Improvement of METANET Model Based on Critical Density Modeling for Traffic Control

Freeway control has been increasingly used as an innovative approach to ease traffic congestion, improve traffic safety and reduce exhaust emissions. As an important predictive model involved in freeway control, the predictive performance of METANET greatly influences the effect of freeway control. This paper focuses on modifying the METANET model by modeling the critical density. Firstly, the critical density model is deduced based on the catastrophe theory. Then, the perturbation wave and traveling wave that are obtained using the macro and micro data, respectively, have been developed to modify the above proposed critical density model. Finally, the numerical simulation is established to evaluate the effectiveness of the modified METANET model based on the field data from the realistic motorway network. The results show that overall, the predicted data from the modified METANET model are closer to the field data than those obtained from the original model.

Method for Imputing Missing Data using Online Calibration for Urban Freeway Control

Real-time traffic control systems are widely implemented on roadways around the world as a measure to improve freeway mobility. However, the systems, which rely on data from road-side and on-road sensors and other electronic equipment, continue to suffer from issues related to missing and erroneous data. While many data imputation methods are documented in the related literature, traffic control systems still lack an imputation method that is applicable in practice, accurate in imputation, and simple in computation. In response, this paper puts forth a linear imputation model that considers both temporal traffic trend and spatial detector correlations. To adapt the model to dynamic traffic variations, the imputation method was equipped with an online calibration module. The proposed imputation method was evaluated with field data from two stations on the Whitemud Drive, a busy urban freeway in Edmonton, Alberta, Canada. The proposed model benefited from its time-of-day temporal trend and outperforms the previous model that considers only spatial correlations. Moreover, the online calibration module was effective in improving imputation accuracy. Finally, the sensitivity of imputation performance was analyzed. The results show that the imputation with online calibration is more sensitive to missing data ratios than that with offline calibration. The sensitivity analysis revealed that imputation with online calibration is more suitable for online imputation in traffic control implementations.