This paper discusses diagnosis problems in distributed systems within the context of a language- theoretic discrete event formalism. A distributed system is seen as a system with multiple spatially separated sites with each site having a diagnoser that observes some of the events generated by the system and diagnoses the faults associated with the site. We allow the diagnosers to share information by sending messages to each other. Distributed systems are classified as being centrally, decentrally, and independently diagnosable. We characterize the class of distributed systems for which there exists a centralized diagnoser but there exists no inter-diagnoser messaging scheme that can replicate the information available to a centralized diagnoser. Plant properties that are necessary and sufficient for the three kinds of diagnosability are derived. The formulation and results are motivated by a discussion on the diagnosis of failures in a wireless LAN used to support the real-time operation of automated vehicles.

This paper describes a modular compilation scheme for distributed synchronous programming. The approach is first described mathematically and then implemented as a library to distribute Simulink (59). Application of the scheme is illustrated by developing a control system to coordinate traffic signals.

We consider a Generalized, Multiple Depot Hamiltonian Path Problem (GMDHPP) and show that it has an algorithm with an approximation ratio of 3/2 if the costs are symmetric and satisfy the triangle inequality. This improves on the 2-approximation algorithm already available for the same.

Though 2-approximation algorithms are available for several Multiple Depot Travelling Salesman Problems (TSPs)and Hamiltonian Path Problems (HPPs), there are no algorithms in the literature for any multiple depot variant of TSP or HPP that has an approximation ratio better than 2. This paper addresses one variant of the Multiple Depot HPP and provides the first 5/3-approximation algorithm for the same when the costs are symmetric and satisfy triangle inequality.

In this dissertation, we develop two experimental methods for the problem of pricing or incentivizing use of a transportation service and apply them to the pricing of employee parking at the University of California, Berkeley.

The University of California, Berkeley, with 23,962 employees is the largest employer in the eastern half of the San Francisco Bay Area and has a problem with employee parking. The university wants to explore a daily parking cash-out program, named the FlexPass, to make employees more mindful of their parking consumption. We use a Randomized Controlled Trial(RCT) to reveal the causal power of the cash-out. The RCT is applied to 392 employees, representing 10% of the university employees driving alone and parking, over three months using an IT system able to collect daily parking consumption, weekly commute mode reports and location data. The FlexPass treatment reduced consumption by 6.1% with high significance.

Our second experiment is focused on measuring an incentive response curve. We use a repeated 2nd price reverse auction, in which 215 parking permit holders participate for 61 working days. Our method measures the incentive response curve for our subjects and we estimate the curve for the employee population using a quantile regression. We find the known and heavy overhead of repeated bidding can be removed by a lightweight IT system compressed of apps on iPhone and Android and a server in the cloud.

Finally, we build a two-stage signaling game and design a variable-rate daily incentive scheme, where the incentive changes based on weekday and weather. The variable-rate daily incentive outperforms the fixed-rate daily incentive on both parking cruising times and leftover parking spaces.

This paper is about the modular compilation and distribution of a sub-class of Simulink programs [10] across networks using bounded FIFO queues. The problem is first addressed mathematically. Then, based on these formal results, a software library for the modular compilation and distribution of Simulink program is given. The performance the library is given. The value of synchronous programming for the next generation of traffic control value is discussed. The adoption of these tools seems to be the natural candidate to address the needs of the traffic engineers. As a case study we present an implementation in Simulink of a controller for coordinated traffic signal in an asymmetric peak hour traffic scenario and we evaluate its computational performances in a distributed environment.

Traffic control systems have reached a high level of sophistication: they are general purpose machines that can, in principle, run any traffic control software. The firmware they are running turns them into special purpose machines able to operate only according to some pre-defined rules. The firmware usually allows limited customizations through parameters, but it does not support the introduction of new control schemes. As a result, implementing a new traffic control scheme requires the re-implementation of the firmware, a complex task given the low-level programming required. The complexity of the task has created a rift between the academic community and the roadside equipment manufacturers’ community. As a result, the traffic control firmware does not benefit from the work and novel ideas of the academic community and it is often suboptimal when it comes to issue of interoperability, integration and cost-effectiveness. At the same time the efforts of the academic research are often wasted by an incorrect understanding of the underlying system: many sophisticated control schemes proposed by the researcher are never used because they are not implementable over the existing hardware. This paper introduces a software suite, the Tools for the development of Traffic Control Systems (TTCS), meant to bridge the gap between these two communities. The key concept is the introduction of the Control logic / Hardware Abstraction Layer (CHAL) between the control logic and the hardware, reducing the complexity of developing a new traffic control scheme. It enables the control designer to re-use the code developed in the design, testing and simulation phases directly on the traffic controller. Conforming to the CHAL architecture ensures that the design is implementable over the target hardware. Moreover CHAL decouples the control logic and the hardware (ensuring software portability), easing both hardware and software upgrades (avoiding the need to update the software when the hw changes and vice versa). A software library for the integration of Quadstone Paramics 5.4 into the CHAL architecture is presented.

Unmanned Aircraft Systems (UAS), commonly known as drones, are becoming increasingly popular in civilian applications. However, the infrastructure to ensure the safe operations are still largely missing. In this dissertation, I addressed two problems related to a flight mission. First, I propose a flight planning system specifically for single multirotor UAS operations. The system generates a 4D trajectory at the pre-flight stage before an actual flight taking place. Safety is achieved by ensuring enough energy to finish a trip under environmental influences such as wind, as well as avoiding static obstacles. Second, I proposed a hybrid controller capable of performing trajectory following as well as single-helicopter collision avoidance. The controller enables a multi-rotor to follow the planned trajectory.

In the first part of the dissertation, I first divide the flight planning system into two components: energy demand and energy supply. The demand side is determined by the performance of the UAS, the environment, and the flight trajectory, while the energy supply depends on the power source. Then, I focused on two specific problems on the energy demand side. First, I quantify the energy performance of a UAS by developing a theoretical power consumption model for multi-rotor Unmanned Aircraft Systems (UAS). The model is derived from the helicopter literature. The model parameters are identified and validated experimentally by flying an IRIS+ quadrotor UAS. Second, I solved the trajectory generation problem by formulating it as an optimal control problem. The cost to be optimized is the energy consumption or flight duration at the cruise phase of a flight mission, taking into account 3-dimensional wind field and terrain variation. The problem is solved numerically by the Ordered Upwind Method (OUM). By combining the optimized path with timestamps, we can generate 4D trajectories typically used in aviation flight planning, and thus maximize the software compatibility between manned and unmanned aviation. Simulation shows that the generated trajectories are able to avoid obstacles such as high-rise buildings and give energy consumption estimates along the entire trajectory.

In the second part of the dissertation, I addressed the trajectory following problem with collision avoidance capability given a planned 4D trajectory. I analyzed the tracking performance of an IRIS+ quadrotor for a challenging trajectory. In the scenario, wind played a big role in the cross-track errors. The collision avoidance capability, or a safety controller, is integrated to the trajectory following controller by introducing a hybrid automaton, deciding when to start the avoidance action and what control actions to take. The hybrid controller is examined in a simple straight-line-following scenario to avoid a manned helicopter.

This report studies the effects of Advanced Traveler Information Systems (ATIS) on traffic congestion in the Smart Corridor of the Santa Monica Freeway. Simulation modeling is used to estimate the potential travel time savings to divert traffic from the Smart Corridor to arterial roads when incidents occur. The study attempts to establish relationships between traffic management variables, such as incident detection time, incident duration, capacity reduction, percentage of traffic diversion, and duration of traffic diversion.