Traffic congestion, high gas price and inadequate public transportation are major challenges for any country, business or individual. The traditional approach to solving these problems has been to improve public transportation and use greener energy. These approaches require huge investment, research and time, and can only be carried out by governments or businesses. An alternative solution seeks to reduce the number of vehicles on the road based on ridesharing. Nevertheless, ridesharing is not a popular form of public transportation. A ridesharing application involves planning and collaboration in setting up rides. Some of the concerns of users of these applications are social discomfort that arises due to lack of trust amongst co-passengers and inconvenience in scheduling rides. In this work we are trying to understand the perceptions of trust in a software application that integrates collaboration and social networks. We conducted a rideshare needs assessment survey conducted within the Virginia Tech community. The purpose behind this survey was to understand commuters travel patterns, their needs and to identify their preferences for private vehicles and public transit for a variety of travel needs. The survey results indicate that users are willing to increase participation in ridesharing programs if three core issues are addressed trust, convenience and incentives. Based on the results and analysis of our survey results we present an iPhone based ridesharing application that would leverage social networks to embed trust. To overcome the complexity in scheduling rides, we have made our application available on mobile phones (iPhone) so that users are connected on the go and make use of GPS for location awareness to plan their rides instantaneously. Our social software application is easy and intuitive to use, helps users find trusted rideshares and reduces the carbon footprint of the individual. / Master of Science

This paper presents a case for the expansion of a commuting system called casual carpool. The system is a grassroots network of carpoolers that collaborate to form rideshare for their commute to the downtown areas of San Francisco, Houston and Washington DC. Through the author’s experience with the system and personal attempts to deliver a rideshare network to the public, a framework of a greater casual carpool organization is presented.

Uber and Lyft, the “unregulated taxis” that are putting traditional taxi companies out of business, are expanding quickly and changing the landscape of urban transportation as they go. This thesis analyzes the environmental impacts of Transportation Network Companies, particularly in California, with respect to travel behavior, congestion, and fuel efficiency. The analysis suggests that fuel efficient taxis are being replaced by less fuel efficient Uber and Lyft vehicles. Linear regressions were run on data from the Clean Vehicle Rebate Project’s Electric Vehicle Consumer Survey of electric vehicle owners in California. The findings indicate that Uber drivers are more reliant upon the state rebate than the general population of electric vehicle owners in California.

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.

Objectives: Research suggests the general public has a lack of faith in highly automated vehicles (HAV) stems from a lack of system transparency while in motion (e.g., the user not being informed on roadway perception or anticipated responses of the car in certain situations). This problem is particularly prevalent in public transit or ridesharing applications, where HAVs are expected to debut, and when the user has minimal training on, and control over, the vehicle. To improve user trust and their perception of comfort and safety, this study aimed to develop more detailed and tailored human-machine interfaces (HMI) aimed at relying automated vehicle intended actions (i.e., "intentions") and perceptions of the driving environment to the user. Methods: This project developed HMI systems, with a focus on visual and auditory displays, and implemented them into a HAV developed at the Virginia Tech Transportation Institute (VTTI). Volunteer participants were invited to the Smart Roads at VTTI to experience these systems in real-world driving scenarios, especially ones typically found in rideshare or public transit operations. Participant responses and opinions about the HMIs and their perceived levels of comfort, safety, trust, and situational awareness were captured via paper-based surveys administered during experimentation. Results: There was a considerable link found between HMI modality and users' reported levels of comfort, safety, trust, and situational awareness during experimentation. In addition, there were several key behavioral factors that made users more or less likely to feel comfortable in the HAV. Conclusions: Moving forward, it will be necessary for HAVs to provide ample feedback to users in an effort to increase system transparency and understanding. Feedback should consistently and accurately represent the driving landscape and clearly communicate vehicle states to users. / Master of Science / One of the greatest barriers to the entry of highly automated vehicles (HAV) into the market is the lack of user trust in the vehicle. Research has shown that this lack of faith in the system primarily stems from a lack of system transparency while in motion (e.g., the user not being told how the car will react in a certain situation) and not having an effective way to control the vehicle in the event of a system failure. This problem is particularly prevalent in public transit or ridesharing applications, where HAVs are expected to first appear and where the user has less training and control over the vehicle. To improve user trust and perceptions of comfort and safety, this study developed human-machine interface (HMI) systems, focusing on visual and auditory displays, to better relay automated vehicle "intentions" and the perceived driving environment to the user. These HMI systems were then implemented into a HAV developed at the Virginia Tech Transportation Institute (VTTI) and tested with volunteer participants on the Smart Roads.