# Estimating Destination of Bus Trips Considering Trip Type Characteristics
###### Abstract  
Recently, local governments have been using transportation card data to monitor the use of public transport and improve the service. However, local governments that are applying a single-fare scheme are experiencing difficulties in using data for accurate identification of real travel patterns or policy decision support due to missing information on alighting stops of users. This policy limits its functionality of utilizing data such as accurate identification of real travel patterns, policy decision support, etc. In order to overcome these limitations, various methods for estimating alighting stops have been developed. This study classifies trips with missing alighting stop information into trip four types and then applies appropriate alighting stop estimation methodology for each trip type in stages. The proposed method is evaluated by utilizing transportation card data of the Seoul metropolitan area and checking the accuracy for each standard of allowable error for sensitivity analysis. The analysis shows that the stage-by-stage estimation methodology based on the trip type proposed in this study can estimate users' destinations more accurately than the methodologies of previous studies. Furthermore, based on the construction of nearly 100% valid tag data, this study differs from prior studies.  
public transit transaction data; estimation of destination; categorization of trip types; trip chain; travel pattern; historical travel data +
Footnote †: journal:  
there are several studies focusing on alighting stop estimation methodologies based on the connectivity between trips, i.e., under the assumption that the next trip starts near the destination of this trip [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. Furthermore, studies on alighting stop estimation by applying machine learning methods and analyzing travel patterns based on the past travel history data have been conducted [12; 13; 14; 15; 16]. However, relatively few studies have been conducted on the estimation of more complete travel pattern information in public transit systems by combining various alighting stop estimation methodologies.  
The main purpose of this study is to propose a method for estimating alighting stops to build reliable OD data so that public transportation card data can be used in actual fields. In particular, it focused on improving the accuracy of estimating alighting stops and inferring alighting stops for all trips. The present study, estimated travel patterns of public transport users more accurately and completely, by comprehensively considering the characteristics of various alighting stop estimation methodologies and the characteristics of the trips for which alighting tag information is missing; this was to contribute to more effective public transport operation monitoring and policy decision-support of local governments. Accordingly, we reviewed various alighting stop estimation methods found in previous studies. We considered the characteristics of each method to define the types of travel with missing alighting stop information and set the stages for the alighting stop estimation. The accuracy of the alighting stop information estimation methodology was verified using the data from the Seoul metropolitan area, which includes alighting tag information for most of the trips, and the accuracy and valid data ratio were analyzed based on the results estimated after completely deleting the alighting stop information. Furthermore, we derived implications by examining the accuracies of alighting stop estimation for each stage and for each standard of allowable error, respectively.