As autonomous vehicle (AV) technology evolves and matures, automated public transit (APT) is gaining attention due to its flexibility, cost-effectiveness, and efficiency. This report explores various algorithms for allocating vehicles to passengers within APT systems. It aims to organize and propose effective allocation strategies and validate them through comparative analyses on test networks. Overall, the paper introduces several algorithms, with six specifically compiled and tested using the VIPSim simulator across four traffic networks. Two of these networks are basic, while the other two are more complex and represent real-world scenarios. Through these numerical experiments, the algorithm that maximizes network operational efficiency was identified, and several instructive conclusions were drawn from the comparative analysis.

The focus of this thesis is to study what and how information flows can be used to improve Demand responsive transport (DRT) systems by understanding potential users and how they could be willing to participate in DRT to a higher degree. The viewpoint of this thesis tends to lean towards a DRT service of a public transport type. This thesis studies users in relation to what interaction and information they perceive to be needed in dealing with a DRT service and the different pros and cons with various approaches. The study gathers information by performing adapted qualitative interviews with a select number of users between the ages of 20-35. The participants give their views on three DRT scenarios and reflect on DRT in general as a concept presented to them through a tangible mocked-up interactive prototype.  The thesis makes several distinct findings. The importance of pricing a DRT service correctly is vital to the users, as several participants in the study relied on pricing for decision-making. It also finds that the usage of zones as nomenclature is confusing to many users. The services must be dependable and punctual to both attract users, keep users, and build trust among the general populace. This study shows that DRT services could be a difficult concept to introduce to users. DRT could be introduced as a complement or as an alternative to conventional public transport. An important factor is a well-designed flow of information in the application to keep the user engaged and involved. It is shown that the usability of the application is a cornerstone for a theoretical DRT service to excel. Context is important where DRT and ridesharing would have a higher success rate. Nighttime in urban areas could be a niche market, due to the irregularity, delay, or interruption of regular public transport services at these hours.

The present thesis aims to establish ideas and technical solutions that can have a positive impact on women's perceived safety while traveling in autonomous vehicles, made for sharing with strangers. The method follows the Design Thinking model which contributes to a user-centered design approach. Initial literature research was performed to understand the problem area, which included women's issues in public transportation, the development of autonomous vehicles, the foundation of a trusting behavior and authentication technologies for identifying users. Following ideation workshops with eight potential users of the service contributed with ideas based on the female perspective and their expectations of traveling in a shared mobility alternative. These results provide a foundation that contributes to a specific purpose of the thesis to create and evaluate strategies for authentication of co-passengers due to being advocated by the participants. Two versions of a high-fidelity mobile application prototype were created in Figma with different strategies for how to interact with the service and authentication methods to align with the autonomous vehicle prototype provided by NEVS during the following tests.  The final user tests, with 14 participants, indicate that an identification method should be included in the service, especially during the night. Six of seven female participants appreciate a combination of Bank ID while requesting a ride and facial recognition when boarding the vehicle. However, the results of the male participants vary to a larger extent. The results do not indicate where the identification technology should be implemented, in the private phone or the vehicle doors. To create a solution available to a larger target group, the mobile application need to adopt and provide option alternatives regarding identification methods due to individual differences and previous experiences which lays a foundation for the users' ability to contribute to a trusting behavior. Furthermore, an onboarding process for the first-time user is proposed to prepare the user and describe how the service could be used and what is expected by them.

Ride-hailing platforms (RHP) are sharing economy platforms that connect passengers who need to order a private ride or to share a vehicle with drivers who want to share a ride. However, after rapid growth, major RHPs (e.g., Uber, Lyft, and Didi) have begun to face severe driver shortages. Attracting and maintaining a large driver base is critical to the survival and success of any RHP no matter its size. While practitioners are urging to seek suggestions from academia to prevent driver loss, limited research attention has been paid to RHP drivers’ discontinuance. To fill this gap, this dissertation aims to explore factors that motivate drivers to discontinue using RHPs from the perspective of algorithm unfairness. The algorithm is the boss of ride-hailing drivers as they are matched, paid, and evaluated by various algorithms. While algorithms have the potential to make the ride-hailing process more efficient, they also yield socially biased outcomes which create inequalities and uncomfortable experiences for both drivers and riders which may further influence their decisions to use to not use RHPs. Following the logic, the research question of the current dissertation is “how does algorithm unfairness of RHPs affect drivers’ discontinuance?” Stressor-strain-outcome model and organizational justice theory are adapted to the ride-hailing context based on the contextualization approach to serve as theoretical frameworks of the current study. An online survey is conducted to empirically test drivers’ discontinuance of ride-hailing platforms. Research participants of the studies are recruited by employing the service provided by Prolific. co. Data analysis is conducted by employing the covariance-based structural equation modeling approach by following previously defined approaches. The results support most of the hypotheses. The study is expected to contribute to the current literature on information systems discontinuance, ride-hailing, IT stress, AI-empowered algorithm management, algorithm unfairness, dark side of AI, stressor-strain-outcome model, and organizational justice theory. My dissertation is also expected to offer rich insights into how to retain the user base effectively for practitioners in emerging sharing economy platforms. Moreover, the results of the current dissertation also offer rich implications on how to manage dispersed workforces using AI-empowered algorithms.

abstract: Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). Previous research has made a number of important contributions to the challenging pickup and delivery problem along different formulation or solution approaches. However, there are a number of modeling and algorithmic challenges for a large-scale deployment of a vehicle routing and scheduling algorithm, especially for regional networks with various road capacity and traffic delay constraints on freeway bottlenecks and signal timing on urban streets. The main thrust of this research is constructing hyper-networks to implicitly impose complicated constraints of a vehicle routing problem (VRP) into the model within the network construction. This research introduces a new methodology based on hyper-networks to solve the very important vehicle routing problem for the case of generic ride-sharing problem. Then, the idea of hyper-networks is applied for (1) solving the pickup and delivery problem with synchronized transfers, (2) computing resource hyper-prisms for sustainable transportation planning in the field of time-geography, and (3) providing an integrated framework that fully captures the interactions between supply and demand dimensions of travel to model the implications of advanced technologies and mobility services on traveler behavior. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2018

Ride sharing—the bundling of simultaneous trips of several people in one vehicle—may help to reduce the carbon footprint of human mobility. However, the complex collective dynamics pose a challenge when predicting the efficiency and sustainability of ride sharing systems. Standard door-to-door ride sharing services trade reduced route length for increased user travel times and come with the burden of many stops and detours to pick up individual users. Requiring some users to walk to nearby shared stops reduces detours, but could become inefficient if spatio-temporal demand patterns do not well fit the stop locations. Here, we present a simple model of dynamic stop pooling with flexible stop positions. We analyze the performance of ride sharing services with and without stop pooling by numerically and analytically evaluating the steady state dynamics of the vehicles and requests of the ride sharing service. Dynamic stop pooling does a priori not save route length, but occupancy. Intriguingly, it also reduces the travel time, although users walk parts of their trip. Together, these insights explain how dynamic stop pooling may break the trade-off between route lengths and travel time in door-to-door ride sharing, thus enabling higher sustainability and service quality.

The convergence of various technological and operational advancements has reinstated the interest in On-Demand Air Service (ODAS) as a viable mode of transportation. ODAS enables an end-user to be transported in an aircraft between their desired origin and destination at their preferred time without advance notice. Industry, academia, and the government organizations are collaborating to create technology solutions suited for large-scale implementation of this mode of transportation. Market studies suggest reducing vehicle operating cost per passenger as one of the biggest enablers of this market. To enable ODAS, an ODAS operator controls a fleet of aircraft that are deployed across a set of nodes (e.g., airports, vertiports) to satisfy end-user transportation requests. There is a gap in the literature for a tractable and online methodology that can enable ride-sharing in the on-demand operations while maintaining a publicly acceptable level of service (such as with low waiting time). The need for an approach that not only supports a dynamic-stochastic formulation but can also handle uncertainty with unknowable properties, drives me towards the field of Reinforcement Learning (RL). In this work, a novel two-layer hierarchical RL framework is proposed that can distribute a fleet of aircraft across a nodal network as well as perform real-time scheduling for an ODAS operator. The top layer of the framework - the Fleet Distributor - is modeled as a Partially Observable Markov Decision Process whereas the lower layer - the Trip Request Manager - is modeled as a Semi-Markov Decision Process. This framework is successfully demonstrated and assessed through various studies for a hypothetical ODAS operator in the Chicago region. This approach provides a new way of solving fleet distribution and scheduling problems in aviation. It also bridges the gap between the state-of-the-art RL advancements and node-based transportation network problems. Moreover, this work provides a non-proprietary approach to reasonably model ODAS operations that can be leveraged by researchers and policy makers.