Characterizing multicity urban traffic conditions using crowdsourced data

Abstract

Road traffic congestion continues to manifest and propagate in cities around the world. The recent technological advancements in intelligent traveler information have a strong influence on the route choice behavior of drivers by enabling them to be more flexible in selecting their routes. Measuring traffic congestion in a city, understanding its spatial dispersion, and investigating whether the congestion patterns are stable (temporally, such as on a day-to-day basis) are critical to developing effective traffic management strategies. In this study, with the help of Google Maps API, we gather traffic speed data of 29 cities across the world over a 40-day period. We present generalized congestion and network stability metrics to compare congestion levels between these cities. We find that (a) traffic congestion is related to macroeconomic characteristics such as per capita income and population density of these cities, (b) congestion patterns are mostly stable on a day-to-day basis, and (c) the rate of spatial dispersion of congestion is smaller in congested cities, i.e. the spatial heterogeneity is less sensitive to increase in delays. This study compares the traffic conditions across global cities on a common datum using crowdsourced data which is becoming readily available for research purposes. This information can potentially assist practitioners to tailor macroscopic network congestion and reliability management policies. The comparison of different cities can also lead to benchmarking and standardization of the policies that have been used to date.

Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations

“…Then, each of these network-only images is converted into a matrix, with each pixel inside turned into a normalized value in [0.0, 1.0] according to color of that pixel's congestion level. Although the derived properties of traffic flow, such as the congestion intensity [32] and congestion index [33], in the existing literature are defined on wider ranges of values, such value ranges are inappropriate as direct inputs to deep learning models for the prediction of traffic congestion, because without being normalized they cause a problem known as internal covariate shift [34]. Such matrices form a set of original traffic congestion matrices.…”

A simple crowdsourced delay-based traffic signal control

“…Navigation companies like Google, Here, and TomTom extensively utilize these forms of data collected from the crowd to provide real-time traffic information back to their customers. These types of data, also called "crowdsourced data" have been used to understand mobility behavior [7,8] and congestion patterns [9]. The ubiquity and accuracy [9] of this type of crowdsourced data has made this high-quality real-time traffic data easily accessible at a substantially cheaper cost.…”

Exploring the Potential of Web Based Information of Business Popularity for Supporting Sustainable Traffic Management

“…The API calculates a representative speed value from the available crowdsourced data on a road link at any time of the day and estimated a congestion index for 29 major cities. Google speed data reflects loop detector speed both at the corridor level and at smaller of road sections within a corridor (Nair et al, 2019).…”