Abstract
In recent time, road traffic accidents are increased day by day due to incremental growth in number of vehicles. Traffic accidents are also the crucial and important issue for achieving the sustainable transportation development. One of the tasks of sustainable transportation is to reduce the number of accidents and also design the traffic assessment policy. Many researchers consider the traffic accident issue of sustainable transportation and developed prediction models for measuring severity of accidents. But the accuracy of accident severity is one of the major issues. In this work, an attempt is made to improve the accuracy of accident severity. To achieve the same, a particle swarm optimization-based algorithm is applied for evaluating the accident severity. Prior to implement the PSO, two modification are incorporated into PSO algorithm, called improved PSO. These modifications can be described as mutation operator and trail candidate generation. The performance of improved PSO is examined over accident traffic severity dataset and results are evaluated using accuracy, recall@5 and precision@5 metrics. Several existing techniques are considered for comparing the results of IPSO algorithm. It is revealed that IPSO achieves more accurate results among all techniques.
Highlights
In the era of sustainable transportation development, one of the important issues is traffic safety assessment and accident prediction [1]
By using the traffic accident prediction, the traffic safety assessment policy can be designed on the basis of existing road accident data
Several existing algorithms are considered for comparing simulation results of improved particle swarm optimization (PSO) algorithm
Summary
In the era of sustainable transportation development, one of the important issues is traffic safety assessment and accident prediction [1]. The main task of the researcher worked in the field of traffic accident and assessment is to reduce the traffic accidents and determine the factors that can responsible for accidents. Prior to implement the PSO algorithm, two modifications are incorporated in PSO algorithm, called improved PSO to make it more efficient and balancing the search processes for getting the optimal solutions. These modifications can be described in terms of mutation operator and trial candidate generation.
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More From: International Journal of Electrical and Electronics Research
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