book/book chapter

Conclusion: Reflections and Lessons from the Pandemic

Publication Date

January 1, 2023

Author(s)

Alexandre M. Bayen, R. (Jay) Jayakrishnan, Giovanni Circella, Anastasia Loukaitou-Sideris

Abstract

This concluding chapter presents a summary of the research findings in the previous chapters, along with some reflections for each of the five themes of the book and a discussion of necessary future responses (post-pandemic or in the event of a new pandemic) and topics that require further exploration. The pandemic brought into sharp relief pre-existing social disparities and affected vulnerable populations the most. The economic impacts of the pandemic were diverse and varied by geography, but again certain geographies and economic sectors were more buffered from negative outcomes than others. A lesson and a challenge for policymakers is to find ways to understand and reduce these disparities, instead of pushing them under the rug. The impacts on mobility and travel were dramatic as total trips decreased, transit usage fell dramatically, and telecommuting and active modes of transportation increased. Some positive impacts included an improved air quality, a reduced number of traffic crashes, and a proliferation of walking and biking in some neighbourhoods. As cities are slowly recovering from the pandemic, the challenge is to keep the positive impacts but also find ways to help the transit industry rebound from its plunge. Long-term impacts of the pandemic in terms of changing patterns of work and work arrangements, shopping, recreation, and other human activities that will affect travel need additional time and more research to discern.

Suggested Citation
Alexandre M. Bayen, R. Jayakrishnan, Giovanni Circella and Anastasia Loukaitou-Sideris (2023) “Conclusion: Reflections and Lessons from the Pandemic”, in A. Loukaitou-Sideris, A.M. Bayen, G. Circellaand R. Jayakrishnan (eds.) Pandemic in the Metropolis: Transportation Impacts and Recovery. Cham: Springer International Publishing (Springer Tracts on Transportation and Traffic), pp. 333–345. Available at: 10.1007/978-3-031-00148-2_21.

published journal article

Valuing Sequences of Lives Lost or Saved Over Time: Preference for Uniform Sequences

Decision Analysis

Publication Date

March 1, 2020

Author(s)

Jeffery L. Guyse, Robin Keller, Candice H. Huynh

Abstract

Policymakers often make decisions involving human-mortality risks and monetary outcomes that span across different time periods and horizons. Many projects or environmental-regulation policies involving risks to life, such as toxic exposures, are experienced over time. The preferences of individuals on lives lost or saved over time should be understood to implement effective policies. Using a within-subject survey design, we investigated our participants’ elicited preferences (in the form of ratings) for sequences of lives saved or lost over time at the participant level. The design of our study allowed us to directly observe the possible preference patterns of negative time discounting or a preference for spreading from the responses. Additionally, we embedded factors associated with three other prevalent anomalies of intertemporal choice (gain/loss asymmetry, short/long asymmetry, and the absolute magnitude effect) into our study for control. We find that our participants exhibit three of the anomalies: preference for spreading, absolute magnitude effect, and short/long-term asymmetry. Furthermore, fitting the data collected, Loewenstein and Prelec’s model for the valuation of sequences of outcomes allowed for a more thorough understanding of the factors influencing the individual participants’ preferences. Based on the results, the standard discounting model does not accurately reflect the value that some people place on sequences of mortality outcomes. Preferences for uniform sequences should be considered in policymaking rather than applying the standard discounting model.

Suggested Citation
Jeffery L. Guyse, L. Robin Keller and Candice H. Huynh (2020) “Valuing Sequences of Lives Lost or Saved Over Time: Preference for Uniform Sequences”, Decision Analysis, 17(1), pp. 24–38. Available at: 10.1287/deca.2019.0397.

conference paper

Modeling ridesourcing trip generation: Chicago case study

Proceedings of the 99th annual meeting of the transportation research board

Publication Date

January 1, 2020
Suggested Citation
Arash Ghaffar, Suman Mitra and Michael Hyland (2020) “Modeling ridesourcing trip generation: Chicago case study”, in Proceedings of the 99th annual meeting of the transportation research board.

presentation

Quantifying the Value of Smart and Bidirectional Charging at Airport Passenger Parking Facilities: A San Jose Mineta International Airport Case Study

Publication Date

August 13, 2026

Abstract

We compared the costs of implementing unidirectional and bidirectional charging within a full-year simulation of passenger charging stations at the San Jose Mineta International Airport, using real charging sessions, the facility’s PG&E flex pricing tariff, and a battery-electric fleet representative for Santa Clara County. We find that demand chargers are critical in avoiding new unidirectional smart charging peaks formed by following low-cost electricity rate hours. Also, an equal mix of Level 1 and Level 2 chargers can cut the facility’s peak demand by roughly 40% and still fully charge 94% of vehicles before their departure.

Presented by: Amin Akbari, MS, Graduate student at UC Irvine
Presented at: Bridging Transportation Researchers Conference (BTR #8, Western Track Session 9: Electric Vehicles & Sustainable Freight)

Phd Dissertation

Novel Vulnerability Discoveries, Measurements, and Attack Designs for Safety-Critical Autonomous Systems from Practicality Perspectives

Publication Date

January 1, 2024

Author(s)

Abstract

Autonomous systems, such as autonomous driving (AD), rely heavily on real-time perception systems to detect and interpret their surroundings, such as traffic cones, pedestrians, traffic signs, vehicles, etc. These perception systems predominantly employ Deep Neural Networks (DNNs) for tasks such as real-time object detection due to their superior performance. However, DNNs are inherently vulnerable to adversarial attacks—maliciously crafted inputs designed to cause the DNNs to malfunction. Given the safety- and mission-critical nature of autonomous systems, it is crucial to systematically investigate the potential security vulnerabilities of these systems in real-world settings. So far, one of the most general yet crucial limitations for prior research works in this area is their limited practicality in real-world autonomous system setups, either due to their sole focus on the AI component alone, which makes it non-trivial to transfer their component-only attack effects to the system level, or due to their research scopes limited to academic prototypes instead of real-world systems. For example, almost all prior adversarial attacks on Traffic Sign Recognition (TSR) systems have only assessed the effects on academic TSR models, leaving the impacts on real-world commercial TSR systems largely unexplored. While a few recent works have attempted to evaluate the impact on commercial TSR systems, these efforts are typically confined to a single vehicle model, sometimes even an unidentified one, raising questions about both the generalizability and representativeness of their findings. In this dissertation, I present a suite of research efforts toward novel vulnerability discoveries, measurements, and attack designs for safety-critical autonomous systems from practicality perspectives. By systematically discovering and understanding the security vulnerabilities at both the DNN model level and autonomous system level, these research efforts aim to provide new and useful insights that can inspire further exploration of this largely under-explored aspect in this research area.

Suggested Citation
Ningfei Wang (2024) Novel Vulnerability Discoveries, Measurements, and Attack Designs for Safety-Critical Autonomous Systems from Practicality Perspectives. PhD Dissertation. UC Irvine. Available at: https://uci.primo.exlibrisgroup.com/permalink/01CDL_IRV_INST/17uq3m8/alma991035677822804701.

conference paper

Leveraging Food Delivery Programs as a Community Resilience Resource: A Demand-Driven Spatial and Temporal Analysis of Need

Transportation Research Board 103rd Annual Meeting

Publication Date

January 1, 2024

Author(s)

G Bella, Elisa Borowski, A Stathopolous
Suggested Citation
G Bella, Elisa Borowski and A Stathopolous (2024) “Leveraging Food Delivery Programs as a Community Resilience Resource: A Demand-Driven Spatial and Temporal Analysis of Need”.

Preprint Journal Article

Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides

Publication Date

August 14, 2023

Author(s)

Lianfa Li, Roxana Khalili, Frederick Lurmann, Nathan Pavlovic, Jun Wu, Yan Xu, Yisi Liu, Karl O'Sharkey, Beate Ritz, Luke Oman, Meredith Franklin, Theresa Bastain, Shohreh F. Farzan, Carrie Breton, Rima Habre

Abstract

Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects. Machine learning (ML) methods have significantly enhanced our capacity to predict NOx concentrations at ground-level with high spatiotemporal resolution but may suffer from high estimation bias since they lack physical and chemical knowledge about air pollution dynamics. Chemical transport models (CTMs) leverage this knowledge; however, accurate predictions of ground-level concentrations typically necessitate extensive post-calibration. Here, we present a physics-informed deep learning framework that encodes advection-diffusion mechanisms and fluid dynamics constraints to jointly predict NO2 and NOx and reduce ML model bias by 21-42%. Our approach captures fine-scale transport of NO2 and NOx, generates robust spatial extrapolation, and provides explicit uncertainty estimation. The framework fuses knowledge-driven physicochemical principles of CTMs with the predictive power of ML for air quality exposure, health, and policy applications. Our approach offers significant improvements over purely data-driven ML methods and has unprecedented bias reduction in joint NO2 and NOx prediction.

Suggested Citation
Lianfa Li, Roxana Khalili, Frederick Lurmann, Nathan Pavlovic, Jun Wu, Yan Xu, Yisi Liu, Karl O'Sharkey, Beate Ritz, Luke Oman, Meredith Franklin, Theresa Bastain, Shohreh F. Farzan, Carrie Breton and Rima Habre (2023) “Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides”. arXiv. Available at: 10.48550/arXiv.2308.07441.

published journal article

An elementary mechanism for simultaneously modeling discrete decisions and decision times

System Dynamics Review

Publication Date

July 1, 2022

Author(s)

Abstract

Abstract In the field of system dynamics (SD), there has been a missing set of theoretically sound techniques for explicitly modeling dynamics during discrete decision‐making processes across varying levels and types of decision pressures. Purchasing a property, filing a divorce, approving a merger, imposing a tariff, and launching a war are examples of actions that have broader ramifications; in these cases, the decisions and timing of those decisions are crucial in understanding and predicting the interactions between the decision‐makers and their environments. Sequential Sampling Models (SSMs) have remained commonplace in cognitive psychology (CP) for decades because of their utility in simultaneously capturing individual decisions and decision‐time distributions. This article reviews existing SSM literature and proposes a generalized, elementary mechanism distilled from existing SSMs, which establishes a connection between SD and CP in the hope of benefiting both fields. © 2022 System Dynamics Society.

Suggested Citation
Jiangbo Yu (2022) “An elementary mechanism for simultaneously modeling discrete decisions and decision times”, System Dynamics Review, 38(3), pp. 215–245. Available at: 10.1002/sdr.1712.

conference paper

No one in the middle. Enabling Network Access Control Via Transparent Attribution

Proceedings of the 2018 on asia conference on computer and communications security - ASIACCS '18

Publication Date

January 1, 2018

Author(s)

Jeremy Erickson, Qi Alfred Chen, Xiaochen Yu, Erinjen Lin, Robert Levy, Z. Morley Mao
Suggested Citation
Jeremy Erickson, Qi Alfred Chen, Xiaochen Yu, Erinjen Lin, Robert Levy and Z. Morley Mao (2018) “No one in the middle. Enabling Network Access Control Via Transparent Attribution”, in Proceedings of the 2018 on asia conference on computer and communications security - ASIACCS '18. ACM Press, pp. 651–658. Available at: 10.1145/3196494.3196498.