A Dynamic Model for Bus Arrival Time Estimation based on Spatial Patterns using Machine Learning

TL;DR

This paper presents a machine learning-based dynamic bus arrival time prediction model that uses spatial patterns and limited data, improving accuracy in cities with minimal traffic infrastructure.

Contribution

It introduces a novel approach combining spatial pattern analysis with XGBoost for bus arrival prediction in data-scarce environments.

Findings

The model achieved higher R-squared values compared to baseline methods.

Spatial pattern-based models outperform non-pattern models.

The approach is adaptable to other cities with limited traffic data.

Abstract

The notion of smart cities is being adapted globally to provide a better quality of living. A smart city's smart mobility component focuses on providing smooth and safe commuting for its residents and promotes eco-friendly and sustainable alternatives such as public transit (bus). Among several smart applications, a system that provides up-to-the-minute information like bus arrival, travel duration, schedule, etc., improves the reliability of public transit services. Still, this application needs live information on traffic flow, accidents, events, and the location of the buses. Most cities lack the infrastructure to provide these data. In this context, a bus arrival prediction model is proposed for forecasting the arrival time using limited data sets. The location data of public transit buses and spatial characteristics are used for the study. One of the routes of Tumakuru city service, Tumakuru, India, is selected and divided into two spatial patterns: sections with intersections and sections without intersections. The machine learning model XGBoost is modeled for both spatial patterns individually. A model to dynamically predict bus arrival time is developed using the preceding trip information and the machine learning model to estimate the arrival time at a downstream bus stop. The performance of models is compared based on the R-squared values of the predictions made, and the proposed model established superior results. It is suggested to predict bus arrival in the study area. The proposed model can also be extended to other similar cities with limited traffic-related infrastructure.