Using smart card data to develop origin-destination matrix-based business analytics for bus rapid transit systems: case study of Jakarta, Indonesia

References

• Alsger, A., Assemi, B., Mesbah, M., & Ferreira, L. (2016). Validating and improving public transport origin-destination estimation algorithm using smart card fare data. Transportation Research Part C: Emerging Technologies, 68, 490–506. https://doi.org/10.1016/j.trc.2016.05.004
   Web of Science ®Google Scholar
• Andrienko, G., Andrienko, N., Demsar, U., Dransch, D., Dykes, J., Fabrikant, S. I., Jern, M., Kraak, M. J., Schumann, H., & Tominski, C. (2010). Space, Time, and Visual Analytics. International Journal of Geographical Information Science, 24(10), 1577–1600. https://doi.org/10.1080/13658816.2010.508043
   Web of Science ®Google Scholar
• Andrienko, N., Andrienko, G., Patterson, F., & Stange, H. (2019). Visual Analysis of Place Connectedness by Public Transport. IEEE Transactions on Intelligent Transportation Systems, 21(8), 3196–3208. https://doi.org/10.1109/tits.2019.2924796
   Web of Science ®Google Scholar
• Bae, J. K., & Kim, J. (2011). Product development with data mining techniques: A case on design of digital camera. Expert Systems with Applications, 38(8), 9274–9280. https://doi.org/10.1016/j.eswa.2011.01.030
   Web of Science ®Google Scholar
• Baek, J., & Sohn, K. (2016). Deep-Learning Architectures to Forecast Bus Ridership at the Stop and Stop-To-Stop Levels for Dense and Crowded Bus Networks. Applied Artificial Intelligence, 30(9), 861–885. https://doi.org/10.1080/08839514.2016.1277291
   Web of Science ®Google Scholar
• Barry, J. J., Newhouser, R., Rahbee, A., & Sayeda, S. (2002). Origin and destination estimation in New York City with automated fare system data. Transportation Research Record, 1817, 183–187. https://doi.org/10.3141/1817-24
   Google Scholar
• Bendre, M. R., & Thool, V. R. (2016). Analytics, challenges and applications in big data environment: a survey. Journal of Management Analytics, 3(3), 206–239. https://doi.org/10.1080/23270012.2016.1186578
   Web of Science ®Google Scholar
• Brandt, T., Bendler, J., & Neumann, D. (2017). Information & Management Social media analytics and value creation in urban smart tourism ecosystems. Information & Management, 54(6), 703–713. https://doi.org/10.1016/j.im.2017.01.004
   Web of Science ®Google Scholar
• Carter, E., Adam, P., Tsakis, D., Shaw, S., Watson, R., & Ryan, P. (2020). Enhancing pedestrian mobility in Smart Cities using Big Data. Journal of Management Analytics, 7(2), 173–188. https://doi.org/10.1080/23270012.2020.1741039
   Web of Science ®Google Scholar
• Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly: Management Information Systems, https://doi.org/10.2307/41703503
   Web of Science ®Google Scholar
• Cyril, A., George, V., & Mulangi, R. H. (2018). Electronic ticket machine data analytics for public bus transport planning. 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017, 3917–3922. https://doi.org/10.1109/ICECDS.2017.8390198
   Google Scholar
• Deng, T., & Nelson, J. D. (2013). Bus Rapid Transit implementation in Beijing: An evaluation of performance and impacts. Research in Transportation Economics, 39(1), 108–113. https://doi.org/10.1016/j.retrec.2012.06.002
   Web of Science ®Google Scholar
• Farzin, J. M. (2008). Constructing an automated bus origin-destination matrix using farecard and global positioning system data in São Paulo, Brazil. Transportation Research Record, 2072, 30–37. https://doi.org/10.3141/2072-04
   Web of Science ®Google Scholar
• Figueiras, A. (2013). A typology for data visualization on the web. Proceedings of the International Conference on Information Visualisation, 351–358. https://doi.org/10.1109/IV.2013.45
   Google Scholar
• Global BRT Data. (2021). Global BRT Data.
   Google Scholar
• Huang, D., Yu, J., Shen, S., Li, Z., Zhao, L., & Gong, C. (2020). A Method for Bus OD Matrix Estimation Using Multisource Data. Journal of Advanced Transportation, 2020, 5740521. https://doi.org/10.1155/2020/5740521
   Web of Science ®Google Scholar
• Huang, Z., Li, Q., Li, F., & Xia, J. (2019). A Novel Bus-Dispatching Model Based on Passenger Flow and Arrival Time Prediction. IEEE Access, 7, 106453–106465. https://doi.org/10.1109/ACCESS.2019.2932801
   Web of Science ®Google Scholar
• Jin, M., Wang, M., Gong, Y., & Liu, Y. (2022). Spatio-temporal constrained origin-destination inferring using public transit fare card data. Physica A: Statistical Mechanics and Its Applications, 127642. https://doi.org/10.1016/j.physa.2022.127642
   Web of Science ®Google Scholar
• Kahneman, D. (2003). A Perspective on Judgment and Choice: Mapping Bounded Rationality. American Psychologist, 58(9), 697–720.
   PubMed Web of Science ®Google Scholar
• Koutsopoulos, H. N., Ma, Z., Noursalehi, P., & Zhu, Y. (2018). Transit data analytics for planning, monitoring, control, and information. In Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling. Elsevier Inc. https://doi.org/10.1016/B978-0-12-812970-8.00010-5
   Google Scholar
• Kumar, P., Khani, A., & He, Q. (2018). A robust method for estimating transit passenger trajectories using automated data. Transportation Research Part C: Emerging Technologies, 95(February), 731–747. https://doi.org/10.1016/j.trc.2018.08.006
   Google Scholar
• Lakshminarayan, K., Harp, S. A., & Samad, T. (1999). Imputation of missing data in industrial databases. Applied Intelligence, 11(3), 259–275. https://doi.org/10.1023/A:1008334909089
   Web of Science ®Google Scholar
• Lee, S. G., & Hickman, M. (2014). Trip purpose inference using automated fare collection data. Public Transport, 6(1–2), 1–20. https://doi.org/10.1007/s12469-013-0077-5
   Google Scholar
• Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50(April 2019), 57–70. https://doi.org/10.1016/j.ijinfomgt.2019.04.003
   Web of Science ®Google Scholar
• Li, G., & Chen, A. (2022). Frequency-based path flow estimator for transit origin-destination trip matrices incorporating automatic passenger count and automatic fare collection data. Transportation Research Part E: Logistics and Transportation Review, 163, 102754. https://doi.org/10.1016/j.tre.2022.102754
   Web of Science ®Google Scholar
• Munizaga, M. A., & Palma, C. (2012). Estimation of a disaggregate multimodal public transport Origin-Destination matrix from passive smartcard data from Santiago, Chile. Transportation Research Part C: Emerging Technologies, 24, 9–18. https://doi.org/10.1016/j.trc.2012.01.007
   Web of Science ®Google Scholar
• Nassir, N., Khani, A., Lee, S. G., Noh, H., & Hickman, M. (2011). Transit stop-level origin-destination estimation through use of transit schedule and automated data collection system. Transportation Research Record, 2263, 140–150. https://doi.org/10.3141/2263-16
   Google Scholar
• Pi, X., Egge, M., Whitmore, J., Sean Qian, Z., & Silbermann, A. (2018). Understanding transit system performance using AVL-APC data: An analytics platform with case studies for the Pittsburgh Region. Journal of Public Transportation, 21(2), 19–40. https://doi.org/10.5038/2375-0901.21.2.2
   Web of Science ®Google Scholar
• PT Transportasi, J. (2021). PPID PT Transportasi Jakarta : Pejabat Pengelola Informasi dan Dokumentasi.
   Google Scholar
• Sun, F., Pan, Y., White, J., & Dubey, A. (2016). Real-Time and Predictive Analytics for Smart Public Transportation Decision Support System. 2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016, https://doi.org/10.1109/SMARTCOMP.2016.7501714
   Google Scholar
• Trépanier, M., Tranchant, N., & Chapleau, R. (2007). Individual trip destination estimation in a transit smart card automated fare collection system. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 11(1), 1–14. https://doi.org/10.1080/15472450601122256
   Web of Science ®Google Scholar
• Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626–639. https://doi.org/10.1016/j.ejor.2017.02.023
   Web of Science ®Google Scholar
• Wang, W., Attanucci, J. P., & Wilson, N. H. M. (2011). Bus passenger origin-destination estimation and related analyses using Automated Data Collection Systems. Journal of Public Transportation, 14(4), 131–150. https://doi.org/10.5038/2375-0901.14.4.7
   Web of Science ®Google Scholar
• Xu, G., Qiu, X., Fang, M., Kou, X., & Yu, Y. (2019). Data-driven operational risk analysis in E-Commerce Logistics. Advanced Engineering Informatics, 40(September 2018), 29–35. https://doi.org/10.1016/j.aei.2019.03.001
   Google Scholar
• Zhang, J., Wang, F. Y., Wang, K., Lin, W. H., Xu, X., & Chen, C. (2011). Data-driven intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 12(4), 1624–1639. https://doi.org/10.1109/TITS.2011.2158001
   Web of Science ®Google Scholar
• Zhang, M., Yen, B. T. H., Mulley, C., & Sipe, N. (2020). How does an open system bus rapid transit (BRT) facilitate inter and intra-modal mobility? A visual analytic analysis of Brisbane, Australia. Research in Transportation Economics, 83(April), https://doi.org/10.1016/j.retrec.2020.100906
   Google Scholar
• Zhang, T., Li, Y., Yang, H., Cui, C., Li, J., & Qiao, Q. (2020). Identifying primary public transit corridors using multi-source big transit data. International Journal of Geographical Information Science, 34(6), 1137–1161. https://doi.org/10.1080/13658816.2018.1554812
   Web of Science ®Google Scholar
• Zhao, J., Rahbee, A., & Wilson, N. H. M. (2007). Estimating a rail passenger trip origin-destination matrix using automatic data collection systems. Computer-Aided Civil and Infrastructure Engineering, 22(5), 376–387. https://doi.org/10.1111/j.1467-8667.2007.00494.x
   Web of Science ®Google Scholar
• Zhu, L., Yu, F. R., Wang, Y., Ning, B., & Tang, T. (2019). Big Data Analytics in Intelligent Transportation Systems: A Survey. In IEEE Transactions on Intelligent Transportation Systems, https://doi.org/10.1109/TITS.2018.2815678
   Google Scholar