A simulation process is presented for providing a point estimate of average traffic delay on minor roads at urban unsignalized intersections during peak hours. A performing process of microsimulation was carried out, and a good level of conformity was obtained between the traffic simulated in the peak hours and the corresponding amount of traffic measured in the field. Traffic performance measures expressed in terms of average delay suffered by vehicles on minor roads were used in the statistical analysis. A negative binomial regression model, jointly applied to conflict traffic volume (VHPc) and traffic volume entering intersection from minor roads (VHPe), was used to model the average delay. A dummy variable D was also introduced to test the adequacy of the regression model through the range of hourly conflict traffic volumes. Regression parameters were estimated by the maximum likelihood method and the significance of the covariate was evaluated by using the likelihood ratio test (LRT). The results show that the average delay on minor roads is positively associated with VHPc, VHPe, and D, and these variables are all statistically significant. The regression model was found to fit well the average delays computed in simulation. Additionally, a stop-time model and a maximum queue length model were also developed.

Delay Time Model at Unsignalized Intersections

CALIENDO, Ciro
2014

Abstract

A simulation process is presented for providing a point estimate of average traffic delay on minor roads at urban unsignalized intersections during peak hours. A performing process of microsimulation was carried out, and a good level of conformity was obtained between the traffic simulated in the peak hours and the corresponding amount of traffic measured in the field. Traffic performance measures expressed in terms of average delay suffered by vehicles on minor roads were used in the statistical analysis. A negative binomial regression model, jointly applied to conflict traffic volume (VHPc) and traffic volume entering intersection from minor roads (VHPe), was used to model the average delay. A dummy variable D was also introduced to test the adequacy of the regression model through the range of hourly conflict traffic volumes. Regression parameters were estimated by the maximum likelihood method and the significance of the covariate was evaluated by using the likelihood ratio test (LRT). The results show that the average delay on minor roads is positively associated with VHPc, VHPe, and D, and these variables are all statistically significant. The regression model was found to fit well the average delays computed in simulation. Additionally, a stop-time model and a maximum queue length model were also developed.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4439658
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