Travel times have been traditionally estimated from data collected by roadway sensors. Recently, new tech- nologies, such as cell phone tracking, license plate matching, automatic vehicle identifications and video det...Travel times have been traditionally estimated from data collected by roadway sensors. Recently, new tech- nologies, such as cell phone tracking, license plate matching, automatic vehicle identifications and video detection, are employed for this purpose. In this study, the data collected by TRANSMIT readers, Bluetooth sensors, and INRIX are assessed by comparing each to the ＂ground truth＂ travel times collected by probe vehicles carrying GPS-based naviga- tion devices. Travel times of probe vehicles traveling on the study segment of 1-287 in New Jersey were collected in 2009. Statistical measures, such as standard deviation, average absolute speed error, and speed error bias, were used to make an in-depth analysis. The accuracy of each travel time estimation method is analyzed. The data collected by Bluetooth sensors and the TRANSMIT readers seem more consistent with the ground true data, and slightly outperform the data reported by 1NRIX. This study established a procedure for analyzing the accuracy of floating car data （FCD） collected by different technologies.展开更多
This paper describes procedure for estimation of travel time on signalized arterial roads based on multiple data sources with application of dimensionality reduction. Travel time estimation approach incorporates forec...This paper describes procedure for estimation of travel time on signalized arterial roads based on multiple data sources with application of dimensionality reduction. Travel time estimation approach incorporates forecast of transportation nodes impendence and travel time on network links. Forecasting period is two hours and the estimation is based on historical data and real time data on traffic conditions. Travel time estimation combines multivariate regression, principal component analysis, KNN （k-nearest neighbours）, cross validation and EWMA （exponentially weighted moving average） methods. When comparing estimation methodologies, relevantly better results were achieved by KNN method than with EWMA method. This is true for every time interval considered except for evening time interval when signalized arterial roads were uncongested.展开更多
文摘Travel times have been traditionally estimated from data collected by roadway sensors. Recently, new tech- nologies, such as cell phone tracking, license plate matching, automatic vehicle identifications and video detection, are employed for this purpose. In this study, the data collected by TRANSMIT readers, Bluetooth sensors, and INRIX are assessed by comparing each to the ＂ground truth＂ travel times collected by probe vehicles carrying GPS-based naviga- tion devices. Travel times of probe vehicles traveling on the study segment of 1-287 in New Jersey were collected in 2009. Statistical measures, such as standard deviation, average absolute speed error, and speed error bias, were used to make an in-depth analysis. The accuracy of each travel time estimation method is analyzed. The data collected by Bluetooth sensors and the TRANSMIT readers seem more consistent with the ground true data, and slightly outperform the data reported by 1NRIX. This study established a procedure for analyzing the accuracy of floating car data （FCD） collected by different technologies.
文摘This paper describes procedure for estimation of travel time on signalized arterial roads based on multiple data sources with application of dimensionality reduction. Travel time estimation approach incorporates forecast of transportation nodes impendence and travel time on network links. Forecasting period is two hours and the estimation is based on historical data and real time data on traffic conditions. Travel time estimation combines multivariate regression, principal component analysis, KNN （k-nearest neighbours）, cross validation and EWMA （exponentially weighted moving average） methods. When comparing estimation methodologies, relevantly better results were achieved by KNN method than with EWMA method. This is true for every time interval considered except for evening time interval when signalized arterial roads were uncongested.
