Investigating Mode Choice of Ridesourcing Services: Accounting for Attitudes and Market Segmentation

The phenomenal development of ridesourcing is possibly one of the greatest revolutions that have happened to transportation networks. Ridesourcing improves mobility and mitigates traffic congestion by reducing vehicle ownership and serving as a first/last-mile feeder to public transportation. This tremendous growth created a burgeoning literature exploring ridesourcing users\u27 characteristics, yet there is no clear picture of its market. In the absence of sufficient information, policymakers face a major challenge in planning equitable and accessible transportation systems. This dissertation presents a detailed analysis of individuals’ decisions to adopt ridesourcing, focusing on three main objectives that have not been addressed previously. First, a reduced fare of ridesourcing was considered to explore its adoption beyond cost constraints. Second, the effect of attitudes on the choice of ridesourcing was explored. Lastly, the adoption of ridesourcing across various market segments was examined. Advanced economic models were applied to the data from a stated preference survey, which is a rich database of attitudes and mobility patterns. The results indicate that attitudes play a major role in the adoption of ridesourcing and considering the impact of attitudinal factors could provide valuable insights into individuals’ behavior toward ridesourcing. It was shown that attitudinal factors (e.g., technology-savviness, driving enjoyment) could explain individuals\u27 choice behavior in a way that cannot be clarified by socioeconomic and demographic factors. The market segment-based analysis of ridesourcing adoption demonstrated that different segments have distinct perceptions and attitudes toward ridesourcing. For instance, for regular transit users, travel time and cost perceptions are decisive factors in adopting ridesourcing. In contrast, visitors (i.e., auto users when their vehicle is unavailable) will adopt ridesourcing when it provides higher utility regarding time, cost, and convenience. Moreover, regarding the impact of ridesourcing experience on the adoption of these services, it was shown that individuals with no ridesourcing experience are more sensitive to traveling with strangers, worry about the higher travel time, and are more attached to their vehicles. Finally, considering the role of generational effects on ridesourcing adoption, it was shown that Generation Xers\u27 choice highly depends on the perceived utility of shared mobility and their desires for mobility for non-drivers features. Contrarily, Millennials’ choices are more likely to be affected by their preference toward technology and driving stress relief

An Integrated Choice and Latent Variable Model to Explore the Influence of Attitudinal and Perceptual Factors on Shared Mobility Choices and Their Value of Time Estimation

This work studies how the usage of shared mobility services could be influenced by latent factors. An integrated choice and latent variable model is adopted to explore the effects of three attitudinal and perceptual factors on bike- and car-sharing choices while simultaneously investigating the causes associated with each of the latent variables. A group of Chinese commuters’ stated preference mode choice data are collected. It is found that the probability to choose bike-sharing could be positively affected by “willingness to be a green traveler” and “satisfaction with cycling environment,” and car-sharing choice is positively correlated with “advocacy of car-sharing service.” By taking into account the interaction effects between the latent variables and travel time of the two services, a significant difference is discovered on the estimated value of travel time savings (VTTS) compared with other more restrictive model specifications. The finding highlights the importance to derive different VTTS for travelers with differentiated attitudes and perceptions as the tastes toward travel time spent could vary substantially. In other words, there would be different trade-off preferences across attitudinal groups, according to which transport service operators could customize their strategies on prices and levels of service offered

Migrating towards Using Electric Vehicles in Fleets – Proposed Methods for Demand Estimation and Fleet Design

Carsharing and electric vehicles have emerged as sustainable transportation alternatives to mitigate transportation, environmental, and social issues in cities. This dissertation combines three correlated topics: carsharing feasibility, electric vehicle carsharing fleet optimization, and efficient fleet management. First, the potential demand for electric vehicle carsharing in Beijing is estimated using data from a survey conducted the summer of 2013 in Beijing. This utilizes statistical analysis method, binary logit regression. Secondly, a model was developed to estimate carsharing mode split by the function of utilization and appropriate carsharing fleet size was simulated under three different fleet types: an EV fleet with level 2 chargers, an EV fleet with level 3 chargers, and a gasoline vehicle fleet. This study also performs an economic analysis to determine the payback period for recovering the initial EV charging infrastructure costs. Finally, this study develops a fleet size and composition optimization model with cost constraints for the University of Tennessee, Knoxville motor pool fleet. This will help the fleet manage efficiently with minimum total costs and greater demand satisfaction. This dissertation can help guide future sustainable transportation planning and policy

Long-term mobility choice considering availability effects of shared and new mobility services

E-bikes, shared and new mobility services such as Mobility-as-a-Service (MaaS) are emerging as sustainable and healthy alternatives to private cars, introducing complexities in household mobility decisions and potential substitution between transportation modes and services. However, existing studies primarily examined the potential long-term adoption of these emerging mobilities separately, leaving a gap in understanding the interplay among various emerging mobilities and conventional cars. This study therefore addresses this portfolio choice incorporating a stated portfolio choice experiment encompassing pedelecs, speed pedelecs, MaaS, Shared e-Mobilities, and electric and conventional cars. Results from a random effects error component mixed logit model, based on an online survey conducted in the Netherlands, indicate significant availability effects of shared and new mobility services on personal mobility ownership decisions, and a substantial demand for pedelecs. The findings contribute to facilitating the adoption of emerging mobilities with enhanced synergy, as shared and new mobility services are gradually becoming available

Understanding acceptance of Autonomous Mobility Services using statistical and deep learning approaches

The emergence of vehicle automation and its subsequent growth has led to new transport service offerings, generally known as Autonomous Mobility Services (AMS), that have the potential to facilitate or even replace human-operated vehicles. AMS contains different forms of potential mobility options which may contradict current transport modal concepts in terms of functionalities. For example, an autonomous shuttle bus which is a form of autonomous transit may serve similarly as an autonomous taxi/robo-taxi in terms of functionalities, coinciding with the concept of Shared Autonomous Mobility Services (SAMS). Even if the functionalities or operational principles are different, peoples' perceptions of sharing rides in any of these services may be alike. Apart from these confusions in functionalities mentioned above, peoples' attitudes and acceptance of AMS, once it's implemented in any form in the public road environment, remains a significant research aspect. To address these issues, this thesis tried to first clearly distinguish different types of AMS. Second, it tried to assemble the progress till now in acceptance-related research of AMS while reviewing the previous study approaches, outcomes, policy implications, and future research directions. Third, this study attempted to understand the acceptance of AMS using statistical and deep learning approaches leveraging both survey and social media data. Fourth, this study tried to present the consequent applicabilities and limitations of using both types of data sources for autonomous vehicle acceptance research. Eventually, this thesis intends to present an overall idea of the AMS acceptance research with future directions leveraging both data sources in an individualistic or combined manner.Includes bibliographical references

A Mode Choice Study on Shared Mobility Services: Policy Opportunities for a Developing Country

This research aims to investigate the mode choice behaviour associated with bike-sharing and car-sharing, and the strategies for encouraging their demand in order to pull people away from using private cars. In particular, we reveal the factors that could affect the choices of both services and explore their associated modal substitution patterns. Key interests are put on air pollution’s impact on bike-sharing choice and the sources of demand for car-sharing (i.e. from private car users or public transport users). Moreover, we look at in what ways attitudinal factors could influence shared mobility choices and hence identify any implications. Furthermore, we are also interested in any measures from the habitual level that may help control private car usage in addition to the tactical-level efforts. The mode choice and related data employed in this work were collected by a paper-based questionnaire survey launched in 2015 at a Chinese city. Discrete choice modelling techniques are extensively applied, including the mixed logit (ML), mixed nested logit (mixed NL) and integrated choice and latent variable (ICLV) models. Our findings are compared to those from developed countries for any similarities and differences that lie between, though by addressing several key research gaps in the field, the findings will also significantly enrich the literature on shared mobility choice behaviour as well as disclosing implications for practitioners from both developed and developing countries for take-away and formulating the corresponding demand management policies

Estimación del impacto ambiental y social de los nuevos servicios de movilidad

El transporte es fuente de numerosas externalidades negativas, como los accidentes de tráfico, la congestión en las zonas urbanas y la falta de calidad del aire. El transporte también es un sector que contribuye sustancialmente a la crisis climática con más del 16% de las emisiones globales de gases de efecto invernadero como resultado de las actividades de transporte. Muchos creen que la introducción de nuevos servicios de movilidad podría ayudar a reducir esas externalidades. Sin embargo, con cada introducción de un nuevo servicio de movilidad podemos observar factores que podrían contribuir negativamente a la sostenibilidad del sistema de transporte: una cadena de cambios de comportamiento causados por la introducción de posibilidades completamente nuevas. El objetivo de esta tesis es investigar cómo los nuevos servicios de movilidad, habilitados por la electrificación, la conectividad y la automatización, podrían impactar en las externalidades causadas por el transporte. En particular, el objetivo es desarrollar y validar un marco de modelado capaz de capturar la complejidad del sistema de transporte y aplicarlo para evaluar el impacto potencial de los vehículos automatizados.Transport is a source of numerous negative externalities, such as road accidents, congestion in urban areas and lacking air quality. Transport is also a sector substantially contributing to climate crisis with more than 16% of global greenhouse gas emissions being a result of transport activities. Many believe that the introduction of new mobility services could help reduce those externalities. However, with each introduction of a new mobility service we can observe factors that could negatively contribute to the sustainability of the transport system – a chain of behavioural changes caused by introduction of entirely new possibilities. The aim of this thesis is to investigate how the new mobility services, enabled by electrification, connectivity and automation, could impact the externalities caused by transport. In particular the objective is to develop and validate a modelling framework able to capture the complexity of the transport system and to apply it to assess the potential impact of automated vehicles.This work was realised with the collaboration of the European Commission Joint Research Centre under the Collaborative Doctoral Partnership Agreement N035297. Moreover, this research has been partially funded by the Spanish Ministry of Science and Innovation through the project: AUTONOMOUS – InnovAtive Urban and Transport planning tOols for the implementation of New mObility systeMs based On aUtonomouS driving”, 2020-2023, ERDF (EU) (PID2019-110355RB-I00)

Behavioural Considerations in Route Choice Modelling

RÉSUMÉ La modélisation de choix d'itinéraires, i.e. de la route empruntée par les individus entre une paire origine et destination, est probablement l'un des problèmes les plus complexes et laborieux de l’analyse des comportements de déplacement. L'objectif principal de cette thèse est d'améliorer la compréhension comportementale du choix des itinéraires routiers en observant le processus sous-jacent de prise de décision des conducteurs.----------ABSTRACT Route choice modelling is probably one of the most complex and challenging problems in travel behaviour analysis. It investigates the process of route selection by an individual, making a trip between predefined origin-destination pairs. The main objective of this thesis is to enhance the behavioural understanding of drivers’ route choice decisions by observing drivers’ underlying process of decision-makin

Optimizing Transportation Systems with Information Provision, Personalized Incentives and Driver Cooperation

Poor performance of the transportation systems has many detrimental effects such as higher travel times, increased travel costs, higher energy consumption, and greenhouse gas emissions, etc. This thesis optimizes the transportation systems by addressing the traffic congestion problem and climate change impact resulting from the inefficient operation of these systems. I first focus on the key player of the transportation systems e.g., human being/traveler, and model travelers\u27 route choice behavior with real-time information. In this study, I define looking-ahead behavior in route choice as a traveler\u27s taking into account future diversion possibilities enabled by real-time information in a network with random travel times. Subjects participated in route-choice experiments in a driving simulator as well a PC-based environment. Three types of maps in increasing levels of complexity and information availability are used. Aggregate data analysis shows that network complexity negatively affects subjects\u27 ratio of choosing the risky route given an experiment environment. Higher cognitive load in the driving simulator results in a higher level of risk aversion than in the PC-based environment for the simplest map. I specify and estimate a mixed logit model with two latent classes, looking-ahead and myopic, taking into account the panel effect. The estimated latent class membership function suggests that some subjects can look ahead while others are myopic in making their route choices, and drivers learn to look ahead over time. The experiment environment plays a role in the risk attitude of myopic subjects. A bias against information is found for subjects who look ahead, however, is not significant among myopic subjects. I then shift my focus to influencing the travel patterns of individual travelers to reduce the energy and environmental impacts of the transportation sector. I present the system optimization (SO) framework of Tripod, an integrated bi-level transportation management system aimed at maximizing energy savings of the multi-modal transportation systems. From the user\u27s perspective, Tripod is a smartphone app, accessed before performing trips. The app proposes a series of alternatives each with an amount of tokens which the user can later redeem for goods or services. The role of SO is to compute the optimized set of tokens associated to the available alternatives, in order to minimize the system-wide energy consumption, under a limited token budget. I present a method to solve this complex optimization problem and describe the system architecture, the multimodal simulation-based optimization model and the heuristic method for the on-line computation of the optimized token allocation. I then present the framework with the simulation results. Finally, I optimize the systems travel time by addressing the equity issue of congestion pricing. I propose an alternative approach to an equitable and Pareto-improving transportation systems based on cooperation among travelers assisted by defector penalty. Theoretical analysis shows the existence condition of the cooperative scheme for heterogeneous value of time (VOT) of travelers. I formulate a mathematical programming problem for the optimal cooperative scheme problem in a general network with Pareto-improving constraints and practical considerations on the length the cooperation cycle. I then conduct computational tests on a simple network and evaluate the solutions in terms of efficiency improvement (total system travel time) and equitability (Gini index)

Investigating individual preferences for new mobility services: the case of “mobility as a service” products

In just a few years, the Mobility as a Service (MaaS) concept has gone from an idea discussed by very few, to being a prominent topic in any transportation related debate. However, within this time, there have only been few rigorous studies that explore the various aspects of MaaS. This thesis aims to contribute to existing knowledge by providing empirical evidence on individual preferences for MaaS plans and their components. In doing so, first desk-research is conducted to summarise existing MaaS schemes and outline the MaaS ecosystem. Next, MaaS surveys that are able to capture individual preferences for MaaS products are designed and specific challenges in the design process identified. The MaaS surveys, including MaaS plan stated preference experiments, are applied in two case study areas of London and Greater Manchester. Using the novel data collected, individual preferences for MaaS plans are examined using two distinct studies: (1) a mixed methods research conducted in London, which expands the survey by adding a qualitative (in-depth interview) element to examine user preferences for MaaS plans and the ways individuals choose between them; and (2) a latent class choice model based on data collected from Manchester to examine whether there is heterogeneity in preferences. Finally, implications for industry and policy stakeholders are discussed as well as interventions that can best support the widespread adoption of MaaS. The results of this thesis show there is interest in the concept of MaaS among potential users as many see value in a single app that integrates different transport modes into a single service. In general, individuals are hesitant in purchasing pre-payed MaaS plans and would be more comfortable with a pay-as-you-go product option. While many people are reluctant towards MaaS plans, the results indicate that heterogeneity exists in preferences towards them and there are different user groups based on socio-demographic characteristics and current mobility habits. Smaller, less expensive plans including modes such as public transport and bike sharing can be used to target students or middle-income people with have high overall mode usage. Larger, more expensive plans that include modes such as taxi and car sharing in addition to public transport, will be attritive to individuals who are likely younger, male, well-educated, have higher income and already use many transport modes. Older population groups, individuals with low income and those that do not use any transport modes or are uni-modal are least likely to adopt MaaS plans. The thesis also provides insights into individuals’ preferences towards transport modes within MaaS plans. The analysis showed that respondents classify modes within MaaS plans into three categories: ‘essential’ modes that are pivotal to the individual and which they most likely already frequently use; ‘considered’ modes are those that they would be willing to include but may not yet use; and ‘excluded’ modes are those that they definitely do not want in their plans and would eliminate any plan that included these. Public transport consistently proved to be an essential mode, while taxi, car sharing and bike sharing could be ‘essential’, ‘considered’ or ‘excluded’ depending on the characteristics of the individual. The main contributions of this thesis are the novel data collected in two case study cities about individuals’ preferences for MaaS plans and the findings gained through the analysis providing insights into possible target audiences and product designs for MaaS plans