Dynamic autonomous vehicle fleet operations: Optimization-based strategies to assign AVs to immediate traveler demand requests

2018 ◽  
Vol 92 ◽  
pp. 278-297 ◽  
Author(s):  
Michael Hyland ◽  
Hani S. Mahmassani
Keyword(s):  
Autonomous Vehicle ◽  
Vehicle Fleet ◽  
Operations Optimization ◽  
Fleet Operations

scholarly journals An Initial Investigation of the Effects of a Fully Automated Vehicle Fleet on Geometric Design

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
John Khoury ◽  
Kamar Amine ◽  
Rima Abi Saad
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Real Life ◽  
Geometric Design ◽  
Design Elements ◽  
Sight Distance ◽  
Human Driver ◽  
Projected Change ◽  
Vehicle Fleet ◽  
Stopping Sight Distance

This paper investigates the potential changes in the geometric design elements in response to a fully autonomous vehicle fleet. When autonomous vehicles completely replace conventional vehicles, the human driver will no longer be a concern. Currently, and for safety reasons, the human driver plays an inherent role in designing highway elements, which depend on the driver’s perception-reaction time, driver’s eye height, and other driver related parameters. This study focuses on the geometric design elements that will directly be affected by the replacement of the human driver with fully autonomous vehicles. Stopping sight distance, decision sight distance, and length of sag and crest vertical curves are geometric design elements directly affected by the projected change. Revised values for these design elements are presented and their effects are quantified using a real-life scenario. An existing roadway designed using current AASHTO standards has been redesigned with the revised values. Compared with the existing design, the proposed design shows significant economic and environmental improvements, given the elimination of the human driver.

Implementation of an Intralogistics Routing-Service Basing on Decentralized Workspace Digitization

2018 ◽  
Vol 882 ◽  
pp. 90-95 ◽  
Author(s):  
Michael Scholz ◽  
Xu Zhang ◽  
Jörg Franke
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Routing Algorithm ◽  
Automated Guided Vehicles ◽  
Calculation Time ◽  
Vehicle Type ◽  
Moving Obstacles ◽  
Computing Unit ◽  
Vehicle System ◽  
Vehicle Fleet

The paper presents an intralogistics routing-service for autonomous and versatile transport vehicles. An infrastructural sensor digitize the workspace of the vehicle and is the basis for the vehicle-specific routing plan. Nowadays, a central computing unit allocates transportation task to a known number of automated guided vehicles, which are usually of the same type. Furthermore, this device generates a routing appropriate to the dimensions and the kinematic gauge of the vehicle fleet. The pathing for each specific vehicle is calculated and the result is send to the different entities. The approach of this paper bases on the digitization of the workspace with a ceiling camera, which divides the scenery into moving obstacles and an adaptive background picture. A central computing unit receives the background picture of several cameras and stitch them together to an overview of the entire workspace, e.g. a production hall. Furthermore, the approach includes the development of automated guided vehicles to versatile autonomous vehicles, were each entity is able to calculate the pathing on a given routing plan. A fleet of versatile autonomous vehicles consists of vehicles with task-specific dimensions and kinematic gauges. Therefore, each vehicle needs its own routing-plan. The solution is that each vehicles uses a vehicle parameter-server and register itself with these parameters at the routing unit. This unit is calculating a routing-plan for each specific vehicle dimension and gauge and providing it. When getting a new task, the vehicles uses this routing-plan to do the pathing. The routing-algorithm is implemented inside the service-layer of the versatile autonomous vehicle system. This approach lowers the amount of data, which is send between the service layer and the transportation entities by reducing the information of the workspace to the possible routes of each specific vehicle. Furthermore, the calculation time for routing and pathing is lowered, because each vehicle is calculating its task-specific path, but the route-map is calculated once for each vehicle-type by the routing-service.

Comparison of Light Rail Streetcar Against Shared Autonomous Vehicle Fleet for Brooklyn–Queens Connector in New York City

2017 ◽  
Vol 2650 (1) ◽  
pp. 142-151 ◽  
Author(s):  
Lucas Mestres Mendes ◽  
Manel Rivera Bennàssar ◽  
Joseph Y. J. Chow
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Light Rail ◽  
Rail System ◽  
Total Travel Time ◽  
Vehicle Fleet ◽  
Event Based

Policy makers predict that autonomous vehicles will have significant market penetration in the next decade or so. In several simulation studies shared autonomous vehicle fleets have been shown to be effective public transit alternatives. This study compared the effectiveness of a shared autonomous vehicle fleet with an upcoming transit project proposed in New York City, the Brooklyn–Queens Connector light rail project. The study developed an event-based simulation model to compare the performance of the shared autonomous vehicle system with the light rail system under the same demand patterns, route alignment, and operating speeds. The simulation experiments revealed that a shared autonomous vehicle fleet of 500 vehicles of 12-person capacity (consistent with the EZ10 vehicle) would be needed to match the 39-vehicle light rail system if operated as a fixed-route system. However, as a demand-responsive system, a fleet of only 150 vehicles would lead to the same total travel time (22 min) as the 39-vehicle fleet light rail system. Furthermore, a fleet of 450 12-person vehicles in a demand-responsive operation would have the same average wait times while reducing total travel times by 36%. The implications for system resiliency, idle vehicle allocation, and vehicle modularity are discussed.

Utilizing Automated Report Generation and Data Acquisition Tools to Guide Fuel Cell Vehicle Fleet Operations

2008 ◽  
Author(s):  
Tim McGuire ◽  
Taylor Roche ◽  
Andreas Weinberger ◽  
Peter Friebe ◽  
Juergen Friedrich
Keyword(s):  
Fuel Cell ◽  
Data Acquisition ◽  
Fuel Cell Vehicle ◽  
Report Generation ◽  
Vehicle Fleet ◽  
Fleet Operations

A German Passenger Car and Heavy Vehicle Stock Model: Towards an Autonomous Vehicle Fleet

2018 ◽  
Vol 2672 (46) ◽  
pp. 55-63 ◽  
Author(s):  
Martin Hartmann ◽  
Peter Vortisch
Keyword(s):  
Autonomous Vehicle ◽  
Road User ◽  
Heavy Vehicle ◽  
Automated Vehicles ◽  
Passenger Car ◽  
Regulatory Policies ◽  
Vehicle Fleet ◽  
Stock Model ◽  
Vehicle Purchase ◽  
Automation Technology

Automated vehicles are becoming a reality. Many pilot projects have already begun demonstrating the technological capabilities, as public authorities now allow the testing of automated vehicles in real traffic. To smooth the transition from a conventional to an automated fleet, effective fiscal and regulatory policies must be developed by governmental agencies. But at what rate will automated vehicles actually be adopted, and what automation technology will be available for use in new cars joining the national fleet? A national vehicle stock model can be used to answer these questions and to observe the aggregate impact of governmental policies on individual vehicle purchase decisions. In this paper, we present a passenger car and heavy vehicle stock cohort model that forecasts the diffusion of automation technology in Germany. The model uses national data on vehicle stock and vehicle utilization patterns on German freeways and predicts market shares of generic automation levels in predefined instances of a trend scenario. Results point toward market saturation of automated vehicles beyond 2050, with almost 90% of the passenger car fleet being classified as at least partially automatized by this date. The results also suggest that technology diffusion will be faster in the heavy vehicle fleet than in the passenger car fleet. This implies a positive correlation between emission-linked road user charges for heavy vehicles on the freeway network and the renewal rate of the heavy vehicle fleet. The forecast shares of automated vehicles can be used as an input for traffic flow simulations or as a basis for those infrastructure measures and traffic policies that are sensitive to the share of automated vehicles.

scholarly journals Operations of Shared Autonomous Vehicle Fleet for Austin, Texas, Market

2016 ◽  
Vol 2563 (1) ◽  
pp. 98-106 ◽  
Author(s):  
Daniel J. Fagnant ◽  
Kara M. Kockelman ◽  
Prateek Bansal
Keyword(s):  
Autonomous Vehicle ◽  
Vehicle Fleet
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