Identifying Risk Factors for Autos and Trucks on Highway-Railroad Grade Crossings Based on Mixed Logit Model

Abstract

This study aimed to determine different influencing factors associated with the injury outcomes of heavy vehicle and automobile drivers at highway–rail grade crossings (HRGCs). A mixed logit model was adopted using the Federal Railroad Administration (FRA) dataset (n = 194,385 for 2011–2020). The results show that drivers’ injury severities at HRGCs are enormously different between automobile and truck/truck–trailer drivers. It was found that vehicle speed and train speed significantly affect the injury severity in automobile and truck drivers. Driver characteristics such as gender and driver actions significantly impact the injury severity in automobile drivers, while HRGC attributes such as open space, rural areas, and type of warning device become significant factors in truck models. This study gives us a better understanding of the differences in the types of determinants between automobiles and trucks and their implications on differentiated policies for car and truck drivers.

Exploring Influential Factors of Free-Floating Bike-Sharing Usage Frequency before and after COVID-19

“…As for the results of parallel line test of ordered logit models, the chi-square values of the two models are 59.789 and 90.803, respectively, and the significance level p is greater than 0.05 for both, meaning that the parallel line test is passed and the ordered logit models could be used in the current research. For the fitting information of the ordered logit models, the Chi-square values of the likelihood ratio test were 77.759 and 97.101, respectively, and the significance level p-values are both 0.000 (<0.05), indicating that the explanatory power of the ordered logit models is far superior to that of the zero models (the models containing only intercept terms), and the models established are fit well [46,47].…”

Modeling Autonomous Vehicles’ Altruistic Behavior to Human-Driven Vehicles in the Car following Events and Impact Analysis

“…Te car following event declares that vehicle should stay in the same lane, and duration time should be no less than 15 s. At last, 1027 car following events are extracted to ft our environment. It is necessary to declare that car following events in this paper should involve 3 vehicles, i.e., one (1) Randomly initialize critic network Q(s, a | θ Q ) and actor μ(s | θ μ ) with weights θ Q and θ μ (2) Initialize target network Q ′ and μ ′ with weights θ Q ′ ←θ Q , θ μ ′ ←θ μ (3) Initialize replay bufer R (4) For episode � 1, M do (5) Initialize a random process N for action exploration (6) Receive initial observation state s 1 (7) For t � 1, T do (8) Select action a t � μ(s t | θ μ ) + N t according to current policy and exploration noise (9) Execute action a t and observe reward r t and observe new state s t+1 (10) Store transition (s t , a t , r t , s t+1 ) in R (11) Sample a random minibatch of n transitions (s i , a i , r i , s i+1 ) from R (12) Set…”

“…Car following is a base phenomenon for trafc fow consisting of HDVs [1]. Te research models can be divided into physical models and data-driven models.…”

Study on Driver Behavior Pattern in Merging Area under Naturalistic Driving Conditions

“…It can be found that the current studies related to driving behavior mostly focus on the analysis of driving behavior characteristics of urban roads [24][25][26][27][28][29][30][31][32][33][34][35][36]. Tere is a lack of studies focusing on the driving behavior for highway merging areas.…”