An Investigation of Parallel Road Map Inference from Big GPS Traces Data

AbstractWith the increased use of GPS sensors in several everyday devices, persons trip data are be- coming very abundant. Many opportunities for exploration of the wealth GPS data and in this paper, we inferred, the geometry of road maps in Tunisia and the connectivity between them. This phenomenon is known as map generation and also map inference procedure. For that, we gathered big GPS data from about ten thousands of vehicles equipped with GPS receivers and circulating in Tunisia, which does not have a road map like other developing countries. We collected a big database with approximately 100 gigabytes. After preprocessing it, we were obliged to partition data in order to facilitate handling an unstructured database with a such size. In fact, we used for that K-means with its sequential mode and the parallel mode based on Mapreduce, which is one of the most famous proposed solution to analyse the rapidly growing data. The proposed parallel k-means algorithm was tested with our GPS data and the results are efficient in processing large datasets. It is a parallel data processing tool which is gathering significant importance from industry and academia especially with appearance of a new term to describe massive datasets having large-volume, high-complexity and growing data from different sources, “big data”

TOWARDS AN EFFICIENT TRAFFIC CONGESTION PREDICTION METHOD BASED ON NEURAL NETWORKS AND BIG GPS DATA

ABSTRACT: The prediction of accurate traffic information such as speed, travel time, and congestion state is a very important task in many Intelligent Transportations Systems (ITS) applications. However, the dynamic changes in traffic conditions make this task harder. In fact, the type of road, such as the freeways and the highways in urban regions, can influence the driving speeds and the congestion state of the corresponding road. In this paper, we present a NNs-based model to predict the congestion state in roads. Our model handles new inputs and distinguishes the dynamic traffic patterns in two different types of roads: highways and freeways. The model has been tested using a big GPS database gathered from vehicles circulating in Tunisia. The NNs-based model has shown their capabilities of detecting the nonlinearity of dynamic changes and different patterns of roads compared to other nonparametric techniques from the literature. ABSTRAK: Ramalan maklumat trafik yang tepat seperti kelajuan, masa perjalanan dan keadaan kesesakan adalah tugas yang sangat penting dalam banyak aplikasi Sistem Pengangkutan Pintar (ITS). Walau bagaimanapun, perubahan keadaan lalu lintas yang dinamik menjadikan tugas ini menjadi lebih sukar. Malah, jenis jalan raya, seperti jalan raya dan lebuh raya di kawasan bandar, boleh mempengaruhi kelajuan memandu dan keadaan kesesakan jalan yang sama. Dalam makalah ini, kami membentangkan model berasaskan NN untuk meramalkan keadaan kesesakan di jalan raya. Model kami mengendalikan input baru dan membezakan corak trafik dinamik dalam dua jenis jalan raya yang lebuh raya dan jalan raya. Model ini telah diuji menggunakan pangkalan data GPS yang besar yang dikumpulkan dari kenderaan yang beredar di Tunisia. Model berasaskan NNs telah menunjukkan keupayaan mereka untuk mengesan ketiadaan perubahan dinamik dan pola jalan yang berbeza berbanding dengan teknik nonparametrik yang lain dari kesusasteraan