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.

翻译：本文提出了一种基于机器学习的方法，以解决设计者无法从车辆出行需求角度优化城市土地利用规划的问题。研究表明，我们的计算模型能够帮助设计者快速获取车辆出行需求的反馈，包括基于设计者设计的城市功能分布所产生的出行总量及时间分布，并协助从车辆出行视角对城市功能分布进行设计优化与评估。通过收集城市兴趣点数据和在线车辆数据，我们获取城市功能分布信息及车辆小时出行数据，并选取预测性能最佳的人工神经网络模型。利用不同区域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，我们评估了该计算模型在不同区域的适用性，以期减轻未来城市研究者的工作量。最后，我们展示了该计算模型在帮助设计者获取建筑环境中车辆出行需求反馈中的应用，并联合遗传算法对当前城市环境状态进行优化，为设计者提供建议。