In this paper, we propose a machine learning-based approach to address the
lack of ability for designers to optimize urban land use planning from the
perspective of vehicle travel demand. Research shows that our computational
model can help designers quickly obtain feedback on the vehicle travel demand,
which includes its total amount and temporal distribution based on the urban
function distribution designed by the designers. It also assists in design
optimization and evaluation of the urban function distribution from the
perspective of vehicle travel. We obtain the city function distribution
information and vehicle hours traveled (VHT) information by collecting the city
point-of-interest (POI) data and online vehicle data. The artificial neural
networks (ANNs) with the best performance in prediction are selected. By using
data sets collected in different regions for mutual prediction and remapping
the predictions onto a map for visualization, we evaluate the extent to which
the computational model sees use across regions in an attempt to reduce the
workload of future urban researchers. Finally, we demonstrate the application
of the computational model to help designers obtain feedback on vehicle travel
demand in the built environment and combine it with genetic algorithms to
optimize the current state of the urban environment to provide recommendations
to designers.

本文提出了一种基于机器学习的方法，以解决设计师在优化城市土地利用规划方面缺乏优化交通出行需求能力的问题。研究表明，我们的计算模型可以帮助设计师快速获得有关交通出行需求的反馈，包括总量和时间分布，这是基于设计师设计的城市功能分布。它还有助于从交通出行的角度进行设计优化和评估城市功能分布。我们通过收集城市兴趣点（POI）数据和在线车辆数据获取城市功能分布信息和车辆行驶时间（VHT）信息。选择了预测性能最好的人工神经网络（ANNs）。通过使用不同地区收集的数据集进行相互预测，并将预测结果重新映射到地图上进行可视化，我们评估了计算模型在不同地区的使用程度，以减轻未来城市研究人员的工作负担。最后，我们展示了计算模型在帮助设计师获取建筑环境中交通出行需求的反馈方面的应用，并与遗传算法相结合来优化当前城市环境的状态，为设计师提供建议。