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

View conference paper

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

Author(s)

Zhe (Jared) Sun, Wenlong Jin, Stephen Ritchie

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

View Preprint Journal Article

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.

conference paper

A Deep-Learning Approach to Detect and Classify Heavy-Duty Trucks in Satellite Images

Transportation Research Board 103rd Annual Meeting

Publication Date

January 1, 2024

Author(s)

Xingwei Liu, Yiqiao Li, Langting Sizemore, Xiaohui Xie, Jun Wu

Suggested Citation

Xingwei Liu, Yiqiao Li, Langting Sizemore, Xiaohui Xie and Jun Wu (2024) “A Deep-Learning Approach to Detect and Classify Heavy-Duty Trucks in Satellite Images”. Transportation Research Board 103rd Annual Meeting.

published journal article

Control of a lane-drop bottleneck through variable speed limits

Transportation Research Part C: Emerging Technologies

Publication Date

September 1, 2015

Author(s)

Hui-Yu Jin, Wenlong Jin

View published journal article

Suggested Citation

Hui-Yu Jin and Wen-Long Jin (2015) “Control of a lane-drop bottleneck through variable speed limits”, Transportation Research Part C: Emerging Technologies, 58, pp. 568–584. Available at: 10.1016/j.trc.2014.08.024.

published journal article

Why do they live so far from work? Determinants of long-distance commuting in California

Journal of Transport Geography

Publication Date

October 1, 2019

Author(s)

Suman Mitra, Jean-Daniel Saphores

View published journal article

Abstract

The determinants of long-distance commuting (trips longer than 50â?¯miles one-way) in the U.S. appear to be poorly understood even though long-distance commuting likely has substantial environmental, social, and economic impacts. A review of the literature shows that that few papers have considered how housing costs influence long-distance commuting. Moreover, residential self-selection has rarely been accounted for in commuting studies. To start addressing these gaps, the authors analyze the long-distance travel component of the 2012 California Household Travel Survey via a generalized structural equation model. In their model, land use and housing costs are explained by household and head of household characteristics; together with these characteristics, land use and housing costs influence long-distance commuting. The authors find that the probability of a household commuting long-distance decreases with an increase in the job-housing ratio (ORâ?¯=â?¯0.914***) and most importantly, with the median home value of the census tract where a household resides (ORâ?¯=â?¯0.488***). Conversely, this probability increases with the median census tract home values where household members work (ORâ?¯=â?¯1.765***). Finally, the authors’ model results confirm the presence of small residential self-selection effects. These results highlight the importance of providing more affordable housing and mixed development options to reduce long-distance commuting and its associated environmental impacts.

Suggested Citation

Suman K. Mitra and Jean-Daniel M. Saphores (2019) “Why do they live so far from work? Determinants of long-distance commuting in California”, Journal of Transport Geography, 80, p. 102489. Available at: 10.1016/j.jtrangeo.2019.102489.

published journal article

Association between Airport Ultrafine Particles and Lung Cancer Risk: The Multiethnic Cohort Study

Cancer Epidemiology, Biomarkers & Prevention

Publication Date

May 1, 2024

Author(s)

Arthur Bookstein, Justine Po, Chiuchen Tseng, Timothy V. Larson, Johnny Yang, Sung-shim L. Park, Jun Wu, Salma Shariff-Marco, Pushkar P. Inamdar, Ugonna Ihenacho, Veronica W. Setiawan, Mindy C. DeRouen, Loïc Le Marchand, Daniel O. Stram, Jonathan Samet, Beate Ritz, Scott Fruin, Anna H. Wu, Iona Cheng

View published journal article

Abstract

Ultrafine particles (UFP) are unregulated air pollutants abundant in aviation exhaust. Emerging evidence suggests that UFPs may impact lung health due to their high surface area-to-mass ratio and deep penetration into airways. This study aimed to assess long-term exposure to airport-related UFPs and lung cancer incidence in a multiethnic population in Los Angeles County.Within the California Multiethnic Cohort, we examined the association between long-term exposure to airport-related UFPs and lung cancer incidence. Multivariable Cox proportional hazards regression models were used to estimate the effect of UFP exposure on lung cancer incidence. Subgroup analyses by demographics, histology and smoking status were conducted.Airport-related UFP exposure was not associated with lung cancer risk [per one IGR HR, 1.01; 95% confidence interval (CI), 0.97–1.05] overall and across race/ethnicity. A suggestive positive association was observed between a one IQR increase in UFP exposure and lung squamous cell carcinoma (SCC) risk (HR, 1.08; 95% CI, 1.00–1.17) with a Phet for histology = 0.05. Positive associations were observed in 5-year lag analysis for SCC (HR, 1.12; 95% CI, CI, 1.02–1.22) and large cell carcinoma risk (HR, 1.23; 95% CI, 1.01–1.49) with a Phet for histology = 0.01.This large prospective cohort analysis suggests a potential association between airport-related UFP exposure and specific lung histologies. The findings align with research indicating that UFPs found in aviation exhaust may induce inflammatory and oxidative injury leading to SCC.These results highlight the potential role of airport-related UFP exposure in the development of lung SCC.

Suggested Citation

Arthur Bookstein, Justine Po, Chiuchen Tseng, Timothy V. Larson, Juan Yang, Sung-shim L. Park, Jun Wu, Salma Shariff-Marco, Pushkar P. Inamdar, Ugonna Ihenacho, Veronica W. Setiawan, Mindy C. DeRouen, Loïc Le Marchand, Daniel O. Stram, Jonathan Samet, Beate Ritz, Scott Fruin, Anna H. Wu and Iona Cheng (2024) “Association between Airport Ultrafine Particles and Lung Cancer Risk: The Multiethnic Cohort Study”, Cancer Epidemiology, Biomarkers & Prevention, 33(5), pp. 703–711. Available at: 10.1158/1055-9965.EPI-23-0924.

conference paper

Study of a Dynamic Cooperative Trading Queue Routing Control Scheme for Freeways and Facilities with Parallel Queues

Proceedings of the 97th annual meeting of the transportation research board

Publication Date

January 1, 2018

Author(s)

Roger Lloret-Batlle, R. (Jay) Jayakrishnan

View conference paper

Abstract

This article explores the coalitional stability of a new cooperative control policy for freeways and parallel queuing facilities with multiple servers. Based on predicted future delays per queue or lane, a VOT-heterogeneous population of agents can agree to switch lanes or queues and transfer payments to each other in order to minimize the total cost of the incoming platoon. The strategic interaction is captured by an n-level Stackelberg model with coalitions, while the cooperative structure is formulated as a partition function game (PFG). The stability concept explored is the strong-core for PFGs which we found appropiate given the nature of the problem. This concept ensures that the efficient allocation is individually rational and coalitionally stable. We analyze this control mechanism for two settings: a static vertical queue and a dynamic horizontal queue. For the former, we first characterize the properties of the underlying cooperative game. Our simulation results suggest that the setting is always strong-core stable. For the latter, we propose a new relaxation program for the strong-core concept. Our simulation results on a freeway bottleneck with constant outflow using Newell’s car-following model show the imputations to be generally strong-core stable and the coalitional instabilities to remain small with regard to users’ costs.

Suggested Citation

Roger Lloret-Batlle and R. Jayakrishnan (2018) “Study of a Dynamic Cooperative Trading Queue Routing Control Scheme for Freeways and Facilities with Parallel Queues”, in Proceedings of the 97th annual meeting of the transportation research board. arXiv, p. 6p. Available at: 10.48550/ARXIV.1803.01265.

published journal article

Subprime mortgages and the housing bubble

Journal of Urban Economics

Publication Date

March 1, 2012

Author(s)

Jan Brueckner, Paul S. Calem, Leonard I. Nakamura

View published journal article

Abstract

This paper explores the link between the house-price expectations of mortgage lenders and the extent of subprime lending. It argues that bubble conditions in the housing market are likely to spur subprime lending, with favorable price expectations easing the default concerns of lenders and thus increasing their willingness to extend loans to risky borrowers. Since the demand created by subprime lending feeds back onto house prices, such lending also helps to fuel an emerging housing bubble. These ideas are illustrated in a theoretical model, and tentative support is found in empirical work exploring the connection between price expectations and the extent of subprime lending. (C) 2011 Elsevier Inc. All rights reserved.

Suggested Citation

Jan K. Brueckner, Paul S. Calem and Leonard I. Nakamura (2012) “Subprime mortgages and the housing bubble”, Journal of Urban Economics, 71(2), pp. 230–243. Available at: 10.1016/j.jue.2011.09.002.

presentation

One Year into the Pandemic: Heterogeneity in COVID19 Spread Patterns and Human Mobility Characteristics across US Counties

Publication Date

January 1, 2022

Author(s)

Yusuf Sarwar Uddin, Rezwana Rafiq

Suggested Citation

Md Yusuf Sarwar Uddin and Rezwana Rafiq (2022) “One Year into the Pandemic: Heterogeneity in COVID19 Spread Patterns and Human Mobility Characteristics across US Counties”. 101st Annual Meeting of the Transportation Research Board.