Addressing the "minimum parking" problem for on-demand mobility

TL;DR

This paper presents a data-driven framework to determine the minimum parking infrastructure needed for on-demand mobility in cities, revealing significant tradeoffs between parking reduction and increased vehicle travel.

Contribution

It introduces a systematic, shareability network-based methodology to quantify minimum parking needs and analyze the tradeoffs with vehicle kilometers traveled in urban settings.

Findings

Up to 86% parking reduction possible in Singapore.

Parking reduction leads to 24% increase in vehicle kilometers traveled.

Modest 57% parking reduction incurs only 1.3% more VKT.

Abstract

Parking infrastructure is pervasive and occupies large swaths of land in cities. However, on-demand (OD) mobility -- such as commercial services Uber, Grab or Didi -- has started reducing parking needs in urban areas around the world. This trend is expected to grow significantly with the advent of autonomous driving, which might render on-demand mobility predominant. Recent studies have started looking at expected parking reductions with on-demand mobility, but a systematic framework is still lacking. In this paper, we apply a data-driven methodology based on shareability networks to address what we call the "minimum parking" problem: what is the minimum parking infrastructure needed in a city for given on-demand mobility needs? While solving the problem, we also identify a critical tradeoff between two public policy goals: less parking means increased vehicle travel from deadheading between trips. By applying our methodology to the city of Singapore we discover that parking infrastructure reduction of up to 86% is possible, but at the expense of a 24% increase in traffic measured as vehicle kilometers travelled (VKT). However, a more modest 57% reduction in parking is achievable with only a 1.3% increase in VKT. We find that the tradeoff between parking and traffic obeys an inverse exponential law which is invariant with the size of the vehicle fleet, leading to a simple methodology to estimate aggregate parking demand in a city. Finally, we analyze parking requirements due to passenger pick-ups and show that increasing convenience produces a substantial increase in parking for passenger pickup/dropoff. The above mathematical findings can inform policy-makers, mobility operators, and society at large on the tradeoffs required in the transition towards pervasive on-demand mobility.