International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
INTELLIGENT TRAFFIC MANAGEMENT FOR ZERO VIOLATIONS AND ENHANCE THE ROAD SAFETY
Sivaranjani M1, Rosini S2, Pooja M3, Soniya S4
1Assistant professor,2student,3student,4student Department of ComputerScience andEngineering,Paavai EngineeringCollege,Tamil Nadu, India
Abstract To developasmart traffic monitoringsystem that can detect violations in real-time send alerts and further follow up with rewarding positive behavior. Backed by smart technologies like speed sensors, surveillance cameras, and AI-based analytics, the system detects potential violations, like High-Speed, without helmet and seatbelt users and notifies drivers directly, even before a challan is issued. Promotes rule abiding can also be introduced where the drivers abiding by laws can be rewarded if their records for a certain period have also been clean . The system promotes a culture of safety by motivating drivers to maintain a good driving record, leading to reduced accidents and improved traffic management. This dualapproach model not only dissuades bad behavior through potential punishment but gives zero traffic violations.
Keywords: Real-time, Speed Sensor, Rule abiding, Rewards, Dual approach, zero traffic Violation.
1. INTRODUCTION
The rapid growth of urbanization and the exponential increase in the flow of cars have presented challenges for themaintenanceofroadswithpropertrafficmanagement. Even though traffic laws are well established, there's no denying that high speed, not wearing a helmet or seatbelt andcountlessotherviolationsstillhappenlikeit’snormal. Hence, road safety is a global concern that is directly connected to the health and sustainability of society and the economy, and it can be improved by the use of technology and the internet and, in large part, by addressing road violations. Road violations endanger the livesofroadusersandareoneofthemajorcontributorsto traffic congestion, increase in accident rates, and pressure onthelawenforcementsystem.
There are some measures including issuing challans or fines after the violation. Although the technological solutions achieve some level of deterrence, they are insufficient for driving long-term behavioral change in drivers. A renewed focus is needed on a more proactive
integrated system that goes beyond detecting and sanctioning breaches of the rules, by acting, promptly, to encourage compliance and discourage bad behaviors but alsobyrewardingpositivebehavior.
In this research, we will develop a Smart Traffic Monitoring System which, complementarily integrated speed sensors, surveillance cameras, and artificial intelligence (AI)-based analytics would enable real-time traffic violation detection. In contrast to conventional systems, the proposed model enables prompt alert notifications to be delivered to individuals when a potential violation is identified, such as overspeeding or lack of a helmet or seatbelt, allowing for corrective measurestobetakenbeforepenalactionisimplemented.
Additionally, the system features this new reward mechanism that identifies favorably those drivers that regularlyfollowtrafficrulesoveraspecifictime.Byusinga car like this as a reward for careful, late-night driving, the system helps create a positive reinforcement of responsible driving habits to cultivate a culture of safety andaccountabilityamongdriversontheroad.
The unique combination of real-time behavior detection and reward-based motivation distinguishes this system from current models. The goal is zero traffic violations,zeroroadaccidents,and better adherence. This system looks to revolutionize traffic management in cities through effective automation and by encouraging better drivinghabits.
2. EXISTING SYSTEM
Automated Number Plate Recognition (ANPR): ANPR systems are commonly installed at traffic junctions toidentify vehicles violating signals,speeding, or entering restricted zones. They capture the vehicle’s number plate, record the violation, and automatically generate challans, whicharesenttotheregisteredowner.
Speed Detection Systems: speed cameras are used to monitorvehiclespeedonhighwaysandurbanroads.These devices capture vehicles exceeding speed limits and initiatefinegeneration.
International Research Journal of Engineering and Technology (IRJET)
Volume: 12 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
CCTV Surveillance and Manual Monitoring:
Manycitiesutilizeclosed-circuittelevision(CCTV)cameras at intersections and critical road points. These feeds are monitored either manually or with limited AI support to detectviolationslikered-lightjumping.
Helmet and Seatbelt Detection via Computer Vision:
Recent developments have integrated AI-based computer vision algorithms to detect helmet and seatbelt violations from live camera feeds. These systems can flag violations, butagain,theresponseislargelypunitive,andtheviolator isinformed after theviolation.
E-Challan Systems:
Once a violation is detected, an e-challan (electronic fine) is generated and sent to the driver via SMS or through a centraltrafficportal.Thisdigitalfinesystemhasimproved transparencybutremainsapost-incidentmechanism.
3. PROPOSED SYSTEM
The proposed system is designed as a smart, proactive traffic monitoring solution that goes beyond traditional enforcement. Instead of focusing solely on penalizing violations, this approach integrates real-time detection withpreventivealertsandbehavioralmotivation.Thegoal istonotonlyreducethenumberoftrafficoffensesbutalso to create a culture of consistent, rule-abiding driving, ultimatelyaimingforzerotrafficviolations.
Real-Time Detection of Violations
At the core of the system is a network of intelligent surveillance cameras, AI-based image recognition, and speed-sensing units installed at critical points such as intersections, highways, and urban roads. This setup allows for continuous monitoring of vehicles and can accuratelydetectcommontrafficviolations,including:
• Overspeeding
• Ridingwithoutahelmet(two-wheelers)
• Drivingwithoutaseatbelt(four-wheelers)
• Red-lightjumping (optional extension)
• Lanedisciplineviolations (optional extension) The detectionprocesscombines:
• Radar and speed sensors to measure vehicle speed,
• Computer vision models to identify helmet and seatbeltcomplianceinreal-time,and
• Automatic Number Plate Recognition (ANPR) to link detected violations to the respective driver profiles.
Instant Alerts and Preventive Feedback
One of the key features that differentiate this system is its ability to warn drivers in real-time. As soon as a potential violation is detected, the driver receives an immediate alert through a connected mobile application, SMS, or
even vehicle dashboard (in IoT-enabled vehicles). This serves as a warning, giving the driver a chance to rectify thebehaviorinstantly.
Ifthedriveradjuststheirbehavior suchasslowingdown after a speed alert the incident is logged as a warning, not a fine. However, if the violation continues, the system proceeds with issuing an e-challan. This preventive layer reduces unnecessary penalties and encourages immediate self-correction.
Driver Behavior Profiling
Each driver or registered vehicle is associated with a digital profile maintained in the system. This profile continuouslylogs:
• Thenumberofalertsreceived,
• Confirmedviolations,
• Duration of clean, violation-free driving behavior. By maintaining and analyzing this data, authorities can identify high-risk drivers, reward consistent rulefollowers, and even tailor enforcement policies based on behavioraltrends.
Incentive and Reward Mechanism
To encourage long-term adherence to traffic rules, the systemintroducesapositivereinforcementmodel.Drivers who maintain a clean record over a certain period (e.g., three months) become eligible for various rewards, such as:
• Discountsonvehicleinsurancepremiums,
• Redeemabletrafficpoints,
• Digitalbadgesorpublicrecognition,
• Givediscountsfortolls
This motivational framework is aimed at shifting the perception of traffic rules from fear of punishment to prideinresponsibledriving
4. WORKFLOW
Theentiresystemoperatesthroughthefollowingsteps:
1. Vehicles are monitored continuously by smart sensorsandAI-enabledcameras.
2. Upon detection of a potential violation,an instant alertissenttothedriver.
3. Ifanyonedoesn’tfollowthe trafficrules,itgivesa warningthroughSMSforthefirsttime.
4. Ifthebehaviorpersists,ane-challanisissued.
5. Eachdriver'sprofileisupdatedinthemobileapp.
6. Drivers with clean records are recognized and rewarded.
5. SYSTEM ARCHITECTURE
1. Perception Layer – Real-Time Data Capture
This is the edge layer responsible for sensing and capturingtrafficbehaviorinreal-time.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
• Intelligent Surveillance Cameras – Highresolution,AI-integratedCCTVunits.
• Radar&SpeedSensors–Measurevehiclevelocity withprecision.
• Computer Vision Modules – Embedded on camerasoredgeprocessorstodetect:
1) Helmets(2-wheelers)
2) Seatbeltusage(4-wheelers)
3) Trafficlightstatus(optional)
4) ANPR Cameras – Capture license plate data forviolationattribution.
2. Edge Processing & Event Classification Layer This layerensureslow-latencyprocessingofcaptureddata.
• EmbeddedAIModels:
1) Helmet & seatbelt detection via YOLO or similarCVmodels.
2) Speedviolationdetectionfromsensorfusion.
• EventClassifier:
1) Validatestheviolationtype.
2) Assignsseveritylevel.
• BehaviorResponseTrigger:
1) Instantly communicates with the mobile app/vehiclesystemforalerts.
3. Communication & Alerting Layer
This layer handles instant feedback loops between the systemanddrivers.
• DriverMobileApp/WebApp: Displays warnings, e-challans, and behavior reports.
• SMSGateway(fallbackcommunication)
• Real-timedashboardnotifications.
4. Behavioral Profiling & Driver History Layer
Centralized, cloud-based service that builds a dynamic digitaldriverprofile.
• DriverBehaviorDatabase: Stores alerts, confirmed violations, and clean periods.
• ProfileScoringEngine: Calculatesrisklevelor"driverscore."
• GamificationLogic: Triggersrewards,achievements,andincentives.
5. Incentive & Reward System
Motivationalsystemlinkedwithdriverperformance.
• Points&BadgesSystem
• Insurance API Integration (e.g., for premium discounts)
6. Central Traffic Intelligence Dashboard Admin-facing interfaceusedbytrafficauthorities.
• Real-time Analytics Panel o Heatmaps of violations,time-basedtrendsand regionalstats.
• Behavioral Insights Engine o Identifies repeat offenders,safedriversandriskyzones.
• Policy Recommendation System o Suggests road designtweaks andenforcementneeds.
Fig -1: Flowchart
The flowchart represents the working process of an AIdriven traffic monitoring system deployed at automated checkpoints. The main goal of this system is to improve roadsafetybyidentifyingcommontrafficruleviolations When a vehicle approaches the AI checkpoint, the system automatically activates and begins observing the driver’s behavior. This monitoring is done in real-time using advanced AI techniques, including computer vision and sensor-basedanalysis.
Thesystemchecksforthreespecifictypesofviolations: 1. Helmet Usage:
Ifatwo-wheelerriderisnotwearingahelmet,the systemrecognizesthisasaviolationand immediatelysendsanalertmessage.However,if
Volume: 12 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
therideriswearingahelmet,thesystemignoresit andcontinuesmonitoring.
2. SpeedLimit:
The system evaluates whether the vehicle is adheringtothedesignatedspeedlimit.Ifspeeding isdetected,analertissent.Ifthevehicleiswithin thespeedlimit,noactionistaken.
3. Other Traffic Violations (e.g., Red-Light Jumping):
Thesystemisalsocapableofidentifyingadditional ruleviolations,suchasrunninga redlight.Ifsuch an action is observed, the system sends out a notificationalert.
Any time a violation is detected whether related to helmet usage, speeding, or other violation rules an alert messageisgeneratedandsenttotheuserthroughSMS.
This AI-based approach helps reduce manual monitoring, ensureszeroviolationintraffic,andultimatelycontributes tosaferroads.
6. METHODOLOGY
In this project, we’re using Artificial Intelligence to make our roads safer by reducing traffic violations. The system works step by step, just like how we would track and manage things manually but now it's all automatic and smart.
1. Getting the Traffic Data
First,wetakevideofootagefromtrafficcamerasatsignals or highways. These videos are used to watch how vehicles aremovingandwhetherthey'refollowingtherulesornot.
2. Detecting Number Plates (ANPR)
Then, our AI system looks at each frame of the video to spot vehicle number plates. We use a powerful model like YOLO(YouOnlyLookOnce),whichisveryfastandaccurate indetectingthingslikeplates.
3. Reading the Plate Numbers
Once the plate is found, we use OCR (Optical Character Recognition) to read the text on it. It’s just like how we humansread, butherethecomputerreadsand converts it intoactualtext.
4. Checking for rule-breaking
Now,thisplatenumberischeckedfortrafficviolations:
Ifsomeone breaks a rule for the first time, they’ll getan alert through SMS
Iftheyrepeatit,a challan issent.
If they keep breaking the rules, the system will automaticallysendthelicensesuspension.
5. Public Reporting through the App
People can use a mobile app to report any unsafe driving or road issues. These complaints are reviewed by the admin,andactionistakenwhenneeded.
6. Giving Rewards for Good Drivers
Noteveryonebreaksrules soforthepeople whofollow trafficrulesregularly,thesystemgivesthem rewards,like:
Discountsontollpasses
Lowerinsurancerates
7. Admin Monitoring
Allofthisisshownonan admin dashboard,wherethe authoritiescan:
Seetrafficreports
Checkwhobroketherules
Managerewards
7. PRIVACY & DATA PROTECTION MEASURES
1. Data Encryption
Allsensitivedata,includingvehiclenumbers,user details,andviolationhistory,willbeencrypted duringstorageinthedatabase.
Communicationbetweentheuserapp, surveillancecameras,andtheserverwillutilize HTTPStosafeguardagainsthackingor unauthorizedaccess.
2. Role-Based Access
Accesstosensitivedatawillberestrictedto authorizedusersonly(suchastrafficpoliceor admins).
Thesystemwillimplementloginauthentication anddefineuserroles:
a. Admin:Fullsystemaccess
b. Police:Limitedaccesstonecessarydata
c. Public:Accesstoviewonlytheirdata
3. Anonymized Public Data
Whengeneratingchartsorpublicreports, personalinformationlikenamesorfullvehicle platenumberswillbehidden.
Complaintdatacollectedviathemobileappwillbe anonymizedtopreventmisuse.
4. User Consent
Themobileappwill**askforpermission**before collectinguserlocationorfeedback,ensuringthat consentisclear.
Userswillhavetheoptionto**opt-out**of sharingnon-essentialdata.
5. Secure Database
• Measuressuchasfirewalls,regularbackups,and accesslogswillbeusedtosecurethebackend database.
• Strongpasswords andmulti-factorauthentication (MFA)willbeenforcedforadminlogintoenhance security.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
6.Automatic Data Cleanup
Olddatathatisnolongerneeded(suchasexpired challansor inactive users) will undergo automatic deletion after a designated timeframe (e.g., 6 months), minimizing risks associated with data retention.
7. CONCLUSIONS
Thesystemproposedinthispaperaimstoshiftthewaywe think about traffic rule enforcement. Rather than just focusing on punishing violations, it takes a more balanced and proactive approach by detecting violations in realtime,sendingimmediatealertstodrivers,andencouraging good behavior through rewards. This kind of setup not only helps reduce accidents and traffic offenses but also motivates drivers to build better habits behind the wheel. Bycombiningtechnologieslikesmartsensors,AI-powered cameras, and a user-friendly mobile app, the system creates a more connected and responsive traffic environment.
Ultimately, this approach isn't just about catching offenders it'saboutguidingbehavior,makingroadssafer, and working toward a future with zero traffic violations. With the right support and implementation, this system could make a meaningful impact on traffic management andpublicsafety.
REFERENCES
[1] A. Atzori, S. Barra, S. Carta, G. Fenu, and A. S. Podda, “Heimdall: An AI-based infrastructure for traffic monitoring and anomalies detection,” arXiv preprint arXiv:2103.01506,Mar.2021.
[2] E. M. Mohamed, “Autonomous real-time speedlimit violation detection and reporting systems based on the Internet of Vehicles (IoV),” Security and Privacy, vol. 4, no.3,pp.1–15,2021,doi:10.1155/2021/9888789.
[3] V.Mandal,A.R.Mussah,P.Jin,and Y. Adu-Gyamfi, “Artificial Intelligence enabled traffic monitoring system,” arXiv preprint arXiv:2010.01217,Oct.2020.
[4] A. Ghosh, A. Roy, and M. Basu, “Mobile-based traffic rule violation detection and driver behavior monitoring system,” International Journal of Scientific and Technology Research, vol. 8, no. 9, pp. 1001–1004, Sept. 2019.