Geospatial big data handling theory and methods: A review and research challenges
Big data has now become a strong focus of global interest that is increasingly attracting the
attention of academia, industry, government and other organizations. Big data can be …
attention of academia, industry, government and other organizations. Big data can be …
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 …
[HTML][HTML] Does technical assistance alleviate energy poverty in sub-Saharan African countries? A new perspective on spatial spillover effects of technical assistance
Abstract The sub-Saharan Africa (SSA) region gathers the most vulnerable countries, which
are criss-crossed geographically, making SSA countries extremely vulnerable to the …
are criss-crossed geographically, making SSA countries extremely vulnerable to the …
A unified spatio-temporal model for short-term traffic flow prediction
This paper proposes a unified spatio-temporal model for short-term road traffic prediction.
The contributions of this paper are as follows. First, we develop a physically intuitive …
The contributions of this paper are as follows. First, we develop a physically intuitive …
Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity
Accurate and robust short-term traffic forecasting is a critical issue in intelligent
transportation systems and real-time traffic-related applications. Existing short-term traffic …
transportation systems and real-time traffic-related applications. Existing short-term traffic …
A novel residual graph convolution deep learning model for short-term network-based traffic forecasting
Short-term traffic forecasting on large street networks is significant in transportation and
urban management, such as real-time route guidance and congestion alleviation …
urban management, such as real-time route guidance and congestion alleviation …
Estimation of trip travel time distribution using a generalized Markov chain approach
Z Ma, HN Koutsopoulos, L Ferreira… - … Research Part C …, 2017 - Elsevier
The increasing availability of opportunistic and dedicated sensors is transforming a once
data-starved transport field into one of the most data-rich. While link-level travel time …
data-starved transport field into one of the most data-rich. While link-level travel time …
A graph deep learning method for short‐term traffic forecasting on large road networks
Short‐term traffic flow prediction on a large‐scale road network is challenging due to the
complex spatial–temporal dependencies, the directed network topology, and the high …
complex spatial–temporal dependencies, the directed network topology, and the high …
Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks
Dynamic human activity intensity information is of great importance in many location-based
applications. However, two limitations remain in the prediction of human activity intensity …
applications. However, two limitations remain in the prediction of human activity intensity …
Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events
The predictive hotspot mapping of sparse spatio-temporal events (eg, crime and traffic
accidents) aims to forecast areas or locations with higher average risk of event occurrence …
accidents) aims to forecast areas or locations with higher average risk of event occurrence …