Exploring Factors Associated with Cyclist Injury Severity in Vehicle-Electric Bicycle Crashes Based on a Random Parameter Logit Model

Abstract

Electric bicyclists are vulnerable road users and play an important role in traffic safety. The focus of this research is on analyzing cyclists’ injury severity in vehicle-electric bicycle collisions. It is an exploratory analysis that was conducted based on samples obtained from video data provided by the police of Xi’an China. Three types of severity include fatal, injury, and property-damage-only (PDO). A random parameter logit (RPL) model was specified to gain more insights into factors related to the injury severity level, including human behaviors, vehicle characteristics, roadway attributes, and environmental conditions. Some factors not included in previous research were introduced into this study, especially precrash behaviors of drivers and cyclists. The direct pseudo-elasticity effects of variables were compared to investigate the stability of individual parameter estimates on the severity categories. The results indicated that variables that significantly increment the probability of fatal accidents were as follows: driver violation behaviors (speeding, red-light violation, driving in the opposite direction), cyclist violation behaviors (speeding, red-light violation), day of time (nighttime), visibility restrictions (fixed obstacles), and vehicle type (larger bus, small truck, and larger truck). Based on these findings, we suggested measures such as strengthening law enforcement by installing cameras, implementing zero tolerance for cyclist violations, promoting education by completing training courses for cyclists, and enhancing traffic safety awareness through educational activities. The research results can provide a theoretical basis for formulating strategies to improve cyclist safety.

An Assessment of the Safety Effects of Roadside Barriers on Reducing Mountainous Crash Injury Severity

“…where vectors X in represent the explanatory variables infuencing the level of driver injury severity i (no injury: NI, minor injury: MI, or severe injury: SI) in crash n, β i comprises estimable parameters associated with the respective variables, and ε in denotes the error term assumed to conform to an independent and identically distributed pattern with a mean of zero and a variance of σ 2 . To account for unobserved heterogeneity, random parameters with heterogeneity in means and variances (RPLHMVs) are introduced as follows [16,[22][23][24][25]:…”

The Effect of Age on Motorcyclist Injury Severities in Multi-Vehicle Motorcycle Crashes: Accounting for Unobserved Heterogeneity and Insights From Out-of-Sample Prediction

“…The corresponding estimable parameters are denoted by β i , and the error term, ε in , is assumed to follow an independent and identical distribution with zero mean and variance σ 2 . Using a random parameters multinomial logit model, one can obtain the injury severity probabilities as in [34], [41], [42], and [43]:…”

“…To estimate the random parameters multinomial logit model, a simulated maximum likelihood method is employed, with 1,000 Halton draws used to achieve stable parameter estimates as reported in [44]. The normal distribution is adopted for the distribution of the random parameters to achieve the best goodness of fit, as noted in [43].…”

“…In this paper, all variables used in the estimated models are binary indicator variables. The pseudo-elasticities are a measure of the change in the probability of the outcome (i.e., the severity of injuries) resulting from a change in an explanatory variable from ''0'' to ''1,'' as stated in [39] and [43].…”

Analysis of Injury Severity of Drivers Involved Different Types of Two-Vehicle Crashes Using Random-Parameters Logit Models with Heterogeneity in Means and Variances

“…Separate random-parameter logit models with heterogeneity in means and variances (RPLHMV) were estimated to identify the factors infuencing the driver's injury and severity involved in diferent vehicle crashes. To begin with, an injury-severity function, Y in , that determines the driverinjury-severity level i in crash n, is specifed as follows [36][37][38]:…”