چكيده به لاتين
Today, transportation has become an inseparable part of modern human life. Among the modes of transportation, railways hold a special place due to their high safety and cost-effectiveness. However, there are points in the railway network where intersections with road transport compromise the safety and economic viability of the railway network. These points, known as level crossings, annually impose financial losses and cause injuries and fatalities among rail and road users. For instance, in the United States, over twelve thousand accidents occurred at these points in the five years leading up to 2020, resulting in more than fifteen hundred deaths and over fifty-one hundred injuries. In other words, in this country, one person is involved in a train collision at a crossing every three hours. In Iran, the statistics include one hundred and twenty-three annual accidents resulting in at least sixty deaths, despite the fact that the number of crossings in Iran is significantly fewer than in industrialized countries.
Therefore, the present study aims to model the accident data at Iran’s authorized level crossings and investigate the factors influencing accidents at these crossings. The data used includes the number of accidents at authorized level crossings in Iran from 2012 to 2022, characteristics of the authorized level crossings, and the population within a five-mile radius of the crossings, derived from the 2006 population blocks.
Due to the high number of zeros and excessive dispersion in the accident data, a zero-inflated Poisson regression was used. The modeling of factors affecting the number of accidents at Iran’s authorized rail-road level crossings showed that road usage type, the ratio of male to female population, the distance of the crossing from the nearest road intersection, the total number of passing trains, and the total population within five miles of the crossing had positive coefficients in the Poisson equation, increasing the number of accidents. Conversely, the number of rails at the crossing in this equation reduced the number of accidents. However, in the logistic equation, the presence of an alternative route when the crossing is closed and the difference in male to female population increased the likelihood of no accidents occurring at the crossing, while the average daily number of accidents reduced this likelihood. Ultimately, variables were categorized into three groups: rail-related variables, road-related variables, and population-related variables, and suggestions were made to reduce the number of accidents.