Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement.

Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.

机场处于重大创新的最前沿，它使更多的人和货物可以更快，更便宜，更方便地移动。随着航空旅行的持续增长，机场在应对日益增长的乘用车流量方面将面临挑战，这将导致运营效率降低，空气质量差和安全问题。本文评估了结合交通微观模拟的交通需求预测方法，这将使机场运营人员能够准确地预测交通和拥堵状况。我们使用了两年的详细数据来描述达拉斯-沃思堡（DFW）国际机场的各个车辆的进/出场，飞机起降和天气情况，我们评估了多种预测方法，包括自动回归综合移动平均值（ARIMA）系列模型，传统的机器学习模型和DeepAR（现代的递归神经网络（RNN））。我们发现，这些算法能够捕获地面流量的昼夜趋势，并且在预测下一个30分钟的需求量时，效果都很好。较长的预测范围是中等有效的，这说明了此问题的挑战，并强调了有前途的技术以及潜在的改进领域。

交通需求不是导致终端拥堵的唯一因素，因为对道路网络的临时更改（例如封闭车道）会使良性交通需求高度拥挤。将需求预测与交通微仿真框架相结合，可以提供交通及其后果的完整信息。结果是一个可操作的情报平台，用于探索政策变化以及基础架构扩展和破坏情况。为了证明这种方法的价值，我们提供了DFW机场案例研究的结果，评估了高需求情况下政策变更对车辆路线的影响。该框架可以为像DFW这样的机场提供帮助，帮助他们应对日常运营挑战，并探索新兴技术的集成以及将其服务扩展到长期计划中。