research report
published journal article
Fatigue-related crashes, which are mainly caused by drowsy or distracted driving, account for a significant portion of fatal accidents on highways. Smart vehicle technologies can address this issue of road safety to improve the sustainability of transportation systems. Advanced driver-assistance system (ADAS) can aid drowsy drivers by recommending and guiding them to rest locations. Past research shows a significant correlation between driving distance and driver fatigue, which has been actively studied in the analysis of resting behavior. Previous research efforts have mainly relied on survey methods at specific locations, such as rest areas or toll booths. However, such traditional methods, like field surveys, are expensive and often produce biased results, based on sample location and time. This research develops methods to better estimate travel resting behavior by utilizing a large-scale dataset obtained from car navigation systems, which contain 591,103 vehicle trajectories collected over a period of four months in 2014. We propose an algorithm to statistically categorize drivers according to driving distances and their number of rests. The main algorithm combines a statistical hypothesis test and a random sampling method based on the renowned Monte-Carlo simulation technique. We were able to verify that cumulative travel distance shares a significant relationship with one’s resting decisions. Furthermore, this research identifies the resting behavior pattern of drivers based upon their travel distances. Our methodology can be used by sustainable traffic safety operators to their driver guiding strategies criterion using their own data. Not only will our methodology be able to aid sustainable traffic safety operators in constructing their driver guidance strategies criterion using their own data, but it could also be implemented in actual car navigation systems as a mid-term solution. We expect that ADAS combined with the proposed algorithm will contribute to improving traffic safety and to assisting the sustainability of road systems.
conference paper
published journal article
This paper investigates the historical sources of soil-lead contamination in Santa Ana, California. Even though dangerous levels of soil-lead have been found in a wide variety of communities across the United States, public health institutions lack clarity on the historical origins of these crises. This study uses geo-spatial data collected through archival research to estimate the impact of two potential sources of lead contamination in the past — lead-paint and leaded gasoline. It examines, through a combination of statistical and historical methods, the association between lead concentrations in contemporary soil samples and patterns in the evolution of the city’s physical features, such as the growth of urbanized areas and the historical flow of traffic. We emphasize the value of historical data collected through archival research for understanding the sources of environmental lead, particularly leaded gasoline, which our study found to be the most likely and most prominent contributor to soil-lead in Santa Ana’s environment. This research contributes to environmental-justice advocacy efforts to reframe lead poisoning as a systemic environmental issue and outlines the path forward to community-level remediation strategies.
Phd Dissertation
Modern computer hardware (multi-core, multi gigahertz processors with gigabytes of RAM and terabytes of disk) along with IDEs allows programmers to build computer programs which are bigger and more complex than they can understand or keep in their working memories. Additionally, the problems these programs are designed to model are ever more complicated. Consequently, programs are full of inconsistencies, mistakes, and incompleteness’s. These problems are difficult to detect, difficult to locate, and difficult to correct. Often a change is made by a programmer to fix a problem for which understanding all the repercussions of the change is difficult. Consequently, further bugs are introduced into the code base. Because of the pervasiveness of software in society and the potential severity of the consequences of bugs, software developers need ever better tools to help them understand, navigate, and follow the consequences of their development and maintenance activities. This dissertation presents a novel framework based on tree/graph searching and parsing, deductive retrieval, dynamic analysis, symbolic execution, aspect oriented programming, and an open interpreter to allow a software developer to navigate, locate features, find bugs, and abstract information in software. The system is designed to have a fast modify-test cycle such that the programmer can search and test the software as it is being edited without time consuming recompilation, reinstrumenting, or database repopulating each time an edit is made to the code base. The system is language independent, requiring only files to specify the language grammar, control flow graph transformation, and execution semantics. In addition, because of the flexibility and programmability of the system it is an excellent environment to perform further research on program analysis techniques such as dynamic analysis, symbolic execution and abstract interpretation. A prototype system has been built along with data files for the C programming language which demonstrates the feasibility of the system and its ability to scale to “modern-sized” programs.
working paper
Comparison of performance of transit systems and discussion of changes in the transit industry across years is facilitated by comparison of systems which are similar in their operating characteristics. Analysts and policy makers can be misled by comparing performance of systems which are essentially unlike one another. Construction of peer groups of transit systems which are similar in their operating characteristics allows individual systems to be compared to others which are similar, the relationship between operating characteristics and performance to be examined by focusing on differences between groups and to detect changes across years in the performance of systems with certain operating characteristics. This report focuses on fixed route motor bus transit syst~ms which reported data for Section 15 for FY1980 and FY1981. Peer groups of these systems were constructed on the basis of variables reflecting inherent differences in operations and operating environments. Ideally one would use demographic variables to determine operating environment for each system. However, since these are not available in a form compatible with the level of reporting in the Section 15 data, four variables, tapping different aspects of the operating characteristics, of transit systems were selected as the basis for measuring the similarity among transit systems.
published journal article
published journal article
conference paper
Intersection movement assist (IMA) is a connected vehicle (CV) application to improve vehicle safety. GPS spoofing attack is one major threat to the IMA application since inaccurate localization results may generate fake warnings that increase rear-end crashes, or cancel real warnings that may lead to angle or swipe crashes. In this work, we first develop a GPS spoofing attack model to trigger the IMA warning of entry vehicles at a roundabout driving scenario. The attack model can generate realistic trajectories while achieving the attack goal. To defend against such attacks, we further design a one-class classifier to distinguish the normal vehicle trajectories from the trajectories under attack. The proposed model is validated with a real-world data set collected from Ann Arbor, Michigan. Results show that although the attack model triggers the IMA warning in a short time (i.e., in a few seconds), the detection model can still identify the abnormal trajectories before the attack succeeds with low false positive and false negative rates.
published journal article