Об использовании данных мобильных абонентов в цифровой урбанистике
Аннотация
По исследованиям международного агентства “We are social” за 2018 год в мире насчитывается 5.135 миллиарда пользователей мобильных устройств, что составляет 68% от общей численности населения. В процессе функционирования каждого из мобильных устройств порождаются данные, которые могут быть использованы для решения прикладных задач. Статья состоит из двух частей. В первой части приведен краткий обзор методов обработки четырех видов данных, получаемых со смартфонов: данных сотового оператора, данных, полученных с акселерометров, GPS и Wi-Fi датчиков. Обзор каждой группы методов систематизирован в виде таблиц, которые помогут быстро найти метод для решения нужной задачи или для обработки имеющихся данных. Вторая часть посвящена выявлению основных преимуществ и недостатков использования каждого типа данных. Эта информация так же представлена в виде таблицы, которая позволит выбрать нужный тип данных для решения задачи. В работе приведен обзор статей, посвященных анализу основных недостатков и путей их преодоления, в частности, краудсенсингу. В заключении делается вывод о том, что дальнейшее развитие в области анализа данных, полученных со смартфонов, может состоять в решении существующих проблем, поиске новых приложений и совместном использовании разных типов данных.
Литература
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[14] Wu H., Liu L., Yu Y., Peng Z. Evaluation and Planning of Urban Green Space Distribution Based on Mobile Phone Data and Two-Step Floating Catchment Area Method. Sustainability. 2018; 10(1):214. (In Eng.) DOI: 10.3390/su10010214
[15] Chu J. et al. Passenger Demand Prediction with Cellular Footprints. In: 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Hong Kong, 2018, pp. 1-9. (In Eng.) DOI: 10.1109/SAHCN.2018.8397114
[16] Nyhan M.M., Kloog I., Britter R., Ratti C., Koutrakis P. Quantifying population exposure to air pollution using individual mobility patterns inferred from mobile phone data. Journal of Exposure Science & Environmental Epidemiology. 2019; 29:238-247. (In Eng.) DOI: 10.1038/s41370-018-0038-9
[17] Liu S. Navigation devices & usage - Statistics & Facts. Jan 31, 2018. Available at: https://www.statista.com/topics/2221/navigation-devices-and-usage/ (accessed 16.05.2019). (In Eng.)
[18] Zhang H., Shi B., Zhuge C., Wang W. Detecting Taxi Travel Patterns using GPS Trajectory Data: A Case Study of Beijing. KSCE Journal of Civil Engineering. 2019; 23(4):1797-1805. (In Eng.) DOI: 10.1007/s12205-019-0580-6
[19] Hu X., An S., Wang J. Taxi Driver’s Operation Behavior and Passengers’ Demand Analysis Based on GPS Data. Journal of Advanced Transportation. 2018; 2018:6197549. (In Eng.) DOI: 10.1155/2018/6197549
[20] Zhao J., et al. Truck Traffic Speed Prediction Under Non-Recurrent Congestion: Based on Optimized Deep Learning Algorithms and GPS Data. IEEE Access. 2019; 7:9116-9127. (In Eng.) DOI: 10.1109/ACCESS.2018.2890414
[21] Galpern P., Ladle A., Uribe F.A., Sandalack B., Doyle-Baker P. Assessing urban connectivity using volunteered mobile phone GPS locations. Applied Geography. 2018; 93:37-46. (In Eng.) DOI: 10.1016/j.apgeog.2018.02.009
[22] Zhai Yu., Baran P.K., Wu C. Can trail spatial attributes predict trail use level in urban forest park? An examination integrating GPS data and space syntax theory. Urban Forestry & Urban Greening. 2018; 29:171-182. (In Eng.) DOI: 10.1016/j.ufug.2017.10.008
[23] Chen P., Shen Q., Childress S. A GPS data-based analysis of built environment influences on bicyclist route preferences. International Journal of Sustainable Transportation. 2018; 12(3):218-231. (In Eng.) DOI: 10.1080/15568318.2017.1349222
[24] Deng M., Huang J., Zhang Yu., Liu H., Tang L., Tang J., Yang X. Generating urban road intersection models from low-frequency GPS trajectory data. International Journal of Geographical Information Science. 2018; 32:(12):2337-2361. (In Eng.) DOI: 10.1080/13658816.2018.1510124
[25] Yun H.J., Kang D.J., Lee M.J. Spatiotemporal distribution of urban walking tourists by season using GPS-based smartphone application. Asia Pacific Journal of Tourism Research. 2018; 23(11):1047-1061. (In Eng.) DOI: 10.1080/10941665.2018.1513949
[26] Yang X., Stewart K., Tang L., Xie Z., Li Q. A Review of GPS Trajectories Classification Based on Transportation Mode. Sensors. 2018; 18(11):3741. (In Eng.) DOI: 10.3390/s18113741
[27] Duan Z., Yang Y., Zhang K., Ni Y., Bajgain S. Improved Deep Hybrid Networks for Urban Traffic Flow Prediction Using Trajectory Data. IEEE Access. 2018; 6:31820-31827. (In Eng.) DOI: 10.1109/ACCESS.2018.2845863
[28] Brezmes T., Gorricho J-L., Cotrina J. Activity Recognition from Accelerometer Data on a Mobile Phone. In: Omatu S. et al. (eds) Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol. 5518. Springer, Berlin, Heidelberg, 2009, pp. 796-799. (In Eng.) DOI: 10.1007/978-3-642-02481-8_120
[29] Zhao H., Hou C., Alrobassy H., Zeng X. Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network. Journal of Computer Networks and Communications. 2019; 2019: 4967261. (In Eng.) DOI: 10.1155/2019/4967261
[30] Ignatov A. Real-time human activity recognition from accelerometer data using Convolutional Neural Networks. Applied Soft Computing. 2018; 62:915-922. (In Eng.) DOI: 10.1016/j.asoc.2017.09.027
[31] Almaslukh B., Al Muhtadi J., Artoli A. A robust convolutional neural network for online smartphone-based human activity recognition. Journal of Intelligent & Fuzzy Systems. 2018; 35(2): 1609-1620. (In Eng.) DOI: 10.3233/JIFS-169699
[32] Lee K., Kwan M-P. Physical activity classification in free-living conditions using smartphone accelerometer data and exploration of predicted results. Computers, Environment and Urban Systems. 2018; 67:124-131. (In Eng.) DOI: 10.1016/j.compenvurbsys.2017.09.012
[33] Lu D-N., Nguyen D-N., Nguyen T-H., Nguyen H- Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones. Sensors. 2018; 18(4):1036. (In Eng.) DOI: 10.3390/s18041036
[34] Won M., Sayan S., Park K-J. DeepWiTraffic: Low Cost WiFi-Based Traffic Monitoring System Using Deep Learning. CoRR. 2018; abs/1812.08208. Available at: http://arxiv.org/abs/1812.08208 (accessed 16.05.2019). (In Eng.)
[35] Depatla S., Muralidharan A., Mostofi Y. Occupancy estimation using only WiFi power measurements. IEEE Journal on Selected Areas in Communications. 2015; 33(7):1381-1393. (In Eng.) DOI: 10.1109/jsac.2015.2430272
[36] De Montjoye Y-A., Hidalgo C.A., Verleysen M., Blondel V.D. Unique in the crowd: The privacy bounds of human mobility. Scientific reports. 2013; 3: 1376. (In Eng.) DOI: 10.1038/srep01376
[37] Roth J.D., Tummala M., McEachen J.C., Scrofani J.W. Quantifying Location Privacy in Urban Next-Generation Cellular Networks. In: Proceedings of the 51st Hawaii International Conference on System Sciences. 2018, pp. 5831-5833. (In Eng.) DOI: 10.24251/hicss.2018.731
[38] Smartphone data offered to exchange for wireless energy for charging. In: Indicator. 08.09.2018. Available at: https://indicator.ru/mathematics/mobilnyj-kraudsensing-08-09-2018.htm (accessed 16.05.2019). (In Russ.)
[39] Lane N.D., Chon Y., Zhou L., Zhang Y., Li F., Kim D., Ding G., Zhao F., Cha H. Piggyback CrowdSensing (PCS): energy efficient crowdsourcing of mobile sensor data by exploiting smartphone app opportunities. In: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys ’13). Association for Computing Machinery, New York, NY, USA, 2013, Article 7, pp. 1-14. (In Eng.) DOI: 10.1145/2517351.2517372
[40] Namiot D., Sneps-Sneppe M. A Survey of Smart Cards Data Mining. In: Supplementary Proceedings of the Sixth International Conference on Analysis of Images, Social Networks and Texts (AIST 2017). CEUR Workshop Proceedings. 2017; 1975:314-325. Available at: http://ceur-ws.org/Vol-1975/paper33.pdf (accessed 16.05.2019). (In Eng.)
[41] Namiot D., Pokusaev O., Lazutkina V. On passenger flow data models for urban railways. International Journal of Open Information Technologies. 2018; 6(3):9-14. Available at: https://elibrary.ru/item.asp?id=32595087 (accessed 16.05.2019). (In Russ., abstract in Eng.)
[42] Namiot D.E., Pokusaev O.N., Kupriyanovsky V.P. On Patterns for the Use of Railway Stations. Sovremennye informacionnye tehnologii i IT-obrazovanie = Modern Information Technologies and IT-Education. 2018; 14(3):756-761. (In Russ., abstract in Eng.) DOI: 10.25559/SITITO.14.201803.756-761
[43] Nekraplonna M., Namiot D. Metro correspondence matrix analysis. International Journal of Open Information Technologies. 2019; 7(7):68-80. Available at: https://elibrary.ru/item.asp?id=38532115 (accessed 16.05.2019). (In Russ., abstract in Eng.)
[44] Namiot D., Sneps-Sneppe M. On Crowd Sensing Back-end. In: Data Analytics and Management in Data Intensive Domains - Selected Papers (DAMDID/RCDL 2016). CEUR Workshop Proceedings. 2016; 1752:168-175. Available at: http://ceur-ws.org/Vol-1752/paper28.pdf (accessed 16.05.2019). (In Eng.)
[45] Namiot D., Sneps-Sneppe M. On Data Persistence Models for Mobile Crowdsensing Applications. In: Kalinichenko L., Kuznetsov S., Manolopoulos Y. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2016. Communications in Computer and Information Science, vol. 706. Springer, Cham, 2017, pp. 192-204. (In Eng.) DOI: 10.1007/978-3-319-57135-5_14
[46] Daradkeh Y.I., ALdhaifallah M., Namiot D. Mobile Clouds for Smart Cities. International Journal of Online and Biomedical Engineering. 2017; 13(1):76-86. (In Eng.) DOI: 10.3991/ijoe.v13i01.6320
[2] Wang S., Min J., Yi B.K. Location-Based Services Mobiles: Technologies and Standards. Tutorial given at IEEE ICC 2008, Beijing, 2008. (In Eng.)
[3] Calabrese F., Ferrari L., Blondel V.D. Urban Sensing Using Mobile Phone Network Data: A Survey of Research. ACM Computing Surveys. 2014; 47(2):25. (In Eng.) DOI: 10.1145/2655691
[4] Chen X. et al. Data-Driven Prediction System of Dynamic People-Flow in Large Urban Network Using Cellular Probe Data. Journal of Advanced Transportation. 2019; 2019:9401630. (In Eng.) DOI: 10.1155/2019/9401630
[5] Yang F., Yao Z., Jin P.J., Xiong Y. Arterial link travel time estimation considering traffic signal delays using cellular handoff data. IET Intelligent Transport Systems. 2019; 13(3):461-468. (In Eng.) DOI: 10.1049/iet-its.2018.5186
[6] Roth J., Tummala M., McEachen J., Scrofani J. Quantifying Location Privacy in Urban Next-Generation Cellular Networks. In: Proceedings of the 51st Hawaii International Conference on System Sciences. 2018, pp. 5831-5838. (In Eng.) DOI: 10.24251/hicss.2018.731
[7] Candia J., Gonzalez M.C., Wang P., Schoenharl T., Madey G., Barabasi A-L. Uncovering individual and collective human dynamics from mobile phone records. Journal of Physics A: Mathematical and Theoretical. 2007; 41(22):224015. (In Eng.) DOI: 10.1088/1751-8113/41/22/224015
[8] Breyer N., Gundlegård D., Rydergren C. Cellpath Routing and Route Traffic Flow Estimation Based on Cellular Network Data. Journal of Urban Technology. 2018; 25(2):85-104. (In Eng.) DOI: 10.1080/10630732.2017.1386939
[9] Puura A., Silm S., Ahas R. The Relationship between Social Networks and Spatial Mobility: A Mobile-Phone-Based Study in Estonia. Journal of Urban Technology. 2018; 25(2):7-25. (In Eng.) DOI: 10.1080/10630732.2017.1406253
[10] Eagle N., Pentland A., Lazer D. Inferring Friendship Network Structure by Using Mobile Phone Data. Proceedings of the National Academy of Sciences of the United States of America. 2009; 106(36):15274-15278. (In Eng.) DOI: 10.1073/pnas.0900282106
[11] Wang D., Pedreschi D., Song C., Giannotti F., Barabasi A-L. Human mobility, social ties, and link prediction. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ’11). Association for Computing Machinery, New York, NY, USA, 2011, pp. 1100-1108. (In Eng.) DOI: 10.1145/2020408.2020581
[12] Farrahi K., Gatica-Perez D. Discovering routines from large-scale human locations using probabilistic topic models. ACM Transactions on Intelligent Systems and Technology. 2011; 2(1):3. (In Eng.) DOI: 10.1145/1889681.1889684
[13] Deville P., Linard C., Martin S., Gilbert M., Stevens F.R., Gaughan A.E., Blondel V.D., Tatem A.J. Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences of the United States of America. 2014. 6 p. (In Eng.) DOI: 10.1073/pnas.1408439111
[14] Wu H., Liu L., Yu Y., Peng Z. Evaluation and Planning of Urban Green Space Distribution Based on Mobile Phone Data and Two-Step Floating Catchment Area Method. Sustainability. 2018; 10(1):214. (In Eng.) DOI: 10.3390/su10010214
[15] Chu J. et al. Passenger Demand Prediction with Cellular Footprints. In: 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Hong Kong, 2018, pp. 1-9. (In Eng.) DOI: 10.1109/SAHCN.2018.8397114
[16] Nyhan M.M., Kloog I., Britter R., Ratti C., Koutrakis P. Quantifying population exposure to air pollution using individual mobility patterns inferred from mobile phone data. Journal of Exposure Science & Environmental Epidemiology. 2019; 29:238-247. (In Eng.) DOI: 10.1038/s41370-018-0038-9
[17] Liu S. Navigation devices & usage - Statistics & Facts. Jan 31, 2018. Available at: https://www.statista.com/topics/2221/navigation-devices-and-usage/ (accessed 16.05.2019). (In Eng.)
[18] Zhang H., Shi B., Zhuge C., Wang W. Detecting Taxi Travel Patterns using GPS Trajectory Data: A Case Study of Beijing. KSCE Journal of Civil Engineering. 2019; 23(4):1797-1805. (In Eng.) DOI: 10.1007/s12205-019-0580-6
[19] Hu X., An S., Wang J. Taxi Driver’s Operation Behavior and Passengers’ Demand Analysis Based on GPS Data. Journal of Advanced Transportation. 2018; 2018:6197549. (In Eng.) DOI: 10.1155/2018/6197549
[20] Zhao J., et al. Truck Traffic Speed Prediction Under Non-Recurrent Congestion: Based on Optimized Deep Learning Algorithms and GPS Data. IEEE Access. 2019; 7:9116-9127. (In Eng.) DOI: 10.1109/ACCESS.2018.2890414
[21] Galpern P., Ladle A., Uribe F.A., Sandalack B., Doyle-Baker P. Assessing urban connectivity using volunteered mobile phone GPS locations. Applied Geography. 2018; 93:37-46. (In Eng.) DOI: 10.1016/j.apgeog.2018.02.009
[22] Zhai Yu., Baran P.K., Wu C. Can trail spatial attributes predict trail use level in urban forest park? An examination integrating GPS data and space syntax theory. Urban Forestry & Urban Greening. 2018; 29:171-182. (In Eng.) DOI: 10.1016/j.ufug.2017.10.008
[23] Chen P., Shen Q., Childress S. A GPS data-based analysis of built environment influences on bicyclist route preferences. International Journal of Sustainable Transportation. 2018; 12(3):218-231. (In Eng.) DOI: 10.1080/15568318.2017.1349222
[24] Deng M., Huang J., Zhang Yu., Liu H., Tang L., Tang J., Yang X. Generating urban road intersection models from low-frequency GPS trajectory data. International Journal of Geographical Information Science. 2018; 32:(12):2337-2361. (In Eng.) DOI: 10.1080/13658816.2018.1510124
[25] Yun H.J., Kang D.J., Lee M.J. Spatiotemporal distribution of urban walking tourists by season using GPS-based smartphone application. Asia Pacific Journal of Tourism Research. 2018; 23(11):1047-1061. (In Eng.) DOI: 10.1080/10941665.2018.1513949
[26] Yang X., Stewart K., Tang L., Xie Z., Li Q. A Review of GPS Trajectories Classification Based on Transportation Mode. Sensors. 2018; 18(11):3741. (In Eng.) DOI: 10.3390/s18113741
[27] Duan Z., Yang Y., Zhang K., Ni Y., Bajgain S. Improved Deep Hybrid Networks for Urban Traffic Flow Prediction Using Trajectory Data. IEEE Access. 2018; 6:31820-31827. (In Eng.) DOI: 10.1109/ACCESS.2018.2845863
[28] Brezmes T., Gorricho J-L., Cotrina J. Activity Recognition from Accelerometer Data on a Mobile Phone. In: Omatu S. et al. (eds) Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol. 5518. Springer, Berlin, Heidelberg, 2009, pp. 796-799. (In Eng.) DOI: 10.1007/978-3-642-02481-8_120
[29] Zhao H., Hou C., Alrobassy H., Zeng X. Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network. Journal of Computer Networks and Communications. 2019; 2019: 4967261. (In Eng.) DOI: 10.1155/2019/4967261
[30] Ignatov A. Real-time human activity recognition from accelerometer data using Convolutional Neural Networks. Applied Soft Computing. 2018; 62:915-922. (In Eng.) DOI: 10.1016/j.asoc.2017.09.027
[31] Almaslukh B., Al Muhtadi J., Artoli A. A robust convolutional neural network for online smartphone-based human activity recognition. Journal of Intelligent & Fuzzy Systems. 2018; 35(2): 1609-1620. (In Eng.) DOI: 10.3233/JIFS-169699
[32] Lee K., Kwan M-P. Physical activity classification in free-living conditions using smartphone accelerometer data and exploration of predicted results. Computers, Environment and Urban Systems. 2018; 67:124-131. (In Eng.) DOI: 10.1016/j.compenvurbsys.2017.09.012
[33] Lu D-N., Nguyen D-N., Nguyen T-H., Nguyen H- Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones. Sensors. 2018; 18(4):1036. (In Eng.) DOI: 10.3390/s18041036
[34] Won M., Sayan S., Park K-J. DeepWiTraffic: Low Cost WiFi-Based Traffic Monitoring System Using Deep Learning. CoRR. 2018; abs/1812.08208. Available at: http://arxiv.org/abs/1812.08208 (accessed 16.05.2019). (In Eng.)
[35] Depatla S., Muralidharan A., Mostofi Y. Occupancy estimation using only WiFi power measurements. IEEE Journal on Selected Areas in Communications. 2015; 33(7):1381-1393. (In Eng.) DOI: 10.1109/jsac.2015.2430272
[36] De Montjoye Y-A., Hidalgo C.A., Verleysen M., Blondel V.D. Unique in the crowd: The privacy bounds of human mobility. Scientific reports. 2013; 3: 1376. (In Eng.) DOI: 10.1038/srep01376
[37] Roth J.D., Tummala M., McEachen J.C., Scrofani J.W. Quantifying Location Privacy in Urban Next-Generation Cellular Networks. In: Proceedings of the 51st Hawaii International Conference on System Sciences. 2018, pp. 5831-5833. (In Eng.) DOI: 10.24251/hicss.2018.731
[38] Smartphone data offered to exchange for wireless energy for charging. In: Indicator. 08.09.2018. Available at: https://indicator.ru/mathematics/mobilnyj-kraudsensing-08-09-2018.htm (accessed 16.05.2019). (In Russ.)
[39] Lane N.D., Chon Y., Zhou L., Zhang Y., Li F., Kim D., Ding G., Zhao F., Cha H. Piggyback CrowdSensing (PCS): energy efficient crowdsourcing of mobile sensor data by exploiting smartphone app opportunities. In: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys ’13). Association for Computing Machinery, New York, NY, USA, 2013, Article 7, pp. 1-14. (In Eng.) DOI: 10.1145/2517351.2517372
[40] Namiot D., Sneps-Sneppe M. A Survey of Smart Cards Data Mining. In: Supplementary Proceedings of the Sixth International Conference on Analysis of Images, Social Networks and Texts (AIST 2017). CEUR Workshop Proceedings. 2017; 1975:314-325. Available at: http://ceur-ws.org/Vol-1975/paper33.pdf (accessed 16.05.2019). (In Eng.)
[41] Namiot D., Pokusaev O., Lazutkina V. On passenger flow data models for urban railways. International Journal of Open Information Technologies. 2018; 6(3):9-14. Available at: https://elibrary.ru/item.asp?id=32595087 (accessed 16.05.2019). (In Russ., abstract in Eng.)
[42] Namiot D.E., Pokusaev O.N., Kupriyanovsky V.P. On Patterns for the Use of Railway Stations. Sovremennye informacionnye tehnologii i IT-obrazovanie = Modern Information Technologies and IT-Education. 2018; 14(3):756-761. (In Russ., abstract in Eng.) DOI: 10.25559/SITITO.14.201803.756-761
[43] Nekraplonna M., Namiot D. Metro correspondence matrix analysis. International Journal of Open Information Technologies. 2019; 7(7):68-80. Available at: https://elibrary.ru/item.asp?id=38532115 (accessed 16.05.2019). (In Russ., abstract in Eng.)
[44] Namiot D., Sneps-Sneppe M. On Crowd Sensing Back-end. In: Data Analytics and Management in Data Intensive Domains - Selected Papers (DAMDID/RCDL 2016). CEUR Workshop Proceedings. 2016; 1752:168-175. Available at: http://ceur-ws.org/Vol-1752/paper28.pdf (accessed 16.05.2019). (In Eng.)
[45] Namiot D., Sneps-Sneppe M. On Data Persistence Models for Mobile Crowdsensing Applications. In: Kalinichenko L., Kuznetsov S., Manolopoulos Y. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2016. Communications in Computer and Information Science, vol. 706. Springer, Cham, 2017, pp. 192-204. (In Eng.) DOI: 10.1007/978-3-319-57135-5_14
[46] Daradkeh Y.I., ALdhaifallah M., Namiot D. Mobile Clouds for Smart Cities. International Journal of Online and Biomedical Engineering. 2017; 13(1):76-86. (In Eng.) DOI: 10.3991/ijoe.v13i01.6320
Опубликована
2019-09-30
Как цитировать
BULYGIN, Mark Valerevich; NAMIOT, Dmitry Evgenyevich.
Об использовании данных мобильных абонентов в цифровой урбанистике.
Современные информационные технологии и ИТ-образование, [S.l.], v. 15, n. 3, p. 755-766, sep. 2019.
ISSN 2411-1473. Доступно на: <http://sitito.cs.msu.ru/index.php/SITITO/article/view/556>. Дата доступа: 25 apr. 2024
doi: https://doi.org/10.25559/SITITO.15.201903.755-766.
Раздел
Умные города: стандарты, когнитивно-информационные технологии и их приложения
Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.
Редакционная политика журнала основывается на традиционных этических принципах российской научной периодики и строится с учетом этических норм работы редакторов и издателей, закрепленных в Кодексе поведения и руководящих принципах наилучшей практики для редактора журнала (Code of Conduct and Best Practice Guidelines for Journal Editors) и Кодексе поведения для издателя журнала (Code of Conduct for Journal Publishers), разработанных Комитетом по публикационной этике - Committee on Publication Ethics (COPE). В процессе издательской деятельности редколлегия журнала руководствуется международными правилами охраны авторского права, нормами действующего законодательства РФ, международными издательскими стандартами и обязательной ссылке на первоисточник.
Журнал позволяет авторам сохранять авторское право без ограничений. Журнал позволяет авторам сохранить права на публикацию без ограничений.
Издательская политика в области авторского права и архивирования определяются «зеленым цветом» в базе данных SHERPA/RoMEO.
Все статьи распространяются на условиях лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная, которая позволяет другим использовать, распространять, дополнять эту работу с обязательной ссылкой на оригинальную работу и публикацию в этом журналe.
