Estimating the potential for shared autonomous scooters

Recent technological developments have shown significant potential for transforming urban mobility. Considering first- and last-mile travel and short trips, the rapid adoption of dockless bike-share systems showed the possibility of disruptive change, while simultaneously presenting new challenges, such as fleet management or the use of public spaces. In this paper, we evaluate the operational characteristics of a new class of shared vehicles that are being actively developed in the industry: scooters with self-repositioning capabilities. We do this by adapting the methodology of shareability networks to a large-scale dataset of dockless bike-share usage, giving us estimates of ideal fleet size under varying assumptions of fleet operations. We show that the availability of self-repositioning capabilities can help achieve up to 10 times higher utilization of vehicles than possible in current bike-share systems. We show that actual benefits will highly depend on the availability of dedicated infrastructure, a key issue for scooter and bicycle use. Based on our results, we envision that technological advances can present an opportunity to rethink urban infrastructures and how transportation can be effectively organized in cities

Smart Sustainable Mobility: Analytics and Algorithms for Next-Generation Mobility Systems

To this date, mobility ecosystems around the world operate on an uncoordinated, inefficient and unsustainable basis. Yet, many technology-enabled solutions that have the potential to remedy these societal negatives are already at our disposal or just around the corner. Innovations in vehicle technology, IoT devices, mobile connectivity and AI-powered information systems are expected to bring about a mobility system that is connected, autonomous, shared and electric (CASE). In order to fully leverage the sustainability opportunities afforded by CASE, system-level coordination and management approaches are needed. This Thesis sets out an agenda for Information Systems research to shape the future of CASE mobility through data, analytics and algorithms (Chapter 1). Drawing on causal inference, (spatial) machine learning, mathematical programming and reinforcement learning, three concrete contributions toward this agenda are developed. Chapter 2 demonstrates the potential of pervasive and inexpensive sensor technology for policy analysis. Connected sensing devices have significantly reduced the cost and complexity of acquiring high-resolution, high-frequency data in the physical world. This affords researchers the opportunity to track temporal and spatial patterns of offline phenomena. Drawing on a case from the bikesharing sector, we demonstrate how geo-tagged IoT data streams can be used for tracing out highly localized causal effects of large-scale mobility policy interventions while offering actionable insights for policy makers and practitioners. Chapter 3 sets out a solution approach to a novel decision problem faced by operators of shared mobility fleets: allocating vehicle inventory optimally across a network when competition is present. The proposed three-stage model combines real-time data analytics, machine learning and mixed integer non-linear programming into an integrated framework. It provides operational decision support for fleet managers in contested shared mobility markets by generating optimal vehicle re-positioning schedules in real time. Chapter 4 proposes a method for leveraging data-driven digital twin (DT) frameworks for large multi-stage stochastic design problems. Such problem classes are notoriously difficult to solve with traditional stochastic optimization. Drawing on the case of Electric Vehicle Charging Hubs (EVCHs), we show how high-fidelity, data-driven DT simulation environments fused with reinforcement learning (DT-RL) can achieve (close-to) arbitrary scalability and high modeling flexibility. In benchmark experiments we demonstrate that DT-RL-derived designs result in superior cost and service-level performance under real-world operating conditions

The future of fully automated vehicles : opportunities for vehicle- and ride-sharing, with cost and emissions savings

textFully automated or autonomous vehicles (AVs) hold great promise for the future of transportation, with Google and other auto manufacturers intending on introducing self-driving cars to the public by 2020. New automation functionalities will produce dramatic transportation system changes, across safety, mobility, travel behavior, and the built environment. This work’s results indicate that AVs may save the U.S. economy up to $37.7 billion from safety, mobility and parking improvements at the 10$447.1 billion with 90% market penetration. With only 10% market share, over 1,000 lives could be saved annually. However, realizing these potential benefits while avoiding pitfalls requires overcoming significant barriers including AV costs, liability, security, privacy, and missing research. Additionally, once fully self-driving vehicles can safely and legally drive unoccupied, a new personal travel transportation mode looks set to arrive. This new mode is the shared automated vehicle (SAV), combining on-demand service features with self-driving capabilities. This work simulates a fleet of SAVs operating within Austin, Texas, first using an idealized grid-based representation, and next using Austin’s actual transportation network and travel demand flows. This second model incorporates dynamic ride-sharing (DRS), allowing two or more travelers with similar origins, destinations and departure times to share a ride. Model results indicate that each SAV could replace around 10 conventionally-owned household vehicles while serving over 56,000 person-trips. SAVs’ ability to relocate unoccupied between serving one traveler and the next may cause an increase of 7-10% more travel; however, DRS can result in reduced overall VMT, given enough SAV-using travelers willing to ride-share. Furthermore, using DRS results in overall lower wait and service times for travelers, particularly from pooling rides during peak demand. SAVs should produce favorable emissions outcomes, with an estimated 16% less energy use and 48% lower volatile organic compound (VOC) emissions, per person-trip compared to conventional vehicles. Finally, assuming SAVs cost $70,000 each, an SAV fleet in Austin could provide a 19$1 per trip-mile served. In summary, this new paradigm holds much promise that technological advances may soon realized.Civil, Architectural, and Environmental Engineerin

Service Region Design for Urban Electric Vehicle Sharing Systems

Emerging collaborative consumption business models have shown promise in terms of both generating business opportunities and enhancing the efficient use of resources. In the transportation domain, car sharing models are being adopted on a mass scale in major metropolitan areas worldwide. This mode of servicized mobility bridges the resource efficiency of public transit and the flexibility of personal transportation. Beyond the significant potential to reduce car ownership, car sharing shows promise in supporting the adoption of fuel- efficient vehicles, such as electric vehicles (EVs), due to these vehicles special cost structure with high purchase but low operating costs. Recently, key players in the car sharing business, such as Autolib, Car2Go and DriveNow, have begun to employ EVs in an operations model that accommodates one-way trips. On the one hand (and particularly in free-floating car sharing), the one-way model results in significant improvements in coverage of travel needs and therefore in adoption potential compared with the conventional round-trip-only model (advocated by ZipCar, for example). On the other hand, this model poses tremendous planning and operational challenges. In this work, we study the planning problem faced by service providers in designing a geographical service region in which to operate the service. This decision entails trade-offs between maximizing customer catchment by covering travel needs and controlling fleet operations costs. We develop a mathematical programming model that incorporates details of both customer adoption behavior and fleet management (including EV repositioning and charging) under imbalanced travel patterns. To address inherent planning uncertainty with regard to adoption patterns, we employ a distributionally robust optimization framework that informs robust decisions to overcome possible ambiguity (or lacking) of data. Mathematically, the problem can be approximated by a mixed integer second-order cone program, which is computationally tractable with practical scale data. Applying this approach to the case of Car2Go’s service with real operations data, we address a number of planning questions and suggest that there is potential for the future development of this service

A Better Match for Drivers and Riders: Reinforcement Learning at Lyft

To better match drivers to riders in our ridesharing application, we revised Lyft's core matching algorithm. We use a novel online reinforcement learning approach that estimates the future earnings of drivers in real time and use this information to find more efficient matches. This change was the first documented implementation of a ridesharing matching algorithm that can learn and improve in real time. We evaluated the new approach during weeks of switchback experimentation in most Lyft markets, and estimated how it benefited drivers, riders, and the platform. In particular, it enabled our drivers to serve millions of additional riders each year, leading to more than $30 million per year in incremental revenue. Lyft rolled out the algorithm globally in 2021

Optimization of profits in one-way free-floating car-sharing services, with a user-based relocation strategy that apply dynamic pricing and urban area demand defined gathering real vehicle-sensor data.

Rapid growing in urbanization and miles driven in the city will triple urban mobility by 2050. This explosion in demand requires switching to Mobility-as-a-Service (MaaS) models, such as Car-sharing. However, a critical issue for Car-sharing one-way free-floating services is the imbalance problem that requires to solve the conflict between the positioning of vehicles “at the right place and time” and the freedom for customers to return vehicles where and when they want. To better understand the impact of the imbalance problem, we propose to use a grid partition of the served city into zones with different demand potentials. To this aim as first step of the research real data related to vehicle positions of three Car-sharing services have been collected for approximately three months in the cities of Rome, Milan, Turin and Florence (Italy). In the experimental results data of the city of Rome have been used. This part of the research focuses on analysing user behaviour by using the number of stops in selected city zones (Stop Density) and the duration of any stop (Average Stop Duration); in fact, all the stops of each vehicle belonging to any car-sharing operator, are uniquely associated and mapped to exactly one cell of the city grid representing the Urban Areas, also tracking stop start/end time and trip start/end time. This spatial association is used to calculate Stop Density and Average Stop Duration of each urban area and to map stops to specific time-slots. Consequently, in each urban area, the Urban Area Value is calculated as a function of Stop Density and Average Stop Duration belonging to the urban area; the results of this research confirm that Urban Area Value is high where high values of Stop Density and low value of Average Stop Duration occurs. Urban Areas are ranked using the Urban Area Value calculated by considering all Car-sharing services operating in the eco-system; a spatial analysis with a thermographic map of Urban Area Value allows to visualize the existence of city zones with crucial different demand potentials. The analysis derived from such Urban Area Value and from a time-slot dynamic of the Urban Areas Values themselves, that suggested to split the standard operating day in five hourly ranges, is then used to construct a flexible and dynamic pricing mathematical programming model that has been used to derive an optimal setting of tariffs and to perform a validation phase. In this model the trip fare is defined, based on a trip planning trigger, applying a bonus/malus mechanism to a basic tariff, which considers vehicle service cost, staff relocation saving and the difference of demand value between origin and destination Urban Areas. If the user desired destination is planned in an urban area which is adjoining urban areas with higher values, alternatives with lower fees are proposed. This approach is applicable, in the reality, to several Car-sharing operators and mobility-sharing aggregators such as Urbi. The model and the outcomes of Urban Area Values have been validated in a study based on real data collected in the city of Rome (Italy) during an observation period of 49 days from April 28th to June 16th, in 2016, and where 287.975 stops observation referring to 1.271 distinct vehicles have been collected. All the stops have been observed in the city of Rome whose grid representation has been partitioned in 636 cells. These results have been presented to the 2017 COMPSAC Conference, July 7th, 2017 in the Workshop “Smart Sharing Mobility in Smart Cities” 1. These data have been used to construct an integer linear programming model where only a grid of 25 cells has been considered over the same period of 49 days. The resulting model (which has 84.500 variables and 87.750 constraints) has been solved using AMPL/CPLEX and validated by simulating a trip demand over an observed period. The result of this pricing scheme seems to produce interesting results with a business applicability in urban car–sharing market. The thesis is organized as follows. Chapter 1 is focused on the analysis of main challenges of urban mobility, and the role that car-sharing systems can play. Chapters 2, 3, 4 are devoted to the introduction and a systematic review of the literature. In Chapter 5 the data collection and cleaning are described and the final Data set is presented. Chapter 6 includes the grid partition of a city and the procedure to evaluate the Urban Area Value. Chapter 7 presents a review of the up-to-date pricing models for Car sharing that are used for defining some parameters in the optimization model presented in Chapter 8. Finally, in Chapter 9 the results obtained on the available Data set for the city of Rome are presented

Intelligent Operation System for the Autonomous Vehicle Fleet

Modular vehicles are vehicles with interchangeable substantial components also known as modules. Fleet modularity provides extra operational flexibility through on-field actions, in terms of vehicle assembly, disassembly, and reconfiguration (ADR). The ease of assembly and disassembly of modular vehicles enables them to achieve real-time fleet reconfiguration, which is proven as beneficial in promoting fleet adaptability and in saving ownership costs. The objective of military fleet operation is to satisfy uncertain demands on time while providing vehicle maintenance. To quantify the benefits and burdens from modularity in military operation, a decision support system is required to yield autonomously operation strategies for comparing the (near) optimal fleet performance for different vehicle architectures under diverse scenarios. The problem is challenging because: 1) fleet operation strategies are numerous, especially when modularity is considered; 2) operation actions are time-delayed and time-varying; 3) vehicle damages and demands are highly uncertain; 4) available capacity for ADR actions and vehicle repair is constrained. Finally, to explore advanced tactics enabled by fleet modularity, the competition between human-like and adversarial forces is required, where each force is capable to autonomously perceive and analyze field information, learn enemy's behavior, forecast enemy's actions, and prepare an operation plan accordingly. Currently, methodologies developed specifically for fleet competition are only valid for single type of resources and simple operation rules, which are impossible to implement in modular fleet operation. This dissertation focuses on a new general methodology to yield decisions in operating a fleet of autonomous military vehicles/robots in both conventional and modular architectures. First, a stochastic state space model is created to represent the changes in fleet dynamics caused by operation actions. Then, a stochastic model predictive control is customized to manage the system dynamics, which is capable of real-time decision making. Including modularity increases the complexity of fleet operation problem, a novel intelligent agent based model is proposed to ensure the computational efficiency and also imitate the collaborative decisions making process of human-like commanders. Operation decisions are distributed to several agents with distinct responsibility. Agents are designed in a specific way to collaboratively make and adjust decisions through selectively sharing information, reasoning the causality between events, and learning the other's behavior, which are achieved by real-time optimization and artificial intelligence techniques. To evaluate the impacts from fleet modularity, three operation problems are formulated: (i) simplified logistic mission scenario: operate a fleet to guarantee the readiness of vehicles at battlefields considering the stochasticity in inventory stocks and mission requirements; (ii) tactical mission scenario: deliver resources to battlefields with stochastic requirements of vehicle repairs and maintenance; (iii) attacker-defender game: satisfy the mission requirements with minimized losses caused by uncertain assaults from an enemy. The model is also implemented for a civilian application, namely the real-time management of reconfigurable manufacturing systems (RMSs). As the number of RMS configurations increases exponentially with the size of the line and demand changes frequently, two challenges emerge: how to efficiently select the optimal configuration given limited resources, and how to allocate resources among lines. According to the ideas in modular fleet operation, a new mathematical approach is presented for distributing the stochastic demands and exchanging machines or modules among lines (which are groups of machines) as a bidding process, and for adaptively configuring these lines and machines for the resulting shared demand under a limited inventory of configurable components.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147588/1/lixingyu_2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147588/2/lixingyu_1.pd

Optimization Approaches for Mobility and Service Sharing

Mobility and service sharing is undergoing a fast rise in popularity and industrial growth in recent years. For example, in patient-centered medical home care, services are delivered to patients at home, who share a group of medical staff riding together in a vehicle that also carries shared medical devices; companies such as Amazon and Meijer have been investing tremendous human effort and money in grocery delivery to customers who share the use of delivery vehicles and staff. In such mobility and service sharing systems, decision-makers need to make a wide range of system design and operational decisions, including locating service facilities, matching supplies with demand for shared mobility services, dispatching vehicles and staff, and scheduling appointments. The complexity of the linking decisions and constraints, as well as the dimensionality of the problems in the real world, pose challenges in finding optimal strategies efficiently. In this work, we apply techniques from Operations Research to investigate the optimal and practical solution approaches to improve the quality of service, cost-effectiveness, and operational efficiency of mobility and service sharing in a variety of applications. We deploy stochastic programming, integer programming, and approximation algorithms to address the issues in decision-making for seeking fast and reliable solutions for planning and operations problems. This dissertation contains four main chapters. In Chapter 2, we consider a class of vehicle routing problems (VRPs) where the objective is to minimize the longest route taken by any vehicle as opposed to the total distance of all routes. In such a setting, the traditional decomposition approach fails to solve the problem effectively. We investigate the hardness result of the problem and develop an approximation algorithm that achieves the best approximation ratio. In Chapter 3, we focus on developing an efficient computational algorithm for the elementary shortest path problem with resource constraints, which is solved as the pricing subproblem of the column generation-based approach for many VRP variants. Inspired by the color-coding approach, we develop a randomized algorithm that can be easily implemented in parallel. We also extend the state-of-the-art pulse algorithm for elementary shortest path problem with a new bounding scheme on the load of the route. In Chapter 4, we consider a carsharing fleet location design problem with mixed vehicle types and a restriction on CO2 emission. We use a minimum-cost flow model on a spatial-temporal network and provide insights on fleet location, car-type design, and their environmental impacts. In Chapter 5, we focus on the design and operations of an integrated car-and-ride sharing system for heterogeneous users/travelers with an application of satisfying transportation needs in underserved communities. The system aims to provide self-sustained community-based shared transportation. We address the uncertain travel and service time in operations via a stochastic integer programming model and propose decomposition algorithms to solve it efficiently. Overall, our contributions are threefold: (i) providing mathematical models of various complex mobility and service sharing systems, (ii) deriving efficient solution algorithms to solve the proposed models, (iii) evaluating the solution approaches via extensive numerical experiments. The models and solution algorithms that we develop in this work can be used by practitioners to solve a variety of mobility and service sharing problems in different business contexts, and thus can generate significant societal and economic impacts.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155115/1/miaoyu_1.pd

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)