01746945

Component

https://doi.org/10.1177/0361198120934476

Complex maneuvering patterns are typical of motorized two-wheelers (MTWs), and their widespread adoption in many countries has spurred a growing response from transport researchers to model their dynamic behavior realistically. Considering the increased vulnerability of MTW drivers in dense urban mixed traffic systems, proper evaluation and modeling of lateral interactions between the drivers/riders moving abreast need to be addressed. A proper investigation can essentially help in understanding the behavioral aspects of riders in accepting shorter lateral clearances, design of exclusive motorcycle lanes, improved reliability of microsimulation models, and safety evaluation of the riders in a cognitive architecture. The current study therefore attempts to develop a novel symbolic regression model using a multigene genetic programming algorithm to generate and evaluate lateral clearance models naturally from field data for MTW interactions at mid-block sections, data being collected using video recorders. A binary logit model is initially developed to investigate the factors associated with the riders’ decisions to accept critical lateral clearances. Considering highly non-linear variations in data, the symbolic regression models were further developed and a comparison with the existing linear regression based lateral clearance models indicated that the symbolic model could generalize the non-linear structure of the data realistically and performed significantly better than the existing models.

© National Academy of Sciences: Transportation Research Board 2020.

English

pp 1120-1135

2020-9

Transportation Research Record: Journal of the Transportation Research Board

Web

References (46)

Genetic algorithms; Lateral placement; Linear regression analysis; Logits; Motorcycles; Traffic safety; Two wheeled vehicles; Vehicle mix

Highways; Operations and Traffic Management; Safety and Human Factors; Vehicles and Equipment

TRIS, TRB, ATRI

Jul 24 2020 3:06PM