用人群移动数据预测多模式交通拥堵，提升出行效率
Mobility-based Traffic Forecasting in a Multimodal Transport System
- 基于人群节点间移动数据建模交通流
- 通过历史移动模式预测网络拥堵概率
- 适合智慧交通与城市规划研究者
我们研究基于人群从一个节点到另一个节点的移动行为，以观察、度量并预测交通对这些移动的响应。道路拥堵频率直接影响经济与社会福祉。本文聚焦于探索机器学习方法，利用人群移动数据，在多模式交通网络中预测交通状况（具有一定的概率）。通过分析人群移动对交通网络的影响，并基于历史数据，对网络中的拥堵情况做出合理预测。
原文摘要 · Abstract (English)
We study the analysis of all the movements of the population on the basis of their mobility from one node to another, to observe, measure, and predict the impact of traffic according to this mobility. The frequency of congestion on roads directly or indirectly impacts our economic or social welfare. Our work focuses on exploring some machine learning methods to predict (with a certain probability) traffic in a multimodal transportation network from population mobility data. We analyze the observation of the influence of people's movements on the transportation network and make a likely prediction of congestion on the network based on this observation (historical basis).
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