According to congestion on the passenger road transport system is expected to increase by 36% by 2020 and conventional strategies like the expansion of transport infrastructure cannot meet the demand of use of this infrastructure and thus transportation planning authorities are shifting from infrastructure expansion to more intelligent methods of transportation systems and demand management. Travel patterns have changed over the years due to industrialization, car ownership increment, urbanization and many other factors, leading to an increase in road traffic congestion in urban areas. The study has also proved the viability of this modeling method to investigate policy measures to reduce peak period congestion. The high significance ratios of results prove that these chosen variables are suitable for investigations into peak hour travel pattern studies.
The study also discovered that choice of road type and car type, have varying influence on peak hour travels. Using vehicular movement data from Radio Frequency Identification for Nanjing, China, for the month of May 2014, it was revealed that in most of the cases, weekday travels influence peak hour travels more than weekends and that off-peak hour travels for both weekdays and weekends show little variations. This study utilizes structural equation model (SEM) to investigate the vehicular movements influence of weekdays, weekends, road type choice and car type on two peak hour periods 6 am to 9 am and 4 pm to 7 pm and one off-peak hour 9 am to 12 noon. A study of vehicular movement patterns during these times can influence and impact on planning decisions for transportation engineers. In this paper, it is proposed to investigate explicitly, the effect of weekday and weekend travel variability and road type on peak hour vehicular movement which leads to congestion. Although there have been studies on peak period travels, these studies have only implicitly considered weekday, weekend and road type in their investigations. The main congestion on roads occur during peak hours, apart from incidents such as road accidents and construction works.
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