Traffic control optimization is a challenging task for various traffic centers around the world and the majority of existing approaches focus only on developing adaptive methods under normal (recurrent) traffic conditions. Optimizing the control plans when severe incidents occur still remains an open problem, especially when a high number of lanes or entire intersections are affected.This paper aims at tackling this problem and presents a novel methodology for optimizing the traffic signal timings in signalized urban intersections, under non-recurrent traffic incidents. With the purpose of producing fast and reliable decisions, we combine the fast running Machine Learning (ML) algorithms and the reliable Genetic Algorithms (GA) into a single optimization framework. As a benchmark, we first start with deploying a typical GA algorithm by considering the phase duration as the decision variable and the objective function to minimize the total travel time in the network. We fine tune the GA for crossover, mutation, fitness calculation and obtain the optimal parameters. Secondly, we train various machine learning regression models to predict the total travel time of the studied traffic network, and select the best performing regressor which we further hypertune to find the optimal training parameters. Lastly, we propose a new algorithm BGA-ML combining the GA algorithm and the extreme-gradient decision-tree, which is the best performing regressor, together in a single optimization framework. Comparison and results show that the new BGA-ML is much faster than the original GA algorithm and can be successfully applied under non-recurrent incident conditions.
This paper presents a framework for short-term travel time prediction in a motorway with a three-stage architecture: traffic flow forecasting, traffic flow generation and travel time extraction. Traffic flow forecasting reads the historical traffic data and utilizes a forecasting model -Autoregressive Integrated Moving Average (ARIMA) to predict short-term traffic flow. The traffic flow generation utilizes the Cell Transmission Model (CTM) to generate outgoing flow of a road of interest based on the predicted incoming flow from ARIMA. Predicted short term travel times can then be obtained through N-Curve Analysis. Compared to most studies, this paper presents a historical data-driven framework for travel time prediction that can be trained based on specific profiles of routes and cities. The motorway M4 in Sydney, Australia was used to test this framework. It is shown that the predicted travel times can be used to anticipate congestion episodes at the network level.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.