IRJET- Road Traffic Prediction using Machine LearningIRJET Journal
This document summarizes a research paper on predicting road traffic using machine learning. The paper aims to develop accurate prediction models using accident data to identify factors that contribute to accidents. This will help develop safety measures to prevent accidents. The paper reviews previous literature on identifying accident-prone locations and factors. It then describes the methodology used, which involves collecting accident data and dividing it into categories based on accident severity. Statistical analysis is performed on the data and results show predictions of accidents in urban, rural and other areas over time. The conclusions are that a broader analysis of more accident factors can improve predictions and help reduce accidents.
Traffic Monitoring and Control System Using IoTijtsrd
This research aims to monitor the traffic condition and to control the traffic light. This system can reduce not only traffic congestion but also waiting time. This system is designed to be implemented in places nearing the junctions. This research is based on the effective use of Internet of Things IoT . This system will display the traffic conditions in the website and the traffic light can be controlled from this website. This system has been implemented by using esp8266, ultrasonic sensor and arduino. Esp8266 nodemcu which is IoT device is used to transmit the traffic information to the website which is connected with this device. Ultrasonic sensors are placed on each road to sense the presence and absence of vehicles. Traffic information is received from these sensors. Traffic light prototype is built by using an arduino UNO. This traffic light can be controlled from the website. The system will display the traffic states in the website that can guide the drivers to select the right way and avoid traffic congestions. Ei Swe Zin | Kyaw Zin Latt ""Traffic Monitoring and Control System Using IoT"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25138.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/25138/traffic-monitoring-and-control-system-using-iot/ei-swe-zin
Computer Vision for Traffic Sign Recognitionthevijayps
This document discusses a project to develop a system for traffic sign recognition using computer vision. The system aims to detect and recognize traffic signs independently of variations in appearance, perspective, lighting, and partial occlusions. The objectives are outlined as making the system invariant to these factors and able to provide information on visibility, condition, and placement of signs. An approach is presented involving video segmentation, color-based and shape-based detection methods. MATLAB is identified as a tool for image processing tasks like reading, displaying, and compressing images. Algorithms and pseudo-code are discussed for tasks like video segmentation and image compression. The conclusion states that the algorithm can generalize to other object recognition and considers difficulties of outdoor environments.
Driver drowsiness monitoring system using visual behavior and Machine Learning.AasimAhmedKhanJawaad
Drowsy driving is one of the major causes of road accidents and death. Hence, detection of
driver’s fatigue and its indication is an active research area. Most of the conventional methods are
either vehicle based, or behavioral based or physiological based. Few methods are intrusive and
distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low
cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In the
developed system, a webcam records the video and driver’s face is detected in each frame employing
image processing techniques. Facial landmarks on the detected face are pointed and subsequently the
eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their
values, drowsiness is detected based on developed adaptive thresholding. Machine learning
algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and
specificity of 100% has been achieved in Support Vector Machine based classification.
The project is designed to develop a density based dynamic traffic signal system having remote override facilities. During normal time the signal timing changes automatically on sensing the traffic density at the junction but in the event of any emergency vehicle like ambulance, fire brigade etc. requiring priority are built in with RF remote control to override the set timing by providing instantaneous green signal in the desired direction while blocking the other lanes by red signal for some time. Traffic congestion is a severe problem in many major cities across the world thus it is felt imperative to provide such facilities to important vehicles.
Conventional traffic light system is based on fixed time concept allotted to each side of the junction which cannot be varied as per varying traffic density. Junction timings allotted are fixed. Sometimes higher traffic density at one side of the junction demands longer green time as compared to standard allotted time. The proposed system using a PIC microcontroller duly interfaced with sensors, changes the junction timing automatically to accommodate movement of vehicles smoothly avoiding unnecessary waiting time at the junction. The sensors used in this project are IR, are in line of sight configuration across the loads to detect the density at the traffic signal. The override feature is activated by an on board RF transmitter operated from the emergency vehicle.
AUTOMATIC LICENSE PLATE RECOGNITION SYSTEM FOR INDIAN VEHICLE IDENTIFICATION ...Kuntal Bhowmick
Automatic License Plate Recognition (ANPR) is a practical application of image processing which uses number (license) plate is used to identify the vehicle. The aim is to design an efficient automatic vehicle identification system by using the
vehicle license plate. The system is implemented on the entrance for security control of a highly restricted area like
military zones or area around top government offices e.g.Parliament, Supreme Court etc.
It is worth mentioning that there is a scarcity in researches that introduce an automatic number plate recognition for indian vechicles.In this paper, a new algorithm is presented for Indian vehicle’s number plate recognition system. The proposed algorithm consists of two major parts: plate region extraction and plate recognition.Vehicle number plate region is extracted using the image segmentation in a vechicle image.Optical character recognition technique is used for the character recognition. And finally the resulting data is used to compare with the records on a database so as to come up with the specific information like the vehicle’s owner, registration state, address, etc.
The performance of the proposed algorithm has been tested on real license plate images of indian vechicles. Based on the experimental results, we noted that our algorithm shows superior performance special in number plate recognition phase.
IRJET- Road Traffic Prediction using Machine LearningIRJET Journal
This document summarizes a research paper on predicting road traffic using machine learning. The paper aims to develop accurate prediction models using accident data to identify factors that contribute to accidents. This will help develop safety measures to prevent accidents. The paper reviews previous literature on identifying accident-prone locations and factors. It then describes the methodology used, which involves collecting accident data and dividing it into categories based on accident severity. Statistical analysis is performed on the data and results show predictions of accidents in urban, rural and other areas over time. The conclusions are that a broader analysis of more accident factors can improve predictions and help reduce accidents.
Traffic Monitoring and Control System Using IoTijtsrd
This research aims to monitor the traffic condition and to control the traffic light. This system can reduce not only traffic congestion but also waiting time. This system is designed to be implemented in places nearing the junctions. This research is based on the effective use of Internet of Things IoT . This system will display the traffic conditions in the website and the traffic light can be controlled from this website. This system has been implemented by using esp8266, ultrasonic sensor and arduino. Esp8266 nodemcu which is IoT device is used to transmit the traffic information to the website which is connected with this device. Ultrasonic sensors are placed on each road to sense the presence and absence of vehicles. Traffic information is received from these sensors. Traffic light prototype is built by using an arduino UNO. This traffic light can be controlled from the website. The system will display the traffic states in the website that can guide the drivers to select the right way and avoid traffic congestions. Ei Swe Zin | Kyaw Zin Latt ""Traffic Monitoring and Control System Using IoT"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25138.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/25138/traffic-monitoring-and-control-system-using-iot/ei-swe-zin
Computer Vision for Traffic Sign Recognitionthevijayps
This document discusses a project to develop a system for traffic sign recognition using computer vision. The system aims to detect and recognize traffic signs independently of variations in appearance, perspective, lighting, and partial occlusions. The objectives are outlined as making the system invariant to these factors and able to provide information on visibility, condition, and placement of signs. An approach is presented involving video segmentation, color-based and shape-based detection methods. MATLAB is identified as a tool for image processing tasks like reading, displaying, and compressing images. Algorithms and pseudo-code are discussed for tasks like video segmentation and image compression. The conclusion states that the algorithm can generalize to other object recognition and considers difficulties of outdoor environments.
Driver drowsiness monitoring system using visual behavior and Machine Learning.AasimAhmedKhanJawaad
Drowsy driving is one of the major causes of road accidents and death. Hence, detection of
driver’s fatigue and its indication is an active research area. Most of the conventional methods are
either vehicle based, or behavioral based or physiological based. Few methods are intrusive and
distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low
cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In the
developed system, a webcam records the video and driver’s face is detected in each frame employing
image processing techniques. Facial landmarks on the detected face are pointed and subsequently the
eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their
values, drowsiness is detected based on developed adaptive thresholding. Machine learning
algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and
specificity of 100% has been achieved in Support Vector Machine based classification.
The project is designed to develop a density based dynamic traffic signal system having remote override facilities. During normal time the signal timing changes automatically on sensing the traffic density at the junction but in the event of any emergency vehicle like ambulance, fire brigade etc. requiring priority are built in with RF remote control to override the set timing by providing instantaneous green signal in the desired direction while blocking the other lanes by red signal for some time. Traffic congestion is a severe problem in many major cities across the world thus it is felt imperative to provide such facilities to important vehicles.
Conventional traffic light system is based on fixed time concept allotted to each side of the junction which cannot be varied as per varying traffic density. Junction timings allotted are fixed. Sometimes higher traffic density at one side of the junction demands longer green time as compared to standard allotted time. The proposed system using a PIC microcontroller duly interfaced with sensors, changes the junction timing automatically to accommodate movement of vehicles smoothly avoiding unnecessary waiting time at the junction. The sensors used in this project are IR, are in line of sight configuration across the loads to detect the density at the traffic signal. The override feature is activated by an on board RF transmitter operated from the emergency vehicle.
AUTOMATIC LICENSE PLATE RECOGNITION SYSTEM FOR INDIAN VEHICLE IDENTIFICATION ...Kuntal Bhowmick
Automatic License Plate Recognition (ANPR) is a practical application of image processing which uses number (license) plate is used to identify the vehicle. The aim is to design an efficient automatic vehicle identification system by using the
vehicle license plate. The system is implemented on the entrance for security control of a highly restricted area like
military zones or area around top government offices e.g.Parliament, Supreme Court etc.
It is worth mentioning that there is a scarcity in researches that introduce an automatic number plate recognition for indian vechicles.In this paper, a new algorithm is presented for Indian vehicle’s number plate recognition system. The proposed algorithm consists of two major parts: plate region extraction and plate recognition.Vehicle number plate region is extracted using the image segmentation in a vechicle image.Optical character recognition technique is used for the character recognition. And finally the resulting data is used to compare with the records on a database so as to come up with the specific information like the vehicle’s owner, registration state, address, etc.
The performance of the proposed algorithm has been tested on real license plate images of indian vechicles. Based on the experimental results, we noted that our algorithm shows superior performance special in number plate recognition phase.
This document summarizes a seminar presentation on image processing. It defines image processing as processing of digital images, which are arrays of numbers represented by bits. It lists common applications such as face detection, medical imaging, and remote sensing. The purposes of image processing include visualization, image sharpening, measurement, and recognition. It discusses types of image processing including analog, digital and optical. It outlines the components and future scope of image processing and provides advantages and disadvantages. In conclusion, it states that image processing techniques can be used to enhance, analyze and synthesize images.
This document discusses various domain-specific Internet of Things (IoT) applications. It outlines IoT applications for homes, cities, the environment, energy systems, retail, logistics, industry, agriculture, and health and lifestyle. It then provides more details on specific IoT applications for homes (smart lighting, smart appliances, intrusion detection, smoke/gas detectors), cities (smart parking, smart road lighting, smart roads, structural health monitoring, surveillance, emergency response) and the environment (weather monitoring, air pollution monitoring, noise pollution monitoring, forest fire detection, river flood detection).
Artificial intelligence in autonomous vehicleGwenaël C
Présentation réalisé pour le cours d'anglais de la Licence 3 Miashs parcours Miage réalisée l'université de Toulouse Capitole conjointement à l'université Toulouse Paul Sabatier
GTSRB Traffic Sign recognition using machine learningRupali Aher
This document discusses traffic sign detection and classification. It outlines challenges like variable visual appearances and conditions. The goal is high accuracy recognition in real worlds. It explores feature extraction methods like raw pixels, color histograms, and HOG. Classification algorithms tested include MLP, KNN, SVM, random forest, and CNN using the German Traffic Sign Recognition Benchmark dataset. HOG performed best, and MLP achieved highest accuracy at 98%. The conclusion is HOG is most efficient for extraction and MLP performs best for classification.
The project is about building a human-computer interaction system
using hand gesture by cheap alternative to depth camera. We present
a robust , efficient and real-time technique for depth mapping using
normal 2D -camera and Infrared LED arrays . We use HOG feature
based SVM classifiers to predict hand pose and dynamic hand gestures . The system also tracks hand movements and events like grabbing and
clicking bythe hand.
This document provides an overview of the proposed Extended E-Challan system. The system aims to provide instant notifications to vehicle owners on their mobile if they violate any traffic rules, reducing the burden on traffic departments for challan management. It includes modules for administration, challan management, payment management, vehicle security management, and feedback management. The system is intended to function on Windows operating systems using HTML, Oracle, and Eclipse IDE.
The document describes an IoT-based smart car parking system using Raspberry Pi. The system aims to address issues with current manual parking systems like wasted time finding spots. It allows users to book parking spots online, see availability in real-time on a graphical interface, and pay electronically. Sensors detect occupied/empty spots which update the system in real-time. The system is cost-effective, saves user time, and maximizes parking space utilization.
The document discusses Automatic Number Plate Recognition (ANPR) systems. It provides the following key points:
1. ANPR uses optical character recognition on images captured by specialized cameras to read license plates on vehicles.
2. The cameras capture images that are then processed by ANPR software to detect, segment, and identify the license plate numbers.
3. ANPR systems are commonly used for electronic toll collection, traffic management, parking enforcement, and border control by storing images and license plate data.
Drowsiness Detection using machine learning (1).pptxsathiyasowmi
The document describes a proposed system to detect driver drowsiness using OpenCV and machine learning techniques. The system would use computer vision and facial landmark detection on video from an in-vehicle camera to monitor the driver's eyes and mouth for signs of fatigue like blinking rate, yawning and prolonged eye closures. If drowsiness is detected, the system will alert the driver with an alarm sound and may also activate a self-driving mode if the driver's eyes are closed for over 60 seconds. The proposed system aims to reduce accidents caused by fatigued driving and promote road safety.
License Plate Recognition Using Python and OpenCVVishal Polley
License Plate Recognition Systems use the concept of optical character recognition to read the characters on a vehicle license plate. In other words, LPR takes the image of a vehicle as
the input and outputs the characters written on its license plate.
Number Plate Recognition (NPR) is a computer vision technology that captures images of vehicles using a camera. It extracts the vehicle's number plate to identify the owner's details by matching it to a database. The system works by capturing images, preprocessing them, detecting the number plate using YOLO, recognizing the characters, and outputting the results to a database. It has benefits like saving time, reducing errors, and aiding in tracking criminals. Potential future improvements include enhancing plate recognition for different fonts/sizes and speeding up the system.
The document discusses the concept of the Internet of Vehicles (IoV), which connects vehicles through vehicle-to-vehicle communication networks. It describes how IoV relates to the broader Internet of Things by allowing vehicles to exchange information. Examples of IoV applications include early warning systems, detour applications, social media portals for vehicle owners, and using collected data for transportation analysis. The document outlines future areas of work with IoV including content sharing between vehicles, sensor data collection, intelligent transportation routing, and increased vehicle autonomy through technologies like platoons.
This document summarizes a seminar presentation on Mobile Ad-Hoc Networks (MANETs). It introduces MANETs as networks without infrastructure where nodes can connect in dynamic and flexible topologies. It discusses routing challenges in MANETs due to the dynamic topology. It also summarizes several routing protocols used in MANETs like DSR, DSDV, CGSR, ABR and SSR, which aim to establish and maintain routes between nodes that are moving. Finally, it discusses security and performance issues in MANETs and proposes the dynamic virtual backbone approach to abstract node mobility.
ppt on accident detection system based on Iotrahul ranjan
The document describes a proposed system to detect accidents on highways using an Internet of Things (IoT) approach. The system would use wireless sensor networks deployed along highways to detect accidents. Sensors would detect accidents and transmit location data via protocols like Zigbee to a gateway. The gateway would send the data to the cloud to be accessed by an Android app, allowing users to see accident locations in real-time. The document reviews related work on IoT and wireless sensor networks for smart cities and transportation. It outlines the proposed system architecture, hardware components, and communication methods to realize the accident detection system.
In this presentation, Swetha presents an innovative solution that aims to solve traffic problems in the country. Her solution uses sensors to collect information and relay the information to users over a mobile app.
This is a presentation on Handwritten Digit Recognition using Convolutional Neural Networks. Convolutional Neural Networks give better results as compared to conventional Artificial Neural Networks.
This document discusses hand gesture recognition using an artificial neural network. It aims to classify hand gestures into five categories (pointing one to five fingers) using a supervised feed-forward neural network and backpropagation algorithm. The objective is to facilitate communication for deaf people by automatically translating hand gestures into text. The system requires software like Pandas, Numpy and Matplotlib as well as hardware with a quad core processor and 16GB RAM. It explains key concepts of neural networks like neurons, weights, biases, activation functions and their advantages in handling large datasets and inferring unseen relationships.
Intelligent traffic information and control systemSADEED AMEEN
This document proposes an intelligent traffic information and control system that uses image processing and wireless communication to control traffic lights. A camera at intersections will capture images and detect vehicle presence to adjust light durations accordingly. An emergency vehicle clearance system will turn all lights green on its path. Zigbee modules allow wireless communication between an ambulance and traffic controller. Additionally, a traffic management system and chatbot provide traffic information to users. The system will use incremental development, initially controlling lights with Arduino then adding congestion control with image processing.
Automatic number plate recognition (ANPR) uses optical character recognition on images to read vehicle registration plates. It has seven elements: cameras, illumination, frame grabbers, computers, software, hardware, and databases. ANPR detects vehicles, captures plate images, and processes the images to recognize plates. It has advantages like improving safety and reducing crime. Applications include parking, access control, tolling, border control, and traffic monitoring.
IOT in transportation can improve passenger experiences, safety, and efficiency. IOT connects physical devices like vehicles, infrastructure, and mobile devices to the internet. This allows transportation systems to gather more data which can transform industries by improving traveler experiences, smart road management, parking, safety monitoring, and more. Key applications of IOT in transportation include fleet management, public transit management, smart inventory, asset utilization tracking, and geo-fencing. IOT connectivity in vehicles and infrastructure will change transportation by allowing vehicle-to-vehicle communication and connection to infrastructure for improved navigation, remote access features, and traffic optimization.
Intelligent transportation system based on iot service for traffic controlMahmudulHasan474
This document discusses the development of an intelligent transportation system based on IoT to help control traffic and reduce congestion. It proposes a system that would use sensors and real-time data collection to monitor traffic conditions and automatically adjust traffic light signals accordingly. This is aimed to improve traffic flow and reduce time wasted in traffic jams. The system would collect data from vehicles and sensors around roads to analyze traffic patterns and control lights based on current traffic density to minimize congestion in a smart, dynamic way.
Taking into consideration the drivers’ state might be a serious challenge for designing new advanced driver
assistance systems. During this paper we present a driver assistance system strongly coupled to the user. Driver
Assistance by Augmented Reality for Intelligent Automotive is an augmented reality interface informed by a several
sensors. Communicating the presence of pedestrians or bicyclists to vehicle drivers may end up in safer interactions
with these vulnerable road users. Advanced knowledge about the presence of these users on the roadway is
particularly important when their presence isn't expected or when these users are out of range of the advanced safety
systems that are becoming a daily feature in vehicles today. For example, having advanced knowledge of a pedestrian
walking along a rural roadway is important to increasing driver awareness through in-vehicle warning messages that
provide an augmented version of the roadway ahead. Voice recognition system through an android platform adds
some good flavour during this project. The strategy of voice recognition through this platform is achieved by
converting the input voice signal into text of string and subsequently it's transmitted to embedded system which
contains an arduino atmega328 microcontroller through Bluetooth as a technique of serial communication between an
android application and a control system. The received text string on an arduino is also displayed on the AR Glass. As
connected vehicles start to enter the market, it's conceivable that when the vehicle sensors detect a pedestrian on a
rural roadway, the pedestrian presence is also communicated to vehicles upstream of the pedestrian location that
haven't reached the destination. This paper presents a survey of studies related to perception and cognitive attention
of drivers when this information is presented on Augmented Reality
This document summarizes a seminar presentation on image processing. It defines image processing as processing of digital images, which are arrays of numbers represented by bits. It lists common applications such as face detection, medical imaging, and remote sensing. The purposes of image processing include visualization, image sharpening, measurement, and recognition. It discusses types of image processing including analog, digital and optical. It outlines the components and future scope of image processing and provides advantages and disadvantages. In conclusion, it states that image processing techniques can be used to enhance, analyze and synthesize images.
This document discusses various domain-specific Internet of Things (IoT) applications. It outlines IoT applications for homes, cities, the environment, energy systems, retail, logistics, industry, agriculture, and health and lifestyle. It then provides more details on specific IoT applications for homes (smart lighting, smart appliances, intrusion detection, smoke/gas detectors), cities (smart parking, smart road lighting, smart roads, structural health monitoring, surveillance, emergency response) and the environment (weather monitoring, air pollution monitoring, noise pollution monitoring, forest fire detection, river flood detection).
Artificial intelligence in autonomous vehicleGwenaël C
Présentation réalisé pour le cours d'anglais de la Licence 3 Miashs parcours Miage réalisée l'université de Toulouse Capitole conjointement à l'université Toulouse Paul Sabatier
GTSRB Traffic Sign recognition using machine learningRupali Aher
This document discusses traffic sign detection and classification. It outlines challenges like variable visual appearances and conditions. The goal is high accuracy recognition in real worlds. It explores feature extraction methods like raw pixels, color histograms, and HOG. Classification algorithms tested include MLP, KNN, SVM, random forest, and CNN using the German Traffic Sign Recognition Benchmark dataset. HOG performed best, and MLP achieved highest accuracy at 98%. The conclusion is HOG is most efficient for extraction and MLP performs best for classification.
The project is about building a human-computer interaction system
using hand gesture by cheap alternative to depth camera. We present
a robust , efficient and real-time technique for depth mapping using
normal 2D -camera and Infrared LED arrays . We use HOG feature
based SVM classifiers to predict hand pose and dynamic hand gestures . The system also tracks hand movements and events like grabbing and
clicking bythe hand.
This document provides an overview of the proposed Extended E-Challan system. The system aims to provide instant notifications to vehicle owners on their mobile if they violate any traffic rules, reducing the burden on traffic departments for challan management. It includes modules for administration, challan management, payment management, vehicle security management, and feedback management. The system is intended to function on Windows operating systems using HTML, Oracle, and Eclipse IDE.
The document describes an IoT-based smart car parking system using Raspberry Pi. The system aims to address issues with current manual parking systems like wasted time finding spots. It allows users to book parking spots online, see availability in real-time on a graphical interface, and pay electronically. Sensors detect occupied/empty spots which update the system in real-time. The system is cost-effective, saves user time, and maximizes parking space utilization.
The document discusses Automatic Number Plate Recognition (ANPR) systems. It provides the following key points:
1. ANPR uses optical character recognition on images captured by specialized cameras to read license plates on vehicles.
2. The cameras capture images that are then processed by ANPR software to detect, segment, and identify the license plate numbers.
3. ANPR systems are commonly used for electronic toll collection, traffic management, parking enforcement, and border control by storing images and license plate data.
Drowsiness Detection using machine learning (1).pptxsathiyasowmi
The document describes a proposed system to detect driver drowsiness using OpenCV and machine learning techniques. The system would use computer vision and facial landmark detection on video from an in-vehicle camera to monitor the driver's eyes and mouth for signs of fatigue like blinking rate, yawning and prolonged eye closures. If drowsiness is detected, the system will alert the driver with an alarm sound and may also activate a self-driving mode if the driver's eyes are closed for over 60 seconds. The proposed system aims to reduce accidents caused by fatigued driving and promote road safety.
License Plate Recognition Using Python and OpenCVVishal Polley
License Plate Recognition Systems use the concept of optical character recognition to read the characters on a vehicle license plate. In other words, LPR takes the image of a vehicle as
the input and outputs the characters written on its license plate.
Number Plate Recognition (NPR) is a computer vision technology that captures images of vehicles using a camera. It extracts the vehicle's number plate to identify the owner's details by matching it to a database. The system works by capturing images, preprocessing them, detecting the number plate using YOLO, recognizing the characters, and outputting the results to a database. It has benefits like saving time, reducing errors, and aiding in tracking criminals. Potential future improvements include enhancing plate recognition for different fonts/sizes and speeding up the system.
The document discusses the concept of the Internet of Vehicles (IoV), which connects vehicles through vehicle-to-vehicle communication networks. It describes how IoV relates to the broader Internet of Things by allowing vehicles to exchange information. Examples of IoV applications include early warning systems, detour applications, social media portals for vehicle owners, and using collected data for transportation analysis. The document outlines future areas of work with IoV including content sharing between vehicles, sensor data collection, intelligent transportation routing, and increased vehicle autonomy through technologies like platoons.
This document summarizes a seminar presentation on Mobile Ad-Hoc Networks (MANETs). It introduces MANETs as networks without infrastructure where nodes can connect in dynamic and flexible topologies. It discusses routing challenges in MANETs due to the dynamic topology. It also summarizes several routing protocols used in MANETs like DSR, DSDV, CGSR, ABR and SSR, which aim to establish and maintain routes between nodes that are moving. Finally, it discusses security and performance issues in MANETs and proposes the dynamic virtual backbone approach to abstract node mobility.
ppt on accident detection system based on Iotrahul ranjan
The document describes a proposed system to detect accidents on highways using an Internet of Things (IoT) approach. The system would use wireless sensor networks deployed along highways to detect accidents. Sensors would detect accidents and transmit location data via protocols like Zigbee to a gateway. The gateway would send the data to the cloud to be accessed by an Android app, allowing users to see accident locations in real-time. The document reviews related work on IoT and wireless sensor networks for smart cities and transportation. It outlines the proposed system architecture, hardware components, and communication methods to realize the accident detection system.
In this presentation, Swetha presents an innovative solution that aims to solve traffic problems in the country. Her solution uses sensors to collect information and relay the information to users over a mobile app.
This is a presentation on Handwritten Digit Recognition using Convolutional Neural Networks. Convolutional Neural Networks give better results as compared to conventional Artificial Neural Networks.
This document discusses hand gesture recognition using an artificial neural network. It aims to classify hand gestures into five categories (pointing one to five fingers) using a supervised feed-forward neural network and backpropagation algorithm. The objective is to facilitate communication for deaf people by automatically translating hand gestures into text. The system requires software like Pandas, Numpy and Matplotlib as well as hardware with a quad core processor and 16GB RAM. It explains key concepts of neural networks like neurons, weights, biases, activation functions and their advantages in handling large datasets and inferring unseen relationships.
Intelligent traffic information and control systemSADEED AMEEN
This document proposes an intelligent traffic information and control system that uses image processing and wireless communication to control traffic lights. A camera at intersections will capture images and detect vehicle presence to adjust light durations accordingly. An emergency vehicle clearance system will turn all lights green on its path. Zigbee modules allow wireless communication between an ambulance and traffic controller. Additionally, a traffic management system and chatbot provide traffic information to users. The system will use incremental development, initially controlling lights with Arduino then adding congestion control with image processing.
Automatic number plate recognition (ANPR) uses optical character recognition on images to read vehicle registration plates. It has seven elements: cameras, illumination, frame grabbers, computers, software, hardware, and databases. ANPR detects vehicles, captures plate images, and processes the images to recognize plates. It has advantages like improving safety and reducing crime. Applications include parking, access control, tolling, border control, and traffic monitoring.
IOT in transportation can improve passenger experiences, safety, and efficiency. IOT connects physical devices like vehicles, infrastructure, and mobile devices to the internet. This allows transportation systems to gather more data which can transform industries by improving traveler experiences, smart road management, parking, safety monitoring, and more. Key applications of IOT in transportation include fleet management, public transit management, smart inventory, asset utilization tracking, and geo-fencing. IOT connectivity in vehicles and infrastructure will change transportation by allowing vehicle-to-vehicle communication and connection to infrastructure for improved navigation, remote access features, and traffic optimization.
Intelligent transportation system based on iot service for traffic controlMahmudulHasan474
This document discusses the development of an intelligent transportation system based on IoT to help control traffic and reduce congestion. It proposes a system that would use sensors and real-time data collection to monitor traffic conditions and automatically adjust traffic light signals accordingly. This is aimed to improve traffic flow and reduce time wasted in traffic jams. The system would collect data from vehicles and sensors around roads to analyze traffic patterns and control lights based on current traffic density to minimize congestion in a smart, dynamic way.
Taking into consideration the drivers’ state might be a serious challenge for designing new advanced driver
assistance systems. During this paper we present a driver assistance system strongly coupled to the user. Driver
Assistance by Augmented Reality for Intelligent Automotive is an augmented reality interface informed by a several
sensors. Communicating the presence of pedestrians or bicyclists to vehicle drivers may end up in safer interactions
with these vulnerable road users. Advanced knowledge about the presence of these users on the roadway is
particularly important when their presence isn't expected or when these users are out of range of the advanced safety
systems that are becoming a daily feature in vehicles today. For example, having advanced knowledge of a pedestrian
walking along a rural roadway is important to increasing driver awareness through in-vehicle warning messages that
provide an augmented version of the roadway ahead. Voice recognition system through an android platform adds
some good flavour during this project. The strategy of voice recognition through this platform is achieved by
converting the input voice signal into text of string and subsequently it's transmitted to embedded system which
contains an arduino atmega328 microcontroller through Bluetooth as a technique of serial communication between an
android application and a control system. The received text string on an arduino is also displayed on the AR Glass. As
connected vehicles start to enter the market, it's conceivable that when the vehicle sensors detect a pedestrian on a
rural roadway, the pedestrian presence is also communicated to vehicles upstream of the pedestrian location that
haven't reached the destination. This paper presents a survey of studies related to perception and cognitive attention
of drivers when this information is presented on Augmented Reality
Public transport service is one of the most preferred
modes of transportation in today’s smart cities. People prefer
public transport mainly for the cost benefit reasons. The
problems faced by the people while using the public transport
can be overcome by the technology such as Internet of Things
(IOT). In this paper, we present how this technology can be
applied to eliminate the problems faced by the passengers of the
public bus transport service. The Internet of Things technology is
used to provide the passengers waiting at the bus stop with real
time information of the arriving buses. Information such as
arrival time, crowd density and traffic information of the
arriving buses are predetermined and provided to the passengers
waiting at the bus stop. The display boards fitted at the bus stops
provide the real time bus navigation information to the waiting
passengers. This Smart Bus Navigation system enables the
passengers to make smart decisions regarding their bus journey.
This system reduces the anxiety and the waiting time of the
passenger’s at the bus stop. The smart bus navigation system
creates a positive impact and increases the number of people who
prefer to use the public mode of transportation.
This document summarizes research on analyzing driving safety risks using naturalistic driving data. Key points:
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The emerging increase in vehicles and very high traffic, demands the need for improved Intelligent Transport Systems (ITS). The available ITSs do not meet all the requirements of the present day situation in providing safetravels and avoidance of congestionin spite of its limitations on road. Intelligent Transport Systemsrequiremore research and implementation of better solutions on the traffic network with increased mobility and more rapid acquisition of data by sense network technology. In this paper a review is made on the present ITS where research is required so that improvement in the course of implementing reality mining can enhance the behavior of ITS. This will breed a forward leap in the improvement of safety and convenience of personal and commercial travel and in turn guarantee an ultimate drop in fatality in the society.
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Traffic Prediction for Intelligent Transportation System using Machine Learning
1. A SEMINAR REPORT ON
TRAFFIC PREDICTION FOR INTELLIGENT TRANSPORTATION
SYSTEM USING MACHINE LEARNING
SUBMITTED TO THE SAVITRIBAI PHULE PUNE UNIVERSITY, PUNE
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF
THIRD YEAR COMPUTER
ENGINEERING SUBMITTED BY
OM DNYANOBA SURYAWANSHI
UNDER THE GUIDANCE OF
Prof. Grishma Bobhate
DEPARTMENT OF COMPUTER ENGINEERING
RMD SINHGAD SCHOOL OF ENGINEERING, WARJE, PUNE
2021-22
AFFILIATED TO
2. CERTIFICATE
This is to certify that the seminar report entitles
” Traffic Prediction for Intelligent Transportation System using
Machine Learning”
submitted by
Mr. Om Dnyanoba
Suryawanshi Roll No :- 56
is a bonafide work carried out by above student under the supervision of Prof. Grishma Bobhate
and it is approved for the partial fulfillment of the requirement of Savitribai Phule Pune
University, Pune for the award of the degree of Bachelor of Engineering(Computer Engineering).
Prof. Grishma Bobhate Ms. Vina M. Lomate
Seminar Guide Head of Department
Computer Engineering Computer Engineering
Dr. V.V. Dixit
RMD SINHGAD SCHOOL OF ENGINEERING, WARJE, PUNE
Examiner Name & Sign :
Place: Pune
3. ii
ACKNOWLEDGEMENT
With due respect and gratitude I take the opportunity to thank those who have helped me directly
and indirectly. I convey my sincere thanks to Ms. Vina M. Lomate, HOD Computer Dept. and
Prof. Grishma Bobhate for their help in selecting the seminar topic and support.
I thank to my seminar guide Prof. Grishma Bobhate for her guidance, timely help andvaluable
suggestions without which this seminar would not have been possible. Her direction has always
been encouraging as well as inspiring for me. Attempts have been made to minimize the errors in
the report.
I would also like to express my appreciation and thanks to all my friends who knowingly or
unknowingly have assisted and encourage me throughout my hard work.
MR.OM DNYANOBA SURYAWANSHI (ROLL.NO : 56)
T.E COMPUTER(III year) 2021
4. iii
ABSTRACT
The aim is to develop a tool for predicting accurate and timely traffic flow Information. Traffic
Environment involves everything that can affect the traffic flowing on the road, whether it’s
traffic signals, accidents, rallies, even repairing of roads that can cause a jam. If we have prior
information which is very near approximate about all the above and many more daily life
situations which can affect traffic then, a driver or rider can make an informed decision. Also, it
helps in the future of autonomous vehicles. In the current decades, traffic data have been
generating exponentially, and we have moved towards the big data concepts for transportation.
Available prediction methods for traffic flow use some traffic prediction models and are still
unsatisfactory to handle real-world applications. This fact inspired us to work on the traffic flow
forecast problem build on the traffic data and models.It is cumbersome to forecast the traffic
flow accurately because the data available for the transportation system is insanely huge. In this
work, we planned to use machine learning, genetic, soft computing, and deep learning algorithms
to analyse the big-data for the transportation system with much-reduced complexity. Also, Image
Processing algorithms are involved in traffic sign recognition, which eventually helps for the
right training of autonomous vehicles.
5. iv
List of Figures
Fig 1.1: Machine Learning
Fig 1.2: ITS Depiction
Fig 3: Traffic Jam
Fig 5.1.1: System Architechture
Fig 5.1.2: Process
Fig 5.2.1: Support Vector Machine
Fig 5.2.2: Random Forest
Fig 5.2.3: Support Vector Regression
Fig 5.2.4:Decision Tree
Fig 5.2.5 :Evaluation
6. v
Contents
ACKNOWLEDGEMENT
………………………………………………………. ii
ABSTRACT
………………………………………………………………………... iii
List of
Figures…………………………………………………………………….
. iv
1. INTRODUCTION 1
1.1 MACHINE
LEARNING………………………………………………… 1
1.2 INTELLIGENT TRANSPORTATION SYSTEM (ITS)…………2
1.3 NEED FOR ITS !
…………………………………………………………. 3
2. OBJECTIVES 4
3. MOTIVATION 4
4. LITERATURE SURVEY 5
5. METHODOLOGY 7
5.1 SYSTEM DESIGN
………………………………………………………. 7
5.2 PREDICTION ALGORITHS
……………………………………….. 9
5.3 Proposed algorithm for predicting the traffic congestion........... 13
8. 1
INTRODUCTION
1.1 MACHINE LEARNING
Machine learning (ML) is the study of computer algorithms that can improve automatically
through experience and by the use of data. It is seen as a part of artificial intelligence. Machine
learning algorithms build a model based on sample data, known as training data, in order to
make predictions or decisions without being explicitly programmed to do so. Machine learning
algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech
recognition, and computer vision, where it is difficult or unfeasible to develop conventional
algorithms to perform the needed tasks.
A subset of machine learning is closely related to computational statistics, which focuses on
making predictions using computers; but not all machine learning is statistical learning. The
study of mathematical optimization delivers methods, theory and application domains to the
field of machine learning. Data mining is a related field of study, focusing on
exploratory data analysis through unsupervised learning.Some implementations of machine
learning use data and neural networks in a way that mimics the working of a biological brain.In
its application across business problems, machine learning is also referred to as predictive
analytics.
Fig 1.1: Machine Learning
9. 2
1.2 INTELLIGENT TRANSPORTATION SYSTEM
An intelligent transportation system (ITS) is an advanced application which aims to provide
innovative services relating to different modes of transport and traffic management and enable
users to be better informed and make safer, more coordinated, and 'smarter' use of transport
networks.
Some of these technologies include calling for emergency services when an accident occurs,
using cameras to enforce traffic laws or signs that mark speed limit changes depending on
conditions.
Although ITS may refer to all modes of transport, the directive of the European Union
2010/40/EU, made on July 7, 2010, defined ITS as systems in which information and
communication technologies are applied in the field of road transport, including infrastructure,
vehicles and users, and in traffic management and mobility management, as well as for interfaces
with other modes of transport.[1] ITS may improve the efficiency and safety of transport in a
number of situations, i.e. road transport, traffic management, mobility, etc.[2] ITS technology is
being adopted across the world to increase capacity of busy roads and reduce journey times
Fig 1.2: ITS Depiction
10. 3
1.3 NEED FOR ITS
Various Business sectors and government agencies and individual travellers require precise and
appropriately traffic flow information. It helps the riders and drivers to make better travel
judgement to alleviate traffic congestion, improve traffic operation efficiency, and reduce carbon
emissions. The development and deployment of Intelligent Transportation System (ITSs) provide
better accuracy for Traffic flow prediction. It is deal with as a crucial element for the success of
advanced traffic management systems, advanced public transportation systems, and traveller
information systems. [1]. The dependency of traffic flow is dependent on real-time traffic and
historical data collected from various sensor sources, including inductive loops, radars, cameras,
mobile Global Positioning System, crowd sourcing, social media. Traffic data is exploding due
to the vast use of traditional sensors and new technologies, and we have entered the era of a large
volume of data transportation. Transportation control and management are now becoming more
data-driven. [2], [3].However, there are already lots of traffic flow prediction systems and
models; most of them use shallow traffic models and are still somewhat failing due to the
enormous dataset dimension. Recently, deep learning concepts attract many persons involving
academicians and industrialist due to their ability to deal with classification problems,
understanding of natural language, dimensionality reduction, detection of objects, motion
modelling. DL uses multi-layer concepts of neural networks to mining the inherent properties in
data from the lowest level to the highest level [4]. They can identify massive volumes of
structure in the data, which eventually helps us to visualize and make meaningful inferences
from the data. Most of the ITS departments and researches in this area are also concerned about
developing an autonomous vehicle, which can make transportation systems much economical
and reduce the risk of lives. Also, saving time is the integrative benefit of this idea. In current
decades the lots of attention have made towards the safe automatic driving. It is necessary that
the information will be provided in time through driver assistance system (DAS), autonomous
vehicles (AV)and Traffic Sign Recognition (TSR) [5].
11. 4
OBJECTIVES
The aim is to research different machine learning algorithms capable of producing
accurate traffic flow prediction.
Which technique is best ?
What is proposed method ?
MOTIVATION
India is a country of huge population. The Road traffic in all cities of India is of greater concern.
There is always a long wait for the people on the roades of the cities. India is among the top
countries with large traffic index in the worldand, it is also 4th among the traffic index rankings
of 2019 [3]. With high time index and also the C02 (Carbon di oxide) percent among all the
cities [3]. So it is important to find effective solutions through ML to solve traffic problem.
Fig 3: Traffic Jam
12. 5
LITERATURE SURVEY
PAPER NAME - Traffic Prediction for Intelligent Transportation System using Machine
Learning
AUTHORS - Gaurav Meena,Deepanjali Sharma, Mehul Mahrishi.
PUBLICATION - IEEE(2020)
INFERENCE - ITS provides a smooth and safe movement of road transportation. Decision
Tree ,Random forest and SVM algorithm are used to identify classification and regression .
PAPER NAME- Smart Traffic Analysis using Machine Learning
AUTHORS- Aditya Krishna K.V.S, Abhishek K, Allam Swaraj, Shantala Devi Patil, Gopala
Krishna Shyam
PUBLICATION – IJEAT(2019)
INFERENCE- Analysis using Random Forest Algorithm, predicting the Mean square
error(MSE), calculate Mean Absolute error(MAE) which means the difference between two
continuous variables may be X and Y, also calculating the Root mean squared error(RMSE)
which means the frequently used measure of difference in the values predicted by th machine
learning model.
PAPER NAME- Parallel Control and Management for Intelligent Transportation Systems:
Concepts, Architectures, and Applications
AUTHORS- Fei-Yue Wang
PUBLICATION – IEEE(2011)
INFERENCE- ACP-based parallel control and management systems And use of ATS(Artificial
Transportation System) in it.Studied about 5 components of ATS and also System Architecture
of PTMS.
13. 6
PAPER NAME- A Decentralized Approach for Anticipatory Vehicle Routing Using Delegate
Multiagent Systems
AUTHORS- Rutger Claes, Tom Holvoet, and Danny Weyns
PUBLICATION- IEEE(2011)
INFERENCE – This paper presents a decentralized approach for anticipatory vehicle routing
that is particularly useful in large-scale dynamic environments. The approach is based on
delegate multiagent systems.
PAPER NAME- Index point detection and semantic indexing of videos - a comparative review
AUTHORS- Mehul Mahrishi and Sudha Morwal
PUBLICATION-IEEE(2021)
INFERENCE- To study the existing methods of automatic video indexing and annotation to
analyze the outcomes and gaps by Use of YoloV4.
PAPER NAME- Decision tree methods: applications for classification and prediction
AUTHORS- Yan-yan SONG, Ying LU
PUBLICATION- Shanghai Archives of Psychiatry(2015)
INFERENCE- Decision tree methodology is a commonly used data mining method for
establishing classification systems based on multiple covariates or for developing prediction
algorithms for a target variable.
14. 7
METHODOLOGY
5.1 SYSTEM ARCHITECTURE
Fig 5.1.1: System Architechture
1) Dataset Generation:
The dataset for this project is generated based on available datasets for traffic analysis. The
dataset is created for a particular location in bangalore called Yelahanka For easy understanding.
The dataset will be in the form of a .csv file
1) Dataset Generation
5) Verification
2) Feature
Identification
4)Machine Learning
Algorithm used for
Analysis
3) Feature
Extraction
15. 8
2) Feature Identification:
The neccessary features for the project are to be identified like time, distance, delay, Vehicle
Number etc. The features which are associated with the project are identified for the dataset by
using which the analysis could be easily performed
3) Feature Extraction:
Feature extraction will in general make use of the dimensionality reduction procedure to reduce
and consider only those neccessary attributes neccessary for the project like time
,distances, Nodes between which the traffic in general is identified.
4) Machine Learning Algorithm used for Analysis:
The Machine learning algorithm that is used for the traffic analysis we have used for our TAM
algorithm is the Algorithm.The Algorithm will help in classifying whether the traffic is more or
less in a particular area based on the dataset loaded to the algorithm
5) Verification:
The Verification step will check whether the analysis done on the dataset is proper or not. This
means that the analysis step is giving the proper result or not.
Fig 5.1.2: Process
16. 9
5.2 PREDICTION ALGORITHS :-
1) Support Vector Machine (SVM)
Fig 5.2.1: Support Vector Machine
Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms,
which is used for Classification as well as Regression problems. However, primarily, it is used
for Classification problems in Machine Learning.
The goal of the SVM algorithm is to create the best line or decision boundary that can segregate
n-dimensional space into classes so that we can easily put the new data point in the correct
category in the future. This best decision boundary is called a hyperplane.
SVM chooses the extreme points/vectors that help in creating the hyperplane. These extreme
cases are called as support vectors, and hence algorithm is termed as Support Vector Machine.
Consider the below diagram in which there are two different categories that are classified using a
decision boundary or hyperplane:
17. 10
2) Random Forest
Fig 5.2.2: Random Forest
Random Forest is a popular machine learning algorithm that belongs to the supervised learning
technique. It can be used for both Classification and Regression problems in ML. It is based on
the concept of ensemble learning, which is a process of combining multiple classifiers to solve a
complex problem and to improve the performance of the model.
As the name suggests, "Random Forest is a classifier that contains a number of decision
trees on various subsetsof thegiven dataset andtakestheaverage to improve the
predictive accuracy of that dataset." Instead of relying on one decision tree, the random
forest takes the prediction from each tree and based on the majority votes of predictions, and it
predicts the final output.
The greater number of trees in the forest leads to higher accuracy and prevents the
problem of overfitting.
18. 11
3) Support Vector Regression (SVR)
Fig 5.2.3: Support Vector Regression
Support Vector Regression is a supervised learning algorithm that is used to predict discrete values.
Support Vector Regression uses the same principle as the SVMs. The basic idea behind SVR is to
find the best fit line. In SVR, the best fit line is the hyperplane that has the maximum number of
points.
Unlike other Regression models that try to minimize the error between the real and predicted
value, the SVR tries to fit the best line within a threshold value. The threshold value is the
distance between the hyperplane and boundary line. The fit time complexity of SVR is more
than quadratic with the number of samples which makes it hard to scale to datasets with more
than a couple of 10000 samples.
19. 12
4) Decision Tree
Fig 5.2.4:Decision Tree
A decision tree is a decision support tool that uses a tree-like model of decisions and their
possible consequences, including event change outcomes, resource costs, and utility. It is one
way to display an algorithm that only contains conditional control statements.
Decision trees are commonly used in operations research, specifically in decision analysis, to
help identify a strategy most likely to reach a goal, but are also a popular tool in Machine
Learning.
Decision tree methodology is a commonly used data mining method for establishing
classification systems based on multiple covariates or for developing prediction algorithms for a
target variable. This method classifies a population into branch-like segments that construct an
inverted tree with a root node, internal nodes, and leaf nodes. The algorithmis non-parametric
and can efficiently deal with large, complicated datasets without imposing a complicated
parametric structure. When the sample size is large enough, study data can be divided into
training and validation datasets. Using the training dataset to build a decision tree model and a
validation dataset to decide on the appropriate tree size needed to achieve the optimal final
model.
20. 13
Fig 5.2.5 :Evaluation
5.3 Proposed algorithm for predicting the traffic
congestion which can be seen below:
Step 1: For identifying the congested situation :
Collect the traffic data in every 5 min with various features
Group every 5 min interval with their corresponding data
Calculate the distance between each vehicle with all another vehicles within
specified junction.
if the distance is less than the specific threshold between two vehicles then
those vehicles are considered to be the neighbourhood vehicles
else
Not considered as neighbour vehicles.
end if
21. 14
Step 2: For classifying the congested situation
This will eventually give us the matrix A.
Now assign 1 to A[i, j]
if A[i, j] < threshold then
A[i, j] = 1
else
A[i, j] = 0
endif
Count A[i, j]=1 and label i, j as neighbourhood vehicles
Repeat above steps in every 5 min for 45 min
Plot the graph between neighbourhood vehicles and time interval
Step 3 : Evaluation
if the neighbourhood vehicles shows an increasing graph
then
else
the traffic congestion is identified
No traffic
end if
22. 15
FUTURE WORK
For future work it would be interesting to attempt the same experiments but exam- ine
more complex versions of the models used. For example by using more neurons and
hidden layers in the neural network architectures. Doing this would however re- quire
better hardware, such that the training phase execution time does not become unfeasible.
The hardware in mind would be some high end Graphics Processing Unit as they are well
optimized for matrix operations, which is a large part of training neural networks. Even
better would be to perhaps utilize a new technology released by Google in 2017 called
Tensor Processing Units (TPU) [57]. TPUs were built specifically for training neural
networks, and are available for usage in the Google Cloud [58]. Also we have planned to
integrate the web server and the application.
Also, considering entirely new models would also be an option. One example is to utilize
both CNN and LSTM networks in the same model. This has been tried in several previous
projects with success [59], [60]. This works by using a CNN network to capture the spatial
correlations, and letting the LSTM deal with the temporal dependencies. Another
interesting idea was proposed by Ma et al. (2017) [61], where they forecast future traffic
patterns based on images. In other words, they interpret the traffic speed at various
locations of some road network as an image. A CNN network is then used to learn the
patterns of the images.
Finally, for a future project, much more data would be needed as this would allow the
model to learn all the traffic patterns over an entire year. This would likely improve the
results because no matter where the test set is put in time, the training set will at some point
have included similar patterns
CONCLUSION
It is clear that machine learning has great potential when it comes to time series
forecasting. This has been shown in this thesis as well as in other referenced liter- ature.
Existing statistical approaches should however not be underestimated. The baseline
methods did in fact achieve decent results and are faster to evaluate com- pared to the ML
techniques. When faced with a forecasting problem, whether its traffic forecasting or
something else, the traditional approaches should always be tried first. If they do not
perform as well as expected, one could try experimenting with machine learning. If this
option is considered, a few things are important to keep in mind. Powerful hardware is
crucial as this allows one to train very large and complex ML models at fast speeds.
Increased performance due to hardware will in turn open up many doors for further
improvement of the ML models. For one, it
will speed up grid search optimizations which helps finding better hyperparameters.
23. 16
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