The general plan of this study is to sketch the beginning of Ogden settlement and the early history up to the beginnings of the influence of the coming of the railroad, then to follow in more detail the conditions and influences that developed as the railroad was built and the changes that followed the completion of the line.

Transportation is a function that affects nearly all life decisions, but is often not given much thought by the average individual throughout their daily routines. Most of this complacency streams from the mainstream development patterns in the United States that have changed little from the end of World War II. During the immediate post-war years a perfect mix for suburban living came together: the mass production of automobiles, guaranteed mortgages from the federal government through the G.I. Bill, and in 1956 the passage of the National Interstate and Defense Highways Act. These factors, along with the dominate social paradigm that the "American Dream" was to have a personal front and back yard, helped profoundly transform development in the country. Over half a century later, the United States is now experiencing the consequences of this sprawled, auto-dependent development pattern. Energy prices have increased substantially over the past decade, which were only contained momentarily by a worldwide recession that was arguably caused by the same development patterns. Environmental consequences are becoming increasingly evident, ranging from contaminated storm-water runoff, to global climate change. Similarly, mental and physical health has degraded rapidly, with a soaring depression and obesity rates. The United States can, and should do better than this. Transit Oriented Development (TOD) offers a solution to help alleviate many of the complex issues that many communities must address. While there is no perfect template, TOD is an important step forward for the overall quality of life for individuals throughout the nation. This report will look at the steps that have been taken in the Portland Oregon Metropolitan Area to discourage sprawl development, measuring the effects of their actions on environmental, economic and health factors.

In an age of data explosion, almost every aspect of social activities is impacted by the abundance of information. The information, characterized by alarming volume, velocity and variety, is often referred to as "Big Data". As one fundamental elements of human life, transportation also confronts the promises and challenges brought about by the Big Data era. Big Data in the transportation arena, enabled by the rapid popularization of Intelligent Transportation Systems (ITS) in the past few decades, are often collected continuously from different sources over vast geographical scale. Huge in size and rich in information, the seemingly disorganized data could considerably enhance experts' understanding of their system. In addition, proactive traffic management for better system performance is made possible due to the real-time nature of the Big Data in transportation. Operation efficiency and traffic safety have long been deemed as priorities among highway system performance measurement. While efficiency could be evaluated in terms of traffic congestion, safety is studied through crash analysis. Extensive works have been conducted to identify the contributing factors and remedies of traffic congestion and crashes. These studies lead to gathering consensus that operation and safety have played as two sides of a coin, ameliorating either would have a positive effect on the other. With the advancement of Big Data, monitoring and improvement of both operation and safety proactively in real-time have become an urgent call. In this study, the urban expressway network operated by Central Florida Expressway Authority's (CFX) traffic safety and efficiency was investigated. The expressway system is equipped with multiple Intelligent Transportation Systems (ITS). CFX utilizes Automatic Vehicle Identification (AVI) system for Electronic Toll Collection (ETC) as well as for the provision of real-time information. Recently, the authority introduced Microwave Vehicle Detection System (MVDS) on their expressways for more precise traffic monitoring. These traffic detection systems collect different types of traffic data continuously on the 109-mile expressway network, making them one of the sources of Big Data. In addition, multiple Dynamic Message Signs are currently in use to communicate between CFX and motorists. Due to their dynamic nature, they serve as an ideal tool for efficiency and safety improvement. Careful examination of the Big Data from the ITS traffic detection systems was carried out. Based on the characteristics of the data, three types of congestion measures based on the AVI and MVDS system were proposed for efficiency evaluation. MVDS-based congestion measures were found to be better at capturing the subtle changes in congestion in real-time compared with the AVI-based congestion measure. Moreover, considering the high deployment density of the MVDS system, the whole expressway network is well covered. Thus congestion could be evaluated at the microscopic level in both spatial and temporal dimensions. According to the proposed congestion measurement, both mainline congested segments and ramps experiencing congestion were identified. For congestion alleviation, the existing DMS that could be utilized for queue warning were located. In case of no existing DMS available upstream to the congestion area, the potential area where future DMS could be considered was suggested. Substantial efforts have also been dedicated to Big Data applications in safety evaluation and improvement. Both aggregate crash frequency modeling and disaggregate real-time crash prediction were constructed to explore the use of ITS detection data for urban expressway safety analyses. The safety analyses placed an emphasis on the congestion's effects on the Expressway traffic safety. In the aggregate analysis the three congestion measures developed in this research were tested in the context of safety modeling and their performances compared. Multi-level Bayesian ridge regression was utilized to deal with the multicollinearity issue in the modeling process. While all of the congestion measures indicated congestion was a contributing factor to crash occurrence in the peak hours, they suggested that off-peak hour crashes might be caused by factors other than congestion. Geometric elements such as the horizontal curves and existence of auxiliary lanes were also identified to significantly affect the crash frequencies on the studied expressways. In the disaggregate analysis, rear-end crashes were specifically studied since their occurrence was believed to be significantly related to the traffic flow conditions. The analysis was conducted in Bayesian logistic regression framework. The framework achieved relatively good classifier performance. Conclusions confirmed the significant effects of peak hour congestion on crash likelihood. Moreover, a further step was taken to incorporate reliability analysis into the safety evaluation. With the developed logistic model as a system function indicating the safety states under specific traffic conditions, this method has the advantage that could quantitatively determine the traffic states appropriate to trigger safety warning to motorists. Results from reliability analysis also demonstrate the peak hours as high risk time for rear-end crashes. Again, DMS would be an essential tool to carry the messages to drivers for potential safety benefits. In existing safety studies, the ITS traffic data were normally used in aggregated format or only the pre-crash traffic data were used for real-time prediction. However, to fully realize their applications, this research also explored their use from a post-crash perspective. The real-time traffic states immediately before and after crash occurrence were extracted to identify whether the crash caused traffic deterioration. Elements regarding spatial, temporal, weather and crash characteristics from individual crash reports were adopted to analyze under what conditions a crash could significantly worsen traffic conditions on urban expressways. Multinomial logit model and two separate binomial models were adopted to identify each element's effects. Expected contribution of this work is to shorten the reaction and clearance time to those crashes that might cause delay on expressways, thus reducing congestion and probability of secondary crashes simultaneously. Finally, potential relevant applications beyond the scope of this research but worth investigation in the future were proposed.

The weather condition is a crucial influence factor on road safety issues. Fog is one of the most noticeable weather conditions, which has a significant impact on traffic safety. Such condition reduces the road's visibility and consequently can affect drivers' vision, perception, and judgments. The statistical data shows that many crashes are directly or indirectly caused by the low-visibility weather condition. Hence, it is necessary for road traffic engineers to study the relationship of road traffic accidents and their influence factors. Among these factors, the traffic volume and the speed limits in poor visibility areas are the primary reasons that can affect the types and occurring locations of road accidents. In this thesis, microscopic traffic simulation, through the use of VISSIM software, was used to study the road safety issue and its influencing factors due to limited visibility. A basic simulation model was built based on previously collected field data to simulate Interstate 4 (I-4)'s environment, geometry characteristics, and the basic traffic volume composition conditions. On the foundation of the basic simulation model, an experimental model was built to study the conflicts' types and distribution places under several different scenarios. Taking into consideration the entire 4-mile study area on I-4, this area was divided into 3 segments: section 1 with clear visibility, fog area of low visibility, and section 2 with clear visibility. Lower speed limits in the fog area, which were less than the limits in no-fog areas, were set to investigate the different speed limits' influence on the two main types of traffic conflicts: lane-change conflicts and rear-end conflicts. The experimental model generated several groups of traffic trajectory data files. The vehicle conflicts data were stored in these trajectory data files which, contains the conflict locations' coordinates, conflict time, time-to-conflict, and post-encroachment-time among other measures. The Surrogate Safety Assessment Model (SSAM), developed by the Federal Highway Administration, was applied to analyze these conflict data. From the analysis results, it is found that the traffic volume is an important factor, which has a large effect on the number of conflicts. The number of lane-change and rear-end conflicts increases along with the traffic volume growth. Another finding is that the difference between the speed limits in the fog area and in the no-fog areas is another significant factor that impacts the conflicts' frequency. Larger difference between the speed limits in two nearing road sections always leads to more accidents due to the inadequate reaction time for vehicle drivers to brake in time. And comparing to the scenarios that with the reduced speed limits in the low visibility zone, the condition that without the reduced speed limit has higher conflict number, which indicates that the it is necessary to put a lower speed limit in the fog zone which has a lower visibility. The results of this research have a certain reference value for studying the relationship between the road traffic conflicts and the impacts of different speed limits under fog condition. Overall, the findings of this research suggest follow up studies to further investigate possible relationships between conflicts as observed by simulation models and reported crashes in fog areas.

The goal of this thesis is to evaluate travelers' experience with Highway Advisory Radio (HAR) and Citizens' Band Radio Advisory System (CBRAS) technologies on both Florida Interstate Highway system (FIH) and the Florida Turnpike Enterprise (FTE) toll roads. To achieve this goal, two different survey tools were used. The first tool is a random digit dialing phone survey known as CATI (Computer-Assisted Telephone Interviewing). The second tool is a field survey that intercepts travelers at the Florida Turnpike Enterprise (FTE) service plazas and the Florida Interstate Highway (FIH) rest areas. HAR and CBRAS are traditional components of the Advanced Traveler Information Systems (ATIS). This thesis pays special attention to the effectiveness of HAR and CBRAS in improving travelers' experience. Feedback to analyze these two technologies was collected via a telephonic survey and a field survey. Two different field surveys (one for HAR and one for CBRAS) were designed and implemented to obtain feedback on these technologies. The field survey for CBRAS is unique and has never been done before for this purpose. A sample size of 1000 HAR surveys was collected through the CATI phone survey. Field surveys were collected at five locations across the state, including central, southeast, and southwest regions of Florida. The HAR field survey sample size was 1610 and the CBRAS field survey sample size was 613. All field surveys were conducted by UCF students at each of the five locations, over a 13-week data collection period. The HAR messages were designed to alert drivers of any adverse roadway traffic or weather conditions. The CBRAS is limited to truck drivers with the closed system radio pre-installed in their vehicles. However, truck drivers were also asked some questions on HAR if they do not use CBRAS. Basic statistical analysis was used to determine a number of performance indicators which include system's use and awareness, usability of provided information, route diversion, and travelers' demographics. In addition, the two HAR phone and field samples were combined together and examined using a decision tree model. Target questions were selected from the survey to build the tree network. The tree model aimed at identifying trends between categorical differences of travelers with respect to specific questions. Understanding travelers' satisfaction with HAR is critical to knowing its benefits. The ending results indicated that both basic statistical analysis and the decision tree model are in agreement. A comparison between HAR phone and field surveys indicates the following. Travelers interviewed for the HAR field survey were more aware of the HAR than travelers surveyed by phone. A small portion of the surveyed samples used HAR (22% and this was consistent between the phone and the field surveys). Also, 80% or more were satisfied with HAR for both phone and field samples and the majority (85% or more) supported its continuation as an indication of willingness to use it in the future, especially in emergency conditions. In terms of the types of messages they want to hear from HAR, traffic congestion was the most common. Dynamic Message Signs (DMS) were the most preferred source of travel information and were the alternative for HAR, if HAR gets terminated. This was followed by smartphone applications which received twice as much support from field surveyed travelers (28%) when compared to phone surveyed travelers (15%). The CATI Phone Survey was biased towards elderly people (60% of the sample) and mainly females (58%) that use the FTE roadway system. Users satisfied with the system are those who only use these roadways once per week or less. The survey ultimately shows that travelers rely on modern modes of obtaining traffic information than traditional ones, such as HAR. DMS, and smart phone applications are leading communication tools among all type of travelers. The HAR field survey was less biased with respect to age and gender distribution (56% were under 50 and 62% were males). Both surveys indicate that the sample is well educated (about 60% have an associate degree or higher). CBRAS serves a small segment of commercial truck drivers (only 12% out of 613 used CBRAS). However, this small segment used it heavily (84% used it sometimes, often, or always). And 92% of CBRAS users were satisfied or strongly satisfied with it. CBRAS was used mostly for route divergence, with 72% of the drivers relying on it for this purpose. Truck drivers who never used CBRAS (88% of the sample) were asked questions about HAR. Only 27% of them used HAR and 57% of these used it sometimes, often, or always with 72% of the truck users being satisfied with HAR compared to the 92% satisfied with CBRAS. The most common complaint about HAR by truck drivers was that it is not easy to access or understand. Based on responses of truck drivers for both HAR and CBRAS field surveys above, it seems that GPS navigation was the most preferred source of travel information (28%). In addition to the basic statistics, a decision tree model, using SAS Enterprise Miner was performed. The statistical analysis results indicated satisfaction of travelers. The decision tree model was used to predict and profile responses to all answered questions that each survey shared. Training data was included in the model and the model was able to leverage the questions. Results of the decision tree model predicted high user satisfaction rates. Analyses of the three implemented surveys show that HAR and CBRAS technologies are not used by a large proportion of travelers, but their users are typically satisfied with these technologies. A small portion of the surveyed sample of truck drivers uses CBRAS but they use it heavily and were very satisfied with it. The travelers' satisfaction level with HAR was high. The HAR and CBRAS systems are in the middle of a heated competition lead by digital communication, it may be a sign of the time to create HAR/CBRAS smart phone applications for the longevity of these traditional technologies.

As the second-largest university in the United States, UCF has experienced the largest enrollment in its history. A more densely populated campus has in turn caused increased traffic congestion. Despite increased parking permit fees and newly constructed parking garages, traveling and parking on campus is unpredictable. In effort to reduce congestion on campus, a rideshare program was implemented in Summer 2010. Several universities across the nation have successfully used carpooling as a viable alternative mode to manage traffic and parking demand. This thesis evaluates the UCF rideshare program, Zimride, using stated- and revealed-preference surveys. Preliminary results indicate most students prefer to commute to campus using their own car and without incentives there is no reason to change mode choice, regardless of associated costs—e.g. decal cost, parking time and frustration. Despite 70% of respondents considering themselves environmentally friendly and over 80% are aware of savings in money and productive by using alternative modes, 70% still use their car to commute to campus. Using Explanatory Factor Analysis (EFA) and Structural Equation Modeling (SEM), the observed variables were organized into three (3) latent variables based on the correlation among them. The SEM results of the revealed-preference survey indicate current travel behavior significantly influences attitudes towards carpooling and demographics have a significant effect on current travel behavior. It was also found that demographics influences attitudes towards carpooling at a non statistically significant level.

Automated vehicles are those which are capable of sensing their environments in order to perform at least some aspects of the safety-critical control (like steering, throttling, or braking) without direct human input. As a guide for planners and policymakers, the objective of this thesis is to develop a strong foundation for anticipating the potential impacts resulting from advancements in vehicle automation. To establish the foundation, this thesis uses a robust qualitative methodology, coupling a review of literature on the potential advantages and disadvantages of vehicle automation and lessons from past innovations in transportation, with recent trends of the Millennial Generation, carsharing services, and a series of interviews with thought-leaders in automation, planning, policymaking, transportation, and aviation. Five significant findings emerged from this thesis: (1) the impacts of vehicle automation differ depending on one’s visions of what automation means, how it is implemented, what the automation does, and where it operates; (2) current limitations of vehicle automation to perform all aspects of the dynamic driving task in all driving conditions make it difficult to move from level-4 to level-5 automation; (3) level-5 automation is required to have any effect on carsharing, mobility, and quality of life; (4) assuming effective planning and policymaking techniques, housing preferences, urban growth, and increases in total VMT will likely not be significantly impacted by vehicle automation; (5) human drivers may never be allowed to disengage their attention from a partially-automated vehicle, specifically in applications where drivers are expected to reengage their attention in safety-critical situations. From the perspective of understanding the bigger picture, this thesis developed a proposed future scenario of vehicle automation in the next five to ten years that is used to suggest guiding principles for policymakers, and key recommendations for planners, engineers, and researchers.

Bluetooth technology applications have improved travel time data collection efforts and allowed for collection of large data sets at a low cost per data unit. Mean travel times between pairs of points are available, but the primary value of this technique is the availability of the entire distribution of travel times throughout multiple days and time periods, allowing for a greater understanding of travel time variations and reliability. The use of these data for transportation planning, engineering and operations continues to expand. Previous applications of similar data sources have included travel demand and simulation model validation, work zone traffic patterns, transit ridership and reliability, pedestrian movement patterns, and before-after studies of transportation improvements. This thesis investigates the collection and analysis of Bluetooth-enabled travel time data along a multimodal arterial corridor in San Luis Obispo, California. Five BlueMAC devices collected multimodal travel time data in January and February 2016 along Los Osos Valley Road. These datasets were used to identify and process known sources of error such as occasions where vehicles using the roadway turn off and make an intermediate stop and multiple reads from the same vehicle; quantify travel time performance and reliability along arterial streets; and compare transit, bicycle, and pedestrian facility performance. Additionally, a travel time model was estimated based on segment characteristics and Bluetooth data to estimate average speeds and travel time distributions.

A transportation system designed to prioritize the mobility of automobiles cannot accommodate the growing number of road users. The Complete Streets policy plays a crucial part in transforming streets to accommodate multiple modes of transportation, especially active modes like biking and walking. Complete streets are referred to as streets designed for everyone and enable safety and mobility to all users. A strategy of complete streets transformation is to connect isolated complete street segments to form a complete network that improves active mobility and public transit ridership. This research assessed the impact of efficiently and equitably connecting and expanding the biking network using dedicated lanes on the safety and operation of the network in Atlanta, Georgia. These connections are aimed at increasing the multimodal use of the streets in midtown and downtown Atlanta and achieving the mobility and public health goals through the integration of various modes of travel. The evaluation was done by modeling a well-calibrated and validated network of Midtown and Downtown Atlanta in VISSIM using existing travel demand and traffic design conditions (i.e., the baseline or Scenario 0). A total of three different conditions: existing, proposed, and alternative conditions, were modeled to see the effectiveness of bike infrastructure design improvement and expansion. Three scenarios were then modeled as variations of modal demand of the different condition models. Scenarios modeled are based on input from the City and Community stakeholders. Using the trajectory data from microsimulation, the surrogate safety assessment model (SSAM) from FHWA was used to analyze the safety effect on the bike infrastructure improvement and expansion. Results of this study showed a positive impact of complete streets transformation on the streets of Midtown and Downtown Atlanta. These impacts are quantified in this thesis.

Transit travel time and operating speed influence service attractiveness, operating cost, system efficiency and sustainability. The Tri-County Metropolitan Transportation District of Oregon (TriMet) provides public transportation service in the tri-county Portland metropolitan area. TriMet was one of the first transit agencies to implement a Bus Dispatch System (BDS) as a part of its overall service control and management system. TriMet has had the foresight to fully archive the BDS automatic vehicle location and automatic passenger count data for all bus trips at the stop level since 1997. More recently, the BDS system was upgraded to provide stop-level data plus 5-second resolution bus positions between stops. Rather than relying on prediction tools to determine bus trajectories (including stops and delays) between stops, the higher resolution data presents actual bus positions along each trip. Bus travel speeds and intersection signal/queuing delays may be determined using this newer information. This thesis examines the potential applications of higher resolution transit operations data for a bus route in Portland, Oregon, TriMet Route 14. BDS and 5-second resolution data from all trips during the month of October 2014 are used to determine the impacts and evaluate candidate trip time models. Comparisons are drawn between models and some conclusions are drawn regarding the utility of the higher resolution transit data. In previous research inter-stop models were developed based on the use of average or maximum speed between stops. We know that this does not represent realistic conditions of stopping at a signal/crosswalk or traffic congestion along the link. A new inter-stop trip time model is developed using the 5-second resolution data to determine the number of signals encountered by the bus along the route. The variability in inter-stop time is likely due to the effect of the delay superimposed by signals encountered. This newly developed model resulted in statistically significant results. This type of information is important to transit agencies looking to improve bus running times and reliability. These results, the benefits of archiving higher resolution data to understand bus movement between stops, and future research opportunities are also discussed.