ZAC: A Zone pAth Construction approach for effective real-time ridesharing

National Research Foundation (NRF) Singapore under SMART and Future Mobilit

Collaborative consumption in Southeast Asian cities: Prospects and challenges for environmental sustainability

The rise of collaborative consumption and the sharing economy suggests a growing acceptability of ownerless consumption, which could enable more resource efficient use of goods. These phenomena have largely been studied in high income countries, however, businesses enabling shared-access to goods or services have been emerging around the world. In Asia, rapid economic growth is enabling vast numbers of ‘new consumers’ to access a middle-class lifestyle. In this context, it is important to examine the potential for nascent movements such as collaborative consumption to offer more sustainable alternatives to mass consumption. This thesis examines the use of collaborative consumption businesses in emerging economies in Southeast Asia, focusing on the cities of Hanoi, Bangkok and Manila. The aim is to understand the prospects and challenges for these businesses to offer more environmentally sustainable modes of consuming. Using an interdisciplinary approach, I investigate the prospects for collaborative consumption (CC) businesses in terms of environmental sustainability and with regard to the institutional and personal dimensions of their use. I examine the broader cultural, social, legal, political and economic contexts for CC businesses in these three cities as well as specific case study businesses. I primarily use qualitative research methods, but also develop some quantitative estimates of resource use. The scope is limited to product-service systems for households that enable shared-access to goods, or offer services to replace products. I undertook over forty interviews with businesses, consumers, academics, policymakers and other stakeholders in Bangkok and Hanoi. CC businesses interviewed included rideshare, taxishare, bikeshare, laundry services and rental for tools, toys, clothing and designer bags. In Manila, I undertook nineteen interviews for a case study focused on comparing individual and shared laundering methods. Four distinct journal articles were developed for this thesis. In these articles, I have: characterized the current business types and users in these cities; examined CC business sustainability practices; explored consumer practices and resource use with regard to shared and individual laundering; and identified the socio-cultural and institutional barriers and enablers for CC businesses. I have drawn on different theories for each article, including: adaptive theory, social practice theory and multi-level perspective. I use a social practice theory framework to integrate the findings of the four articles and to develop the conclusions. Collaborative consumption (CC) businesses in our study appear to be used by niche groups: university students, young families, people living in densely populated areas, and emerging and upper middle-class people keen to access better quality and more diverse goods. CC businesses in our study were inclined to use durable, quality goods, to undertake maintenance, and sell their goods for second hand use and potential remanufacturing. These businesses may be more likely to achieve environmentally sustainable outcomes in high density environments, where home storage is limited and where transport needs can be reduced. Our detailed case study on laundering found that social changes and the nature of housing is likely to influence the choice of individual or shared laundering methods. The socio-cultural and commercial regimes in Bangkok and Hanoi present major barriers to further adoption of CC, with regards to “ownership cultures” and resistance from incumbent industries. However, attitudes are changing and many of the physical drivers for CC, such as high-density living, are prominent in Southeast Asia. Many of the barriers to CC can be addressed through supportive policies and institutional arrangements such as: appropriate legal definitions and frameworks, business permits, and programs to facilitate financing for CC businesses. In all cases, positive social and environmental outcomes will need to be encouraged or incentivised by governments

Emerging transport technologies and the modal efficiency framework: A case for mobility as a service (MaaS)

The land passenger transport sector lies on the cusp of a major transformation, guided by collaborative consumption, next generation vehicles, demographic change and digital technologies. Whilst there is widespread enthusiasm across the community for this nexus of disruptors, the wholescale implications on road capacity, traffic congestion, land use and the urban form remains unclear, and by extension, whether this emerging transport paradigm will bring a net benefit to the transport system and our communities. Some issues include the proliferation of point-to-point transportation, a continuation of universal vehicle ownership, and the demise of fixed route public transport—all envisaged by various industry leaders in technology and transportation. In this paper, we develop the modal efficiency framework, with axes representing spatial and temporal efficiency to illustrate why some of these developments may be geometrically incompatible with dense urban environments. We then investigate three potential scenarios likely to emerge and explain why they may be problematic with reference to this framework. Mobility as a service (MaaS) based on shared mobility and modal integration is then introduced as a sustainable alternative which accounts for the realities of spatial and temporal efficiency. Various models for implementing MaaS are evaluated including the distinction between commercially-motivated models (presently well advanced in research and development), and systems which incorporate an institutional overlay. The latter, government-led MaaS, is recommended for implementation given the opportunity for incorporating road pricing as an input into package price, defined by time of day, geography and modal efficiency. In amidst the hype of this emerging transport paradigm, a critical assessment of the realm of possibilities can better inform government policy and ensure that digital disruption occurs to our advantage.Institute of Transport and Logistics Studies. Faculty of Economics and Business. The University of Sydne

Emerging transport technologies and the modal efficiency framework: A case for mobility as a service (MaaS)

The land passenger transport sector lies on the cusp of a major transformation, guided by collaborative consumption, next generation vehicles, demographic change and digital technologies. Whilst there is widespread enthusiasm across the community for this nexus of disruptors, the wholescale implications on road capacity, traffic congestion, land use and the urban form remains unclear, and by extension, whether this emerging transport paradigm will bring a net benefit to the transport system and our communities. Some issues include the proliferation of point-to-point transportation, a continuation of universal vehicle ownership, and the demise of fixed route public transport—all envisaged by various industry leaders in technology and transportation. In this paper, we develop the modal efficiency framework, with axes representing spatial and temporal efficiency to illustrate why some of these developments may be geometrically incompatible with dense urban environments. We then investigate three potential scenarios likely to emerge and explain why they may be problematic with reference to this framework. Mobility as a service (MaaS) based on shared mobility and modal integration is then introduced as a sustainable alternative which accounts for the realities of spatial and temporal efficiency. Various models for implementing MaaS are evaluated including the distinction between commercially-motivated models (presently well advanced in research and development), and systems which incorporate an institutional overlay. The latter, government-led MaaS, is recommended for implementation given the opportunity for incorporating road pricing as an input into package price, defined by time of day, geography and modal efficiency. In amidst the hype of this emerging transport paradigm, a critical assessment of the realm of possibilities can better inform government policy and ensure that digital disruption occurs to our advantage

A Machine Learning Recommender Model for Ride Sharing Based on Rider Characteristics and User Threshold Time

In the present age, human life is prospering incredibly due to the 4th Industrial Revolution or The Age of Digitization and Computing. The ubiquitous availability of the Internet and advanced computing systems have resulted in the rapid development of smart cities. From connected devices to live vehicle tracking, technology is taking the field of transportation to a new level. An essential part of the transportation domain in smart cities is Ride Sharing. It is an excellent solution to issues like pollution, traffic, and the rapid consumption of fuel. Even though Ride Sharing has several benefits, the current usage is significantly low due to limitations like social barriers and long rider waiting times. The thesis proposes a novel Ride Sharing model with two matching layers to eliminate most of the observed issues in the existing Ride Sharing applications like UberPool and LyftLine. The first matching layer matches riders based on specific human characteristics, and the second matching layer provides riders the option to restrict the waiting time by using personalized threshold time. At the end of trips, the system collects user feedback according to five characteristics. Then, at most, two main characteristics that are the most important to riders are determined based on the collected feedback. The registered characteristics and the two main determined characteristics are fed as the inputs to a Machine Learning classification module. For newly registering users, the module predicts the two main characteristics of riders, and that assists in matching with other riders having similar determined characteristics. The thesis includes subjecting the proposed model to an extensive simulation for measuring system efficiency. The model simulations have utilized the real-time New York City Cab traffic data with real-traffic conditions using Google Maps Application Programming Interface (API). Results indicate that the proposed Ride Sharing model is feasible, and efficient as the number of riders increases while maintaining the rider threshold time. The expected outcome of the thesis is to help service providers increase the usage of Ride Sharing, complete the pool for the maximum number of trips in minimal time and perform maximum rider matches based on similar characteristics, thus providing an energy-efficient and a social platform for Ride Sharing

Putting ridesharing to the test: efficient and scalable solutions and the power of dynamic vehicle relocation

We study the optimization of large-scale, real-time ridesharing systems and propose a modular design methodology, Component Algorithms for Ridesharing (CAR). We evaluate a diverse set of CARs (14 in total), focusing on the key algorithmic components of ridesharing. We take a multi-objective approach, evaluating 10 metrics related to global efficiency, complexity, passenger, and platform incentives, in settings designed to closely resemble reality in every aspect, focusing on vehicles of capacity two. To the best of our knowledge, this is the largest and most comprehensive evaluation to date. We (i) identify CARs that perform well on global, passenger, or platform metrics, (ii) demonstrate that lightweight relocation schemes can significantly improve the Quality of Service by up to 50 % , and (iii) highlight a practical, scalable, on-device CAR that works well across all metrics

Vehicle dispatch in high-capacity shared autonomous mobility-on-demand systems

Ride-sharing is a promising solution for transportation issues such as traffic congestion and parking land use, which are brought about by the extensive usage of private vehicles. In the near future, large-scale Shared Autonomous Mobility-on-Demand (SAMoD) systems are expected to be deployed with the realization of self-driving vehicles. It has the potential to encourage a car-free lifestyle and create a new urban mobility mode where ride-sharing is widely adopted among people. This thesis addresses the problem of improving the efficiency and quality of vehicle dispatch in high-capacity SAMoD systems. The first part of the thesis develops a dispatcher which can efficiently explore the complete candidate match space and produce the optimal assignment policy when only deterministic information is concerned. It uses an incremental search method that can quickly prune out infeasible candidates to reduce the search space. It also has an iterative re-optimization strategy to dynamically alter the assignment policy to take into account both previous and newly revealed requests. Case studies of New York City using real-world data shows that it outperforms the state-of-the-art in terms of service rate and system scalability. The dispatcher developed in this part can serve as a foundation for the next two parts, which consider two kinds of uncertain information, stochastic travel times and the dynamic distribution of requests in the long-term future, respectively. The second part of the thesis describes a framework which makes use of stochastic travel time models to optimize the reliability of vehicle dispatch. It employs a candidate match search method to generate a candidate pool, uses a set of preprocessed shortest path tables to score the candidates and provides an assignment policy that maximizes the overall score. Two different dispatch objectives are discussed: the on-time arrival probabilities of requests and the profit of the platform. Experimental studies show that higher service rates, reliability and profits can be achieved by considering travel time uncertainty. The third part of the thesis presents a deep reinforcement learning based approach to optimize assignment polices in a more far-sighted way. It models the vehicle dispatch problem as a Markov Decision Process (MDP) and uses a policy evaluation method to learn a value function from the historic movements of drivers. The learned value function is employed to score candidate matches to guide a dispatcher optimizing long-term objective, and will be continually updated online to capture the real-time dynamics of the system. It is shown by experiments that the value function helps the dispatcher to yield higher service rates