The present study focuses on the relationship between the Covid-19 pandemic and mobility patterns. By using data from an international survey, transport users’ socio-demographic features and travel characteristics during the pre-pandemic and pandemic time are analyzed.Afterward, the pandemic commuting travel behavior is modeled. The multinomial model shows that public transport users are 31.5, 10.6, and 6.9 times more likely to change their commuting transportation mode than car users, motorcycle users, and walkers, respectively. The results demonstrate that travel distance and income level are the two most influential factors in pandemic decision-making. These results confirm the reflection of spatial-economic inequalities during the pandemic. Active modes, motorcycle, and personal car are perceived by the participants as the least risky urban transportation modes during the pandemic. Thus, a considerable growth in individual transportation modes, up to 26% increase for commuting and up to 15% for leisure activities, can be recognized.

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 socio-economic indicators from 151 urban areas and 51 countries, based on comparable, directly observable open-source data such as OpenstreetMap (OSM) and the TomTom database. 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 socio-economic 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. The main results reconfirm previous findings but with a larger sample size and more comparable data. Good correlation values between infrastructure accessibility, socio-economic 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.

Socio-technical systems have a very complex structure presenting multidimensional co-evolutionary dynamics between people and technical factors. Each urban area has a different sociocultural structure, where the built environment and user choices vary. Understanding how urban transportation is shaped by socio-technical factors as well as how these factors affect transportation users and system performance is important to support sustainable transportation planning. This paper aims to provide a holistic view of socio-technical factors shaping urban transportation for the use of transport planners and decision-makers. The current review focuses on understanding which alternative transport modes can be sustainable under specific socio-technical conditions and how to attract travelers toward these sustainable modes. Moreover, under which socio-technical conditions an urban transport system can minimize its socio-economic costs and environmental impacts are investigated. Furthermore, the interrelations among system dynamics, their influence on transport users as well as on system externalities are identified and integrated as a system model perspective for a holistic view.

Introduction: As the global warming threat has become more concrete in recent years, there is a need to update transport energy consumptions of cities and to understand how they relate to population density and transport infrastructure. Transportation is one of the major sources of global warming and this update is an important warning for urban planners and policy makers to take action in a more consistent way. Analysis: This paper estimates and analyzes the passenger transport energy per person per year with a large and diverse sample set based on comparable, directly observable open-source data of 57 cities, distributed over 33 countries. The freight transport energy consumption, which accounts for a large portion of urban transport energy, is not considered. The main focus of the analysis is to establish a quantitative relation between population density, transport infrastructure and transport energy consumption. Results: In a first step, significant linear relations have been found between road length per inhabitant, the road infrastructure accessibility (RIA) and private car mode share as well as between RIA and public transport mode share. Results show further relation between travel distance, population density and RIA. In a second step, a simplified model has been developed that explains the non-linear relation between the population density and RIA. Finally, based on this relation and the above findings, a hyperbolic function between population density and transport energy has been calibrated, which explains the rapid increase of transport energy consumption of cities with low population density. Conclusions: The result of the this study has clearly identified the high private car mode share as main cause for the high transport energy usage of such cities, while the longer average commute distance in low-population density cities has a more modest influence on their transport energy consumption.

Introduction—culture is an interpretation code of societies, which may explain common preferences in a place. Prediction of alternative transport systems, which could be adopted in a city at peace can help urban transport planners and policy makers adjust urban environments in a more sustainable manner. This paper attempts to investigate the role of Hofstede’s culture dimensions (HCD) on urban travel patterns in 87 urban areas and 41 countries. Analysis—this is the first, systematic analysis investigating the effect of culture on urban travel patterns with open source data from different urban areas around the world. The relationship between HCD and some urban travel patterns such as mode choices (individual transportation and public transportation), car ownership, and infrastructure accessibility (road infrastructure per capita) was demonstrated. In addition, the relationship between culture and some demographic indicators (population density and GDP per capita) closely associated with travel choices are checked. The relations between indicators were identified through correlations and regression models, and calibrated to quantify the relation between indicators. Results and Conclusions—good correlation values between Hofstede’s fundamental culture dimension: individualism/collectivism (IND/COL) and urban travel patterns were demonstrated with a reasonably good fit. The analysis showed that countries with higher individualism build more individualistic transport-related environments, which in turn result in more driving. On the other hand, collective nations tend to use more public transportation. There is significant evidence that, in the case of nations, an increase in tree culture dimensions: collectivism, uncertainty, and masculinity, results in greater usage of public transport.

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