Traffic observatory

Ribeiro, Sílvio S.; Davis, Clodoveu A.; Oliveira, Diogo Rennó Rocha de; Meira, Wagner; Gonçalves, Tatiana Santos; Pappa, Gisele L.

doi:10.1145/2442796.2442800

Cited by 37 publications

(6 citation statements)

References 9 publications

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“…• Online newspaper: Web pages from many electronic newspapers such as: CBS Chicago, Chicago tribune etc • Sensors: Traffic data collected from Loop detectors • Geo-location data: Traffic GPS data including the latitude, longitude, elevation, date and time • Unmanned Aerial vehicle: Traffic image data • Closed-circuit television: Traffic video data However, in the streaming mode, we are using Twitter only as this data source has largely been used by citizens and administrations for publishing traffic incidents. In fact, Ribeiro et al (2012) propose a system to detect and locate traffic events with Twitter in Belo Horizonte. They found that there is a significant correlation between real traffic conditions and tweets talking about traffic conditions.…”

Section: Data Sources Layermentioning

confidence: 99%

Towards a Generic Multimodal Architecture for Batch and Streaming Big Data Integration

Yousfi

Rhanoui²,

Chiadmi³

2019

Journal of Computer Science

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Big Data are rapidly produced from various heterogeneous data sources. They are of different types (text, image, video or audio) and have different levels of reliability and completeness. One of the most interesting architectures that deal with the large amount of emerging data at high velocity is called the lambda architecture. In fact, it combines two different processing layers namely batch and speed layers, each providing specific views of data while ensuring robustness, fast and scalable data processing. However, most papers dealing with the lambda architecture are focusing one single type of data generally produced by a single data source. Besides, the layers of the architecture are implemented independently, or, at best, are combined to perform basic processing without assessing either the data reliability or completeness. Therefore, inspired by the lambda architecture, we propose in this paper a generic multimodal architecture that combines both batch and streaming processing in order to build a complete, global and accurate insight in near-real-time based on the knowledge extracted from multiple heterogeneous Big Data sources. Our architecture uses batch processing to analyze the data structures and contents, build the learning models and calculate the reliability index of the involved sources, while the streaming processing uses the built-in models of the batch layer to immediately process incoming data and rapidly provide results. We validate our architecture in the context of urban traffic management systems in order to detect congestions.

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Section: Data Sources Layermentioning

confidence: 99%

Towards a Generic Multimodal Architecture for Batch and Streaming Big Data Integration

Yousfi

Rhanoui²,

Chiadmi³

2019

Journal of Computer Science

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“…BECKER & GRAVANO (2011) and JACKOWAY et al (2011) for example identify real-world events and news content on Twitter by extracting and classifying topics using term frequency analysis and naive bayes classifiers. Using spatiotemporal and textual information from Twitter, researchers aim to discover events in the area of disaster-(SAKAKI et al 2010), crisis-(STEFANIDIS et al 2011), and mobility management (RIBEIRO et al 2012). In the latter application, a number of studies aim to infer general human mobility using Twitter.…”

Section: Related Workmentioning

confidence: 99%

Uncovering Latent Mobility Patterns from Twitter During Mass Events

Steiger¹,

Ellersiek²,

Resch³

et al. 2015

giforum

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The investigation of human activity in location-based social networks such as Twitter is one promising example of exploring spatial structures in order to infer underlying mobility patterns. Previous work regarding Twitter analysis is mainly focused on the spatiotemporal classification of events. However, since the information about the occurrence of a general event can in many cases be considered as given, one identified research gap is the exploration of human spatial behavior within specific mass events to potentially characterize underlying, locally occurring mobility clusters. One key challenge is to explore whether this noisy biased dataset can be a reliable source for the knowledge discovery of human mobility during mass events. In this paper we therefore present an advanced methodologycal framework, including a generative semantic topic modeling and local spatial autocorrelation approach, to observe both spatiotemporal and semantic clusters during a major sports event in Boston in the US. Our results of the observed spatiotemporally and semantically clustered tweets within the selected case study area have shown the possibility of deriving intra-urban event related mobility patterns with similar spatiotemporal movement.

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“…Sakaki & Matsuo [12] have a similar approach in Japan with an additional classification of driving information from Twitter. Gerais et al [13] are detecting and locating traffic events with twitter by georeferencing traffic Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored.…”

Section: State Of the Art And Related Workmentioning

confidence: 99%

Explorative public transport flow analysis from uncertain social media data

Steiger

Ellersiek

Zipf

2014

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information

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In this paper, we propose a framework to detect human mobility transportation hubs and infer public transport flows from unstructured georeferenced social media data using semantic topic modeling and spatial clustering techniques. An infrastructure for receiving and storing large sets of social media data has been developed together with an ad hoc processing framework in order to consider the high uncertainty of our retrieved data. Given the detected and extracted social media signals indicating human mobility, we compared the results with the public transport network from OpenStreetMap and classified observed mobility patterns for an exemplary case study. To analyze collected datasets a web based visualization tool has been setup.

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Traffic observatory

Cited by 37 publications

References 9 publications

Towards a Generic Multimodal Architecture for Batch and Streaming Big Data Integration

Towards a Generic Multimodal Architecture for Batch and Streaming Big Data Integration

Uncovering Latent Mobility Patterns from Twitter During Mass Events

Explorative public transport flow analysis from uncertain social media data

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