published journal article
conference paper
Phd Dissertation
This research describes an innovative distributed approach for the provision of real-time decision support to Transportation Management Center (TMC) operators for coordinated, multi-jurisdictional traffic congestion management on freeway and arterial networks. Coordinated responses among the agencies that share responsibilities for urban traffic management avoids the implementation of operations that may be conflicting or counter-productive. A distributed software architecture, called CARTESIUS (Coordinated Adaptive Real-Time Expert System for Incident management in Urban Systems) was designed, developed and evaluated. CARTESIUS is composed of two interacting, real-time decision-support systems for TMC operator that are able to perform cooperative reasoning and resolve conflicts, for the analysis of non-recurring congestion and the formulation of suitable integrated control responses. The two agents support incident management operations for, respectively, a freeway and an adjacent arterial subnetwork. Each module interacts with a human operator in one of the agencies, is able to receive real-time traffic and control data, and provides the operator with control recommendations in response to the occurrence of incidents. The multi-decision making approach adopted by CARTESIUS reflects the spatial and administrative organization of traffic management agencies, providing a coordinated solution that attempts to satisfy all parties, preserves their own levels of authority, and reflects the inherent distribution of the decision-making power. The structure of the distributed processing and the interaction between the agents is based on the Functionally Accurate, Cooperate (FA/C) paradigm, a distributed problem solving approach aimed at producing consistent global solutions even when complete and up-to-date information is not directly available to the agents, in order to reduce communication requirements and synchronization time delays. The contribution of this research lies in demonstrating the validity of the assumption that satisficing control solutions can be efficiently obtained by relaxing the requirements that agents have shared access to all globally available information, and the application of theoretical principles of the FA/C paradigm to traffic control, through the development of CARTESIUS. The simulation-based validation of the system performance has demonstrated the effectiveness of such an approach in producing real-time, integrated traffic control solutions that reduce the adverse impact of incidents on traffic circulation, network-wide.
conference paper
published journal article
Abstract This paper provides evidence on the effect of international airline alliances on fares. The main finding is that alliance partners charge interline fares that are approximately 25 percent below those charged by nonallied carriers. According to our theoretical model, the main source of this fare reduction is the internalization of a negative externality that arises from the uncoordinated choice of interline “sub-fares” in the absence of an alliance. The paper also looks for evidence of an anti-competitive alliance effect in the gateway-to-gateway markets. While the point estimates show that an alliance between two previously competitive carriers would raise fares by about 5 percent, this effect is not statistically significant.
working paper
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Oct 1993
working paper |
Jan 1997
book/book chapter |
Oct 1998
working paper |
Sep 1995
working paper |
Sep 1994
working paper |
Jul 1997
Preprint Journal Article
In high-level Autonomous Driving (AD) systems, behavioral planning is in charge of making high-level driving decisions such as cruising and stopping, and thus highly securitycritical. In this work, we perform the first systematic study of semantic security vulnerabilities specific to overly-conservative AD behavioral planning behaviors, i.e., those that can cause failed or significantly-degraded mission performance, which can be critical for AD services such as robo-taxi/delivery. We call them semantic Denial-of-Service (DoS) vulnerabilities, which we envision to be most generally exposed in practical AD systems due to the tendency for conservativeness to avoid safety incidents. To achieve high practicality and realism, we assume that the attacker can only introduce seemingly-benign external physical objects to the driving environment, e.g., off-road dumped cardboard boxes. To systematically discover such vulnerabilities, we design PlanFuzz, a novel dynamic testing approach that addresses various problem-specific design challenges. Specifically, we propose and identify planning invariants as novel testing oracles, and design new input generation to systematically enforce problemspecific constraints for attacker-introduced physical objects. We also design a novel behavioral planning vulnerability distance metric to effectively guide the discovery. We evaluate PlanFuzz on 3 planning implementations from practical open-source AD systems, and find that it can effectively discover 9 previouslyunknown semantic DoS vulnerabilities without false positives. We find all our new designs necessary, as without each design, statistically significant performance drops are generally observed. We further perform exploitation case studies using simulation and real-vehicle traces. We discuss root causes and potential fixes.
working paper
Modeling personal travel behavior is complex, particularly when one tries to adhere closely to actual causal mechanisms while predicting human response to changes in the transport environment. There has long been a need for explicitly modeling the underlying determinant of travel – the demand for participation in out-of-home activities; and progress is being made in this area, primarily through discrete-choice models coupled with continuous-duration choices. However, these models tend to be restricted in size and conditional on a wide variety of other choices that could be modeled more endogenously.
This dissertation derives a system of demands for activity participation and other travel-related goods that is rigorously linked to theories of utility maximization. Two difficulties inherent in the modeling of travel – the discrete nature of many travel-related demands and the formal recognition of a time budget, not just a financial one – are dealt with explicitly. The dissertation then empirically evaluates several such demand systems, based on flexible specifications of indirect utility. The results provide estimates of activity generation and distribution and of economic parameters such as demand elasticities. Several hypotheses regarding travel behavior are tested, and estimates are made of welfare effects generated by changes in the travel environment.
The models presented here can be extended to encompass more disaggregate consumption bundles and stronger linkages between consumption of out-of-home activities and other goods. The flexibility and strong behavioral basis of the approach make it a promising new direction for travel demand modeling.
working paper |
Oct 1993
working paper |
Jan 1997
book/book chapter |
Oct 1998
working paper |
Sep 1995
working paper |
Sep 1994
working paper |
Jul 1997
published journal article
Phd Dissertation
While the industry of civilian unmanned aerial vehicles (UAV) or drones has seen rapid expansion in the past decade, few studies have systematically examined the dynamics between this disruptive technology and various aspects of cities. Employing quantitative methods, this dissertation explores 1) the diffusion and adoption patterns of civilian drones; 2) how cities manage the challenges of increasing drone activities; and 3) the supply-side opportunities and constraints associated with the deployment of Urban Air Mobility (UAM) in built-out metropolitan areas. The results of the first county level study might suggest (Chapter 2) that the digital divide has magnified the uneven and nonlinear diffusion of drones across time and space. Furthermore, the strength of state-level interventions correlates with the intensity of local drone adoption, even though the regulatory effects are different among drone user groups. People living in neighborhoods with a higher adoption rate of drones are on average younger, more affluent, and Whiter. An extension of the first study at the zip code level (Chapter 3) has retested the key results and provided additional insights into the spatial dependence effects that affect the drone adoption patterns. Furthermore, the results of the second study (Chapter 4) indicate that local drone policy adoption among communities of color trails behind that of other communities. Although drone policy adoption at the local level has been shaped by both motivation and capacity factors, the desire to protect public facilities appears to motivate localities to adopt regulatory measures. In particular, policy adoption is influenced by what nearby cities do, suggesting that strategic interaction is at play among local governments. In the third study (Chapter 5), I evaluate the supply-side opportunities and constraints associated with UAM adoption through a systematic scenario analysis. The results of the third study indicate that current supply-side infrastructure opportunities in Southern California, like helipads and elevated parking structures, are widely available to accommodate the regional deployment of UAM service although current spatial constraints can significantly limit the location choice of UAM landing sites (vertiports) for electric vertical take-off and landing (eVTOL) aircraft. Moreover, the low-income and young populations tend to live relatively farther away from the supply-side opportunities compared to the general population. The third study also proposes a network of UAM stations in Southern California based on the joint considerations of available infrastructure and home-workplace commuting flows.