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Urban traffic congestion estimation and prediction based on floating car trajectory data

机译：基于浮动汽车轨迹数据的城市交通拥堵估计与预测

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摘要

Traffic flow prediction is an important precondition to alleviate traffic congestion in large-scale urban areas. Recently, some estimation and prediction methods have been proposed to predict the traffic congestion with respect to different metrics such as accuracy, instantaneity and stability. Nevertheless, there is a lack of unified method to address the three performance aspects systematically. In this paper, we propose a novel approach to estimate and predict the urban traffic congestion using floating car trajectory data efficiently. In this method, floating cars are regarded as mobile sensors, which can probe a large scale of urban traffic flows in real time. In order to estimate the traffic congestion, we make use of a new fuzzy comprehensive evaluation method in which the weights of multi-indexes are assigned according to the traffic flows. To predict the traffic congestion, an innovative traffic flow prediction method using particle swarm optimization algorithm is responsible for calculating the traffic flow parameters. Then, a congestion state fuzzy division module is applied to convert the predicted flow parameters to citizens' cognitive congestion state. Experimental results show that our proposed method has advantage in terms of accuracy, instantaneity and stability.
机译：交通流量预测是缓解大规模城市地区交通拥堵的重要前提。近来，已经提出了一些估计和预测方法来针对诸如准确性，瞬时性和稳定性之类的不同度量来预测交通拥堵。然而，缺乏统一的方法来系统地解决三个性能方面。在本文中，我们提出了一种新颖的方法来利用浮动汽车的轨迹数据来有效地估计和预测城市交通拥堵。在这种方法中，浮动汽车被视为移动传感器，它可以实时探测大规模的城市交通流量。为了估计交通拥堵，我们使用了一种新的模糊综合评估方法，其中根据交通流量分配多指标的权重。为了预测交通拥堵，使用粒子群优化算法的创新交通流预测方法负责计算交通流参数。然后，使用拥塞状态模糊划分模块将预测的流量参数转换为市民的认知拥塞状态。实验结果表明，本文提出的方法在准确性，瞬时性和稳定性方面均具有优势。

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