Analysis and evaluation of hourly changes in travel consumption volume and the number of severe accidents in inner-city roads (case study of Qom city)

Document Type : Original Article

Authors
Amir Hossein Jashnian 1
Mohammad Amin Ebrahimzadeh 2
Shabnam Yaghoobii 3
1 Department of Civil Engineering, SR.C., Islamic Azad University, Tehran, Iran.
2 Assistant Professor, Department of Civil Engineering, Islamic Azad University, Karaj Branch, Iran.
3 Department of Civil Engineering, Faculty of Civil Engineering, Architectural Art, Azad University of Science and Research, Tehran, Iran.
10.22034/road.2024.416743.2201
Abstract
Today, the issue of providing safe traffic at the level of the intra-city road network is one of the basic principles governing traffic engineering and transportation planning.As a pilgrimage base, Qom city is the origin and destination of many pilgrimages. Due to the importance of modeling the number of severe accidents, the importance of different factors in the incidents can be obtained and by managing it, the level of safety can be increased. In this study, this relationship has been analyzed using 2 linear and non-linear regression models and using 10,764 severe accident reports. Taking into account the hourly changes in travel demand, it has been observed that the linear regression model does not have the ability to estimate the probability of predicting severe accidents, but the nonlinear regression models have a better description for predicting the number of severe accidents in Qom city. For modeling, the variables of the number of severe accidents between 7:00 am and 7:00 pm in Qom's inner city axes, the hourly traffic volume of ride sharing, the hourly traffic volume of heavy vehicles, and the hourly traffic volume of private vehicles were used. It should also be noted that with hourly changes in the volume of travel demand, the number of severe accidents also changed, but in general, it cannot be said that these two always have a positive relationship, and in some hours, other variables cause an increase in severe accidents.
Keywords
Road Safety
Severe Accident
traffic Flow Volume
Travel Demand
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