Prediction of Bus Passenger Traffic using Gaussian Process Regression

Vidya G S; Hari V S

doi:10.1007/s11265-022-01774-3

Prediction of Bus Passenger Traffic using Gaussian Process Regression

J Signal Process Syst. 2023;95(2-3):281-292. doi: 10.1007/s11265-022-01774-3. Epub 2022 Jun 4.

Authors

Vidya G S¹, Hari V S¹

Affiliation

¹ Department of Electronics, College of Engineering Chengannur, A P J Abdul Kalam Technological University, Kerala 689121 Thiruvananthapuram, India.

Abstract

The paper summarizes the design and implementation of a passenger traffic prediction model, based on Gaussian Process Regression (GPR). Passenger traffic analysis is the present day requirement for proper bus scheduling and traffic management to improve the efficiency and passenger comfort. Bayesian analysis uses statistical modelling to recursively estimate new data from existing data. GPR is a fully Bayesian process model, which is developed using PyMC3 with Theano as backend. The passenger data is modelled as a Poisson process so that the prior for designing the GP regression model is a Gamma distributed function. It is observed that the proposed GP based regression method outperforms the existing methods like Student-t process model and Kernel Ridge Regression (KRR) process.

Keywords: Bayesian analysis; Gamma prior; Gaussian process regression; Poisson process; PyMC3; Student-t.