One-way car-sharing services (CSSs) are believed to be a promising transportation mode for urban mobility. Due to the disparity of city functional areas and population, travel demand and vehicle supply in a CSS may inevitably tend to be imbalanced as well. Therefore, an essential requirement of one-way CSSs is the capability of providing fleet management solutions to improve quality of service and system performance. In other words, a CSS depends heavily on technologies that offer strategic decisions on topics like Fleet sizing Location and capacity of depots and charging stations Matching of travelers with vehicles Relocation of vehicles and dispatchers for fleet rebalancing Balancing and charging schedules of electric vehicles Car-sharing Mobility-on-Demand Systems addresses trending CSS technologies and outlines some insights into the existing unsettled issues and potential solutions. The discussions and outlook are presented as a collection of key points encountered in system planning, configuration, and especially fleet operation. In doing so, the focus is on innovation in technologies, policies, operations, and regulations that impact operators, users, and transport management authorities. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2022018
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".
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.
Autonomous Vehicles and Future Mobility presents novel methods for examining the long-term effects on individuals, society, and on the environment for a wide range of forthcoming transport scenarios, such as self-driving vehicles, workplace mobility plans, demand responsive transport analysis, mobility as a service, multi-source transport data provision, and door-to-door mobility. With the development and realization of new mobility options comes change in long-term travel behavior and transport policy. This book addresses these impacts, considering such key areas as the attitude of users towards new services, the consequences of introducing new mobility forms, the impacts of changing work related trips, and more. By examining and contextualizing innovative transport solutions in this rapidly evolving field, the book provides insights into the current implementation of these potentially sustainable solutions. It will serve as a resource of general guidelines and best practices for researchers, professionals and policymakers.
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.
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.
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.
Mobility On Demand (MOD) systems are creating a paradigm shift in transportation, where mobility is provided not through personally owned vehicles but rather through a fleet of shared vehicles. To maintain a high customer quality of service (QoS), MOD systems need to manage the distribution of vehicles under spatial and temporal fluctuations in customer demand. A challenge for MOD systems is developing and informing a customer demand model. A new proactive demand model is presented which correlates real-time traffic data to predict customer demand on short timescales. Traditional traffic data collection approaches use pervasive fixed sensors which are costly for system-wide coverage. To address this, new frameworks are presented for measuring real-time traffic data using MOD vehicles as mobile sensors. The frameworks are evaluated using hardware and simulation implementations of a real-world MOD system developed for MIT campus. First, a mobile sensing framework is introduced that uses camera and Lidar sensors onboard MOD shuttles to observe system-wide traffic. Through a principled approach for decoupling dependencies between observation data and vehicle motion, the framework provides traffic rate estimates comparable to those of costly fixed sensors. Second, an active sensing framework is introduced which quantifies demand uncertainty with a Bayesian model and routes mobile sensors to reduce parameter uncertainty. The active sensing framework reduces error in demand estimates over both short and long timescales when compared to baseline approaches. Given estimates of customer demand, the challenge for MOD systems is maintaining high customer QoS through fleet management. New automated fleet management planners are introduced for improving customer QoS in ride hailing, ride requesting, and ridesharing MOD operating frameworks. The planners are evaluated using data-driven simulation of the MIT MOD system. For ride hailing, to address the challenge of missed customers, a chance-constrained planner is introduced for positioning vehicles at likely customer hailing locations. The chance-constrained planner provides a significant improvement in the number of served hailing customers over a baseline exploration approach. For ride requesting, to address the challenge of high customer wait times, a predictive positioning planner is introduced to position vehicles at key locations in the MOD system based on customer demand. The predictive positioning planner provides a reduction in service times for requesting customers compared to a baseline waiting approach. For ridesharing, incorrect assumptions on customer preference for transit delays can lead to poor realized customer QoS. A ridesharing planner is introduced for assigning customers to vehicles based on a trained ratings-based QoS model. The ridesharing planner provides robust performance over a range of unknown customer preferences compared to approaches with assumed customer preferences.
This edited book comprises papers about the impacts, benefits and challenges of connected and automated cars. It is the third volume of the LNMOB series dealing with Road Vehicle Automation. The book comprises contributions from researchers, industry practitioners and policy makers, covering perspectives from the U.S., Europe and Japan. It is based on the Automated Vehicles Symposium 2015 which was jointly organized by the Association of Unmanned Vehicle Systems International (AUVSI) and the Transportation Research Board (TRB) in Ann Arbor, Michigan, in July 2015. The topical spectrum includes, but is not limited to, public sector activities, human factors, ethical and business aspects, energy and technological perspectives, vehicle systems and transportation infrastructure. This book is an indispensable source of information for academic researchers, industrial engineers and policy makers interested in the topic of road vehicle automation.
