City-scale car traffic and parking density maps from Uber Movement travel time data

Arsam Aryandoust,Oscar van Vliet,Anthony Patt

doi:10.1038/s41597-019-0159-6

Abstract

Car parking is of central importance to congestion on roads and the urban planning process of optimizing road networks, pricing parking lots and planning land use. The efficient placement, sizing and grid connection of charging stations for electric cars makes it even more important to know the spatio-temporal distribution of car parking densities on the scale of entire cities. Here, we generate car parking density maps using travel time measurements only. We formulate a Hidden Markov Model that contains non-linear functional relationships between the changing average travel times among the zones of a city and both the traffic activity and flow direction probabilities of cars. We then sample the traffic flow for 1,000 cars per city zone for each city from these probability distributions and normalize the resulting spatial parking distribution of cars in each time step. Our results cover the years 2015–2018 for 34 cities worldwide. We validate the model for Melbourne and reach about 90% accuracy for parking densities and over 93% for circadian rhythms of traffic activity.

Highlights

Car parking is of central importance to congestion on roads and their social, environmental and economic impacts on society
We explore if car parking density maps on the scale of entire cities can be estimated from travel time measurements among different zones of a city only
If temporal car parking density maps can be modeled on the scale of an entire city, given travel time measurements between the zones of that city only

Summary

Introduction

Car parking is of central importance to congestion on roads and their social, environmental and economic impacts on society. Knowing where cars are parked at what times could support urban planning in the process of optimizing road networks, pricing parking lots and planning land use. The electrification of the mobility sector makes it even more important to know where cars are parked at what times. At times and locations where large numbers of cars are parked with high density and must charge simultaneously for their upcoming trips, their additional electricity consumption can cause stresses to the local grid[4]. For an efficient placement, sizing and grid connection of charging stations, it is important to know the spatio-temporal distribution of car parking densities on the scale of entire cities[8,9,10]

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Conclusion