sibadiНаучный рецензируемый журнал "Вестник СибАДИ"The Russian Automobile and Highway Industry Journal2071-72962658-5626The Siberian State Automobile and Highway University10.26518/2071-7296-2021-18-1-52-71sibadi-1224Research ArticleТРАНСПОРТTRANSPORTОбследование пассажирских потоков путем анализа валидаций электронных проездных билетовTransit ridership survey by analysis validation of electronic pass ticketsФадеевА. И.FadeevA. I.

Фадеев Александр Иванович - кандидат технических наук, доц. кафедры транспорта Scopus ID: 57208356151.

660074, Красноярск, ул. Академика Киренского, д. 26

Aleksandr I. Fadeev - Cand. of Sci., Associate Professor of the Transport department, Scopus ID: 57208356151.

660074, Krasnoyarsk city, Akademika Kirenskogost., 26

fai@ak1967.ruhttps://orcid.org/0000-0003-3028-0675АлхуссейниС.AlhusseiniS.

Алхуссейни Сами - аспирант кафедры транспорта СФУ, Scopus ID: 57212171306, Web of Science ID: AAC-6792-2020.

660074, Красноярск, ул. Академика Киренского, д. 26

Sami Alhusseini - Postgraduate student of the Transport department, Scopus ID: 57212171306, Web of Science ID: AAC-6792-2020.

660074, Krasnoyarsk city, Akademika

eng.sami20143@gmail.comСибирский федеральный университетРоссияSiberian Federal UniversityRussian Federation2021290320211815271Copyright © Фадеев А.И., Алхуссейни С., 20212021Фадеев А.И., Алхуссейни С.Fadeev A.I., Alhusseini S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.sibadi.org/jour/article/view/1224Введение

Введение. Применяемые сегодня методы определения спроса общественного транспорта предполагают большие затраты времени, ресурсов и высокую трудоемкость. В этой связи особую перспективу представляет изучение транспортного спроса на основе сбора, интеграции и анализа больших и разнородных данных, генерируемых различными источниками в пространствах жизнедеятельности человека: Urban computing, Big data, Internet of things.

Материалы и методы

Материалы и методы. В настоящей статье представлен метод определения (восстановления) корреспонденций пассажиров общественным транспортом посредством интеллектуального анализа операций валидации электронных проездных билетов (electronic travel tickets): смарт-карты (smart card), транспортной карты, магнитной карты, мобильного телефона или других электронных устройств (electronic gadget), реквизиты которых при выполнении валидации фиксируются в автоматизированной системе управления перевозками.

Результаты

Результаты. Алгоритм расчета пассажирских корреспонденций реализован в компьютерной программе с использованием реляционной СУБД MS SQL Server. Апробация эффективности разработанной методики и программного обеспечения осуществлена по данным системы пассажирского транспорта г. Красноярска.

Обсуждение и заключение

Обсуждение и заключение. Описанный в статье метод расчета пассажирских потоков путем анализа операций валидации электронных проездных билетов и данных системы диспетчерского управления движением транспортных средств позволяет определять выполненные маршрутные и сетевые корреспонденции пассажиров и на этой основе осуществлять объективную оценку спроса общественного транспорта и технико-эксплуатационных показателей транспортной системы.

Introduction

Introduction. The methods used today for determining the demand on public transport come with a big waste of time, resources and a need for great effort. In this regard, a special perspective is to study the transit demand based on the collection, integration and analysis of large and diverse data, which were generated by various sources of human life: Urban computing, Big data, Internet of things.

Materials and methods

Materials and methods. This article presents a method for determining (restoring) the correspondence of transit passengers by means of intelligent analysis of validation operations data of electronic travel tickets (smart card, transport card, magnetic card, mobile phone or other electronic devices (electronic gadgets)), which are recorded in the automated transportation management system during validation.

Results

Results. The algorithm for calculating passenger correspondence is implemented in a computer program using the relational DBMS MS SQL Server. The effectiveness of the proposed algorithm was verified by calculating the passenger correspondence of public transport in the city of Krasnoyarsk (Russia).

Discussion and conclusion. The described method for calculating passenger flows, based on analyzing the data of validation operations of electronic tickets and data from the transit dispatch control system, makes possible to determine the route and passengers correspondence and, to carry out an objective assessment of the demand for public transport and the technical and operational indicators of the transit system.

пассажирский потоктранспортный спроспассажирские корреспонденцииматрица пассажирских корреспонденцийобщественный городской транспортpassenger flowtransit demandpassenger correspondencematrix of passenger correspondencepublic urban transportReferencesШтоцкая А.А. Оценка транспортной подвижности населения на основе дезагрегированных моделей // Вестник Иркутского государственного технического университета. 2017. 21 (5). С. 199-207.Shtockaja A.A. Mihajlov A.Ju. Ocenka transportnoj podvizhnosti naselenija na osnove dezagregi-rovannyh modelej [Assessment of transport mobility of the population based on disaggregated models] Vestnik Irkutskogo gosudarstvennogo tehnicheskogo universiteta. 2017; 2.(5): 199-207.(In Russian)Семёнов В.В., Ермаков А.В. Исторический анализ моделирования транспортных процессов и транспортной инфраструктуры // Препринты ИПМ им. М.В.Келдыша. 2015. № 3. 36 с. URL: http://library. keldysh.ru/preprint.asp?id=2015-3Semjonov V.V., Ermakov A.V. Istoricheskij analiz modelirovanija transportnyh processov i transportnoj infrastruktury [Historical analysis of modeling transport processes and transport infrastructure] Preprinty IPMim. M.V.Keldysha. 2015; 3(36). URL: http://library. keldysh.ru/preprint.asp?id=2015-3. (In Russian)Ortuzar J. D., Willumsen L. G. Modelling transport. John Willey & Sons, 2011. 30 p.Ortuzar J. D., Willumsen L. G. Modelling transport. John Willey & Sons, 2011.Lee D. Requiem for large-scale models. Journal of the American Institute of Planners, Vol 39, No 3, May, 1973. p. 63-78Lee D. Requiem for large-scale models. Journal of the American Institute of Planners, Vol 39, No 3, May, 1973.Atkins S. Transportation planning models: What the papers say. Traffic Engineering and Control, Vol 27, No 9, September, 1986.Atkins S. Transportation planning models: What the papers say. Traffic Engineering and Control, Vol 27, No 9, September, 1986.Fadeev A.I. Alhusseini S, Belova E.N. Monitoring Public Transport Demand Using Data from Automated Fare Collection System. Advances in Engineering Research, volume 158: Proceedings of the International Conference “Aviamechanical engineering and transport”. 2018. p. 5-12Fadeev A.I. Alhusseini S, Belova E.N. Monitoring Public Transport Demand Using Data from Automated Fare Collection System. Advances in Engineering Research, volume 158: Proceedings of the International Conference “Aviamechanical engineering and transport". 2018; 5: 12.Fadeev A, Alhusseini S. Using Automated Fare Collection System Data To Determine Transport Demand. Advances in Engineering Research, volume 188. International Conference on Aviamechanical Engineering and Transport (AviaENT 2019). Pp. 1-9Fadeev A, Alhusseini S. Using Automated Fare Collection System Data To Determine Transport Demand. Advances in Engineering Research, volume 188. International Conference on Aviamechanical Engineering and Transport (AviaENT 2019): 1-9.Fadeev A, Alhusseini S. Determining The Public Transport Demand by Validation Data Of the Electronic Tickets. MIST: Aerospace 2019 IOP Conf. Series: Materials Science and Engineering 734 (2020) 012148 IOP Publishing.Fadeev A, Alhusseini S. Determining The Public Transport Demand by Validation Data Of the Electronic Tickets. MIST: Aerospace 2019 IOP Conf. Series: Materials Science and Engineering 734 (2020) 012148 IOP Publishing.Fadeev A.I, Alhusseini S. Passenger trips analysis determined by processing validation data of the electronic tickets in public transport. “IOP Conference Series: Materials Science and Engineering”.Fadeev A.I, Alhusseini S. Passenger trips analysis determined by processing validation data of the electronic tickets in public transport. “IOP Conference Series: Materials Science and Engineering”.Zheng Y, Capra L, Wolfson O, and Yang H, Urban computing: Concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol., vol. 5, no. 3. pp. 1-55, Sep. 2014.Zheng Y, Capra L, Wolfson O, and Yang H, Urban computing: Concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol., 2014; 3(5): 1-55.Chen M. Shiwen Mao, Yin Zhang, Victor C.M. Leung.Big Data. Related Technologies. Challenges, and Future Prospects, Spinger, 2014. 100 p.Chen M. Shiwen Mao, Yin Zhang, Victor C.M. Leung.Big Data. Related Technologies. Challenges, and Future Prospects, Spinger, 2014: 100.Barry J.J. Newhouser R., Rahbee A., Sayeda S. Origin and destination estimation in New York City with automated fare system data. Transportation Research, Record 1817, 2002. pp.183-187.Barry J.J. Newhouser R., Rahbee A., Sayeda S. Origin and destination estimation in New York City with automated fare system data. Transportation Research, Record 1817, 2002:183-187.Zhao J., Rahbee A., Wilson N. Estimating a rail passenger trip origin- destination matrix using automatic data collection systems. Aided Civil and Infrastructure Engineering, vol. 22, 2007. pp.376-387.Zhao J., Rahbee A., Wilson N. Estimating a rail passenger trip origin- destination matrix using automatic data collection systems. Aided Civil and Infrastructure Engineering, vol. 22, 2007: 376-387.Trepanier M., Tranchant N., Chapleau R. Individual trip destination estimation in a transit smart card automated fare collection system. Journal of Intelligent Transportation Systems, vol. 11, 2007. pp.1-14.Trepanier M., Tranchant N., Chapleau R. Individual trip destination estimation in a transit smart card automated fare collection system. Journal of Intelligent Transportation Systems. 2007; 7: 1-14.Devillaine F., Munizaga M.A., M. Trepanier, Detection activities of public transport users by analyzing smart card data. Transportation Research Record: Journal of the Transportation Research Board, vol. 2276, 2012. pp.48-55.Devillaine F., Munizaga M.A., M. Trepanier, Detection activities of public transport users by analyz-ing smart card data. Transportation Research Record: Journal of the Transportation Research Board. 2012; 2276: 48-55.Munizaga M., Palma C. Estimation of a disaggregate multimodal public transport OD matrix from passive smartcard data from Santiago, Chile. Transportation Research Part C, Vol. 24, 2012. pp.9-18.Munizaga M., Palma C. Estimation of a disaggregate multimodal public transport OD matrix from passive smartcard data from Santiago, Chile. Transportation Research Part C. 2012; 24: 9-18.Bagchi M., White P What role for smart-card data from bus systems?. Munic. Eng., vol. 157, 2004. pp.39-46.Bagchi M., White P. What role for smart-card data from bus systems?. Munic. Eng. 2004; 157: 39-46.Nassir N. Khani A., Lee S.G., Noh H., Hickman M. Transit stop-level origin-destination estimation through use of transit schedule and automated data collection system. Transportation Research Record: Journal of the Transportation Research Board, vol. 2263, 2011. pp.140-150.Nassir N. Khani A., Lee S.G., Noh H., Hickman M. Transit stop-level origin-destination estimation through use of transit schedule and automated data collection system. Transportation Research Record: Journal of the Transportation Research Board. 2011; 2263: 140-150.Cui A. Bus passenger origin-destination matrix estimation using automated data collection systems master's dissertation. Massachusetts Institute of Technology, 2006.Cui A. Bus passenger origin-destination matrix estimation using automated data collection systems master's dissertation. Massachusetts Institute of Technology, 2006.Wang W., John P, Nigel H.M., Bus passenger origin-destination estimation and travel behavior using automated data collection systems in London. Journal of Public Transportation, Vol. 14, No.4, 2011.Wang W., John P, Nigel H.M., Bus passenger origin-destination estimation and travel behavior using automated data collection systems in London. Journal of Public Transportation. 2011; 14(4).Alsger A. Mesbah M., Ferreira L., Safi H. Use of smart card fare data to estimate public transport origin-destination matrix. Transportation Research Record: Journal of the Transportation Research Board, vol. 2535, 2015. pp.88-96.Alsger A. Mesbah M., Ferreira L., Safi H. Use of smart card fare data to estimate public transport origin-destination matrix. Transportation Research Record: Journal of the Transportation Research Board. 2015; 2535: 88-96.Barry J.J., Freimer R., Slavin H.L. Use of entry-only automatic fare collection data to estimate linked transit trips in New York City. Transp. Res. Rec. J. Transp. Res. Board, vol. 2112, 2009. pp.53-61.Barry J.J., Freimer R., Slavin H.L. Use of entry-only automatic fare collection data to estimate linked transit trips in New York City. Transp. Res. Rec. J. Transp. Res. Board. 2009; 2112:.53-61.Mahrsi K.E. Come E., Oukhellou L., Verleysen M. Clustering smart card data for urban mobility analysis. IEEE Transactions on intelligent transportation systems, vol. 18, NO. 3, March 2017.Mahrsi K.E. Come E., Oukhellou L., Verleysen M. Clustering smart card data for urban mobility analysis. IEEE Transactions on intelligent transportation systems.March 2017; 3(18).Jinhua Zhao J, Rahbee A. Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems. Computer-Aided Civil and Infrastructure Engineering 22 (2007). pp 376-387.Jinhua Zhao J, Rahbee A. Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems. Computer-Aided Civil and Infrastructure Engineering 22 (2007): 376-387Nunes, A.A., Dias, T.G., Cunha, J.F., 2016. Passenger journey destination estimation from automated fare collection system data using spatial validation. IEEE Trans. Intell. Transp. Syst. 17. pp 133-142. DOI:10.1109/TITS.2015.2464335.Nunes, A.A., Dias, T.G., Cunha, J.F., 2016. Passenger journey destination estimation from automated fare collection system data using spatial validation. IEEE Trans. Intell. Transp. Syst. 17: 133-142. DOI:10.1109/TITS.2015.2464335.Joana Horaa. Teresa Galvao Diasa , Ana Camanhoa , Thiago Sobral. Estimation of Origin-Destination matrices under Automatic Fare Collection: the case study of Porto transportation system. Transportation Research Procedia 27 (2017). pp. 664-671.Joana Horaa. Teresa Galvao Diasa , Ana Camanhoa , Thiago Sobral. Estimation of Origin-Destination matrices under Automatic Fare Collection: the case study of Porto transportation system. Transportation Research Procedia. 2017; (27): 664-671.Munizaga M. Devillaine F., Navarrete C., Silva D. Validating travel behavior estimated from smartcard data.Transportation Research Part C Emerging Technologies. July 2014. pp. 1-18.Munizaga M. Devillaine F., Navarrete C., Silva D. Validating travel behavior estimated from smartcard data. Transportation Research Part C Emerging Technologies. July 2014: 1-18.

The authors declare that there are no conflicts of interest present.