• Sajith Sharwan BV
• Sandeep J
• Norton Stanley S A
• Smera C
• Sreeja C S

This review explores how predictive modeling revolutionizes urban transportation by enhancing efficiency and resource allocation. By leveraging machine learning algorithms like RNNs and LSTM networks, transportation patterns are forecasted accurately. Spatial-temporal analysis aids in optimizing operations, while optimization techniques improve decision-making processes. Evaluation metrics ensure model accuracy. The study emphasizes the importance of integrating contextual factors and passenger behaviors for comprehensive urban mobility planning. Future research directions include refining prediction accuracy and exploring innovative algorithms for enhanced efficiency in transportation systems.

Predictive modeling, Transportation systems, Machine learning algorithms, Ride-sharing, Demand prediction, Urban mobility

"Advancements in Predictive Modeling for RideShare Systems with Optimal Features", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.548-554, May-2024, Available :http://www.jetir.org/papers/JETIRGG06086.pdf

"Advancements in Predictive Modeling for RideShare Systems with Optimal Features", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. pp548-554, May-2024, Available at : http://www.jetir.org/papers/JETIRGG06086.pdf