@ARTICLE{Munshi_Aarohi_Kumar_Genetic_2024,
 author={Munshi, Aarohi Kumar and Patnaik, Ashish Kumar},
 volume={72},
 number={3},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e149180},
 howpublished={online},
 year={2024},
 abstract={The study concentrates on two different genetic programming approaches for determining passenger car equivalent (PCE) values and observing the impact on capacity estimation at urban unsignalized intersections. Considering heterogeneous traffic conditions, a new PCE value is introduced to encompass sustainable modes of public transit vehicles, specifically slow-moving three-wheelers (SM3W), commonly known as E-Rickshaws. Since PCE value is considered an important parameter for capacity calculations, the present study considered 14 unsignalized intersections located in Ranchi city of India. An automatic plate recognition system is employed to have the count of vehicular traffic. The methodologies include age-layered population structure genetic programming (ALPSGP), and the offspring selection genetic programming (OSGP) approach that incorporates static and dynamic variables. Based on the significance test and ranking of the genetic programming (GP) models, the OSGP model is recommended as the most appropriate model for heterogeneous traffic. Sensitivity analysis reported that lagging headway (����) is the most contributing factor in PCE estimation. The PCE value of SM3W is found to be 0.81 and that could be incorporated as a new classification of vehicles in Indo-HCM. It is observed that evaluated capacity based on PCE values of OSGP performed admirably in both normal and congested traffic situations.},
 type={Article},
 title={Genetic programming for estimating passenger car equivalent in unsignalized intersections},
 URL={http://journals.pan.pl/Content/130421/PDF-MASTER/BPASTS_2024_72_3_4154.pdf},
 doi={10.24425/bpasts.2024.149180},
 keywords={passenger car rquivalent (PCE), unsignalized intersection, slow-moving three-wheelers (SM3W), urban traffic, sustainable mode, genetic programming},
}