Capacity Impacts and Optimal Geometry of Automated Cars’ Surface Parking Facilities

You Kong,Scott Le Vine,Xiaobo Liu

doi:10.1155/2018/6908717

You Kong, Scott Le Vine + Show 1 more

Open Access

https://doi.org/10.1155/2018/6908717

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Abstract

The impact of Automated Vehicles (AVs) on urban geography has been widely speculated, though there is little quantitative evidence in the literature to establish the magnitude of such effects. To quantify the impact of the greater precision of automated driving on the spatial efficiency of off-street parking facilities, we develop a mixed integer nonlinear model (solved via a branch-and-cut approach) and present comparisons against industry-standard requirements for human-driving operation. We demonstrate that gains on the order of 40–50% in spatial efficiency (parking spaces per unit area) are in principle achievable while ensuring that each parked vehicle is independently accessible. We further show that the large majority of these efficiency gains can be obtained under current automotive engineering practice in which only the front two wheels pivot. There is a need for standardized methods that take the parking supply of a city as an input and calculate both the aggregate (citywide) efficiency impacts of automated driving and the spatial distribution of the effects. This study is intended as an initial step towards this objective.

Highlights

Automation technologies for automobiles and other road vehicles are becoming increasingly available commercially, with further advances anticipated in coming years in both fundamental technology and its manifestation as consumer products
Toing/froing Translation (b) spaces designed for human drivers. (b) shows analogous curves for facilities designed for Automated Vehicles (AVs) usage, using each of the four maneuvers. For both human and AV operations, it can be see that spatial efficiency increases with facility size and that the sensitivity of efficiency to 1󸀠-increment increases in facility size decreases with facility size
In this study we develop and implement a mixed integer nonlinear optimization model to evaluate the impact of AVs’ precise physical maneuvering on the spatial efficiency of surface parking facilities

Summary

Introduction

Automation technologies for automobiles and other road vehicles are becoming increasingly available commercially, with further advances anticipated in coming years in both fundamental technology and its manifestation as consumer products. This paper’s specific focus is the impacts of AVs on the spatial efficiency (vehicles stored per unit area) of parking facilities (NB: we neglect the efficiency of internal circulation provided that the maneuvers can in principle be performed, in order to focus exclusively on spatial efficiency) This is an important research question, because any increase in the spatial efficiency of vehicle storage would affect the density of land use and the valuation of urban land, independent of any change in the location of vehicle storage (see previous paragraph). Our objective in this study is to provide a contribution by directly addressing the narrow research question of how automated cars will affect vehicle storage capacity and thereby provide an incremental advance towards the broader research question “How will automated cars affect urban geography via the mechanism of impacts to the vehicle-parking ecosystem?” To address this topic, in this study we develop a mixed integer nonlinear programming (MINLP) approach to compare the spatial efficiency of human-driven and AV surface parking facilities and subsequently implement it.

Background

Methods

Numerical Analysis

Conclusions

Findings

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