Measuring traffic congestion: An approach based on learning weighted inequality, spread and aggregation indices from comparison data

• Published in: Applied soft computing Vol. 67; pp. 910 - 919
• Main Authors: Beliakov, Gleb, Gagolewski, Marek, James, Simon, Pace, Shannon, Pastorello, Nicola, Thilliez, Elodie, Vasa, Rajesh
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2018
• Subjects: Aggregation functions; Congestion; Inequality indices; Learning weights; Spread measures; Traffic analysis; Aggregation functions; Congestion; Inequality indices; Spread measures; Traffic analysis; Learning weights
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2017.07.014

Comparison of smoothed time series data for velocity, volume, weighted spread (using proportions calculated from relative volume) and an adjusted volume index. [Display omitted] •We formulate the parameter (weight) learning problems for weighted spread and inequality related functions.•We showcase an application of these ideas to the analysis and identification of traffic congestion.•We use real data as a means for demonstrating our techniques. As cities increase in size, governments and councils face the problem of designing infrastructure and approaches to traffic management that alleviate congestion. The problem of objectively measuring congestion involves taking into account not only the volume of traffic moving throughout a network, but also the inequality or spread of this traffic over major and minor intersections. For modeling such data, we investigate the use of weighted congestion indices based on various aggregation and spread functions. We formulate the weight learning problem for comparison data and use real traffic data obtained from a medium-sized Australian city to evaluate their usefulness.