Fully automated or autonomous vehicles (AVs) hold great promise for the future of transportation, with Google and other auto manufacturers intending on introducing self-driving cars to the public by 2020. New automation functionalities will produce dramatic transportation system changes, across safety, mobility, travel behavior, and the built environment. This work’s results indicate that AVs may save the U.S. economy up to $37.7 billion from safety, mobility and parking improvements at the 10% market penetration level (in terms of system-wide vehicle-miles traveled [VMT]), and up to $447.1 billion with 90% market penetration. With only 10% market share, over 1,000 lives could be saved annually. However, realizing these potential benefits while avoiding pitfalls requires overcoming significant barriers including AV costs, liability, security, privacy, and missing research. Additionally, once fully self-driving vehicles can safely and legally drive unoccupied, a new personal travel transportation mode looks set to arrive. This new mode is the shared automated vehicle (SAV), combining on-demand service features with self-driving capabilities. This work simulates a fleet of SAVs operating within Austin, Texas, first using an idealized grid-based representation, and next using Austin’s actual transportation network and travel demand flows. This second model incorporates dynamic ride-sharing (DRS), allowing two or more travelers with similar origins, destinations and departure times to share a ride. Model results indicate that each SAV could replace around 10 conventionally-owned household vehicles while serving over 56,000 person-trips. SAVs’ ability to relocate unoccupied between serving one traveler and the next may cause an increase of 7-10% more travel; however, DRS can result in reduced overall VMT, given enough SAV-using travelers willing to ride-share. Furthermore, using DRS results in overall lower wait and service times for travelers, particularly from pooling rides during peak demand. SAVs should produce favorable emissions outcomes, with an estimated 16% less energy use and 48% lower volatile organic compound (VOC) emissions, per person-trip compared to conventional vehicles. Finally, assuming SAVs cost $70,000 each, an SAV fleet in Austin could provide a 19% return on investment, when charging $1 per trip-mile served. In summary, this new paradigm holds much promise that technological advances may soon realized. / text

Denna studie är ämnad att utreda hur samåkningsföretag kan arbeta medvarumärkesprofilering för att stärka sitt varumärkeskapital. Detta har undersökts genom attstudera vilka faktorer som är viktiga för kunder vid valet att samåka med fokus på densvenska marknaden. Dessa undersökningar har baserat sig på intervjuer med företagenSkjutsgruppen och GoMore samt deras kunder. Studien visar att de viktigaste faktorerna förkunder vid valet att samåka på den svenska marknaden är ekonomiska incitament, miljö,trygghet och säkerhet, flexibilitet samt social interaktion. Av dessa är de ekonomiskaincitamenten viktigast för valet att samåka. Studien har sedan undersökt hursamåkningsföretag, baserat på dessa identifierade faktorer, kan arbeta med sinvarumärkesprofilering för att stärka företagets varumärkeskapital. Studien resulterar i en modell enligt vilken samåkningsföretag kan arbeta med sinvarumärkesprofilering för att stärka sitt varumärkeskapital. Modellen föreslår attsamåkningsföretagen skapar kännedom om varumärket genom marknadsföringsåtgärder somuppmärksammar kunderna på varumärket. Detta ökar varumärkets relevans, det vill sägavarumärket finns med i kundernas minne då de väljer tjänst. Marknadsföringen syftar ocksåtill att profilera företagets varumärke genom att förbättra kundernas inställning till varumärket.Målet med detta är att etablera en emotionell relation mellan kund och företag genom attfokusera på en faktor som lyfts fram som extra betydelsefull. Modellen föreslår också attföretagen samtidigt kan differentiera sig inom flera faktorer i att syfta att erbjuda en bättretjänst än konkurrenterna.

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New companies with business models based on technology-enabled sharing have emerged as the hot topic in technology in recent years. Uber, the sharing-economy’s poster-child, is now the world’s most valuable start-up by far. Lyft, its younger competitor, is seeing year over year growth in the hundreds of percentage points. This growth is coming at the cost of the incumbent taxi industry, and this is what this study sets out to examine. What is the effect of Uber, Lyft, and other ride-sharing services on the taxi industry? My study reveals that there has been an extremely negative effect on taxicabs, and that there are only a few last strands of hope for ways taxis can compete. Based on my study and learning, in final I forecast the ways that the firms in this space can continue to grow and dominate the ride-sharing market, and beyond.

<div>In recent years, there has been considerable growth in the adoption and technology development of electric vehicles (EV), autonomous vehicles (AV), and ride sharing (RS). These technologies have the potential to improve transportation sustainability. Many studies have evaluated the environmental impacts of these technologies but the existing literature has three major gaps: (1) the adoption of these three technologies need to be evaluated considering their impact on each other, (2) many existing models do not evaluate systems on a common ground, and (3) the heterogeneous preferences of riders towards these emerging technologies are not fully incorporated. To address these gaps, this work studies and quantifies the environmental and efficiency gains that can be gained through these emerging transportation technologies by developing a Parameterized Preference-based Shared Autonomous Electric Vehicle (PP-SAEV) agent-based model. The model is then applied to a case study of New York City (NYC) taxis to evaluate the system performance with increasing AV, EV, and RS adoption.</div><div><br></div><div>The outputs from the PP-SAEV model show that replacing taxi cabs in NYC with AVs along with RS potentially can reduce CO\textsubscript{2} emissions by 866 metric Tones per day and increase average vehicle occupancy from 1.2 to 3 persons in vehicles with passenger seating capacity of 4. A prediction model based on the PP-SAEV output recommends that 6000 vehicles are needed to maintain the current level of service with 100\% AV and RS adoption using capacity 4 taxis. Taxi fleets with capacity 4 with high RS and low AV adoption are also found to have the least CO\textsubscript{2} emissions. Because the heterogeneous sharing preferences of riders have shown as the major limiting factor to ride sharing, these heterogeneous sharing preferences are further modelled. The results show that high service levels are achieved when all the riders are open to sharing, and the maximum service level is reached when 30\% of riders will only accept shared rides and 70\% of the riders are either indifferent to sharing or prefer to use ride sharing over riding alone. Additionally, the service level and waiting time of riders that are inflexible (will accept only shared or non-shared rides) are greatly impacted by varying mix of riders with different sharing preference. Finally, an optimization model was built to site charging stations in a system with continually increasing EV adoption. Using the best charging station locations, transforming a fleet of autonomous or traditional vehicles to electric vehicles does not significantly change the system service level. The results show that increasing the EV adoption in fleets with 100\% RS and AV adoption reduced the daily CO\textsubscript{2} emissions by about 861 Tones and transforming a fleet of traditional taxi cabs to electric taxi cabs reduced the daily CO\textsubscript{2} emissions by 1100 Tones.</div><div><br></div><div>In summary, this dissertation evaluates the potential growth of autonomous vehicles, ride sharing, and electric vehicles in systems where riders may have heterogeneous sharing preferences, from a system performance`s perspective and assesses the environmental impacts. The developed model and the insights gained from this study can inform policy makers to develop sustainable transportation systems incorporating the emerging transportation technologies.</div>

abstract: This dissertation examines travelers’ innovation adoption and repurchase behaviors in the sharing economy. The central question is to what extent the tourism industry embraces service innovations in the sharing economy. Predicated upon behavioral reasoning theory, this research makes a contribution to the tourism study and diffusion of innovation literature, by exploring the influence of travelers’ reasonings in the innovation decision process. The dissertation follows a two-study format. The analysis contextualizes reasons for and against adoption, by incorporating appropriate constructs relevant to service innovations in social dining services (Study 1) and ride-sharing services (Study 2). An exploratory mixed methods approach is taken in both studies. The survey data and the semi-structured interviews are used to identify the context-specific reasons for and against adoption. And, a series of statistical analyses are employed to examine how reasonings influence intentions to adopt social dining services (Study 1) and intentions to repurchase ride-sharing services for the next trip (Study 2). The main results suggest that both reasons for and reasons against adoption have countervailing influences in the psychological processing, supporting the validity of the research models. The findings also reveal that different psychological paths in travelers’ adoption and repurchase intentions. In Study 1, the trustworthiness of service providers attenuates the reasons against adoption and enhances the likelihood of adopting social dining services in the pre-adoption stage. In Study 2, attitude strength functions as an additional construct, which mediates travelers’ attitudes and ultimately intentions to repurchase ride-sharing services for the next trip in the post-adoption stage. By developing and testing a framework comprising a set of consumers’ beliefs, reasonings for adoption and resistance, attitudes towards adoption, and behavioral responses to the sharing economy, the insights gleaned from this research allow practical recommendations to be made for service providers, platform providers, and policy makers in the tourism industry. / Dissertation/Thesis / Doctoral Dissertation Community Resources and Development 2019

The world faces human-made hazardous weather events such as heat waves, droughts, floods andwildfires in dimensions which have never been seen before. A crucial contributor to this negative trendis the constantly growing transportation sector. In addition, most urban regions suffer from trafficcongestions which lead among others to local emissions, the loss of time and noise pollution. Onepromising approach to reduce the amount of transport related emissions is ride-sharing. This paperfocuses on the possibilities and barriers of ride-sharing for the daily commute to and from work. To gainreliably results, a real-life test trial was implemented at a Swedish corporation. The gatheredquantitative and qualitative datasets were analysed with the framework of Social Practice Theory, whichsplits up the practice into its three elements of materials, meanings and competences and therebydevelops revealing insights. The reason for the low participation rate during the test trial can be tracedback to the potential loss of flexibility. Despite a high environmental awareness and a deep trust relationto colleagues, the potential loss of flexibility was for most participants the crucial factor to not start ridesharing.Even though individuals’ opinions were very positive towards the idea of ride-sharing, theparticipation rate during the real-life study shows that the perception of ride-sharing highly derivatesfrom the action.

The aim of this report is to provide an analysis of potential benefits of a fleet of Shared Autonomous Vehicles (SAV) providing a taxi service to replace private car commuter trips in a metropolitan area. We develop a framework for dynamic allocation of SAVs to passenger trips, empty-vehicle routing and multi-criteria evaluation with regard to passenger waiting time, trip time and fleet size. Using a representation of current private trip demand for the Stockholm metropolitan area and a detailed road network representation, different scenarios (varying levels of accepted passenger waiting time at origin and accepted increase in travel time) are compared with respect to passenger travel time, number of vehicles needed and vehicle mileage. In a second step the environmental impacts of the different scenarios are assessed and compared using a life cycle approach. The assessment includes both a fleet consisting of currently in use gasoline and diesel cars as well as electrical cars. The results show that an SAV-based personal transport system has the potential to provide an on-demand door-to-door transport with a high level of service, using less than 10 % of today's private cars and parking places. In order to provide an environmental benefit and lower congestion an SAV-based personal transport system requires users to accept ride-sharing, allowing a maximum 30% increase of their travel time (15% on average) and a start time window of 10 minutes. In a scenario where users are not inclined to accept any reduced level of service, i.e. no ride-sharing and no delay, empty vehicle drive of an SAV-based personal transport system will lead to increased road traffic increasing environmental impacts and congestion. Envisioning a future using electrical cars a SAV-based system and electrical vehicle technology seems to be a “perfect” match that could definitely contribute to a sustainable transport system in Stockholm.

In recent years, the paradigm of personal urban mobility has radically evolved as an increasing number of Mobility-on-Demand (MoD) systems continue to revolutionize urban transportation. Hailed as the future of sustainable transportation, with significant implications on urban planning, these systems typically utilize a fleet of shared vehicles such as bikes, electric scooters, cars, etc., and provide a centralized matching platform to deliver point-to-point mobility to passengers. In this dissertation, we study MoD systems along three operational directions – (1) modeling: developing analytical models that capture the rich stochasticity of passenger demand and its impact on the fleet distribution, (2) economics: devising strategies to maximize revenue, and (3) control: developing coordination mechanisms aimed at optimizing platform throughput. First, we focus on the metropolitan bike-sharing systems where platforms typically do not have access to real-time location data to ascertain the exact spatial distribution of their fleet. We formulate the problem of accurately predicting the fleet distribution as a Markov Chain monitoring problem on a graph representation of a city. Specifically, each monitor provides information on the exact number of bikes transitioning to a specific node or traversing a specific edge at a particular time. Under budget constraints on the number of such monitors, we design efficient algorithms to determine appropriate monitoring operations and demonstrate their efficacy over synthetic and real datasets. Second, we focus on the revenue maximization strategies for individual strategic driving partners on ride-hailing platforms. Under the key assumption that large-scale platform dynamics are agnostic to the actions of an individual strategic driver, we propose a series of dynamic programming-based algorithms to devise contingency plans that maximize the expected earnings of a driver. Using robust optimization techniques, we rigorously reason about and analyze the sensitivity of such strategies to perturbations in passenger demand distributions. Finally, we address the problem of large-scale fleet management. Recent approaches for the fleet management problem have leveraged model-free deep reinforcement learning (RL) based algorithms to tackle complex decision-making problems. However, such methods suffer from a lack of explainability and often fail to generalize well. We consider an explicit need-based coordination mechanism to propose a non-deep RL-based algorithm that augments tabular Q-learning with a combinatorial optimization problem. Empirically, a case study on the New York City taxi demand enables a rigorous assessment of the value, robustness, and generalizability of the proposed approaches.

This thesis explores the puzzling subnational variation of policymaking for the online ride-hailing industry (ORS) in China. Chengdu and Jinan are two similar cities on many economic and political levels. They are both capital cities of their provinces, new first-tier cities in terms of their economic size and both have a large population. Yet, they adopted significantly different ORS policies. This thesis asks why two similar cities in China have diverging policy outcomes. Using a method of difference (MOD) strategy to compare these two similar cases with diverging outcomes, the thesis evaluates three potential explanations. They are as follows: (1) historical legacies and political communities, (2) the cadre evaluation system (CES), and (3) government-business relationships. Using a historical institutionalism theoretical framework with comparative capitalism and economic sociology roots, this thesis finds that a mixture of CES incentives and government business relationship patterns has had a determining impact on diverging outcomes in Jinan and Chengdu. There have been two phases of ORS policymaking in China until now. In phase 1, Chengdu had a laxer ORS policy than Jinan. A comparison of historical legacies and political communities tells us that Chengdu has been a more market-oriented city than Jinan. More importantly, Jinan’s government had a more intimate relationship with local taxi agencies, which proved to be the major cause of subnational differences. In phase 2, Chengdu’s ORS policy was found to be more stringent than Jinan’s. In this case, the significant variable leading to Chengdu’s tightened policy was the target-setting of the cadre evaluation system (CES). The CES specifically required Chengdu’s government to ban non-green vehicles from the ORS market while Jinan did not encounter the same requirement. Moreover, Jinan can exclude non-green vehicles from its environmental protection plan while Chengdu cannot. This thesis observes a structural distortion caused by the CES. Throughout the two phases of ORS policymaking, both governments play a consistently dominant role. However, the government-business relationship remains flexible. If the relationship is viewed as an institution, it is composed of informal procedures, conventions, and orders where actors accommodate each other. The largest privately-owned ORS enterprise, Didi, has declared that their preferences are taken into consideration by the government. Some questions remain as to how the government processes those preferences and how much importance it attaches to them, but this illustrates the mutual accommodation of the government and an enterprise within an informal institution. By some unwritten but conventional procedures, they coordinate with each other. This thesis furthers the study of the government-business relationship in China. It not only unearths the institutional factors of subnational variation for ORS policymaking, but also verifies the presence of institutional flexibility in China. This thesis is an important addition to the literature on government-business ties in China because it does beyond the study of rent-seeking to evaluate the multifaceted ways in which the Chinese government can build relationships with enterprises.