A Comprehensive Comparative Analysis of Passenger Demand Prediction for Improving the Urban Bus Transportation System (UBTS)

A Comprehensive Comparative Analysis of Passenger Demand Prediction for Improving the Urban Bus Transportation System (UBTS)

  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-9
Year of Publication : 2022
Authors : Archana M. Nayak, Akhilesh Ladha, Nirbhay Kumar Chaubey
DOI : 10.14445/22315381/IJETT-V70I9P227

How to Cite?

Archana M. Nayak, Akhilesh Ladha, Nirbhay Kumar 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. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I9P227

Abstract
Cities play a vital role in promoting commercial growth and wealth. The development of cities is mainly based on their social, physical, and institutional infrastructure. In this situation, the significance of urban transportation is dominant. Urban area transportation is a common public bus service and is mostly used to transport many people in urban areas. In this paper, a survey on important parameters for improving the UBTS is reviewed. At first, the article reviewed the flow of passenger prediction based on the UBTS. Here, the UBTS issues like the prediction of passenger flow, fleet size, passenger comfort perception, delay, driver's behaviour, sound level, vehicle breakdowns, and so on are reviewed. To overcome these issues, the authors have presented some techniques and solutions for urban transportation, which are reviewed. A systematic literature review is conducted for the urban transportation system from 2011 until 2021. This survey provides a technical direction for researchers' work, and the potential future aspects have been discussed.

Keywords
UBTS, Passenger Flow, Passenger Comfort Perception, Drivers Behaviours.

Reference
[1] C.W. Tsai, C.H. Hsia, S.J. Yang, S.J. Liu and Z.Y. Fang, “Optimizing Hyperparameters of Deep Learning in Predicting Bus Passengers Based on Simulated Annealing,” Applied Soft Computing, pp. 106068, 2020.
[2] J. Huang, F. Shao and S. Yang, “Passenger Flow Prediction Based on Recurrent Neural Networks and Wavelet Transform,” Journal of Physics: Conference Series, vol. 1486, pp. 022021, 2020.
[3] I.K. Isukapati, C. Igoe, E. Bronstein, V. Parimi and S.F. Smith, “Hierarchical Bayesian Framework for Bus Dwell Time Prediction,” IEEE Transactions on Intelligent Transportation Systems, 2020.
[4] G.S. Vidya, V.S. Hari and S. Shivasagaran, “Estimation of Passenger Flow in A Bus Route Using Kalman Filter,” in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) IEEE, pp. 1248-1251, 2020.
[5] RB Sharmila, N.R. Velaga and P. Choudhary, “Bus Arrival Time Prediction and Measure of Uncertainties Using Survival Models,” IET Intelligent Transport Systems, 2020.
[6] M. As, T. Mine and T. Yamaguchi, “Prediction of Bus Travel Time Over Unstable Intervals Between Two Adjacent Bus Stops,” International Journal of Intelligent Transportation Systems Research, vol. 18, no. 1, pp. 53-64, 2020.
[7] Q. Han, K. Liu, L. Zeng, G. He, L. Ye and F. Li, “A Bus Arrival Time Prediction Method Based on Position Calibration and LSTM,” IEEE Access, vol. 8, pp. 42372-42383, 2020.
[8] Y. Jing, J. Weng, Z. Zhang, J. Wang and H. Qian, “Public Traffic Passenger Flow Prediction Model for Short-Term Large Scale Activities Based on Wavelet Analysis, in Green,” Smart and Connected Transportation Systems Springer, Singapore, pp. 1281- 1294, 2020.
[9] Y.W. Hsu, T.Y. Wang and JW Perng, “Passenger Flow Counting in Buses Based on Deep Learning Using Surveillance Video,” Optik. Vol. 202 , pp. 163675, 2020.
[10] Y. Ye, L. Chen and F. Xue, “Passenger Flow Prediction in BTS using ARIMA Models with Big Data,” In 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (Cyberc) IEEE, pp. 436-443, 2019.
[11] H. Liu, H. Xu, Y. Yan, Z. Cai, T. Sun and W. Li, “Bus Arrival Time Prediction Based on LSTM and Spatial-Temporal Feature Vector,” IEEE Access, vol. 8, pp. 11917-11929, 2020.
[12] M. Handajani and AK Nugroho, “The Efficiency of a Bus Rapid Transit Utilizing a Passenger Information System,” in 2nd International Symposium on Transportation Studies in Developing Countries (ISTSDC 2019) Atlantis Press, pp. 8-12, 2020.
[13] C. Colombaroni, G. Fusco and N. Isaenko, “A Simulation-Optimization Method for Signal Synchronization with Bus Priority and Driver Speed Advisory to Connected Vehicles,” Transportation Research Procedia, vol. 45, pp. 890-897, 2020.
[14] Z. Huang, Q. Li, F. Li and J. Xia, “A Novel Bus-Dispatching Model Based on Passenger Flow and Arrival Time Prediction,” IEEE Access, vol. 7 , pp. 106453-106465, 2019.
[15] R. Gummadi and S.R. Edara, “Prediction of Passenger Flow of Transit Buses Over a Period of Time Using Artificial Neural Network,” In Third International Congress on Information and Communication Technology Springer, Singapore, pp. 963-971, 2019.
[16] X. Luo, Y. Jiang, Z. Yao, Y. Tang and Y. Liu, “Designing Limited-Stop Transit Service With Fixed Fleet Size in Peak Hours by Exploiting Transit Data,” Transportation Research Record, vol. 2647, no. 1, pp. 134-141, 2017.
[17] E. Calvo and M. Ferrer, “Evaluating the Quality of the Service Offered by a Bus Rapid Transit System: the Case of Transmetro BRT System in Barranquilla, Colombia,” International Journal of Urban Sciences, vol. 22, no. 3, pp. 392-413, 2018.
[18] M. Handte, S. Foell, S. Wagner, G. Kortuem and P.J. Marrón, “An Internet-of-Things Enabled Connected Navigation System for Urban Bus Riders,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 735-744, 2016.
[19] M. Yap, O. Cats and B. Van Arem, Crowding Valuation in Urban Tram and Bus Transportation Based on Smart Card Data, Transportmetrica A: Transport Science, pp. 1-20, 2018.
[20] C.O. Escolano, R.K.C. Billones, E. Sybingco, A.D. Fillone and E.P. Dadios, “Passenger Demand Forecast Using Optical Flow Passenger Counting System for Bus Dispatch Scheduling,” In 2016 IEEE Region 10 Conference (TENCON) IEEE, pp.1875-1878, 2016.
[21] Y. Sun, B. Leng and W. Guan, “A Novel Wavelet-SVM Short-Time Passenger Flow Prediction in Beijing Subway System,” Neuro computing, vol. 166, pp. 109-121, 2015.
[22] MD. Yap, S. Nijënstein and N. Van Oort, “Improving Predictions of Public Transport Usage During Disturbances Based on Smart Card Data,” Transport Policy, vol. 61, pp. 84-95, 2018.
[23] J. Amita, S.S. Jain and P.K. Garg, “Prediction of Bus Travel Time Using ANN: A Case Study in Delhi,” Transportation Research Procedia, vol. 17, pp. 263-272, 2016.
[24] C. Bai, Z.R. Peng, Q.C. Lu and J. Sun, “Dynamic Bus Travel Time Prediction Models on Road With Multiple Bus Routes,” Computational Intelligence and Neuroscience, vol. 2015, pp. 63, 2015.
[25] K. Bauer, T. Bosker, K.N. Dirks and P. Behrens, “The Impact of Seating Location on Black Carbon Exposure in Public Transit Buses: Implications for Vulnerable Groups,” Transportation Research Part D: Transport and Environment, vol. 62, pp. 577-583, 2018.
[26] C. Crump, R. Brinkerhoff and D. Young, “ Passenger Seat Belt Usage Rates on Shuttle Buses, In Proceedings of the Human Factors and Ergonomics Society Annual Meeting Sage CA: Los Angeles, CA: SAGE Publications, vol. 61, no. 1 , pp. 1674-1678, 2017.
[27] Y. Ji, X. Yang and Y. Du,“Optimal Design of a Short‐Turning Strategy Considering Seat Availability,” Journal of Advanced Transportation, vol. 50, no. 7, pp. 1554-1571, 2016.
[28] E.F. Sam, T.K. Ojo, S. Siita, A. Sarpong, I.K. Baffour and E. Abenyi, “Determinants of Public Transport Passengers' Choice of Seating Positions in Ghana, Urban,” Planning and Transport Research, vol. 6, no. 1, pp. 148-158, 2018.
[29] J.D. Schmöcker, A. Fonzone, H. Shimamoto, F. Kurauchi and MG Bell, “Frequency-Based Transit Assignment Considering Seat Capacities,” Transportation Research Part B: Methodological, vol. 45, no. 2, pp. 392-408, 2011.
[30] J. Du, S.F. Cheng and H.C, “Lau Designing Bus Transit Services for Routine Crowd Situations At Large Event Venues,” In International Conference on Computational Logistics Springer, Cham, pp. 704-718, 2015.
[31] D. Mukundan and P. Ezhumalai, “Crowd Conscious Internet of Things Enabled Smart Bus Navigation System,” International Journal of Computer Science and Information Security (IJCSIS), vol. 16, no. 3, 2018.
[32] Sisay Alemu Munea, Dr. Raju Ramesh Reddy, Gebrefilmuna Abera, "Evaluating the Impact of Various Geometric Characteristics of Rural Two Lane Road on Traffic Safety in Ethiopia," SSRG International Journal of Civil Engineering, vol. 7, no. 6, pp. 1-11, 2020. Crossref, https://doi.org/10.14445/23488352/IJCE-V7I6P101.
[33] M.N. Mustaqeem, F. Jalaluddin and R. Hassan, “Bus Network Coverage Analysis of Dhaka City Along with its Service Quality,” AJIRSET, 2018.
[34] J. Wu, M. Yang, S. Rasouli and C. Xu, “Exploring Passenger Assessments of Bus Service Quality Using Bayesian Networks,” Journal of Public Transportation, vol. 19, no. 3, pp. 3, 2016.
[35] S. Chakrabarti and G. Giuliano, “Does Service Reliability Determine Transit Patronage? Insights From the Los Angeles Metro Bus System,” Transport Policy, vol. 42, pp. 12-20, 2015.
[36] W. Shen, W. Xiao and X. Wang, “Passenger Satisfaction Evaluation Model for Urban Rail Transit: A Structural Equation Modeling Based on Partial Least Squares,” Transport Policy, vol. 46, pp. 20-31, 2016.
[37] J. Weng, X. Di, C. Wang, J. Wang and L. Mao, “A Bus Service Evaluation Method From Passenger's Perspective Based on Satisfaction Surveys: A Case Study of Beijing, China,” Sustainability, vol. 10, no. 8, pp. 2723, 2018.
[38] X. Cheng, Y. Cao, K. Huang and Y. Wang, “Modeling the Satisfaction of Bus Traffic Transfer Service Quality at a High-Speed Railway Station,” Journal of Advanced Transportation, 2018.
[39] M. Quddus, F. Rahman, F. Monsuur, J. De Ona and M. Enoch, “Analyzing Bus Passengers' Satisfaction in Dhaka Using Discrete Choice Models,” Transportation Research Record, vol. 2673, no. 2, pp. 758-768, 2019.
[40] S. Liang, M. Ma and S. He, “Multiobjective Optimal Formulations for Bus Fleet Size of Public Transit Under Headway-Based Holding Control,” Journal of Advanced Transportation, vol. 2019, 2019.
[41] S. Liang, M. Ma, S. He and H. Zhang, “The Impact of Bus Fleet Size on Performance of Self-Equalize Bus Headway Control Method,” In Proceedings of the Institution of Civil Engineers-Municipal Engineer Thomas Telford Ltd, vol. 172, no. 4, pp. 246-256, 2019.
[42] M. Rogge, E. Van Der Hurk, A. Larsen and D.U. Sauer, “Electric Bus Fleet Size and Mix Problem With Optimization of Charging Infrastructure,” Applied Energy, vol. 211 , pp. 282-295, 2018.
[43] L. Li, H.K. Lo, F. Xiao and X. Cen, “Mixed Bus Fleet Management Strategy for Minimizing Overall and Emissions External Costs,” Transportation Research Part D: Transport and Environment, vol. 60 , pp. 104-118, 2018.
[44] H.L. Khoo and M. Ahmed, “Modeling of Passengers' Safety Perception for Buses on Mountainous Roads,” Accident Analysis & Prevention, vol. 113, pp. 106-116, 2018.
[45] H. He, M. Yan, C. Sun, J. Peng, M. Li and H. Jia, “Predictive Air-Conditioner Control for Electric Buses with Passenger Amount Variation Forecast,” Applied Energy, vol. 227, pp. 249-261.
[46] D. Göhlich, T.A. Ly, A. Kunith and D. Jefferies, “Economic Assessment of Different Air-Conditioning and Heating Systems for Electric City Buses Based on Comprehensive Energetic Simulations,” World Electric Vehicle Journal, vol. 7, no. 3, pp. 398-406, 2015.
[47] E. Tosun, M. Bilgili, G. Tuccar, A. Yasar and K. Aydin, “Exergy Analysis of an Inter-City Bus Air-Conditioning System,” International Journal of Exergy, vol. 20, no. 4, pp. 445-464, 2016.
[48] C. Schulze, G. Raabe, W.J. Tegethoff and J. Koehler, “Transient Evaluation of A City Bus Air Conditioning System with R-445A as Drop-in–From the Molecules to the System,” International Journal of Thermal Sciences, vol. 96 , pp. 355-361, 2015.
[49] Unnikrishnan Menon, Divyani Panda, "Design and Evaluation of Electric Bus Systems for Metropolitan Cities," SSRG International Journal of Mechanical Engineering, vol. 7, no. 10, pp. 16-23, 2020. Crossref, https://doi.org/10.14445/23488360/IJMEV7I10P104.
[50] F. Quatmann and S. Mazidi, Airbus Operations Gmbh, “Seat Modification Assembly and Aircraft Passenger Seat Comprising a Seat Modification Assembly,” Us Patent, vol. 9, no. 487, pp. 298, 2016.
[51] P.S. Bruno, Q.R. Marcos, C. Amanda and Z.H. Paulo, “Annoyance Evaluation and the Effect of Noise on the Health of Bus Drivers,” Noise and Health, vol. 15, no. 66, pp. 301, 2013.
[52] Y. Huo, W. Li, J. Zhao and S. Zhu, “Modelling Bus Delay At Bus Stop,” Transport, vol. 33, no. 1, pp. 12-21, 2018.
[53] T. Nagatani, “Delay Effect on Schedule in Shuttle Bus Transportation Controlled By Capacity,” Physica A: Statistical Mechanics and Its Applications, vol. 391, no. 11 , pp. 3266-3276, 2012.
[54] B. Shi, L. Xu, J. Hu, Y. Tang, H. Jiang, W. Meng and H. Liu, “Evaluating Driving Styles By Normalizing Driving Behavior Based on Personalized Driver Modeling,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 12, pp. 1502-1508, 2015.
[55] O. Cats, “Determinants of Bus Riding Time Deviations: Relationship Between Driving Patterns and Transit Performance,” Journal of Transportation Engineering, Part A: Systems, vol. 145, no. 1, pp. 04018078, 2018.
[56] S. Varmazyar, SB Mortazavi, E. Hajizadeh and S. Arghami, “The Relationship Between Driving Aberrant Behavior and SelfReported Accidents Involvement Amongst Professional Bus Drivers in the Public Transportation Company,” 2013.
[57] B. Armaselu and O. Daescu, “Interactive Assistive Framework for Maximum Profit Routing in Public Transportation in Smart Cities,” in Proceedings of the 10th International Conference on Pervasive Technologies Related to Assistive Environments ACM, 2017.
[58] S. Zhang, Y. Wu, H. Liu, R. Huang, L. Yang, Z. Li, L. Fu and J. Hao, “Real-World Fuel Consumption and CO2 Emissions of Urban Public Buses in Beijing,” Applied Energy, vol. 113, pp. 1645-1655, 2014.
[59] A. Lajunen and T. Lipman, “Lifecycle Cost Assessment and Carbon Dioxide Emissions of Diesel, Natural Gas, Hybrid Electric, Fuel Cell Hybrid and Electric Transit Buses,” Energy, vol. 106, pp. 329-342, 2016.
[60] S. Tarulescu, R. Tarulescu, A. Soica and C.I. Leahu, “Smart Transportation CO2 Emission Reduction Strategies,” In IOP Conference Series: Materials Science and Engineering IOP Publishing, vol. 252, no. 1 , pp. 012051, 2017.
[61] Q. Han, K. Liu, L. Zeng, G. He, L. Ye and F. Li, “A Bus Arrival Time Prediction Method Based on Position Calibration and LSTM,” IEEE Access. vol.8, pp. 42372-42383, 2020.
[62] A. Khadhir, B. Anil Kumar and L.D. Vanajakshi, “Analysis of Global Positioning System Based Bus Travel Time Data and Its Use for Advanced Public Transportation System Applications,” Journal of Intelligent Transportation Systems, vol. 25, no. 1, pp. 58-76, 2021.
[63] C. Chen, H. Wang, F. Yuan, H. Jia and B. Yao, “Bus Travel Time Prediction Based on Deep Belief Network with BackPropagation,” Neural Computing and Applications, vol. 32, no. 14, pp. 10435-10449, 2020.
[64] J.C. Miles, “Intelligent Transport Systems: Overview and Structure , History, Applications, and Architectures,” Encyclopedia of Automotive Engineering, pp. 1-16, 2014.
[65] R. Mangiaracina, A. Perego, G. Salvadori and A. Tumino, “A Comprehensive View of Intelligent Transport Systems for Urban Smart Mobility,” International Journal of Logistics Research and Applications, vol. 20, no. 1, pp. 39-52, 2017.
[66] F. Jiao, L. Huang, R. Song and H. Huang, “An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction During the COVID-19 Pandemic,” Sensors, vol. 21, no. 17, pp. 5950, 2021.
[67] 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, 12519-12542, 2022.
[68] S. Kalra, S. Momin, T.S. Kulkarni and V. Lohani, “Real Time Re-Routing of Public Transportation System,” In 2019 IEEE Bombay Section Signature Conference (IBSSC), pp. 1-5, 2019.
[69] AA Kutty, N. Al-Jurf, A.F. Naser, M. Kucukvar, H. Ayad, M. Al-Obadi, G.M. Abdella, M.E. Bulak and J.M. Elkharaz, “Optimizing University Campus Shuttle Bus Congestion Focusing on System Effectiveness and Reliability: A Combined Modeling BasedRouting Approach,” in Proceedings of the International Conference on Industrial Engineering and Operations Management, Sao Paulo, Brazil, pp. 5-8, 2020.
[70] Z. Khan, S. Fang, A. Koubaa, P. Fan, F. Abbas and H. Farman, “Street-Centric Routing Scheme Using Ant Colony OptimizationBased Clustering for Bus-Based Vehicular Ad-Hoc Network,” Computers & Electrical Engineering, vol. 86, pp. 106736, 2020.
[71] W. Lv, Y. Lv, Q. Ouyang and Y. Ren, “A Bus Passenger Flow Prediction Model Fused With Point-of-Interest Data Based on Extreme Gradient Boosting,” Applied Sciences, vol. 12, no. 3, pp. 940, 2022.
[72] J. Yu, Z. Xie, Z. Dong, H. Song, J. Su, H. Wang, J. Xiao, X. Liu and J. Yang, “Intelligent Bus Scheduling Control Based on onBoard Bus Controller and Simulated Annealing Genetic Algorithm,” Electronics, vol. 11, no. 10, pp. 1520, 2022.
[73] ST Bhattarai, “Passenger Satisfaction Towards Services of Public Transportation: Butwal – Bhairahawa,” SSRG International Journal of Economics and Management Studies, vol. 6, no. 11, pp. 29-33, 2019, Crossref, https://doi.org/10.14445/23939125/IJEMSV6I11P104.
[74] X. Yang, Y. Ji, Y. Du and H.M. Zhang, “Bi-Level Model for Design of Transit Short-Turning Service Considering Bus Crowding,” Transportation Research Record, vol. 2649, no. 1, pp. 52-60, 2019.
[75] A.O. Ajayeoba and L.O. Adekoya, “Evaluation of the Ergonomic Suitability of Passenger Seats in Molue Buses in Nigeria,” Journal of Mechanical Engineering, vol. 1, no. 2, pp. 4-11, 2012.