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Various non-pharmaceutical interventions were adopted by countries worldwide in the fight against the COVID-19 pandemic with adverse socioeconomic side effects, which raises the question about their differential effectiveness. We estimate the average dynamic effect of each intervention on the incidence of COVID-19 and on people’s whereabouts by developing a statistical model that accounts for the contemporaneous adoption of multiple interventions. Using daily data from 175 countries, we show that, even after controlling for other concurrent lockdown policies, cancelling public events , imposing restrictions on private gatherings and closing schools and workplaces had significant effects on reducing COVID-19 infections. Restrictions on internal movement and public transport had no effects because the aforementioned policies, imposed earlier on average, had already de facto reduced human mobility. International travel restrictions, although imposed early, had a short-lived effect failing to prevent the epidemic from turning into a pandemic because they were less stringent. We interpret the impact of each intervention on containing the pandemic using a conceptual framework which relies on their effects on human mobility behaviors in a manner consistent with time-use and epidemiological factors.

A discussion of the sustainability and travel behaviour impacts of ride-hailing is provided, based on an extensive literature review of studies from both developed and developing countries. The effects of ride-hailing on vehicle-kilometres travelled (VKT) and traffic externalities such as congestion, pollution and crashes are analysed. Modal substitution, user characterisation and induced travel outputs are also examined. A summary of findings follows. On the one hand, ride-hailing improves the comfort and security of riders for several types of trips and increases mobility for car-free households and for people with physical and cognitive limitations. Ride-hailing has the potential to be more efficient for rider-driver matching than street-hailing. Ride-hailing is expected to reduce parking requirements, shifting attention towards curb management. On the other hand, results on the degree of complementarity and substitution between ride-hailing and public transport and on the impact of ride-hailing on VKT are mixed; however, there is a tendency from studies with updated data to show that the ride-hailing substitution effect of public transport is stronger than the complementarity effect in several cities and that ride-hailing has incremented motorised traffic and congestion. Early evidence on the impact of ride-hailing on the environment and energy consumption is also concerning. A longer-term assessment must estimate the ride-hailing effect on car ownership. A social welfare analysis that accounts for both the benefits and costs of ride-hailing remains unexplored. The relevance of shared rides in a scenario with mobility-as-a-service subscription packages and automated vehicles is also highlighted.

We conduct an artefactual field experiment using a diversified sample of passengers of public transportation to study attitudes toward dishonesty. We find that the diversity of behavior in terms of (dis)honesty in laboratory tasks and in the field correlate. Moreover, individuals who have just been fined in the field behave more honestly in the lab than the other fare dodgers, except when context is introduced. Overall, we show that simple tests of dishonesty in the lab can predict moral firmness in life, although fraudsters who care about social image cheat less when behavior can be verified ex post by the experimenter. Data and the online appendix are available at https://doi.org/10.1287/mnsc.2016.2616 . This paper was accepted by Uri Gneezy, behavioral economics .

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we give a general framework for the algorithms categorized under various settings: unsupervised versus (semi-)supervised approaches, for static versus dynamic graphs, for attributed versus plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly attribution and highlight the major techniques that facilitate digging out the root cause, or the ‘why’, of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field.

Investigation of premature mortality by seven emission sources of atmospheric pollutants shows that outdoor air pollution, mostly by fine particulate matter, leads to more than three million premature deaths per year worldwide, which could double by 2050. Links between air pollution and premature mortality Premature mortality can be linked to a wide range of causes including the effect of outdoor air pollutants such as ozone and fine particulate matter on human health. This paper investigates the link between premature mortality and seven sources of atmospheric pollutants in urban and rural environments. Jos Lelieveld et al . find that outdoor air pollution, mostly by fine particulate matter, leads to around three million premature deaths per year worldwide. Emissions from residential energy use such as heating and cooking, prevalent in India and China, have the largest effect on premature mortality globally. In large areas of the United States and a few other countries, emissions from traffic and power generation are important, whereas in the eastern USA, Europe, Russia and East Asia agricultural emissions make the largest relative contribution to fine particulate matter, with the overall health effect depending on assumptions regarding particle toxicity. Assessment of the global burden of disease is based on epidemiological cohort studies that connect premature mortality to a wide range of causes 1 , 2 , 3 , 4 , 5 , including the long-term health impacts of ozone and fine particulate matter with a diameter smaller than 2.5 micrometres (PM 2.5 ) 3 , 4 , 5 , 6 , 7 , 8 , 9 . It has proved difficult to quantify premature mortality related to air pollution, notably in regions where air quality is not monitored, and also because the toxicity of particles from various sources may vary 10 . Here we use a global atmospheric chemistry model to investigate the link between premature mortality and seven emission source categories in urban and rural environments. In accord with the global burden of disease for 2010 (ref. 5 ), we calculate that outdoor air pollution, mostly by PM 2.5 , leads to 3.3 (95 per cent confidence interval 1.61–4.81) million premature deaths per year worldwide, predominantly in Asia. We primarily assume that all particles are equally toxic 5 , but also include a sensitivity study that accounts for differential toxicity. We find that emissions from residential energy use such as heating and cooking, prevalent in India and China, have the largest impact on premature mortality globally, being even more dominant if carbonaceous particles are assumed to be most toxic. Whereas in much of the USA and in a few other countries emissions from traffic and power generation are important, in eastern USA, Europe, Russia and East Asia agricultural emissions make the largest relative contribution to PM 2.5 , with the estimate of overall health impact depending on assumptions regarding particle toxicity. Model projections based on a business-as-usual emission scenario indicate that the contribution of outdoor air pollution to premature mortality could double by 2050.

Given the rapid recent trend of urbanization, a better understanding of how urban infrastructure mediates socioeconomic interactions and economic systems is of vital importance. While the accessibility of location-enabled devices as well as large-scale datasets of human activities, has fueled significant advances in our understanding, there is little agreement on the linkage between socioeconomic status and its influence on movement patterns, in particular, the role of inequality. Here, we analyze a heavily aggregated and anonymized summary of global mobility and investigate the relationships between socioeconomic status and mobility across a hundred cities in the US and Brazil. We uncover two types of relationships, finding either a clear connection or little-to-no interdependencies. The former tend to be characterized by low levels of public transportation usage, inequitable access to basic amenities and services, and segregated clusters of communities in terms of income, with the latter class showing the opposite trends. Our findings provide useful lessons in designing urban habitats that serve the larger interests of all inhabitants irrespective of their economic status.

To systematically study the implications of additional information about routes provided to certain users (e.g., via GPS-based route guidance systems), we introduce a new class of congestion games in which users have differing information sets about the available edges and can only use routes consisting of edges in their information set. After defining the notion of an information-constrained wardrop equilibrium (ICWE) for this class of congestion games and studying its basic properties, we turn to our main focus: whether additional information can be harmful (in the sense of generating greater equilibrium costs/delays). We formulate this question in the form of an informational Braess’ paradox (IBP), which extends the classic Braess’ paradox in traffic equilibria and asks whether users receiving additional information can become worse off. We provide a comprehensive answer to this question showing that in any network in the series of linearly independent (SLI) class, which is a strict subset of series-parallel networks, the IBP cannot occur, and in any network that is not in the SLI class, there exists a configuration of edge-specific cost functions for which the IBP will occur. In the process, we establish several properties of the SLI class of networks, which include the characterization of the complement of the SLI class in terms of embedding a specific set of networks, and also an algorithm that determines whether a graph is SLI in linear time. We further prove that the worst-case inefficiency performance of ICWE is no worse than the standard Wardrop equilibrium.

The growth of literature in the field of quality of service in the public transport (PT) sector shows increasing concern for a better understanding of the factors affecting service quality (SQ) in PT organizations and companies. A large variety of approaches to SQ have been developed in recent years owing to the complexity of the concept; the broad range of attributes required to evaluate SQ; and the imprecision, subjectivity, and heterogeneous nature of the data used to analyze it. Most of these approaches are based on customer satisfaction surveys. This paper seeks to summarize the evolution of research and current thinking as it relates to the different methodological approaches for SQ evaluation in the PT sector over the years and to provide a discussion of future directions.

Background: The transportation sector has wide-ranging effects on the human societies. Public transportation has a key and undeniable role in the lives of people in society and affects important aspects such as economic, social, cultural and environmental. Therefore, assessing the sustainability of public transportation in urban areas can be considered as a challenge for transportation policy makers. Methods: In this study, a novel hybrid multi-criteria decision-making (MCDM) method is proposed to evaluate sustainable public transportation in Tehran. Evaluation criteria have been identified using the literature and experts’ opinion. The proposed method integrates the fuzzy best-worst method (FBWM) and the multi-attributive border approximation area comparison (MABAC) method. a group of three experts determined the weight and importance of each criterion using FBWM. The MABAC method was then used to rank sustainable public transport alternatives. Results: The results indicate the reliability of the proposed method. Also, we can see that the results are congruent with the actual conditions of public transportation. The studied alternatives have been evaluated, and according to the decision criteria, metro and e-hailing have been the most sustainable alternatives. It is noteworthy that the economic and financial sustainability, service availability and environment sustainability have been the most important criteria. Conclusion: The proposed framework in this study can be used by public transportation planners and policy makers to identify sustainable options in order to consider facilities and implement incentive policies in this field. Also, the results of the proposed method used in this study can be used as a suitable guidance to assess the sustainability of public transportation.

Cities worldwide are pursuing policies to reduce car use and prioritise public transit (PT) as a means to tackle congestion, air pollution, and greenhouse gas emissions. The increase of PT ridership is constrained by many aspects; among them, travel time and the built environment are considered the most critical factors in the choice of travel mode. We propose a data fusion framework including real-time traffic data, transit data, and travel demand estimated using Twitter data to compare the travel time by car and PT in four cities (São Paulo, Brazil; Stockholm, Sweden; Sydney, Australia; and Amsterdam, the Netherlands) at high spatial and temporal resolutions. We use real-world data to make realistic estimates of travel time by car and by PT and compare their performance by time of day and by travel distance across cities. Our results suggest that using PT takes on average 1.4–2.6 times longer than driving a car. The share of area where travel time favours PT over car use is very small: 0.62% (0.65%), 0.44% (0.48%), 1.10% (1.22%) and 1.16% (1.19%) for the daily average (and during peak hours) for São Paulo, Sydney, Stockholm, and Amsterdam, respectively. The travel time disparity, as quantified by the travel time ratio R (PT travel time divided by the car travel time), varies widely during an average weekday, by location and time of day. A systematic comparison between these two modes shows that the average travel time disparity is surprisingly similar across cities: R < 1 for travel distances less than 3 km, then increases rapidly but quickly stabilises at around 2. This study contributes to providing a more realistic performance evaluation that helps future studies further explore what city characteristics as well as urban and transport policies make public transport more attractive, and to create a more sustainable future for cities.