A Work Zone Simulation Model for Travel Time Prediction in a Connected Vehicle Environment

TL;DR

This study develops a simulation-based travel time prediction model for work zones using connected vehicle data and compares different statistical modeling approaches to optimize accuracy and resource use.

Contribution

It introduces a novel simulation framework integrating connected vehicle data with multiple modeling techniques for work zone travel time prediction.

Findings

All models showed similar RMSE performance.

Stepwise regression requires only two RSUs for effective prediction.

Model choice can be based on RSU placement and data availability.

Abstract

A work zone bottleneck in a roadway network can cause traffic delays, emissions and safety issues. Accurate measurement and prediction of work zone travel time can help travelers make better routing decisions and therefore mitigate its impact. Historically, data used for travel time analyses comes from fixed loop detectors, which are expensive to install and maintain. With connected vehicle technology, such as Vehicle-to-Infrastructure, portable roadside unit (RSU) can be located in and around a work zone segment to communicate with the vehicles and collect traffic data. A PARAMICS simulation model for a prototypical freeway work zone in a connected vehicle environment was built to test this idea using traffic demand data from NY State Route 104. For the simulation, twelve RSUs were placed along the work zone segment and sixteen variables were extracted from the simulation results to explore travel time estimation and prediction. For the travel time analysis, four types of models were constructed, including linear regression, multivariate adaptive regression splines (MARS), stepwise regression and elastic net. The results show that the modeling approaches under consideration have similar performance in terms of the Root of Mean Square Error (RMSE), which provides an opportunity for model selection based on additional factors including the number and locations of the RSUs according to the significant variables identified in the various models. Among the four approaches, the stepwise regression model only needs variables from two RSUs: one placed sufficiently upstream of the work zone and one at the end of the work zone.