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Author: Mohammad Abdollahi (Industrial engineer)
Publisher:
ISBN:
Category : Industrial engineering
Languages : en
Pages : 0
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Book Description
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.
Author: Mohammad Abdollahi (Industrial engineer)
Publisher:
ISBN:
Category : Industrial engineering
Languages : en
Pages : 0
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Book Description
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.
Author: Amaral, António Manuel
Publisher: IGI Global
ISBN: 1799816168
Category : Transportation
Languages : en
Pages : 293
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Book Description
With the recent advancements and implementations of technology within the global community, various regions of the world have begun to transform. The idea of smart transportation and mobility is a specific field that has been implemented among countless areas around the world that are focused on intelligent and efficient environments. Despite its strong influence and potential, sustainable mobility still faces multiple demographic and environmental challenges. New perspectives, improvements, and solutions are needed in order to successfully apply efficient and sustainable transportation within populated environments. Implications of Mobility as a Service (MaaS) in Urban and Rural Environments: Emerging Research and Opportunities is a pivotal reference source that provides vital research on recent transportation improvements and the development of mobility systems in populated regions. While highlighting topics such as human-machine interaction, alternative vehicles, and sustainable development, this publication explores competitive solutions for transport efficiency as well as its impact on citizens’ quality of life. This book is ideally designed for researchers, environmentalists, civil engineers, architects, policymakers, strategists, academicians, and students seeking current research on mobility advancements in urban and rural areas across the globe.
Author: Yue Guan (Ph. D.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 192
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Book Description
Mobility of people and goods has been critical to urban life ever since cities emerged thousands of years ago. With the ushering in Cyber-Physical Systems enabled by the development of smart mobile devices, telecommunication technologies, as well as affordable, accessible and powerful computing resources, new paradigms are revolutionizing urban mobility. Among these, Shared Mobility on Demand Service (SMoDS) has changed the landscape of urban transportation, providing alternatives with a customized combination of affordability, flexibility, and carbon footprint. Dynamic routing and dynamic pricing are two central pillars of an SMoDS solution, where the former offers customized routes according to the specific passenger request and real time traffic conditions, and the latter provides incentive signals that appropriately influence the passengers’ subscription of the service. Although emerging SMoDS solutions have seen remarkable successes, further improvements are in need. In this thesis, we present an integrated SMoDS design with dynamic routing and dynamic pricing that introduces two major improvements over the state of the art: (i) enhanced optimality in travel times through dynamic routing with added spatial flexibility, and (ii) explicit accommodation of behavioral modelling of empowered passengers so as to lead to an accurate dynamic pricing strategy. The first part of this thesis focuses on the development of the dynamic routing framework with a new concept of space window. To accommodate the complexity introduced by space window in the optimization of dynamic routes, we propose an algorithm based upon the Alternating Minimization (AltMin) paradigm, and demonstrate an order of magnitude improvement in computational efficiency compared to benchmarks provided by standard solvers. The second part of this thesis, related to dynamic pricing, is broken down into two modules, with the first related to behavioral modelling of empowered passengers based on Cumulative Prospect Theory (CPT). The CPT based behavioral model is able to capture the subjective and potentially irrational behaviors of passengers when deciding upon the SMoDS ride offer amidst uncertainties and risks associated with framing effects, loss aversion, diminishing sensitivity, and probability distortion. Key properties and the implications of the CPT based passenger behavioral model on dynamic pricing are discussed in detail. The second module of dynamic pricing determines the desired probability of acceptance from each passenger so as to optimize key performance indicators of the SMoDS such as the estimated waiting time. A Reinforcement Learning (RL) based approach combined with the problem formulation in the form of a Markov Decision Process (MDP) is used to estimate this desired probability of acceptance. The proposed RL algorithm deploys an integrated planning and learning architecture where the planning phase is carried out by a lookahead tree search, and the learning phase is achieved via value iteration using a neural network as the value function approximator. Two major challenges that arise in this context is the varying dimension of the underlying state and the arrival of information in a sequential manner where long-term dependency needs to be preserved. These are addressed through the incorporation of Long Short-Term Memory (LSTM), convolutional and fully-connected layers. Their judicious incorporation in the underlying neural network architecture allows the extraction of this information and successful estimation of the desired probability of acceptance that leads to the optimization of the SMoDS. A number of computational experiments are carried out using various datasets of large-scale problems and are shown to result in a superior capability of the proposed RL algorithm.
Author: Jian Wen (S. M.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 91
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Book Description
The concept of shared mobility-on-demand (MoD) systems describes an innovative mode of transportation in which rides are tailored as per the immediate requests in a shared manner. Convenience of hailing, ease of transactions, and economic efficiency of crowd-sourcing the rides have made these services very attractive today. It is anticipated that autonomous vehicle (AV) technology may further improve the economics of such services by reducing the operational costs. The design and operation of such an shared autonomous mobility-on-demand (AMoD) system is therefore an important research direction that requires significant investigation. This thesis mainly addresses three issues revolving around the dispatching strategies of shared AMoD systems. First, it responds to the special dispatching need that is critical for effective AMoD operation. This includes a dynamic request-vehicle assignment heuristic and an optimal rebalancing policy. In addition, the dispatching strategies also reflect transit-oriented designs in two ways: (a) the objective function embodies the considerations of service availability and equity through the support of various hailing policies; and (b), the service facilitates first-mile connections to public transportation. Second, this thesis models the interaction between demand and supply through simulation. Using the level of service as interface, this mechanism enables feedback between operators and travelers to more closely represent the choices of both parties. A fixed-point approach is then applied to reach balance iteratively, estimating both the demand volume and the system performance at equilibrium. The results from the simulation support decision-making with regard to comprehensive system design problems such as fleet sizing, vehicle capacities and hailing policies. Third, the thesis evaluates the value of demand information through simulation experiments. To quantify the system performance gain that can be derived from the demand information, this thesis proposes to study two dimensions, level of information and value of information, and builds up the relationship between them. The numerical results help rationalize the efforts operators should spend on data collection, information inference and advanced dispatching algorithms. This thesis also implements an agent-based modeling platform, amod-abm, for simulating large-scale shared AMoD applications. Specifically, it models individual travelers and vehicles with demand-supply interaction and analyzes system performance through various metrics of indicators. This includes wait time, travel time, detour factor and service rate at the traveler's side, as well as vehicle distance traveled, load and profit at the operator's side. A case study area in London is selected to support the presentation of methodology. Results show that encouraging ride-sharing and allowing in-advance requests are powerful tools to enhance service efficiency and equity. Demand information from in-advance requests also enables the operator to plan service ahead of time, which leads to better performance and higher profit. The thesis concludes that the demand-supply interaction can be effective for defining and assessing the roles of AV technology in our future transportation systems. Combining efficient dispatching strategies and demand information management tools is also important for more affordable and efficient services.
Author: Ata M. Khan
Publisher: IET
ISBN: 1785618628
Category : Technology & Engineering
Languages : en
Pages : 519
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Book Description
Shared mobility is gaining increasing attention in private and public sectors. Serving as a source of information on how best to shape shared vehicle systems of the future, this book contributes knowledge on key facets of shared mobility. It includes shared vehicle systems as well as shared automated vehicle systems.
Author: Yu Xin Leo Chen
Publisher:
ISBN:
Category :
Languages : en
Pages : 97
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Book Description
The autonomous vehicle (AV) is poised to be one of the most disruptive technologies in the transportation industry. The advent of three major trends in transportation: automation, on-demand mobility and ride-sharing, are set to revolutionize the way we travel. The forthcoming adoption and commercialization of AVs are expected to have extensive impacts on our road networks, congestion, safety, land use, public transportation (PT) and more. Rapid advances in AV technology are convincing many that AV services will play a significant role in future transportation systems. The advancement of AVs presents both opportunities and threats to transportation. It has the potential to significantly impact traffic congestion, traffic accidents, parking and VMT (vehicle miles traveled), especially for people that are not able to drive such as children and elderly people. Motivated by the potential of autonomous vehicles, authorities around the world are preparing for this revolution in transport and deems this an important research direction that requires significant investigation. This thesis tackled and contributed to three main research questions related to the impact of autonomous vehicles on transportation systems. First, this thesis proposes a simulation-based approach to the design and evaluation of integrated autonomous vehicle and public transportation systems. We highlight the transit-orientation by respecting the social-purpose considerations of transit agencies (such as maintaining service availability and ensuring equity) and identifying the synergistic opportunities between AV and PT. Specifically, we identified that AV has a strong potential to serve first-mile connections to the PT stations and provide efficient and affordable shared mobility in low-density suburban areas that are typically inefficient to serve by conventional fixed-route PT services. The design decisions reflect the interest of multiple stakeholders in the system. Second, the interaction between travelers (demand) and operators (supply) is modeled using a system of equations that is solved as a fixed-point problem through an iterative procedure. In this, we developed demand and supply as two sub-problems. The demand will be predicted using a nested logit model to estimate the volume for different modes based on modal attributes. The supply will use a simulation platform capable of incorporating critical operational decisions on factors including fleet sizes, vehicle capacities, sharing policies, fare schemes and hailing strategies such as in-advance and on-demand requests. Using feedback between demand and supply, we enable interactions between the decisions of the service operator and those of the travelers, in order to model the choices of both parties. Finally, this thesis systematically optimizes service design variables to determine the best outcome in accordance to AV+PT stakeholder goals. Optimization objective functions can be formulated to reflect the different objectives of different stakeholders. In this paper, we specifically propose and develop a simulation-based service design method where we quantify various benefits and costs to reflect the objectives of key AV+PT stakeholders. We simulate the service with different sets of system settings and identify the highest performing set. We employ a case study of regional service contracting to showcase the ability of this method to inform AV+PT service design. We tested our approach with a case study area in a major European city on an agent-based simulation platform, amod-abm. Agent-based simulation has the advantage of capturing individual (agent) behaviors and the interactions of the various individual agents in a realistic synthetic environment where the intent is to re-create a complex phenomenon of mobility on demand service delivered by AV. Although this thesis will focus on a major European city, the general framework and methodologies proposed here can be widely applicable. The thesis concludes that the demand-supply interaction can be effective for designing and assessing the role of AV technology in our mobility systems. Moreover, simulation-based optimization can be an effective method for transit agencies to make decisions that support their overall AV related transport strategy as well as operational planning.
Author: Evangelos Simoudis
Publisher:
ISBN: 9780998067711
Category : Automobile industry and trade
Languages : en
Pages :
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Book Description
From Detroit to Germany, Japan, and Korea, within the incumbent automotive industry there is amplifying conversation about the magnitude, extent and timing of the disruption that will result from the introduction of autonomous and driverless vehicles. This disruption will in turn result from innovations in technology and business models and changing attitudes toward car ownership. Catalyzed by the development of Autonomous, Connected and Electrified (ACE) vehicles and Mobility Services, the emerging hybrid mobility model will blend car ownership with on-demand car access. Big data generated inside and outside ACE vehicles and the exploitation of that data by machine intelligence technologies are key ingredients in this next generation of mobility. Together they offer a unique and still overlooked value creation opportunity. The book presents a strategy for capitalizing on the opportunities presented in our driverless future through the combination of startup innovations with corporate innovation efforts.
Author: Yoshiki Yamagata
Publisher: Elsevier
ISBN: 0128162937
Category : Political Science
Languages : en
Pages : 462
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Book Description
Urban Systems Design: Creating Sustainable Smart Cities in the Internet of Things Era shows how to design, model and monitor smart communities using a distinctive IoT-based urban systems approach. Focusing on the essential dimensions that constitute smart communities energy, transport, urban form, and human comfort, this helpful guide explores how IoT-based sharing platforms can achieve greater community health and well-being based on relationship building, trust, and resilience. Uncovering the achievements of the most recent research on the potential of IoT and big data, this book shows how to identify, structure, measure and monitor multi-dimensional urban sustainability standards and progress. This thorough book demonstrates how to select a project, which technologies are most cost-effective, and their cost-benefit considerations. The book also illustrates the financial, institutional, policy and technological needs for the successful transition to smart cities, and concludes by discussing both the conventional and innovative regulatory instruments needed for a fast and smooth transition to smart, sustainable communities. Provides operational case studies and best practices from cities throughout Europe, North America, Latin America, Asia, Australia, and Africa, providing instructive examples of the social, environmental, and economic aspects of “smartification Reviews assessment and urban sustainability certification systems such as LEED, BREEAM, and CASBEE, examining how each addresses smart technologies criteria Examines existing technologies for efficient energy management, including HEMS, BEMS, energy harvesting, electric vehicles, smart grids, and more
Author: Constantinos Antoniou
Publisher: Elsevier
ISBN: 012815019X
Category : Business & Economics
Languages : en
Pages : 314
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Book Description
Demand for Emerging Transportation Systems: Modeling Adoption, Satisfaction, and Mobility Patterns comprehensively examines the concepts and factors affecting user quality-of-service satisfaction. The book provides an introduction to the latest trends in transportation, followed by a critical review of factors affecting traditional and emerging transportation system adoption rates and user retention. This collection includes a rigorous introduction to the tools necessary for analyzing these factors, as well as Big Data collection methodologies, such as smartphone and social media analysis. Researchers will be guided through the nuances of transport and mobility services adoption, closing with an outlook of, and recommendations for, future research on the topic. This resource will appeal to practitioners and graduate students. Examines the dynamics affecting adoption rates for public transportation, vehicle-sharing, ridesharing systems and autonomous vehicles Covers the rationale behind travelers’ continuous use of mobility services and their satisfaction and development Includes case studies, featuring mobility stats and contributions from around the world
Author: Babak Heydari
Publisher: Cambridge University Press
ISBN: 1108853277
Category : Law
Languages : en
Pages : 259
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Book Description
The current sharing economy suffers from system-wide deficiencies even as it produces distinctive benefits and advantages for some participants. The first generation of sharing markets has left us to question: Will there be any workers in the sharing economy? Can we know enough about these technologies to regulate them? Is there any way to avoid the monopolization of assets, information, and wealth? Using convergent, transdisciplinary perspectives, this volume examines the challenge of reengineering a sharing economy that is more equitable, democratic, sustainable, and just. The volume enhances the reader's capacity for integrating applicable findings and theories in business, law and social science into ethical engineering design and practice. At the same time, the book helps explain how technological innovations in the sharing economy create value for different stakeholders and how they impact society at large. Reengineering the Sharing Economy is also available as Open Access on Cambridge Core.
