Artificial Neural Network Model for Estimating Temporal and Spatial Freeway Work Zone Delay Using Probe-Vehicle Data

Abstract

Highway lane closures due to road reconstruction and the resulting work zones have been a major source of nonrecurring congestion on freeways. It is extremely important to calculate the safety and cost impacts of work zones: the use of new technologies that track drivers and vehicles make that possible. A multilayer feed-forward artificial neural network (ANN) model is developed in this paper to estimate work zone delay by using the probe-vehicle data. The probe data include the travel speeds under normal and work zone conditions. Unlike previous models, the proposed model estimates temporal and spatial delays, which are applied to a real world case study in New Jersey. The work zone data (i.e., starting time, duration, length, and number of closed lanes) were collected on New Jersey freeways in 2014 together with actual probe-vehicle speeds. A comparative analysis was conducted; the results indicate that the ANN model outperforms the traditional deterministic queuing model in terms of the accuracy in estimating travel delays. The ANN model can be used to calculate contractor penalty in terms of cost overruns as well as incentivize a reward schedule in case of early work competition. The model can assist work zone planners in designing optimal start and end time of work zone as function of time of day. In assessing the performance of work zones, the model can assist transportation engineers to better develop and evaluate traffic mitigation and management plans.

Predicting Freeway Work Zone Delays and Costs with a Hybrid Machine-Learning Model

“…The root mean square error (RMSE) is employed for performance evaluation. Comparing with a previous ANN model [7], we found that the developed model here with the work zone capacities suggested by HCM [33] and SVM has demonstrated itself as a sound model which improved prediction accuracy in terms of reduced RMSE. The results also suggest that the proposed hybrid machine-learning model with SVM outperforms the others for all three real-world study cases with greater prediction accuracy, especially when work zones are placed in daytime facing high traffic volumes.…”

“…Du and Chien [6] formulated the work zone delay considering time-varying traffic pattern, work zone capacity adjustment factors (e.g., light condition, heavy-vehicle percentage, and lane width), and shoulder usage. Deterministic queuing models are suitable for predicting delay for planning purposes but sometimes they have a limited ability to provide accurate prediction (i.e., delays), especially under significantly fluctuated traffic condition over time [7,8]. Further, the applied work zone capacity was either given or based on some simplified empirical equations, which also degrade the accuracy of prediction.…”

“…Many studies have successfully applied ANNs to predict various traffic measures, such as traffic flow [16,17], freeway work zone capacity [18,19], and work zone delay [7,8,20]. GhoshDastidar and Adeli [20] presented a multilayer feedforward neural network model (i.e., Levenberg-Marquardt neural network model) for delay and queue length prediction at freeway work zones.…”

“…This new and rich data has made way for big data analytics as an emerging method for predicting freeway spatiotemporal work zone delay. Du et al [7] developed a multilayer feedforward ANN model to predict work zone delay using the probe-vehicle data (i.e., speeds under normal and work zone conditions) subject to the condition when traffic volume and capacity information are missing. Based on the prediction results of three examples, we found that the ANN model outperformed analytical models in terms of the accuracy in predicting delays caused by reconstruction projects.…”

“…It is a previous ANN model developed by Du et al [7] in which the relationship between approaching traffic volume and work zone capacity was not applied to predict work zone speeds.…”