目的 随着城市交通拥堵问题的日益严重，建立有效的道路拥堵可视化系统，对智慧城市建设起着重要作用。针对目前基于车辆密度分析法、车速判定法、行驶时间判定法等模式单一，可信度低的问题，提出了一种基于DBSCAN+（density-based spatial clustering of applications with noise plus）的道路拥堵识别可视化方法。方法 引入分块并行计算，相较于传统密度算法，可以适应大规模轨迹数据，并行降维聚类速度快。对结果中缓行区类簇判别路段起始点和终止点，通过曲线拟合和拓扑网络纠偏算法，将类簇中轨迹样本点所表征的路段通过地图匹配算法匹配在电子地图中，并结合各类簇中浮动车平均行驶速度判别道路拥堵程度，以颜色深浅程度进行区分可视化。结果 实验结果表明，DBSCAN+算法相较现有改进的DBSCAN算法时间复杂度具有优势，由指数降为线性，可适应海量轨迹点。相较主流地图产品，利用城市出租车车载OBD（on board diagnostics）数据进行城区道路拥堵识别，提取非畅通路段总检出长度相较最优产品提高28.9%，拥堵识别命中率高达91%，较主流产品城区拥堵识别平均命中率提高15%。结论 在城市路网中，基于DBSCAN+密度聚类和缓行区平均移动速度的多表征道路拥堵识别算法与主流地图产品相比，对拥堵识别率、通勤程度划分更具代表性，可信度更高，可以为道路拥堵识别的实时性提供保障。

Objective With the continuous improvement of people's living standards and the advancement of urbanization, the number of private cars has been increasing, leading to the growing problem of road congestion. Road congestion will increase social costs, such as fuel consumption, waste of resources, travel time, emissions, and environmental pollution. If these problems are not resolved in time, then immeasurable harm will come to the future city development. The main reason for the traffic congestion is that the existing traffic structure system cannot meet the growing travel needs of people. To improve the traffic structure system, areas where traffic congestion is common must be identified. The establishment of an effective road congestion visualization system plays an important role in the construction of smart cities. In this paper, a road congestion based on density-based spatial clustering of applications with noise plus (DBSCAN+) is proposed to deal with single mode and low credibility. This algorithm is based on vehicle density analysis, vehicle speed determination, and driving time judgment methods. Method DBSCAN+ first introduces block parallel computing. In comparison with the traditional density algorithm, DBSCAN+ can adapt to large-scale trajectory data and has fast parallel dimensionality reduction clustering. This algorithm solves the time-consuming problem of the traditional density-based clustering algorithm (DBSCAN), which scans all data points for each sample point with large-scale data. DBSCAN+ performs block-wise parallel calculation of the data and iterates the results again until reaching the iteration termination condition. Then, the algorithm discriminates the start and end points of the link segment from the slow-running class cluster in the result. For each trajectory data point in the slow-moving area cluster, the surface distance between the data points is calculated and marked by latitude and longitude data. The marked data points are no longer repeated in the comparison. The two points farthest from each other are selected. Finally, through curve fitting and topological network rectification algorithm, the road segments represented by the trajectory sample points in the cluster are matched to the electronic map via the map matching algorithm. The average driving speed of the floating vehicles in each cluster is used to determine the degree of road congestion. The degree of congestion is visualized by the color depth. Result Experiments show that the DBSCAN+ algorithm has advantages over the existing improved DBSCAN algorithm in terms of time complexity from exponential to linear, adapting to massive trajectory points. In comparison with the mainstream map products, the urban taxi congestion OBD (on board diagnostics) data are used to identify the urban road congestion. The total detection length of the nonsmooth path segment is 28.9% higher than that of the optimal product. The experiment simultaneously conducted hit rate detection on the mainstream map products. The tested products included Baidu map, Gaode map, and Tencent map. The experimental results were compared, showing that the method has an advantage in detecting the hit rate of urban congestion events. The hit rate of congestion identification reaches 91%, which is 15% higher than the average detection hit rate of mainstream products. Conclusion On the basis of DBSCAN+ density clustering and slow moving average moving speed, the multicharacterized road congestion identification algorithm is more representative of the congestion identification rate and commute degree in the urban road network than the mainstream map products. The algorithm provides real-time protection for urban traffic congestion identification. In this study, the taxi GPS (global positioning system) trajectory data of Huai'an City are used to utilize DBSCAN+, average commute speed, and other multifeature methods comprehensively for clustering the GPS trajectory of the taxis in the urban commuting area. The visualization method and system of road congestion identification can be well adapted to the GPS trajectory data of the large-scale urban taxi OBD terminal, which is convenient for identifying the urban road congestion situation and distinguishing the congestion degree in real time. In comparison with traditional map manufacturers, the number of urban taxis' GPS data is large and widely distributed, which can further effectively analyze the commute status of urban roads. The innovative parallel multithread clustering algorithm can efficiently fit the calculation demands. On the basis of the topological road network matching correction algorithm and congestion identification model, urban taxi distribution can be used to visualize the actual congestion situation. Furthermore, it can provide scientific decision-making for passenger travel and scheduling of public transport.