| People | Locations | Statistics |
|---|---|---|
| Serhiienko, Serhii |
| |
| Schmalz, Ulrike |
| |
| Oliveira, Marisa |
| |
| Ribeiro Pereira, Maria Teresa |
| |
| Bellér, Gábor |
| |
| Araujo, M. |
| |
| Frey, Michael | Karlsruhe |
|
| Coutinho-Rodrigues, João | Coimbra |
|
| Wouters, Christian Guillaume Louise | Aachen |
|
| Kessel, Paul J. Van Van |
| |
| Árpád, István |
| |
| Fontul, Simona |
| |
| Kocsis, Dénes |
| |
| Cigada, Alfredo | Milan |
|
| Oort, Neils Van | Delft |
|
| Agárdi, Anita | Miskolc |
|
| Andrews, Gordon E. |
| |
| Sousa, Nuno |
| |
| Witlox, Frank Jacomina Albert | Ghent |
|
| Dobruszkes, Frederic |
| |
| Kiss, Judit T. |
| |
| Hadachi, Amnir | Saint-Étienne-du-Rouvray |
|
| Hamilton, Carl J. | Kunovice |
|
| Misiura, Serhii |
| |
| Schimpf, Marina |
|
Hadachi, Amnir
Institut National des Sciences Appliquées de Rouen
in Cooperation with on an Cooperation-Score of 37%
Topics
- crash
- machinery
- learning
- machine learning
- injury
- data
- case study
- city
- simulation
- estimating
- optimisation
- implementation
- employed
- modal split
- calibration
- smart city
- real time control
- pedestrian
- sensor
- urban area
- pedestrian flow
- transport demand
- vision
- transformer
- researcher
- decision making
- architecture
- internet
- cloud
- fog
- migration
- camera
- laser radar
- deep learning
- survey
- travel
- travel time
- telephone
- cellular telephone
- mobility pattern
- customer
- noise
- data file
- filtration
- reconstruction
- antenna
- driving
- autonomous automobile
- trajectory
- particulate
- filter
- positioning
- position fixing
- modeling
- neural network
- pavement
- accelerometer
- commuting
- mining
- public transport
- traffic congestion
- road
- highway traffic
- forecasting
- experiment
- weather condition
- commuter
- network model
- assessment
- algorithm
- hub
- map
- handheld computer
- vehicle
- data collection
- planning
- behavior
- app
- foundation
- recommendation
- big data
- state of the art
- scientist
- SUMO
- intelligent transportation system
- location based service
- transceiver
- rural mobility
- chain
- Markov chain
- estimate
- Monte Carlo method
- radar
- driver
- computer science
- intersection
- flexibility
- probe vehicle
- rural area
- time interval
- digital map
- traffic signal
- referencing
- show 73 more
Publications
- 2023Exploring Machine Learning Techniques to Identify Important Factors Leading to Injury in Curve Related Crashes
- 2022Real-Time System for Daily Modal Split Estimation and OD Matrices Generation Using IoT Data: A Case Study of Tartu Citycitations
- 2022A Real-Time Model for Pedestrian Flow Estimation in Urban Areas based on IoT Sensorscitations
- 2022Real-Time Calibration of Disaggregated Traffic Demand
- 2022Transformer for Multiple Object Tracking: Exploring Locality to Vision
- 2022Adaptive Edge Process Migration for IoT in Heterogeneous Fog and Edge Computing Environments
- 2021NetCalib: A Novel Approach for LiDAR-Camera Auto-calibration Based on Deep Learning
- 2021A Survey on the Advancement of Travel Time Estimation Using Mobile Phone Network Datacitations
- 2021CDR Based Trajectories: Tentative for Filtering Ping-pong Handover
- 2021This is The Way: Sensors Auto-calibration Approach Based on Deep Learning for Self-driving Carscitations
- 2021Mobile Positioning and Trajectory Reconstruction Based on Mobile Phone Network Data: A Tentative Using Particle Filtercitations
- 2021Towards state-full positioning of mobile subscribers through advanced cell coverage modeling techniquecitations
- 2020Road Surface Recognition Based on DeepSense Neural Network using Accelerometer Datacitations
- 2020Unveiling large-scale commuting patterns based on mobile phone cellular network datacitations
- 2020Mining Large-scale Mobility Patterns Using Mobile Phone Network Data
- 2020Adaptive Edge Process Migration for IoT in Heterogeneous Fog and Edge Computing Environmentscitations
- 2020Neural Networks Model for Travel Time Prediction Based on ODTravel Time Matrix
- 2020Evaluation of the Robustness of Visual SLAM Methods in Different Environments
- 2020Mobile Fog Computing
- 2019Mobility Episode Discovery in the Mobile Networks Based on Enhanced Switching Kalman Filtercitations
- 2019From Mobility Analysis to Mobility Hubs Discovery: A Concept Based on Using CDR Data of the Mobile Networkscitations
- 2019Exploring a New Model for Mobile Positioning Based on CDR Data of The Cellular Networks
- 2019OD-Matrix Extraction based on Trajectory Reconstruction from Mobile Datacitations
- 2018Real-time Vehicles Tracking Based on Mobile Multi-sensor Fusioncitations
- 2018Mobile Big Data: Foundations, State of the Art, and Future Directionscitations
- 2018Introducing Cellular Network Layer into SUMO for Simulating Vehicular Mobile Devices' Interactions in Urban Environmentcitations
- 2017A new approach for mobile positioning using the CDR data of cellular networkscitations
- 2017Spatio-temporal mobility analysis for community detection in the mobile networks using CDR datacitations
- 2017The Impact of Morphological Processing and Feature Selection on Handwriting Recognition Accuracy
- 2015Real Time Movement Labeling of Mobile Event Datacitations
- 2015Mobility episode detection from CDR's data using switching Kalman filtercitations
- 2014Cell Phone Subscribers Mobility Prediction Using Enhanced Markov Chain Algorithm
- 2013Approach to estimate travel time using sparsely sampled GPS data in urban networkscitations
- 2012Travel Time Estimation Using Cooperative Probes Vehicles
- 2012Practical Testing Application of Travel Time Estimation Using Applied Monte Carlo Method and Adaptive Estimation from Probes
- 2011An Application of the Sequential Monte Carlo to Increase the Accuracy of Travel Time Estimation in Urban Areascitations
Places of action
document
CDR Based Trajectories: Tentative for Filtering Ping-pong Handover
Abstract
Call Detail Records (CDRs) coupled with the coverage area locations provide the operator with an incredible amount of information on its customers' whereabouts and movement. Due to the non-static and overlapping nature of the antenna coverage area there commonly exist situations where cellphones geographically close to each other can be connected to different antennas due to handover rule - the operator hands over a certain cellphone to another antenna to spread the load between antennas. Hence, this aspect introduces a ping-pong handover phenomena in the trajectories extracted from the CDR data which can be misleading in understanding the mobility pattern. To reconstruct accurate trajectories it is a must to reduce the number of those handovers appearing in the dataset. This letter presents a novel approach for filtering ping-pong handovers from CDR based trajectories. Primarily, the approach is based on anchors model utilizing different features and parameters extracted from the coverage areas and reconstructed trajectories mined from the CDR data. Using this me thodology we can significantly reduce the ping-pong handover noise in the trajectories, which gives a more accurate reconstruction of the customers' movement pattern.
Topics
Search in FID move catalog