01659661

Component

OD matrix estimation is an important problem in bus transit systems for transportation system analyses, planning and ridership analysis. However, smart cards of most bus transit system only record boarding stops of passengers, which hinders direct exaction of transit OD matrix. In this paper, the authors associate smart card data, bus GPS data and static transit network information to derive transit OD matrix. Passengers are segmented into regular and irregular groups using K-means clustering based on number of trips per day and days with trip records per week. Regular users are believed to have higher tendency to return to previously visited locations and exhibit high predictability. Deterministic model such as the trip chain analysis is conducted on these passengers. Around 84.55% records can be determined using the developed model. For irregular users with uncertainty and limited records, machine learning algorithms are developed in alighting stop classification. The performance of Naïve Bayesian, SVM, decision tree, random forest, KNN and ensemble learning are then compared and an average accuracy of around 70% is achieved. Transfer trip recognition helps to distinguish transfer trips from single trips. This research sheds light on forming a set of simple and applicable methodologies in alighting stop determination and OD matrix estimation using smart card data.

This paper was sponsored by TRB committee AP000 Public Transportation Group.

18-03715

English

7p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References; Tables

Alighting; Bus stops; Bus transit; Estimating; Origin and destination; Smart cards; Transit riders

Shenzhen (China)

Operations and Traffic Management; Planning and Forecasting; Public Transportation

Transportation Research Board Annual Meeting 2018 Paper #18-03715

TRIS, TRB, ATRI

Jan 8 2018 10:55AM