Disparities in travel times between car and transit: Spatiotemporal patterns in cities

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 (S\ue3o 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\ua0favours 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 S\ue3o Paulo, Sydney, Stockholm, and Amsterdam, respectively. The travel time disparity, as quantified by the travel time ratio\ua0R (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:\ua0R<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

Feasibility of estimating travel demand using geolocations of social media data

Travel demand estimation, as represented by an origin–destination (OD) matrix, is essential for urban planning and management. Compared to data typically used in travel demand estimation, the key strengths of social media data are that they are low-cost, abundant, available in real-time, and free of geographical partition. However, the data also have significant limitations: population and behavioural biases, and lack of important information such as trip purpose and social demographics. This study systematically explores the feasibility of using geolocations of Twitter data for travel demand estimation by examining the effects of data sparsity, spatial scale, sampling methods, and sample size. We show that Twitter data are suitable for modelling the overall travel demand for an average weekday but not for commuting travel demand, due to the low reliability of identifying home and workplace. Collecting more detailed, long-term individual data from user timelines for a small number of individuals produces more accurate results than short-term data for a much larger population within a region. We developed a novel approach using geotagged tweets as attraction generators as opposed to the commonly adopted trip generators. This significantly increases usable data, resulting in better representation of travel demand. This study demonstrates that Twitter can be a viable option for estimating travel demand, though careful consideration must be given to sampling method, estimation model, and sample size

Integration of tools for decision making in vehicular congestion

Este estudio tiene como finalidad presentar un análisis de la utilización e integración de herramientas tecnológicas que ayudan a tomar decisiones en situaciones de congestión vehicular. La ciudad de Quito-Ecuador es considerada como un caso de estudio para el trabajo realizado. La investigación se presenta en función del desarrollo de una aplicación, haciendo uso de herramientas Big Data (Apache Flume, Apache Hadoop, Apache Pig), que permiten el procesamiento de gran cantidad de información que se requiere recolectar, almacenar y procesar. Uno de los aspectos innovadores de la aplicación es el uso de la red social Twitter como fuente de origen de datos. Para esto se utilizó su interfaz de programación de aplicaciones (Application Programming Interface, API), la cual permite tomar datos de esta red social en tiempo real e identificar puntos probables de congestión. Este estudio presenta resultados de pruebas realizadas con la aplicación, durante un período de 9 meses.The purpose of this study is to present an analysis of the use and integration of technological tools that help decision making in situations of vehicular congestion. The city of Quito-Ecuador is considered as a case study for the done work. The research is presented according to the development of an application, using Big Data tools (Apache Flume, Apache Hadoop, Apache Pig), favoring the processing of a lot of information that is required to collect, store and process. One of the innovative aspects of the application is the use of Twitter social network as source of origin. For this, it used its application programming interface (API), which allows to take data from this social network and identify probable points of congestion. This study presents results of tests carried out with the application, in a period of 9 months

Scoping out urban areas of tourist interest though geolocated social media data: Bucharest as a case study

Social media data has frequently sourced research on topics such as traveller planning or the factors that influence travel decisions. The literature on the location of tourist activities, however, is scarce. The studies in this line that do exist focus mainly on identifying points of interest and rarely on the urban areas that attract tourists. Specifically, as acknowledged in the literature, tourist attractions produce major imbalances with respect to adjacent urban areas. The present study aims to fill this research gap by addressing a twofold objective. The first was to design a methodology allowing to identify the preferred tourist areas based on concentrations of places and activities. The tourist area was delimited using Instasights heatmaps information and the areas of interest were identified by linking data from the location-based social network Foursquare to TripAdvisor’s database. The second objective was to delimit areas of interest based on users’ existing urban dynamics. The method provides a thorough understanding of functional diversity and the location of a city’s different functions. In this way, it contributes to a better understanding of the spatial distribution imbalances of tourist activities. Tourist areas of interest were revealed via the identification of users’ preferences and experiences. A novel methodology was thus created that can be used in the design of future tourism strategies or, indeed, in urban planning. The city of Bucharest, Romania, was taken as a case study to develop this exploratory research.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research has been partially funded by the Valencian Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana and the European Social Fund (ACIF/2020/173); and by the University of Alicante—Vicerrectorado de Investigación (GRE 21-15)

Participatory Sensor Networks as Sensing Layers

International audienceParticipatory sensor networks (PSNs) regards smartphone users as consumers as well as active producers of data. A sensing layer represents a type of data, coming from a given source of data, such as web services, traditional wireless sensor networks, and PSNs. In this work, we show the usefulness and potential of having sensing layers in PSNs. We also show how we can formalize the concept of sensing layers in participatory sensor networks. Furthermore, we demonstrate how to derive and create new applications and services that are not promptly available from different sensing layers, opening up very interesting research opportunities

Evaluation of COVID-19 Spread Effect on the Commercial Instagram Posts using ANN: A Case Study on The Holy Shrine in Mashhad, Iran

The widespread deployment of social media has helped researchers access an enormous amount of data in various domains, including the the COVID-19 pandemic. This study draws on a heuristic approach to classify Commercial Instagram Posts (CIPs) and explores how the businesses around the Holy Shrine were impacted by the pandemic. Two datasets of Instagram posts (one gathered data from March 14th to April 10th, 2020, when Holy Shrine and nearby shops were closed, and one extracted data from the same period in 2019), two word embedding models – aimed at vectorizing associated caption of each post, and two neural networks – multi-layer perceptron and convolutional neural network – were employed to classify CIPs in 2019. Among the scenarios defined for the 2019 CIPs classification, the results revealed that the combination of MLP and CBoW achieved the best performance, which was then used for the 2020 CIPs classification. It was found out that the fraction of CIPs to total Instagram posts has increased from 5.58% in 2019 to 8.08% in 2020, meaning that business owners were using Instagram to increase their sales and continue their commercial activities to compensate for the closure of their stores during the pandemic. Moreover, the portion of non-commercial Instagram posts (NCIPs) in total posts has decreased from 94.42% in 2019 to 91.92% in 2020, implying the fact that since the Holy Shrine was closed, Mashhad residents and tourists could not visit it and take photos to post on their Instagram accounts

Understanding Human Mobility with Emerging Data Sources: Validation, spatiotemporal patterns, and transport modal disparity

Human mobility refers to the geographic displacement of human beings, seen as individuals or groups, in space and time. The understanding of mobility has broad relevance, e.g., how fast epidemics spread globally. After 2030, transport is likely to become the sector with the highest emissions in the 2\ub0C\ua0scenario. Better informed policy-making requires up-to-date empirical mobility data with good quality. However, the conventional methods are limited when dealing with new challenges. The prevalence of digital technologies enables a large-scale collection of human mobility traces, through social media data and GPS-enabled devices etc, which contribute significantly to the understanding of human mobility. However, their potentials for the further application are not fully exploited.This thesis uses emerging data sources, particularly Twitter data, to enhance the understanding of mobility and apply the obtained knowledge in the field of transport. The thesis answers three questions: Is Twitter a feasible data source to represent individual and population mobility? How are Twitter data used to reveal the spatiotemporal dynamics of mobility? How do Twitter data contribute to depicting the modal disparity of travel time by car vs public transit? In answering these questions, the methodological contribution of this thesis lies in the applied side of data science.Using geotagged Twitter data, mobility is firstly described by abstract metrics and physical models; in Paper A to reveal the population heterogeneity of mobility patterns using data mining techniques; and in Paper B to estimate travel demand with a novel approach to address the sparsity issue of Twitter data. In Paper C, GIS techniques are applied to combine the travel demand as revealed by Twitter data and the transportation network to give a more realistic picture of the modal disparity in travel time between car and public transit in four cities in different countries at a high spatial and temporal granularity. The validation of using Twitter data in mobility study contributes to better utilisation of this low-cost mobility data source. Compared with a static picture obtained by conventional data sources, the dynamics introduced by social media data among others contribute to better-informed policymaking and transport planning