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.
Highlights
Growing metropolitan cities with strong economies continue to sprawl into suburbs with the desire for private transportation and on-time access to goods and services placing pressure on road networks
In recent years, crowdsourced data has become increasingly popular among transport agencies as it provides easy accessibility to the way individuals travel in a road network
This study shows the utility of crowdsourced data to determine traffic conditions and, to the best of our knowledge, proposes a standard data source which facilitates comparison of traffic congestion across multiple cities of the world
Summary
Growing metropolitan cities with strong economies continue to sprawl into suburbs with the desire for private transportation and on-time access to goods and services placing pressure on road networks. It can be argued that the proportion of users whose travel data is recorded and processed by these apps is relatively small resulting in a potential bias in representing the real traffic situation These studies ( still only a handful) demonstrate the feasibility of using Maps APIs to reflect traffic patterns that are recorded by the traditional techniques (which, by and large, are considered to be the accurate reflection of the real-world traffic conditions). Despite the robust methodologies and meticulous implementation of those congestion matrices, the high costs associated with obtaining the data have proven to be the bottleneck in traffic congestion mitigation and management where most of the rapidly growing cities are looking for monitoring the traffic conditions around the clock Using these congestion metrics to measure congestion, in not a single city but many at once has been a particular challenge in this line of research. We analyze the spatial dispersion of traffic congestion in the network and discuss the stability of network equilibrium under various congestion levels and traffic conditions
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