Abstract
More often than not, commuters are left stranded at pick-up spots – clueless about the availability and proximity of public transport vehicles hence the stigma of public transport being unreliable, especially in developing countries. This is a result of poorly managed fleets, caused by varying demands and rigid schedules. In this paper, we present an intelligent real-time transport information system to keep commuters informed about the status of buses currently in transit, and also provide an insight to bus managers based on ridership data and commuter behavior. The system is composed of three subsystems designed to cater for commuters, bus-drivers and bus managers respectively. This system is developed on the Backend-as-a-Service (BaaS) platform Firebase. Furthermore, a neural network is trained to provide predictions to bus managers on the expected ridership numbers per route. The trained model is integrated with a web application for bus managers. An Android application used by bus drivers collects the ridership data being fed to the network. The proposed system was evaluated with a real-world data set that contains the daily ridership on a per-route basis dating back to 2001. Evaluation results confirm the effectiveness of the new system in reducing the total mileage used to deliver commuters, reducing fuel costs, increasing the profit of bus operators, and increasing the percentage of satisfied ridership requests.
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References
Statistics South Africa. Measuring household expenditure on public transport (2015). http://www.statssa.gov.za/?p=5943
Sungur, C., Sungur, I., Babaoglu, A.: Smart bus station-passenger information system. In: 2015 2nd International Conference on Information Science and Control Engineering (2015)
Google Developers. Get started — directions api. https://developers.google.com/maps/documentation/directions/start?hl=enUS
Monchambert, G., De Palma, A.: Public transport reliability and commuter strategy. J. Urban Econ. 81, 14–29 (2014)
Shirisha, K., Sivaprasad, T.: Acquire bus information using GSM technology. Int. J. Adv. Technol. (2016)
Kumbhar, M., Survase, M., Mastud, P., Salunke, A., Sirdeshpande, S.: Real time web based bus tracking system. Int. Res. J. Eng. Technol. (IRJET) 3(02), 632–635 (2016)
Moroney, L.: The firebase realtime database (2017)
Moroney, L.: Using authentication in firebase. In: The Definitive Guide to Firebase. Apress, Berkeley (2017)
UN DESA — United Nations Department of Economic and Social Affairs. 68 per cent of the world population projected to live in urban areas by 2050, says un, 2018. https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html
Independent Communications Authority of South Africa. State of ICT sector in South Africa - 2019 report (2019). https://www.icasa.org.za/legislation-and-regulations/state-of-ict-sectorin-south-africa-2019-report
Lahudkar, P., Sawale, S., Deshmane, V., Bharambe, K.: NoSQL database-Google’s firebase: a review. Int. J. Innov. Res. Sci. Eng. Technol. 7(3), 243–250 (2018)
Manikandan, R., Niranjani, S.: Implementation on real time public transportation information using GSM query response system. Contemp. Eng. Sci. 7(05), 2014 (2014)
Smaato. How to optimize your location data’s accuracy (2019). https://www.smaato.com/blog/optimize-your-location-data-accuracy/
Smartdraw. Selecting the right software development tools for your developers. https://www.smartdraw.com/technology/right-softwaredevelopment-tools-for-developers.htm
WhiteSource Software. When to consider a NoSQL vs relational database. https://resources.whitesourcesoftware.com/blogwhitesource/when-to-consider-a-nosql-vs-relational-database
StackShare. Amazon EC2 vs firebase — what are the differences? https://stackshare.io/stackups/amazon-ec2-vs-firebase
The Verge. Why GPS-dependent apps deplete your smartphone battery (2018). https://www.theverge.com/2018/8/17/17630872/smartphonebattery-gps-location-services
Hameed, S., Chida, J.: Mastering Android Wear Application Development. Packt Publishing Ltd (2016). https://eng.uber.com/activity-service-dependency-android-architecture/
The Statistics Portal. Global market share held by the leading smartphone operating systems in sales to end users from 1st quarter 2009 to 2nd quarter 2018. Statistics Portal (2018). https://www.statista.com/statistics/266136/global-market-share-held-by-smartphone-operating-systems
CTA - Ridership - Bus Routes - Daily Totals by Route. https://data.cityofchicago.org/Transportation/CTA-Ridership-Bus-Routes-Daily-Totals-by-Route/jyb9-n7fm
Annual Ridership Report: Calendar Year 2018 (PDF). Transitchicago.com. https://www.transitchicago.com/assets/1/6/2018_Annual_Report_-_v3_04.03.2019.pdf
Machine Learning Mastery https://machinelearningmastery.com/how-to-improve-neural-network-stability-and-modeling-performance-with-data-scaling/
Bacciu, D., Carta, A., Gnesi, S., Semini, L.: An experience in using machine learning for short-term predictions in smart transportation systems. J. Log. Algebraic Methods Program. 1(87), 52–66 (2017)
Agarwal, P.K., Gurjar, J., Agarwal, A.K., Birla, R.: Application of artificial intelligence for development of intelligent transport system in smart cities. J. Traffic Transp. Eng. 1(1), 20–30 (2015)
Othman, M.S., Tan, G.: Machine learning aided simulation of public transport utilization. In: 2018 IEEE/ACM 22nd International Symposium on Distributed Simulation and Real Time Applications (DS-RT), 15 October 2018, pp. 1–2. IEEE (2018)
Toqué, F., Khouadjia, M., Come, E., Trepanier, M., Oukhellou, L.: Short & long term forecasting of multimodal transport passenger flows with machine learning methods. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 16 October 2017, pp. 560–566. IEEE (2017)
Liu, G., Yin, Z., Jia, Y., Xie, Y.: Passenger flow estimation based on convolutional neural network in public transportation system. Knowl.-Based Syst. 123, 102–115 (2017)
Van Oort, N., Brands, T., de Romph, E.: Short term ridership prediction in public transport by processing smart card data. Transp. Res. Rec. (2015)
Skhosana, M., Ezugwu, A.E.: Irenbus: A real-time public transport management system. In: 2020 Conference on Information Communications Technology and Society (ICTAS), 11 March 2020, pp. 1–7. IEEE (2020). https://doi.org/10.1109/ictas47918.2020.234000
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Smart Irenbus GitHub repository with source code including raw and processed training data, https://github.com/m3n2ie/Irenbus.
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Skhosana, M., Ezugwu, A.E., Rana, N., Abdulhamid, S.M. (2020). An Intelligent Machine Learning-Based Real-Time Public Transport System. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12254. Springer, Cham. https://doi.org/10.1007/978-3-030-58817-5_47
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