published journal article
Evaluation and modification of constant volume sampler based procedure for plug-in hybrid electric vehicle testing
SAE Int. J. Alt. Power.
Publication Date
August 1, 2011
Archives: Research Products
conference paper
Behavioral model for Ingress/Egress decision on buffer-separated HOV facilities for microsimulation model
Proceedings of the 89th annual meeting of the transportation research board
Publication Date
January 1, 2010
Author(s)
Abstract
High-Occupancy Vehicle (HOV) lanes, commonly known as carpool lanes, have been accepted as a cost-effective and environmental-friendly alternative in many metropolitan areas. It is thus very important to find ways to evaluate HOV strategies during the decision making process, and prior to any implementation. An alternative approach is microscopic traffic simulation. However, current micro-simulation models have limited capabilities to model HOV driver behaviors, particularly with respect to buffer-separated facilities. This study proposed a probabilistic HOV driver behavioral model based on discrete choice modeling derived from random utility principles, which includes a preferred access choice model for examining travel time savings and a traffic model for calculating the acceptable gap to get in/out the HOV lane. A HOV access plugin was also developed using a micro-simulation tool, Paramics, based on the proposed model. Its theoretical capability is extended to real-world application via providing additional â??implementationâ?? parameters, such as ingress and egress points selection control, and update frequency of traffic information. A real-world freeway network, SR-57 in Orange County, California, was selected to analyze the reasonableness of the model through sensitivity analysis, and was further investigated for model validation purposes. The results have shown the reasonableness of the proposed model under various traffic conditions. The proposed model also demonstrated its feasibility and applicability via setting various calibration parameters and control parameters.
Suggested Citation
Shin-Ting (Cindy) Jeng, Will Recker and Lianyu Chu (2010) “Behavioral model for Ingress/Egress decision on buffer-separated HOV facilities for microsimulation model”, in Proceedings of the 89th annual meeting of the transportation research board, p. 32p. Phd Dissertation
Exploring Delivery Service Substitution of Travel: Optimized Fleet Systems and Household Activity Patterns
Publication Date
January 1, 2024
Author(s)
Areas of Expertise
Abstract
This dissertation delves into the intersection of two critical elements shaping the future of transportation: opportunities and the challenges presented by shopping delivery services, particularly same-day delivery (SDD), and the necessity to anticipate and explore the forthcoming transportation paradigm with the new possibilities offered by Autonomous Vehicles (AVs). This study investigates the transformative potential of SDD services facilitated by a fleet of shared autonomous vehicles (SAVs) to reshape daily shopping trips and activities.With a dual focus on both the network and household layers, the dissertation addresses the viability of SDD services, considering vehicle miles traveled (VMT) savings and operational strategies for efficient fleet management on one side, and the impacts on travel patterns on the other. Leveraging real-world data for the network of Irvine, CA, and employing optimization methodologies, this dissertation (i) investigates the potential VMT savings from SDD compared to the base scenario where households conduct their own shopping activities, (ii) analyzes the optimal fleet size needed to achieve significant VMT reductions, and (iii) evaluates operational strategies for cost-effective and efficient service delivery. In this dissertation, I analyze the optimal fleet size and system design settings needed to achieve significant VMT reductions without losing profitability and I evaluate operational strategies for cost-effective and time-sensitive service delivery. At the network layer, the system is modeled as a multi-Vehicle and Multi-Depot Pickup and Delivery Problem with Time Windows (m-MDPDPTW), which was implemented in Google OR-Tools. The depots are assumed to be at the warehouse locations from where shopping goods deliveries are made. An analysis is presented for a delivery service comprising an AV fleet serving households on their daily shopping trips for the case study of the City of Irvine, CA. The results indicate these services can significantly decrease the distance traveled and the time spent for shopping trips. The dissertation tests several scenarios to determine how varying possible service operation parameters as well as demand characteristics would affect the results. Scenarios involving varying percentage of the service demand, time window for deliveries, loading/unloading time, and warehouse distribution are considered. At the household layer, the dissertation examines how the SDD service influences household travel patterns and savings, using output from the California Statewide Travel Demand Model (CSTDM) for the City of Irvine. The time saved is used as an accessibility measure. Using the Household Activity Travel Pattern Problem (HAPP), formulated as a pickup and delivery problem with time windows for household daily activities, time saved is compared over four distinct scenarios: a base (existing) case with CSTDM patterns, the HAPP-optimized version of the base case, the base case excluding shopping trips, and its HAPP-optimized version. HAPP-based analysis sheds light on new opportunities in travel and activity planning enabled by AVs as well as insights into future activity patterns shaped by subscription services that may lead to more optimized travel patterns. High Performance Computing is used to tackle the NP-Hard computational problem involved in HAPP in the real world case study with a large set of households. This research is also intended to establish the viability of operationalizing a HAPP-methodology for analyzing realistic travel network contexts, for transportation policies that involve innovative vehicle usage and routing patterns. A HAPP solution is not a model for the actual household-level travel behavior, but rather a constraint-driven optimal version of it. Nonetheless, with the availability of rich individual level activity data now and in the future, HAPP can indeed become an optimizer for households, if computational problems can be surmounted. This dissertation establishes that computational problems are not insurmountable with current cloud and advanced computing options, even for 4-member households with activities substitutable across individuals, which past research had generally avoided. The research illustrated that, for a real-world network that has an individual and household-level activity-based planning model, or at least a synthesized model of that kind, policy analysis for future transportation options can be done using HAPP to find an optimized implementation of the policy when the behavioral response to such policy is not available in the existing activity models or data. The dissertation also points to future research possibilities involving faster optimizations that can be achieved if HAPP can be implemented with starting feasible solutions that may be developed from existing networks.
Suggested Citation
Marjan Mosslemi (2024) Exploring Delivery Service Substitution of Travel: Optimized Fleet Systems and Household Activity Patterns. Ph.D.. UC Irvine. Available at: https://uci.primo.exlibrisgroup.com/permalink/01CDL_IRV_INST/u4evf/cdi_proquest_journals_3087787095 (Accessed: October 23, 2024). research report
Exploratory use of raster images as a data source for agricultural commodity transportation modeling
Publication Date
January 1, 2014
Author(s)
Suggested Citation
Pedro V Camargo, Michael G McNally and Stephen G Ritchie (2014) Exploratory use of raster images as a data source for agricultural commodity transportation modeling. conference paper
Revisiting Physical-World Adversarial Attack on Traffic Sign Recognition: A Commercial Systems Perspective
ISOC Network and Distributed System Security Symposium (NDSS) 2025
Publication Date
February 1, 2025
Author(s)
Abstract
Traffic Sign Recognition (TSR) is crucial for safe and correct driving automation. Recent works revealed a general vulnerability of TSR models to physical-world adversarial attacks, which can be low-cost, highly deployable, and capable of causing severe attack effects such as hiding a critical traffic sign or spoofing a fake one. However, so far existing works generally only considered evaluating the attack effects on academic TSR models, leaving the impacts of such attacks on real-world commercial TSR systems largely unclear. In this paper, we conduct the first large-scale measurement of physical-world adversarial attacks against commercial TSR systems. Our testing results reveal that it is possible for existing attack works from academia to have highly reliable (100%) attack success against certain commercial TSR system functionality, but such attack capabilities are not generalizable, leading to much lower-than-expected attack success rates overall. We find that one potential major factor is a spatial memorization design that commonly exists in today’s commercial TSR systems. We design new attack success metrics that can mathematically model the impacts of such design on the TSR system-level attack success, and use them to revisit existing attacks. Through these efforts, we uncover 7 novel observations, some of which directly challenge the observations or claims in prior works due to the introduction of the new metrics.
Suggested Citation
Ningfei Wang, Shaoyuan Xie, Takami Sato, Yunpeng Luo, Kaidi Xu and Qi Alfred Chen (2025) “Revisiting Physical-World Adversarial Attack on Traffic Sign Recognition: A Commercial Systems Perspective”, in ISOC Network and Distributed System Security Symposium (NDSS) 2025. Available at: https://ics.uci.edu/~alfchen/pubs/ningfei_ndss25.pdf. published journal article
Real-time inductive-signature-based level of service for signalized intersections
Transportation Research Record
Publication Date
January 1, 2002
Author(s)
Suggested Citation
Cheol Oh and Stephen G. Ritchie (2002) “Real-time inductive-signature-based level of service for signalized intersections”, Transportation Research Record, 1802(1), pp. 97–104. Available at: 10.3141/1802-12. published journal article
Meta-analysis of shared micromobility ridership determinants
Transportation Research Part D: Transport and Environment
Publication Date
August 1, 2023
Associated Project
Author(s)
Areas of Expertise
Abstract
Shared micromobility (SμM)—shared e-scooters and (e-)bikes—offer moderate-speed, space-efficient, and carbon-light mobility, promoting environmental sustainability and healthy travel. SμM benefits and SμM data availability have fueled a growing literature that analyses SμM ridership. We present a meta-analysis of 29 studies that estimate statistical models of zone- or station-based SμM trip counts, including 22 that examine station-based bikeshare systems. The meta-analysis reveals positive elasticities between SμM usage and population density (median elasticity of 0.16), employment density (0.07), median household income (0.33), bus stops (0.12), metro stations (0.17), bike infrastructure (0.09), and nearby station capacity (0.32). In contrast, station elevation has a negative elasticity. These magnitudes can inform SμM providers and transportation planners seeking to plan/design SμM systems to promote environmentally sustainable travel. Additionally, we critique the existing literature’s failure to (i) capture spatial dependencies, and (ii) discuss the practical implications of model parameters. Finally, we identify themes for future research.
Suggested Citation
Arash Ghaffar, Michael Hyland and Jean-Daniel Saphores (2023) “Meta-analysis of shared micromobility ridership determinants”, Transportation Research Part D: Transport and Environment, 121, p. 103847. Available at: 10.1016/j.trd.2023.103847.Related Publications
policy brief |
Aug 2023
What Drives Shared Micromobility Ridership?
conference paper
Hypercongestion
Annual Meeting of the American Real Estate and Urban Economics Association, Jan 1997
Publication Date
December 13, 2000
Associated Project
Author(s)
Areas of Expertise
Abstract
The standard economic model for analyzing traffic congestion, due to A.A. Walters, incorporates a relationship between speed and traffic flow. Empirical measurements indicate a region, known as hypercongestion, in which speed increases with flow. We argue that this relationship is unsuitable as a supply curve for equilibrium analysis because hypercongestion occurs as a response to transient demand fluctuations. We then present tractable models for handling such fluctuations, both for a uniform expressway and for a dense street network such as in a central business district (CBD). For the CBD model, we consider both exogenous and endogenous time patterns for demand, and we make use of an empirical speed-density relationship for Dallas, Texas to characterize both congested and hypercongested conditions.
Suggested Citation
Kenneth A. Small and Xuehao Chu (2000) “Hypercongestion”. Annual Meeting of the American Real Estate and Urban Economics Association, Jan 1997, New Orleans, LA. Available at: https://escholarship.org/uc/item/3nn3733q?conferencePaper.Related Publications
working paper |
Sep 1994
Population and Employment Densities: Structure and Change
Phd Dissertation |
Jun 1996
A Neuro-Genetic-Based Universally Transferable Freeway Incident Detection Framework
working paper |
Oct 1995
Clean on Paper, Dirty on the Road: Troubles with California's Smog Check
working paper |
Mar 1997
Hypercongestion
working paper |
Sep 1998
A Utility-Theory-Consistent System-of-Demand-Equations Approach to Household Travel Choice
working paper |
Mar 1999
A Simultaneous Model of Household Activity Participation and Trip Chain Generation
Preprint Journal Article
Using machine learning to estimate wildfire PM2.5 at California ZIP codes (2006-2020)
Publication Date
October 18, 2021
Author(s)
Rosana Aguilera, Nana Luo, Rupa Basu, Jun Wu, Alexander Gershunov, Tarik Benmarhnia
Abstract
Epidemiological studies on the detrimental health impacts of exposure to fine particulate matter (PM2.5) from different sources of emission can inform regulatory policy and identify vulnerable communities. Though PM2.5 has decreased in the U.S. in the two past decades, the increasing frequency and severity of wildfires contribute to episodically impair air quality in wildfire-prone regions and beyond. Monitoring air quality extensively is challenging. Since government-operated monitors are sparsely located across California and the U.S., several regions and populations remain unmonitored. Current approaches to estimate PM2.5 concentrations in unmonitored areas often rely on gathering large amounts of data, such as satellite-derived aerosol properties and meteorological variables. and direct use of low-cost air sensor measurements that may be associated with substantial uncertainty Furthermore, modelling wildfire-specific PM2.5 is often based on chemical transport model predictions, which results in highly computationally intensive efforts. Our study used an ensemble model that integrated multiple machine learning algorithms and a large set of predictor variables to estimate daily PM2.5 at the ZIP code level, a relevant spatio-temporal resolution for epidemiological and public health studies. Our models achieved comparable results to previous machine learning studies for PM2.5 prediction, but avoided processing larger, computationally intensive datasets. In addition, we use machine learning to estimate the wildfire-specific PM2.5 concentrations through a novel multiple imputation approach.
Suggested Citation
Rosana Aguilera, Nana Luo, Rupa Basu, Jun Wu, Alexander Gershunov and Tarik Benmarhnia (2021) “Using machine learning to estimate wildfire PM2.5 at California ZIP codes (2006-2020)”. ChemRxiv. Available at: 10.26434/chemrxiv-2021-9hk6q. working paper
Real-Time Mass Passenger Transport Network Optimization Problems
Publication Date
June 1, 2005
Author(s)
Areas of Expertise
Abstract
The aim of Real-Time Mass Transport Vehicle Routing Problem (MTVRP) is to find a solution to route n vehicles in real time to pick up and deliver m passengers. This problem is described in the context of flexible large-scale mass transportation options that use new technologies for communication among passengers and vehicles. The solution of such a problem is relevant to future transportation options involving large scale real-time routing of shared-ride fleet transit vehicles. However, the global optimization of a complex system involving routing and scheduling multiple vehicles and passengers as well as design issues has not been strictly studied in the past. This research proposes a methodology to solve it by using a three level hierarchical optimization approach. Within the optimization process, a Mass Transport Network Design Problem (MTNDP) is solved. This paper introduces MTVRP and presents a scheme to solve it. Then, the associated algorithm to perform the MTNDP optimization is described in detail. An instance for the city of Barcelona, Spain is solved, showing promising results with regard to the applicability of the methodology for large scale transit problems.