AN INTEGRATED SCORE-BASED TRAFFIC LAW ENFORCEMENT AND NETWORK MANAGEMENT IN CONNECTED VEHICLE ENVIRONMENT

The increasing number of traffic accidents and the associated traffic congestion have prompted the development of innovative technologies to curb such problems. This dissertation introduces a novel Score-Based Traffic Law Enforcement and Network Management System (SLEM), which leverages connected vehicle (CV) and telematics technologies. SLEM assigns a score to each driver which reflects her/his driving performance and compliance with traffic laws over a predefined period of time. The proposed system adopts a rewarding mechanism that rewards high-performance drivers and penalizes low-performance drivers who fail to obey traffic laws. The reward mechanism is in the form of a route guidance strategy that restricts low-score drivers from accessing certain roadway sections and time periods that are strategically selected in order to shift the network traffic distribution pattern from the undesirable user equilibrium (UE) pattern to the system optimal (SO) pattern. Hence, it not only incentivizes drivers to improve their driving performance, but it also provides a mechanism to manage network congestion in which high-score drivers experience less congestion and a higher level of safety at the expense of low-performing drivers. This dissertation is divided into twofold. iv First, a nationwide survey study was conducted to measure public acceptance of the SLEM system. Another survey targeted a focused group of traffic operation and safety professionals. Based on the results of these surveys, a set of logistic regression models was developed to examine the sensitivity of public acceptance to policy and behavioral variables. The results showed that about 65 percent of the public and about 60.0 percent of professionals who participated in this study support the real-world implementation of SLEM. Second, we present a modeling framework for the optimal design of SLEM’s routing strategy, which is described in the form of a score threshold for each route. Under SLEM’s routing strategy, drivers are allowed to use a particular route only if their driving scores satisfy the score threshold assigned to that route. The problem is formulated as a bi-level mathematical program in which the upper-level problem minimizes total network travel time, while the lower-level problem captures drivers’ route choice behavior under SLEM. An efficient solution methodology developed for the problem is presented. The solution methodology adopts a heuristic-based approach that determines the score thresholds that minimize the difference between the traffic distribution pattern under SLEM’s routing strategy and the SO pattern. The framework was applied to the network of the US-75 Corridor in Dallas, Texas, and a set of simulation-based experiments was conducted to evaluate the network performance given different driver populations, score class aggregation levels, recurrent and non-recurrent congestion scenarios, and driver compliance rates

AN INTEGRATED SCORE-BASED TRAFFIC LAW ENFORCEMENT AND NETWORK MANAGEMENT IN CONNECTED VEHICLE ENVIRONMENT

The increasing number of traffic accidents and the associated traffic congestion have prompted the development of innovative technologies to curb such problems. This dissertation introduces a novel Score-Based Traffic Law Enforcement and Network Management System (SLEM), which leverages connected vehicle (CV) and telematics technologies. SLEM assigns a score to each driver which reflects her/his driving performance and compliance with traffic laws over a predefined period of time. The proposed system adopts a rewarding mechanism that rewards high-performance drivers and penalizes low-performance drivers who fail to obey traffic laws. The reward mechanism is in the form of a route guidance strategy that restricts low-score drivers from accessing certain roadway sections and time periods that are strategically selected in order to shift the network traffic distribution pattern from the undesirable user equilibrium (UE) pattern to the system optimal (SO) pattern. Hence, it not only incentivizes drivers to improve their driving performance, but it also provides a mechanism to manage network congestion in which high-score drivers experience less congestion and a higher level of safety at the expense of low-performing drivers. This dissertation is divided into twofold. iv First, a nationwide survey study was conducted to measure public acceptance of the SLEM system. Another survey targeted a focused group of traffic operation and safety professionals. Based on the results of these surveys, a set of logistic regression models was developed to examine the sensitivity of public acceptance to policy and behavioral variables. The results showed that about 65 percent of the public and about 60.0 percent of professionals who participated in this study support the real-world implementation of SLEM. Second, we present a modeling framework for the optimal design of SLEM’s routing strategy, which is described in the form of a score threshold for each route. Under SLEM’s routing strategy, drivers are allowed to use a particular route only if their driving scores satisfy the score threshold assigned to that route. The problem is formulated as a bi-level mathematical program in which the upper-level problem minimizes total network travel time, while the lower-level problem captures drivers’ route choice behavior under SLEM. An efficient solution methodology developed for the problem is presented. The solution methodology adopts a heuristic-based approach that determines the score thresholds that minimize the difference between the traffic distribution pattern under SLEM’s routing strategy and the SO pattern. The framework was applied to the network of the US-75 Corridor in Dallas, Texas, and a set of simulation-based experiments was conducted to evaluate the network performance given different driver populations, score class aggregation levels, recurrent and non-recurrent congestion scenarios, and driver compliance rates

Optimal Performance of Photovoltaic-Powered Water Pumping System

Photovoltaic (PV) systems are one of the promising renewable energy sources that have many industrial applications; one of them is water pumping systems. This paper proposes a new application of a PV system for water pumping using a three-phase induction motor while maximizing the daily quantity of water pumped while considering maximizing both the efficiency of the three-phase induction motor and the harvested power from the PV system. This harvesting is performed through maximum power point tracking (MPPT) of the PV system. The proposed technique is applied to a PV-powered 3 phase induction motor water pumping system (PV-IMWPS) at any operating point. Firstly, an analytical approach is offered to find the optimal firing pattern of the inverter (V-F) for the motor through optimal flux control. This flux control is presented for maximizing the pump flow rate while achieving MPPT for the PV system and maximum efficiency of the motor at any irradiance and temperature. The provided analytical optimal flux control is compared to a fixed flux one to ascertain its effectiveness. The obtained feature of the suggested optimal flux control validates a significant improvement in the system performances, including the daily pumped quantity, motor power factor, and system efficiency. Then converting the data from the first analytical step into an intelligent approach using an adaptive neuro-fuzzy inference system (ANFIS). This ANFIS is trained offline with the input (irradiance and temperature) while the output is the inverter pattern to enhance the performance of the proposed pumping system, PV-IMWPS