On the Empty Miles of Ride-Sourcing Services: Theory, Observation and Countermeasures

The proliferation of smartphones in recent years has catalyzed the rapid growth of ride-sourcing services such as Uber, Lyft, and Didi Chuxing. Such on-demand e-hailing services significantly reduce the meeting frictions between drivers and riders and provide the platform with unprecedented flexibility and challenges in system management. A big issue that arises with service expansion is the empty miles produced by ride-sourcing vehicles. To overcome the physical and temporal frictions that separate drivers from customers and effectively reposition themselves towards desired destinations, ride-sourcing vehicles generate a significant number of vacant trips. These empty miles traveled result in inefficient use of the available fleet and increase traffic demand, posing substantial impacts on system operations. To tackle the issues, my dissertation is dedicated to deepening our understanding of the formation and the externalities of empty miles, and then proposing countermeasures to bolster system performance. There are two essential and interdependent contributors to empty miles generated by ride-sourcing vehicles: cruising in search of customers and deadheading to pick them up, which are markedly dictated by forces from riders, drivers, the platform, and policies imposed by regulators. In this dissertation, we structure our study of this complex process along three primary axes, respectively centered on the strategies of a platform, the behaviors of drivers, and the concerns of government agencies. In each axis, theoretical models are established to help understand the underlying physics and identify the trade-offs and potential issues that drive behind the empty miles. Massive data from Didi Chuxing, a dominant ride-sourcing company in China, are leveraged to evidence the presence of matters discussed in reality. Countermeasures are then investigated to strengthen management upon the empty miles, balance the interests of different stakeholders, and improve the system performance. Although this dissertation scopes out ride-sourcing services, the models, analyses, and solutions can be readily adapted to address related issues in other types of shared-use mobility services.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163209/1/xzt_1.pd

Shared autonomous vehicle services: A comprehensive review

© 2019 Elsevier Ltd The actions of autonomous vehicle manufacturers and related industrial partners, as well as the interest from policy makers and researchers, point towards the likely initial deployment of autonomous vehicles as shared autonomous mobility services. Numerous studies are lately being published regarding Shared Autonomous Vehicle (SAV) applications and hence, it is imperative to have a comprehensive outlook, consolidating the existing knowledge base. This work comprehensively consolidates studies in the rapidly emerging field of SAV. The primary focus is the comprehensive review of the foreseen impacts, which are categorised into seven groups, namely (i) Traffic & Safety, (ii) Travel behaviour, (iii) Economy, (iv) Transport supply, (v) Land–use, (vi) Environment & (vii) Governance. Pertinently, an SAV typology is presented and the components involved in modelling SAV services are described. Issues relating to the expected demand patterns and a required suitable policy framework are explicitly discussed

Modeling and Optimizing Routing Decisions for Travelers and On-demand Service Providers

This thesis investigates the dynamic routing decisions for individual travelers and on-demand service providers (e.g., regular taxis, Uber, Lyft, etc). For individual travelers, this thesis models and predicts route choice at two time-scales: the day-to-day and within-day. For day-to-day route choice, methodological development and empirical evidences are presented to understand the roles of learning, inertia and real-time travel information on route choices in a highly disrupted network based on data from a laboratory competitive route choice game. The learning of routing policies instead of simple paths is modeled when real-time travel information is available, where a routing policy is defined as a contingency plan that maps realized traffic conditions to path choices. Using data from a competitive laboratory experiment, prediction performance is then measured in terms of both one-step and full trajectory predictions. For within day route choice, a recursive logit model is formulated in a stochastic time-dependent (STD) network without sampling any choice sets. A decomposition algorithm is then proposed so that the model can be estimated in reasonable time. Estimation and prediction results of the proposed model are presented using a data set collected from a subnetwork of Stockholm, Sweden. Taxis and ride-sourcing vehicles play an important role in providing on-demand mobility in an urban transportation system. Unlike individual travelers, they do not have a clear destination when there\u27s no passenger on board. The optimal routing of a vacant taxi is formulated as a Markov Decision Process (MDP) problem to maximize long-term profit over the full working period. Two approaches are proposed to solve the problem. One is the model-based approach where a model of the state transitions of the environment is obtained from queuing-theory based passenger arrival and competing taxi distribution processes. An enhanced value iteration for solving the MDP problem is then proposed making use of efficient matrix operations. The other is the model-free Reinforcement Learning (RL) approach, which learns the best policy directly from observed trajectory data. Both approaches are implemented and tested in a mega city transportation network with reasonable running time, and a systematic comparison of the two approaches is also provided

Context-aware mobility analytics and trip planning

The study of user mobility is to understand and analyse the movement of individuals in the spatial and temporal domains. Mobility analytics and trip planning are two vital components of user mobility that facilitate the end users with easy to access navigational support through the urban spaces and beyond. Mobility context describes the situational factors that can influence user mobility decisions. The context-awareness in mobility analytics and trip planning enables a wide range of end users to make effective mobility decisions. With the ubiquity of urban sensing technologies, various situational factors related to user mobility decisions can now be collected at low cost and effort. This huge volume of data collected from heterogeneous data sources can facilitate context-aware mobility analytics and trip planning through intelligent analysis of mobility contexts, mobility context prediction, mobility context representation and integration considering different user perspectives. In each chapter of this thesis such issues are addressed through the development of case-specific solutions and real-world deployments. Mobility analytics include prediction and analysis of many diverse mobility contexts. In this thesis, we present several real-world user mobility scenarios to conduct intelligent contextual analysis leveraging existing statistical methods. The factors related to user mobility decisions are collected and fused from various publicly available open datasets. We also provide future prediction of important mobility contexts which can be utilized for mobility decision making. The performance of context prediction tasks can be affected by the imbalance in context distribution. Another aspect of context prediction is that the knowledge from domain experts can enhance the prediction performance however, it is very difficult to infer and incorporate into mobility analytics applications. We present a number of data-driven solutions aiming to address the imbalanced context distribution and domain knowledge incorporation problems for mobility context prediction. Given an imbalanced dataset, we design and implement a framework for context prediction leveraging existing data mining and sampling techniques. Furthermore, we propose a technique for incorporating domain knowledge in feature weight computation to enhance the task of mobility context prediction. In this thesis, we address key issues related to trip planning. Mobility context inference is a challenging problem in many real-world trip planning scenarios. We introduce a framework that can fuse contextual information captured from heterogeneous data sources to infer mobility contexts. In this work, we utilize public datasets to infer mobility contexts and compute trip plans. We propose graph based context representation and query based adaptation techniques on top of the existing methods to facilitate trip planning tasks. The effectiveness of trip plans relies on the efficient integration of mobility contexts considering different user perspectives. Given a contextual graph, we introduce a framework that can handle multiple user perspectives concurrently to compute and recommend trip plans to the end user. This thesis contains efficient techniques that can be employed in the area of urban mobility especially, context-aware mobility analytics and trip planning. This research is built on top of the existing predictive analytics and trip planning techniques to solve problems of contextual analysis, prediction, context representation and integration in trip planning for real-world scenarios. The contributions of this research enable data-driven decision support for traveling smarter through urban spaces and beyond

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency

Affärsmodell för operation av ett enmotorigt flygplan i Europa

This thesis aims at developing a practical business model for operating Pilatus PC-12 aircraft in Europe. The underlying question is whether commercial operations with this aircraft can be profitable. The main goal is to answer a set of sub-questions derived from a business model framework covering financial, operational, technical and structural aspects. A secondary objective is to offer decision makers a better understanding of underlying decisions within the business aviation sector. This thesis, written in cooperation with Hendell Aviation, is eventually useful for the company, its partners and the whole industry. The main research data used in this thesis includes literature, market data, expert interview data and survey data. This comprehensive industry insight is used to both identify and analyse different business model options and choose the optimum model. Categorised interview data is analysed by both deductive and inductive content analysis methods that look for patterns, repetitive claims and relationship to theory. Interview data driven SWOT analysis serves as an aid to find the best model out of research data. Survey data is mainly analysed qualitatively, but also quantitative methods of ranking and scoring results are used. A separate business model evaluation frameworks is used to test the proposed business model. Thesis research suggests that the Pilatus PC-12 aircraft can be profitably operated in Europe. Furthermore, it can offer a cost advantage against existing air travel business models. Findings signal the aircraft is most suitable for a high aircraft utilisation rate model focusing operations outside of major hubs, preferably at easily accessible secondary airports with a high demand. Research also shows the aircraft is optimal for short air travel distances and that the most important customer value proposition is saved travel time. The study implies key resources to be aircraft maintenance, the right employees, experienced pilots and a robust business model.Syftet med detta diplomarbete är att utveckla en affärsmodell för att operera Pilatus PC- 12 flygplan i Europa. Diplomarbetet försöker även besvara frågan ifall kommersiell flygverksamhet med denna flygplansmodell kan vara lönsamt. Det huvudsakliga målet är att hitta svar på ekonomiska, operativa, tekniska och strukturella affärsval. Dessutom försöker arbetet erbjuda beslutsfattarna bättre förståelse om affärsluftfartssektorn. Arbetet är nyttigt för branschen samt för samarbetsbolaget Hendell Aviation och dess partners. Diplomarbetet utnyttjar litteratur, marknads-, intervju- och undersökningsdata för att identifiera alternativa affärsmodeller och välja den optimala modellen. Kategoriserad intervjudata analyseras genom deduktiva och induktiva analysmetoder för att hitta relation till teori eller skapa ny teori. En ”SWOT” analys används för att hitta den bästa affärsmodellen för denna flygplansmodell. En skild online-undersökning analyserad med kvalitativa metoder förstärker forskningen. Den valda affärsmodellen är utvärderad med Michael Porters välkända ”five force” ramverk. Forskingsresultat tyder på att en kommersiell Pilatus PC-12 operator lönsamt kan fungera i Europa. Dessutom kan denna affärsmodell bjuda på kostnadsfördelar jämfört med existerande modeller. Forskningen visar att denna affärsmodell är lämpligast för ett högt årligt bruk av flygplanen med fokus utanför primära flygplatser. Flygplansmodellen är dessutom optimal för korta flygsträckor som sparar på resetid jämfört med landtransport. De viktigaste resurserna för att driva dessa flygoperationer innefattar ett starkt tekniskt underhåll, kunnig arbetskraft, erfarna piloter och en stark affärsmodell

Mechanics of Mobilization: The Making of a Taxi Workers Alliance

This dissertation examines the political engagement and occupational struggles of South Asian immigrant taxi workers in an American metropolis, and their participation in a taxi workers alliance. Contrary to popular understandings of South Asian immigrants as a model minority --- politically passive and economically successful professionals --- these South Asian immigrant taxi workers often struggle to make ends meet, and are actively involved in both leadership positions of their workers alliance as well as the rank and file. Further, their political engagements and their creative strategies to survive, maximize income, and achieve upward mobility show how poorer immigrants in non-professional occupations cannot be viewed simplistically as victims of structural exploitation. Using primary data (interviews with stakeholders in the taxicab industry) and secondary data (legislative records, Census data, newspaper archives), this study explores if, how and why immigrant workers chose to participate in this political mobilization. It recognizes three important factors that enabled the successful mobilization of this occupational group. Firstly, immigrant organizers used their social networks and ethnic ties to influence immigrant drivers\u27 decisions to participate in alliance activities; these ties variously enabled and constrained their participation. Secondly, non-profit organizations assisted the fledgling alliance with logistics and strategizing. Thirdly, national unions have been growing more receptive to nontraditional labor organizing, and provided institutional support to taxi worker organizations across cities in the United States

Practice and Innovations in Sustainable Transport

The book continues with an experimental analysis conducted to obtain accurate and complete information about electric vehicles in different traffic situations and road conditions. For the experimental analysis in this study, three different electric vehicles from the Edinburgh College leasing program were equipped and tracked to obtain over 50 GPS and energy consumption data for short distance journeys in the Edinburgh area and long-range tests between Edinburgh and Bristol. In the following section, an adaptive and robust square root cubature Kalman filter based on variational Bayesian approximation and Huber’s M-estimation is proposed to accurately estimate state of charge (SOC), which is vital for safe operation and efficient management of lithium-ion batteries. A coupled-inductor DC-DC converter with a high voltage gain is proposed in the following section to match the voltage of a fuel cell stack to a DC link bus. Finally, the book presents a review of the different approaches that have been proposed by various authors to mitigate the impact of electric buses and electric taxis on the future smart grid