Factors Affecting Red-light Running Behaviors of Takeaway Delivery Riders Considering Heterogeneity in the Means and Variances

To address the frequent occurrences of takeaway delivery riders running red-light and the high risk of crashes associated with this behavior, a filed survey is conducted at multiple signalized intersections in Xi'an, the red-light running (RLR) behaviors of delivery riders are investigated. The RLR behavior is taken as the dependent variable, while independent variables included rider personal characteristics, crossing behavior characteristics, and traffic and environmental features. A random parameter Logit model considering heterogeneity in the means and variances was constructed. Parameter estimation was carried out using Halton sequence sampling, and the impact of each independent variable on the dependent variable was quantitatively analyzed through the estimation results and average marginal effects. The findings indicate that Eleme and UU delivery riders have a lower probability of RLR. Variables such as arriving during the second or third phase of the red light, waiting behind the stop line for the green light, and higher conflicting direction traffic volumes significantly reduce the probability of RLR. Conversely, an increase in the number of violators in the same direction, the noon peak hours and evening peak hours significantly increase the probability of RLR. Among these, the variable that most significantly increases the probability of RLR is the evening peak hour, with an average marginal effect of 0.278; the variable that most significantly decreases the probability of RLR is waiting behind the stop line, with an average marginal effect of －0.222. Besides, the parameters of waiting behind the stop line and evening peak hours are random parameter variables, following a normal distribution with means and standard deviations of －1.379, 1.359 and 2.502, 5.360, respectively. Besides, both random parameters exhibit significant heterogeneity in means and variances. For the variable of waiting behind the stop line, arriving during the second phase of the red light increases both the mean and variance of this variable's parameter, hence increasing the probability of RLR and the randomness of its impact on this behavior. For the evening peak hour, a higher volume of motor vehicle traffic reduces both its parameter's mean and variance, thus lowering the probability of RLR and reducing the randomness of its impact on this behavior. Additionally, having only one violator also reduces the variance of the evening peak hour's parameter.