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摘要: 通过文献梳理、专家访谈和试验场景构建等方法，分析了道路指定断面和区域路网宏观交通流预测的国内外研究现状和发展趋势，归纳了局部断面交通流预测方法，包括传统机器学习、递归神经网络和混合模型，分析了卷积神经网络、图神经网络和融合多因素网络的特点，阐述了方法的原理、优势、局限性和应用场景，总结了现有场景交通数据集类别，从采样周期与采集方式角度归纳了国内外主流交通数据集。分析结果表明：递归神经网络可以有效获取交通数据的历史规律，但存在梯度爆炸、计算复杂度高、长时预测准确度不佳等问题；图神经网络针对路网拓扑连接关系引入了图结构，在考虑路网和交通流数据的时空相关性上具有明显优势；融合多因素网络充分考虑天气、道路、事故等内外部因素的影响，有效提升了交通流预测的实时性和鲁棒性；由于交通数据采集困难、外部因素影响难以量化、机器学习方法可解释性差等原因，交通流预测方法的改进受到了限制；未来应从交通信息有效挖掘和图卷积方法完善两方面入手，拓宽图结构在交通领域的应用和考虑非常态交通场景，进一步揭示交通数据的内在规律，开发更准确、高效的交通流预测方法，推动交通流预测在工业界的落地应用。Abstract: The research status and development trend of macro traffic flow prediction of designated road sections and regional road network at home and abroad were analyzed by literature review, expert interview, and experimental scenario construction. Local section traffic flow prediction methods were summarized, including traditional machine learning, recurrent neural networks, and hybrid models. The characteristics of convolutional neural networks, graph neural networks, and fusion multi-factor networks were discussed.The principles, advantages, limitations, and application scenarios of the methods were explained. The types of existing scenario traffic datasets and the mainstream traffic datasets at home and abroad were summarized from the perspectives of sampling periods and collecting methods. Analysis results show that recurrent neural networks can effectively obtain the historical laws of traffic data, but there are some problems such as gradient explosion, high computational complexity, and poor accuracy of long-time prediction. Graph neural networks introduce graph structures for road network topological connection relationships, which has obvious advantage in considering the spatiotemporal correlation of road network and traffic flow data. Fusion multi-factor methods fully consider the influence of internal and external factors such as weather, roads, and accidents, effectively improving the real-time performance and robustness of traffic flow prediction. The improvements of traffic flow prediction methods have limitations due to the difficult traffic data collection and external factor influence quantification, as well as the poor interpretability of machine learning methods. The future research should start from two aspects of starting the efficient mining of traffic information and the perfection of graph convolution methods, broaden the application of graph structures in the traffic field, and consider non-constant traffic scenarios. So as to further reveal the inherent laws of traffic data, develop more accurate and efficient traffic flow prediction methods, and promote the application of traffic flow prediction in industry.
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图 1 陕西省西安市某指定断面交通流
Figure 1. Traffic flow at a designated section in Xi'an Shaanxi Province
图 2 K-NN方法内部数据流传递关系
Figure 2. Transfer relationship of internal data flow in K-NN method
图 5 基于递归神经网络的交通流预测主流方法
Figure 5. Mainstream methods of traffic flow prediction based on recurrent neural network
图 15 陕西省186个收费站点交通流数据
Figure 15. Traffic flow data of 186 toll stations in Shaanxi Province
表 1 三种断面交通流预测方法对比
Table 1. Comparison of three section traffic flow prediction methods
方法 原理 局限性 适用场景 传统机器学习 训练使用统计学知识和交通特征建立的回归方法 体系结构较浅，适用范围极其有限, 必须手动选取特征 预测时间短、精度需求低、数据量小的单一场景 递归神经网络 借助记忆单元学习历史数据对当前的影响 长时依赖性获取不足，训练成本高 精度需求高与数据量大的单一场景 混合模型 衔接几种方法或并行计算多个方法 未充分考虑路网的空间特性，计算复杂度高 预测精度和时间要求高的复杂场景 
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表 2 十种典型方法的试验结果对比
Table 2. Experimental result comparison of 10 typical methods
方法 平均绝对误差 平均绝对离差 均方根误差 训练时间/min 时序图卷积网络
(Temporal Graph Convolutional Network，T-GCN)2.915/3.154
3.429 /3.6800.709/0.711
0.723/0.7288.532/9.019
9.608 /10.129120 STGCN 3.124/3.467
3.826/4.1740.828/0.862
0.831 /0.8539.199/10.371
11.706/12.92322 DCRNN 4.130/5.120
6.430 /7.5260.964/1.120
1.428/1.6349.650/10.620
11.882 /12.949160 时间卷积网络
(Temporal Convolutional Network, TCN)5.372/9.305
14.575 /18.9533.280/5.859
10.040/13.1539.664/12.520
19.574 /23.82964 GRU 6.547/10.130
16.570/21.1053.586/6.847
11.544 /15.2839.981/15.328
25.114/31.94178 SAE 7.625/11.985
16.980 /21.5513.626/5.897
10.662/13.76410.136/15.525
25.744 /32.74388 LSTM 7.727/12.232
17.246/21.9213.370/5.920
10.626 /13.89410.332/16.354
26.288/33.61496 支持向量回归
(Support Vector Regression，SVR)6.705/11.406
22.808 /20.7433.923/6.478
15.186/19.79220.017/34.670
67.055 /87.619130 ARIMA 12.094/22.913
43.934/58.1537.605/14.742
30.161 /40.05816.961/31.009
57.026/75.063210 历史平均
(Historical Average, HA)14.602/26.620
45.830 /60.2453.751/7.000
12.583/16.6136.309/64.627
108.666/142.22190
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表 3 三种区域交通流预测方法对比
Table 3. Comparison of three regional traffic flow prediction methods
方法 原理 优势 局限性 适用场景 后续应用 卷积神经网络 采用2个连续的卷积层和池化层提取路网空间信息 局部连接性，输出神经元只连接到附近的局部输入神经元；池化机制，很大程度上减少了训练CNN所需的参数数量，同时保证了最重要的特征被保留；全连接性，应用于最后阶段的全连接层使得输入层的数据维度是可控的 在具有非欧几里得拓扑的运输网络中具有局限性 路网结构较为简单的场景 交通态势评估、通行能力分析与收费站人力规划 图神经网络 利用图神经网络来建模交通网络中的空间依赖性，再利用卷积从根本上提高图分析效率 空间解释性好；图结构的引入有效地捕获了非欧结构的路网特性 未考虑天气等其他因素对路网的影响 路网结构复杂、外部因素影响弱的场景 交通信号协调控制与地铁车辆调度 融合多因素 利用多源数据并将外部影响因素整合到方法中 实用性高；考虑外部因素对交通状况的影响 相关性因素的融合仍不够成熟 外部因素对交通状况影响较大的场景 交通事故分析
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表 4 交通预测公开数据集
Table 4. Public datasets for traffic prediction
分类 数据集名称 采样周期 采集方式 固定式采集数据集 交通性能测量系统数据集 5 min 车辆检测器 多尺度时空交通估计与预测数据集 5 min 车辆检测器 洛杉矶高速公路数据集 5 min 环形检测器 西雅图城市交通流数据集 5 min 地感线圈 纽约市自行车共享行程数据集 自行车数据 上海地铁客流数据集、深圳地铁客流数据集 15 min 地铁站点 移动式采集数据集 马德里城市交通追踪数据集 0.5 s 车辆轨迹采集 纽约市出租车和豪华轿车委员会行程数据集 出租车记录 北京市出租车GPS轨迹数据集 30 min GPS数据 上海市交通行程时间及车速数据集 10 min 出租车记录 深圳市浮动车数据集 15 min 出租车轨迹 北京市出租车GPS轨迹数据集 出租车轨迹 滴滴出行交通流量预测数据集 网约车记录 科隆市的推特交通信息语料库 1 s 车辆轨迹采集 弗吉尼亚理工大学自然驾驶研究数据集 1 s 车辆轨迹采集
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