Taxi-aware map: identifying and predicting vacant taxis in the city

Knowing where vacant taxis are and will be at a given time and location helps the users in daily planning and scheduling, as well as the taxi service providers in dispatching. In this paper, we present a predictive model for the number of vacant taxis in a given area based on time of the day, day of the week, and weather condition. The history is used to build the prior probability distributions for our inference engine, which is based on the naïve Bayesian classifier with developed error-based learning algorithm and method for detecting adequacy of historical data using mutual information. Based on 150 taxis in Lisbon, Portugal, we are able to predict for each hour with the overall error rate of 0.8 taxis per sq. km

Tracking trash

Using active self reporting tags we were able to follow the journey of 2,000 objects through the waste management system of Seattle. We used this data to define measures of efficiency for what could be called the ‘removal chain’. We found that over 95% of the traces reached a compliant end destination. However, there were concerns with special categories of waste (cellphones, e-waste, and household hazardous waste) and specific geographic locations (trash from Bellevue and Redmond in particular did not follow the recommended best practices). We believe that similar studies may increase knowledge and systemic performance of waste management systems and, at a personal level, reduce the ‘out of sight out of mind’ attitude to trash

MIT GEOblog: a platform for digital annotation of space for collective community based digital story telling

This paper focuses on guidelines in designing platforms for collective, location-sensitive user generated content, built upon a system that allows for locating mobile subjects within the space. The process of conceptual design, design development, and technical implementation of MIT GEOblog project from a user-interaction design point of view, is used to illustrate the applicability of the guidelines. GEOblog is a web-based platform that allows people to annotate the space, through geo-tagging and sharing user generated content or, in other words, placing digital content over spatial zones that can be retrieved by others based on their real-time sensed location by the system

Perspectives on semantics of the place from online resources

We present a methodology for extraction of semantic indexes related to a given geo-referenced place. These lists of words correspond to the concepts that should be semantically related to that place, according to a number of perspectives. Each perspective is provided by a different online resource, namely upcoming.org, Flickr, Wikipedia or open Web search (using Yahoo! search engine). We describe the process by which those lists are obtained, present experimental results and discuss the strengths and weaknesses of the methodology and of each perspective

Quantifying urban attractiveness from the distribution and density of digital footprints

In the past, sensors networks in cities have been limited to fixed sensors, embedded in particular locations, under centralised control. Today, new applications can leverage wireless devices and use them as sensors to create aggregated information. In this paper, we show that the emerging patterns unveiled through the analysis of large sets of aggregated digital footprints can provide novel insights into how people experience the city and into some of the drivers behind these emerging patterns. We particularly explore the capacity to quantify the evolution of the attractiveness of urban space with a case study of in the area of the New York City Waterfalls, a public art project of four man-made waterfalls rising from the New York Harbor. Methods to study the impact of an event of this nature are traditionally based on the collection of static information such as surveys and ticket-based people counts, which allow to generate estimates about visitors’ presence in specific areas over time. In contrast, our contribution makes use of the dynamic data that visitors generate, such as the density and distribution of aggregate phone calls and photos taken in different areas of interest and over time. Our analysis provides novel ways to quantify the impact of a public event on the distribution of visitors and on the evolution of the attractiveness of the points of interest in proximity. This information has potential uses for local authorities, researchers, as well as service providers such as /nmobile network operators

Understanding individual and collective mobility patterns from smart card records: A case study in Shenzhen

Understanding the dynamics of the inhabitants' daily mobility patterns is essential for the planning and management of urban facilities and services. In this paper, novel aspects of human mobility patterns are investigated by means of smart card data. Using extensive smart card records resolved in both time and space, we study the mean collective spatial and temporal mobility patterns at large scales and reveal the regularity of these patterns. We also investigate patterns of travel behavior at the individual level and show that the concentricity and regularity of mobility patterns. The analytical methodologies to spatially and temporally quantify, visualize, and examine urban mobility patterns developed in this paper could provide decision support for transport planning and management

Quantifying urban attractiveness from the distribution and density of digital footprints

In the past, sensors networks in cities have been limited to fixed sensors, embedded in particular locations, under centralised control. Today, new applications can leverage wireless devices and use them as sensors to create aggregated information. In this paper, we show that the emerging patterns unveiled through the analysis of large sets of aggregated digital footprints can provide novel insights into how people experience the city and into some of the drivers behind these emerging patterns. We particularly explore the capacity to quantify the evolution of the attractiveness of urban space with a case study of in the area of the New York City Waterfalls, a public art project of four man-made waterfalls rising from the New York Harbor. Methods to study the impact of an event of this nature are traditionally based on the collection of static information such as surveys and ticket-based people counts, which allow to generate estimates about visitors’ presence in specific areas over time. In contrast, our contribution makes use of the dynamic data that visitors generate, such as the density and distribution of aggregate phone calls and photos taken in different areas of interest and over time. Our analysis provides novel ways to quantify the impact of a public event on the distribution of visitors and on the evolution of the attractiveness of the points of interest in proximity. This information has potential uses for local authorities, researchers, as well as service providers such as /nmobile network operators