Table 3 Personalized route recommendation research.

Cao et al. proposed a genetic algorithm solution for determining the shortest tourism route³⁶

The Improved Genetic Algorithm can be effectively applied to multi-destination route planning and selecting the shortest travel routes

https://doi.org/10.1155/2022/7665874

Ke et al. used a fusion convolutional network to address spatiotemporal challenges in tourist flow prediction³⁷

Training on Didi Chuxing data reduced the root mean square error by 48.3%

https://doi.org/10.1016/j.trc.2017.10.016

Chang et al. introduced a VANET-based A* (VBA*) route planning algorithm to compute routes with minimum travel time or fuel consumption³⁸

VBA* significantly reduced the average travel time and fuel consumption of planned routes

https://doi.org/10.1155/2013/794521

Zeng et al. stacked Gated Recurrent Unit (GRU) and LSTM models for traffic prediction³⁹

The stacked GRU-LSTM model improved prediction accuracy and reduced prediction time

https://doi.org/10.1109/ACCESS.2022.3171330