Comparing the Regression and Utility Theory Models in Predicting Yellow/Red Light Running Decisions Considering Spatial and Drivers’ Heterogeneity

Articles in Press

Document Type : Scientific - Research

Authors

1 Faculty of Civil Engineering, Shahrood University of Technolog, Shahrood, Iran

2 Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran

3 Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

Modeling the drivers’ decision to stop or pass when facing a yellow light plays an important role in dilemma protection, driver-assistive, and driver warning systems. The wrong decision leads to accidents. The models for predicting stop/go decisions are divided into two groups: regression and utility theory models. But it is not clear which group of these models has better predictive power. Moreover, in previous studies, all three decisions of stopping, passing a yellow light, and passing a red light are rarely considered together. The purpose of this article is to compare the goodness of fit and the correct predictions of these two groups of models considering the unsafe decision of red light running. Wide range of utility theory models, including multinomial, nested, and combined logit, and multinomial, ordered, and probit logistic regression models were calibrated. Then the predictive ability of these two groups were compared. It was also investigated whether the consideration of drivers’ random taste variations and spatial heterogeneity will improve the model accuracy. For modeling, the data of three intersections of Nehzat, Modares, and Mazandaran in Semnan, Iran were extracted with the help of filming. The results showed that the goodness of fit of regression models is better than utility theory models. Validation with the help of 20% of the data also confirmed the same. But the advantage of utility theory models was to demonstrate the heterogeneity of drivers' tastes and spatial heterogeneity. The multinomial logistic regression model was recognized as the best model with 99.43% correct predictions.

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Quarterly Journal of Transportation Engineering

Articles in Press, Accepted Manuscript
Available Online from 09 July 2023

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History

  • Receive Date: 21 March 2023
  • Revise Date: 04 June 2023
  • Accept Date: 24 June 2023

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