Doctor of Philosophy

dissertationTraffic congestion is an increasing problem in most urban areas in the United States. One of the sources of this problem is the automobile-oriented development that encourages automobile use and suppresses other transportation modes. A good transit system can satisfy most of the requirements of a transportation system user. A transit system must be efficient, safe, comfortable, and competitive to private cars in order to attract more riders. Transit Signal Priority (TSP) is an operational strategy that facilitates transit vehicles at signalized intersections. It improves transit efficiency and helps transit offer travel times competitive to private cars. A lot of studies conducted in the past 40 years show the major possibilities and benefits of TSP. The goal of this research is to develop a simulation-based methodology for the evaluation and improvement of TSP strategies. The objectives consist of evaluating existing and future TSP systems, and developing field-ready algorithms that provide adaptive ways for achieving different levels of TSP and improving its operation. The focus of the research is on using traffic microsimulation to evaluate and improve TSP, but it also looks into some field-based implementations and evaluations for additional support. The analysis of different TSP strategies is performed on existing and future rapid transit mode implementations, namely Bus Rapid Transit (BRT) and Light Rail Transit (LRT). The results from the presented studies show the major benefits of TSP implementations for transit operations and small disruptions for vehicular traffic. Depending on the selected strategies and level of TSP, the travel time savings for transit can be between 10% and 30%, the reduction in intersection delay can exceed 60%, while running time reliability and headway adherence are greatly improved. These improvements in transit operations can make transit more efficient and competitive to private cars, justifying the TSP implementation. This research offers significant contributions to the state of TSP practice and research. It provides detailed insights into TSP operations, develops methods for its evaluation, and describes algorithms for achieving different levels of TSP. A significant part of the research is dedicated to the use of Software-in-the-Loop (SIL) traffic controllers in microsimulation. Through this research, SIL is proven to be a powerful tool for simulating complex traffic signal operations and TSP

Arterial traffic signal optimization: a person-based approach

This paper presents a real-time signal control system that optimizes signal settings based on minimization of person delay on arterials. The system’s underlying mixed integer linear program minimizes person delay by explicitly accounting for the passenger occupancy of autos and transit vehicles. This way it can provide signal priority to transit vehicles in an efficient way even when they travel in conflicting directions. Furthermore, it recognizes the importance of schedule adherence for reliable transit operations and accounts for it by assigning an additional weighting factor on transit delays. This introduces another criterion for resolving the issue of assigning priority to conflicting transit routes. At the same time, the system maintains auto vehicle progression by introducing the appropriate delays associated with interruptions of platoons. In addition to the fact that it utilizes readily available technologies to obtain the inputs for the optimization, the system’s feasibility in real-world settings is enhanced by its low computation time. The proposed signal control system is tested on a four-intersection segment of San Pablo Avenue arterial located in Berkeley, California. The findings show the system’s capability to outperform pretimed (i.e., fixed-time) optimal signal settings by reducing total person delay. They have also demonstrated its success in reducing bus person delay by efficiently providing priority to transit vehicles even when they travel in conflicting directions

Policy support for autonomous swarms of drones

In recent years drones have become more widely used in military and non-military applications. Automation of these drones will become more important as their use increases. Individual drones acting autonomously will be able to achieve some tasks, but swarms of autonomous drones working together will be able to achieve much more complex tasks and be able to better adapt to changing environments. In this paper we describe an example scenario involving a swarm of drones from a military coalition and civil/humanitarian organisations that are working collaboratively to monitor areas at risk of flooding. We provide a definition of a swarm and how they can operate by exchanging messages. We define a flexible set of policies that are applicable to our scenario that can be easily extended to other scenarios or policy paradigms. These policies ensure that the swarms of drones behave as expected (e.g., for safety and security). Finally we discuss the challenges and limitations around policies for autonomous swarms and how new research, such as generative policies, can aid in solving these limitations

Refining, Implementing, and Evaluating the Extended Continuous Variable-Specific Resolutions of Feature Interactions

Systems that involve feature-oriented software development suffer from feature interactions, in which features affect one another’s behaviour in surprising ways. As the number of features increases, the complexity of examining feature combinations and fixing undesired interactions increases exponentially, such that the workload of resolving interactions comes to dominate feature development. The Feature Interaction Problem results from aiming resolve feature interaction by providing optimal resolutions. Resolution strategies combat the Feature Interaction Problem by offering default strategies that resolve entire classes of interactions, thereby reducing the work of the developer who is charged with the task of resolving interactions. However, most such approaches employ coarse-grained resolution strategies (e.g., feature priority) or a centralized arbitrator. This thesis focuses on evaluating and refining a proposed architecture that resolves features’ conflicting actions on system’s outputs. In this thesis, we extend a proposed architecture based on variable-specific resolution to enable co-resolution of related outputs and to promote smooth continuous resolutions over execution sequences. We implemented our approach within the PreScan simulator for advanced driver assistance systems, and performed a case study involving 15 automotive features that we implemented. We also devised and implemented three resolution strategies for the features’ outputs. The results of the case study show that the approach produces smooth and continuous resolutions of interactions throughout interesting scenarios

Collaborative Diagnosis of Over-Subscribed Temporal Plans

PhD thesisOver-subscription, that is, being assigned too many tasks or requirements that are too demanding, is commonly encountered in temporal planning problems. As human beings, we often want to do more than we can, ask for things that may not be available, while underestimating how long it takes to perform each task. It is often difficult for us to detect the causes of failure in such situations and then find resolutions that are effective. We can greatly benefit from tools that assist us by looking out for these plan failures, by identifying their root causes, and by proposing preferred resolutions to these failures that lead to feasible plans. In recent literature, several approaches have been developed to resolve such over-subscribed problems, which are often framed as over-constrained scheduling, configuration design or optimal planning problems. Most of them take an all-or-nothing approach, in which over-subscription is resolved through suspending constraints or dropping goals. While helpful, in real-world scenarios, we often want to preserve our plan goals as much possible. As human beings, we know that slightly weakening the requirements of a travel plan, or replacing one of its destinations with an alternative one is often sufficient to resolve an over-subscription problem, no matter if the requirement being weakened is the duration of a deep-sea survey being planned for, or the restaurant cuisine for a dinner date. The goal of this thesis is to develop domain independent relaxation algorithms that perform this type of slight weakening of constraints, which we will formalize as continuous relaxation, and to embody them in a computational aid, Uhura, that performs tasks akin to an experienced travel agent or ocean scientists. In over-subscribed situations, Uhura helps us diagnose the causes of failure, suggests alternative plans, and collaborates with us in order to resolve conflicting requirements in the most preferred way. Most importantly, the algorithms underlying Uhura supports the weakening, instead of suspending, of constraints and variable domains in a temporally flexible plan. The contribution of this thesis is two-fold. First, we developed an algorithmic framework, called Best-first Conflict-Directed Relaxation (BCDR), for performing plan relaxation. Second, we use the BCDR framework to perform relaxation for several different families of plan representations involving different types of constraints. These include temporal constraints, chance constraints and variable domain constraints, and we incorporate several specialized conflict detection and resolution algorithms in support of the continuous weakening of them. The key idea behind BCDR's approach to continuous relaxation is to generalize the concepts of discrete conflicts and relaxations, first introduced by the model-based diagnosis community, to hybrid conflicts and relaxations, which denote minimal inconsistencies and minimal relaxations to both discrete and continuous relaxable constraints

Overview of Infrastructure Charging, part 4, IMPROVERAIL Project Deliverable 9, “Improved Data Background to Support Current and Future Infrastructure Charging Systems”

Improverail aims are to further support the establishment of railway infrastructure management in accordance with Directive 91/440, as well as the new railway infrastructure directives, by developing the necessary tools for modelling the management of railway infrastructure; by evaluating improved methods for capacity and resources management, which allow the improvement of the Life Cycle Costs (LCC) calculating methods, including elements related to vehicle - infrastructure interaction and external costs; and by improving data background in support of charging for use of railway infrastructure. To achieve these objectives, Improverail is organised along 8 workpackages, with specific objectives, responding to the requirements of the task 2.2.1/10 of the 2nd call made in the 5th RTD Framework Programme in December 1999.This part is the task 7.1 (Review of infrastructure charging systems) to the workpackage 7 (Analysis of the relation between infrastructure cost variation and diversity of infrastructure charging systems).Before explaining the economic characteristics of railway and his basic pricing principles, authors must specify the objectives of railways infrastructure charging.principle of pricing ; rail infrastructure charging ; public service obligation ; rail charging practice ; Europe ; Improverail

Quantifying the Mobility and Safety Benefits of Transit Signal Priority

The continuous growth of automobile traffic on urban and suburban arterials in recent years has created a substantial problem for transit, especially when it operates in mixed traffic conditions. As a result, there has been a growing interest in deploying Transit Signal Priority (TSP) to improve the operational performance of arterial corridors. TSP is an operational strategy that facilitates the movement of transit vehicles (e.g., buses) through signalized intersections that helps transit service be more reliable, faster, and more cost-effective. The goal of this research was to quantify the mobility and safety benefits of TSP. A microscopic simulation approach was used to estimate the mobility benefits of TSP. Microscopic simulation models were developed in VISSIM and calibrated to represent field conditions. Implementing TSP provided significant savings in travel time and average vehicle delay. Under the TSP scenario, the study corridor also experienced significant reduction in travel time and average vehicle delay for buses and all other vehicles. The importance and benefits of calibration of VISSIM model with TSP integration were also studied as a part of the mobility benefits. Besides quantifying the mobility benefits, the potential safety benefits of the TSP strategy were also quantified. An observational before-after full Bayes (FB) approach with a comparison-group was adopted to estimate the crash modification factors (CMFs) for total crashes, fatal/injury (FI) crashes, property damage only (PDO) crashes, rear-end crashes, sideswipe crashes, and angle crashes. The analysis was based on 12 corridors equipped with the TSP system and their corresponding 29 comparison corridors without the TSP system. Overall, the results indicated that the deployment of TSP improved safety. Specifically, TSP was found to reduce total crashes by 7.2% (CMF = 0.928), FI crashes by 14% (CMF = 0.860), PDO crashes by 8% (CMF = 0.920), rear-end crashes by 5.2% (CMF = 0.948), and angle crashes by 21.9% (CMF = 0.781). Alternatively, sideswipe crashes increased by 6% (CMF = 1.060), although the increase was not significant at a 95% Bayesian credible interval (BCI). These results may present key considerations for transportation agencies and practitioners when planning future TSP deployments

Modeling and optimizing network infrastructure for autonomous vehicles

Autonomous vehicle (AV) technology has matured sufficiently to be in testing on public roads. However, traffic models of AVs are still in development. Most previous work has studied AV technologies in micro-simulation. The purpose of this dissertation is to model and optimize AV technologies for large city networks to predict how AVs might affect city traffic patterns and travel behaviors. To accomplish these goals, we construct a dynamic network loading model for AVs, consisting of link and node models of AV technologies, which is used to calculate time-dependent travel times in dynamic traffic assignment. We then study several applications of the dynamic network loading to predict how AVs might affect travel demand and traffic congestion. AVs admit reduced perception-reaction times through technologies such as (cooperative) adaptive cruise control, which can reduce following headways and increase capacity. Previous work has studied these in micro-simulation, but we construct a mesoscopic simulation model for analyses on large networks. To study scenarios with both autonomous and conventional vehicles, we modify the kinematic wave theory to include multiple classes of flow. The flow-density relationship also changes in space and time with the class proportions. We present multiclass cell transmission model and prove that it is a Godunov approximation to the multiclass kinematic wave theory. We also develop a car-following model to predict the fundamental diagram at arbitrary proportions of AVs. Complete market penetration scenarios admit dynamic lane reversal -- changing lane direction at high frequencies to more optimally allocate road capacity. We develop a kinematic wave theory in which the number of lanes changes in space and time, and approximately solve it with a cell transmission model. We study two methods of determining lane direction. First, we present a mixed integer linear program for system optimal dynamic traffic assignment. Since this program is computationally difficult to solve, we also study dynamic lane reversal on a single link with deterministic and stochastic demands. The resulting policy is shown to significantly reduce travel times on a city network. AVs also admit reservation-based intersection control, which can make greater use of intersection capacity than traffic signals. AVs communicate with the intersection manager to reserve space-time paths through the intersection. We create a mesoscopic node model by starting with the conflict point variant of reservations and aggregating conflict points into capacity-constrained conflict regions. This model yields an integer program that can be adapted to arbitrary objective functions. To motivate optimization, we present several examples on theoretical and realistic networks demonstrating that naïve reservation policies can perform worse than traffic signals. These occur due to asymmetric intersections affecting optimal capacity allocation and/or user equilibrium route choice behavior. To improve reservations, we adapt the decentralized backpressure wireless packet routing and P0 traffic signal policies for reservations. Results show significant reductions in travel times on a city network. Having developed link and node models, we explore how AVs might affect travel demand and congestion. First, we study how capacity increases and reservations might affect freeway, arterial, and city networks. Capacity increases consistently reduced congestion on all networks, but reservations were not always beneficial. Then, we use dynamic traffic assignment within a four-step planning model, adding the mode choice of empty repositioning trips to avoid parking costs. Results show that allowing empty repositioning to encourage adoption of AVs could reduce congestion. Also, once all vehicles are AVs, congestion will still be significantly reduced. Finally, we present a framework to use the dynamic network loading model to study shared AVs. Results show that shared AVs could reduce congestion if used in certain ways, such as with dynamic ride-sharing. However, shared AVs also cause significant congestion. To summarize, this dissertation presents a complete mesoscopic simulation model of AVs that could be used for a variety of studies of AVs by planners and practitioners. This mesoscopic model includes new node and link technologies that significantly improve travel times over existing infrastructure. In addition, we motivate and present more optimal policies for these AV technologies. Finally, we study several travel behavior scenarios to provide insights about how AV technologies might affect future traffic congestion. The models in this dissertation will provide a basis for future network analyses of AV technologies.Civil, Architectural, and Environmental Engineerin