Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas

2016 ◽  
Vol 45 (1) ◽  
pp. 143-158 ◽  
Author(s):  
Daniel J. Fagnant ◽  
Kara M. Kockelman
2020 ◽  
pp. 1-11
Author(s):  
Billi Sam

New technologies have revolutionized nearly every aspect of human existence, including the ways that firms market products and services to consumers. Along with now familiar innovations like the Internet, greater computing capacity, mobile devices and applications, and social media, more radical innovations are emerging. Related to artificial intelligence (AI) (Davenport 2018), the Internet of things (IoT) (Hoffman and Novak 2018), and robotics (Mende et al. 2019), these technological advances are exerting profound effects on the practice of marketing. Thus, it should come as no surprise that firms across nearly every business sector (e.g., retailing, manufacturing, healthcare, financial) keep steadily increasing their technology spending, driven to reach various objectives. For example, many manufacturing firms seek cost savings through mechanized and robotic production processes, which both limit labor costs and increase production efficiencies. Retailers and service firms devote more spending to online, mobile, and social media platforms in attempts to better communicate and connect with customers (both current and potential), thereby increasing their revenues. Early adopters of each new technology change the rules of the game (e.g., Grewal 2019). Consider Amazon as an example: It leads the pack in adopting a host of technological innovations. Its fulfillment centers feature robotic technologies to assist workers, increase efficiencies, and drive down costs. Amazon is actively experimenting with drone delivery (a service it calls Prime Air). Furthermore, it is known for its predictive analytic capabilities, uses AI to establish and maintain its sophisticated personalized recommendation system, and has developed an innovative, patented, one-click ordering system. Ride-sharing firms like Uber and Lyft similarly have revolutionized traditional taxi and limousine industries, as well as providing novel work opportunities and greater customer control over their rides. Such groundbreaking shifts also depend heavily on the available technology, including geofencing and social media ratings capabilities. Newer options, such as autonomous vehicles, are on the horizon and likely to shake up the ride-sharing industry and ultimately the entire transportation industry. Waymo (Google’s self-driving vehicle), Tesla, and Volvo are all racing to introduce the first driverless test vehicles to create value for consumers and business customers.


2020 ◽  
Author(s):  
Amanda Camacho Novaes de Oliveira ◽  
Amit Bhaya ◽  
Daniel Ratton Figueiredo

Public transportation in urban centers is of fundamental importance, being a widely investigated topic. Smart autonomous vehicles (SAVs) present a great potential in revolutionizing transportation systems in urban areas, providing more flexible and efficient solutions. This work proposes a new transportation model based on SAVs that provides a station-based, point-to-point service, with distributed coordination. The model offers two different modes of operation, one with exclusive rides, and the other with ride sharing between clients. A simulator has been developed, through which the system’s characteristics are analyzed, and the two modes of operation compared. It was observed that with the increase in the system client demand over time the ride sharing mode gets more efficient than the mode with exclusive rides, both in terms to the average time required to deliver clients and the total distance traveled.


2017 ◽  
Vol 4 (2) ◽  
pp. 611-618 ◽  
Author(s):  
Ahmed B. T. Sherif ◽  
Khaled Rabieh ◽  
Mohamed M. E. A. Mahmoud ◽  
Xiaohui Liang

2017 ◽  
Vol 114 (3) ◽  
pp. 462-467 ◽  
Author(s):  
Javier Alonso-Mora ◽  
Samitha Samaranayake ◽  
Alex Wallar ◽  
Emilio Frazzoli ◽  
Daniela Rus

Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). We present a more general mathematical model for real-time high-capacity ride-sharing that (i) scales to large numbers of passengers and trips and (ii) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. We quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ∼3 million rides extracted from the New York City taxicab public dataset. Our experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems.


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