Network systems, including transportation and logistic systems, electric power generation and distribution networks as well as financial networks, provide the critical infrastructure for the functioning of our societies and economies. The understanding of the dynamic behavior of such systems is also crucial to national security and prosperity. The identification of new connections between distinct network systems is the inspiration for the research in this dissertation. In particular, I answer two questions raised by Beckmann, McGuire, and Winsten (1956) and Copeland (1952) over half a century ago, which are, respectively, how are electric power flows related to transportation flows and does money flow like water or electricity? In addition, in this dissertation, I achieve the following: (1) I establish the relationships between transportation networks and three other classes of complex network systems: supply chain networks, electric power generation and transmission networks, and financial networks with intermediation. The establishment of such connections provides novel theoretical insights as well as new pricing mechanisms, and efficient computational methods. (2) I develop new modeling frameworks based on evolutionary variational inequality theory that capture the dynamics of such network systems in terms of the time-varying flows and incurred costs, prices, and, where applicable, profits. This dissertation studies the dynamics of such network systems by addressing both internal competition and/or cooperation, and external changes, such as varying costs and demands. (3) I focus, in depth, on electric power supply chains. By exploiting the relationships between transportation networks and electric power supply chains, I develop a large-scale network model that integrates electric power supply chains and fuel supply markets. The model captures both the economic transactions as well as the physical transmission constraints. The model is then applied to the New England electric power supply chain consisting of 6 states, 5 fuel types, 82 power generators, with a total of 573 generating units, and 10 demand markets. The empirical case study demonstrates that the regional electricity prices simulated by the model match very well the actual electricity prices in New England. I also utilize the model to study interactions between electric power supply chains and energy fuel markets.

Older drivers are over-represented in angled crashes when compared with younger experienced drivers. Past research primarily points to age-related cognitive and physical decline, which can impede older drivers' ability to monitor their driving environment efficiently and decrease their ability to maintain adequate situational awareness. Despite compensatory behaviors such as driving less, driving more slowly or avoiding driving in inclement conditions, there is evidence that in some cases these drivers may be under-compensating, as older drivers are still involved in more angled crashes than any other category. Of particular concern are intersections in which other vehicles can approach from the side. Two experiments described here investigate whether tailored feedback based on a driver's own unsafe behaviors and active, situation-based training in a simulator can change drivers' attitudes about their own abilities, raise their awareness of the crash risks for older drivers and lead to long-term improvements of driving behavior such as increased side-to-side scanning while negotiating intersections. Experiment 1 investigated whether customized feedback tailored to the individual's specific unsafe driving behaviors in a simulator can successfully alter an older driver's perceptions of his driving skills. Experiment 2 compared how effectively customized feedback about a driver's specific unsafe driving behaviors on the open road followed by active situation-based training in a simulator can improve road scanning and head turning behavior when compared with lecture-style training. The results from Experiment 1 demonstrated that letting drivers make errors in a simulator and then providing customized feedback was successful in changing older drivers' perception of their ability, making them more willing to change driving behavior. The results from Experiment 2 indicated that capturing drivers' errors on the road, providing customized feedback, and then adding active training in a simulator increased side-to-side scanning in intersections by nearly 100% in both post-training simulator and field drives. A second group, which received passive classroom-style training, demonstrated no significant improvement. In summary, compared with passive training programs, error capture, feedback, and active situation-based practice in a simulated environment is a much more effective strategy for raising awareness and increasing the road scanning behavior of older drivers.

The study of user-behavior and decision-making dynamics in transportation network are vital in modeling and simulation of user interactions. Different users access transportation network in order to accomplish different activities. Such activities can be regular commuting, transit services, commercial taxicabs, deliveries, long distance trips, logistics or fleet services, etc. While the world is becoming increasingly urbanized reliable and cost effective movement of people and goods is important for the productivity and economic growth at large. Urbanization and population growth have created the shift in how travel activities are tied to the economy. In today's economy, businesses and individuals are looking for ways of making their fiscal resources and workforce more efficient. However, traffic congestion dampens the efficiency and prosperity by imposing additional operating costs, slowing mobility and causing wastage of time and by hindering efficient metropolitan services such as deliveries, public safety and maintenance. Traffic congestion in the United States in 2011 for instance, caused urban commuters to travel 5.5 billion hours more and to purchase an extra 2.9 billion gallons of fuel (enough to fill Superdome in New Orleans, two times) for a congestion cost of $121 billion. In larger cities and in busy expressways, traffic infrastructures are already operating at near or full capacity. With today's shrinking budgets, often no funding is available to rebuild or expand an aging public transportation infrastructure, making it crucial to devise ways to optimize the performance of existing transportation assets. Since the recurring congestions in large metropolitan areas are mainly due to predictable behavioral activity scheduling, traffic management efforts should be geared towards behavior analysis and modeling. Modeling behavior and decisions, pertinent to route choice and activity scheduling dynamics are crucial for capturing microscopic and mesoscopic nature of traffic flow patterns. In this research, the focus is placed on the development of multi-agent transportation demand estimation and simulation framework to be used by the public entities for performance optimization of existing transportation network and scenario evaluation of new investments. The framework employs several mathematical and statistical methods for the derivation of sampling distributions of users' (i.e., agents') behavior and travel characteristics for the initial network demand generation. The processes of deriving sampling distributions of agents' behavior and travel characteristics largely rely on the quantity, quality and resolution of the available data of the region under study. Travel characteristics/travel surveys data from South East Florida Regional Planning Model (SERPM) region and the National Household Travel Survey (NHTS) data contained individuals' travel characteristics such as origin, destination, departure and arrival time, chain of activities and tours within the trip. These are micro-information needed for the derivation of household and individual agent's travel behavior. The data was processed to develop probability distributions for groups of agents with similar travel behavior, given the agents' household characteristics. In a similar fashion, with agents' household characteristics given, the logit models for agents' activity and locations choices were developed. Besides behavior simulation and demand estimation, the developed framework included an ad-on module for lane choice and pricing approaches applicable to dynamic high occupancy toll (HOT) lanes pricing. The reinforcement learning (RL) approach was used for updating the optimal pricing strategy in a given traffic condition. The pricing controller was configured to start with a predefined base price at a given traffic level, and then in the process of learning, it varies the price in accordance with the acceptable price levels at a given level of service (LOS). In this way, the pricing controller learns the states in which a higher price is more beneficial and those in which a lower price is more beneficial, and then adjusts the parameters of the pricing function to minimize the difference between the current computed price and the posted price. The framework was tested and validated for the scenario based on the data from SERPM region. The scenario was simulated in Multi-Agent Transport Simulation (MATSim). In MATSim, the simulation is constructed around the notion of agents that make independent decisions about their actions. Each traveler of the real system is modeled as an individual agent. Generally, the observation of network traffic evolution from the simulation showed the expected traffic patterns for both morning peak and afternoon peak traffic. One of the most important aspects of travel behavior is the characterization of travel activities by trip duration. The distribution of travel activities by trip duration is the reflection of user behavior in the study area. This determines the expected users departing, en-route, stuck, and arriving to their destinations at a particular time interval. In this research, the simulation results show that network users in our case consist mainly of regular commuters (≥ 20%) whose trips take about 15 minutes. As any other research study, there are some limitations with this work. Due to lack of relevant data, transit use and other modes other than personal vehicle were not considered. Future directions for this research include the inclusion of other data sources and optimization of the demand estimation framework in order to scale-down the computation cost. In addition to the reduction of computation cost, focus will be on development and implementation of modules for simulating dynamic toll pricing on high occupancy toll lanes and assessing the effects of social media information exchange among the agents on mobility. / A Dissertation submitted to the Department of Civil and Environmental Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester, 2015. / January 23, 2015. / Activity-based modeling and simulation, Multi-agent simulation, Network demand estimation, Network optimization, Smart mobility, Travel Behavior / Includes bibliographical references. / Ren Moses, Professor Directing Dissertation; Mark W. Horner, University Representative; Eren Erman Ozguven, Committee Member; John O. Sobanjo, Committee Member; Chiwoo Park, Committee Member.

Air pollution is a very critical non-natural hazard that adversely affects human health as well as the environment itself in the context of climate change. One of the biggest contributors to air pollution is the transportation industry. According to the U.S. Environmental Protection Agency (EPA), transportation is the second leading source for greenhouse gas (GHG) emissions, contributing to GHG emissions by 28%. Researchers and practitioners have been working on developing techniques to estimate and reduce transportation-related emissions by the help of various types of technologies. As such, this study aimed to investigate the effect of vehicle operating modes (i.e., constant running, idling, accelerating, and braking) on vehicle exhaust emissions in order to highlight the importance of occasionally disregarded factors that exacerbate the transportation-related air pollution problem. In order to accomplish this goal, this study adopted an approach involving two frequently used software for estimating emissions, namely VISSIM (a microscopic traffic simulation software) and EPA’s Motor Vehicle Emission Simulator (MOVES). The input data required for these software was collected, processed, and introduced into the models in order to estimate the emissions. First, a corridor was simulated within the VISSIM. This corridor is located in the City of Tallahassee, Florida, which is highly congested during the peak hours, and approximately 7.7 miles long, with 22 signalized intersections. Next, the outputs of VISSIM were collected and provided to MOVES by developing an integration tool. First, average speed and volume data were provided to MOVES only for the whole corridor, and VISSIM and MOVES emissions for carbon monoxide (CO) and nitrogen oxides (NOx) were compared. Note that VISSIM provides only emissions for CO and NOx. After observing the massive difference between VISSIM and MOVES emissions, the importance of using operating mode distribution file in MOVES was pointed out. To meet this end, the integration tool was enhanced to compute the vehicle operating mode distribution file based on second-by-second vehicle trajectory output. This was provided to MOVES in order obtain more accurate emission estimation results since only average speed and volume data could not provide accurate emission values disregarding the different vehicle operating modes. For this purpose, an algorithm, named as operating mode calculation algorithm (OMCA), was developed in Python 3.0 to create operating mode distribution input by using second-by-second vehicle trajectory data of VISSIM. This type of analysis focusing on the emissions of individual vehicles provided more accurate emission results. Now that these results were obtained, the focus of the thesis shifted towards analyzing the impact of vehicle connectedness on the air pollution. Two intersections of the selected highway corridor were modelled and simulated with a connected environment using one of the widely used vehicle-to-infrastructure (V2I) communication application called Green Light Optimized Speed Advisory (GLOSA). The GLOSA was implemented on the major leg of these intersections only with different Connected Vehicle (CV) penetration rates. One of the selected legs was the most congested link of the corridor. After extensive simulations, second-by-second VISSIM trajectory data were provided to OMCA, which converted them to MOVES operating mode distribution input files. Finally, MOVES was run in order to estimate carbon monoxide (CO), nitrogen oxides (NOx), primary exhaust smaller than 2.5 micrometer (PM2.5) and primary exhaust smaller than 10 micrometer (PM10) emissions. Findings of the study can aid researchers in understanding the effect of different operation modes on the exhaust emissions, understanding the effect of smoother and lower number of stop-and-go driving operations in the context of the connected vehicle impact on the exhaust emission, and quantifying the potential operational and environmental benefits of connected vehicles (CV’s). / A Thesis submitted to the Department of Civil and Environmental Engineering in partial fulfillment of the requirements for the degree of Master of Science. / Spring Semester 2019. / March 27, 2019. / Connected vehicle, Emission, GLOSA, MOVES, Simulation, Traffic / Includes bibliographical references. / Eren Erman Ozguven, Professor Directing Thesis; John O. Sobanjo, Committee Member; Ren Moses, Committee Member.

Efficient freight mobility plays a major role in the economy, and its performance is closely related to the quality of the transportation system. Requirements for funding transportation infrastructure projects often do not specify the analytical tools planners should use to request funding. Critical Urban and Rural Freight Corridors are sections of the National Highway Freight Network providing critical connectivity of goods and must have improved system performance. This research study offers a method for identifying these corridors considering temporal and spatial inputs. For this end, a multi-criteria spatial decision support system (MC-SDSS) was developed. This framework attributes a score to highway corridors (links) based on policy eligibility and prioritization. We apply the Analytic Hierarchy Process (AHP) to structure the problem and consider different stakeholder preferences and available data. The product of this study is a tool for decisionmakers to optimize the selection of critical freight corridors and analyze alternatives. It also offers flexibility to manipulate the framework to meet various agency goals, using the State of Florida as a case study. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

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This research sought to understand the relationship between licensing policy and the opportunity for the development of a scientifically-based approach to identifying high risk older drivers based on prior driving history. This research focused on five tasks: (1) review of the literature, (2) compilation of information on licensing policy for use by decision-makers, (3) assessment of charges and payer source for older driver crashes using linked crash and hospital data, and (4) the development, and (5) validation of an older driver crash prediction model. There is relatively little available in the way of information for policymakers regarding licensing, and there is even less information available on evaluation of licensing practice effectiveness. Emergency department charges for older males were lower than females even though males accounted for a larger percentage of the injured population. Older drivers were no more likely to be covered by public insurance than the comparison group. Crash and citation data used to develop a driver history showed no differences between drivers in injury causing crashes and drivers in non-injury crashes. Logistic regression, Poisson regression, and negative binomial regression models were unable to effectively predict crash involvement based on driver history. This is likely due to self-selection bias for older drivers and truncated distribution of count variable (injury causing crashes). Recommendations resulting from this research include Massachusetts and national policy recommendations and additional research. Massachusetts should expand beyond its referral-based system for reviewing older drivers, consider restriction rather than only revocation, review medical advisory board practices, conduct evaluation of any policies it does implement, and conduct a thorough review of alternative transportation options. Nationally, efforts should focus on developing effective cognitive/functional testing by licensing agents, identification of effective second phase of testing, determination of a mechanism for determining when to retest, and assessment of the differences between older males and females for potential use in training, education, and testing. Research recommendations include continued exploration of the potential for systematic identification of high risk drivers using administrative data and in-depth analyses of the differences between males and females in terms of aging and driver safety.

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This research is an evaluation of the addition of a citizen involvement process that has come to be known as "visioning" or "community visioning" to the traditional process of developing a state's transportation plan, a process which has typically been very much an in-government and esoteric province of professionals in transportation planning. The research specifically focuses on the Commonwealth of Virginia and its addition of three citizen participation components the Commonwealth labeled "community visioning" to the traditional transportation planning process. The research examines the three components of "community visioning" with regard to: (1) their impact on the state's transportation plan (VTrans2025); (2) the degree to which they met the expectations of the regulations and best practices requirements of federal oversight; (3) the degree to which they met the expectations of the advocates of visioning and of more "democratic participation" in pubic administrative and policy processes; and (4) the degree to which they could affect the final outcome of transportation policy. Visioning is a relatively new approach to citizen involvement in the planning process. It places the citizen involvement at the beginning of the process instead of the end. Visioning asks citizens key questions about what they envision as a positive future for their community. The purpose or goal of this new visioning is to have the final plans reflect the vision drawn from the citizens and public officials and reached through consensus. This dissertation determined that Virginia put forth a good faith effort to involve citizens of the Commonwealth. Collectively, the citizen involvement activities in VA’s visioning process were reasonable and meaningful. Additionally, Virginia’s vision statement was heavily influenced by the citizen participation activities. However, there are three aspects of Virginia’s vision that are troubling from an implementation standpoint. In short, this dissertation found that the vision is what the people want, but the comprehensive plan does not tell the citizens how the Commonwealth intends on achieving that vision. / Ph. D.

TRANSIMS is a disaggregate, behavioral transportation planning package developed under US DOT's and EPA funding at the Los Alamos National Laboratory (LANL). It is an integrated system of travel forecasting models designed to give transportation planners accurate, complete information on traffic impacts, congestion, and pollution by simulating second-by-second movements of every person and every vehicle through the transportation network of a large metropolitan area. There is no built-in module for travellers' mode choices In TRANSIMS. The modes going with the shortest path are always taken. In Portland Study, a mode choice methodology implemented by a series of feedback processes is proposed. However, it uses aggregate, deterministic mode choice model. There is little solid theoretic ground for the format and coefficients of the generalized costs used in the calibration process. The accessibility to a mode, especially to Transit, was also not included in the model. In the thesis, a disaggregate and deterministic mode choice methodology in TRANSIMS is developed. The accessibility to each mode is analyzed and included in the model. The methodology is then implemented on the Blacksburg transportation planning study, namely Blacksburg_Lite. The analysis of the result is based on the indicator of mode choice, mode split between Transit and Auto. The indicator is close to that in survey data and converged fast. Therefore, this mode choice methodology could be used within TRANSIMS framework. / Master of Science