Machine Learning Approach for Ecological Public Transport Systems

Abstract

Using Convolutional neural networks and Genetic programming, this study presents a new composite technique for modeling bicycle traffic in the town of Novo mesto, Slovenia. Every town needs public passenger transportation because the current transportation system has well-known issues like congestion, environmental effect, a lack of parking spaces, increased safety hazards, and excessive energy consumption. Urban transport is crucial for the functionality of any city. High-quality and usable urban transport not only affects the functionality of the city as an economic and social center, but it also reduces the number of passenger cars on the streets. The Novo mesto region, which has a population of around 30 000 people, is a major industrial center that is strongly reliant on metropolitan transportation. Unfortunately, the urban traffic of Novo mesto still has a relatively weak influence on the transport connectivity of the wider area. The study's goal is to examine and simulate bicycle rentals. For 35 weeks, convolutional neural networks and genetic programming were utilized to anticipate bicycle traffic. Three types of models were applied to study the impact of weather conditions on bicycle traffic: linear regression, genetic programming, and feed-forward neural networks. The proposed approach will be useful for cities with similar needs around the world.