Introduction: This study presents the prediction of driver yielding compliance and pedestrian tendencies to press pushbuttons at signalized mid-block Danish offset crosswalks.
Method: It applies Bayesian Networks (BNs) analysis, which is basically a graphical non-functional form model, on observational survey data collected from five signalized crosswalks in Las Vegas, Nevada. The BNs structures were learnt from the data by the application of several score functions. By considering prediction accuracy and the Area under the Receiver Operating Characteristic (ROC) curves, the BN learnt using the Bayesian Information Criterion (BIC) score resulted as the best network structure, compared to the ones learnt using K2 and the Akaike Information Criterion (AIC). The BIC score-based structure was then used for parameter learning and probabilistic inference.
Results: Results show that, when considering an individual scenario, the highest predicted yielding compliance (81%) is attained when pedestrians arrive at the crosswalk while the flashes are active, whereas the lowest predicted yielding compliance (23.4%) is observed when the pedestrians cross between the yield line and advanced pedestrian crosswalk sign. On the other hand, crossing within marked stripes, approaching the crosswalk from the near side of the pushbutton pole, inactive flashing lights, and being the first to arrive at the crosswalk result in relatively high-predicted probabilities of pedestrians pressing pushbutton. Furthermore, with a combination of scenarios, the maximum achievable predicted yielding probability is 87.5%, while that of pressing the button was 96.3%. Practical applications: Traffic engineers and planners may use these findings to improve the safety of crosswalk users.
Keywords: Bayesian networks; Drivers yielding compliance; Pedestrians' behaviors.
Published by Elsevier Ltd.