16.7.2.5.10 Traffic Flow Analysis Using Phone Signals, Cell Data, Wi-Fi data

Chapter Contents (Back)
Traffic Flow. Smart Highways. Phone Data. Using phone data for traffic. Including pedestrian movement.
See also Indoor Localization, Navigation Issues, Non-Image, Wi-Fi Positioning. Non-phone GPS papers:
See also Traffic Flow Analysis, GPS, GNSS.

Ferrari, L.[Laura], Mamei, M.[Marco],
Discovering City Dynamics through Sports Tracking Applications,
Computer(44), No. 12, December 2011, pp. 63-68.
IEEE DOI 1112
Tracking data from the phone directly. BibRef

Calabrese, F., Colonna, M., Lovisolo, P., Parata, D., Ratti, C.,
Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome,
ITS(12), No. 1, March 2011, pp. 141-151.
IEEE DOI 1103
BibRef

Pan, J.J.F.[Jeffrey Jun-Feng], Pan, S.J.[Sinno Jialin], Yin, J.[Jie], Ni, L.M.[Lionel M.], Yang, Q.A.[Qi-Ang],
Tracking Mobile Users in Wireless Networks via Semi-Supervised Colocalization,
PAMI(34), No. 3, March 2012, pp. 587-600.
IEEE DOI 1201
BibRef

Caceres, N., Romero, L.M., Benitez, F.G., del Castillo, J.M.,
Traffic Flow Estimation Models Using Cellular Phone Data,
ITS(13), No. 3, September 2012, pp. 1430-1441.
IEEE DOI 1209
BibRef

Gisdakis, S., Manolopoulos, V., Tao, S., Rusu, A., Papadimitratos, P.,
Secure and Privacy-Preserving Smartphone-Based Traffic Information Systems,
ITS(16), No. 3, June 2015, pp. 1428-1438.
IEEE DOI 1506
Authentication BibRef

Engelbrecht, J., Booysen, M.J., van Rooyen, G.J., Bruwer, F.J.,
Survey of smartphone-based sensing in vehicles for intelligent transportation system applications,
IET-ITS(9), No. 10, 2015, pp. 924-935.
DOI Link 1512
computerised monitoring BibRef

Jahangiri, A., Rakha, H.A.,
Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data,
ITS(16), No. 5, October 2015, pp. 2406-2417.
IEEE DOI 1511
decision trees BibRef

Wahlström, J., Skog, I., Händel, P.,
Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary,
ITS(18), No. 10, October 2017, pp. 2802-2825.
IEEE DOI 1710
Internet of Things, Smartphones, driver classification, telematics, usage-based-insurance, vehicle, navigation BibRef

Gan, H.C.[Hong-Cheng],
To switch travel mode or not? Impact of Smartphone delivered high-quality multimodal information,
IET-ITS(9), No. 4, 2015, pp. 382-390.
DOI Link 1506
mobile computing BibRef

Janecek, A., Valerio, D., Hummel, K.A., Ricciato, F., Hlavacs, H.,
The Cellular Network as a Sensor: From Mobile Phone Data to Real-Time Road Traffic Monitoring,
ITS(16), No. 5, October 2015, pp. 2551-2572.
IEEE DOI 1511
cellular radio BibRef

Lv, M., Chen, L., Wu, X., Chen, G.,
A Road Congestion Detection System Using Undedicated Mobile Phones,
ITS(16), No. 6, December 2015, pp. 3060-3072.
IEEE DOI 1512
Accelerometers BibRef

Assemi, B., Safi, H., Mesbah, M., Ferreira, L.,
Developing and Validating a Statistical Model for Travel Mode Identification on Smartphones,
ITS(17), No. 7, July 2016, pp. 1920-1931.
IEEE DOI 1608
data privacy BibRef

Demissie, M.G., Phithakkitnukoon, S., Sukhvibul, T., Antunes, F., Gomes, R., Bento, C.,
Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal,
ITS(17), No. 9, September 2016, pp. 2466-2478.
IEEE DOI 1609
Africa BibRef

Xu, Y.[Yang], Shaw, S.L.[Shih-Lung], Fang, Z.X.[Zhi-Xiang], Yin, L.[Ling],
Estimating Potential Demand of Bicycle Trips from Mobile Phone Data: An Anchor-Point Based Approach,
IJGI(5), No. 8, 2016, pp. 131.
DOI Link 1609
BibRef

Lai, W.K.[Wei-Kuang], Kuo, T.H.[Ting-Huan],
Vehicle Positioning and Speed Estimation Based on Cellular Network Signals for Urban Roads,
IJGI(5), No. 10, 2016, pp. 181.
DOI Link 1610
BibRef

Yang, X.P.[Xi-Ping], Fang, Z.X.[Zhi-Xiang], Xu, Y.[Yang], Shaw, S.L.[Shih-Lung], Zhao, Z.Y.[Zhi-Yuan], Yin, L.[Ling], Zhang, T.[Tao], Lin, Y.[Yunong],
Understanding Spatiotemporal Patterns of Human Convergence and Divergence Using Mobile Phone Location Data,
IJGI(5), No. 10, 2016, pp. 177.
DOI Link 1610
BibRef

Lu, S.W.[Shi-Wei], Fang, Z.X.[Zhi-Xiang], Zhang, X.R.[Xi-Rui], Shaw, S.L.[Shih-Lung], Yin, L.[Ling], Zhao, Z.Y.[Zhi-Yuan], Yang, X.P.[Xi-Ping],
Understanding the Representativeness of Mobile Phone Location Data in Characterizing Human Mobility Indicators,
IJGI(6), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Su, X., Caceres, H., Tong, H., He, Q.,
Online Travel Mode Identification Using Smartphones With Battery Saving Considerations,
ITS(17), No. 10, October 2016, pp. 2921-2934.
IEEE DOI 1610
Global Positioning System BibRef

Zhong, G., Wan, X., Zhang, J., Yin, T., Ran, B.,
Characterizing Passenger Flow for a Transportation Hub Based on Mobile Phone Data,
ITS(18), No. 6, June 2017, pp. 1507-1518.
IEEE DOI 1706
Base stations, Matrix converters, Mobile communication, Mobile handsets, Time series analysis, Transportation, Urban areas, Base station, mobile phone data, passenger transportation hub, temporal-spatial clustering, wireless, communication BibRef

Zhou, C., Jia, H., Juan, Z., Fu, X., Xiao, G.,
A Data-Driven Method for Trip Ends Identification Using Large-Scale Smartphone-Based GPS Tracking Data,
ITS(18), No. 8, August 2017, pp. 2096-2110.
IEEE DOI 1708
Big data, Data models, Global Positioning System, Internet, Timing, Transportation, GPS tracking data processing, data-driven method, random forest, trip, ends, identification BibRef

Basyoni, Y.[Yarah], Abbas, H.M.[Hazem M.], Talaat, H.[Hoda], El Dimeery, I.[Ibrahim],
Speed prediction from mobile sensors using cellular phone-based traffic data,
IET-ITS(11), No. 7, September 2017, pp. 387-396.
DOI Link 1710
BibRef

Lemmens, R.[Rob], Lungo, J.[Juma], Georgiadou, Y.[Yola], Verplanke, J.[Jeroen],
Monitoring Rural Water Points in Tanzania with Mobile Phones: The Evolution of the SEMA App,
IJGI(6), No. 10, 2017, pp. xx-yy.
DOI Link 1710
BibRef

Michau, G., Nantes, A., Bhaskar, A., Chung, E., Abry, P., Borgnat, P.,
Bluetooth Data in an Urban Context: Retrieving Vehicle Trajectories,
ITS(18), No. 9, September 2017, pp. 2377-2386.
IEEE DOI 1709
vehicular ad hoc networks, Bluetooth data sensor, spatially constrained shortest path analysis, traffic information gathering, travel time analysis, trip information extraction, vehicle trajectory retrieval, Roads, Trajectory, Vehicles, constrained shortest path, trajectories. BibRef

Jin, C.J.[Cheng-Jie], Knoop, V.L.[Victor L.], Jiang, R.[Rui], Wang, W.[Wei], Wang, H.[Hao],
Calibration and validation of cellular automaton traffic flow model with empirical and experimental data,
IET-ITS(12), No. 5, June 2018, pp. 359-365.
DOI Link 1805
BibRef

Yin, M., Sheehan, M., Feygin, S., Paiement, J.F., Pozdnoukhov, A.,
A Generative Model of Urban Activities from Cellular Data,
ITS(19), No. 6, June 2018, pp. 1682-1696.
IEEE DOI 1806
Activity recognition, Analytical models, Context modeling, Data models, Global Positioning System, Hidden Markov models, latent variables BibRef

Rupi, F.[Federico], Schweizer, J.[Joerg],
Evaluating cyclist patterns using GPS data from smartphones,
IET-ITS(12), No. 4, May 2018, pp. 279-285.
DOI Link 1804
BibRef

Wang, B., Gao, L., Juan, Z.,
Travel Mode Detection Using GPS Data and Socioeconomic Attributes Based on a Random Forest Classifier,
ITS(19), No. 5, May 2018, pp. 1547-1558.
IEEE DOI 1805
Automobiles, Feature extraction, Global Positioning System, Public transportation, Smart phones, GPS data, ROC curve, socioeconomic attributes BibRef

Wang, X.O.[Xi-Ong], Zhang, J.B.[Jin-Bei], Tian, X.H.[Xiao-Hua], Gan, X.Y.[Xiao-Ying], Guan, Y.F.[Yun-Feng], Wang, X.B.[Xin-Bing],
Crowdsensing-Based Consensus Incident Report for Road Traffic Acquisition,
ITS(19), No. 8, August 2018, pp. 2536-2547.
IEEE DOI 1808
Roads, Reliability, Sensors, Mobile communication, Inference algorithms, Smart phones, Wireless sensor networks, incentive BibRef

Batran, M.[Mohamed], Mejia, M.G.[Mariano Gregorio], Kanasugi, H.[Hiroshi], Sekimoto, Y.[Yoshihide], Shibasaki, R.[Ryosuke],
Inferencing Human Spatiotemporal Mobility in Greater Maputo via Mobile Phone Big Data Mining,
IJGI(7), No. 7, 2018, pp. xx-yy.
DOI Link 1808
BibRef

Senaratne, H., Mueller, M., Behrisch, M., Lalanne, F., Bustos-Jiménez, J., Schneidewind, J., Keim, D., Schreck, T.,
Urban Mobility Analysis With Mobile Network Data: A Visual Analytics Approach,
ITS(19), No. 5, May 2018, pp. 1537-1546.
IEEE DOI 1805
Antennas, Data visualization, GSM, Mobile communication, Trajectory, Urban areas, Visual analytics, Visual analytics, urban dynamics BibRef

Kim, Y.S.[Young-Sung], Ghorpade, A.[Ajinkya], Zhao, F.[Fang], Pereira, F.C.[Francisco C.], Zegras, P.C.[P. Christopher], Ben-Akiva, M.[Moshe],
Activity Recognition for a Smartphone and Web-Based Human Mobility Sensing System,
IEEE_Int_Sys(33), No. 4, July 2018, pp. 5-23.
IEEE DOI 1810
BibRef
Earlier: A1, A4, A3, A2, A5, A6:
Activity Recognition for a Smartphone Based Travel Survey Based on Cross-User History Data,
ICPR14(432-437)
IEEE DOI 1412
Frequency modulation, Quantization (signal), Activity recognition, Global Positioning System, Sociology, interactive data collection. Accuracy. Transport models, why the trip is made. BibRef

Zhou, Z.P.[Zhu-Ping], Yang, J.[Jiwei], Qi, Y.[Yong], Cai, Y.F.[Yi-Fei],
Support vector machine and back propagation neutral network approaches for trip mode prediction using mobile phone data,
IET-ITS(12), No. 10, December 2018, pp. 1220-1226.
DOI Link 1812
BibRef

Ashqar, H.I., Almannaa, M.H., Elhenawy, M., Rakha, H.A., House, L.,
Smartphone Transportation Mode Recognition Using a Hierarchical Machine Learning Classifier and Pooled Features From Time and Frequency Domains,
ITS(20), No. 1, January 2019, pp. 244-252.
IEEE DOI 1901
Feature extraction, Frequency-domain analysis, Transportation, Sensors, Time-domain analysis, Global Positioning System, hierarchical modeling BibRef

Ghahramani, M., Zhou, M., Hon, C.T.,
Extracting Significant Mobile Phone Interaction Patterns Based on Community Structures,
ITS(20), No. 3, March 2019, pp. 1031-1041.
IEEE DOI 1903
Mobile handsets, Urban areas, Data analysis, Clustering methods, Transportation, Planning, Spatial databases, Big data, spatio-temporal analysis BibRef

Gao, Z.Q.[Zhi-Qiang], Liu, D.[Dawei], Huang, K.[Kaizhu], Huang, Y.[Yi],
Context-Aware Human Activity and Smartphone Position-Mining with Motion Sensors,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Demissie, M.G., Phithakkitnukoon, S., Kattan, L.,
Trip Distribution Modeling Using Mobile Phone Data: Emphasis on Intra-Zonal Trips,
ITS(20), No. 7, July 2019, pp. 2605-2617.
IEEE DOI 1907
Mobile handsets, Poles and towers, Data models, Estimation, Gravity, Transportation, Sociology, Gravity model, intra-zonal trips, trip distribution model BibRef

He, S.L.[Shang-Lu], Ding, F.[Fan], Zhou, Y.[Yang], Cheng, Y.[Yang], Ran, B.[Bin],
Investigating and modelling the relationship between traffic volume and extracts from cellphone activity data,
IET-ITS(13), No. 8, August 2019, pp. 1299-1308.
DOI Link 1908
BibRef

Wu, H.[Hao], Liu, L.[Lingbo], Yu, Y.[Yang], Peng, Z.H.[Zheng-Hong], Jiao, H.Z.[Hong-Zan], Niu, Q.A.[Qi-Ang],
An Agent-based Model Simulation of Human Mobility Based on Mobile Phone Data: How Commuting Relates to Congestion,
IJGI(8), No. 7, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Rupi, F.[Federico], Poliziani, C.[Cristian], Schweizer, J.[Joerg],
Data-driven Bicycle Network Analysis Based on Traditional Counting Methods and GPS Traces from Smartphone,
IJGI(8), No. 8, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Gore, N.[Ninad], Arkatkar, S.[Shriniwas], Joshi, G.[Gaurang], Bhaskar, A.[Ashish],
Exploring credentials of Wi-Fi sensors as a complementary transport data: an Indian experience,
IET-ITS(13), No. 12, December 2019, pp. 1860-1869.
DOI Link 1912
BibRef

Derrmann, T., Frank, R., Viti, F., Engel, T.,
How Road and Mobile Networks Correlate: Estimating Urban Traffic Using Handovers,
ITS(21), No. 2, February 2020, pp. 521-530.
IEEE DOI 2002
Handover, Roads, Data models, Mobile handsets, Urban areas, Mobile network, cellular, traffic state, traffic flow theory, macroscopic fundamental diagram BibRef

Ebrahimpour, Z.[Zeinab], Wan, W.[Wanggen], García, J.L.V.[José Luis Velázquez], Cervantes, O.[Ofelia], Hou, L.[Li],
Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data,
IJGI(9), No. 2, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Liu, X.D.[Xu-Dong], Tian, Y.Z.[Yong-Zhong], Zhang, X.Q.[Xue-Qian], Wan, Z.[Zuyi],
Identification of Urban Functional Regions in Chengdu Based on Taxi Trajectory Time Series Data,
IJGI(9), No. 3, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Mei, Z.Y.[Zhen-Yu], Ding, W.[Wenchao], Feng, C.[Chi], Shen, L.[Liting],
Identifying commuters based on random forest of smartcard data,
IET-ITS(14), No. 4, April 2020, pp. 207-212.
DOI Link 2004
BibRef

Kim, K.,
Identifying the Structure of Cities by Clustering Using a New Similarity Measure Based on Smart Card Data,
ITS(21), No. 5, May 2020, pp. 2002-2011.
IEEE DOI 2005
Urban areas, Public transportation, Smart cards, Clustering algorithms, Feature extraction, Detection algorithms, data mining BibRef

Gong, L.S.[Lun-Sheng], Jin, M.H.[Mei-Han], Liu, Q.A.[Qi-Ang], Gong, Y.X.[Yong-Xi], Liu, Y.[Yu],
Identifying Urban Residents' Activity Space at Multiple Geographic Scales Using Mobile Phone Data,
IJGI(9), No. 4, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Sakamanee, P.[Pitchaya], Phithakkitnukoon, S.[Santi], Smoreda, Z.[Zbigniew], Ratti, C.[Carlo],
Methods for Inferring Route Choice of Commuting Trip From Mobile Phone Network Data,
IJGI(9), No. 5, 2020, pp. xx-yy.
DOI Link 2005
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Bicakci, Y.S.[Yunus Serhat], Seker, D.Z.[Dursun Zafer], Demirel, H.[Hande],
Location-Based Analyses for Electronic Monitoring of Parolees,
IJGI(9), No. 5, 2020, pp. xx-yy.
DOI Link 2005
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Yazdizadeh, A., Patterson, Z., Farooq, B.,
Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey,
ITS(21), No. 6, June 2020, pp. 2232-2239.
IEEE DOI 2006
Global Positioning System, Trajectory, Transportation, Predictive models, Feature extraction, machine learning BibRef

Peng, Z.H.[Zheng-Hong], Wang, R.[Ru], Liu, L.[Lingbo], Wu, H.[Hao],
Fine-Scale Dasymetric Population Mapping with Mobile Phone and Building Use Data Based on Grid Voronoi Method,
IJGI(9), No. 6, 2020, pp. xx-yy.
DOI Link 2006
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He, L.[Li], Páez, A.[Antonio], Jiao, J.[Jianmin], An, P.[Ping], Lu, C.[Chuntian], Mao, W.[Wen], Long, D.P.[Dong-Ping],
Ambient Population and Larceny-Theft: A Spatial Analysis Using Mobile Phone Data,
IJGI(9), No. 6, 2020, pp. xx-yy.
DOI Link 2006
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Zhang, G.Y.[Guang-Yuan], Rui, X.P.[Xiao-Ping], Poslad, S.[Stefan], Song, X.F.[Xian-Feng], Fan, Y.[Yonglei], Wu, B.[Bang],
A Method for the Estimation of Finely-Grained Temporal Spatial Human Population Density Distributions Based on Cell Phone Call Detail Records,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
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Zheng, L., Xia, D., Chen, L., Sun, D.,
Understanding Citywide Resident Mobility Using Big Data of Electronic Registration Identification of Vehicles,
ITS(21), No. 10, October 2020, pp. 4363-4377.
IEEE DOI 2010
Trajectory, Automobiles, Public transportation, Roads, Global Positioning System, Urban areas, attractive area BibRef

Carboni, A.[Angela], Deflorio, F.[Francesco], Chiara, B.D.[Bruno Dalla],
Monitoring truck's operations at freight intermodal terminals: traffic observation by scanning on-board devices,
IET-ITS(14), No. 12, December 2020, pp. 1638-1646.
DOI Link 2011
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Das, R.D., Purves, R.S.,
Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India,
ITS(21), No. 12, December 2020, pp. 5213-5222.
IEEE DOI 2012
Twitter, Roads, Sensors, Task analysis, Urban areas, Accidents, Georeference, jaccard distance, placename, toponym, traffic, tweet, geographical information science (GIS) BibRef

Scholz, J.[Johannes], Jeznik, J.[Janja],
Evaluating Geo-Tagged Twitter Data to Analyze Tourist Flows in Styria, Austria,
IJGI(9), No. 11, 2020, pp. xx-yy.
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Liang, X., Zhang, Y., Wang, G., Xu, S.,
A Deep Learning Model for Transportation Mode Detection Based on Smartphone Sensing Data,
ITS(21), No. 12, December 2020, pp. 5223-5235.
IEEE DOI 2012
Transportation, Accelerometers, Sensors, Gravity, Acceleration, Deep learning, Data models, Transportation mode, deep learning, accelerometer BibRef

Tu, L., Wang, S., Zhang, D., Zhang, F., He, T.,
ViFi-MobiScanner: Observe Human Mobility via Vehicular Internet Service,
ITS(22), No. 1, January 2021, pp. 280-292.
IEEE DOI 2012
Wireless fidelity, Global Positioning System, Sensors, Web and internet services, Urban areas, Observability, multi-source data mining BibRef

Sun, R., Cheng, Q., Xie, F., Zhang, W., Lin, T., Ochieng, W.Y.,
Combining Machine Learning and Dynamic Time Wrapping for Vehicle Driving Event Detection Using Smartphones,
ITS(22), No. 1, January 2021, pp. 194-207.
IEEE DOI 2012
Smart phones, Sensors, Event detection, Machine learning algorithms, Vehicles, Bagging, Acceleration, dynamic time wrapping BibRef

Wei, M., Liu, T., Sun, B.,
Optimal Routing Design of Feeder Transit With Stop Selection Using Aggregated Cell Phone Data and Open Source GIS Tool,
ITS(22), No. 4, April 2021, pp. 2452-2463.
IEEE DOI 2104
Routing, Mathematical model, Transportation, Analytical models, Programming, Tools, Mathematical programming, open source GIS tool BibRef

Liu, J.[Jian], Meng, B.[Bin], Wang, J.[Juan], Chen, S.[Siyu], Tian, B.[Bin], Zhi, G.Q.[Guo-Qing],
Exploring the Spatiotemporal Patterns of Residents' Daily Activities Using Text-Based Social Media Data: A Case Study of Beijing, China,
IJGI(10), No. 6, 2021, pp. xx-yy.
DOI Link 2106
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Wang, B.Q.[Bing-Qing], Meng, B.[Bin], Wang, J.[Juan], Chen, S.[Siyu], Liu, J.[Jian],
Perceiving Residents' Festival Activities Based on Social Media Data: A Case Study in Beijing, China,
IJGI(10), No. 7, 2021, pp. xx-yy.
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Zhang, X.M.[Xiao-Ming], Gao, F.[Feng], Liao, S.[Shunyi], Zhou, F.[Fan], Cai, G.[Guanfang], Li, S.[Shaoying],
Portraying Citizens' Occupations and Assessing Urban Occupation Mixture with Mobile Phone Data: A Novel Spatiotemporal Analytical Framework,
IJGI(10), No. 6, 2021, pp. xx-yy.
DOI Link 2106
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Wan, W.[Wei], Cai, M.[Ming],
Phone-vehicle trajectory matching framework based on ALPR and cellular signalling data,
IET-ITS(15), No. 1, 2021, pp. 107-118.
DOI Link 2106
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Cumbane, S.P.[Silvino Pedro], Gidófalvi, G.[Gyozo],
Spatial Distribution of Displaced Population Estimated Using Mobile Phone Data to Support Disaster Response Activities,
IJGI(10), No. 6, 2021, pp. xx-yy.
DOI Link 2106
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Zhu, X.Y.[Xiao-Yu], Luo, Y.[Yueyi], Liu, A.[Anfeng], Tang, W.J.[Wen-Juan], Bhuiyan, M.Z.A.[Md. Zakirul Alam],
A Deep Learning-Based Mobile Crowdsensing Scheme by Predicting Vehicle Mobility,
ITS(22), No. 7, July 2021, pp. 4648-4659.
IEEE DOI 2107
Sensors, Urban areas, Trajectory, Task analysis, Recruitment, Prediction algorithms, Data centers, Mobile crowdsensing, data collection BibRef

Shit, R.C.[Rathin Chandra], Sharma, S.[Suraj], Yelamarthi, K.[Kumar], Puthal, D.[Deepak],
AI-Enabled Fingerprinting and Crowdsource-Based Vehicle Localization for Resilient and Safe Transportation Systems,
ITS(22), No. 7, July 2021, pp. 4660-4669.
IEEE DOI 2107
Location awareness, Fingerprint recognition, Machine learning, Training, Databases, Machine learning algorithms, fingerprinting BibRef

Antar, A.D.[Anindya Das], Ahmed, M.[Masud], Ahad, M.A.R.[Md Atiqur Rahman],
Recognition of human locomotion on various transportations fusing smartphone sensors,
PRL(148), 2021, pp. 146-153.
Elsevier DOI 2107
Activity recognition, Deep learning, Feature selection, Mode technique, Post processing, Statistical classification, Smartphone sensors impact BibRef

Liu, S.J.[Shao-Jun], Long, Y.[Yi], Zhang, L.[Ling], Liu, H.[Hao],
Semantic Enhancement of Human Urban Activity Chain Construction Using Mobile Phone Signaling Data,
IJGI(10), No. 8, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Wang, C.X.[Chen-Xing], Luo, H.Y.[Hai-Yong], Zhao, F.[Fang], Qin, Y.J.[Yan-Jun],
Combining Residual and LSTM Recurrent Networks for Transportation Mode Detection Using Multimodal Sensors Integrated in Smartphones,
ITS(22), No. 9, September 2021, pp. 5473-5485.
IEEE DOI 2109
Feature extraction, Global Positioning System, Smart phones, Public transportation, Intelligent sensors, Context awareness, activity recognition BibRef

Determe, J.F.[Jean-François], Singh, U.[Utkarsh], Horlin, F.[François], de Doncker, P.[Philippe],
Forecasting Crowd Counts With Wi-Fi Systems: Univariate, Non-Seasonal Models,
ITS(22), No. 10, October 2021, pp. 6407-6419.
IEEE DOI 2110
Wireless fidelity, Sensors, Forecasting, Predictive models, Probes, Monitoring, Computational modeling, Crowd monitoring and control, Box-Cox transformation BibRef


Miao, Y.[Yu], Tang, X.H.[Xue-Hua], Wang, Z.Y.[Zhong-Yuan],
An Automatic Semantic Map Generation Method Using Trajectory Data,
ISPRS20(B4:63-67).
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Features for cell phone trajectory data. BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Traffic Flow Analysis, GPS, GNSS .


Last update:Nov 1, 2021 at 09:26:50