Spatiotemporal traffic forecasting: review and proposed directions
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 …
A hybrid deep learning based traffic flow prediction method and its understanding
Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic
flow with big data. While existing DNN models can provide better performance than shallow …
flow with big data. While existing DNN models can provide better performance than shallow …
Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies
Multistep traffic forecasting on road networks is a crucial task in successful intelligent
transportation system applications. To capture the complex non-stationary temporal …
transportation system applications. To capture the complex non-stationary temporal …
Traffic flow prediction on urban road network based on license plate recognition data: combining attention-LSTM with genetic algorithm
Exploring traffic flow characteristics and predicting its variation patterns are the basis of
Intelligent Transportation Systems. The intermittent characteristics and intense fluctuation on …
Intelligent Transportation Systems. The intermittent characteristics and intense fluctuation on …
Identifying service bottlenecks in public bikesharing flow networks
Abstract Service bottlenecks are a key barrier to building a resilient public transport system.
In this paper, we propose a new approach to automatically extract the role of a station in …
In this paper, we propose a new approach to automatically extract the role of a station in …
Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal …
Obtaining accurate information about future traffic flows of all links in a traffic network is of
great importance for traffic management and control applications. This research studies two …
great importance for traffic management and control applications. This research studies two …
Copula ARMA-GARCH modelling of spatially and temporally correlated time series data for transportation planning use
Time series analysis has been used extensively in transport research in various areas, such
as traffic management and transport planning. Time-series data may contain temporal and …
as traffic management and transport planning. Time-series data may contain temporal and …
Link traffic speed forecasting using convolutional attention-based gated recurrent unit
G Khodabandelou, W Kheriji, FH Selem - Applied Intelligence, 2021 - Springer
Traffic speed forecasting becomes a thriving research area in modern transportation
systems. The intensification of travel flow volumes due to fast urbanization, vehicle path …
systems. The intensification of travel flow volumes due to fast urbanization, vehicle path …
Data mining and information technology in transportation—a review
J Ganapathy, FP García Márquez - Proceedings of the Fifteenth …, 2021 - Springer
Traffic management is an integral part of intelligent transport system (ITS). At present, the
focus of the research community is on the innovation in technology-driven traffic …
focus of the research community is on the innovation in technology-driven traffic …
Spatiotemporal short-term traffic forecasting using the network weight matrix and systematic detrending
This study examines the spatiotemporal dependency between traffic links. We model the
traffic flow of 140 traffic links in a sub-network of the Minneapolis-St. Paul highway system for …
traffic flow of 140 traffic links in a sub-network of the Minneapolis-St. Paul highway system for …