Factors Affecting Traffic Management using Two Step Cluster

Abstract

The concept of traffic congestion and traffic management is ambiguous in nature. The traffic and congestion is dependent on a number of factors that might impact the stretch of road or the framework of the traffic management systems. As the evolution of internet in last one decade and its reach to the very last person on this planet, this provides the basket of new opportunity of managing the traffic and its patterns on the basis of live traffic data from the onsite cameras, sensors, and the google maps traffic forecast the situation of traffic congestion would be avoided, which directly helps in reducing the load on environment and saving some valuable time of the commuters, and indirectly having large savings on the countries resources. In this research paper the authors have identified the factors affecting the management, flow, and working of traffic on the toll roads, national highways, and dedicated fright corridors in specific from the literature. The identified factors have been analyzed using quantitative statistical tools such as: relative importance index, Cronbach’s alpha, and cluster analysis to know the predictor importance. For this study a total of 192 valid responses were received using structured questionnaire survey. On the basis of data analysis the recommendation have be drawn and shared with the government authorities to be implemented on the highways to facilitate the commuters and all the other stakeholders associated with the traffic and traffic management. The findings of the relative importance index conclude that the most significant attributes of traffic management are No tolling for e-vehicles, use of information boards to avoid any traffic situations, and savings on fuels. Furthermore the findings of the cluster analysis concludes that the most important predictor is no-tolling for e-vehicles, followed by savings on fuels.

Methods and tools for the development of a Product-Service System: Proposal of a conceptual model

“…Results above 0.7 are considered satisfactory (Fadheel et al, 2018). Cronbach's alpha coefficient is used to check the level of data consistency (Yaduvanshi et al, 2019) and the reliability of the measurement instrument (Durán and Condorí, 2019). Its results should also be above 0.7, indicating statistical significance (Coşkun et al, 2019).…”

The Classification of Application Users Supporting and Facilitating Travel Mobility Using Two-Step Cluster Analysis

“…Another English study focused on the approach to the analysis of travel workflows based on geodemographic classification [20]. The recognition of the important predictor was identified through cluster analysis in traffic congestion, while the factors influencing the management, flow, and functioning of traffic were outlined [21]. The impact of transport on carbon dioxide emissions in Chinese cities was also examined using a two-stage cluster analysis, which was divided into various categories based on the degree of impact of each driver [22].…”