book/book chapter
published journal article
Structural inequalities provide an important context for understanding and responding to the impact of high traffic densities on disadvantaged neighborhoods. Emerging atmospheric science and epidemiological research indicates hazardous vehicle-related pollutants (e.g., diesel exhaust) are highly concentrated near major roadways, and the prevalence of respiratory ailments and mortality are heightened in these high-traffic corridors. This article builds on recent findings that low-income and minority children in California disproportionately reside in high-traffic areas by demonstrating how the urban structure provides a critical framework for evaluating the causes, characteristics, and magnitude of traffic, particularly for disadvantaged neighborhoods. We find minority and high-poverty neighborhoods bear over two times the level of traffic density compared to the rest of the Southern California region, which may associate them with a higher risk of exposure to vehicle-related pollutants. Furthermore, these areas have older and more multifamily housing, which is associated with higher rates of indoor exposure to outdoor pollutants, including intrusion of motor vehicle exhaust. We discuss the implications of these patterns on future planning and policy strategies for mitigating the serious health consequences of exposure to vehicle-related air pollutants.
conference paper
published journal article
MS Thesis
This thesis focuses on the operational analysis, evaluation, and comparison between freeways with limited access HOV (High Occupancy Vehicle) lanes and those with continuous access HOV lanes. The study site is the SR-55 and SR-57 freeways in Orange County, California, which are managed by Caltrans District 12. The study focuses on a comparison of before and after volume-occupancy traffic-flow fundamental diagrams and the parameters derived from these diagrams. Determining critical occupancy and critical volume–both of which have heretofore been subjective due to noisy data and subject to traffic engineers’ experience–is crucial to this analysis. To both remove the data collection labor and subjectivity associate with quantifying critical occupancy and volume, a Bayesian approach is proposed to develop a tool to systematically find the critical occupancy and critical volume by Markov chain Monte Carlo methods combined with piecewise linear regression. This research found two main results. First, freeway’s performance after conversion from limited-access HOV facility to continuous-access HOV facility is uncertain. For the two study sites, SR-55 performed better, but SR-57 performed worse after conversion. Second, a common characteristic for the continuous-access HOV facility is that the shockwave speed became faster with conversion, which may cause the slow-down area in the upstream to be more extensive.
published journal article
This study develops the Perception-Intention-Adaptation (PIA) framework to examine the role of attitudes, perceptions, and norms in public transportation ridership. The PIA framework is then applied to understand the relative importance of socio-demographic, built environment, transit service, and socio-psychological factors on public transit use for 279 residents of south Los Angeles, California, a predominately low-income, non-white neighborhood. Confirmatory factor analysis based on 21 survey items resulted in six transit-relevant socio-psychological factors which were used in regression models of two measures of transit use: the probability of using transit at least once in the 7-day observation period, and the mean number of daily transit trips. Our analysis indicates that two PIA constructs, attitudes toward public transportation and concerns about personal safety, significantly improved the model fit and were robust predictors of transit use, independent of built environment factors such as near-residence street network connectivity and transit service level. Results indicate the need for combined policy approaches to increasing transit use that not only enhance transit access, but also target attitudes about transit service and perceptions of crime on transit. (C) 2013 Elsevier Ltd. All rights reserved.
conference paper
conference paper
conference paper
published journal article
Understanding the spatiotemporal distribution of commercial vehicles is essential for facilitating strategic pavement design, freight planning, and policy making. Hence, transportation agencies have been increasingly interested in collecting truck body configuration data due to its strong association with industries and freight commodities, to better understand their distinct operational characteristics and impacts on infrastructure and the environment. The rapid advancement of Light Detection and Ranging (LiDAR) technology has facilitated the development of non-intrusive detection solutions that are able to accurately classify truck body types in detail. This paper proposes a new truck classification method using a LiDAR sensor oriented to provide a wide field-of-view of roadways. In order to enrich the sparse point cloud obtained from the sensor, point clouds originating from the same truck across consecutive frames were grouped and combined using a two-stage vehicle reconstruction framework to generate a dense three-dimensional (3D) point cloud representation of each truck. Subsequently, PointNet – a deep representation learning algorithm – was adopted to train the classification model from reconstructed point clouds. The model utilizes low-level features extracted from the 3D point clouds and detects key features associated with each truck class. Finally, model ensemble techniques were explored to reduce the generalization error by averaging the results of seven PointNet models and further enhancing the overall model performance. The optimal number of models in the ensemble was determined through a comprehensive sensitivity analysis with the consideration of the average correct classification rate (CCR), the variability of the prediction results, and the computation efficiency. The developed model is capable of distinguishing passenger vehicles and 29 different truck body configurations with an average CCR of 83 percent. The average correct classification rate of the developed method on the test dataset was 90 percent for trucks pulling a large trailer(s).