Electric bus energy consumption prediction is conducive to bus route scheduling and
charging optimization management. However, existing prediction models do not …

Given the vehicle speed during actual driving, it is possible to apply an advanced energy
management strategy for achieving better efficiency and less emission. We conducted a …

The energy consumption of electric vehicles is closely related to the problems of charging
station planning and vehicle route optimization. However, due to various factors, such as …

Travel time data is an important factor for evaluating the performance of a public transport
system. In terms of time and space within the nature of uncertainty, bus travel time is …

Advocating public transport travel can reduce carbon emissions and pollutant emissions,
alleviating the problem of global warming. Accurate bus travel time prediction can improve …

This letter proposes random neural networks (RNNs) to randomly train several neural
network (NN) models for the promotion of traditional NN. Moreover, an arrival time prediction …

The estimation of energy consumption under real-world driving conditions is a prerequisite
for optimizing bus scheduling and meeting the requirements of route operation, thereby …

Achieving accurate speed prediction provides the most critical support parameter for high-
level energy management of plug-in hybrid electric vehicles. Nowadays, people often drive …

With the increasing popularity of electric buses (EBs), accurate estimation of the trip-level
energy consumption of EBs has become increasingly essential. In this paper, an effective …

The paper presents a novel approach for predicting battery energy consumption in electric
city buses (e-buses) by means of a trip-based data-driven regression model. The model was …