Survey on traffic prediction in smart cities
The rapid development in machine learning and in the emergence of new data sources
makes it possible to examine and predict traffic conditions in smart cities more accurately …
makes it possible to examine and predict traffic conditions in smart cities more accurately …
Enhancing transportation systems via deep learning: A survey
Abstract Machine learning (ML) plays the core function to intellectualize the transportation
systems. Recent years have witnessed the advent and prevalence of deep learning which …
systems. Recent years have witnessed the advent and prevalence of deep learning which …
Long short-term memory neural network for traffic speed prediction using remote microwave sensor data
Neural networks have been extensively applied to short-term traffic prediction in the past
years. This study proposes a novel architecture of neural networks, Long Short-Term Neural …
years. This study proposes a novel architecture of neural networks, Long Short-Term Neural …
Travel time prediction with LSTM neural network
Travel time is one of the key concerns among travelers before starting a trip and also an
important indicator of traffic conditions. However, travel time acquisition is time delayed and …
important indicator of traffic conditions. However, travel time acquisition is time delayed and …
Spatiotemporal traffic forecasting: review and proposed directions
A Ermagun, D Levinson - Transport Reviews, 2018 - Taylor & Francis
This paper systematically reviews studies that forecast short-term traffic conditions using
spatial dependence between links. We extract and synthesise 130 research papers …
spatial dependence between links. We extract and synthesise 130 research papers …
Dynamic origin-destination prediction in urban rail systems: A multi-resolution spatio-temporal deep learning approach
P Noursalehi, HN Koutsopoulos… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Short-term demand predictions, typically defined as less than an hour into the future, are
essential for implementing dynamic control strategies and providing useful customer …
essential for implementing dynamic control strategies and providing useful customer …
Research on travel time prediction model of freeway based on gradient boosting decision tree
To improve the prediction accuracy of traffic flow, a travel time prediction model based on
gradient boosting decision tree (GBDT) is proposed. In order to test the applicability of …
gradient boosting decision tree (GBDT) is proposed. In order to test the applicability of …
Trend modeling for traffic time series analysis: An integrated study
This paper discusses the trend modeling for traffic time series. First, we recount two types of
definitions for a long-term trend that appeared in previous studies and illustrate their intrinsic …
definitions for a long-term trend that appeared in previous studies and illustrate their intrinsic …
Using an ARIMA-GARCH modeling approach to improve subway short-term ridership forecasting accounting for dynamic volatility
Subway short-term ridership forecasting plays an important role in intelligent transportation
systems. However, limited efforts have been made to forecast the subway short-term …
systems. However, limited efforts have been made to forecast the subway short-term …
Real-time prediction model for indoor temperature in a commercial building
Indoor environmental parameters have significant influence on commercial building energy
consumption and indoor thermal comfort. Prediction of these parameters, especially that of …
consumption and indoor thermal comfort. Prediction of these parameters, especially that of …