# A Path Recommendation Method Considering Individual Driving Preferences
## 6 Conclusions  
In this paper, we proposed a path satisfaction model based on ALPR data and applied it to a path recommendation method based on individual preferences. The main conclusions of this work are as follows:  
1. Proposed path satisfaction model based on ALPR data  
ALPR data were used to extract individual preferences instead of questionnaire surveys, and the pth percentile was used to value indicators in the improved AHP, instead of expert scores. The constructed path satisfaction model could successfully identify the changes in road network status and driver preferences under the scenario designed, providing the basis for personalized path recommendation.
2. Personalized path recommendation case study  
Based on real trajectory data, we extracted the preferences of drivers. Different scenarios were designed to verify the reliability of the satisfaction model, and the satisfaction model was used to conduct a preliminary analysis of the urban traffic conditions. The results demonstrated that the average satisfaction of all drivers was increased by 12.76%, 33.36% of drivers reached the threshold for a secondary recommendation, and 41.67% of the drivers who were given a secondary recommendation were advised to drive from arterial roads to minor roads, thus increasing their satisfaction while reducing the traffic pressure on the arterial roads.  
In the future, work should be carried out to address the following areas:  
1. While this study considered changes in traffic conditions due to time variations in making secondary path recommendations, it is worth exploring changes in traffic conditions due to path selections chosen by drivers with multi-agent and reinforcement learning methods.
2. The individual preferences extracted in this study were based on a fixed OD pair. It is important that whether these preferences can be directly used to all OD pairs, as well as to what extent adjustments are needed to make them applicable to other OD pairs. Further research could determine the conditions under which these preferences can be applied.  
**Author Contributions:** Conceptualization, Y.L. and M.H.; methodology, Y.L. and M.H.; software, Y.L.; validation, Y.L. and M.H.; formal analysis, Y.L. and M.H.; investigation, Y.L. and M.H.; re-sources, M.H.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L. and M.H.; visualization, Y.L.; supervision, M.H.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China, grant number 2020 YFB1600400.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank the National Key Research and Development Program of China (2020 YFB1600400) for its support. The authors also wish to thank anonymous referees for their valuable comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.