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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.

For transport planners and land use practitioners, many are the qualitative guidelines provided regarding the location and quality of separated bicycle facilities. When separated bicycle facilities are poorly designed or placed in less than optimum locations, their intended use is less than anticipated. An interesting element in the evaluation of bicycle facilities that have received less attention revolve around the disturbance due to the presence of other users on cyclists way. Other users consists in cyclists and pedestrians for off-street bicycle facilities, and motorized vehicles on the roadway. This study focuses on quantifying the role of disturbances encountered on separated cycling facilities, compared to disturbances from cycling mixed with traffic, assuming cyclists speed as a performance measure and analysing the cyclist speed reductions from different types of disturbances. Collecting data on three segments of Bologna's cycling network (Italy), we measured the frequency, type, and speed reduction attributed to different types of disturbances. The data collected shows that pedestrian disturbances on the separated facility are highly frequent but associated with moderate speed reductions, while disturbances in the mixed traffic environment can be relatively fewer but have more severe speed reductions. Moreover, our results suggest that design elements of separated facilities can play a role in affecting the frequency, type, and severity of disturbances. This work helps lay the foundation for outlining the existing relationship between bicycle travel speeds and non stationary disturbances.

A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built and validated for the city of Bologna (Italy) during the morning peak hour. Large-scale microsimulations enable the evaluation of city-wide effects of novel and complex transport technologies and services, such as intelligent traffic lights or shared autonomous vehicles. Large-scale microsimulations can be seen as an interdisciplinary project where transport planners and technology developers can work together on the same scenario; big data from OpenStreetMap, traffic surveys, GPS traces, traffic counts and transit details are merged into a unique transport scenario. The employed activity-based demand model is able to simulate and evaluate door-to-door trip times while testing different mobility strategies. Indeed, a utility-based mode choice model is calibrated that matches the official modal split. The scenario is implemented and analyzed with the software SUMOPy/SUMO which is an open source software, available on GitHub. The simulated traffic flows are compared with flows from traffic counters using different indicators. The determination coefficient has been 0.7 for larger roads (width greater than seven meters). The present work shows that it is possible to build realistic microsimulation scenarios for larger urban areas. A higher precision of the results could be achieved by using more coherent data and by merging different data sources.