published journal article
conference paper
Development of advanced technologies for real-time road traffic congestion detection is required by advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS). However, provision of dynamic intra-lane and inter-lane traffic information such as queuing and lane-changing remains incomplete in emerging technologies. This paper is intended to introduce a prototype of a new framework capable of real-time detection of incident and non-incident congestion on freeways. It employs dynamic estimates of lane traffic characteristics including queue lengths in blocked lanes tin the case of lane-blocking incidents), the number of vehicle in each lane, and mandatory lane-changing fractions in lanes with traffic congestion for the use of real-time road traffic congestion detection. On-line lane traffic count and occupancy data collected from point detectors are used as the major input to the proposed framework. The framework is founded on the basis of 1) nonlinear stochastic system modeling and estimation which involves the use of an extended Kalman filter and 2) the modified sequential probability ratio test technology (MSPRT). Preliminary tests had been conducted, indicating the feasibility of employing the proposed framework for the use in real-time incident detection on freeways. Further tasks will include tests for the case of non-incident congestion. The research presented here may help stimulate research in related areas such as incident management systems, automatic vehicle tracking and monitoring systems, and automatic road congestion warning systems for further use in ATMS and ATIS.
published journal article
conference paper
research report
This report presents a data collection plan for evaluating the greenhouse gas (GHG) impacts of high-occupancy toll (HOT) lane projects implemented under Caltrans’ Carbon Reduction Strategy. Because HOT lanes can affect route choice, departure time, vehicle occupancy, and destination choice beyond the managed lane corridor, the report recommends evaluating impacts across the regional travel shed. The research team recommend two approaches for reliable before-and-after estimates of vehicle miles traveled (VMT) by speed bin. The household-based survey approach uses OBD-II, GPS, or smartphone-based tools to observe a representative sample of households within the travel shed, yielding the most detailed evidence on regional VMT and how impacts vary across the population. The infrastructure-based approach combines continuous freeway sensor data with sampled counts and speeds on arterials, local roads, and ramps; it is less costly and easier to implement but offers less insight into the travelers behind observed changes. For each approach, the report describes what data to collect, where and when to collect it, and how to validate and integrate it with EMFAC. The report also includes order-of-magnitude cost estimates for the two alternatives.
Phd Dissertation
This dissertation outlines three topics on urban transportation energy, emphasizing the role of transportation energy policy, and aims to provide a single comprehensive framework to evaluate and compare different pricing and regulatory policy options for reducing transportation fuel consumption in the United States. In the first chapter, I examine the effect of population density on motor fuel (i.e., highway gasoline) consumption, controlling for other variables such as gas price, income, vehicle stock and so on, using state level aggregate cross-sectional time series data from 1966 to 2004. By estimating the impact of density on fuel consumption, I improve the understanding of the conventional logic that there is a negative correlation between population density and transportation energy use due to reduced average travel distance and availability of alternative modes in denser area. In the second part, I examine various transportation energy policy instruments such as a fuel tax, a mileage based VMT tax, Corporate Average Fuel Economy (CAFE) standards, a Pay-at-the-pump (PATP), and a Pay-as-you-drive (PAYD) insurance premium to measure policy impacts through computerized policy simulations. By fully integrating three interrelated economic demand decisions—size of vehicle stock, use of the vehicle stock, and energy efficiency—it can predict short-run, long-run, and dynamic effects of a policy change. The impacts are measured in terms of vehicle miles traveled, fuel consumption, greenhouse gas emissions, and cost savings. I also examine the impact of transportation energy policies on traffic safety in terms of the number of traffic accidents, traffic fatalities, and total accident costs. The outcome of this research provides a set of specific results comparing policy scenarios in a consistent manner. The results will provide guidance concerning whether the policy option would reduce energy dependency as well as undesirable side effects such as environmental problems and safety problems of motor-vehicle travel.
published journal article
conference paper
A microsimulation method is presented for evaluating the effectiveness of potential intelligent transportation system (ITS) strategies under non-recurrent congestion. The evaluated ITS strategies include incident management, adaptive ramp metering, traveler information systems, arterial management, and a combination of those strategies. These strategies are implemented and evaluated over a road network in Irvine, California, with the microsimulation model PARAMICS. The evaluation results show that all ITS strategies have positive effects on network performance. Because of the network topology (one major freeway with two parallel arterial streets), real-time traveler information has the greatest benefits among all single ITS strategies. However, a combination of ITS strategies can further increase benefits.
published journal article
conference paper
Cyber-physical systems (CPS) play a pivotal role in modern critical infrastructure, spanning sectors such as energy, transportation, healthcare, and manufacturing. These systems combine digital and physical elements, making them susceptible to a new class of threats known as cyber kinetic vulnerabilities. Such vulnerabilities can exploit weaknesses in the cyber world to force physical consequences and pose significant risks to both human safety and infrastructure integrity. This paper presents a novel tool, named Rampo, that can perform binary code analysis to identify cyber kinetic vulnerabilities in CPS. The proposed tool takes as input a Signal Temporal Logic (STL) formula that describes the kinetic effect—i.e., the behavior of the “physical” system—that one wants to avoid. The tool then searches the possible “cyber” trajectories in the binary code that may lead to such “physical” behavior. This search integrates binary code analysis tools and hybrid systems falsification tools using a Counter-Example Guided Abstraction Refinement (CEGAR) approach. In particular, Rampo starts by analyzing the binary code to extract symbolic constraints that represent the different paths in the code. These symbolic constraints are then passed to a Satisfiability Modulo Theories (SMT) solver to extract the range of control signals that can be produced by each of the paths in the code. The next step is to search over possible “physical” trajectories using a hybrid systems falsification tool that adheres to the behavior of the “cyber” paths and yet leads to violations of the STL formula. Since the number of “cyber” paths that need to be explored increases exponentially with the length of “physical” trajectories, we iteratively perform refinement of the “cyber” path constraints based on the previous falsification result and traverse the abstract path tree obtained from the control program to explore the search space of the system. To illustrate the practical utility of binary code analysis in identifying cyber kinetic vulnerabilities, we present case studies from diverse CPS domains, showcasing how they can be discovered in their control programs. In particular, compared to off-the-shelf tools, our tool could compute the same number of vulnerabilities while leading to a speedup that ranges from 3× to 98×.