Nowadays traffic congestion has become significantly worse. Not only has it led to economic losses, but also to environmental damages, wastage of time and energy, human stress and pollution. Generally, traffic congestion is a ripple effect of a road congestion on neighbouring roads. When congestion occurs, it will propagate through the road network due to increasing traffic flow. One of the complexities of traffic congestion is unpredictability, thus it is difficult to represent traffic flows by numerical equations. One possible approach is to use the spatial historical data of traffic flow and relate them with traffic condition (congestion or clear) using statistical approach. Studies on traffic flow propagation generally involves visualization with real time GPS trajectory data to help analyze traffic flow propagation using human vision. Our research focuses on traffic flow pattern based on data from sensors without having information about the connected roads. We study spatial and temporal factors that influence traffic flow near a congested road in a neighbouring area. Hence, our study investigates the relationship of roads in a neighbouring area based on the similarity of traffic condition. Roads with high relationship with other neighbouring roads are identified by extracting spatial and temporal features using traffic state clustering. Grey level of co-occurrence matrix (GLCM) is utilized with spectral clustering to cluster road segments that have the same duration of road congestion in terms of day and time intervals. The emission probability is then calculated for prediction of traffic state impact of road congestion in neighboring area using Hidden Markov Model (HMM). We proposed HMM together with our clustering method to predict traffic state impact of road congestion. The experimental results show that the accuracy of prediction using the proposed HMM achieve 89%.

中文翻译：

---

使用隐马尔可夫模型预测基于邻近道路拥堵的交通流传播


如今，交通拥堵已经变得更加严重。它不仅导致经济损失，还造成环境破坏、时间和能源浪费、人类压力和污染。一般来说，交通拥堵是道路拥堵对邻近道路的连锁反应。当拥堵发生时，由于交通流量的增加，拥堵将通过道路网络传播。交通拥堵的复杂性之一是不可预测性，因此很难用数值方程来表示交通流。一种可能的方法是使用交通流的空间历史数据，并使用统计方法将它们与交通状况（拥堵或畅通）联系起来。对交通流传播的研究通常涉及利用实时 GPS 轨迹数据进行可视化，以帮助利用人类视觉分析交通流传播。我们的研究重点是基于传感器数据的交通流模式，而无需有关所连接道路的信息。我们研究影响邻近地区拥堵道路附近交通流量的空间和时间因素。因此，我们的研究基于交通状况的相似性来调查邻近地区道路的关系。通过使用交通状态聚类提取空间和时间特征来识别与其他相邻道路具有较高关系的道路。灰度共生矩阵 (GLCM) 与谱聚类结合使用，对在天数和时间间隔方面具有相同道路拥堵持续时间的路段进行聚类。然后使用隐马尔可夫模型（HMM）计算排放概率，以预测邻近地区道路拥堵的交通状态影响。 我们提出了 HMM 和我们的聚类方法来预测道路拥堵对交通状态的影响。实验结果表明，使用所提出的HMM进行预测的准确率达到89%。