Graph Based Traffic Analysis and Delay Prediction
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Abstract (arXiv)
This research is focused on traffic congestion in the small island of Malta which is the most densely populated country in the EU with about 1,672 inhabitants per square kilometre (4,331 inhabitants/sq mi). Furthermore, Malta has a rapid vehicle growth. Based on our research, the number of vehicles increased by around 11,000 in a little more than 6 months, which shows how important it is to have an accurate and comprehensive means of collecting data to tackle the issue of fluctuating traffic in Malta. In this paper, we first present the newly built comprehensive traffic dataset, called MalTra. This dataset includes realistic trips made by members of the public across the island over a period of 200 days. We then describe the methodology we adopted to generate syntactic data to complete our data set as much as possible. In our research, we consider both MalTra and the Q-Traffic dataset, which has been used in several other research studies. The statistical ARIMA model and two graph neural networks, the spatial temporal graph convolutional network (STGCN) and the diffusion convolutional recurrent network (DCRNN) were used to analyse and compare the results with existing research. From the evaluation, we found that the DCRNN model outperforms the STGCN with the former resulting in MAE of 3.98 (6.65 in the case of the latter) and a RMSE of 7.78 (against 12.73 of the latter).
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 43 references that are in the map. First posted on arXiv 28 Oct 2024.
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10 of 43 references.
References · 10
- 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
- 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
- 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
- 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 330
- 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
- 2018Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecastingcited by 145
- 2018Deep Sequence Learning with Auxiliary Information for Traffic Predictioncited by 29
- 2020TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecastingcited by 14
- 2018Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Datacited by 10
- 2021Graph Neural Networks for Traffic Forecastingcited by 7
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