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.

本文总结了基于高斯过程回归 (GPR) 的客运量预测模型的设计和实现。客运分析是当今对适当的公交车调度和交通管理以提高效率和乘客舒适度的要求。贝叶斯分析使用统计模型从现有数据中递归地估计新数据。GPR 是一个完全贝叶斯过程模型，它是使用 PyMC3 开发的，以 Theano 为后端。乘客数据被建模为泊松过程，因此用于设计 GP 回归模型的先验是 Gamma 分布函数。据观察，所提出的基于 GP 的回归方法优于现有方法，如 Student-t 过程模型和内核岭回归 (KRR) 过程。