Amnir Hadachi

Cell phone subscribers mobility prediction using enhanced Markov Chain algorithm

This article presents a mobility prediction method for mobile phone users based on an enhanced Markov Chain algorithm. The mobile phone data has a highly dynamic nature and a sparcely sampled aspect; therefore, the prediction of users mobility location poses a challenge. Our enhancement approach can be summarized as an embedded association of rules applied to a Markov chain algorithm. The proposed solution is encouraging for the next generation of mobile networks and it can be used to optimize the existing mobile network infrastructure, road traffic, tracking systems and localization. Validation of our system was carried out using real data collected from the field.

An application of the Sequential Monte Carlo to increase the accuracy of travel time estimation in urban areas

This paper presents an application of the Sequential Monte Carlo that will help to increase the accuracy of travel time estimations in our historical data. Our estimation filter is based on the Monte Carlo Method and was modeled in such a way as to be applicable to our new kind of data in order to estimate travel time per section of road. We took into consideration the delay time while changing the sections to symbolize the delay due to traffic lights or crossroads. We worked on an urban zone of Rouen, a French city, to evaluate our application. In this application, information is collected from a specific GPS system that warns drivers of the location of both fixed and mobile speed radars. Unlike the classical GPS system, this system is characterized by the data flow frequency where the GPS data is received from the probe vehicles at one minute intervals. After receiving the data we apply the map matching method in order to correct the GPS errors. Also, our geo-referencing system has special features; each road or section of road is formed by nodes and segments, and the intersection between each section is called a PUMAS points. The PUMAS Points are GPS coordinate points on a digital map which can be propagated or moved without cost, providing total flexibility to mesh a city or rural area. Over all the performance of the filter estimator is around 85% if we set our threshold at 50%.

Practical testing application of travel time estimation using applied Monte Carlo Method and adaptive estimation from probes

This paper presents a practical testing of two different methods to estimate the travel time in urban areas. The purpose behind this testing is to validate the behavior of each method regarding the road aspect in urban areas. The first method is based on Monte Carlo Method and the second one is based on adaptive estimation from probes. Both methods were modified to be adapted to our case and also to the nature of our data. The paper also describes an experiment with real-world data that was used in the testing of the two methods. Moreover it contains the architecture of the system used in order to make the tests. This work yeilded interesting results based on real-world experiments which give clear feedback about the application of the two methods to compute the travel time estimation per road section that can be used for processing the historical database as well as real time data. In general this work is a suitable validation of the two methods and encouraging for our future perspectives.

Introducing Cellular Network Layer into SUMO for Simulating Vehicular Mobile Devices' Interactions in Urban Environment.

During the last decade researchers have been demonstrating the importance of mobile data or CDR data in depicting the human mobility patterns. However, this type of data is not easy to get access to from mobile operators. Besides, in order to make this type of data available and enable their usage for the scientific communities the process can face many constraints that can constitute obstacle. From this perspective, this paper introduces a way to produce realistic real-life mobility logs through the traffic simulation tool SUMO, which has been enhanced with a cellular network layer to mimic cellular networking behavior.

A new approach for mobile positioning using the CDR data of cellular networks

Nowadays, mobile devices are equipped with a number of radio transceivers which are active every day and everywhere. As a result, vast amounts of data and technical logs are collected by mobile operators. For this reason, mobile phones have a great potential for sensing urban and rural mobility and population displacement. Therefore, in this article, we are proposing a new approach for estimating the location of mobile subscribers within the coverage area of a mobile network. The method created is based on enhanced Kalman filter featured with integrated mobility models. The algorithm allows estimating location of mobile subscribers by knowing only the network coverage cell to which they are connected. The results are very encouraging and they can be very beneficial for applications in intelligent transportation systems and location based services based on the use of Call Detail Records (CDRs) data.

Mobility episode detection from CDR's data using switching Kalman filter

The detection of stay-jump-and-moving movement episodes using only cellular data is a big challenge due to the nature of the data. In this article, we propose a method to automatically detect the movement episodes (stay-jump-and-moving) from sparsely sampled spatio-temporal data, in our case Call Detail Records (CDRs), using switching Kalman filter with a new integrated movement model and cellular coverage optimization approach. The algorithm is capable of estimating the movement episodes and classifying the trajectory sequences associated to a stay, a jump or a moving action. The result of this approach can be beneficial for applications using cellular data related to traffic management, mobility profiling, and semantic enrichment.