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JACIII Vol.26 No.6 pp. 983-994
doi: 10.20965/jaciii.2022.p0983
(2022)

Paper:

Strategic Transit Route Recommendation Considering Multi-Trip Feature Desirability Using Logit Model with Optimal Travel Time Analysis

Marielet A. Guillermo, Maverick C. Rivera, Kervin Joshua C. Lucas, Ronnie S. Concepcion II, Argel A. Bandala, Robert Kerwin C. Billones, Edwin Sybingco, Alexis M. Fillone, and Elmer P. Dadios

De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

Corresponding author

Received:
May 5, 2022
Accepted:
July 15, 2022
Published:
November 20, 2022
Keywords:
logit model, optimal strategy, path finding algorithm, public transportation, transit route recommendation
Abstract

Route recommendation continues to manifest noteworthy contributions to the intelligent transportation system field of research as it evolves through time. Early related studies helped passengers and tourists experience a more convenient travel. At the same time, these helped transport planners analyze people’s trip preferences and its correlation with the region-specific economic status in a more time-relevant data. Majority, however, require historical data and heavy data collection methods. For user quantified metrics such as route cost in terms of travel time and distance, the complexity and sparsity of preferences between travelers are persistent challenges. The strategic transit route recommendation proposed in this study takes into account multiple trip features (both quantitative and qualitative) desirability using logit model and the optimal travel time with respect to a given road traffic condition, headway, and passenger demand. The chosen area of study is the Western Visayas region of the Philippines specific to the public utility bus (PUB) and jeepney (PUJ) transit routes. The results of the research exhibited the feasibility of an optimal and strategic recommendation of public transportation route for passengers considering present time relevant trip conditions rather than relying on the historical data which are difficult to obtain, or worse, non-existent.

Recommended route using STRAT model

Recommended route using STRAT model

Cite this article as:
M. Guillermo, M. Rivera, K. Lucas, R. Concepcion II, A. Bandala, R. Billones, E. Sybingco, A. Fillone, and E. Dadios, “Strategic Transit Route Recommendation Considering Multi-Trip Feature Desirability Using Logit Model with Optimal Travel Time Analysis,” J. Adv. Comput. Intell. Intell. Inform., Vol.26 No.6, pp. 983-994, 2022.
Data files:
References
  1. [1] Iloilo City, Local Public Transport Route Plan (LPTRP), “Iloilo City Public Transportation Route Plan,” Vol.1, No.1, pp. 1-105, 2021.
  2. [2] IMD, “Smart City Index 2021.” https://www.imd.org/smart-city-observatory/smart-city-index/ [accessed July 10, 2022]
  3. [3] National Economic and Development Authority, “Western Visayas Water Supply and Sanitation Databook and Regional Roadmap,” Vol.2, 2019.
  4. [4] ALMEC Corporation, JICA, and Department of Transportation and Communications (DOTC), “The Project for Capacity Development on Transportation Planning and Database Management in the Republic of the Philippines,” Vol.3, p. 51, 2015.
  5. [5] J. R. F. Regidor, “Current State of Transportation Data and Statistics in the Philippines and Opportunities for Improvement Towards Usability,” 14th Natl. Conv. Stat., Vol.1, No.1, pp. 1-17, 2019.
  6. [6] Y. Ge et al. “Route Recommendations for Intelligent Transportation Services,” IEEE Trans. Knowl. Data Eng., Vol.33, No.3, pp. 1169-1182, doi: 10.1109/TKDE.2019.2937864, 2021.
  7. [7] V. T. F. Chow et al., “Utilizing real-time travel information, mobile applications and wearable devices for smart public transportation,” Proc. 7th Int. Conf. Cloud Comput. Big Data (CCBD), pp. 138-144, doi: 10.1109/CCBD.2016.036, 2016.
  8. [8] P. Campigotto et al., “Personalized and Situation-Aware Multimodal Route Recommendations: The FAVOUR Algorithm,” IEEE Trans. Intell. Transp. Syst., Vol.18, No.1, pp. 92-102, doi: 10.1109/TITS.2016.2565643, 2017.
  9. [9] J.-X. Zhang et al., “Mixed Traffic Mode Based Travel Route Recommendation,” Proc. 4th Annu. Int. Conf. Netw. Inf. Syst. Comput. (ICNISC), pp. 372-376, doi: 10.1109/ICNISC.2018.00082, 2018.
  10. [10] Y. Ishizaki et al., “A route recommendation method based on personal preferences by Monte-Carlo tree search,” IEEE Int. Conf. Consum. Electron. (ICCE-Berlin), pp. 404-409, doi: 10.1109/ICCE-Berlin47944.2019.8966146, 2019.
  11. [11] H. Nakamura et al., “Personalized recommendation for public transportation using user context,” Proc. 5th IIAI Int. Congr. Adv. Appl. Informatics (IIAI-AAI), pp. 224-229, doi: 10.1109/IIAI-AAI.2016.204, 2016.
  12. [12] J. Samraj and N. Menaka, “Sentimental Analysis Based on Cold-Start Recommendation with Deep Neural Learning (SACNN): A Novel Approach for Travel Recommendation in Pandemic,” IEEE Int. Conf. Mob. Networks Wirel. Commun. (ICMNWC), pp. 4-8, doi: 10.1109/ICMNWC52512.2021.9688450, 2021.
  13. [13] W. Q. Al-Salih and D. Esztergár-Kiss, “Linking mode choice with travel behavior by using logit model based on utility function,” Sustain., Vol.13, No.8, doi: 10.3390/su13084332, 2021.
  14. [14] W. Victory and M. A. Ahmed, “Forecasting paratransit utility by using multinomial logit model: A case study,” Int. J. Eng. Technol., Vol.8, No.5, pp. 2193-2198, doi: 10.21817/ijet/2016/v8i5/160805233, 2016.
  15. [15] B. Qu et al., “Profitable Taxi Travel Route Recommendation Based on Big Taxi Trajectory Data,” IEEE Trans. Intell. Transp. Syst., Vol.21, No.2, pp. 653-668, doi: 10.1109/TITS.2019.2897776, 2020.
  16. [16] E. P. Gunawan and C. Tho, “Development of an application for tourism route recommendations with the dijkstra algorithm,” Int. Conf. Inf. Manag. Technol. (ICIMTech), pp. 343-347, doi: 10.1109/ICIMTech53080.2021.9534998, 2021.
  17. [17] D. Ajantha et al., “A user-location vector based approach for personalised tourism and travel recommendation,” Proc. Int. Conf. Big Data Anal. Comput. Intell. (ICBDACI), pp. 440-446, doi: 10.1109/ICBDACI.2017.8070880, 2017.
  18. [18] C. Yuan and M. Uehara, “Improvement of multi-purpose travel route recommendation system based on genetic algorithm,” Proc. 7th Int. Symp. Comput. Netw. Work. (CANDARW), pp. 305-308, doi: 10.1109/CANDARW.2019.00060, 2019.
  19. [19] S. Elkosantini and S. Darmoul, “Intelligent public transportation systems: A review of architectures and enabling technologies,” Int. Conf. Adv. Logist. Transp. (ICALT), pp. 233-238, doi: 10.1109/ICAdLT.2013.6568465, 2013.
  20. [20] M. A. H. Nur et al., “Tracking, Arrival Time Estimator, and Passenger Information System on Bus Rapid Transit (BRT),” 8th Int. Conf. Inf. Commun. Technol. (ICoICT), pp. 2020-2023, doi: 10.1109/ICoICT49345.2020.9166375, 2020.
  21. [21] R. R. Tobias et al., “Design and Construction of a Solar Energy Module for Optimizing Solar Energy Efficiency,” IEEE 12th Int. Conf. Humanoid, Nanotechnology, Inf. Technol. Commun. Control. Environ. Manag. (HNICEM), pp. 3-8, doi: 10.1109/HNICEM51456.2020.9400127, 2020.
  22. [22] J. E. Mmari and S. Markon, “Mobile applications for real time information delivery on rapid bus transit systems in Tanzania,” Proc. 17th Int. Conf. Adv. ICT Emerg. Reg., pp. 54-61, doi: 10.1109/ICTER.2017.8257812, 2017.
  23. [23] F. Z. Rahmanti et al., “Integrated Information System Based on Google Maps APIs: Design of Surabaya Public Transportation System,” Proc. Int. Conf. Comput. Sci. Inf. Technol. Electr. Eng. (ICOMITEE), pp. 154-159, doi: 10.1109/ICOMITEE.2019.8921161, 2019.
  24. [24] S. Lauguico et al., “Machine Vision-Based Prediction of Lettuce Phytomorphological Descriptors using Deep Learning Networks,” IEEE 12th Int. Conf. HNICEM, doi: 10.1109/HNICEM51456.2020.9400103, 2020.
  25. [25] M. A. Guillermo et al., “Content-based Fashion Recommender System Using Unsupervised Learning,” IEEE Region 10 Conf. (TENCON), pp. 29-34, doi: 10.1109/TENCON54134.2021.9707459, 2021.
  26. [26] G. Bajaj et al., “Towards building real-time, convenient route recommendation system for public transit,” IEEE 2nd Int. Smart Cities Conf. (ISC2), doi: 10.1109/ISC2.2016.07580779, 2016.
  27. [27] P. Chen et al., “Intelligent travel route recommendation algorithm based on big data,” 12th Int. Conf. Meas. Technol. Mechatronics Autom. (ICMTMA), pp. 531-534, doi: 10.1109/ICMTMA50254.2020.00120, 2020.
  28. [28] R. Akçelik, “Travel time functions for transport planning purposes: Davidson’s function, its time-dependent form and an alternative travel time function (Minor Revision),” Aust. Road Res., Vol.21, No.3, pp. 49-59, 2000.
  29. [29] R. K. C. Billones et al., “Smart Region Mobility Framework,” Sustain., Vol.13, No.11, doi: 10.3390/su13116366, 2021.

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