Predicting Transjakarta Passengers with LSTM-BiLSTM Deep Learning Models for Smart Transportpreneurship
DOI:
https://doi.org/10.34306/att.v7i1.440Keywords:
Passenger, BRT, Deep Learning, LSTM, BiLSTMAbstract
Travel pattern variations pose challenges in building a prediction model that accurately captures seasonal patterns or precision of BRT passenger numbers. An approach that integrates sophisticated prediction algorithms with high accuracy is needed to address the Transjakarta BRT passenger number prediction model problem. The proposed prediction model with the best accuracy is sought using deep learning on 8 models. The prediction model is used for short-term and long-term predictions, as well as looking for correlations in the prediction results of 13 Transjakarta corridors. The Python programming language with the Deep Learning Tensor Flow framework is run by Google Colaboratory used in the prediction simulation environment. The combination of BiLSTM-CNN was found to have the best accuracy of the evaluation value (SMAPE = 15.9387, MAPE = 0.598, and MSLE = 0.0425), although it has the longest time (134 seconds). Fluctuations in short-term predictions of passenger numbers evenly occur simultaneously across all corridors. Fluctuations in long-term predictions evenly occur simultaneously across all corridors, except in February. There is no negative correlation in the 13 prediction results and there are 8 corridors that have a close positive correlation. The prediction results can be used by transportation operators and the government to optimize resource planning and transportation policies to support sustainable community and economic mobility.
References
M. N. B. A. Rahim, Z. A. Aziz, A. A. M. Hassan, M. F. A. Yazid, M. A. Razik, Z. Miskam et al., “Factors influencing passengers’ preference towards public bus transportation from kelantan to northern region,” Turkish Journal of Computer and Mathematics Education, 2021.
D. F. Vieira and H. G. J´unior, “Public transportation passengers accounting at university by iot device,” in 2022 Symposium on Internet of Things (SIoT). IEEE, 2022, pp. 1–5.
C. Cong, X. Li, S. Yang, Q. Zhang, L. Lu, and Y. Shi, “Impact estimation of unplanned urban rail disruptions on public transport passengers: A multi-agent based simulation approach,” International journal of environmental research and public health, vol. 19, no. 15, p. 9052, 2022.
A. Helfers, N. Schneider, J. Koch, L. Fouckhardt, and C. Sommer, “Visualizing ventilation in the bus: Addressing risk perception in public transport passengers,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 101, pp. 236–249, 2024.
M. Girma and B. Woldetensae, “Passengers’ perceptions on security and safety in public transportation in ethiopia: a case study of addis ababa,” Zeszyty Naukowe. Transport/Politechnika ´Slaska, no. 113, 2021.
U. Hasan, A. Whyte, and H. A. Jassmi, “Public bus transport service satisfaction: Understanding its value to urban passengers towards improved uptake,” Transactions on Transport Sciences, vol. 12, no. 1, pp. 25–37, 2021.
M. R. Amalia, “The impact of digital era 4.0 transformation on human resources management,” Management Studies and Business Journal (PRODUCTIVITY), vol. 1, no. 1, pp. 89–98, 2024.
S. Bwigenge, D. I. Sensuse, R. R. Suryono et al., “Passengers acceptance of cashless payment system for public bus transportation system in kigali city (rwanda),” in 2020 International Conference on Advanced Computer Science and Information Systems (ICACSIS). IEEE, 2020, pp. 341–350.
V. Naumov, B. Zhamanbayev, D. Agabekova, Z. Zhanbirov, and I. Taran, “Fuzzy-logic approach to estimate the passengers’ preference when choosing a bus line within the public transport system,” Communications-Scientific letters of the University of Zilina, vol. 23, no. 3, pp. A150–A157, 2021.
J. Wang, Y. Chen, L. Mao, R. Yang, D. Li, and B. Chen, “Public transport passengers’ classification and path choice characteristics analysis by using c&rt model in beijing,” in IOP Conference Series: Materials Science and Engineering, vol. 688, no. 4. IOP Publishing, 2019, p. 044018.
S. Kraˇsna, A. Keller, A. Linder, A. P. Silvano, J.-C. Xu, R. Thomson, and C. Klug, “Human response to longitudinal perturbations of standing passengers on public transport during regular operation,” Frontiers in bioengineering and biotechnology, vol. 9, p. 680883, 2021.
A. M. Hidayat and K. Choocharukul, “Passengers’ intentions to use public transport during the covid-19 pandemic: A case study of bangkok and jakarta,” Sustainability, vol. 15, no. 6, p. 5273, 2023.
W. Al-Shaar, N. Nehme, O. Bonin, J. Adjizian-G´erard, and M. Al-Shaar, “Passengers receptivity of a new public transport mode: Case of a brt project in lebanon,” Computational Urban Science, vol. 2, no. 1, p. 25, 2022.
B. Z. Malik, Z. ur Rehman, A. H. Khan, and W. Akram, “Investigating users’ travel behaviours and perceptions of single-corridor brt: Lessons from lahore,” Journal of Transport Geography, vol. 91, p. 102942, 2021.
J. Singh, H. K. Chaudhary, and A. Malik, “A case study on indore brts with reference to other indian cities,” International Journal for Research in Applied Science and Engineering Technology, vol. 10, no. 10, pp. 196–203, 2022.
A. Delhi, E. Sana, A. A. Bisty, and A. Husain, “Innovation in business management exploring the path to competitive excellence,” APTISI Transactions on Management, vol. 8, no. 1, pp. 58–65, 2024.
G. Tobias Portis, A. Mota dos Santos, and F. Gioppo Nunes, “Influˆencia da infraestrutura de transportes sobre a temperatura de superf´ıcie em um corredor brt, na cidade de goiˆania/go.” Revista Ateliˆe Geogr´afico, vol. 16, no. 1, 2022.
R. Dantas, J. Dantas, C. Melo, and P. Maciel, “Performance evaluation in brt systems: An analysis to predict the brt systems planning,” Case Studies on Transport Policy, vol. 9, no. 3, pp. 1141–1150, 2021.
C. A. Sakurai, C. F. Fontana, B. D. C. Negr˜ao, and A. G. D. S. Andrade, “System functionalities of its for brt,” International Journal of Computational and Applied Mathematics & Computer Science, vol. 2, pp. 18–25, 2022.
H. A. Winata and F. Simon, “Influence of profitability, audit quality, and corporate governance on earnings management,” APTISI Transactions on Management, vol. 8, no. 2, pp. 93–104, 2024.
H. Thevadass, G. B. Hoe, W. K. Cheong, T. F. Yenn, C. C. M. May, Y. C. Wah, and Y. E. Hwa, “A review on brt scoring system for malaysia practice,” Civil Engineering and Architecture, vol. 9, no. 2, pp. 410–419, 2021.
R. M. Medeiros, F. Duarte, I. Bojic, Y. Xu, P. Santi, and C. Ratti, “Merging transport network companies and taxis in curitiba’s brt system,” Public Transport, vol. 16, no. 1, pp. 269–293, 2024.
C. F. Fontana, C. A. Sakurai, C. L. Marte, J. R. Cardoso, and A. D. S. Andrade, “Power line communication as alternative for data communication channel for brt,” EARTH SCIENCES AND HUMAN CONSTRUCTIONS, vol. 2, pp. 60–67, 2022.
F. Khan, A. Ahmed, M. Ahmed, and M. A. U. Baig, “An evaluation of cost optimization strategies for brt projects in pakistan,” Engineering, Technology & Applied Science Research, vol. 12, no. 4, pp. 8825–8830, 2022.
M. I. N. Hakim, J. Siswanto, and A. A. Nuryono, “Parameter tuning dalam klasifikasi load factor pada bus rapid transit (brt),” JURNAL FASILKOM, vol. 14, no. 2, pp. 500–506, 2024.
L. A. Guzman, H. D. Enr´ıquez, and P. Hessel, “Brt system in bogot´a and urban effects: More residential land premiums?” Research in Transportation Economics, vol. 90, p. 101039, 2021.
M. I. N. Hakim, J. Siswanto, A. A. Qalban, and A. A. Nuryono, “Increasing the accuracy of classification models with a scaler for bus rapid transit (brt) reliability values,” in 2024 International Conference on Electrical Engineering and Computer Science (ICECOS). IEEE, 2024, pp. 211–216.
A. M. Santos and A. G. De Oliveira, “A importˆancia da mobilidade urbana: implantac¸ ˜ao da primeira linha do brt em salvador/the importance of urban mobility: implementation of the first line of brt in salvador,” Brazilian Applied Science Review, vol. 5, no. 4, pp. 1951–1969, 2021.
A. M. Nayak, A. Ladha, and N. K. Chaubey, “A comprehensive comparative analysis of passenger demand prediction for improving the urban bus transportation system (ubts),” International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 269–279, 2022.
G. Luo, H. Kuang, D. Zhang, K. Deng, and J. Li, “A dual-view approach for multistation short-term passenger flow prediction in bus transit systems,” Journal of Advanced Transportation, vol. 2023, no. 1, p. 2009635, 2023.
K. E. Zannat, J. Laudan, C. F. Choudhury, and S. Hess, “Developing an agent-based microsimulation for predicting the bus rapid transit (brt) demand in developing countries: A case study of dhaka, bangladesh,” Transport Policy, vol. 148, pp. 92–106, 2024.
M. Baradaranshahidin, S. Shabani, M. Khalilzadeh, S. Tahmasbi Ashtiani, S. Tajaddini, and M. Azimipour, “Modeling of brt system travel time prediction using avl data and ann approach,” European Transport Trasporti Europei, 2021.
X. Li and C. Wang, “Short-term origin-destination demand forecasting in bus rapid transit based on dual attentive multi-scale convolutional network,” IET Intelligent Transport Systems, vol. 18, no. 1, pp. 29–46, 2024.
A. M. Nayak and N. K. Chaubey, “Intelligent passenger demand prediction-based rerouting for comfort perception in public bus transport systems,” International Journal of Communication Systems, vol. 35, no. 18, p. e5351, 2022.
D. Luo, D. Zhao, Q. Ke, X. You, L. Liu, D. Zhang, H. Ma, and X. Zuo, “Fine-grained service-level passenger flow prediction for bus transit systems based on multitask deep learning,” IEEE transactions on intelligent transportation systems, vol. 22, no. 11, pp. 7184–7199, 2020.
J. Siswanto, U. Rahardja, I. Sembiring, E. A. Lisangan, M. I. N. Hakim, F. Wibowo et al., “Hybrid deep learning model of lstm and bilstm for transjakarta passenger prediction,” in 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT). IEEE, 2024, pp. 1–7.
N. Nagaraj, H. L. Gururaj, B. H. Swathi, and Y.-C. Hu, “Passenger flow prediction in bus transportation system using deep learning,” Multimedia tools and applications, vol. 81, no. 9, pp. 12 519–12 542, 2022.
C. Okoro, F. Muleya, and I. Musonda, “Assessing the role of planning and considerations in the sustainability of brt-ppp projects: A south african case,” Journal of Construction in Developing Countries, vol. 27, no. 2, pp. 205–234, 2022.
S. Halyal, R. Mulangi, and M. Harsha, “Short-term passenger demand modelling using automatic fare collection data: a case study of hubli-dharwad brts.” Advances in Transportation Studies, vol. 59, 2023.
K. Someya, B. Tandel, and R. B. Ranpise, “Evaluation and ann modelling of traffic noise on brts corridors with different median vegetation: A case study of surat city (western india),” Journal of The Institution of Engineers (India): Series A, vol. 103, no. 4, pp. 1325–1339, 2022.
H. Inokuchi, “Combinations of transportation policies to promote brt usage using artificial society model,” Journal of Traffic and Transportation Engineering, vol. 12, no. 1, pp. 2328–2142, 2024.
H. Behbahani and M. Poorjafari, “Passenger-based adaptive transit signal priority for brt systems with multiple loading areas,” IET Intelligent Transport Systems, vol. 18, no. 6, pp. 1089–1108, 2024.
K. Zhang, Y. Wang, Z. Hu, and L. Zhou, “Brt oil price combination forecasting based on the dispersion degree of triangular fuzzy numbers,” Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1143–1166, 2024.
S. Najafi-Zangeneh, N. Shams Gharneh, A. Arjomandi-Nezhad, and E. Hassannayebi, “A hybrid machine learning and optimization model to minimize the total cost of brt brake components,” Journal of Advanced Transportation, vol. 2021, no. 1, p. 5590780, 2021.
D. R. R. Cabrera-Moya, “Econom´ıas de aglomeraci´on, sistemas de transporte p´ublico masivo tipo brt (bus rapid transit) y decisiones de ubicaci´on geogr´afica de empresas,” Urbe. Revista Brasileira de Gest˜ao Urbana, vol. 13, p. e20190217, 2021.
F. Haider, Z. u. Rehman, A. H. Khan, M. Ilyas, and I. Khan, “Performance evaluation of brt standard in decision support system for integrated transportation policy,” Sustainability, vol. 13, no. 4, p. 1957, 2021.
N. M. Haviz, G. B. Utama, A. A. Suryobuwono, P. Widiyanto et al., “Transjakarta bus rapid transit development strategy in supporting intermodal integration,” Advances in Transportation and Logistics Research, vol. 3, pp. 170–177, 2020.
A. D. Nurlukman, Y. Fadli, A. S. Dzulham et al., “Satisfaction impact of transjakarta integrated transportation system quality services,” in Journal of Physics: Conference Series, vol. 1477, no. 7. IOP Publishing, 2020, p. 072014.
R. B. Satrio, A. K. Hidayat, and A. Hayati, “The impact of transjakarta bus rapid transit (brt) operationalization on economic growth and unemployment rate in dki jakarta province,” Quantitative Economics and Management Studies, vol. 4, no. 2, pp. 261–277, 2023.
S. Ali, A. Handayani, M. Sobirin, and M. E. Audifikria, “Analysis of transjakarta bus rapid transit infrastructure services on customer satisfaction performance with large-scale social restrictions in dki jakarta,” IJTI International Journal of Transportation and Infrastructure eISSN 2597-4769 pISSN 2597-4734, vol. 5, no. 01, pp. 1–11, 2021.
C. Ma, G. Dai, and J. Zhou, “Short-term traffic flow prediction for urban road sections based on time series analysis and lstm bilstm method,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 5615–5624, 2021.
M. A. I. Sunny, M. M. S. Maswood, and A. G. Alharbi, “Deep learning-based stock price prediction using lstm and bi-directional lstm model,” in 2020 2nd novel intelligent and leading emerging sciences conference (NILES). IEEE, 2020, pp. 87–92.
J. Siswanto, D. Manongga, I. Sembiring, and S. Wijono, “Deep learning based lstm model for predicting the number of passengers for public transport bus operators,” Jurnal Online Informatika, vol. 9, no. 1, pp. 18–28, 2024.
J. Siswanto, U. Rahardja, I. Sembiring, K. D. Hartomo, H. D. Purnomo, A. Iriani et al., “Number of road accidents predicting using deep learning-based lstm development models,” in 2023 11th International Conference on Cyber and IT Service Management (CITSM). IEEE, 2023, pp. 1–6.
G. Vidya and V. Hari, “Lstm network integrated with particle filter for predicting the bus passenger traffic,” Journal of signal processing systems, vol. 95, no. 2, pp. 161–176, 2023.
J. Siswanto, I. Sembiring, A. Setiawan, and I. Setyawan, “Number of cyber attacks predicted with deep learning based lstm model,” JUITA: Jurnal Informatika, vol. 12, no. 1, pp. 39–48, 2024.
D. Apriani, R. Afrijaldi, N. Auliya, and A. A. Darmawan, “Operating system and server integration for business effectiveness,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 5, no. 2, pp. 91–99, 2024.
I. S. Kervanci, M. F. Akay, and E. ¨Ozceylan, “Bitcoin price prediction using lstm, gru and hybrid lstm-gru with bayesian optimization, random search, and grid search for the next days,” Journal of Industrial and Management Optimization, vol. 20, no. 2, pp. 570–588, 2024.
X. Shu, L. Zhang, Y. Sun, and J. Tang, “Host–parasite: Graph lstm-in-lstm for group activity recognition,” IEEE transactions on neural networks and learning systems, vol. 32, no. 2, pp. 663–674, 2020.
Z. Xiang, J. Yan, and I. Demir, “A rainfall-runoff model with lstm-based sequence-to-sequence learning,” Water resources research, vol. 56, no. 1, p. e2019WR025326, 2020.
J. Du, C.-M. Vong, and C. P. Chen, “Novel efficient rnn and lstm-like architectures: Recurrent and gated broad learning systems and their applications for text classification,” IEEE transactions on cybernetics, vol. 51, no. 3, pp. 1586–1597, 2020.
F. Shahid, A. Zameer, and M. Muneeb, “Predictions for covid-19 with deep learning models of lstm, gru and bi-lstm,” Chaos, Solitons & Fractals, vol. 140, p. 110212, 2020.
Y. Pei, S. Ran, W. Wang, and C. Dong, “Bus-passenger-flow prediction model based on wpd, attention mechanism, and bi-lstm,” Sustainability, vol. 15, no. 20, p. 14889, 2023.
L. Xian and L. Tian, “Passenger flow prediction and management method of urban public transport based on sdae model and improved bi-lstm neural network,” Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10 563–10 577, 2023.
M. Sajjad, S. Kwon et al., “Clustering-based speech emotion recognition by incorporating learned features and deep bilstm,” IEEE access, vol. 8, pp. 79 861–79 875, 2020.
A. Poulose and D. S. Han, “Uwb indoor localization using deep learning lstm networks,” Applied Sciences, vol. 10, no. 18, p. 6290, 2020.
H. Zheng, F. Lin, X. Feng, and Y. Chen, “A hybrid deep learning model with attention-based conv-lstm networks for short-term traffic flow prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 11, pp. 6910–6920, 2020.
Z. Wang, X. Su, and Z. Ding, “Long-term traffic prediction based on lstm encoder-decoder architecture,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 10, pp. 6561–6571, 2020.
C. Sun, H. Li, and S. Fu, “Application of bilstm-transformer in portfolio optimization,” Advances in Economics, Management and Political Sciences, vol. 26, pp. 212–217, 2023.
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