Operational research and simulation methods for autonomous ride-sourcing

Ride-sourcing platforms provide on-demand shared transport services by solving decision problems related to ride-matching and pricing. The anticipated commercialisation of autonomous vehicles could transform these platforms to fleet operators and broaden their decision-making by introducing problems such as fleet sizing and empty vehicle redistribution. These problems have been frequently represented in research using aggregated mathematical programs, and alternative practises such as agent-based models. In this context, this study is set at the intersection between operational research and simulation methods to solve the multitude of autonomous ride-sourcing problems. The study begins by providing a framework for building bespoke agent-based models for ride-sourcing fleets, derived from the principles of agent-based modelling theory, which is used to tackle the non-linear problem of minimum fleet size. The minimum fleet size problem is tackled by investigating the relationship of system parameters based on queuing theory principles and by deriving and validating a novel model for pickup wait times. Simulating the fleet function in different urban areas shows that ride-sourcing fleets operate queues with zero assignment times above the critical fleet size. The results also highlight that pickup wait times have a pivotal role in estimating the minimum fleet size in ride-sourcing operations, with agent-based modelling being a more reliable estimation method. The focus is then shifted to empty vehicle redistribution, where the omission of market structure and underlying customer acumen, compromises the effectiveness of existing models. As a solution, the vehicle redistribution problem is formulated as a non-linear convex minimum cost flow problem that accounts for the relationship of supply and demand of rides by assuming a customer discrete choice model and a market structure. An edge splitting algorithm is then introduced to solve a transformed convex minimum cost flow problem for vehicle redistribution. Results of simulated tests show that the redistribution algorithm can significantly decrease wait times and increase profits with a moderate increase in vehicle mileage. The study is concluded by considering the operational time-horizon decision problems of ride-matching and pricing at periods of peak travel demand. Combinatorial double auctions have been identified as a suitable alternative to surge pricing in research, as they maximise social welfare by relying on stated customer and driver valuations. However, a shortcoming of current models is the exclusion of trip detour effects in pricing estimates. The study formulates a shared-ride assignment and pricing algorithm using combinatorial double auctions to resolve the above problem. The model is reduced to the maximum weighted independent set problem, which is APX-hard. Therefore, a fast local search heuristic is proposed, producing solutions within 10\% of the exact approach for practical implementations.Open Acces

Optimizing Information Values in Smart Mobility

Smart mobility, enabled by advanced sensing, communication, vehicle, and emerging mobility technologies, has transformed transportation systems. Real-time information shared by public and private entities plays a pivotal role in smart mobility, which facilitates informed decision-making, including effective mode choice, dynamic vehicle control, optimized travel routing, and strategic vehicle relocation. While more information is believed to benefit individual decision makers, it is crucial to acknowledge that the effects of information on transportation network performance are contingent; more information may not always benefit the safety and mobility of the whole system. The goal of this dissertation is to investigate the effects of information shared by public and private transportation entities on system-level performance. The challenges are primarily due to the lack of a unified modeling framework to endogenously reflect the decentralized multi-agent interaction involved in the interconnected transportation networks and the resulting computational complexities arising from non-convexity and high dimensionality. To address these challenges, this dissertation proposes novel modeling frameworks and computational solutions for three cutting-edge smart mobility applications. First, to examine the impact of en-route information on a transportation network, we propose a novel two-stage stochastic traffic equilibrium model to characterize the equilibrium traffic patterns considering adaptive routing behavior when locational en-route traffic information is provided through infrastructure-to-vehicles (I2V) communications. This model is formulated as a convex stochastic optimization problem so that efficient stochastic programming algorithms can be directly leveraged to achieve scalability. Second, to achieve optimal control over real-time variable speed limits information sharing and evaluate its impact on the network, we propose a twin-delayed deep deterministic policy gradient model, which converges more reliably than state-of-the-art deep reinforcement learning models. We investigate the transferability of the control algorithm and conduct comparative analyses of different traffic control strategies and spatial distributions of variable speed limit control (VSLC) deployment. Third, to assess the impacts of information provided by private ride-sourcing companies on transportation network congestion, we propose a Stackelberg framework for spatial pricing of ride-sourcing services considering traffic congestion and convex reformulation strategies under mild conditions. We perform numerical experiments on transportation networks of varying scales and with diverse transportation network company (TNC) objectives, aiming to derive policy insights regarding the implications of spatial pricing information on transportation systems

Design and Analysis of Mobility Permit-based Traffic Management Schemes

High demand for mobility has undeniably been causing numerous negative impacts on the economy, the society and the environment. As a potential solution to address this challenge, a rapid transition is taking place in the transportation sector with emerging concepts of mobility marketplace. The basic premise is to treat the transportation system and its use as a collection of commodities or services that can be bought from the transportation market. This concept is increasingly becoming a reality with the technological developments in automotive industry such as connected and autonomous vehicles (CAVs). However, there are many policy, design and operation related issues that must be addressed before these traffic management schemes become reality. This thesis research aims at addressing some of these challenges and issues with a specific focus on the two most promising market-driven instruments, namely, mobility permits (MP)- and mobility credits (MC)-based traffic management schemes, which have been proposed to manage travel demand and mitigate traffic congestion by controlling roadway-use right. This research has made several distinctive contributions into the literature. We first conduct a critical review of the state-of-the-art methodological advances on MP- and MC-based travel demand management schemes. We synthesize the relevant body of literature with an in-depth discussion on related studies to provide an improved understanding of the fundamental constructs of these problems, including problem variants, methodologies, and modeling attributes. We also discuss the research gaps and challenges and suggest some possible perspectives and directions for future research. Based on the gaps identified in the literature review, an integrated framework is proposed for implementing various roadway-use right-based traffic management programs such as MP and MC-based schemes. This framework entails a unique construct for integrating the needs of multiple stakeholders (e.g., road users and authorities), diverse network conditions, and traffic control methods. It allows easy incorporation of different components required for implementing a coordinative mobility scheme, taking into account the influence of the participating players and the underlying issues. The framework can be served as a road-map to future studies on different roadway-use right-based solutions for traffic congestion management. With our proposed framework, we then focus on addressing various specific challenges arising in designing and implementing MP-based and MC-based schemes, such as, representation of realistic user characteristics (e.g., utility function, user priorities and cooperation), availability of information on users and traffic conditions, uncertainty in system conditions and user behaviors, and circulation of mobility rights in market place. For the MP-based scheme, we focus specifically on designing a mobility scheme for single-bottleneck roadways. Roads with bridges, tunnels and business districts with limited parking spaces are the most obvious examples of a simple roadway with a single-bottleneck in a transportation network. We deal with observing operational objectives, specifically, balancing efficiency, equity (users priorities), and revenue outcome of distributing mobility permits under the “fairness” constraint. We explore the theoretical properties of the proposed scheme and show that the proposed scheme can achieve an optimal traffic pattern. Particularly, we show that the proposed scheme is a Pareto-improving and strategy-proof scheme capable of achieving efficient and effective market prices suitable for travelers. Our computational results indicate the effectiveness of the proposed scheme as an alternative solution for MP-based traffic management on single-bottleneck roadways. We then investigate the case of traffic congestion management in a general road network through a MC-based scheme. Specifically, we propose a MC-based traffic management scheme in a road network consisting of a mixed-fleet traffic with connected and autonomous vehicles (CAVs) and conventional vehicles (non-CAVs). The basic premise of the proposed scheme is to regulate or influence travel demand and congestion with regards to the supply (capacity) of road networks, implementing a market-driven traffic management paradigm. A set of revenue-neutral, Pareto-improving MC-based charge and reward policies applicable to stochastic traffic environments are developed, considering different characteristics of users such as cooperative versus selfish routing behaviors, human-associated factors (e.g., level of uncertainty) and interactions due to a shared infrastructure setting. Path-free mathematical programming models are formulated, obviating computationally intractable path enumeration process pertinent to the existing studies. This makes the proposed scheme suitable for examining the theoretical characteristics of large-scale realistic transport networks. We examine several theoretical properties related to the proposed MC-based scheme, including the existence and uniqueness of the equilibrium price, and existence of Pareto-improving credit charges and rewards rates that can promote travel decision behaviors of individual travelers towards a network-wide optimal state. Our comprehensive computational results indicate that the proposed MC-based scheme can be an effective tool for managing travel demand and routing decisions in mixed-vehicle traffic settings

Institutions and Decision Making for Sustainable Development

Economic theory provides a coherent framework for analysing the elements of growth and sustainable development. Robust policies and appropriate institutional structures are essential to achieving sustainable development. Environmental problems are rooted in failed markets and their resolution requires government taking some kind of action – to establish property rights, set standards of liability, apply polluter pays taxes, or regulate. There is ample evidence showing that market based instruments can achieve the same environmental outcome at considerably less cost relative to command and control. Rational policy must seriously consider the use of market-based instruments. A framework for considering the quality of institutional structures vis-à-vis achieving sustainable development is presented. The framework is applied to aspects of the Resource Management Act 1991. Although the Act aims to promote sustainable management it is the primary legal foundation for sustainable development policy. One result of the Act was to devolve a great deal of environmental management and policy to local government. To a limited extent the Act is permissive and creates opportunities for local and regional government to find effective and efficient ways of achieving environmental outcomes that suit their communities. There is a clear preference for command and control in situations where statute provides a legal framework for market based instruments. But the options for using market-based instruments are limited. There are instances where attempts by regional administrators to implement market-based instruments are thwarted either by statute or by coordination difficulties at higher levels of government. Barriers to using market-based instruments are identified along with suggestions for institutional reform.Sustainable development; institutions and decision-making; market-based incentives

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)

Mastering Uncertainty in Mechanical Engineering

This open access book reports on innovative methods, technologies and strategies for mastering uncertainty in technical systems. Despite the fact that current research on uncertainty is mainly focusing on uncertainty quantification and analysis, this book gives emphasis to innovative ways to master uncertainty in engineering design, production and product usage alike. It gathers authoritative contributions by more than 30 scientists reporting on years of research in the areas of engineering, applied mathematics and law, thus offering a timely, comprehensive and multidisciplinary account of theories and methods for quantifying data, model and structural uncertainty, and of fundamental strategies for mastering uncertainty. It covers key concepts such as robustness, flexibility and resilience in detail. All the described methods, technologies and strategies have been validated with the help of three technical systems, i.e. the Modular Active Spring-Damper System, the Active Air Spring and the 3D Servo Press, which have been in turn developed and tested during more than ten years of cooperative research. Overall, this book offers a timely, practice-oriented reference guide to graduate students, researchers and professionals dealing with uncertainty in the broad field of mechanical engineering

Integrating Consumer Flexibility in Smart Grid and Mobility Systems - An Online Optimization and Online Mechanism Design Approach

Consumer flexibility may provide an important lever to align supply and demand in service systems. However, harnessing dispersed flexibility endowments in the presence of self-interested agents requires appropriate incentive structures. This thesis quantifies the potential value of consumers\u27 flexibility in smart grid and mobility systems. In order to include incentives, online optimization approaches are augmented with methods from online mechanism design