Modified Betweenness-Based Measure for Prediction of Traffic Flow on Urban Roads

The conventional network structure measure, betweenness centrality (BC), has been used to predict roadway traffic flow; however, without consideration of variable travel demand on the roadway network, the measure’s prediction capability is limited by its static nature. With the objective of explicitly addressing the effects of travel pattern [e.g., origin–destination (O-D) distribution] on the prediction of urban roadway traffic flow, this paper proposes a modified measure that integrates the BC index and the traditional travel demand metrics (i.e., the O-D demand and the ratios of total demand). In the case study, roadway networks of two cities—San Francisco, California, and Nanjing, China—were selected to demonstrate the effectiveness of the proposed method. Taxi GPS trace data in the two cities were retrieved and applied to calibrate and validate the proposed measure. Correlation analyses were conducted to compare the predictions of the conventional and modified betweenness measures against the observed taxi traffic flows. The results show that, compared with the traditional approach, the modified betweenness measure produces predictions that have better correlations with the observed taxi traffic flows in both case studies. More accurate predictions for the future year traffic flow can be expected with the application of the new modified measure.