Walking is a commonly used movement mode, and the transport mode is denoted by the term pedestrian. Walking is an unavoidable part of almost all kind of trips especially when using public transport services such as trains and buses. Usually, pedestrians need to cross the road in order to access various residential and commercial lands for many purposes. However, many developing countries, such as India, lack suitable pedestrian crossing infrastructure. As a result, pedestrians must coexist with motor activity on the roadway. These facilities are available at a few areas, however, pedestrians are unwilling to utilize them and prefer to cross the road at their own convenience. These crossings are illegal. As previously mentioned, unlawful pedestrian crossings are fairly widespread in many nations. This study is looking into the analysis of the impact of such undesignated crossings on the speed and other traffic characteristics at various urban midblock sections in India with the use of an effective machine learning algorithm, support vector regression (SVR). When there is a large amount of data available and the variability in the data is significantly high, conventional modeling techniques fail to handle them. In that case, data-driven approaches show superior performance over other techniques. So, the prediction of speeds using SVR by considering the effects of pedestrian crossing is considered in this study, and the results are compared with results from traditional techniques. Results show that SVR outperforms the traditional models.
Analysis of Effect of Crossing Pedestrians on Traffic Characteristics at Urban Midblock Sections Using Support Vector Regression
Lecture Notes in Civil Engineering
Recent Advances in Transportation Systems Engineering and Management ; Kapitel : 36 ; 553-567
2022-11-11
15 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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