Downs Law on peak–hour traffic congestion states that peak-hour traffic congestion rises to meet maximum capacity on city highways, thus emphasizing the impact of peak hour movement on traffic congestion. A study of vehicular movement patterns during times which are highly variable like peak periods can influence and have impact on planning decisions for transportation engineers.Įxtensive studies have been carried out on the effects of time of day, on traffic volumes and disparities in traffic volumes over the hours of the day have long been noticed. A prior knowledge of peak hour patterns is therefore useful for the traveler.

Consistency and prior knowledge of travel inconsistency leading to a more reliable transport system are said to be more important to travelers than other factors like reduction in travel time. A lot of policies have been put in place employing cutting-edge technology traffic management tools to reduce traffic congestion during peak hours. Peak hour congestion on roads is one of the main concerns for traffic engineers as congestion during peak hours is the main cause of congestion on roads, apart from incidents such as road accidents, failure of traffic signals and construction works. With the insurgent of big data and corresponding applications, this study is therefore apt and opportune since it uses RFID technology and big data which is in a dynamic stage.

State-of-the-art methodology is therefore needed to cater for this problem and the use of RFID technology has been mentioned as one of the new approaches recommended to collect data to investigate traffic conditions. 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.

Peakhour no interfaces