Smart City Gnosys

Smart city article details

Title	Machine Learning-Driven Passenger Demand Forecasting For Autonomous Taxi Transportation Systems In Smart Cities
ID_Doc	36074
Authors	Munawar A.; Piantanakulchai M.
Year	2025
Published	Expert Systems, 42, 3
DOI	http://dx.doi.org/10.1111/exsy.70014
Abstract	Autonomous Taxis (ATs) have seen remarkable global proliferation in recent years owing to the widespread adoption and advancements in Artificial Intelligence (AI) across various domains. ATs play a crucial role in Intelligent Transportation Systems (ITS) in smart cities. However, the effectiveness of ITS relies heavily on accurately forecasting the passenger demand for ATs, which poses a significant challenge. Precise prediction of passenger demand is essential for minimising waiting times and unnecessary cruising of ATs in metropolitan areas, which helps conserve energy. To address this issue, this study proposed an adaptive Bayesian Regularisation Backpropagation Neural Network (BRBNN) augmented with a Machine Learning (ML) model to predict passenger demand in different regions of metropolitan cities specifically for ATs. The study conducted extensive simulations using a real-world dataset collected from 4781 taxis in Bangkok, Thailand. Using MATLAB2022b, the proposed model compared various state of art methods and existing research. The results indicate that proposed model outperforms existing methods in terms of performance metrics such as Root Mean Square Error (RMSE) and R-squared ((Formula presented.)) for passenger demand forecasting. These findings validated the effectiveness of the prediction model and its ability to accurately forecast passenger demand for ATs, thereby contributing to the advancement of efficient transportation systems in smart cities. © 2025 John Wiley & Sons Ltd.
Author Keywords	autonomous taxis; autonomous vehicle; Bayesian regularisation; demand forecasting; intelligent transportation system; machine learning

Similar Articles

Id	Similarity	Authors	Title	Published
711	0.91	Munawar A.; Piantanakulchai M.	A Collaborative Privacy-Preserving Approach For Passenger Demand Forecasting Of Autonomous Taxis Empowered By Federated Learning In Smart Cities	Scientific Reports, 14, 1 (2024)
54463	0.895	Askari B.; Le Quy T.; Ntoutsi E.	Taxi Demand Prediction Using An Lstm-Based Deep Sequence Model And Points Of Interest	Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020 (2020)
2708	0.883	Boumeddane S.; Hamdad L.; El-Feda Bouregag A.A.; Damene M.; Sadeg S.	A Model Stacking Approach For Ride-Hailing Demand Forecasting : A Case Study Of Algiers	2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-Being, IHSH 2020 (2021)
3322	0.87	Wu Z.; Lian G.	A Novel Dynamically Adjusted Regressor Chain For Taxi Demand Prediction	Proceedings of the International Joint Conference on Neural Networks (2020)
32662	0.869	Chen G.; Zhang J.W.	Intelligent Transportation Systems: Machine Learning Approaches For Urban Mobility In Smart Cities	Sustainable Cities and Society, 107 (2024)
2199	0.866	Liu X.-H.; Guo L.-M.; Yang B.-W.	A Hybrid Network Model Based On The Construction Of Virtual Service Areas For Taxi Demand Prediction	4th International Conference on Intelligent Robotics and Control Engineering, IRCE 2021 (2021)
24000	0.865	Subramanian M.; Cho J.; Veerappampalayam Easwaramoorthy S.; Murugesan A.; Chinnasamy R.	Enhancing Sustainable Transportation: Ai-Driven Bike Demand Forecasting In Smart Cities	Sustainability (Switzerland), 15, 18 (2023)
17407	0.864	Patel M.; Patel S.B.; Swain D.	Data-Driven Decision Support By Utilizing Machine Learning To Predict Passenger Flow For Route And Station Optimization	Journal of The Institution of Engineers (India): Series B (2025)
52828	0.862	Bhanu M.; Priya S.; Moreira J.M.; Chandra J.	St-Agp: Spatio-Temporal Aggregator Predictor Model For Multi-Step Taxi-Demand Prediction In Cities	Applied Intelligence, 53, 2 (2023)
58943	0.861	Kodama Y.; Akeyama Y.; Miyazaki Y.; Takeuchi K.	Travel Demand Prediction With Application To Commuter Demand Estimation On Urban Railways	WWW 2024 Companion - Companion Proceedings of the ACM Web Conference (2024)