# A Path Recommendation Method Considering Individual Driving Preferences
###### Abstract
However, in reality, people cannot obtain all the relevant information and tend to make instantaneous decisions. To address this, random utility theory was developed, incorporating a random error term into the expected utility to simulate human cognitive behavior. Based on this theory, an improved logit model was introduced to analyze driver's path choice characteristics under different preferences [PERSON] _et al._ (2018). Additionally, recognizing the bounded rationality of decision making, some researchers utilized prospect theory and cumulative prospect theory from the field of psychology to describe the limited rationality in decision-making processes [PERSON] _et al._ (2018). Based on prospect theory, scholars such as [PERSON] et al. [PERSON] _et al._ (2018) and [PERSON] _et al._ (2018) incorporated risk preference factors and road network familiarity into path choice behavior models, in order to provide recommendations to travelers. Regarding the data used in path choice behavior research, two main types have been considered [PERSON] _et al._ (2018). Active data collection methods include stated preference (SP) surveys and revealed preference (RP) surveys, while passive data comprises GPS data, automatic license plate recognition (ALPR) data, and other sources that capture the travel trajectories of travelers. For example, [PERSON] _et al._ (2018) collected data on ideal and actual commuting times through questionnaire surveys issued to commuters, allowing for exploration of the factors influencing their commuting time satisfaction. [PERSON] et al. [PERSON] _et al._ (2018) collected data on individual attributes, information preferences, and path choice preferences through RP surveys, then analyzed the influence of individual attributes and information type preferences on the commuter path choice using structural equation modeling. [PERSON] et al. [PERSON] _et al._ (2018) compared user travel paths between origin-destination (OD) pairs considering the K-shortest paths, using GPS data to explore the relationship between traveler path choice behavior and general traffic conditions. While active data obtained through questionnaires can target the specific information needed for research, they suffer from small sample sizes, high acquisition costs, and potential biases due to respondents misunderstanding the options. According to the analysis above, personalized path recommendation research emphasizes the comprehensive extraction of individual preferences, accurate description of path choice behavior, and analysis of the interactions between influencing factors. However, there is still a need for further research on the use of passive data collection techniques, such as ALPR data, for the extraction of individual preferences and construction of path selection models. To maximize the information available in ALPR data and reduce the cost of acquiring individual preferences, we proposed a method that utilizes such data to extract individual travel preferences and recommend paths accordingly. The structure of this article is shown in Figure 1. Firstly, we analyzed the influence of the path, traffic conditions, and individual factors on driver path selection behavior, providing calculation methods for each influencing factor based on ALPR data. Secondly, the traditional AHP was improved by determining the importance of each indicator using the \(p\)th percentile importance score. Finally, by combining the path satisfaction calculation method with path attributes and individual preferences, personalized path recommendations can be made for travelers. The main contributions of this paper are as follows:  
* A path satisfaction calculation method based on ALPR data was proposed based on ALPR data. By analyzing the trajectory data obtained through ALPR data, the driver type was identified and their path selection preferences were extracted. This approach makes the quantification of individual preferences at low cost, providing a new avenue for quantifying preferences using ALPR data.