Transport indicator analysis and comparison of 151 urban areas, based on open source data

Abstract

Introduction: The accurate analysis and comparison of transport indicators from a large variety of urban areas can help to evaluate the performance of different adopted transport policies. This paper attempts to determine important transport and socioeconomic indicators from 151 urban areas and 51 countries, based on comparable, directly observable open-source data such as OpenstreetMap (OSM) and the TomTom database. Analysis: This is the first, systematic indicator-analysis using recent, open source data from different urban areas around the world. The indicator road kilometers per person, sometimes cited as infrastructure accessibility is calculated by processing OSM data. Information on congestion levels have been taken from the TomTom database and socioeconomic data from various, publicly accessible databases. Relations between indicators are identified through correlations and regression models are calibrated, quantifying the relation between transport infrastructure and performance indicators. Three sub-categories of cities with different population sizes (small cities, large cities and metropolises) are defined and studied individually. In addition, a qualitative analysis is performed, putting five different indicators into relation. Results & Conclusions: The main results reconfirm previous findings but with a larger sample size and more comparable data. Good correlation values between infrastructure accessibility, socioeconomic indicators, and congestion levels are demonstrated. It is shown that cities with higher GDP have generally built more infrastructure which in turn reduces their congestion levels. In particular, for cities with low population density (above approximately 1500 inh. Per sq.km), more roads per inhabitant lead to lower congestion levels; cities with high population density have in general lower congestion levels if the rail infrastructure per person ratio is high. Furthermore, these cities increasing railways per person is more effective in reducing congestions than increasing road length per person.

A Macroscopic analysis of transport networks: The influence of network design on urban transportation performance

“…Some studies have also shown that an increase in alternative transport systems' infrastructure alleviates traffic road congestion. This effect has been demonstrated in [25,26] for rail transit systems, in [27,28] for public transportation in general and in [25] for cycling. These results suggest that the IA, in terms of infrastructure per capita, is an important factor for determining the usage of transportation systems.…”

“…A number of studies have found that an increase in road infrastructure increases car ownership and usage [20][21][22] and an increase in rail network infrastructure increases rail mode share, resulting from a modal shifts from car to public transport [22-]. It has been demonstrated that an increase in demand rises the travel delay time, while an increase in IA (as road length per inhabitant) decreases the travel delay time [25]. Some studies have also shown that an increase in alternative transport systems' infrastructure alleviates traffic road congestion.…”

“…An indicator for the amount of installed infrastructure in a city is the IA, which is expressed as infrastructure length per inhabitant as described in [22]. In this study, IA is chosen to be investigated since it is closely correlated with urban dynamics and transport performance [22,25]. Recent population densities and OSM data are used from a previous study [25]: The road infrastructure accessibility (RIA) is expressed in road length per inhabitant, the train infrastructure accessibility (TIA) is expressed in rail length per inhabitant and cycleway infrastructure accessibility (CIA) is expressed in bikeways per inhabitant.…”

“…In this study, IA is chosen to be investigated since it is closely correlated with urban dynamics and transport performance [22,25]. Recent population densities and OSM data are used from a previous study [25]: The road infrastructure accessibility (RIA) is expressed in road length per inhabitant, the train infrastructure accessibility (TIA) is expressed in rail length per inhabitant and cycleway infrastructure accessibility (CIA) is expressed in bikeways per inhabitant. As congestion is more challenging for larger cities, this study considers only large cities with over 1 million inhabitants.…”

Microsimulation of Residential Activity for Alternative Urban Development Scenarios: A Case Study on Brussels and Flemish Brabant

“…This has contributed to an unsustainable mobility model that is excessively dependent on private car use [2]. Predictably, Flemish roads are also among the most congested in Europe [3,4]. Urban sprawl and road congestion contribute significantly to negative environmental and health impacts [5,6], greenhouse gas emission [7], landscape and ecosystem degradation [8][9][10] and economic losses [3,11].…”