published journal article

On-demand ridesourcing for urban emergency evacuation events: An exploration of message content, emotionality, and intersectionality

International Journal of Disaster Risk Reduction

Publication Date

April 1, 2020

Author(s)

Elisa Borowski, Amanda Stathopoulos

Abstract

Evacuation mode choice has been researched over the past decade for disaster management and planning, focusing primarily on established modes such as personal automobiles, carpooling, and transit. Recently, however, on-demand ridesourcing has become a viable mode alternative, most notably through the growth of major transportation network companies, such as Uber and Lyft. The availability of this new transportation option is expected to have important implications for adaptive disaster response. The goal of this work is to investigate the influence of internal and external contextual factors on preferred ridesourcing applications during small-scale urban evacuations. A case study was conducted in the three most populous metropolitan areas in the United States. Data were collected using an internet-based stated preference survey, and a discrete choice model was estimated to analyze the 185 responses. Determinants of on-demand ridesourcing for evacuation include internal factors, such as interactions between race, gender, and income, and external contextual factors, such as the evacuation notification source, consequence severity, immediacy, evacuation distance, unfamiliarity of surroundings, and traveling with others. Findings are illustrated through three ridesourcing applications based on specific evacuation needs. Policy recommendations are provided for the design of equitable evacuation services, soft policy communication strategies, and public-private partnerships.

Suggested Citation
Elisa Borowski and Amanda Stathopoulos (2020) “On-demand ridesourcing for urban emergency evacuation events: An exploration of message content, emotionality, and intersectionality”, International Journal of Disaster Risk Reduction, 44, p. 101406. Available at: 10.1016/j.ijdrr.2019.101406.

book/book chapter

What if psychology redesigned the criminal justice system?

Publication Date

September 1, 2011

Author(s)

Joel A. Dvoskin, Jennifer L. Skeem, Raymond Novaco, Kevin S. Douglas
Suggested Citation
Joel A. Dvoskin, Jennifer L. Skeem, Raymond W. Novaco and Kevin S. Douglas (2011) “What if psychology redesigned the criminal justice system?”, in Using social science to reduce violent offending. Oxford University Press, pp. 291–302. Available at: https://doi.org/10.1093/acprof:oso/9780195384642.003.0069.

working paper

Demand for Clean-Fuel Personal Vehicles in California: A Discrete-Choice Stated Preference Survey

Publication Date

March 1, 1992

Author(s)

David Bunch, Mark Bradley, Thomas Golob, Ryuichi Kitamura, Gareth P. Occhiuzzo

Working Paper

UCI-ITS-WP-92-2

Abstract

A study was conducted to determine how demand for clean-fuel vehicles and their fuels is likely to vary as a function of attributes that distinguish these vehicles from conventional gasoline vehicles. For the purposes of the study, clean-fuel vehicles are defined to encompass both electric vehicles, and unspecified (methanol, ethanol, compressed natural gas or propane) liquid and gaseous fuel vehicles, in both de or multiple-fuel versions. The attributes include vehicle purchase price, fuel operating cost, vehicle range between refueling, availability of fuel, dedicated versus multiple-fuel capability, and the level of reduction in emissions (compared to current vehicles). In a mail-back stated preference survey, approximately 700 respondents in the California South Coast Air Basin gave their choices among sets of hypothetical future vehicles, as well as their choices between alternative fuel versus gasoline for hypothetical multiple-fuel vehicles. Estimates of attribute importance and segment differences are made using discrete-choice nested multinomial logit models for vehicle choice, and binomial logit models for fuel choice. These estimates can be used to modify present vehicle-type choice and utilization models to accommodate clean-fuel vehicles; they can also be used to evaluate scenarios for alternative clean-fuel vehicle and fuel supply configurations. Results indicate that range between refueling is an important attribute, particularly if range for an alternative fuel is substantially less than that for gasoline. For fuel choice, the most important attribute is fuel cost, but the predicted probability of choosing alternative fuel is also affected by emissions levels, which can compensate for differences in fuel prices.

Suggested Citation
David S. Bunch, Mark Bradley, Thomas F. Golob, Ryuichi Kitamura and Gareth P. Occhiuzzo (1992) Demand for Clean-Fuel Personal Vehicles in California: A Discrete-Choice Stated Preference Survey. Working Paper UCI-ITS-WP-92-2. Institute of Transportation Studies, Irvine. Available at: https://escholarship.org/uc/item/91m3m2qt.

published journal article

Calibration of INTRAS for simulation of 30-sec loop detector output

Transportation Research Record

Publication Date

January 1, 1994
Suggested Citation
Ruey L. Cheu, Wilfred W. Recker and Stephen G. Ritchie (1994) “Calibration of INTRAS for simulation of 30-sec loop detector output”, Transportation Research Record, (1457), pp. 208–215.

published journal article

Optimal sensor placement for dilution of precision minimization via quadratically constrained fractional programming

IEEE Transactions on Aerospace and Electronic Systems

Publication Date

August 1, 2019
Suggested Citation
Joe J. Khalife and Zaher Zak M. Kassas (2019) “Optimal sensor placement for dilution of precision minimization via quadratically constrained fractional programming”, IEEE Transactions on Aerospace and Electronic Systems, 55(4), pp. 2086–2096. Available at: 10.1109/taes.2018.2879552.

conference paper

A pattern recognition and feature fusion formulation for vehicle reidentification in Intelligent Transportation Systems

IEEE international conference on acoustics speech and signal processing

Publication Date

May 1, 2002

Author(s)

Ravi P. Ramachandran, Glenn Arr, Carlos Sun, Stephen Ritchie

Abstract

Vehicle reidentification is the process of reidentifying or tracking vehicles from one point on the roadway to the next. By performing vehicle reidentification, important traffic parameters including travel time, section density and partial dynamic origin/destination demands can be obtained. This provides for anonymous tracking of vehicles from site-to-site and has the potential for improving Intelligent Transportation Systems (ITS) by providing more accurate data. This paper presents a new vehicle reidentification algorithm that uses four different features, namely, (1) the inductive signature vector acquired from loop detectors, (2) vehicle velocity, (3) traversal time and (4) color information (based on images acquired from video cameras) to achieve high accuracy. A nearest neighbor approach classifies the features and linear feature fusion is shown to improve performance. With the fusion of four features, more than a 91 percent accuracy is obtained on real data collected from a parkway in California.

Suggested Citation
Ravi P. Ramachandran, Glenn Arr, Carlos Sun and Stephen G. Ritchie (2002) “A pattern recognition and feature fusion formulation for vehicle reidentification in Intelligent Transportation Systems”, in IEEE international conference on acoustics speech and signal processing. IEEE / IEEE Signal Proc Soc (International conference on acoustics speech and signal processing (ICASSP)), pp. 3840–3843. Available at: 10.1109/icassp.2002.5745494.

research report

Simultaneous state and parameter estimation in newell's simplified kinematic wave model with heterogeneous data

Publication Date

January 1, 2015
Suggested Citation
Zhe Sun, Wen-Long Jin and Stephen G Ritchie (2015) Simultaneous state and parameter estimation in newell's simplified kinematic wave model with heterogeneous data.

Preprint Journal Article

Radiance Field Delta Video Compression in Edge-Enabled Vehicular Metaverse

Publication Date

December 31, 2024

Author(s)

Matúš Dopiriak, Eugen Šlapak, Juraj Gazda, Devendra S. Gurjar, Mohammad Al Faruque, Marco Levorato

Abstract

Connected and autonomous vehicles (CAVs) offload computationally intensive tasks to multi-access edge computing (MEC) servers via vehicle-to-infrastructure (V2I) communication, enabling applications within the vehicular metaverse, which transforms physical environment into the digital space enabling advanced analysis or predictive modeling. A core challenge is physical-to-virtual (P2V) synchronization through digital twins (DTs), reliant on MEC networks and ultra-reliable low-latency communication (URLLC). To address this, we introduce radiance field (RF) delta video compression (RFDVC), which uses RF-encoder and RF-decoder architecture using distributed RFs as DTs storing photorealistic 3D urban scenes in compressed form. This method extracts differences between CAV-frame capturing actual traffic and RF-frame capturing empty scene from the same camera pose in batches encoded and transmitted over the MEC network. Experiments show data savings up to 71% against H.264 codec and 44% against H.265 codec under different conditions as lighting changes, and rain. RFDVC also demonstrates resilience to transmission errors, achieving up to +0.29 structural similarity index measure (SSIM) improvement at block error rate (BLER) = 0.35 in non-rainy and +0.25 at BLER = 0.2 in rainy conditions, ensuring superior visual quality compared to standard video coding (VC) methods across various conditions.

Suggested Citation
Matúš Dopiriak, Eugen Šlapak, Juraj Gazda, Devendra S. Gurjar, Mohammad Abdullah Al Faruque and Marco Levorato (2024) “Radiance Field Delta Video Compression in Edge-Enabled Vehicular Metaverse”. arXiv. Available at: 10.48550/arXiv.2411.11857.