International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
SmartRail: AI-Powered Occupancy Prediction for Metro and Rail Networks
* 1,2,3 B.Tech Student, Department of Computer Science and Engineering, LCIT Bilaspur (C.G.)
*4 Assistant Professor, Department of Computer Science and Engineering, LCIT Bilaspur (C.G.)
Abstract - The SmartRail project is a cutting-edge AIpoweredsolutiondesignedtooptimizepassengerdistribution and enhance operational efficiency in metro and railway networks. By leveraging Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), the system provides real-time passenger occupancy predictions. The objectiveofthisstudy istodevelopanautomatedsystemthat dynamically adjusts train schedules, optimizes seating arrangements, and improves passenger safety through datadriven insights. The proposed system incorporates deep learning-based image processing, sensor-based passenger counting, and cloud computing to ensure seamless railway operations.
This paper discusses system architecture, data collection methods,modeldevelopment,challenges,benefits,andfuture enhancements. The research findings suggest that implementing SmartRail leads to improved crowd management,reducedwaittimes,betterutilizationofrailway resources,andenhancedcommutersatisfaction.Additionally, the integration of predictive analytics allows for proactive decision-making, reducing operational inefficiencies.
Keywords: Crowd management, Object detection, Deep learning, Railway occupancy, Passenger safety, AI in transportation, IoT-based monitoring, Cloud computing, Predictiveanalytics,Automatedscheduling.
1.INTRODUCTION
The rapid urbanization and increasing reliance on public transportation have led to overcrowding in metro and railway systems, causing discomfort and inefficiencies. Traditionalmethodsofcrowdmanagementrelyonmanual monitoring,staticschedules,andreactivedecision-making, whichfailtoadaptdynamicallytochangingpassengerloads. Asmetroandrailwaynetworkscontinuetoexpand,thereis anurgentneedforintelligentsolutionsthatoptimizetrain schedules,preventoverutilizationofresources,andenhance passengerconvenience.
Currentrailwaysystemsfacenumerouschallenges,including the lack of real-time passenger data, inefficient train frequencyadjustments,andovercrowding,whichincreases safety risks. Many railway authorities operate on fixed schedules that do not consider real-time variations in passenger demand. This results in either underutilized or
overcrowdedtrains,leadingtopoorserviceefficiencyand increased operational costs. Moreover, the limited digital infrastructure available for real-time monitoring restricts the ability of railway operators to make data-driven decisions. Addressing these issues requires an AI-driven solutioncapableofcontinuously analysingpassengerflow and dynamically adjusting train schedules to optimize performance.
TheSmartRailsystemaimstoaddressthesechallengesby implementinganAI-basedpredictivemodelthatintegrates deeplearning,IoT-basedmonitoring,andcloudcomputing totrackreal-timepassengermovement.Byleveragingrealtimedataanalytics,SmartRailprovidesrailwayauthorities with a robust solution to manage passenger distribution efficiently, ensure safety, and enhance overall railway operations
1.1 SmartRail – AI Based Approach
SmartRail introduces an AI-based approach to real-time passenger occupancy prediction, allowing railway authorities to enhance train frequency, optimize seating arrangements, and provide passengers with informed travel decisions through mobile applications and digital displays.Thissystemwillnotonlyimproveserviceefficiency but also contribute to sustainable urban mobility by reducingtravelcongestionandenergyconsumption.
Fig.1: HomePage
Aryan Agney1, Shubham Gupta2 , Pawash Sharaf3, Muskan Naik4
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
2. LITERATURE REVIEW
SeveralstudieshaveexploredtheapplicationofAI,IoT,and dataanalyticsinpublictransportation.AI-drivenpassenger flowoptimizationhasbeena widelyresearchedarea,with modelslikeYOLO(YouOnlyLookOnce)andSSD(SingleShot MultiBox Detector) proving effective in detecting crowd densitiesintransportationhubs.TheimplementationofIoT sensors, including infrared counters, RFID scanners, and smartcameras,hasenabledreal-timetrackingofpassenger flow,enhancingtheaccuracyofpredictivemodels.
Table -1:
AI and IoT in Railways
Technology Application Benefits
AI for Flow Optimization
YOLO,SSDforcrowd estimation
IoTinTransport Infrared,RFIDfor tracking
ML for Forecasting RNNs, LSTMs for demandprediction
DeepLearning CNNs, R-CNNs for movementanalysis
SmartTicketing AI-based dynamic pricing
Better crowd control,trainuse
Accurate occupancydata
Optimized scheduling
Improvedsecurity, management
More revenue, balancedload
Machine learning techniques such as Recurrent Neural Networks(RNNs),LongShort-TermMemory(LSTMs),and Time-SeriesForecastinghavedemonstratedhighefficiencyin predictingpassengerdemand.Thesemodelsallowrailway operatorstoanticipatepeakhoursandadjusttrainschedules accordingly. Convolutional Neural Networks (CNNs) and Region-basedCNNs(R-CNNs)havebeenemployedincrowd management solutions, enabling real-time monitoring and improvingsafetymeasures.
Furthermore, the integration of big data analytics has facilitatedimprovedcongestionmanagementinmajorcities suchasTokyo,London,andNewYork.Predictiveanalytics tools have enabled railway authorities to optimize train frequency, reduce energy consumption, and enhance passengerconvenience.Additionally,blockchaintechnology is being explored for secure data sharing, while edge computingisbeingutilizedtominimizelatencyinAI-driven decision-makingsystems.
TheSmartRailsystembuildsupontheseexistingstudiesby integrating multiple AI-driven approaches into a unified platform for real-time railway occupancy prediction and optimization.Thispapercontributestotheongoingresearch by proposing a scalable and adaptable model that can be appliedacrossdifferentrailwaynetworksworldwide.
3. CHALLENGES AND LIMITATIONS
Despite the advantages of AI-driven railway occupancy prediction, several challenges and limitations must be addressed. One of the primary concerns is real-time data accuracy. Environmental factors such as poor lighting, shadows, and occlusions in crowded areas can affect the accuracy of CCTV-based passenger detection. Additionally, sensor hardware malfunctions can lead to incorrect occupancy estimates, requiring frequent calibration and maintenance.Datainconsistenciesduetonetworklatencyor sensor malfunctions can introduce errors, leading to unreliablepredictions.
3.1 Privacy and Security Of Passenger Data
Anothermajorchallengeisensuringtheprivacyandsecurity of passenger data. AI-driven railway monitoring systems mustcomplywithglobaldataprotectionregulationssuchas theGeneralDataProtectionRegulation(GDPR)andnational privacy laws. The collection and processing of real-time passengerdataraiseethicalconcernsregardingsurveillance, necessitating robust encryption and anonymization techniquestoprotectuseridentities.Furthermore,AI-based transportation systems are vulnerable to cyber threats, including data breaches and unauthorized access. Any compromise in data security can lead to misinformation, affectingpassengersafetyandoperationaldecision-making.
Fig 2: DataFlowandPre-Processing
Implementing AI in railway networks requires significant computationalpowerandinfrastructure.Thedeploymentof real-timeAImodelsdemandshigh-performancecomputing resources, cloud storage, and advanced edge computing solutions. Many railway authorities may find it financially challenging to adopt AI-driven systems due to the initial investmentinupgradingexistinginfrastructure.Additionally, integrating AI-based solutions into legacy railway systems requires seamless compatibility, often requiring extensive modificationsandretrainingofoperationalstaff.Resistance
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
toadoptingnewtechnologiesandlackoftechnicalexpertise furtherhindersmoothimplementation.
3.2 Scalability
It is another limitation, as AI models trained on specific railwaynetworksmaynotperformefficientlywhendeployed in different regions with varied passenger behaviours and operational policies. The accuracy of predictive models dependsheavilyonthequalityoftrainingdata,whichmay differsignificantlyacrosslocations.Furthermore,thereliance on IoT devices and cloud-based analytics introduces dependenciesonuninterruptedinternetconnectivity,making system performance susceptible to network failures. In developing countries, where railway infrastructure is not technologically advanced, implementing AI-powered monitoringsolutionsremainsasignificantchallenge.
3.3 Ethical Considerations
Additionally,ethicalconsiderationsplayaroleindetermining thefeasibilityofAI-drivenrailwayoccupancyprediction.The implementationofAI-basedmonitoringmayraiseconcerns regardingcontinuoussurveillanceofpassengers,potentially leadingtoa debateonprivacyversusefficiency.Thereisa fine balance between utilizing AI for optimizing railway operationsandensuringthatpassengerrightsandfreedoms are not infringed upon. Public acceptance of AI-driven solutionsiscrucialforlarge-scaleadoption,andtransparency indatacollectionandprocessingmustbemaintainedtobuild trust.
3.3 Reliability Of Predictive Analytics
Anotherlimitationisthereliabilityofpredictiveanalyticsin handlingunexpectedpassengerfluctuations.WhileAImodels can accurately forecast crowd density based on historical data, unforeseen circumstances such as festivals, political rallies,orsuddendisruptionscanleadtoinconsistenciesin predictions. AI models must be adaptive and capable of respondingdynamicallytosuchanomaliestoensurerobust decision-making. Moreover, AI-powered scheduling adjustmentsmustbetestedextensivelytoensurethatthey donotcreatedelaysorimbalancesinserviceefficiency.
Inconclusion,whileAI-drivenrailwayoccupancyprediction offersnumerousbenefits,challengessuchasdataaccuracy, security, infrastructure costs, scalability, and ethical considerationsmustbecarefullyaddressed.Thesuccessful implementation of AI in railway management requires collaborationbetweengovernments,railwayauthorities,and technology providers to create an efficient, secure, and privacy-compliantsolution.Futureresearchshouldfocuson developing more adaptable AI models, enhancing cybersecurity protocols, and ensuring cost-effective deploymentstrategiestoovercometheselimitations.
4. BENEFITS OF OUR PROJECT
The SmartRail system offers multiple benefits for both railway operators and passengers. By providing real-time crowd density estimates, it enables efficient crowd management, allowing passengers to choose less crowded coachesandimprovingtravelcomfort.AI-basedmonitoring ensures that train occupancy data is updated in real-time, allowing railway authorities to implement dynamic schedulingtoaccommodatevaryingdemand.Thispredictive approachhelpspreventcongestionduringpeakhoursand reduceswaitingtimes,significantlyenhancingthepassenger experience.Furthermore,bybalancingpassengerloadacross different coaches, SmartRail helps prevent unnecessary delaysandminimizesdiscomfortcausedbyovercrowding.
4.1 Optimization Of Train Scheduling
One of the key benefits of the SmartRail system is the optimization of train scheduling. Traditional scheduling modelsfollowstatictimetablesthatdonotaccountforrealtime fluctuations in passenger demand. However, AIpowered predictive analytics can dynamically adjust train frequencybasedonoccupancylevels,reducingthenumberof underutilized trains while increasing service availability during peak hours. This optimization leads to increased operationalefficiency,bettertrainfrequencymanagement, andimprovedresourceutilizationforrailwayoperators.
4.2 Enhancement Of Passenger Safety
PassengersafetyissignificantlyenhancedthroughAI-based monitoring,whichpreventsovercrowding-relatedrisksand facilitates better emergency response mechanisms. Overcrowded train compartments can lead to security hazards, discomfort, and even health risks for commuters. With real-time monitoring of occupancy levels, railway authoritiescanidentifypotentialrisksandtakepreventive measures such as restricting entry to overfilled compartmentsordispatchingadditionaltrains.SmartRailcan also integrate with emergency management systems to provide rapid response strategies in case of incidents, ensuringasafertravelenvironmentforallpassengers.
Fig 3 : ScalabilityUsingTensorFlowRTIntegration
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
4.2 AI-Optimized Scheduling
AnothermajoradvantageofSmartRailisitscontributionto energyefficiencyandsustainability.AI-optimizedscheduling reduces unnecessary train operations, thereby minimizing fuel and electricity consumption. When fewer trains run empty or underutilized, the overall carbon footprint of railway operations is significantly reduced. This makes SmartRailaneco-friendlysolutionthatalignswithmodern sustainable transportation initiatives. Additionally, by preventing congestion and optimizing train occupancy, SmartRailcontributestoasmootherflowofrailwaytraffic, reducingtrainidletimesandoperationalinefficiencies.
SmartRail also includes automated maintenance alerts, allowingrailwayauthoritiestopredictequipmentfailuresin advanceandimplementpreventivemaintenancestrategies. AI-driven predictive maintenance systems analyse sensor data from train components to detect potential failures before they occur. By addressing maintenance issues proactively, railway operators can avoid unexpected breakdowns, reduce downtime, and extend the lifespan of railwayassets.Thisapproachnotonlyenhancesoperational reliability but also leads to long-term cost savings by minimizing expensive emergency repairs and service disruptions.
Insummary,theSmartRailsystemprovidesacomprehensive and innovative solution for modern railway network management. Its ability to manage crowd distribution, optimizescheduling,enhancepassengersafety,andpromote sustainabilitymakesitavaluableassetforrailwayauthorities worldwide.Asrailwaynetworkscontinuetoexpandandface increasing passenger demand, AI-driven solutions like SmartRailwillplayacrucialroleinensuringefficient,safe, andeco-friendlytransportationforthefuture.
5. PROBLEM STATEMENT
Withtherapidincreaseinurbanpopulationsandthegrowing demandforefficientpublictransportation,metroandrailway systems are facing significant challenges in managing passenger distribution effectively. The current train schedulingmodelsarelargelystatic,relyingonpredefined timetablesthatdonotaccountforreal-timefluctuationsin passengerdemand.Asaresult,railwayauthoritiesstruggle withoverfilledcoachesduringpeakhoursandunderutilized trainsduringoff-peaktimes,leadingtoinefficienciesinboth operational costs and service delivery. This imbalance not onlyaffectscommutersatisfactionbutalsoincreasesdelays, overcrowding, and the overall strain on railway infrastructure.
5.1 Lack Of Real-Time Monitoring Systems
Oneoftheprimaryissuesfacedbyrailwaynetworksisthe lack of real-time monitoring systems that can provide accurate occupancy predictions. Traditional passenger
countingtechniquesrelyonmanualobservationsorticketing data,whichdonotreflectreal-timepassengermovements. Without an intelligent system to dynamically allocate resources, trains continue to operate with inefficient load distribution, exacerbating congestion and passenger discomfort.Additionally,unforeseenfactorssuchasweather conditions, special events, or sudden increases in demand furtherdisrupttrainschedules,makingitdifficultforrailway authoritiestomaketimelyadjustments.
5.2 Safety In Densely Packed Railway Systems
Another major challenge is ensuring passenger safety and comfort in densely packed railway systems. Overcrowded coachesnotonlycausediscomfortbutalsoposesignificant risks,includingrestrictedemergencyexits,increasedchances of accidents, and higher exposure to airborne diseases. WithoutanAI-drivenoccupancypredictionsystem,railway operators have limited means to manage these risks effectively. There is a growing need for a system that can track passenger movement, predict crowd levels, and optimizetrainschedulesaccordingly.
5.3 Operational Inefficiencies ~ Running Empty Or Overloaded Trains
Operational inefficiencies also lead to increased fuel and electricityconsumption.Runningemptyoroverloadedtrains withoutstrategicschedulingresultsinunnecessaryenergy expenditure, contributing to higher carbon emissions and increasedcostsforrailwayauthorities.Theenvironmental impactofpoorlymanagedrailwaysystemsunderscoresthe needforamoreintelligentandsustainableapproachtotrain operations.
To address these pressing concerns, an AI-driven railway occupancy prediction system is required. By integrating machinelearningalgorithms, real-time passenger tracking through IoT sensors, and cloud-based analytics, railway networkscanmakedata-drivendecisionstooptimizetrain schedules,allocateresourcesmoreefficiently,andimprove overall passenger experience. This system will not only reducecongestionandwaitingtimesbutalsoenhancesafety, loweroperationalcosts,andcontributetoamoresustainable modeofpublictransportation.
6. OBJECTIVE OF STUDY
The primary objective of this study is to design and implementanAI-poweredoccupancypredictionsystemthat enhances the efficiency of metro and railway networks. Traditionalrailwayoperationsoftensufferfrominefficiencies duetostaticscheduling,lackofreal-timepassengertracking, andsuboptimalresourceallocation.Byleveragingartificial intelligenceandmachinelearningtechniques,thisstudyaims to provide railway authorities with an advanced system capable of dynamically adjusting train schedules based on real-time passenger demand. This will help reduce
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
congestion, improve train frequency management, and enhance overall passenger experience. Additionally, the system is expected to enable more effective crowd distribution by guiding passengers toward less crowded coaches,ultimatelyleadingtoasaferandmorecomfortable commutingexperience.
6.1 Sustainability In Railway Operations
Another key objective of this research is to improve sustainability in railway operations by optimizing energy consumption and resource utilization. Running empty or overcrowded trains leads to excessive energy usage and higheroperationalcosts.Byemployingpredictiveanalytics, theSmartRailsystemcanassistinreducingunnecessarytrain operationsandensurethatenergyisutilizedmoreefficiently. The study also seeks to integrate IoT-enabled smart monitoringdevices,suchasinfraredsensorsandcomputer vision-based CCTV analysis, to collect real-time passenger datawithhighaccuracy.Furthermore,thedevelopmentofa centralized cloud-based dashboard will allow railway authorities to monitor, analyse, and make data-driven decisionstoimproveoperationalefficiency.Theultimategoal is to create a scalable and adaptable solution that can be implemented in metro systems worldwide, ensuring seamless,safe,andsustainableurbantransportation
7. WORKING PRINCIPLE
TheSmartRailsystemintegratesAI,IoT,andcloudcomputing tomonitorpassengeroccupancyinreal timeandoptimize train schedules dynamically. It collects data from CCTV cameras, infrared sensors, and RFID scanners, which are processed using machine learning algorithms. These algorithms analyse passenger movement patterns, detect overcrowding,andpredictfutureoccupancylevels.Basedon thisanalysis,thesystemsuggestsreal-timeadjustments,such as increasing train frequency, directing passengers to less crowded coaches, or optimizing station entry points to balancecrowddistribution.
Once the data is analyzed, SmartRail automates decisionmaking by providing railway authorities with actionable insights via a centralized dashboard. If overcrowding is detected,thesystemissuesalertsandrecommendsschedule modificationstopreventcongestion.Conversely,duringlowdemand periods, it optimizes train frequency to reduce energyconsumptionandoperationalcosts.Bycontinuously learning from passenger behavior, SmartRail refines its predictions over time, enhancing railway efficiency, improvingpassengercomfort,andensuringaseamless,datadriventransitexperience.
7.1 Modes of Detection in Model
This mode processes static images to identify and classify objects, extracting key features for analysis. It applies computervisiontechniquestodetectpatternsandanomalies.
Thismodeanalyzesrecordedvideostreams,detectingand tracking objects over time. It helps in extracting motion patternsandidentifyingspecificevents.
Fig 4: AutomatedPACBEDImageProcessingUsingCNN
Fig 5:StaticImageAnalysisMode
Fig 6: RecordedVideoAnalysisMode
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
Fig 7: Real-TimeVideoAnalysisMode
Thismodeprocessesreal-timevideostreamstodetectand analyze objects dynamically. It enables instant decisionmakinginsurveillanceandautomationapplications.
Fig 8: ObjectDetectionwithAccuracyMetrics
Thismodeappliesmachinelearning-basedobjectdetection, assigningconfidencescorestodetectedobjects.Itenhances accuracybyprioritizinghigher-weightedpredictions.
8. CONCLUSIONS AND FUTURE SCOPE
The implementation of SmartRail presents a significant advancementinrailwayandmetrosystemmanagementby integrating AI-driven occupancy prediction and real-time monitoring. The system effectively enhances passenger experience,optimizestrainscheduling,andensuresbetter resource utilization. By addressing issues such as overcrowding,inefficientscheduling,andhighoperational costs, SmartRail contributes to a more reliable and sustainablepublictransportationsystem.Theintegrationof predictive analytics enables railway operators to make informed decisions that improve service efficiency while minimizingdelaysandcongestion.Additionally,theAI-based monitoring system ensures a safer and more comfortable travel experience by mitigating risks associated with overfilled compartments and unbalanced passenger distribution.
Lookingahead,futuredevelopmentsinSmartRailwillfocus on enhancing the accuracy and scalability of AI-driven predictionsthroughimprovedmachinelearningalgorithms and advanced sensor technologies. The integration of blockchain-based data security measures can further enhance the reliability of passenger tracking and privacy protection. Expanding the system to multimodal transportation networks, including buses and trams, will enable seamless and interconnected urban transit. Additionally, incorporating augmented reality (AR)-based navigation systems for passengers and AI-driven ticket pricing strategies could further revolutionize the public transportationexperience.Asurbanpopulationscontinueto grow, SmartRail's continuous advancements will play a crucial role in shaping the future of intelligent and sustainabletransportsystemsworldwide.
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BIOGRAPHIES
AryanAgney 3031022222341
B.Tech CSE
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
ShubhamGupta 3031022222302
B.TechCSE
PawashSharaf 303102221043
B.TechCSE
Ms.MuskanNaik
AsstProfessorLCIT Dept.CSE