Riding into the Future: Transforming Jordan’s Public Transportation with Predictive Analytics and Real-Time Data
DOI:
https://doi.org/10.56294/dm2025887Keywords:
Public Transportation, Predictive Analytics, Real-Time Data Integration, ARIMA, Random Forest, Congestion Management, IoT and GPS Tracking, Metadata ManagementAbstract
Introduction: This study explores how predictive analytics and real-time data integration can improve efficiency in Jordan’s public transportation network. By addressing scheduling, route optimization, and congestion management, it responds to growing urban transit demands in the region.
Methods: Data were collected over three months from official ridership logs, GPS-enabled buses, and traffic APIs. ARIMA-based time-series forecasting captured historical trends, while a Random Forest model incorporated congestion index, average wait times, and other operational variables. Metadata management protocols (JSON/XML) facilitated cross-agency data sharing.
Results: ARIMA proved effective for short-term passenger demand projections, although it occasionally underpredicted sudden ridership peaks. The Random Forest approach yielded stronger overall accuracy, explaining roughly 85% of variation when combining real-time congestion data with historical records. Real-time streams further supported dynamic scheduling and route adjustments.
Conclusion: Combining predictive models with IoT-based data integration can enhance reliability and user satisfaction in Jordan’s public transit system. Although limited by timeframe and route scope, the findings underscore the importance of multi-agency collaboration and ongoing policy support to sustain data-driven innovations.
References
1. Akuh, R., Zhong, M., & Raza, A. (2022). Evaluating a Proposed Urban Transportation System Using Advance Transport and Land-Use Modelling Framework. In Advances in Science and Technology – Research Journal (Vol. 16, Issue 3, p. 234). Society of Polish Mechanical Engineers and Technicians. https://doi.org/10.12913/22998624/149607
2. Watkins, K., Berrebi, S., Erhardt, G. D., Hoque, J. M., Goyal, V., Brakewood, C., Ziedan, A., Darling, W., Hemily, B., & Kressner, J. (2021). Recent Decline in Public Transportation Ridership: Analysis, Causes, and Responses. In Transportation Research Board eBooks. https://doi.org/10.17226/26320
3. TransitCenter, & Technologies, A. P. (2020). Mobility Performance Metrics (MPM) For Integrated Mobility and Beyond. https://doi.org/10.21949/1506055.
4. National Academies of Sciences, Engineering, and Medicine. (2020). Data Sharing Guidance for Public Transit Agencies. https://nap.nationalacademies.org/catalog/25696/data-sharing-guidance-for-public-transit-agencies-now-and-in-the-future
5. Adesina, A. A., Iyelolu, T. V., & Paul, P. O. (2024). Leveraging predictive analytics for strategic decision-making: Enhancing business performance through data-driven insights. In World Journal of Advanced Research and Reviews (Vol. 22, Issue 3, p. 1927). GSC Online Press. https://doi.org/10.30574/wjarr.2024.22.3.1961
6. Rashvand, N., Hosseini, S., Azarbayjani, M., & Tabkhi, H. (2024). Real-Time Bus Arrival Prediction: A Deep Learning Approach for Enhanced Urban Mobility (p. 123). https://doi.org/10.5220/0012365500003639
7. Rao, A. R., Wang, H., & Gupta, C. (2024). Predictive Analysis for Optimizing Port Operations. In arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2401.14498
8. Ge, L., Sarhani, M., Voß, S., & Xie, L. (2021). Review of Transit Data Sources: Potentials, Challenges and Complementarity. In Sustainability (Vol. 13, Issue 20, p. 11450). Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/su132011450
9. Han, C., Talusan, J. P., Freudberg, D., Mukhopadhyay, A., Dubey, A., & Lászka, Á. (2024). Forecasting and Mitigating Disruptions in Public Bus Transit Services. In arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2403.04072 av
10. Barbeau, S. J., Phạm, M. H., Nieves, J. A., & Bertini, R. L. (2020). Improving Transportation Performance Measurement via Open “Big Data” Systems – Phase 1 Transit. https://trid.trb.org/view/1678046
11. Zhang, J., Wada, K., & Oguchi, T. (2023). Morning commute in congested urban rail transit system: a macroscopic model for equilibrium distribution of passenger arrivals. In Transportmetrica B Transport Dynamics (Vol. 11, Issue 1). Taylor & Francis. https://doi.org/10.1080/21680566.2023.2195582
12. Avila, A., & Mezić, I. (2020). Data-driven analysis and forecasting of highway traffic dynamics. In Nature Communications (Vol. 11, Issue 1). Nature Portfolio. https://doi.org/10.1038/s41467-020-15582-5
13. Wang, J., & Shalaby, A. (2024). DST-TransitNet: A Dynamic Spatio-Temporal Deep Learning Model for Scalable and Efficient Network-Wide Prediction of Station-Level Transit Ridership. In arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2410.15013
14. Saini, K. P., & Sharma, A. (2024). A Comparison Between Long Short-Term Memory And Prophet For Time Series Analysis And Forecasting Technique. https://doi.org/10.53555/kuey.v30i4.2816
15. Cheng, Z., Trépanier, M., & Sun, L. (2021). Incorporating travel behaviour regularity into passenger flow forecasting. In Transportation Research Part C Emerging Technologies (Vol. 128, p. 103200). Elsevier BV. https://doi.org/10.1016/j.trc.2021.103200
16. Zheng, Y., Wang, Q., Zhuang, D., Wang, S., & Zhao, J. (2023). Fairness-enhancing deep learning for ride-hailing demand prediction. In arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2303.05698
17. Baimbetova, A., Konyrova, K., Zhumabayeva, A., & Seitbekova, Y. (2021). Bus Arrival Time Prediction: a Case Study for Almaty. In 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST). https://doi.org/10.1109/sist50301.2021.9465963
18. Mittal, V., Devi, P. S., Pandey, A. K., Singh, T., Dhingra, L., & Beliakov, S. (2024). IoT-Enabled Predictive Maintenance for Sustainable Transportation Fleets. In E3S Web of Conferences (Vol. 511, p. 1012). EDP Sciences. https://doi.org/10.1051/e3sconf/202451101012
19. Bellini, P., Bilotta, S., Collini, E., Fanfani, M., & Nesi, P. (2024). Data Sources and Models for Integrated Mobility and Transport Solutions. In Sensors (Vol. 24, Issue 2, p. 441). Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/s24020441
20. Alshawaf, F., Guescini, R., Kotschka, F., Dreyer, M., & Bierwirth, M. (2023). Harmonized Research Information for Classifying and Linking Research Data. In Proceedings of the Conference on Research Data Infrastructure (Vol. 1). https://doi.org/10.52825/cordi.v1i.310
21. Creutzig, F. (2021). From smart city to digital urban commons: Institutional considerations for governing shared mobility data. In Environmental Research Infrastructure and Sustainability (Vol. 1, Issue 2, p. 25004). IOP Publishing. https://doi.org/10.1088/2634-4505/ac0a4e
22. Liu, Y., Zuo, X., Ai, G., & Liu, Y. (2023). A reinforcement learning-based approach for online bus scheduling. In Knowledge-Based Systems (Vol. 271, p. 110584). Elsevier BV. https://doi.org/10.1016/j.knosys.2023.110584
23. Zheng, W., Zhuang, X., Liao, Z., Li, M., & Lin, Z. (2021). All roads lead to the places of your interest: An on‐demand, ride‐sharing visitor transport service. In International Journal of Tourism Research (Vol. 23, Issue 5, p. 871). Wiley. https://doi.org/10.1002/jtr.2449
24. Rozhdestvenskiy, O. I., & Poornima, E. (2024). Enabling Sustainable Urban Transportation with Predictive Analytics and IoT. In MATEC Web of Conferences (Vol. 392, p. 1179). EDP Sciences. https://doi.org/10.1051/matecconf/202439201179
25. Neves, F. T., Neto, M. de C., & Aparício, M. (2020). The impacts of open data initiatives on smart cities: A framework for evaluation and monitoring. In Cities (Vol. 106, p. 102860). Elsevier BV. https://doi.org/10.1016/j.cities.2020.102860
26. Wang, K., Zhao, Y., Gangadhari, R. K., & Li, Z. (2021). Analyzing the Adoption Challenges of the Internet of Things (IoT) and Artificial Intelligence (AI) for Smart Cities in China. In Sustainability (Vol. 13, Issue 19, p. 10983). Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/su131910983
27. Caicedo, J., González, M. C., & Walker, J. L. (2023a). Public Transit Demand Prediction During Highly Dynamic Conditions: A Meta-Analysis of State-of-the-Art Models and Open-Source Benchmarking Infrastructure. In arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arXiv.2306.
28. Li, X., Fan, J., Wu, Y., Chen, J., & Xue-feng, D. (2020). Exploring Influencing Factors of Passenger Satisfaction toward Bus Transit in Small-Medium City in China. In Discrete Dynamics in Nature and Society (Vol. 2020, p. 1). Hindawi Publishing Corporation. https://doi.org/10.1155/2020/8872115
29. Xiao, F., Jeng, W., & He, D. (2018). Investigating metadata adoptions for open government data portals in US cities. In Proceedings of the Association for Information Science and Technology (Vol. 55, Issue 1, p. 573). https://doi.org/10.1002/pra2.2018.14505501062
30. Getahun, S., Kefale, H., & Gelaye, Y. (2024). Application of Precision Agriculture Technologies for Sustainable Crop Production and Environmental Sustainability: A Systematic Review [Review of Application of Precision Agriculture Technologies for Sustainable Crop Production and Environmental Sustainability: A Systematic Review]. The Scientific World JOURNAL, 2024(1). Hindawi Publishing Corporation. https://doi.org/10.1155/2024/2126734
31. Ospina, R., Gondim, J. A. M., Leiva, V., & Castro, C. (2023). An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil. In Mathematics (Vol. 11, Issue 14, p. 3069). Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/math11143069
32. Mohammad AAS, Mohammad SIS, Al‑Daoud KI, Al Oraini B, Vasudevan A, Feng Z. Optimizing the Value Chain for Perishable Agricultural Commodities: A Strategic Approach for Jordan [Internet]. Vol. 6, Research on World Agricultural Economy; 2025 [cited 2025 Feb]. p. 465.
33. Miristice, L. M. B., Gentile, G., Corman, F., Tiddi, D., & Meschini, L. (2023). Real-Time passengers forecasting in congested transit networks considering dynamic service disruptions and passenger count data (p. 1). https://doi.org/10.1109/mt-its56129.2023.10241550
34. Mohammad AAS, Al-Daoud KI, Rusho MA, Alkhayyat A, Doshi H, Dey P, Kiani M. Modeling polyethylene glycol density using robust soft computing methods [Internet]. Vol. 210, Microchemical Journal; 2025 [cited 2025 Feb]. p. 112815.
35. Gastinger, J., Meilicke, C., Errica, F., Sztyler, T., Schuelke, A., & Stuckenschmidt, H. (2024). History repeats itself: A Baseline for Temporal Knowledge Graph Forecasting. In arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2404.16726
36. Mohammad AAS, Mohammad SIS, Al Oraini B, Vasudevan A, Alshurideh MT. Data security in digital accounting: A logistic regression analysis of risk factors [Internet]. Vol. 8, International Journal of Innovative Research and Scientific Studies; 2025 [cited 2025 Feb]. p. 2699.
37. Adewuyi, A. Y., Adebayo, K. B., Adebayo, D., Kalinzi, J. M., Ugiagbe, U. O., Ogunruku, O. O., Samson, O. A., Oladele, O. R., & Adeniyi, S. A. (2024). Application of big data analytics to forecast future waste trends and inform sustainable planning. In World Journal of Advanced Research and Reviews (Vol. 23, Issue 1, p. 2469). GSC Online Press. https://doi.org/10.30574/wjarr.2024.23.1.2229
38. Mohammad AAS. The impact of COVID-19 on digital marketing and marketing philosophy: evidence from Jordan [Internet]. Vol. 48, International Journal of Business Information Systems; 2025 [cited 2025 Feb]. p. 267.
39. Pei, S., Blumberg, S., Vega, J. C., Robin, T., Zhang, Y., Medford, R. J., Adhikari, B., & Shaman, J. (2023). Challenges in Forecasting Antimicrobial Resistance [Review of Challenges in Forecasting Antimicrobial Resistance]. Emerging Infectious Diseases, 29(4), 679. Centers for Disease Control and Prevention. https://doi.org/10.3201/eid2904.221552
40. Mohammad AAS, Al-Hawary SIS, Hindieh A, Vasudevan A, Al-Shorman MH, Al-Adwan AS, Turki Alshurideh M, Ali, I. Intelligent Data-Driven Task Offloading Framework for Internet of Vehicles Using Edge Computing and Reinforcement Learning [Internet]. Vol. 4, Data and Metadata; 2025 [cited 2025 Feb]. P. 521.
41. Faliagka, E., Christopoulou, E., Ringas, D., Politi, C., Kostis, N., Leonardos, D., Tranoris, C., Antonopoulos, C., Denazis, S., & Voros, N. (2024). Trends in Digital Twin Framework Architectures for Smart Cities: A Case Study in Smart Mobility. In Sensors (Vol. 24, Issue 5, p. 1665). Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/s24051665
42. Mohammad AA, Shelash SI, Saber TI, Vasudevan A, Darwazeh NR, Almajali R. Internal audit governance factors and their effect on the risk-based auditing adoption of commercial banks in Jordan [Internet]. Vol. 4, Data and Metadata; 2025 [cited 2025 Feb]. P. 464.
43. Dutta, A., Jain, M., Khan, A. O., & Sathanur, A. V. (2023). Deep Reinforcement Learning to Maximize Arterial Usage during Extreme Congestion. In arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arXiv.2305.
44. Mohammad SIS, Al-Daoud KI, Al Oraini BS, Alqahtani MM, Vasudevan A, Ali I. Impact of Crude Oil Price Volatility on Procurement and Inventory Strategies in the Middle East [Internet]. Vol. 15, International Journal of Energy Economics and Policy; 2025 [cited 2025 Feb]. P. 715.
45. Ma, M., Preum, S. M., Ahmed, M. Y., Tärneberg, W., Hendawi, A., & Stankovic, J. A. (2019). Data Sets, Modeling, and Decision Making in Smart Cities. In ACM Transactions on Cyber-Physical Systems (Vol. 4, Issue 2, p. 1). Association for Computing Machinery. https://doi.org/10.1145/3355283
46. Galdolage BS, Ekanayake EA, Al-Daoud KI, Vasudevan A, Wenchang C, Hunitie MFA, Mohammad SIS. Sustainable Marine and Coastal Tourism: A Catalyst for Blue Economic Expansion in Sri Lanka [Internet]. Vol. 3, Journal of Ecohumanism; 2024 [cited 2025 Feb]. P. 1214.
47. Samrat, S. R., & Manjunath, S. J. (2024). Leveraging IoT, AI, and ML for Enhanced Decision-Making in Karnataka’s Smart Citie. In International Journal of Innovative Science and Research Technology (IJISRT) (p. 2302). https://doi.org/10.38124/ijisrt/ijisrt24jul1317
48. Ekanayake EA, Al-Daoud KI, Vasudevan A, Wenchang C, Hunitie MFA, Mohammad SIS. Leveraging Aquaculture and Mariculture for Sustainable Economic Growth in Sri Lanka: Challenges and Opportunities [Internet]. Vol. 3, Journal of Ecohumanism; 2024 [cited 2025 Feb]. P. 1229.
49. Lai, S., Sorichetta, A., Steele, J., Ruktanonchai, C. W., Cunningham, A., Rogers, G., Koper, P., Woods, D. R., Bondarenko, M., Ruktanonchai, N., Shi, W., & Tatem, A. J. (2022). Global holiday datasets for understanding seasonal human mobility and population dynamics. In Scientific Data (Vol. 9, Issue 1). Nature Portfolio. https://doi.org/10.1038/s41597-022-01120-z
50. Chen W, Vasudevan A, Al-Daoud KI, Mohammad SIS, Arumugam V, Manoharan T, Foong WS. Integrating cultures, enhancing outcomes: Perceived organizational support and its impact on Chinese expatriates' performance in Dubai [Internet]. Vol. 7, Herança; 2024 [cited 2025 Feb]. P. 25.
51. Caicedo, J., González, M. C., & Walker, J. L. (2023b). Public Transit Demand Prediction During Highly Dynamic Conditions: A Meta-Analysis of State-of-the-Art Models and Open-Source Benchmarking Infrastructure. In arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2306.06194
52. Al-Oraini B, Khanfar IA, Al-Daoud K, Mohammad SI, Vasudevan A, Fei Z, Al-Azzam MKA. Determinants of Customer Intention to Adopt Mobile Wallet Technology [Internet]. Vol. 18, Appl. Math; 2024 [cited 2025 Feb]. P. 1331.
53. Ruotsalainen, P., & Blobel, B. (2023). Future pHealth Ecosystem-Holistic View on Privacy and Trust. In Journal of Personalized Medicine (Vol. 13, Issue 7, p. 1048). Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/jpm13071048
54. Ayyalsalman KM, Alolayyan MN, Alshurideh MT, Al-Daoud K, Al-Hawary SIS. Mathematical Model to Estimate The Effect of Authentic Leadership Components on Hospital Performance [Internet]. Vol. 18, Appl. Math; 2024 [cited 2025 Feb]. P. 701.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Anber Abraheem Shlash Mohammad, Suleiman Ibrahim Mohammad, Khaleel Ibrahim Al- Daoud, Badrea Al Oraini, Menahi Mosallam Alqahtani, Asokan Vasudevan, Mohammad Faleh Ahmmad Hunitie (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
