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-

GO-JEK SEBAGAI SIMBOL PERUBAHAN SOSIAL DAN EKONOMI DI KOTA TEGAL

Abstrak Go-Jek merupakan salah satu perusahaan transportasi ojek online di Indonesia yang berdiri sejak tahun 2010, Kehadiran Go-Jek telah mengundang banyak pro dan kontra dimasyarakat, mulai saat berdiri hingga saat ini menimbulkan banyak perdebatan diberbagai kalangan. Tujuan dari penelitan ini adalah untuk meneliti dampak Go-Jek terhadap perubahan sosial dan ekonomi di Kota Tegal baik dari sisi driver Go-Jek, konsumen maupun transportasi konvensional. Penelitian ini menggunakan jenis penelitian kualitatif dengan wawancara secara mendalam. Total responden dalam penelitian ini sebanyak 15 responden, dengan rincian 5 responden dari driver Go-Jek, 5 responden dari konsumen dan 5 responden dari transportasi konvensional. Hasil yang diperoleh dari penelitian ini menerangkan bahwa : (1) Terjadi perubahan perilaku sosial dan ekonomi yang positif pada driver Go-Jek (2) Terjadi perubahan perilaku sosial dan ekonomi yang positif pada konsumen Go-Jek (3) Terjadi perubahan sosial dan ekonomi yang negatif terhadap transportasi konvensional pesaing Go-Jek Kata Kunci: Transportasi Online, Go-Jek, Perubahan Sosial, Perubahan Ekonom

Graph Input Representations for Machine Learning Applications in Urban Network Analysis

Understanding and learning the characteristics of network paths has been of particular interest for decades and has led to several successful applications. Such analysis becomes challenging for urban networks as their size and complexity are significantly higher compared to other networks. The state-of-the-art machine learning (ML) techniques allow us to detect hidden patterns and, thus, infer the features associated with them. However, very little is known about the impact on the performance of such predictive models by the use of different input representations. In this paper, we design and evaluate six different graph input representations (i.e., representations of the network paths), by considering the network's topological and temporal characteristics, for being used as inputs for machine learning models to learn the behavior of urban networks paths. The representations are validated and then tested with a real-world taxi journeys dataset predicting the tips using a road network of New York. Our results demonstrate that the input representations that use temporal information help the model to achieve the highest accuracy (RMSE of 1.42$)

O uso de aplicativos para smartphone no transporte individual : 99Taxis e Uber

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2016.Nos últimos anos, devido ao avanço na tecnologia dos smartphones e à popularização do acesso à internet móvel, diversos aplicativos de transporte individual foram lançados e já fazem parte do cotidiano da população brasileira. Com a recente polêmica gerada pela chegada do Uber ao Brasil, país em que o 99Taxis foi inventado, muitos questionamentos estão sendo levantados a respeito da solicitação de transporte individual por meio de aplicativos para celulares. Este projeto visa identificar quem são os usuários dos aplicativos 99Taxis e Uber, quais as razões que os levam a escolher utilizar um ou outro e qual o nível de satisfação do usuário com relação serviço oferecido pelo aplicativo, tanto no transporte em si como na solicitação. Para isso, foi feita uma revisão sistemática de literatura para entender a evolução da tecnologia no transporte individual e quais mudanças ela gerou. Após a fase inicial de pesquisa bibliográfica foi realizada uma pesquisa de opinião através de uma ferramenta online, com divulgação nacional através de três diferentes plataformas. Como resultado, são apresentados os perfis socioeconômicos dos três tipos de usuários definidos ao longo da pesquisa: usuários do Uber e do 99Taxis (Tipo 1), usuários somente do 99Taxis (Tipo 2) e usuários apenas do Uber (Tipo3). Ademais, são analisados os comportamentos de cada um deles. A maioria dos usuários de aplicativos são jovens adultos com nível superior completo e renda familiar mensal acima de 10 salários mínimos. Não foram encontradas diferenças de comportamento relacionadas ao gênero do usuário. Porém há certas diferenças de comportamento de acordo com o tipo de usuário, sua região de residência e sua frequência de uso quando é feita uma análise mais detalhada

Modeling urban ridesharing for commuting: how to choose the way you move

Taxonomy and analysis of the concepts that constitute ridesharing with car in urban and metropolitan areas: hitch-hiking for free, rider in collaborative economy rides (paying), passenger (for free) in a car driven by a driver (with some proximity link with the rider), carpooling, carsharing (company), uber and conventional taxicab. The thesis will focus on analytical modeling of the options. The application will be in Barcelona