Can Machine Learning Uncover Insights into Vehicle Travel Demand from
  Our Built Environment?
Zixun Huang Hao Zheng
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.