Demand estimation and chance-constrained fleet management for ride hailing

In autonomous Mobility on Demand (MOD) systems, customers request rides from a fleet of shared vehicles that can be automatically positioned in response to customer demand. Recent approaches to MOD systems have focused on environments where customers can only request rides through an app or by waiting at a station. This paper develops MOD fleet management approaches for ride hailing, where customers may instead request rides simply by hailing a passing vehicle, an approach of particular importance for campus MOD systems. The challenge for ride hailing is that customer demand is not explicitly provided as it would be with an app, but rather customers are only served if a vehicle happens to be located at the arrival location. This work focuses on maximizing the number of served hailing customers in an MOD system by learning and utilizing customer demand. A Bayesian framework is used to define a novel customer demand model which incorporates observed pedestrian traffic to estimate customer arrival locations with a quantification of uncertainty. An exploration planner is proposed which routes MOD vehicles in order to reduce arrival rate uncertainty. A robust ride hailing fleet management planner is proposed which routes vehicles under the presence of uncertainty using a chance-constrained formulation. Simulation of a real-world MOD system on MIT's campus demonstrates the effectiveness of the planners. The customer demand model and exploration planner are demonstrated to reduce estimation error over time and the ride hailing planner is shown to improve the fraction of served customers in the system by 73% over a baseline exploration approach.Ford-MIT AllianceFord Motor Compan

Uncertain demand prediction for guaranteed automated vehicle fleet performance

Mobility-on-demand (MoD) services offer a convenient and efficient transportation option, using technology to replace traditional modes. However, the flexibility of MoD services also presents challenges in controlling the system. One of the major issues is supply-demand imbalance, caused by uneven stochastic travel demand. To address this, it is crucial to predict the network behavior and proactively adapt to future travel demand.In this thesis, we present a stochastic model predictive controller (SMPC) that accounts for uncertainties in travel demand predictions. Our method make use of Gaussian Process Regression (GPR) to estimate passenger travel demand and predict time patterns with uncertainty bounds. The SMPC integrates these demand predictions into a receding horizon MoD optimization and uses a probabilistic constraining method with a user-defined confidence interval to guarantee constraint satisfaction. This result in a Chance Constrained Model Predictive Control (CCMPC) solution. Our approach has two benefits: incorporating travel demand uncertainty into the MoD optimization and the ability to relax the solution into a simpler Mixed-Integer Linear Program (MILP). Our simulation results demonstrate that this method reduces median customer wait time by 4% compared to using only the mean prediction from GPR. By adjusting the confidence bound, near-optimal performance can be achieved

Dynamic Stochastic Electric Vehicle Routing with Safe Reinforcement Learning

Dynamic routing of electric commercial vehicles can be a challenging problem since besides the uncertainty of energy consumption there are also random customer requests. This paper introduces the Dynamic Stochastic Electric Vehicle Routing Problem (DS-EVRP). A Safe Reinforcement Learning method is proposed for solving the problem. The objective is to minimize expected energy consumption in a safe way, which means also minimizing the risk of battery depletion while en route by planning charging whenever necessary. The key idea is to learn offline about the stochastic customer requests and energy consumption using Monte Carlo simulations, to be able to plan the route predictively and safely online. The method is evaluated using simulations based on energy consumption data from a realistic traffic model for the city of Luxembourg and a high-fidelity vehicle model. The results indicate that it is possible to save energy at the same time maintaining reliability by planning the routes and charging in an anticipative way. The proposed method has the potential to improve transport operations with electric commercial vehicles capitalizing on their environmental benefit

Data-driven analyses of future electric personal mobility

Personal mobility is moving towards the era of electrification. Adopting electric vehicles (EV) is widely regarded as an effective solution to energy crisis and air pollution. Many automakers have announced their roadmap to electrification in the next 1-2 decades. At the same time, limited electric range and insufficient charging infrastructure are still obstacles to EV large-scale adoption. However, with the emerging technologies of ride-hailing, connected vehicles, and autonomous vehicles, these obstacles are being solved effectively, and the EV market penetration is expected to increase significantly. Among the many kinds of electric mobility, electric taxis and personal battery electric vehicles (BEV) especially are gaining increasing popularity and acceptance among customers. This dissertation studies the future challenges of electric taxis and personal BEVs. First, this dissertation examines the BEV feasibility from the spatial-temporal travel patterns of taxis. The BEV feasibility of a taxi is quantified as the percentage of occupied trips that can be completed by BEVs during a year. It is found that taxis with certain characteristics are more suitable for switching to BEVs, such as fewer daily shifts, shorter daily driving distance, and higher likelihood to dwell at the borough of Manhattan. Second, we model and simulate the operations of electric autonomous vehicle (EAV) taxis. EAV taxis are dispatched by the optimization-based model and the neural network-based model. The neural network dispatch model is able to learn the optimal dispatch strategies and runs much faster. The EAV taxis dispatched by the neural network-based model can improve operational efficiency in term of less empty travel distance and smaller fleet size. Third, this dissertation proposes a cumulative prospect theory (CPT) based modeling framework to describe charging behavior of BEV drivers. A BEV mass-market scenario is constructed using 2017 National Household Travel Survey data. By applying the CPT-based charging behavior model, we examine the battery state-of-charge when drivers decide to charge their vehicles, charging timing and locations, and charging power demand profiles under the mass-market scenario. In addition, sensitivity analyses with respect to drivers’ risk attitude and public charger network coverage are conducted

Uber – everyone's private driver

Uber has a responsibility to continue development and targeted execution, as seen by its overwhelming interest in the advancement of its mobile application. The ability to connect drivers and passengers via mobile devices eliminates the need for Uber to have a physical presence in local areas where they expand operations, resulting in a highly scalable system with few barriers to further development. Uber’s historic development has been marked by controversies, technological advancements, protests, and bright new ideas. To assess their journey to the top of the mobility industry, general highlights about financials, historic developments, and the entrepreneurial transition from startup to scaleup will be showcased. Using the given information as groundwork, the case focuses on Uber’s marketing efforts over the years, how they utilized different strategies, why they used them and lastly – what it can teach students

Shared autonomous vehicle services: A comprehensive review

© 2019 Elsevier Ltd The actions of autonomous vehicle manufacturers and related industrial partners, as well as the interest from policy makers and researchers, point towards the likely initial deployment of autonomous vehicles as shared autonomous mobility services. Numerous studies are lately being published regarding Shared Autonomous Vehicle (SAV) applications and hence, it is imperative to have a comprehensive outlook, consolidating the existing knowledge base. This work comprehensively consolidates studies in the rapidly emerging field of SAV. The primary focus is the comprehensive review of the foreseen impacts, which are categorised into seven groups, namely (i) Traffic & Safety, (ii) Travel behaviour, (iii) Economy, (iv) Transport supply, (v) Land–use, (vi) Environment & (vii) Governance. Pertinently, an SAV typology is presented and the components involved in modelling SAV services are described. Issues relating to the expected demand patterns and a required suitable policy framework are explicitly discussed

Real-Time Data-Driven Optimization Algorithms for Modern Power Systems

Power systems have experienced significant transformations in recent years driven by the integration of new technologies, such as renewable generation and distributed energy resources (DERs). However, the variability of renewable generation and uncontrollable loads, along with the increasing grid-to-user interactions, pose severe challenges to the grid operation. Therefore, ensuring a reliable, safe, and optimal real-time operation of modern power systems requires new scalable optimization approaches that can cope with uncertainty and learn models in real-time. Towards this end, this dissertation presents contributions to the field of time-varying optimization, focusing on applications where renewable generation and controllable DERs interact with the grid. The dissertation is divided into two parts. Part I (Chapter 2 and 3) investigates time-varying optimization problems associated with a physical system and human-in-the-loop interactions. In this part, we study two settings. First, we consider a cost comprising a partially known time-varying function capturing performance metrics of the system, as well as unknown functions associated with users interacting with the physical system. Second, we feature an optimization problem where the objective is to minimize cost functions related to the individuals&rsquo; preferences, subject to timevarying constraints that capture the physical or operational limits of the network. Based on these time-varying optimization problems, we develop learning-based distributed online optimization algorithms. Our focus lies in the synthesis of first-order online methods, where feedback from the user is leveraged to learn the unknown functions concurrently with the execution of the online algorithm, and measurements of the output of the system are used to estimate the gradient and evaluate the partially known engineering functions. Part II (Chapter 4 - 7) focuses on optimization and control techniques for different applications where power systems interact with controllable DERs. First, we present an application of the theoretical framework of Part I to the real-time management of DERs. Second, we present a demand response application on commercial buildings. In this case, we introduce a predictive controller for a grid-interactive multi-zone building where the temperature dynamics are learned via a supervised learning technique. This controller uses the learned dynamics to solve a multi-objective problem to guarantee occupants&rsquo; comfort and energy efficiency during normal conditions and demand response events. Third, we study the estimation of sensitivity matrices in power grids with applications in transmission and distribution systems. By leveraging a low-rank approximation of certain classes of sensitivity matrices, we propose an online proximal-gradient algorithm based on a robust nuclear norm minimization problem to estimate linear sensitivities from measurements. Finally, we address a problem related to the charging schedule of electric vehicles (EVs). We consider a scenario where a ride-service provider utilizes a 100%-EV fleet to serve customers, while a power utility company aims to maximize the utilization of renewable generation at specific charging stations. To achieve this, we propose a novel mechanism that encourages EV charging during hours of high renewable generation while minimizing the impact on the quality of service for the ride-service provider.</p