Using big data and machine learning to rank traffic signals in Tennessee

This paper discusses low-cost approaches capable of ranking traffic intersections for the purpose of signal re-timing.We extracted intersections that are comprised of multiple roads,defined by alphanumeric traffic message channel segment codes per international classification standards.Each of these road segments includes a variety of metrics,including congestion,planning time index,and bottleneck ranking information provided by the Regional Integrated Transportation Information System.Our first approach was to use a ranking formula to calculate intersection rankings using a score between 0 and 10 by considering data for different times of the day and different days of the week,weighting weekdays more heavily than weekends and morning and evening commute times more heavily than other times of day.The second method was to utilize unsupervised machine learning algorithms,primarily k-means clustering,to accomplish the intersection ranking task.We first approach this by checking the performance of basic k-means clustering on our data set.We then explore the ranking problem further by utilizing data provided by traffic professionals in the state of Tennessee.This exploration involves using MATLAB to minimize the mean-squared error of intersection rankings to determine the optimum weights in the ranking formula based on a city’s professional data.We then attempted an optimization of our weights via a brute-force search approach to minimize the distance from ranking formula results to the clustering results.All the ranking information was aggregated into an online SQL database hosted by Amazon web services that utilized the PHP scripting language.

Recent advances in traffic signal performance evaluation

Signal retiming is a prominent way that transportation agencies use to fight congestion and change of traffic pattern.Performance evaluations of traffic conditions at signalized intersections and arterials provide actionable data for agencies to make well-informed and prioritized signal retiming decisions.However,the abundance of data sources,the lack of standardized evaluation methods and oftentimes the shortage of resources make it a difficult endeavor.The review detailed in this paper examines the advances made in traffic signal performance evaluation.We establish the necessity for the evaluations,study the process of continuous improvement of traffic signal performance using the evaluations,and then examine multiple methodologies in a plethora of research endeavors.Particularly,we focus on probe vehicles and sensors data,the two major sources of data.We discuss how sensors are connected to signal controllers to provide relevant in-depth traffic data including speed and occupancy measures.We also review the nature of probe vehicles and the level of penetration.We then define and summarize performance measures derived from both sources,to aid in performance evaluations.For performance evaluation methods,we discuss the research studies and provide summaries including advantages and disadvantages of the methods used,as well as a holistic outlook for future research.This paper is aimed to provide a comprehensive review on the state-of-the-art to benefit researcher,traffic agencies,and commercial entities that thrive to improve safety and efficiency of traffic signals through performance evaluations.