Car Sharing Mobility On Demand Systems
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Car Sharing Mobility On Demand Systems
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Author : Ge Guo
language : en
Publisher:
Release Date : 2022
Car Sharing Mobility On Demand Systems written by Ge Guo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
SAE EDGE Research Reports provide state-of-the-art and state-of-industry examinations of the most significant topics in mobility engineering. SAE EDGE contributors are experts from research, academia, and industry who have come together to explore and define the most critical advancements, challenges, and future direction in areas such as vehicle automation, unmanned aircraft, IoT and connectivity, cybersecurity, advanced propulsion, and advanced manufacturing.
Shared Mobility On Demand System Design
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Author : Mohammad Abdollahi (Industrial engineer)
language : en
Publisher:
Release Date : 2021
Shared Mobility On Demand System Design written by Mohammad Abdollahi (Industrial engineer) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Industrial engineering categories.
Tomorrows mobility will be radically different. Connected, Autonomous, Shared, and Electric Mobility are four main developments that are dramatically altering the automobile industry. We study the shared centralized class of mobility problems which considers a platform of self driving cars. There are new challenges with these systems such as how to balance the idle vehicle, how to price the shared autonomous system, and etc. We are attempting to address the question of how to share passengers ride to maximize satisfaction for riders, and the platform itself. Besides that, to have a good ETA estimate for trips, we develop a data-driven travel time prediction algorithm which can be used in our platform to get a good estimate for scheduling and routing the rides. Finally, we also study the pricing mechanism of these systems using a deep reinforcement learning agent that simulates the rides in New York. We start by studying both static and dynamic (real-time) ride pooling problem with time windows, multiple homogeneous/heterogeneous vehicles, passenger convenience and other business considerations. First, the problems under consideration is modeled as two different static MILP for homogeneous/heterogeneous fleet of vehicles, and also a constraint programming counterpart is provided for the heterogeneous vehicles case. Also to improve the linear relaxation of these models, several pre-processing steps and lifting inequalities are applied. While appealing, exact formulations have integer variables which render them as non-convex optimization problems. Thus, while this approach offers the benefit of system optimality, its formulation here is NP-hard, making it not viable for real world problems. To find a good quality solution, a heuristic decomposition algorithm based on constraint programming and branch and price is proposed to solve static model within a reasonable time for implementation in a real-world situation. Computational results show that the heuristic algorithms are superior compared to the exact algorithms in terms of the calculation time as the problem size (in terms of the number of requests) increases. In phase 2 of this dissertation, we propose a travel time predictive model by developing a integrated multi-step approach to learn the feature space. This multi stage algorithm is initiated by pre-processing task. Subsequently, the feature set is obtained by incorporating some publicly available information. Moreover, a feature engineer ing path is proposed to improve the feature space. This path includes Principal Component Analysis (PCA), geospatial features analysis, and unsupervised learning methods like K-Means and stacked autoencoders. Finally we apply a customized gradient boosting method to estimate travel times and comparing our results with LSTM network which shows superiority of our method in terms of capturing dynamics of traffic through time. Although more data with rare events need to be added in case of experiencing heavy snow or other events which magnifies travel times. Lastly, we developed a fleet management simulation platform where we model pricing problem as a partially observable Markov decision process (POMDP), and DQN agent is developed to estimate fares as a function of real-time interaction with the environment. Fare prices are considered to be continuous and stochastic variables, but for simplicity we have price adjustment in discrete units, and we determine them using a deep neural network (DNN). We compare our algorithm with the one for ride hailing system and see if our pricing mechanism can decrease rejections and cancellation and increase system objective as well as passengers0́9 utility. We illustrate the usefulness of our algorithm by applying it to real-world transportation problem and show that it learns fare estimates to minimize total travel time, maximize revenue, and other weighted objectives. Collectively, this work can be used for designing a ride sharing system of autonomous vehicles in which a controller module with many different predictive and prescriptive analytics engines dispatches vehicles and broadcasts ride fares to optimize system and riders utility.
Electric Vehicles In Shared Fleets Mobility Management Business Models And Decision Support Systems
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Author : Kenan Degirmenci
language : en
Publisher: World Scientific
Release Date : 2022-04-28
Electric Vehicles In Shared Fleets Mobility Management Business Models And Decision Support Systems written by Kenan Degirmenci and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-28 with Business & Economics categories.
The electrification of shared fleets offers numerous benefits, including the reduction of local emissions of pollutants, which leads to ecological improvements such as the improvement of air quality. Electric Vehicles in Shared Fleets considers a holistic concept for a socio-technical system with a focus on three core areas: integrated mobility solutions, business models for economic viability, and information systems that support decision-making for the successful implementation and operation of electric vehicles in shared fleets.In this book, we examine different aspects within these areas including multimodal mobility, grid integration of electric vehicles, shared autonomous electric vehicle services, relocation strategies in shared fleets, and the challenge of battery life of electric vehicles. Insights into the future of transport are provided, which is predicted to be shared, autonomous, and electric. This will require the expansion of the charging infrastructure to provide adequate premises for the electrification of transportation and to create market demand.
Leverage Data Streams For Better Operational Decision Making
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Author : Christoph Prinz
language : en
Publisher: Cuvillier Verlag
Release Date : 2023-05-31
Leverage Data Streams For Better Operational Decision Making written by Christoph Prinz and has been published by Cuvillier Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-31 with Business & Economics categories.
Smart sustainable mobility ecosystems promise to address society’s expectation of environmentally friendly on-demand mobility. While the technology stack to build such ecosystems is just around the corner in the form of connected, automated, and electric vehicles, strategies to deploy and operate such fleets in a coordinated manner must still be advanced. Most of such optimization challenges highly depend on the nature of customer demand, vehicle supply, and environmental influences. Hence, this dissertation investigates how available data streams from mobility ecosystems can be leveraged in Information Systems to solve related decision problems. The overarching goal of this work is to generate design knowledge to improve vehicle availability, provider profitability, and environmental sustainability for such ecosystems. Applying quantitative methods to real-world data from shared vehicle systems generates insights into the nature of demand and supply. Combining it with an analysis of empirical research on vehicle relocation algorithms builds the foundation for two artifact designs. The first artifact enables the development and simulation-based evaluation of operation modes for vehicle fleets. The second artifact enables artificial intelligence-based decision support for the vehicle rebalancing problem. The insights are finally incorporated and generalized to a nascent design theory on data-enabled operational decision-making in the context of smart sustainable mobility environments. The findings have multifaceted implications for researchers concerned with data-enabled value creation in Green IS, shared economy and smart mobility, and business analytics and data science. Furthermore, guidance for fleet providers to improve system attractiveness and for society to experience the potential amount of vehicle access without personal ownership is provided.
Models And Large Scale Coordination Algorithms For Autonomous Mobility On Demand
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Author : Rick Zhang
language : en
Publisher:
Release Date : 2016
Models And Large Scale Coordination Algorithms For Autonomous Mobility On Demand written by Rick Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Urban mobility in the 21st century faces significant challenges, as the unsustainable trends of urban population growth, congestion, pollution, and low vehicle utilization worsen in large cities around the world. As autonomous vehicle technology draws closer to realization, a solution is beginning to emerge in the form of autonomous mobility-on-demand (AMoD), whereby fleets of self-driving vehicles transport customers within an urban environment. This dissertation introduces a systematic approach to the design, control, and evaluation of these systems. In the first part of the dissertation, a stochastic queueing-theoretical model of AMoD is developed, which allows both the analysis of quality-of-service metrics as well as the synthesis of control policies. This model is then extended to one-way car sharing systems, or human-driven mobility-on-demand (MoD) systems. Based on these models, closed-loop control algorithms are designed to efficiently route empty (rebalancing) vehicles in very large systems with thousands of vehicles. The performance of the algorithms and the potential societal benefits of AMoD and MoD are evaluated through case studies of New York City and Singapore using real-world data. In the second part of the dissertation, additional structural and operational constraints are considered for AMoD systems. First, the impact of AMoD on traffic congestion with respect to the underlying structural properties of the road network is analyzed using a network flow model. In particular, it is shown that empty rebalancing vehicles in AMoD systems will not increase congestion, in stark contrast to popular belief. Finally, the control of AMoD systems with additional operational constraints is studied under a model predictive control framework, with a focus on range and charging constraints of electric vehicles. The technical approach developed in this dissertation allows us to evaluate the societal benefits of AMoD systems as well as lays the foundation for the design and control of future urban transportation networks.
Disrupting Mobility
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Author : Gereon Meyer
language : en
Publisher: Springer
Release Date : 2017-01-04
Disrupting Mobility written by Gereon Meyer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-04 with Technology & Engineering categories.
This book explores the opportunities and challenges of the sharing economy and innovative transportation technologies with regard to urban mobility. Written by government experts, social scientists, technologists and city planners from North America, Europe and Australia, the papers in this book address the impacts of demographic, societal and economic trends and the fundamental changes arising from the increasing automation and connectivity of vehicles, smart communication technologies, multimodal transit services, and urban design. The book is based on the Disrupting Mobility Summit held in Cambridge, MA (USA) in November 2015, organized by the City Science Initiative at MIT Media Lab, the Transportation Sustainability Research Center at the University of California at Berkeley, the LSE Cities at the London School of Economics and Politics and the Innovation Center for Mobility and Societal Change in Berlin.
Autonomous Driving
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Author : Markus Maurer
language : en
Publisher: Springer
Release Date : 2016-05-21
Autonomous Driving written by Markus Maurer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-21 with Technology & Engineering categories.
This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies? Experts from Germany and the United States define key societal, engineering, and mobility issues related to the automation of vehicles. They discuss the decisions programmers of automated vehicles must make to enable vehicles to perceive their environment, interact with other road users, and choose actions that may have ethical consequences. The authors further identify expectations and concerns that will form the basis for individual and societal acceptance of autonomous driving. While the safety benefits of such vehicles are tremendous, the authors demonstrate that these benefits will only be achieved if vehicles have an appropriate safety concept at the heart of their design. Realizing the potential of automated vehicles to reorganize traffic and transform mobility of people and goods requires similar care in the design of vehicles and networks. By covering all of these topics, the book aims to provide a current, comprehensive, and scientifically sound treatment of the emerging field of “autonomous driving".
Car Sharing
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Author : Adam Millard-Ball
language : en
Publisher: Transportation Research Board
Release Date : 2005
Car Sharing written by Adam Millard-Ball and has been published by Transportation Research Board this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Transportation categories.
Electric Vehicle Sharing Services For Smarter Cities
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Author : Daniele Fabrizio Bignami
language : en
Publisher: Springer
Release Date : 2017-08-20
Electric Vehicle Sharing Services For Smarter Cities written by Daniele Fabrizio Bignami and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-20 with Technology & Engineering categories.
This book examines electric car sharing in cities from a variety of perspectives, from service design to simulation, from mathematical modeling to technology deployment, and from energy use improvement to the integration of different kinds of vehicle. The contents reflect the outcomes of the Green Move project, undertaken by Politecnico di Milano with the aim of fostering an innovative and easily accessible electric vehicle sharing system. The first section of the book illustrates the car sharing service, covering service design, the configuration of the vehicle sharing model and the Milan mobility pattern, analysis of local demand and supply, testing of the condominium-based car sharing model, and communication design for social engagement. The second section then explains the technological choices, from the architecture of the system and dynamic applications to information management, the smartphone-based energy-oriented driving assistance system, automatic fleet balancing systems, and real-time monitoring of vehicle positions. In the final section, readers will find descriptions of the simulation model, a model to estimate potential users of the service, and a model for a full-scale electric car sharing service in Milan.
Demand Uncensored
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Author : Evan Jerome Fields
language : en
Publisher:
Release Date : 2019
Demand Uncensored written by Evan Jerome Fields and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
In the design and operation of urban mobility systems, it is often desirable to understand patterns in traveler demand. However, demand is typically unobserved and must be estimated from available data. To address this disconnect, we begin by proposing a method for recovering an unknown probability distribution given a censored or truncated sample from that distribution. The proposed method is a novel and conceptually simple detruncation technique based on sampling the observed data according to weights learned by solving a simulation-based optimization problem; this method is especially appropriate in cases where little analytic information about the unknown distribution is available but the truncation process can be simulated. The proposed method is compared to the ubiquitous maximum likelihood (MLE) method in a variety of synthetic validation experiments where it is found that the proposed method performs slightly worse than perfectly specified MLE and competitively with slight misspecified MLE. We then describe a novel car-sharing simulator which captures many of the important interactions between supply, demand, and system utilization while remaining simple and computationally efficient. In collaboration with Zipcar, a leading car-sharing operator in the United States, we demonstrate the usefulness of our detruncation method combined with our simulator via a pair of case studies. These tools allow us to estimate demand for round trip car-sharing services in the Boston and New York metropolitan areas, and the inferred demand distributions contain actionable insights. Finally, we extend the detruncation method to cover cases where data is noisy, missing, or must be combined from different sources such as web or mobile applications. In synthetic validation experiments, the extended method is benchmarked against kernel density estimation (KDE) with Gaussian kernels. We find that the proposed method typically outperforms KDE, especially when the distribution to be estimated is not unimodal. With this extended method we consider the added utility of search data when estimating demand for car-sharing.