Integrating big data into a sustainable mobility policy 2.0 planning support system

It is estimated that each of us, on a daily basis, produces a bit more than 1 GB of digital content through our mobile phone and social networks activities, bank card payments, location-based positioning information, online activities, etc. However, the implementation of these large data amounts in city assets planning systems still remains a rather abstract idea for several reasons, including the fact that practical examples are still very strongly services-oriented, and are a largely unexplored and interdisciplinary field; hence, missing the cross-cutting dimension. In this paper, we describe the Policy 2.0 concept and integrate user generated content into Policy 2.0 platform for sustainable mobility planning. By means of a real-life example, we demonstrate the applicability of such a big data integration approach to smart cities planning process. Observed benefits range from improved timeliness of the data and reduced duration of the planning cycle to more informed and agile decision making, on both the citizens and the city planners end. The integration of big data into the planning process, at this stage, does not have uniform impact across all levels of decision making and planning process, therefore it should be performed gradually and with full awareness of existing limitations

The moving crowd: collecting and processing of crowd behaviour data

The MOVE project focuses on the collection and analyses of crowd behavior data. The two main goals of the project are first, the collection of data through mobile phones. The second goal is to develop new technologies to process and mine the collected data for crowd behaviour analysis. The technology will allow to make advanced interpretations of historic and dynamic mobile crowd data coming from GSM/GPS and from different classes of users (vehicle, pedestrian, indoor/outdoor). Fusion will be made between data coming from different sources (smartphone, navigation device) and external map data. The interpretation will allow the mining of advanced features/geometry from the crowd data as well as interprete the dynamic behaviour of the population

Collection and analyses of crowd travel behaviour data by using smartphones

In 2010 the MOVE project started in the collection and analysis of crowd behaviour data. The two main goals of the project are first, the collection of data through the use of mobile phones. The second goal is to develop new technologies to process and mine the collected data for crowd behaviour analysis. The technology will allow to make advanced interpretations of historic and dynamic mobile crowd data coming from GSM/GPS and from different classes of users (vehicle, pedestrian, indoor/outdoor). Fusion will be made between data coming from different sources (smartphone, navigation device) and external map data. The interpretation will allow the mining of advanced features/geometry from the crowd data as well as the dynamic (travel) behavior of the population

Sensor specific distributions for improved tracking of people

In this paper, we examine sensor specific distributions of local image operators (edge and line detectors), which describe the appearance of people in video sequences. The distributions are used to describe a probabilistic articulated motion model to track the gestures of a person in terms of arms and body movement. The distributions are based on work of Sidenbladh where general distributions are examined, collected over images found on the internet. In our work, we focus on the statistics of one sensor, in our case a standard webcam, and examine the influence of image noise and scale. We show that although the general shape of the distributions published by Sidenbladh are found, important anomalies occur which are due to image noise and reduced resolution. Taking into account the effects of noise and blurring on the scale space response of edge and line detectors improves the overall performance of the model. The original distributions introduced a bias towards small sharp boundaries over large blurred boundaries. In the case of arms and legs which often appear blurred in the image, this bias is unwanted. Incorporating our modifications in the distributions removes the bias and makes the tracking more robust

Combining feature fusion and decision fusion for classification of hyperspectral and LiDAR data

This paper proposes a method to combine feature fusion and decision fusion together for multi-sensor data classification. First, morphological features which contain elevation and spatial information, are generated on both LiDAR data and the first few principal components (PCs) of original hyperspectral (HS) image. We got the fused features by projecting the spectral (original HS image), spatial and elevation features onto a lower subspace through a graph-based feature fusion method. Then, we got four classification maps by using spectral features, spatial features, elevation features and the graph fused features individually as input of SVM classifier. The final classification map was obtained by fusing the four classification maps through the weighted majority voting. Experimental results on fusion of HS and LiDAR data from the 2013 IEEE GRSS Data Fusion Contest demonstrate effectiveness of the proposed method. Compared to the methods using single data source or only feature fusion, with the proposed method, overall classification accuracies were improved by 10% and 2%, respectively

Fietsers kunnen trajecten beoordelen via mobiele app: Bike to Work lanceert smartphone-app 'Rate your Ride'

Bike to Work, een drietalig onlineproject van Fiet- sersbond vzw, ondersteunt Belgische werkgevers het hele jaar door om meer personeelsleden op de fiets te krijgen en te houden. Het unieke fietspun- tensysteem, de zomer- en winterwedstrijd en de meetbare fietsresultaten stimuleren sinds april 2009 bijna 18.000 geregistreerde fietsers uit 286 aangesloten ondernemingen. In samenwerking met de Universiteit Gent werd nu een mobiele applica- tie ontwikkeld. Daarmee kunnen de geregistreerde fietsers de Bike to Work-fietskalender aanvullen, de afstand en snelheid volgen op de fietscomputer, scores geven aan de woon-werkroute en fietspa- den in kaart brengen

Vebimobe: correcte snelheidsinformatie voor correct rijgedrag: onderzoek naar mogelijkheden verkeersbordendatabank voor ITS-toepassingen

Onaangepaste snelheid is naast dronkenschap de hoofdoorzaak van zware ongevallen. Assistentie van de bestuurder bij het snelheidsgedrag is daarom een cruciaal hulpmiddel om ongevallen te voorkomen. De huidige navigatiesystemen geven al wel advi-serende snelheidsinformatie, maar die is niet dwingend en verre van accuraat. In het VEBIMOBE-project van het VIM (Vlaams Instituut voor Mobiliteit) wordt onderzocht hoe de Vlaamse verkeersbordendatabank zou kunnen bijdragen tot correcte snelheidsinformatie en -gedrag. Meer specifiek: er wordt nage-gaan hoe de data van de verkeersbordendatabank naar ITS-standaarden kunnen worden overgezet en hoe die data via innovatieve technieken verfijnd en geactualiseerd kunnen worden

Combining feature fusion and decision fusion for classification of hyperspectral and LiDAR data

This paper proposes a method to combine feature fusion and decision fusion together for multi-sensor data classification. First, morphological features which contain elevation and spatial information, are generated on both LiDAR data and the first few principal components (PCs) of original hyperspectral (HS) image. We got the fused features by projecting the spectral (original HS image), spatial and elevation features onto a lower subspace through a graph-based feature fusion method. Then, we got four classification maps by using spectral features, spatial features, elevation features and the graph fused features individually as input of SVM classifier. The final classification map was obtained by fusing the four classification maps through the weighted majority voting. Experimental results on fusion of HS and LiDAR data from the 2013 IEEE GRSS Data Fusion Contest demonstrate effectiveness of the proposed method. Compared to the methods using single data source or only feature fusion, with the proposed method, overall classification accuracies were improved by 10% and 2%, respectively

Er zit mobiliteit in een mobieltje: het gebruik van mobiele telefoons in het traceren van verplaatsingsgedrag

Het in kaart brengen van de mobiliteit in een bepaalde omgeving is nog steeds een uitdaging voor vele vervoersplanners. Nieuwe technologieën zoals navigatiesystemen laten het toe om personen in hun verplaatsingen te traceren en zodoende deze informatie te gebruiken in mobiliteitsplanning en onderzoek. Aan de Universiteit Gent werd een project opgezet waarbij de Smartphone van studenten werden uitgerust met een programmaatje om hun verplaatsingen te traceren. In ruil kregen de studenten enkele maanden gratis mobiel Internet. Het onderzoek richtte zich vooral op de technische haalbaarheid en de bruikbaarheid van deze data en dit op zo’n manier dat de persoonlijke levenssfeer van de gebruiker maximaal beschermd blijft. In een volgend deel van het onderzoek werd het gekende verplaatsingsdagboek weergegeven op de Smartphone die gebruikers konden invullen; dit om de modi van vooral niet-gemotoriseerde verplaatsingen te kennen. De eerste resultaten scheppen hoopvolle verwachtingen; zelfs een alternatief om automatisch de modi te genereren uit de data werd ontwikkeld zodat de inspanningen van de gebruiker zeer minimaal wordt. Deze ontwikkelingen leiden ertoe dat men sterk denkt om binnen de Universiteit Gent een stedelijk of regionaal mobiliteitsplatform op te richten waarbij men kan beschikken over een grote poel van testgebruikers. De gecollecteerde data zou dan gebruikt kunnen worden in diverse planning- en mobiliteitsdoeleinden