book/book chapter
Area of Expertise: Unspecified
conference paper
A distributed, scalable, and synchronized framework for large-scale microscopic traffic simulation
Proceedings. 2005 IEEE intelligent transportation systems, 2005.
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Author(s)
Suggested Citation
Raymond Klefstad, Yue Zhang, Mingjie Lai, R Jayakrishnan and Riju Lavanya (2005) “A distributed, scalable, and synchronized framework for large-scale microscopic traffic simulation”, in Proceedings. 2005 IEEE intelligent transportation systems, 2005.. IEEE / IEEE, pp. 813–818. Available at: 10.1109/itsc.2005.1520154.conference paper
Using mobile tracking technologies to characterize air pollution exposure in major goods movement corridors
Proceedings of the annual meeting of the association of collegiate schools of planning (ACSP), cincinnati, OH
Publication Date
Author(s)
Suggested Citation
D. Houston, G. Jaimes, J. Wu and D. Yang (2012) “Using mobile tracking technologies to characterize air pollution exposure in major goods movement corridors”, in Proceedings of the annual meeting of the association of collegiate schools of planning (ACSP), cincinnati, OH.published journal article
Comments
Brookings-Wharton Papers on Urban Affairs
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Suggested Citation
Jan K. Brueckner and Douglas Holtz-Eakin (2000) “Comments”, Brookings-Wharton Papers on Urban Affairs, 2000(1), pp. 267–273. Available at: 10.1353/urb.2000.0014.published journal article
Intersectionality of individual and neighborhood-level adverse social determinants of health in early pregnancy
Pregnancy
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Abstract
Introduction Individual- and neighborhood-level social determinants of health (SDOH) have been assessed separately in pregnancy, but their relationship to one another remains uncertain. We investigated the intersectionality of three neighborhood-level SDOH measures with three individual-level SDOH measures. This was done to examine the concomitant experiences of multiple SDOH in pregnancy. Methods A secondary analysis of data from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-To-Be. We assessed three neighborhood-level SDOH measures using geocoded participant home addresses in the first trimester at the census-tract level: (1) high socioeconomic disadvantage (in tertiles) by the 2015 Area Deprivation Index, (2) inadequate food access by the USDA Food Access Research Atlas, and (3) low walkability by the EPA National Walkability Score. We assessed three individual-level SDOH measures: low household income, lower educational attainment, and Medicaid insurance. We examined the combinations of these three neighborhood SDOH and three individual SDOH measures by graphical visualization and using statistical tests to assess overall differences in the distribution of these measures. Results Of 9588 nulliparous individuals, adverse neighborhood-level SDOH [high socioeconomic disadvantage (28%), inadequate food access (24%), and low walkability (66%)] and adverse individual-level SDOH [low household income (19%), lower educational attainment (23%), and Medicaid insurance (33%)] were common in early pregnancy. Six percent of individuals lived in a community with all three adverse neighborhood-level SDOH measures. Of those living in a community with at least two neighborhood-level SDOH measures, 23% lived in areas with inadequate food access and low walkability, 19% with high socioeconomic disadvantage and low walkability, and 1% with high socioeconomic disadvantage and inadequate food access. Overall, 23% lived in a community with no adverse neighborhood-level SDOH, and among this group, 88% had no adverse individual-level SDOH. There were significant differences in adverse individual-level SDOH based on whether individuals lived in a community with all three adverse neighborhood-level measures [low household income (39%), lower educational attainment (44%), Medicaid (55%)], any two measures [low household income (22%), lower educational attainment (27%), Medicaid (37%)], or only one measure [low household income (14%), lower educational attainment (17%), Medicaid (27%)] (p < 0.001 for all). Conclusion Among nulliparous individuals in early pregnancy, the frequency of adverse individual-level SDOH was generally higher when they lived in communities with more adverse neighborhood-level SDOH. Future approaches that identify and classify the multifaceted and multilevel nature of structural determinants as they relate to pregnancy outcomes are needed.
Suggested Citation
Jameaka L. Hamilton, William A. Grobman, Jiqiang Wu, Lynn M. Yee, David Haas, Becky Mcneil, Brian Mercer, Hyagriv Simhan, Uma Reddy, Robert M. Silver, Samuel Parry, George Saade, Jun Wu, Courtney D. Lynch and Kartik K. Venkatesh (2025) “Intersectionality of individual and neighborhood-level adverse social determinants of health in early pregnancy”, Pregnancy, 1(2), p. e70002. Available at: 10.1002/pmf2.70002.conference paper
Modeling, analysis, and optimization of Electric Vehicle HVAC systems
2016 21st asia and south pacific design automation conference (ASP-DAC)
Publication Date
Author(s)
Suggested Citation
Mohammad Abdullah Al Faruque and Korosh Vatanparvar (2016) “Modeling, analysis, and optimization of Electric Vehicle HVAC systems”, in 2016 21st asia and south pacific design automation conference (ASP-DAC). IEEE. Available at: 10.1109/aspdac.2016.7428048.published journal article
Emissions impacts of a modal shift: A case study of the Southern California ports region
Journal of International Logistics and Trade
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Abstract
This paper presents a case study examining emissions impacts of a modal shift from on-road trucks to rail for goods movement through the Southern California ports region, one of the severest nonattainment areas in terms of national air quality standards. Recent completion of the Alameda Corridor, a 20-mile rail expressway connecting the Ports of Long Beach and Los Angeles with rail main lines near downtown Los Angeles, provides substantial reserve capacity for port traffic to be diverted from the severely congested road network to the rail line. On-road vehicle emissions were estimated using California’s mobile-source emissions model EMFAC that incorporates a set of emissions factors for each vehicle type and an estimate of vehicle activity. These emissions were then compared with the emissions generated from trains increased to carry freight volume diverted from truck traffic. On the basis of year 2000 traffic level, it was estimated that for a 20% modal shift of port traffic, mobile-source emissions can be reduced up to 0.86 tons for nitrogen oxides and 16 kg for particulates/day. The analysis results indicate encouraging the modal shift for port-related freight traffic should be an integral part of overall air quality improvement initiatives for the study area.
Suggested Citation
Minyoung Park, Amelia Regan and Choon-Heon Yang (2007) “Emissions impacts of a modal shift: A case study of the Southern California ports region”, Journal of International Logistics and Trade, 5(2), pp. 67–81. Available at: 10.24006/jilt.2007.5.2.67.published journal article
Congestion pricing and the future of transit
Journal of Transport Geography
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Author(s)
Suggested Citation
Gordon J. Fielding (1995) “Congestion pricing and the future of transit”, Journal of Transport Geography, 3(4), pp. 239–246. Available at: 10.1016/0966-6923(95)00023-2.published journal article
Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
IEEE Transactions on Intelligent Transportation Systems
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Author(s)
Abstract
There is considerable evidence that evaluating the subjective risk level of driving decisions can improve the safety of Autonomous Driving Systems (ADS) in both typical and complex driving scenarios. In this paper, we propose a novel data-driven approach that uses scene-graphs as intermediate representations for modeling the subjective risk of driving maneuvers. Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers. To train our model, we formulate subjective risk assessment as a supervised scene classification problem. We evaluate our model on both synthetic lane-changing datasets and real-driving datasets with various driving maneuvers. We show that our approach achieves a higher classification accuracy than the state-of-the-art approach on both large (96.4% vs. 91.2%) and small (91.8% vs. 71.2%) lane-changing synthesized datasets, illustrating that our approach can learn effectively even from small datasets. We also show that our model trained on a lane-changing synthesized dataset achieves an average accuracy of 87.8% when tested on a real-driving lane-changing dataset. In comparison, the state-of-the-art model trained on the same synthesized dataset only achieved 70.3% accuracy when tested on the real-driving dataset, showing that our approach can transfer knowledge more effectively. Moreover, we demonstrate that the addition of spatial and temporal attention layers improves our model’s performance and explainability. Finally, our results illustrate that our model can assess the risk of various driving maneuvers more accurately than the state-of-the-art model (86.5% vs. 58.4%, respectively).
Suggested Citation
Shih-Yuan Yu, Arnav Vaibhav Malawade, Deepan Muthirayan, Pramod P. Khargonekar and Mohammad Abdullah Al Faruque (2022) “Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions”, IEEE Transactions on Intelligent Transportation Systems, 23(7), pp. 7941–7951. Available at: 10.1109/TITS.2021.3074854.published journal article
Globally Optimal Assignment Algorithm for Collective Object Transport Using Air–Ground Multirobot Teams
IEEE Transactions on Control Systems Technology
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Author(s)
Abstract
We consider the problem of collectively transporting multiple objects using air–ground multirobot teams. The objective is to find the optimal matching between the objects and aerial/ground robots that minimizes the energy of the overall system. We reveal the local optimality criteria for this combinatorial problem and prove that combining a branch and bound algorithm with a negative-cycle canceling algorithm (NCCA) yields an efficient algorithm that provides the globally optimal solution of the problem. Numerical experiments demonstrate the performance on practical problems.