The decision to select the best Intelligent Transportation System (ITS) technologies from available options has always been a challenging task. The availability of connected vehicle/automated vehicle (CV/AV) technologies in the near future is expected to add to the complexity of the ITS investment decision-making process. The goal of this research is to develop a multi-criteria decision-making analysis (MCDA) framework to support traffic agencies’ decision-making process with consideration of CV/AV technologies. The decision to select between technology alternatives is based on identified performance measures and criteria, and constraints associated with each technology. Methods inspired by the literature were developed for incident/bottleneck detection and back-of-queue (BOQ) estimation and warning based on connected vehicle (CV) technologies. The mobility benefits of incident/bottleneck detection with different technologies were assessed using microscopic simulation. The performance of technology alternatives was assessed using simulated CV and traffic detector data in a microscopic simulation environment to be used in the proposed MCDA method for the purpose of alternative selection. In addition to assessing performance measures, there are a number of constraints and risks that need to be assessed in the alternative selection process. Traditional alternative analyses based on deterministic return on investment analysis are unable to capture the risks and uncertainties associated with the investment problem. This research utilizes a combination of a stochastic return on investment and a multi-criteria decision analysis method referred to as the Analytical Hierarchy Process (AHP) to select between ITS deployment alternatives considering emerging technologies. The approach is applied to an ITS investment case study to support freeway bottleneck management. The results of this dissertation indicate that utilizing CV data for freeway segments is significantly more cost-effective than using point detectors in detecting incidents and providing travel time estimates one year after CV technology becomes mandatory for all new vehicles and for corridors with moderate to heavy traffic. However, for corridors with light, there is a probability of CV deployment not being effective in the first few years due to low measurement reliability of travel times and high latency of incident detection, associated with smaller sample sizes of the collected data.

Traveler information systems play a significant role in most travelers’ daily trips. These systems assist travelers in choosing the best routes to reach their destinations and possibly select suitable departure times and modes for their trips. Connected Vehicle (CV) technologies are now in the pilot program stage. Vehicle-to-Infrastructure (V2I) communications will be an important source of data for traffic agencies. If this data is processed properly, then agencies will be able to better determine traffic conditions, allowing them to take proper countermeasures to remedy transportation system problems under different conditions. This research focuses on developing methods to assess the potential of utilizing CV data to support the traveler information system data collection process. The results from the assessment can be used to establish a timeline indicating when an agency can stop investing, at least partially, in traditional technologies, and instead rely on CV technologies for traveler information system support. This research utilizes real-world vehicle trajectory data collected under the Next Generation Simulation (NGSIM) program and simulation modeling to emulate the use of connected vehicle data to support the traveler information system. NGSIM datasets collected from an arterial segment and a freeway segment are used in this research. Microscopic simulation modeling is also used to generate required trajectory data, allowing further analysis, which is not possible using NGSIM data. The first step is to predict the market penetration of connected vehicles in future years. This estimated market penetration is then used for the evaluation of the effectiveness of CV-based data for travel time and volume estimation, which are two important inputs for the traveler information system. The travel times are estimated at different market penetrations of CV. The quality of the estimation is assessed by investigating the accuracy and reliability with different CV deployment scenarios. The quality of volume estimates is also assessed using the same data with different future scenarios of CV deployment and partial or no detector data. Such assessment supports the identification of a timeline indicating when CV data can be used to support the traveler information system.

This study links traffic sign visibility and legibility to quantify the effects of damage or deterioration on sign retroreflective performance. In addition, this study proposes GIS-based data integration strategies to obtain and extract climate, location, and emission data for in-service traffic signs. The proposed data integration strategy can also be used to assess all transportation infrastructures’ physical condition. Additionally, non-parametric machine learning methods are applied to analyze the combined GIS, Mobile LiDAR imaging, and digital photolog big data. The results are presented to identify the most important factors affecting sign visual condition, to predict traffic sign vandalism that obstructs critical messages to drivers, and to determine factors contributing to the temporary obstruction of the sign messages. The results of data analysis provide insight to inform transportation agencies in the development of sign management plans, to identify traffic signs with a higher likelihood of failure, and to schedule sign replacement.

Traffic signal systems serve as one of the most powerful control tools in improving the efficiency of surface transportation travel. Traffic operations on arterial roads are particularly complex because of traffic interruptions caused by signalized intersections along the corridor. This dissertation research presents a systematic framework of integrated traffic control in an attempt to break down the complexities into several simpler sub-problems such as pattern recognition, environment-mobility relationships and multi-objective optimization for multi-criterial signal timing design. The overall goal of this dissertation is to develop signal timing plans, including a day plan schedule, cycle length parameters, splits and offsets, which are suitable for real traffic conditions with consideration of multi-criterial performance of the surface transportation system. To this end, the specific objectives are to: (1) identify appropriate time-of-day breakpoints and intervals to accommodate traffic pattern variations for day plan schedule of signal timing; (2) explore the relationship between environmental outcomes (e.g., emissions) from emission estimators and mobility measures (e.g., delay and stops) for different types of intersections; (3) optimize signal timing parameters for multi-criteria objectives (e.g., minimizing vehicular delay, number of stops, marginal costs of emissions and total costs), with the comparison of performance metrics for different objectives, at the intersection level; (4) optimize arterial offsets for different objectives at the arterial level and compare the performance metrics of different objectives to recommend suitable objectives for integrated multi-criteria signal timing design in arterial traffic operations. An extensive review of the literature, which covers existing tools, traffic patterns, traffic control with environmental concerns, and related optimization methods, shows that both opportunities and challenges have emerged for multi-criteria traffic signal timing design. These opportunities include large quantities of traffic condition data collected by system detectors or non-intrusive data collection platforms as well as powerful tools for microscopic traffic modeling and instantaneous emission estimation. The challenge is how to effectively deal with these big data, either from field collection or detailed simulation, and provide useful information for decision makers in practice. Methodologically, there's a tradeoff between the accuracy of objective function values and the computational efficiency of simulation and optimization. To address this need, in this dissertation, traffic signal timing design that systematically enables the use of integrated data and models are investigated and analyzed in the four steps/studies. The technology of identifying time-of-day breakpoints in the first study shows a mathematical way to classify dynamic traffic patterns by understanding dynamic traffic features and instabilities at a macroscopic level on arterials. Given the limitations of using built-in emissions modules within current traffic simulation and signal optimization tools, the metamodeling-based approach presented in the second study makes a methodological contribution. The findings of the second study on environment-mobility relationships set up the base for extensive application of two-stage optimization in the third and fourth studies for sustainable traffic operations and management. The comparison of outputs from an advanced estimator with those from the current tool also addresses improving the emissions module for more accurate analysis (e.g., benefit-cost analysis) in practical signal retiming projects. The third study shows that there are tradeoffs between minimizing delay and minimizing marginal costs of emissions. When total cost (including cost of delay, fuel consumption and emissions) is set as a single objective function, that objective clears the way for relatively reliable results for all the aspects. In the fourth study, the improvements in marginal cost of emissions and total cost by dynamic programming procedure are obvious, which indicates the effectiveness of using total link cost as an objective at the corridor level. In summary, this dissertation advocates a sustainable traffic control system by simultaneously considering travel time, fuel consumption and emissions. The outcomes of this integrated multi-criteria signal timing design can be easily implemented by traffic operators in their daily life of retiming signal timing.

Traffic fatalities accounted for 1.24 million lives lost in 2013 worldwide, and almost 33 thousand of those fatalities were in the U.S. in 2013. The southeastern region of the nation stands out for continuously having higher fatality rates per mile driven than the national average. If one can establish compelling relationships between various factors and fatality rates, then policies and investments can be targeted to increase the safety on the network by focusing on policies that mitigate those factors. In this research effort risk-taking characteristics are explored. These factors have not been as comprehensively reviewed as conventional factors such as vehicle and facility conditions associated with safety. The hypothesis assumes if a person exhibits risk-taking behavior, that risk-taking behavior is not limited to only one aspect of risk, but is likely to occur in multiple facets of the person's life. Some of the risk-taking characteristics explored include credit score, safety belt use, smoking and tobacco use, drug use, mental health, educational attainment, obesity, and overall general health characteristics. All risk-taking characteristics with the exception of mental health were found to have statistically significant correlations with fatality rates alone. However, when a regression model was formed to estimate fatality rates by risk-taking characteristics, only four risk-taking characteristics - credit score, educational attainment, overall poor health, and seat belt use were found to be statistically significant at an integrated level with other demographic characteristics such as unemployment levels and population born is state of residency. By identifying at-risk population segments, education, counseling, enforcement, or other strategies may be deployed to help improve travel safety.

Pedestrians are the most vulnerable road users in terms of traffic safety. Public transit users mostly have a pedestrian trip before and/or after the transit one. Thus, pedestrian activity is produced at transit stops naturally. The main focus of this study is pedestrian safety problems around transit stops, more specifically bus stops. The proposed methodology first includes Geographic Information Systems (GIS) analyses of the pedestrian safety along the study corridors and around bus stops on them / this enables determination of accident-prone corridor segments and bus stops, respectively. Later, two analyses are studied to understand their correlation. Finally, linear regression analyses are performed to find the significant factors affecting pedestrian safety. These analyses use parameters created in the GIS analyses in the first part, as well as others (i.e. built environment, traffic network, etc.) that have potential impact on pedestrian movement or safety. In corridor safety models, the number of pedestrian accidents or accident density (or some transformation of them) is used as the dependent variable / while it is selected as the total number of accidents within a selected buffer zone in the bus stop safety models. The case study corridors are selected based on the high density of pedestrian accidents in Ankara, including the Central Business District (CBD) and four main arterials serve from CBD to different regions. The bus stops on corridors with high motorized and pedestrian flows are found to be more critical than others.

Sustainable transportation aims encouragement of non-motorized (pedestrian and bicyclist) and shared-ride transportation modes instead of car-dependent travels. This is important for university campuses, as they have better chance to implement such policies in a rather controlled traffic network, and can set an example to other communities. Most of sustainable campus transportation programs boil down to reduction of car-based emission cost of campus mobility, which is always the first step in developing more sustainable transportation policies. Middle East Technical University (METU), Ankara has a large campus area and a population over 30,000 people. To develop sustainable campus transportation policies, it was important to quantify the current levels of mobility and vehicle emissions within the campus, which was the main motivation behind this study. This required determination of i) campus origin-destination matrix, ii) in-campus vehicle-km-travelled (vehicle-km), and iii) carbon emissions. Travel data obtained from different sources, including the gate entry with RFID systems enabled analysis of different user groups, such as academic and administrative personnel and students, separately. The traffic simulations were prepared in PTV VISUM, which provided both speed and vehicle-km values for road segments, and could represent multi-user group demand matrices in a single traffic assignment. Based on the base case mobility and emission values, more sustainable campus transportation policies were simulated in PTV VISUM, and assessed in terms of carbon emission impacts. Discouraging of private car usage by students seemed the first and simplest action.

An increasing number of agencies have begun to define sustainability for transportation systems and are taking steps to incorporate the concept into the regional transportation planning process. Planning for sustainable transportation systems should at the very least incorporate their broader impacts on system effectiveness, environmental integrity, economic development, and the social quality of life. This study reviews definitions, performance measures, and evaluation methodologies for transportation system sustainability and demonstrates a framework for incorporating sustainability considerations in transportation planning and decision making. Through a case study using data from the Atlanta Metropolitan Region, the study evaluates competing transportation and land use plans based on a broad range of sustainability parameters using relevant spatial and environmental analyses. A multiple criteria decision making (MCDM) method enables the aggregation of individual performance measures into four basic indexes and further into a composite sustainability index based on regional goals and priorities. The value of the indexes lies in their ability to capture the multidimensional nature of sustainability as well as important tradeoffs among the potentially conflicting decision criteria. A decision support tool is proposed to visualize dominance and tradeoffs when evaluating alternatives and to effectively reflect changing regional priorities over time. The proposed framework should help decision makers with incorporating sustainability considerations into transportation planning as well as identifying superior plans for predetermined objectives.

This study deals with a municipal solid waste management system in which the strategic and tactical decisions are addressed simultaneously. In the system, the number and locations of the transfer facilities which serve to the particular solid waste pick-up points and the landfill are determined. Additionally, routing plans are constructed for the vehicles which collect the solid waste from the pick-up points by regarding the load capacity of the vehicles and shift time restrictions. We formulate this reverse logistics system as a location-routing problem with two facility layers. Mathematical models of the problem are presented, and an iterative capacitated-k-medoids clustering-based heuristic method is proposed for the solution of the problem. Also, a sequential clustering-based heuristic method is presented as a benchmark to the iterative method. Computational studies are performed for both methods on the problem instances including up to 1000 pick-up points, 5 alternative transfer facility sites, and 25 vehicles. The results obtained show that the iterative clustering-based method developed achieves considerable improvement over the sequential clustering-based method.

Traffic safety is an important problem in today&rsquo / s world with increasing number of fatalities and injuries in traffic accidents. For the solution of this problem, determination of accident prone locations on a network and reasons behind is an essential step, which is studied to some extend via different traffic accident analyses in the literature. While major factors affecting accident risk, such as speed, congestion, infrastructural aspects are known, it is still very difficult to figure out the interaction among these factors, due to complexity in the spatial and temporal distribution of the aforementioned factors and traffic network characteristics. While the case of accident analysis on highways is simpler, in case of urban roads, it requires more effort due to more complex traffic networks with quite a number of conflict points and varying flow characteristics. To investigate possible relationships between speed and accident occurrence on urban arterials, a Geographic Information Systems based accident analysis methodology (GIS-TAAM) is developed in this study. This methodology uses time-dependent average link speeds (calculated from GPS-based data) and accident history of links, and three safety measures in thematic accident maps: i) total number of accident, ii) a severity index based on number of fatality and injury accidents, and iii) an alternative severity index based on total number of fatalities and injuries. The implementation of the proposed methodology and its deliverables are discussed over a pilot study on in&ouml / n&uuml / Boulevard &ndash / EskiSehir Road, Ankara.