Prediction of Travel Time Reliability on Interstates Using Linear Quantile Mixed Models

Abstract

Under the Moving Ahead for Progress in the 21st Century Act (MAP-21), state Departments of Transportation (DOTs) are responsible for reporting travel time reliability and also setting targets and showing progress toward those targets. To know how to improve travel time reliability and what to expect from investments in transportation infrastructure, state DOTs need a better understanding of the factors that affect travel time reliability and methods to predict future travel time reliability. This paper proposes linear quantile mixed models (LQMMs) to quantify travel time reliability impact factors and predict selected reliability measures (level of travel time reliability [LOTTR] and the 90th percentile) to address these needs. The method was demonstrated using probe vehicle data from interstate segments in Virginia that had been partitioned into approximately homogeneous clusters based on the similarity of their cumulative distribution functions (CDFs) of travel times. Using clustered data meant that LQMMs were only necessary for a limited number of clusters rather than for hundreds of individual segments, thus making the process more efficient and manageable. The LQMMs showed that frequencies of non-recurrent events, such as incidents and weather, were correlated with higher travel time percentiles. The prediction performance of LQMMs was compared with trend line predictions, a common method used in practice. The results showed that LQMMs significantly improved prediction accuracy.

Travel Time Reliability Prediction Using Random Forests

“…This split could help evaluate model flexibility. GRF and QRF models were created for freeway and interchange segments separately because a previous study found the independent variables could affect these two types of segments differently ( 26 ). The 50th, 80th and 90th percentiles of travel times were predicted at the same time in one model run to facilitate easy implementation in practice.…”

“…For both GRF and QRF models, the prediction errors increase as the travel time percentile being predicted increases. The 90th percentile travel time is often used to represent the extreme situations, and past studies have found it is more challenging to predict 90th than 80th and 50th percentiles ( 26 ). Because freeway segments were longer than interchange segments and the travel times were longer, non-scaled error metrics—such as MAE, MSE, and bias—of freeway models had higher values than interchange models, but the freeway models were generally more accurate than interchange models.…”

“…The length of TMC segment (mile), heavy vehicle percentage (heavy_percent), the number of through lanes (throu_lane), and volume-to-capacity ratio (vc_ratio) were ranked high for both GRF and QRF models, which indicates that these variables have stronger predictive power than other variables for predicting the 50th, 80th, and 90th travel times based on the study dataset. These variables are among the major influencing factors identified in current literature for modeling travel time reliability ( 7 , 26 , 36 ). Compared with all other models, the QRF interchange model gave relatively lower importance to urban/rural designation (rural), and relatively higher importance to v/c ratio, SSP and crash variables.…”

“…Based on a previous study on travel time reliability influencing factors for interstate highways in Virginia ( 26 ), the following variables were considered in modeling.…”

“…The TMC segment was used as the spatial unit of analysis in this study. Data were aggregated at TMC segment level following a data conflation procedure described in Zhang et al ( 26 ). The final dataset prepared for model training and testing included more than 2.55 million rows of records.…”