Modeling and Analysis of a Mobility Portfolio Framework for Shared-Autonomous Transportation Systems

The emerging and rapidly changing landscape of autonomous vehicles and shared mobility technologies bring up possibilities for a paradigm shift in how we model and analyze mobility. Transportation and mobility systems can now be connected continuously and seamlessly, which can make them more flexible and shareable. What can make this possible? Put simply, it would require integrating various mobility options so that travelers can freely hop among them. The demarcation lines among modes can then become increasingly hazy, as every individual trip may include multiple modes to various degrees. This implies that the paradigm shift is in how we view the travel modes. What were traditionally considered as limited discrete mode options, need to be seen as part of a continuum. In turn, we should focus more on mode combinations rather than individual travel modes. In this dissertation, we propose shifting the focus to the new idea of a mode option pool with an associated structure. The option pool would include every type of travel option in a continuous spectrum. This observation motivates the phrase ‘travel-option chain (TOC)’ mode proposed in this dissertation as a combination of travel options in a continuous spectrum. Shared use of vehicles – either time-shared, or seat-shared – expands the travel option pool. Autonomous vehicle technology makes even more time-shared use of vehicles possible, as the driver constraint is also removed, and thus further expands the travel mode option pool. Then the question is on how to make such a larger option pool available for a large number of users, to improve their level of mobility and the productivity of the vehicles as well as the associated infrastructure. One aspect that needs to be addressed is that people cannot be individually owning the vehicles and infrastructure involved in all of the mobility options they use from the pool. Different people may partially or fully own different components such as, for instance, vehicles or spaces where they are parked. Some ownership may be time-shared as well. Publicly provided transit systems with purchased tickets will naturally be part of many TOC modes. Subscription-type ownership is a possibility, if mobility service providers offer the options for purchase, and they can be bundled options as well, similar to phone plans. This fits within Mobility-as-a-Service (MaaS) platforms that have been proposed in recent years. In this dissertation, a powerful user-side concept, ‘mobility portfolios’ is proposed that encompasses MaaS platforms, subscriptions, ownership, bundled plans and selection of optimal TOCs from a continuum spectrum of modes. The question then ensues on how we can find the optimal TOC modes. From an analytical standpoint, this can be solved with a ridematching problem formulation of matching paths in a time-expanded multimodal network. A more vexing problem is how people can travel on these TOC modes unless they have paid for it in a certain way. The mobility portfolio scheme proposed in this dissertation is geared to make it possible for them to pay for it in an efficient way and in a shareable manner with enough flexibility. This dissertation defines mobility portfolio as a “grouping of the number of hours/cost/resources that can be spent on each distinct travel options, so as to fit within a time/cost/resource constraint specified for a given time period”. The portfolio approach compartmentalizes the travel options that are chained, and allocates appropriate “quantities” of them, when we view them as consumable travel commodities and resources. The portfolio scheme incorporates pricing for the commodities and are expected to bring in efficiency and cost savings while increasing shared mobility participation. This is a good approach for controlling TOC mode change travel behaviors and it subsumes currently envisaged ideas such as MaaS mobility bundles in a smart and shared mobility system with subscription options. The focus of this dissertation is on the user level decisions on selecting the TOC modes from their mobility portfolios scheme. Innovative options such as users offering their own resources (e.g., owned vehicles) and their own services (e.g., potential driving for shared rides) are incorporated in the portfolios. We develop an iterative framework which is rooted on a learning-based travel cost perception update model, so as to model the users being provided with the best travel options as well as the best usage plan for mobility portfolios. Performing simulated case studies on a real network, we confirm that the proposed framework converges to the optimum mobility portfolio state for system participants and improves the performance of the system by inducing people to use shared mobility options more

A Study of the Factors Affecting Multimodal Ridesharing with Choice-Based Conjoint Analysis

As public perception about the shared economy evolves, peer-to-peer ridesharing has been gaining increased attention worldwide. Both private and public sector entities have launched mobile app-based ridesharing services, while a range of methodologies and system architectures have been proposed in academia. Whereas traditional ridesharing methods match drivers and riders when their origin and destination are similar, recently proposed algorithms often feature multi-hop and multimodal properties that allow riders to be connected by multiple modes. Such algorithms can reduce travel time and/or travel cost; however, they may also add other travel impedances, such as requiring multiple transfers. Understanding user behavior toward such new ridesharing systems is essential for successful service design. For policymakers and service planners, identifying factors that impact traveler choices can lead to better design and improved services. This research involved a web-based survey to capture traveler preferences using a conjoint analysis framework. A choice-based method was adopted to identify factors for the estimation model and to analyze traveler willingness to pay. Among the proposed factors, the number-of-transfers was shown to be the most important, as was expected. When a multimodal ridesharing system provides less travel time, low travel cost, and sufficient ridesharing incentive, people are more likely to pay for the service

Designing a Transit-Feeder System Using Bikesharing and Peer-to-Peer Ridesharing

Peer-to-peer (P2P) ridesharing is a relatively new concept that aims at providing a sustainable method for transportation in urban areas. This research is on the second phase of a sequence of projects that follows the previously funded UCConnect project titled “Promoting Peer-toPeer Ridesharing Services as Transit System Feeders”. In this phase, the study constructs a multimodal network, which includes P2P ridesharing, transit and city bike-sharing. The research develops schemes to provide travel alternatives, routes and information across multiple modes in the network. In addition, we develop a mobile application that demonstrates the research in the context of Los Angeles, CA, by using a combination of subway transit lines, proposed P2P ridesharing, and bikesharing to provide multi-modal itineraries to users. The Los Angeles Metro’s Red and Gold line subway rail and the downtown bike-share system are included in the network for a case study. The study includes a simulation of the operation of the combined system that provides travel alternatives during morning peak hours for multiple riders. The results indicate that a multi-modal network would expand the coverage of public transit. Ridesharing and bike-sharing could both act as transit feeders when properly designed in the system. Increased travel demand from the system can induce the problem that pick-up and drop-off demand in the bike system is not evenly distributed in space and time, which implies that bike redistribution should be introduced. We also develop algorithms to improve service level and reduce unsatisfied bike demand