Although traffic is one of the massively collected data, it is often only
available for specific regions. One concern is that, although there are studies
that give good results for these data, the data from these regions may not be
sufficiently representative to describe all the traffic patterns in the rest of
the world. In quest of addressing this concern, we propose a speed prediction
method that is independent of large historical speed data. To predict a
vehicle's speed, we use the trajectory road topographical features to fit a
Shared Weight Multilayer Perceptron learning model. Our results show
significant improvement, both qualitative and quantitative, over standard
regression analysis. Moreover, the proposed framework sheds new light on the
way to design new approaches for traffic analysis.

我们提出了一种独立于大规模历史速度数据的车辆速度预测方法，通过使用轨迹道路地形特征来拟合共享权重多层感知器学习模型，我们的结果在定性和定量上都展现了显著的改进，并且为交通分析的新方法设计提供了新的思路。