2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

here, but often with some revealing new insight; and only an exceptionally eclectic and voracious reader would fail to find new material. Even so familiar a subject as the Indian railways produces some fresh touches: Headrick remarks that British investors, under the government guarantee system (of 5 percent return on their capital), were not gambling on the profitability of railway operation in India but on the continuing ability of the British Raj to collect taxes; and India was the only nation with a major rail network that failed to industrialize during its railway boom. He describes a masterful use of technical requirements to keep out commercial rivals while maintaining a free trade ideology and a later masterful use of procrastination and indecision to avoid a hard choice between a declining British industry and an emerging India. Ironies abound. Earnest and laborious, although underfunded, efforts produced successful agricultural research programs (for export cash crops, of course) in Indonesia, India, and Malaya. But although research in Indochina "had been disorganized, unfocused, and erratic," it didn't matter, "because neighboring colonies had developed profitable plants and techniques" (p. 231) readily borrowed or stolen by French planters. Nevertheless, the French regime established something the British Raj in India never did. Its Office Indochinois du Riz, established in 1930-31, applied modern research techniques to a major peasant food and subsistence crop. Some fascinating people figure in these case studies, from the Indian geologist Pramatha Nath Bose—whose work made possible the success of Tata Iron and Steel, but only after he resigned from the service of a British Indian government that opposed such efforts—to men like Nigeria's Herbert S. H. Macaulay, Africa's first black civil engineer, who was sent to England to study surveying, civil engineering, and piano tuning. Colonial administrators often thought of technical education in terms of preindustrial crafts and not modern professions, not just out of racism (although there was plenty of that). Many were conservative, in a sense refugees from their own industrialized cultures, who wished to protect and conserve the "best elements" of the colonial cultures from rapid change. Both colonizers and colonized had ambiguous attitudes, which made it easier to transfer technology for transport and communication but difficult to transfer technology for import substitution. Asians and Africans had entrepreneurial skills, but only rarely combined with capital and an understanding of Western technology; and even a great success, such as the Tatas in India or smallholder rubber growers in Malaya, were at the mercy of government policies.

Ethnic Groups in Conflict

Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild. Despite their ubiquity, these real-world distribution shifts are under-represented in the datasets widely used in the ML community today. To address this gap, we present WILDS, a curated collection of 8 benchmark datasets that reflect a diverse range of distribution shifts which naturally arise in real-world applications, such as shifts across hospitals for tumor identification; across camera traps for wildlife monitoring; and across time and location in satellite imaging and poverty mapping. On each dataset, we show that standard training results in substantially lower out-of-distribution than in-distribution performance, and that this gap remains even with models trained by existing methods for handling distribution shifts. This underscores the need for new training methods that produce models which are more robust to the types of distribution shifts that arise in practice. To facilitate method development, we provide an open-source package that automates dataset loading, contains default model architectures and hyperparameters, and standardizes evaluations. Code and leaderboards are available at this https URL.

WILDS: A Benchmark of in-the-Wild Distribution Shifts

In response to the coronavirus disease 2019 (COVID-19) pandemic, several national governments have applied lockdown restrictions to reduce the infection rate. Here we perform a massive analysis on near-real-time Italian mobility data provided by Facebook to investigate how lockdown strategies affect economic conditions of individuals and local governments. We model the change in mobility as an exogenous shock similar to a natural disaster. We identify two ways through which mobility restrictions affect Italian citizens. First, we find that the impact of lockdown is stronger in municipalities with higher fiscal capacity. Second, we find evidence of a segregation effect, since mobility contraction is stronger in municipalities in which inequality is higher and for those where individuals have lower income per capita. Our results highlight both the social costs of lockdown and a challenge of unprecedented intensity: On the one hand, the crisis is inducing a sharp reduction of fiscal revenues for both national and local governments; on the other hand, a significant fiscal effort is needed to sustain the most fragile individuals and to mitigate the increase in poverty and inequality induced by the lockdown.

https://www.pnas.org/content/pnas/117/27/15530.full.pdf

Economic and social consequences of human mobility restrictions under COVID-19.

Journal of the American Society for Information Science and Technology

High resolution datasets of population density which accurately map sparsely-distributed human populations do not exist at a global scale. Typically, population data is obtained using censuses and statistical modeling. More recently, methods using remotely-sensed data have emerged, capable of effectively identifying urbanized areas. Obtaining high accuracy in estimation of population distribution in rural areas remains a very challenging task due to the simultaneous requirements of sufficient sensitivity and resolution to detect very sparse populations through remote sensing as well as reliable performance at a global scale. Here, we present a computer vision method based on machine learning to create population maps from satellite imagery at a global scale, with a spatial sensitivity corresponding to individual buildings and suitable for global deployment. By combining this settlement data with census data, we create population maps with ~30 meter resolution for 18 countries. We validate our method, and find that the building identification has an average precision and recall of 0.95 and 0.91, respectively and that the population estimates have a standard error of a factor ~2 or less. Based on our data, we analyze 29 percent of the world population, and show that 99 percent lives within 36 km of the nearest urban cluster. The resulting high-resolution population datasets have applications in infrastructure planning, vaccination campaign planning, disaster response efforts and risk analysis such as high accuracy flood risk analysis.

Mapping the World Population One Building at a Time

After a natural disaster or other crisis, humanitarian organizations need to know where affected people are located and what resources they need. While this information is difficult to capture quickly through conventional methods, aggregate usage patterns of social media apps like Facebook can help fill these information gaps. This talk will describe the data and methodology that power Facebook Disaster Maps. These maps utilize information about Facebook usage in areas impacted by natural hazards, producing insights into how the population is affected by and responding to the hazard. In addition to methodology details, including efforts taken to ensure the security and privacy of Facebook users, I'll also discuss how we worked with humanitarian partners to develop the maps, which are actively used in disaster response today. I'll give examples of insights generated from the maps and I'll also discuss some limitations of the current methodologies, challenges, and opportunities for improvement.

Facebook Disaster Maps: Aggregate Insights for Crisis Response & Recovery.

Measuring public sentiment is a key task for researchers and policymakers alike. The explosion of available social media data allows for a more time-sensitive and geographically specific analysis than ever before. In this paper we analyze data from the micro-blogging site Twitter and generate a sentiment map of New York City. We develop a classifier specifically tuned for 140-character Twitter messages, or tweets, using key words, phrases and emoticons to determine the mood of each tweet. This method, combined with geotagging provided by users, enables us to gauge public sentiment on extremely fine-grained spatial and temporal scales. We find that public mood is generally highest in public parks and lowest at transportation hubs, and locate other areas of strong sentiment such as cemeteries, medical centers, a jail, and a sewage facility. Sentiment progressively improves with proximity to Times Square. Periodic patterns of sentiment fluctuate on both a daily and a weekly scale: more positive tweets are posted on weekends than on weekdays, with a daily peak in sentiment around midnight and a nadir between 9:00 a.m. and noon.

pdf/sentiment-in-new-york-city-a-high-resolution-spatial-and-3iav2w09a4.pdf

Sentiment in New York City: A High Resolution Spatial and Temporal View.

We consider the conditions of peace and violence among ethnic groups, testing a theory designed to predict the locations of violence and interventions that can promote peace Violence arises at boundaries between regions that are not sufficiently well defined We model cultural differentiation as a separation of groups whose members prefer similar neighbors with a characteristic group size at which violence occurs Application of this model to the area of the former Yugoslavia and to India accurately predicts the locations of reported conflict Characterizing the model’s success in predicting peace requires examples where peace prevails despite diversity Switzerland is recognized as a country of peace, stability, and prosperity This is surprising because of its linguistic and religious diversity that in other parts of the world lead to conflict and violence Here we analyze how peaceful stability is maintained Our analysis shows that peace does not depend on integrated coexistence, but rather on well-defined topographical and political boundaries separating linguistic and religious groups, respectively In exactly one region, a porous mountain range does not adequately separate linguistic groups and violent conflict has led to the recent creation of the canton of Jura Our analysis supports the hypothesis that violence between groups can be inhibited by both physical and political boundaries A similar analysis of the area of the former Yugoslavia shows that during widespread ethnic violence existing political boundaries did not coincide with the boundaries of distinct groups, but peace prevailed in specific areas where they did coincide

The Geography of Ethnic Violence

In the last several years, remote sensing technology has opened up the possibility of performing large scale building detection from satellite imagery. Our work is some of the first to create population density maps from building detection on a large scale. The scale of our work on population density estimation via high resolution satellite images raises many issues, that we will address in this paper. The first was data acquisition. Labeling buildings from satellite images is a hard problem, one where we found our labelers to only be about 85% accurate at. There is a tradeoff of quantity vs. quality of labels, so we designed two separate policies for labels meant for training sets and those meant for test sets, since our requirements of the two set types are quite different. We also trained weakly supervised footprint detection models with the classification labels, and semi-supervised approaches with a small number of pixel-level labels, which are very expensive to procure.

Building Detection from Satellite Images on a Global Scale.

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