Abstract
Lane changing of traffic flow is a complicated and significant behavior for traffic safety on the road. Frequent lane changing can cause serious traffic safety issues, particularly on a two-lane road section of a freeway. This study aimed to analyze the effect of significant traffic parameters for traffic safety on lane change frequency using the studied calibrated values for driving logic “conscious” in VISSIM. Video-recorded traffic data were utilized to calibrate the model under specified traffic conditions, and the relationship between observed variables were estimated using simulation plots. The results revealed that changes in average desired speed and traffic volume had a positive relationship with lane change frequency. In addition, lane change frequency was observed to be higher when the speed distribution was set large. 3D surface plots were also developed to show the integrated effect of specified traffic parameters on lane change frequency. Results showed that high average desired speed and large desired speed distribution coupled with high traffic volume increased the lane change frequency tremendously. The study also attempted to develop a regression model to quantify the effect of the observed parameters on lane change frequency. The regression model results showed that desired speed distribution had the highest effect on lane change frequency compared to other traffic parameters. The findings of the current study highlight the most significant traffic parameters that influence the lane change frequency.
Highlights
The Global Status Report on Road Safety stated that the number of annual road traffic deaths has reached 1.35 million [1]
Modeling of lane changing for road safety was done by changing the values of the model parameters from the default setting to driving logic “conscious” with basic field input data, such as desired speed and traffic volume as per field observation
The plot results showed a positive relationship between the two variables, with lane change frequency increasing with the increase in traffic volume
Summary
The Global Status Report on Road Safety stated that the number of annual road traffic deaths has reached 1.35 million [1]. 90% of these deaths are centered in low- and middle-income countries, traffic accidents in WHO’s European Region cause at least 120,000 deaths and injure 2.4 million people each year [2]. The Road Safety Action Program (2014–2016) was incorporated into the Hungarian Transport Strategy, which sets targets to decrease the number of road fatalities by 50% between 2010 and 2020 [4]. The situation analysis of the Road Safety Action Program declares that most of the accidents are caused by human-related factors, and tackling them is the most dynamic objective of road safety initiatives [4]. Some previous studies have estimated human factors to be the sole or primary causal factor in approximately 90% of road traffic accidents [6,7,8,9]
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