Congestion Articulation Control Using Machine Learning Technique

Congestion is the most serious issue in both Adhoc mobile networking and regular road traffic systems. The definition of a vehicle is changing as the automotive industry advances. Nowadays, all automobiles are outfitted with the most up-to-date sensors and communication capabilities. Mobile Ad Hoc Network that avoids traffic jams and articulation issues while also saving time by receiving direction from the GPS system on the shortest path using various algorithms. It also provides information on road safety and where to go. It repeatedly recalculates the shortest way using multiple algorithms to ensure that the user does not become stuck and stranded in traffic. From the point of view of research, this paper defines the architecture and protocols. However, VANETs are a subset of MANETs and constitute the future of Intelligent Transportation Systems. The development of big data, the latest sensors and probing vehicle data, as well as the widespread use of machine learning technologies, has given articulation control measurement in the traffic congestion area a completely new and different direction. By examining multiple traffic metrics. With machine learning, it is straightforward to forecast traffic congestion. This study is based on traffic congestion forecasting in real-time. This paper presents a summary of recent research conducted using various AI approaches and machine learning models

Performance Analysis of a Real-Time Adaptive Prediction Algorithm for Traffic Congestion

Traffic congestion is a major factor to consider in the development of a sustainable urban road network. In the past, several mechanisms have been developed to predict congestion, but few have considered an adaptive real-time congestion prediction. This paper proposes two congestion prediction approaches are created. The approaches choose between five different prediction algorithms using the Root Mean Square Error model selection criterion. The implementation consisted of a Global Positioning System based transmitter connected to an Arduino board with a Global System for Mobile/General Packet Radio Service shield that relays the vehicles position to a cloud server. A control station then accesses the vehicles position in real-time, computes its speed. Based on the calculated speed, it estimates the congestion level and it applies the prediction algorithms to the congestion level to predict the congestion for future time intervals. The performance of the prediction algorithms was analysed, and it was observed that the proposed schemes provide the best prediction results with a lower Mean Square Error than all other prediction algorithms when compared with the actual traffic congestion states

SUPPORTING FACTORS AND FACTORY EMPLOYEE’S BEHAVIOUR IN THE USE OF PUBLIC TRANSPORTATION MODE IN JAKARTA

The tendency for people in Jakarta to use public transportation is still relatively low. A study from Jabodetabek Transportation Management Agency shows that only 24 percent of road users chose public land transportation modes (Transjakarta bus and train) from a total of 47.5 million trips in Jabodetabek by 2015. Public transportation trips in Jabodetabek are all people's journeys or the journey to employee work destination located in buffer towns around Jakarta. Employees are among the elements of society that use public transportation. The economic conditions of factory employees that encourage them to work overtime cause differences in attitudes between factory employees and office employees toward using public transportation modes. This study aims to determine the factors that encourage factory employees to choose the mode of transportation to the workplace and analyze their attitude toward using public transportation mode. The results show that the number of factory employees who prefer public transportation mode is still little. However, their potential to move into public transportation is substantial due to the belief in the commitment of the Jakarta government to fixing the public transportation system. Most factory employees also agree that public transport can reduce congestion in Jakarta. There is a need to apply a strategy of the transit development (TOD) to reach public places and places of work

Exploring the Highway Travel Patterns Affected by COVID-19 through Outbreak to Recovery Stages – A Case Study in Guizhou Province

The examination of highway travel behaviour during the COVID-19 pandemic can provide valuable insights into the impacts of the pandemic and associated policies on human mobility patterns. This paper proposes a comprehensive examination, measurement and characterisation approach in the perspective of network and community structure. To capture the changes in travel behaviour, four stages were defined based on four consecutive Augusts from 2019 to 2022, during which varying levels of restrictions were implemented. The findings reveal interesting trends in travel patterns. In 2020, after the clearance of pandemic cases, there was a remarkable increase of over 10% in highway trips. However, in 2021, with the emergence of COVID-19 variants, there was a significant decline of over 30% in highway trips. By employing complex network analysis, key metrics of the primary network, including link weight, node flux and network connectivity, exhibited a notable decrease during the pandemic. These changes in network properties also reflect the spatial heterogeneity of highway travel demand. Moreover, the outcomes of community detection shed light on the evolution of the highway community structure, highlighting the efficacy of a community-collaboration strategy for highway management during public emergency events, as it fosters strong local interaction within the community