published journal article

Tracking daily travel; Assessing discrepancies between GPS-derived and self-reported travel patterns

Transportation Research Part C: Emerging Technologies

Abstract

Global Positioning Systems (GPS) technologies have been used in conjunction with traditional one- or two-day travel diaries to audit respondent reporting patterns, but we used GPS-based monitoring to conduct the first assessment to our knowledge of travel reporting patterns using a seven-day travel log instrument, which could reduce response burden and provide multiple-day, policy-relevant information for evaluation studies. We found substantial agreement between participant-reported daily travel patterns and GPS-derived patterns among 116 adult residents of a largely low-income and non-white transportation corridor in urbanized Los Angeles in 2011-2013. For all modes, the average difference between daily GPS- and log-derived trip counts was only about 0.39 trips and the average difference between daily GPS- and log-derived walking duration was about -11.8 min. We found that the probability that a day would be associated with agreement or discrepancies between these measurement tools varied by travel mode and participant socio-demographic characteristics. Future research is needed to investigate the potential and limitations of this and other self-report instruments for a larger sample and a wider range of population groups and travel patterns. (C) 2014 Elsevier Ltd. All rights reserved.

Suggested Citation
Douglas Houston, Thuy T. Luong and Marlon G. Boarnet (2014) “Tracking daily travel; Assessing discrepancies between GPS-derived and self-reported travel patterns”, Transportation Research Part C: Emerging Technologies, 48, pp. 97–108. Available at: 10.1016/j.trc.2014.08.013.

published journal article

Stochastic preplanned household activity pattern problem with uncertain activity participation (SHAPP)

Transportation Science

Publication Date

August 1, 2013
Suggested Citation
Li Ping Gan and Will Recker (2013) “Stochastic preplanned household activity pattern problem with uncertain activity participation (SHAPP)”, Transportation Science, 47(3), pp. 439–454. Available at: 10.1287/trsc.1120.0426.

working paper

Economic and Occupational Causes of Transit Operator Absenteeism: A Review of Research

Publication Date

March 1, 1984

Author(s)

Abstract

Transit operator absence from work is a costly and pervasive problem within public transport organizations. This paper reviews over forty international studies in order to document significant factors related to this phenomenon. We begin with a brief assessment of the magnitude and costs of operator absence and isolate two major theories which have been proposed to explain operator absence behavior: the income-leisure tradeoff and occupational stress. Case study reports from three U.S. public transport organizations are used to illustrate the range of factors which influence employee absence behavior. We conclude with suggestions for organizational changes which may serve to reduce operator absence and suggestions for further research.

Suggested Citation
Lyn Long and James L. Perry (1984) Economic and Occupational Causes of Transit Operator Absenteeism: A Review of Research. Working Paper UCI-ITS-WP-84-3. Institute of Transportation Studies, Irvine. Available at: https://escholarship.org/uc/item/46s54575.

Phd Dissertation

Diffusion and Management of Disruptive Technology in Cities: The Case of Drones

Abstract

While the industry of civilian unmanned aerial vehicles (UAV) or drones has seen rapid expansion in the past decade, few studies have systematically examined the dynamics between this disruptive technology and various aspects of cities. Employing quantitative methods, this dissertation explores 1) the diffusion and adoption patterns of civilian drones; 2) how cities manage the challenges of increasing drone activities; and 3) the supply-side opportunities and constraints associated with the deployment of Urban Air Mobility (UAM) in built-out metropolitan areas. The results of the first county level study might suggest (Chapter 2) that the digital divide has magnified the uneven and nonlinear diffusion of drones across time and space. Furthermore, the strength of state-level interventions correlates with the intensity of local drone adoption, even though the regulatory effects are different among drone user groups. People living in neighborhoods with a higher adoption rate of drones are on average younger, more affluent, and Whiter. An extension of the first study at the zip code level (Chapter 3) has retested the key results and provided additional insights into the spatial dependence effects that affect the drone adoption patterns. Furthermore, the results of the second study (Chapter 4) indicate that local drone policy adoption among communities of color trails behind that of other communities. Although drone policy adoption at the local level has been shaped by both motivation and capacity factors, the desire to protect public facilities appears to motivate localities to adopt regulatory measures. In particular, policy adoption is influenced by what nearby cities do, suggesting that strategic interaction is at play among local governments. In the third study (Chapter 5), I evaluate the supply-side opportunities and constraints associated with UAM adoption through a systematic scenario analysis. The results of the third study indicate that current supply-side infrastructure opportunities in Southern California, like helipads and elevated parking structures, are widely available to accommodate the regional deployment of UAM service although current spatial constraints can significantly limit the location choice of UAM landing sites (vertiports) for electric vertical take-off and landing (eVTOL) aircraft. Moreover, the low-income and young populations tend to live relatively farther away from the supply-side opportunities compared to the general population. The third study also proposes a network of UAM stations in Southern California based on the joint considerations of available infrastructure and home-workplace commuting flows.

Suggested Citation
XIANGYU LI (2022) Diffusion and Management of Disruptive Technology in Cities: The Case of Drones. PhD Dissertation. UC Irvine. Available at: https://escholarship.org/uc/item/20t4w3kj#main.

Phd Dissertation

Essays in transportation economics

Publication Date

January 1, 2008

Author(s)

Abstract

This dissertation uses industrial organization and econometric techniques in the analysis of transportation issues. The first chapter, titled “The Impact of Regional Jets on Airline Networks” examines the impact of a new technology, in the form of regional jets, on the US airline industry. Similar to large jets, Regional Jets have a lower threshold for providing profitable service. The chapter develops a theoretical framework that predicts passengers with high schedule-delay costs (i.e., business travelers) would take a direct flight that uses a regional jet. Data from 1997 to 2005 are then analyzed to see the impact of regional jet use on hub-spoke and point-to-point service. The second chapter, “Factors that Affect Airline Flight Frequency and Aircraft Size,” assesses the determinants of aircraft size and frequency of flights on airline routes by considering market demographics, airport characteristics, airline characteristics, and route characteristics. The chapter shows that frequency and aircraft size increase with population, income, and runway length. An increase in the proportion of managerial workers in the labor force or the proportion of population below the age of 25 results in greater frequency with the use of small planes. Slot constrained airports and an increase in the number of nearby airports lead to lower flight frequency with the use of smaller planes. Hubs and low cost carriers are associated with larger plane sizes and higher frequency, while regional airline ownership leads to higher frequency and the use of smaller planes. An increase in distance between the endpoints leads to lower frequency with the use of larger planes. As airport delay rises, airlines reduce frequency and use smaller planes, though when airport cancellations rise, flight frequency increases with the use of larger planes. This finding suggests airlines utilize frequency and aircraft size to hedge against flight cancellations. The third chapter, titled “Road Congestion Tolling under Competition,” introduces a tolled road that congests the un-tolled alternative to the model proposed by Verhoef, Nijkamp and Rietveld (1996) and analyzes the toll and welfare outcomes under a social planner’s prospective.

Suggested Citation
Vivek Aravind Pai (2008) Essays in transportation economics. Ph.D.. University of California, Irvine. Available at: https://uci.primo.exlibrisgroup.com/permalink/01CDL_IRV_INST/74dcdl/alma991035093220004701 (Accessed: October 14, 2023).

conference paper

Restaurant Food Consumption in the Time of the Pandemic - A California Case Study

Transportation Research Board 103rd Annual Meeting

Publication Date

January 1, 2024
Suggested Citation
Bumsub Park and Jean-Daniel Saphores (2024) “Restaurant Food Consumption in the Time of the Pandemic - A California Case Study”. Transportation Research Board 103rd Annual Meeting.

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.

conference paper

Measuring consumer willingness to enroll in battery electric vehicle smart charging programs

2024 IEEE vehicle power and propulsion conference (VPPC)

Publication Date

October 1, 2024

Author(s)

Pingfan Hu, Brian Tarroja, Matthew Dean, Kate Forrest, Eric Hittinger, Alan Jenn, John Paul Helveston

Abstract

As Battery Electric Vehicles (BEVs) gain popularity, managing their charging becomes crucial for grid stability. Smart charging programs can help utilities manage this demand and integrate more renewable energy by controlling when and how BEVs are charged. However, these programs require participation from BEV owners, who may be hesitant to freely provide such control. This study uses a discrete choice experiment (also called conjoint analysis) to measure BEV owners’ willingness to participate in smart charging programs under various incentives and features. We examine two types of smart charging: Supplier-Managed Charging (SMC), which controls charging times, and Vehicle-to-Grid (V2G), allowing BEVs to return power to the grid. In an online survey conducted via Facebook and Instagram ads, we collected 858 valid responses, with 815 responses for SMC program choices and 414 for V2G program choices. We used mixed logit (MXL) models to quantify respondents’ willingness to participate. The findings indicate a general reluctance to participate in both programs without some form of incentive, with respondents being most sensitive to recurring monetary incentives. For SMC, there is also concern about ensuring sufficient battery levels in the mornings. Simulations were conducted to predict enrollment rates based on different program features. Additional data will be collected to refine the models in the coming months.

Suggested Citation
Pingfan Hu, Brian Tarroja, Matthew Dean, Kate Forrest, Eric Hittinger, Alan Jenn and John Paul Helveston (2024) “Measuring consumer willingness to enroll in battery electric vehicle smart charging programs”, in 2024 IEEE vehicle power and propulsion conference (VPPC), pp. 1–17. Available at: 10.1109/VPPC63154.2024.10755299.

research report

Transportation Management Center (TMC) Performance Measurement System

Abstract

This project developed a web-based application that addresses the problem of identifying the value of the TMC in managing disruptions to the transportation system by quantifying the delay savings that can be attributed directly to TMC actions. Using event data from TMC activity logs and traffic state data from the PeMS database, the system identifies the time-space impact of events in the activity database using a mathematical-programming formulation to match evidence of disruption to computed time-space bounds. Given this boundary, the actual delay associated with the impacted region is calculated. To compute the savings attributable to the TMC, the activity logs are used to identify when the direct disruption by the event is removed (e.g., when an accident is cleared) and models the increased delay that would occur if this clearance was delayed. Given these calculations, the system allows TMC managers to evaluate the performance of various bundles of TMC technologies and operational policies by mapping their effects onto events in the system that can be measured using existing surveillance systems and daily activity logs. The system is deployed atop the CTMLabs service-oriented architecture and is available as a application on the CTMLabs website for use by authenticated users.

Suggested Citation
Will Recker and Craig Ross Rindt (2010) Transportation Management Center (TMC) Performance Measurement System. Final Report CA11-0975, #UCI-0252. ITS-Irvine. Available at: https://dot.ca.gov/-/media/dot-media/programs/research-innovation-system-information/documents/final-reports/ca11-0975-finalreport-a11y.pdf.

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)

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.