Representation Learning of public transport data. Application to event detection

5th International Workshop and Symposium TransitData 2019, Paris, France, 08-/07/2019 - 10/07/2019On the basis of data collected by counting sensors deployed on trains, this paper deals with a forecasting of passenger load in public transport taking into account train operation. Providing passengers with train load forecasting, in addition to the expected arrival time of the next train, can indeed be useful for a better planning of their journeys, which can prevent over-crowding situations in the trains [6] [7]. The proposed approach is built on both a hierarchy of recurrent neural networks [8] and representation learning [9] with the aim to explore the ability of such mobility data processing to simultaneously perform a forecasting task and highlight the impact of events on the public transport operation and demand. An event refers here to an unexpected passenger transport activity or to a modification in transport operation compared to those corresponding to normal conditions. Two kind of historical data are used, namely train load data and automatic vehicle location (AVL) data. This latter source contains all information related to the train operation (delay, time of arrival/departure of vehicles ...). The proposed methodology is applied on a railway transit network line operated by the French railway company SNCF in the suburban of Paris. The historical dataset used in the experiments covers the period from 2015 to 2016

DATASET2050 D2.1 - Data requirements and acquisition

The purpose of this document, Deliverable 2.1, is to describe the sources of data required by the H2020 coordination and support action DATASET2050. Data requirements have been categorised into seven broad groups to support WP3 and WP4: demographic; passenger demand; passenger type; door-to-kerb; kerb-to-gate; airside capacity and competing services. The current scenario is well supported by existing datasets, however the two future scenarios require modelled data

Geo-Spotting: Mining Online Location-based Services for Optimal Retail Store Placement

The problem of identifying the optimal location for a new retail store has been the focus of past research, especially in the field of land economy, due to its importance in the success of a business. Traditional approaches to the problem have factored in demographics, revenue and aggregated human flow statistics from nearby or remote areas. However, the acquisition of relevant data is usually expensive. With the growth of location-based social networks, fine grained data describing user mobility and popularity of places has recently become attainable. In this paper we study the predictive power of various machine learning features on the popularity of retail stores in the city through the use of a dataset collected from Foursquare in New York. The features we mine are based on two general signals: geographic, where features are formulated according to the types and density of nearby places, and user mobility, which includes transitions between venues or the incoming flow of mobile users from distant areas. Our evaluation suggests that the best performing features are common across the three different commercial chains considered in the analysis, although variations may exist too, as explained by heterogeneities in the way retail facilities attract users. We also show that performance improves significantly when combining multiple features in supervised learning algorithms, suggesting that the retail success of a business may depend on multiple factors.Comment: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, Chicago, 2013, Pages 793-80

No. 17: International Migration and Good Governance in the Southern African Region

Southern Africa has a long history of intra-regional migration, dating back to the mid-nineteenth century. Migration was probably the single most important factor tying together all of the various colonies and countries of the sub-continent into a single regional labour market during the twentieth century. However, entrenched patterns of migration have undergone major restructuring in the last two decades. Southern Africa is now a region on the move (McDonald 2000). Several broader changes underly this shift towards greater and greater intra-regional mobility. First, the end of apartheid, a system designed to control movement and exclude outsiders, produced new opportunities for internal and cross-border mobility and new incentives for moving. The ensuing integration of South Africa with the SADC region brought a major increase in legal and undocumented cross-border flows and new forms of mobility. Second, the region’s reconnection with the global economy has opened it up to forms of migration commonly associated with globalization (Crush and McDonald 2002). Third, growing rural and urban poverty and unemployment have pushed more people out of households in search of a livelihood. One aspect of this has been a significant gender reconfiguration of migration streams (Dodson 1998). Fourth, HIV/AIDS has also impacted considerably on migration. Not only is the rapid diffusion of the epidemic inexplicable without reference to human mobility but new forms of migration are emerging in response (Williams et al. 2003; IOM 2003a). Finally, the countries of the SADC are still dealing with the legacy of mass displacement and forced migration. The impact of the Mozambican and Angolan civil wars continue to reverberate. Recurrent civil strife in the rest of Africa has generated mass refugee movements and new kinds of asylum seeker to and within the region. The cessation of hostilities and threat has confronted countries of asylum with issues of repatriation and integration. Policy responses as the local, national, regional and continental scale must take into account the extraordinary dynamism and instability of migration forms and patterns in the region. Governments wedded to legal frameworks of control and exclusion are finding it increasingly difficult to cope. The fundamental policy challenge is to move the states of Southern Africa to a regionally-harmonized and consistent set of policies that emphasize good governance, sound management and client-centred service delivery (Klaaren and Rutinwa 2004). In addition, because migration is a cross-cutting phenomenon, it needs to be integrated into all facets of state policymaking and planning, including programs and strategies to alleviate poverty and reduce inequality. For this to happen, migration’s key role needs to be documented by researchers and recognized by policy-makers

State of Australian cities 2014-2015: progress in Australian regions

Provides insight into the vital role that Australian cities play in the growth of the countries economy and tracks the overall progress made in Australia\u27s major centres. Summary Since 2010, the State of Australian Cities reports have examined the progress being made in our major cities. These reports have provided insight into the vital role that Australian cities play in the growth of our economy and have tracked the overall progress made in Australia\u27s major centres. The State of Australian Cities 2014–2015 once again looks at the drivers behind some of the public policy issues facing the country today and into the future. Australia is a highly urbanised country. The populations of Australia\u27s major cities are at record levels, as is the number of people employed. It is in our cities that the overwhelming majority of jobs are located and where the most new jobs are being created. The economic output of our major cities has grown and their national importance remains extremely high. However, alongside that growth there is more demand on transport systems in Australia than ever before. This report examines population growth, economic growth and the increased traffic flows through our ports and airports and on our roads and rail lines. Issues of space and the potential conflicts of the usability of cities with the utility and long term capacity of freight hubs, ports and airports and the movement of goods and people in cities is a challenge for policy makers. This report provides the evidence base for policy makers at all levels of government to consider those challenges now and into the future

Optimising the Loading Diversity of Rail Passenger Crowding using On-Board Occupancy Data

Crowded conditions on trains can lead to lower passenger satisfaction, discourage rail travel, result in negative economic impacts and are a factor in a number of health and safety hazards. In the UK there is an annual survey of rail passenger crowding, although the measures used do not reflect coach-by-coach variations, nor do they reflect variations across the peak period. In this MPhil thesis I investigated the application of weight-based automatic passenger counting data to deliver more even loadings on trains through the provision of new real-time and static solutions. In addition I investigated the potential benefits of such solutions in terms of reduced dwell times and reduced crowding. The overall concept proposed was to make the most of the existing available capacity; for example, so that no-one is standing when seats are available. Through analysing a large sample of air suspension data, I identified station-specific trends where some coaches were over capacity while others had spare capacity. I also conducted a critical review of academic research into on-train crowding and solutions that seek to optimise ‘loading diversity’. This study contributes to this emerging subject area in several ways: I propose two new metrics to describe inter-coach loading diversity that, unlike existing metrics, contain information relative to the capacity; I have revealed a link between the inter-coach loading diversity metrics and estimated boarding times, with trains classified as ‘very uneven’ on departure typically having dwell times of approximately five to ten seconds greater than services that were classified as being ‘even’ with a similar total number of passengers on board; and finally I have applied classification supervised learning techniques to predict the load factor for a given service and these predictors were an improvement over taking the historical average

Mining open datasets for transparency in taxi transport in metropolitan environments.

Uber has recently been introducing novel practices in urban taxi transport. Journey prices can change dynamically in almost real time and also vary geographically from one area to another in a city, a strategy known as surge pricing. In this paper, we explore the power of the new generation of open datasets towards understanding the impact of the new disruption technologies that emerge in the area of public transport. With our primary goal being a more transparent economic landscape for urban commuters, we provide a direct price comparison between Uber and the Yellow Cab company in New York. We discover that Uber, despite its lower standard pricing rates, effectively charges higher fares on average, especially during short in length, but frequent in occurrence, taxi journeys. Building on this insight, we develop a smartphone application, OpenStreetCab, that offers a personalized consultation to mobile users on which taxi provider is cheaper for their journey. Almost five months after its launch, the app has attracted more than three thousand users in a single city. Their journey queries have provided additional insights on the potential savings similar technologies can have for urban commuters, with a highlight being that on average, a user in New York saves 6 U.S. Dollars per taxi journey if they pick the cheapest taxi provider. We run extensive experiments to show how Uber's surge pricing is the driving factor of higher journey prices and therefore higher potential savings for our application's users. Finally, motivated by the observation that Uber's surge pricing is occurring more frequently that intuitively expected, we formulate a prediction task where the aim becomes to predict a geographic area's tendency to surge. Using exogenous to Uber data, in particular Yellow Cab and Foursquare data, we show how it is possible to estimate customer demand within an area, and by extension surge pricing, with high accuracy.This is the final version of the article. It was first available from Springer via http://dx.doi.org/10.1140/epjds/s13688-015-0060-

Mining large-scale human mobility data for long-term crime prediction

Traditional crime prediction models based on census data are limited, as they fail to capture the complexity and dynamics of human activity. With the rise of ubiquitous computing, there is the opportunity to improve such models with data that make for better proxies of human presence in cities. In this paper, we leverage large human mobility data to craft an extensive set of features for crime prediction, as informed by theories in criminology and urban studies. We employ averaging and boosting ensemble techniques from machine learning, to investigate their power in predicting yearly counts for different types of crimes occurring in New York City at census tract level. Our study shows that spatial and spatio-temporal features derived from Foursquare venues and checkins, subway rides, and taxi rides, improve the baseline models relying on census and POI data. The proposed models achieve absolute R^2 metrics of up to 65% (on a geographical out-of-sample test set) and up to 89% (on a temporal out-of-sample test set). This proves that, next to the residential population of an area, the ambient population there is strongly predictive of the area's crime levels. We deep-dive into the main crime categories, and find that the predictive gain of the human dynamics features varies across crime types: such features bring the biggest boost in case of grand larcenies, whereas assaults are already well predicted by the census features. Furthermore, we identify and discuss top predictive features for the main crime categories. These results offer valuable insights for those responsible for urban policy or law enforcement

Automatic extraction of mobility activities in microblogs

Tese de Mestrado Integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201