针对马耳他交通拥堵，构建新数据集并用图神经网络预测延迟。
Graph Based Traffic Analysis and Delay Prediction
- 用合成数据补全真实交通数据，构建马拉特交通数据集。
- 扩散卷积循环网络预测误差更低，MAE仅3.98，优于STGCN的6.65。
- 适合交通规划、城市智能系统研究者参考。
本研究聚焦于欧洲人口密度最高的国家马耳他（每平方公里约1,672人）的交通拥堵问题，该国车辆增长迅速，6个多月内车辆数增加约1.1万辆。为此，本文构建了涵盖岛上公众200天真实出行的综合性交通数据集MalTra。为补充数据，采用语法生成方法合成数据。研究对比了统计模型ARIMA与两种图神经网络（STGCN和DCRNN），基于MalTra与现有Q-Traffic数据集进行分析。结果表明，扩散卷积循环网络（DCRNN）性能更优：其平均绝对误差（MAE）为3.98，均方根误差（RMSE）为7.78；而STGCN对应指标分别为6.65和12.73，显著更高。
原文摘要 · Abstract (English)
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).
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