jeffrey Q. Chambers, Robert D. Cooper, Nur Arafeh-Dalmau, Todd Dawson, Aníbal Díaz de la Vega Pérez, Joseph Dimento, Saúl Domínguez Guerrero, Matthew Edwards, Joshua R. Ennen, Hector Estrada-Medina, Natalia Fierro-Estrada, Héctor Gadsden, Patricia Galina-Tessaro, Paul M. Gibbons, Eric V. Goode, Morgan E. Gorris, Thomas Harmon, Susanna Hecht, Marco Antonio Heredia Fragoso, Alan Hernández-Solano, Danae Hernández-Cortés, Gustavo Hernández-Carmona, Scott Hillard, Raymond B. Huey, Matthew B. Hufford, G. Darrel Jenerette, Juan Jiménez-Osornio, Karla Joana López-Nava, Rafael A. Lara Reséndiz, Heather M. Leslie, Alejandro López-Feldman, Víctor H. Luja, Norberto Martínez Méndez, William J. Mautz, Josué Medellín-Azuara, Cristina Meléndez-Torres, Fausto R. Méndez de la Cruz, Fiorenza Micheli, Donald B. Miles, Giovanna Montagner, Gabriela Montaño-Moctezuma, Johannes Muller, Paulina Oliva, José Abraham Ortinez Álvarez, J. Pablo Ortiz-Partida, Julio Palleiro-Nayar, Víctor Hugo Páramo Figueroa, P.Ed. Parnell, Peter Raimondi, Arturo Ramírez-Valdez, James T. Randerson, Daniel C. Reed, Meritxell Riquelme, Teresita Romero Torres, Philip C. Rosen, Jeffrey Ross-Ibarra, Victor Sánchez-Cordero, Samuel Sandoval-Solis, Juan Carlos Santos, Ruairidh Sawers, Barry Sinervo, Jack W. Sites, Oscar Sosa-Nishizaki, Travis Stanton, Jared R. Stapp, Joseph A.E. Stewart, Jorge Torre, Guillermo Torres-Moye, Kathleen K. Treseder, Jorge Valdez-Villavicencio, Fernando I. Valle Jiménez, Mercy Vaughn, Luke Welton, Michael F. Westphal, Guillermo Woolrich-Piña, Antonio Yunez-Naude, José A. Zertuche-González, J. Edward Taylor
Abstract
The US and Mexico share a common history in many areas, including language and culture. They face ecological changes due to the increased frequency and severity of droughts and rising energy demands; trends that entail economic costs for both nations and major implications for human wellbeing. We describe an ongoing effort by the Environment Working Group (EWG), created by The University of California’s UC-Mexico initiative in 2015, to promote binational research, teaching, and outreach collaborations on the implications of climate change for Mexico and California. We synthesize current knowledge about the most pressing issues related to climate change in the US-Mexico border region and provide examples of cross-border discoveries and research initiatives, highlighting the need to move forward in six broad rubrics. This and similar binational cooperation efforts can lead to improved living standards, generate a collaborative mindset among participating universities, and create an international network to address urgent sustainability challenges affecting both countries.
Suggested Citation
Octavio Aburto-Oropeza, Andrew F. Johnson, Mickey Agha, Edith B. Allen, Michael F. Allen, Jesús Arellano González, Diego M. Arenas Moreno, Rodrigo Beas-Luna, Scott Butterfield, Gabriel Caetano, Jennifer E. Caselle, Gamaliel Castañeda Gaytán, Max C.N. Castorani, Linh Anh Cat, Kyle Cavanaugh, Jeffrey Q. Chambers, Robert D. Cooper, Nur Arafeh-Dalmau, Todd Dawson, Aníbal Díaz de la Vega Pérez, Joseph F.C. DiMento, Saúl Domínguez Guerrero, Matthew Edwards, Joshua R. Ennen, Hector Estrada-Medina, Natalia Fierro-Estrada, Héctor Gadsden, Patricia Galina-Tessaro, Paul M. Gibbons, Eric V. Goode, Morgan E. Gorris, Thomas Harmon, Susanna Hecht, Marco Antonio Heredia Fragoso, Alan Hernández-Solano, Danae Hernández-Cortés, Gustavo Hernández-Carmona, Scott Hillard, Raymond B. Huey, Matthew B. Hufford, G. Darrel Jenerette, Juan Jiménez-Osornio, Karla Joana López-Nava, Rafael A. Lara Reséndiz, Heather M. Leslie, Alejandro López-Feldman, Víctor H. Luja, Norberto Martínez Méndez, William J. Mautz, Josué Medellín-Azuara, Cristina Meléndez-Torres, Fausto R. Méndez de la Cruz, Fiorenza Micheli, Donald B. Miles, Giovanna Montagner, Gabriela Montaño-Moctezuma, Johannes Muller, Paulina Oliva, José Abraham Ortinez Álvarez, J. Pablo Ortiz-Partida, Julio Palleiro-Nayar, Víctor Hugo Páramo Figueroa, P.Ed. Parnell, Peter Raimondi, Arturo Ramírez-Valdez, James T. Randerson, Daniel C. Reed, Meritxell Riquelme, Teresita Romero Torres, Philip C. Rosen, Jeffrey Ross-Ibarra, Victor Sánchez-Cordero, Samuel Sandoval-Solis, Juan Carlos Santos, Ruairidh Sawers, Barry Sinervo, Jack W. Sites, Oscar Sosa-Nishizaki, Travis Stanton, Jared R. Stapp, Joseph A.E. Stewart, Jorge Torre, Guillermo Torres-Moye, Kathleen K. Treseder, Jorge Valdez-Villavicencio, Fernando I. Valle Jiménez, Mercy Vaughn, Luke Welton, Michael F. Westphal, Guillermo Woolrich-Piña, Antonio Yunez-Naude, José A. Zertuche-González and J. Edward Taylor (2018) “Harnessing cross-border resources to confront climate change”, Environmental Science & Policy, 87, pp. 128–132. Available at: 10.1016/j.envsci.2018.01.001. published journal article
ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System
IEEE Internet of Things Journal
Publication Date
April 1, 2024
Author(s)
Mojtaba Taherisadr, Mohammad Al Faruque, Salma Elmalaki
Abstract
Thanks to the rapid growth in wearable technologies and advancements in machine learning, monitoring complex human contexts becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously. Nevertheless, a central challenge in designing many of these IoT systems arises from the requirement to infer the human mental state, such as intention, stress, cognition load, or learning ability. While different human contexts can be inferred from the fusion of different sensor modalities that can correlate to a particular mental state, the human brain provides a richer sensor modality that gives us more insights into the required human context. This article proposes ERUDITE, a human-in-the-loop IoT system for the learning environment that exploits recent wearable neurotechnology to decode brain signals. Through insights from concept learning theory, ERUDITE can infer the human state of learning and understand when human learning increases or declines. By quantifying human learning as an input sensory signal, ERUDITE can provide adequate personalized feedback to humans in a learning environment to enhance their learning experience. ERUDITE is evaluated across 15 participants and showed that by using the brain signals as a sensor modality to infer the human learning state and providing personalized adaptation to the learning environment, the participants’ learning performance increased on average by 26%. Furthermore, to evaluate ERUDITE practicality and scalability, we showed that ERUDITE can be deployed on an edge-based prototype consuming 75-mW power on average with 100 MB memory footprint.
Suggested Citation
Mojtaba Taherisadr, Mohammad Abdullah Al Faruque and Salma Elmalaki (2024) “ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System”, IEEE Internet of Things Journal, 11(8), pp. 14532–14550. Available at: 10.1109/JIOT.2023.3343462. published journal article
Beyond visual inspection: capturing neighborhood dynamics with historical Google Street View and deep learning-based semantic segmentation
Journal of Geographical Systems
Publication Date
July 12, 2023
Author(s)
Jae Hong Kim, Donghwan Ki, N Osutei, Sugie Lee, John R. Hipp
Abstract
While street view imagery has accumulated over the years, its use to date has been largely limited to cross-sectional studies. This study explores ways to utilize historical Google Street View (GSV) images for the investigation of neighborhood change. Using data for Santa Ana, California, an experiment is conducted to assess to what extent deep learning-based semantic segmentation, processing historical images much more efficiently than visual inspection, enables one to capture changes in the built environment. More specifically, semantic segmentation results are compared for (1) 248 sites with construction or demolition of buildings and (2) two sets of the same number of randomly selected control cases without such activity. It is found that the deep learning-based semantic segmentation can detect nearly 75% of the construction or demolition sites examined, while screening out over 60% of the control cases. The results suggest that it is particularly effective in detecting changes in the built environment with historical GSV images in areas with more buildings, less pavement, and larger-scale construction (or demolition) projects. False-positive outcomes, however, can emerge due to the imperfection of the deep learning model and the misalignment of GSV image points over years, showing some methodological challenges to be addressed in future research.
Suggested Citation
Jae Hong Kim, Donghwan Ki, Nene Osutei, Sugie Lee and John R. Hipp (2023) “Beyond visual inspection: capturing neighborhood dynamics with historical Google Street View and deep learning-based semantic segmentation”, Journal of Geographical Systems [Preprint]. Available at: 10.1007/s10109-023-00420-1. published journal article
Making probability judgments of future product failures: The role of mental unpacking
Journal of Consumer Psychology
Publication Date
April 1, 2012
Author(s)
Dipayan Biswas, Robin Keller, Bidisha Burman
Suggested Citation
Dipayan Biswas, L. Robin Keller and Bidisha Burman (2012) “Making probability judgments of future product failures: The role of mental unpacking”, Journal of Consumer Psychology, 22(2), pp. 237–248. Available at: 10.1016/j.jcps.2011.03.002. published journal article
DisCovHAR: Contrastive Attention for Human Activity Recognition Under Distribution Shifts
IEEE Internet of Things Journal
Publication Date
June 1, 2025
Author(s)
Luke Chen, Mohanad Odema, Mohammad Al Faruque
Abstract
Advances in Internet of Things (IoT) wearable sensors and edge-artificial intelligence (Edge-AI) have enabled practical realizations of machine learning (ML)-enabled mobile sensing applications like human activity recognition (HAR). The effective deployment of these data-driven models necessitates learning robust representations capable of handling prevalent distribution shifts (DS), including new users, device positions, rotations, and more. In that respect, contrastive learning (CL) has shown promise in learning transformation-invariant features, outperforming traditional HAR methods. However, recent findings reveal that the contrastive loss induces shrinkage and expansion of the feature space which may limit the generalization capacity of the model. To address this, we propose DisCovHAR, a contrastive attention method to selectively apply the contrastive loss to a subset of the feature space through the transformer encoder attention mechanism. Extensive experiments on three HAR datasets (DSADS, PAMAP2, and USCHAD) demonstrate its superiority over state-of-the-art methods. Specifically, our approach yields up to 4.47% and 7.82% average accuracy improvements in subject-wise and position-wise generalization settings. Furthermore, DisCovHAR demonstrates up to 5.07% increased robustness compared to prior methods under multivariate distribution shift scenarios.
Suggested Citation
Luke Chen, Mohanad Odema and Mohammad Abdullah Al Faruque (2025) “DisCovHAR: Contrastive Attention for Human Activity Recognition Under Distribution Shifts”, IEEE Internet of Things Journal, 12(12), pp. 21973–21983. Available at: 10.1109/JIOT.2025.3551263. published journal article
The value of time and reliability: measurement from a value pricing experiment
Transportation Research Part E: Logistics and Transportation Review
Publication Date
April 1, 2001
Author(s)
Abstract
We measure values of time and reliability from 1998 data on actual behavior of commuters on State Route 91 in Orange County, California, where they choose between a free and a variably tolled route. For each route at each time of day and for each day of the week, the distribution of travel times across different weeks is measured using loop detector data. The best-fitting models represent travel-time by its median, and unreliability by the difference between the 90th percentile and the median. We present models of route choice both alone and combined with other choices, namely time of day, car occupancy, and installation of an electronic transponder. In our best model, containing all these choices except time of day, value of time (VOT) is $22.87 per hour, while value of reliability is $15.12 per hour for men and $31.91 for women. These values are 72%, 48%, and 101%, respectively, of the average wage rate in our sample.
Suggested Citation
Terence C. Lam and Kenneth A. Small (2001) “The value of time and reliability: measurement from a value pricing experiment”, Transportation Research Part E: Logistics and Transportation Review, 37(2), pp. 231–251. Available at: 10.1016/S1366-5545(00)00016-8. published journal article
Preference functions for spatial risk analysis. Preference Functions for Spatial Risk Analysis
Risk Analysis
Publication Date
September 1, 2017
Author(s)
Robin Keller, Jay Simon