Developing operating speed models for elevated multilane urban arterials using artificial neural networks

Abstract

Operating speed models help assess and evaluate geometric design consistency along successive road segments. The development of operating speed models has been mostly focused on rural two-lane two-way highways, where horizontal curvature plays a dominant role in speed prediction. The need to enhance the prediction power of operating speed models and ability to capture more complex relationships within an urban setting have motivated this investigation. This research investigates the use of artificial neural networks, to develop operating speed models for multilane urban elevated arterial roads. Variables investigated in this study included geometric/operational features of a road segment in addition to the residual impact of the characteristics of upstream segments. A data collection exercise was undertaken on two major urban elevated arterial roads in Greater Cairo Region, Egypt: 6th of October and Saft Al-Laban corridors. Speed data was extracted from Google Distance Matrix Application Programming Interface and validated using test vehicle speed data. A regression-based modeling exercise was undertaken in the preliminary investigation phase to serve as a benchmark for the intended machine learning modeling exercise. Results showed that the prediction power of the developed ANN models — capturing the residual effect of upstream speeds — outperformed regression-based ones. The best-performing model used operating speeds of two upstream segments in addition to geometric/operational features of the segment under investigation to predict the segment operating speed (the model reported MAPE of 6.7%). Outputs of this model were used in a design consistency evaluation and potential transferability exercises to further investigate the model practicality.