# A Path Recommendation Method Considering Individual Driving Preferences
###### Abstract  
The issue of congestion on urban roads stems from an imbalance between transport demand and supply. It has become imperative to address the problem from the traffic demand side. While managing effective traffic demand relies on understanding the individual preferences of drivers, the current method for gathering preferences (i.e., through questionnaires) is both expensive and may not accurately capture the characteristics of respondents due to their varying interpretations of the options. To overcome these challenges, we proposed a path recommendation method that takes individual travel preferences into consideration by employing automatic license plate recognition (ALPR) data for the extraction of individual travel preferences. We initially identified key factors influencing the path selection behaviors of drivers, including path attributes, travel attributes, and individual attributes. Subsequently, we constructed a path satisfaction model based on individual preferences, employing an improved analytic hierarchy process (AHP). Furthermore, we utilized the \(p\)th percentile approach, rather than expert scores, in order to determine the relative importance of each indicator in the improved AHP. By applying the proposed model to the ALPR data from Xuancheng City, we successfully extracted the path selection preferences of drivers. We designed various scenarios to verify the reliability of the model, and the experimental results demonstrated that the proposed path satisfaction model can effectively capture the influence of underlying indicators on the path selection behavior of individuals with diverse travel preferences, considering different driver types and path attributes. Moreover, compared to the real trajectory, the recommended paths yielded an overall satisfaction improvement of over 10%, confirming the reliability and practicality of our proposed model. intelligent transportation; automatic license plate recognition data; analytic hierarchy process; driving preference; path satisfaction Article  
[PERSON], Y.  
recommendations that cater to individual travel preferences, which can play a pivotal role in balancing the spatial and temporal distribution of travel demand, thereby enhancing traveler satisfaction, enriching ways of traffic management, alleviating traffic congestion, and improving urban traffic efficiency. Previous studies on personalized path recommendations for drivers considered various factors. Traditional studies considered optimization objectives such as the shortest path, shortest travel time, or lowest fuel consumption, and utilized algorithms such as [PERSON]'s algorithm [PERSON] (1988) or the A* algorithm [PERSON] (1988). Subsequent studies incorporated time-varying characteristics of the traffic system, proposing dynamic path planning methods which are applicable to changing road network environments [PERSON] _et al._ (2018). Other research focused on the selection of parking lots as part of the path recommendation process, constructing two-layer planning models that incorporate the length of the path from the starting point and end point to the parking lot [PERSON] _et al._ (2018). Additionally, some studies analyzed the behavioral habits of drivers--such as braking, acceleration, and steering--by utilizing onboard vehicle operation sensors, in order to recommend optimal paths in alignment with the driver's habits [PERSON] _et al._ (2018). While these studies discussed factors influencing path choice from different perspectives and constructed models to describe these objective factors, subjective variations exist among drivers, even under the same objective conditions. Therefore, it is necessary to construct a model that captures the subjective preferences of drivers accurately and reconstructs the decision-making process accordingly. Previous research, rooted in expected utility theory, assumed that drivers possess all relevant information and made optimal choices.