International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
www.irjet.net
Machine Learning Based Traffic Volume Count Prediction Nishant Patil1, Megha Natekar2, Ratan Gore3, Chandrashekhar Raut 4 1,2,3Student,
Computer Engineering Department, Mumbai University, Datta Meghe College of Engineering, Navi Mumbai, Maharashtra, India 4Professor, Computer Engineering Department, Mumbai University, Datta Meghe College of Engineering, Navi Mumbai, Maharashtra, India ---------------------------------------------------------------------***------------------------------------------------------------------1.1 Motivation Abstract - The transportation industry was accountable for 28% of worldwide CO2 emissions in 2014. the number of traffic-related deaths in 2013 was 1.25 million. Additionally, holdup at peak hours reaches unacceptable levels in many parts of the earth these are all serious issues caused by current transportation systems, and optimization through the usage of recent technologies is vital for the required improvements. Tons of the innovation that are a part of the solution already exists. Various Business sectors and government agencies and individual travelers require precise and appropriate traffic flow information. It helps the riders and drivers to make better travel judgments to alleviate traffic congestion, improve traffic operation efficiency, and reduce carbon emissions. Machine learning provides better accuracy for Traffic volume flow prediction. It's addressed as a major element for the success of advanced traffic volume management systems, advanced public transportation systems, and traveler information systems. The rationale of this extension is to develop a prescient demonstration utilizing different machine learning calculations and to record the end-to-end steps. The Metro Interstate Activity Volume dataset could also be a relapse circumstance where we are trying to anticipate the esteem of a ceaseless variable. We'll be analyzing how the drift of month-to-month interstate activity volume changes over an extended time between 2012 and 2018. Concurring to the discoveries, the month-to-month activity volume remains an equivalent indeed even though the knowledge appears a somewhat upward drift a short time recently applying time arrangement strategies.
With the progress of urbanization and therefore the recognition of automobiles, transportation problems are becoming more and more challenging: the traffic volume flow is congested, wear n tear of vehicles, delays end in the late time of arrival at the meeting, accidents are frequent, and wastage of fuel while waiting in traffic, the traffic environment is becoming worse, to unravel this problem and to assist society, we've chosen our topic as traffic volume prediction.
1.2 Problem Definition Now? The question arises of how to improve the capacitor y of the road network. To solve this problem the first solution that occurs to most of us is to build more highways, expanding the number of lanes on the road. However, according to the study done by scholars, expanding the road capacity will cause more serious traffic conditions. Therefore, traffic volume prediction is one of the most famous.
1.3 Objective The objective of this study is to seek out a traffic volume predictor suitable for real implications. This predictor must be accurate in terms of computation cost and power consumption. Within the go after such a predictor, we've included the subsequent contributions: We compare existing schemes to seek out their effectiveness for real-time applications
Key Words: Traffic Volume, Random Forest, Machine Learning, Webapp, prediction, RSME, MAE
2. Literature Review
1. INTRODUCTION
Traffic volume prediction is integrated by a selection of technologies. Machine learning is one of the foremost famous of those systems. It can improve traffic efficiency, ease congestion, increase road capacity, and reduce traffic accidents and environmental pollution. Road sources are mainly gathered from the high mobility vehicles on the highway or on urban roads, which makes it so important to figure out what percentage of vehicles are progressing to be on the given road segment within the longer term. To affect this, the traffic volume prediction system will provide highly reliable future traffic. according to the historical traffic pattern and thus the position over the entire road network.
Traffic jams on urban Network are increasing day by day, because the traffic demand increases, and the speed of the vehicles is drastically reduced thus causing longer vehicular queuing and more such cases substantially hamper the traffic flow by giving rise to holdup. Such situations highlight towards the drawback such as
Increase in pollution Wear and tear of vehicles Delays may result in late arrival etc.
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