published journal article
published journal article
Despite the substantial number of studies on the relationships between crime patterns and built environments, the impacts of street-level built environments on crime patterns have not been definitively determined due to the limitations of obtaining detailed streetscape data and conventional analysis models. To fill these gaps, this study focuses on the non-linear relationships and threshold effects between built environments and local crime patterns at the level of a street segment in the City of Santa Ana, California. Using Google Street View (GSV) and semantic segmentation techniques, we quantify the features of the built environment in GSV images. Then, we examine the non-linear relationships and threshold effects between built environment factors and crime by applying interpretable machine learning (IML) methods. While the machine learning models, especially Deep Neural Network (DNN), outperformed negative binomial regression in predicting future crime events, particularly advantageous was that they allowed us to obtain a deeper understanding of the complex relationship between crime patterns and environmental factors. The results of interpreting the DNN model through IML indicate that most streetscape elements showed non-linear relationships and threshold effects with crime patterns that cannot be easily captured by conventional regression model specifications. The non-linearities and threshold effects revealed in this study can shed light on the factors associated with crime patterns and contribute to policy development for public safety from crime.
published journal article
Abstract COVID‐19 pandemic policies requiring disease testing provide a rich context to build insights on true positives versus false positives. Our main contribution to the pedagogy of data analytics and statistics is to propose a method for teaching updating of probabilities using Bayes’ rule reasoning to build understanding that true positives and false positives depend on the prior probability. Our instructional approach has three parts. First, we show how to construct and interpret raw frequency data tables, instead of using probabilities. Second, we use dynamic visual displays to develop insights and help overcome calculation avoidance or errors. Third, we look at graphs of positive predictive values and negative predictive values for different priors. The learning activities we use include lectures, in‐class discussions and exercises, breakout group problem solving sessions, and homework. Our research offers teaching methods to help students understand that the veracity of test results depends on the prior probability as well as helps students develop fundamental skills in understanding probabilistic uncertainty alongside higher‐level analytical and evaluative skills. Beyond learning to update the probability of having the disease given a positive test result, our material covers naïve estimates of the positive predictive value, the common mistake of ignoring the disease’s base rate, debating the relative harm from a false positive versus a false negative, and creating a new disease test.
published journal article
working paper
This annotated bibliography is prepared for those who are interested in the problem, concerned citizens and planners in small cities and rural areas, and policy analysts in various levels of government. Public transportation planning in nonmetropolitan areas has been disjointed. There are few well developed goals, objectives, and policies. Standards and values are varied among different communities. Consequently, decisions on public transportation services can best be made locally with active citizen participation. This bibliography, together with a review paper (ITS Report D-SR-77-2), provides adequate information for gaining insights into the various facets of the problem. Additional information and assistance can be obtained from the library of the Institute of Transportation Studies (Berkeley and Irvine), the Division of Mass Transportation – California Department of Transportation, the Urban Mass Transportation Administration – U.S. Department of Transportation, and other agencies or institutes. The bibliography is divided into three sections. The first section is a list of references arranged alphabetically by author. Each article is classified by a system of coded keywords. The keyword codes are shown in parentheses after each reference. The second section contains the abstracts of the articles. The third section is an index of articles by subject. The cross-reference is shown by the article number.
published journal article
conference paper
Accurate estimation of aircraft takeoff weight (TOW) and landing weight (LW) is critical for assessing fuel consumption, emissions, noise impacts, and other analyses, yet these parameters are typically unavailable in surveillance data such as Automatic Dependent Surveillance-Broadcast (ADS-B). This study presents a method for estimating aircraft takeoff and landing weights using stabilized airspeed segments from ADS-B surveillance data. The approach is derived by relating lift, weight, and airspeed during stabilized flight phases. The method outlined is validated using one year of operations at Seattle-Tacoma International Airport, analyzing over 10,000 flights across three narrow-body aircraft types: B737-800, B737-900, and A320. Weight estimated from ADS-B airspeed profiles was matched to weight records provided by an airline, achieving mean absolute errors of 5.0–7.4% of maximum takeoff weight (MTOW) for departures and 6.0–7.0% of MTOW for arrivals. The method exhibits minimal systematic bias, with absolute distribution mean errors below 0.4% MTOW in magnitude. The demonstrated accuracy enables applications such as fleet-wide fuel consumption modeling, emissions inventories, and aircraft noise impact assessments, providing a valuable tool for data-driven modeling of aviation operations using existing surveillance infrastructure.
working paper
This paper describes the development of SIMAP, an activity-based microsimulation model for travel demand forecasting, and is part of a larger research effort aimed at the development of innovative transportation planning methodologies designed to address the limitations of current modeling practice in meeting current legislative and judicial mandates. The model builds upon existing research demonstrating that travel behavior should be viewed holistically using activity-travel patterns, a time-dependent representation of the activities and their attributes in which an individual engages. A microsimulation approach integrated with a geographic information system is advanced to synthesize individual, 24-hour activity-travel patterns for households that are reflective of the available transportation and land use system. By using activity-travel patterns as the basis of the SIMAP, the timing, sequencing, and connections between activities are explicitly included in the model where previously they would be disregarded. The final product of this research is a prototype modeling system that has the potential to replace some or all aspects of the traditional ‘four-step’ modeling process. The next section describes the specifics of SIMAP. Section 3 presents a short discussion of the aggregate activity-travel pattern classification and results. Section 4 summarizes the implementation of the generation model, while Section 5 demonstrates a limited application of SIMAP. Finally, Section 6 concludes this paper by describing how this project’s key contribution and suggests some extensions to the work.
published journal article
Many current initiatives to develop the electric vehicle depend upon public perception that electric vehicles (EVs) are good for the environment. This study investigates how people acquire information about the environment and EVs, and whether their opinions about environmental efficacy change over time and experience levels. These issues are explored across two data sets. The first data set is a panel survey of California households (n = 1718) and environmental opinions are tracked over two waves of survey. A decline in the environmental ethos is associated with several factors, including interpersonal communications and exposure to more specialized media. A sample of households from the panel study were subsequently chosen, among others, to participate in a 2-week long trial of EVs (n = 69). Opinions about environmental efficacy are studied as users gain first hand knowledge of an EV. Opinions about the environmental efficacy of the EV show improvement, but trial users become less likely to cite the environmental benefit as a reason for choosing the technology, and they do not change their opinions about providing policy incentives. (C) 1998 Elsevier Science Ltd. All rights reserved.
published journal article