Framework of Big Data and Deep Learning for Simultaneously Solving Space Allocation and Signal Timing Problem

Framework of Big Data and Deep Learning for Simultaneously Solving Space Allocation and Signal Timing Problem

Deep learning

Big data

Developing countries

Intelligent systems

Nearest neighbor search

Motor transportation

Learning systems

Decision trees

Traffic congestion

Timing circuits

Intelligent vehicle highway systems

Traffic signals

Efficient operations at intersections are associated with smooth, safe, and sustainable travel at the network level. It is often challenging to prevent congestion at these locations, especially during rush hours, owing to high traffic demand and restraints of road geometry. The primary cause of intersection traffic congestion is large variations and fluctuations in traffic demand during the day. Dynamic lane assignment (DLA) is an intelligent transportation system (ITS) technology that can improve traffic operations at signalized intersections, making better use of available space by optimizing lane allocation based on real-time fluctuating traffic demands. This study used a brute-force optimization model to produce a vast synthetic dataset of turning movements at a four-legged signalized intersection. The model then found the optimal lane assignment and cycle length for each case. The produced data were used to develop a deep learning model to predict the optimal lane assignment and cycle length simultaneously at any four-legged signalized intersection without applying optimization software per se. The deep learning model results developed were compared with the results of the k-nearest neighbors (KNN), random forest, and decision tree algorithms. It was found that the deep learning model was superior, with accuracy and F1 score of 95.26% and 93.56%, respectively. The proposed solution is expected to require insignificant additional resources and services for successful implementation, particularly in developing countries. 2022 American Society of Civil Engineers.

1

149

Assi, Khaled

Ratrout, Nedal

Nemer, Ibrahim

Rahman, Syed Masiur

Jamal, Arshad

Journal of Transportation Engineering Part A: Systems

2023

journalArticle

24732907

10.1061/JTEPBS.TEENG-7233