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The present study intended to (1) investigate the injury risk of pedestrian casualties involved in traffic crashes at signalized intersections in Hong Kong; (2) determine the effect of pedestrian volumes on the severity levels of pedestrian injuries; and (3) explore the role of spatial correlation in econometric crash-severity models. The data from 1889 pedestrian-related crashes at 318 signalized intersections between 2008 and 2012 were elaborately collected from the Traffic Accident Database System maintained by the Hong Kong Transport Department. To account for the cross-intersection heterogeneity, a Bayesian hierarchical logit model with uncorrelated and spatially correlated random effects was developed. An intrinsic conditional autoregressive prior was specified for the spatial correlation term. Results revealed that (1) signalized intersections with greater pedestrian volumes generally exhibited a lower injury risk; (2) ignoring the spatial correlation potentially results in reduced model goodness-of-fit, an underestimation of variability and standard error of parameter estimates, as well as inconsistent, biased, and erroneous inference; (3) special attention should be paid to the following factors, which led to a significantly higher probability of pedestrians being killed or sustaining severe injury: pedestrian age greater than 65 years, casualties with head injuries, crashes that occurred on footpaths that were not obstructed/overcrowded, heedless or inattentive crossing, crashes on the two-way carriageway, and those that occurred near tram or light-rail transit stops. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper establishes a mathematical model that can represent the conflicting effects of two pedestrian streams that have an oblique intersecting angle in a large crowd. In a previous paper, a controlled experiment in which two streams of pedestrians were asked to walk in designated directions was used to model the bidirectional pedestrian stream of certain intersecting angles. In this paper, the writers revisit that problem and apply the Bayesian inference method to calibrate an improved model with the controlled experiment data. Pedestrian movement data are also collected from a busy crosswalk by using a video observation approach. The two sets of data are used separately to calibrate the proposed model. With the calibrated model, the relationship between speed, density, and flow is studied in both the reference and conflicting streams, and a prediction is made regarding how these factors affected the interactions of moving pedestrian streams. It is found that the speed of one stream not only decreases with its total density, but also decreases with the ratio of its flow relative to the total flow, i.e., the speed of the pedestrians decreases if their stream changes from the major to minor stream. It is also observed that the maximum disruption that was induced by pedestrian flow from an intersecting angle occurs when the angle is approximately 135°. [ABSTRACT FROM AUTHOR]