Bioinspired computational intelligence and transportation systems: a long road ahead
This paper capitalizes on the increasingly high relevance gained by data-intensive
technologies in the development of intelligent transportation system, which calls for the …
technologies in the development of intelligent transportation system, which calls for the …
A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system
Dockless bike-sharing is becoming popular all over the world, and short-term spatiotemporal
distribution forecasting on system state has been further enlarged due to its dynamic …
distribution forecasting on system state has been further enlarged due to its dynamic …
Deep multi-scale convolutional LSTM network for travel demand and origin-destination predictions
Advancements in sensing and the Internet of Things (IoT) technologies generate a huge
amount of data. Mobility on demand (MoD) service benefits from the availability of big data in …
amount of data. Mobility on demand (MoD) service benefits from the availability of big data in …
Exploring human mobility for multi-pattern passenger prediction: A graph learning framework
Traffic flow prediction is an integral part of an intelligent transportation system and thus
fundamental for various traffic-related applications. Buses are an indispensable way of …
fundamental for various traffic-related applications. Buses are an indispensable way of …
Passenger flow prediction in bus transportation system using deep learning
The forecasting of bus passenger flow is important to the bus transit system's operation.
Because of the complicated structure of the bus operation system, it's difficult to explain how …
Because of the complicated structure of the bus operation system, it's difficult to explain how …
[HTML][HTML] AI-based neural network models for bus passenger demand forecasting using smart card data
Accurate short-term forecasting of public transport demand is essential for the operation of
on-demand public transport. Knowing where and when future demands for travel are …
on-demand public transport. Knowing where and when future demands for travel are …
An improved STL-LSTM model for daily bus passenger flow prediction during the COVID-19 pandemic
F Jiao, L Huang, R Song, H Huang - Sensors, 2021 - mdpi.com
The COVID-19 pandemic is a significant public health problem globally, which causes
difficulty and trouble for both people's travel and public transport companies' management …
difficulty and trouble for both people's travel and public transport companies' management …
A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction
Manufacturing quality prediction model, as an effective measure to monitor the quality in
advance, has been developed using various data-driven techniques. However, multi …
advance, has been developed using various data-driven techniques. However, multi …
Hourly PM2. 5 concentration forecast using stacked autoencoder model with emphasis on seasonality
Accurate PM 2.5 forecasting provides a possibility for establishing an early warning system
to notify the public and take precautionary measures to prevent negative effects on ambient …
to notify the public and take precautionary measures to prevent negative effects on ambient …
[HTML][HTML] Estimating on-board passenger comfort in public transport vehicles using incomplete automatic passenger counting data
The prevention of crowding inside buses, trams and trains is an important component of on-
board passenger comfort and is central to the provision of good public transport services. In …
board passenger comfort and is central to the provision of good public transport services. In …