Travel time reliability is deemed as one of the most important factors affecting travelers’ route choice decisions. However, existing practices mostly consider average travel time only. This dissertation establishes a methodology framework to overcome such limitation. Semi-standard deviation is first proposed as the measure of reliability to quantify the risk under uncertain conditions on the network. This measure only accounts for travel times that exceed certain pre-specified benchmark, which offers a better behavioral interpretation and theoretical foundation than some currently used measures such as standard deviation and the probability of on-time arrival. Two path finding models are then developed by integrating both average travel time and semi-standard deviation. The single objective model tries to minimize the weighted sum of average travel time and semi-standard deviation, while the multi-objective model treats them as separate objectives and seeks to minimize them simultaneously. The multi-objective formulation is preferred to the single objective model, because it eliminates the need for prior knowledge of reliability ratios. It offers an additional benefit of providing multiple attractive paths for traveler’s further decision making. The sampling based approach using archived travel time data is applied to derive the path semi-standard deviation. The approach provides a nice workaround to the problem that there is no exact solution to analytically derive the measure. Through this process, the correlation structure can be implicitly accounted for while simultaneously avoiding the complicated link travel time distribution fitting and convolution process. Furthermore, the metaheuristic algorithm and stochastic dominance based approach are adapted to solve the proposed models. Both approaches address the issue where classical shortest path algorithms are not applicable due to non-additive semi-standard deviation. However, the stochastic dominance based approach is preferred because it is more computationally efficient and can always find the true optimal paths. In addition to semi-standard deviation, on-time arrival probability and scheduling delay measures are also investigated. Although these three measures share similar mathematical structures, they exhibit different behaviors in response to large deviations from the pre-specified travel time benchmark. Theoretical connections between these measures and the first three stochastic dominance rules are also established. This enables us to incorporate on-time arrival probability and scheduling delay measures into the methodology framework as well.

Transportation engineering has taken upon a new role; to empower the alternative modes of travel: walking, biking, and bus transit. In this new era, engineers are rethinking a network designed predominately for the automobile. The ultimate goal of this research is to create a process that can make a vehicle dominant corridor a desirable, livable thoroughfare by livability design and context sensitive performance measures. Balancing travel modes requires an account of vehicular traffic and the impact of reconfiguring existing conditions. The analysis herein is conducted by field data collection, transportation equations and microsimulation. Simulating traffic behavior will be the means to apply livable alternatives comparable to existing Southeast Florida conditions. The results herein have shown that micro-simulation can be utilized in transportation planning to reveal good livability alternatives. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2015 / FAU Electronic Theses and Dissertations Collection

novel approach to extend the decision-making capabilities of unmanned surface vehicles (USVs) is presented in this work. A multi-objective framework is described where separate controllers command different behaviors according to a desired trajectory. Three behaviors are examined – transiting, station-keeping and reversing. Given the desired trajectory, the vehicle is able to autonomously recognize which behavior best suits a portion of the trajectory. The USV uses a combination of a supervisory switching control structure and a reinforcement learning algorithm to create a hybrid deliberative and reactive approach to switch between controllers and actions. Reinforcement learning provides a deliberative method to create a controller switching policy, while supervisory switching control acts reactively to instantaneous changes in the environment. Each action is restricted to one controller. Due to the nonlinear effects in these behaviors, two underactuated backstepping controllers and a fully-actuated backstepping controller are proposed for each transiting, reversing and station-keeping behavior, respectively, restricted to three degrees of freedom. Field experiments are presented to validate this system on the water with a physical USV platform under Sea State 1 conditions. Main outcomes of this work are that the proposed system provides better performance than a comparable gain-scheduled nonlinear controller in terms of an Integral of Absolute Error metric. Additionally, the deliberative component allows the system to identify dynamically infeasible trajectories and properly accommodate them. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

In any infrastructure project, managing the built assets is an important task. In the case of transportation asset inventories, a significant cost and effort is spent on recording and storing the asset information. In order to reduce the time and cost involved in road corridor mapping, this paper proposes a low cost MMS (Mobile Mapping System) using an equipped laser scanner and cameras. The process of building the MMS, components and sensors involved and calibration procedures are discussed. The efficiency of this Mobile Mapping System is experimented by mounting it on a truck and golf cart. The paper also provides a framework to extract road assets both automatically and manually using stateof- the-art techniques. The efficiency of this method is compared with traditional field survey methods. Quality of collected data, data integrity and process flow are experimented with a sample asset management framework and a spatial database structure for mapping road corridor features. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Nowadays it is very hard to find available spots in public parking lots and even harder at facilities such as universities and sports venues. A system that provides drivers with parking availability and parking lot occupancy will allow users find a parking space much easier and faster. This thesis presents a system for automatic parking lot occupancy computation using motion tracking. The use of computer vision techniques and low cost video sensors makes it possible to have an accurate system that allows drivers to find a parking spot. Video bitrate and quality reduction and its impact on performance were studied. It was concluded that high quality video is not necessary for the proposed algorithm to obtain accurate results. The results show that relatively inexpensive and low bandwidth networks can be used to develop large scale parking occupancy applications. / by Francisco Alberto Justo Torres. / Thesis (M.S.C.S.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

Although Value Engineering (VE) studies are mandated by the Federal Highway Administration for large ($25 million or more) federal-aid highway projects, many state Departments of Transportation do not conduct voluntary VE studies on smaller projects. Those who have done so have seen project improvements and savings as a result. The success of the existing voluntary VE programs indicates that VE application to small transportation projects represents a significant opportunity for savings. The goal of this thesis work was to develop a methodology for conducting VE studies on small transportation projects that would make efficient use of available personnel and require little VE training. The author examined the results and procedures of several DOT VE programs, including some that conduct studies on projects as small as $1 million. The analyses revealed sources of past savings, trends and common methods in VE studies, and procedures and forms that are best suited to the types of project under study. Based on the research and analyses, the author developed a VE study methodology that is tailored to small transportation projects, including a workbook and both general and specific guidelines. This report proposes an approach to VE on small transportation projects using this methodology, which is characterized by conformance with accepted VE practice and FHWA guidelines, efficient use of personnel, and ease of use. In particular, these recommendations are intended for use by any state DOT with an existing but limited VE program, such as MassHighway, which currently conducts only mandated studies.

This thesis explores the politics and consequences of road construction for local populations and migrant road workers in Ladakh. Through a political ecology framework, I consider road construction as the transformation of an environment in which different agents act through specific socio-political arrangements and for purposes that are socially and culturally mediated. Based on ethnographic fieldwork conducted in remote villages and among groups of Nepali and Jharkhandi road workers in Ladakh, the thesis documents the case of the Zanskar Highway, a 292 km long trans- Himalayan road that has been under construction since the 1970s. It analyses the reasons why states build roads, nationally and more specifically in the contested landscape of Ladakh; why people want roads; how people negotiate roads and their trajectory; and what the consequences of roads and road construction are in terms of mobility, isolation, resource use, livelihoods and well-being. In the thesis, I question the roads-development nexus, and argue that the reasons why states build roads are extremely diverse and have changed over time. I argue that road construction is a highly political process determined by conflicting motivations and perceptions. I also argue that the consequences of roads are complex, often ambiguous and region-specific, and that gains and losses that occur because of roads and their construction are unequally distributed, within and between local and migrant populations. The research makes an original contribution to road studies by studying the political, socio-economic and symbolic consequences of both roads and the process of their construction for the populations that live near new roads and those who build them. It also links ex-ante with ex-post road studies by looking at what happens during the process of construction. Finally, it contributes to Ladakh studies by documenting the history of road construction in the region and providing the first study of migrants in Ladakh.

This dissertation addresses the relationship between roadway segment length and roadway attributes and their relationship to the efficacy of Safety Performance Function (SPF) models. This research focuses on three aspects of segmentation: segment length, roadway attributes, and combinations of the two. First, it is shown that choice of average roadway segment length can result in markedly different priority lists. This leads to an investigation of the effect of segment length on the development of SPFs and identifies average lengths that produce the best-fitting SPF. Secondly, roadway attributes are filtered to test the effect that homogeneity has on SPF development. Lastly, a combination of segment length and attributes are examined in the same context. In the process of conducting this research a tool was developed that provides objective goodness-of-fit measures as well as visual depictions of the model. This information can be used to avoid things like omitted variable bias by allowing the user to include other variables or filter the database. This dissertation also discusses and offers examples of ways to improve the models by employing alternate model forms. This research revealed that SPF development is sensitive to a variety of factors related to segment length and attributes. It is clear that strict base condition filters based on the most predominant roadway attributes provide the best models. The preferred functional form was shown to be dependent on the segmentation approach (fixed versus variable length). Overall, an important step in SPF development process is evaluation and comparison to determine the ideal length and attributes for the network being analyzed (about 2 miles or 3.2 km for Kentucky parkways). As such, a framework is provided to help safety professionals employ the findings from this research.

This research investigates whether Transportation Network Companies (TNCs), such as Uber and Lyft, live up to their stated vision of reducing congestion by complementing transit and reducing car ownership in major cities. The objective of this research study is to answer the question: are TNCs are correlated to traffic congestion in the city of San Francisco? If found to be so, do they increase or decrease traffic congestion for the case of San Francisco? If and how TNC pickups and drop-offs impact traffic congestion within San Francisco? And finally, how does the magnitude of this measured command of TNCs on congestion compare to that caused by pre-existing conventional drivers of traffic and congestion change? Apart from answering these questions, it is also sought to establish a framework to be able to include TNCs, a seemingly fledgling mode of transportation but one that is demonstrably shaping and modifying extant transportation and mode choice trends, as part of the travel demand models estimated by any geographic jurisdiction. Traffic congestion has worsened noticeably in San Francisco and other major cities over the past few years. Part of this change could reasonably be explained by strong economic growth or other standard factors such as road and transit network changes. The sharp increase in travel times and congestion also corresponds to the emergence of TNCs, raising the question of whether the two trends may be related. Existing research has produced conflicting results and been hampered by a lack of data. Using data scraped from the Application Programming Interfaces (APIs) of two TNCs, combined with observed travel time data, this research finds that contrary to their vision, TNCs are the biggest contributor to growing traffic congestion in San Francisco. Between 2010 and 2016, weekday vehicle hours of delay increased by 62%, compared to 22% in a counterfactual 2016 scenario without TNCs. The findings provide insight into expected changes in major cities as TNCs continue to grow, informing decisions about how to integrate TNCs into the existing transportation system. This research also decomposes the contributors to increased congestion in San Francisco between 2010 and 2016, considering contributions from five incremental effects: road and transit network changes, population growth, employment growth, TNC volumes, and the effect of TNC pick-ups and Drop-offs. It is so done through a series of controlled travel demand model runs, supplemented with observed TNC data. The results show that road and transit network changes over this period have only a small effect on congestion, population and employment growth are important contributors, and that TNCs are the biggest contributor to growing congestion over this period, contributing about half of the increase in vehicle hours of delay, and adding to worsening travel time reliability. This research contradicts several studies that suggest TNCs may reduce congestion and adds evidence in support of a recent empirical analysis showing that their net effect is to increase congestion. This research gives transportation planners a better understanding of the causes of growing congestion, allowing them to more effectively craft strategies to mitigate or adapt to it.

Current practices based on estimated volume and basic queuing theory to calculate delay resulting from non-recurrent congestion do not account for the day-to-day fluctuations in traffic. In an attempt to address this issue, probe GPS data are used to develop impact zone boundaries and calculate Vehicle Hours of Delay (VHD) for incidents stored in the Traffic Response and Incident Management Assisting the River City (TRIMARC) incident log in Louisville, KY. Multiple linear regression along with stepwise selection is used to generate models for the maximum queue length, the average queue length, and VHD to explore the factors that explain the impact boundary and VHD. Models predicting queue length do not explain significant amounts of variance but can be useful in queue spillback studies. Models predicting VHD are as effective as the data collected; models using cheaper-to-collect data sources explain less variance; models collecting more detailed data explained more variance. Models for VHD can be useful in incident management after action reviews and predicting road user costs.