Proceedings of 1st Shri Chhatrapati Shivaji Maharaj QIP Conference on Engineering Innovations Organized by Shri. Chhatrapati Shivaji Maharaj College of Engineering, Nepti, Ahmednagar In Association with JournalNX - A Multidisciplinary Peer Reviewed Journal, ISSN No: 2581-4230 21st - 22nd February, 2018
IOT BASED PREDICTION OF BUS ARRIVAL TIME USING GPS SYSTEM Prof. Pritesh Patil1, Manasi Jain2, Pranjal Jain3, Suyog Choudhary4 1(Information Technology, AISSMS IOIT/SPPU, India) 2(Information Technology, AISSMS IOIT/SPPU, India) 3(Information Technology, AISSMS IOIT/SPPU, India) 4(Information Technology, AISSMS IOIT/SPPU, India) ABSTRACT : While selecting for the public-transport, time and forbearance square measure of a lot of concern . we will additionally say , human traveling on public-transport found their loss of your time attributable to waiting at the bus-stops. this method is providing a time period vehicle pursuit via Global-Positioning System technology to find the situation of bus and to use web by a generalpacket-radio service (GPRS) technology to show live pictures on the Google Map or web site app for pursuit location of buses anytime. we have a tendency to square measure exploitation the GPS and GPRS modules, the GPS module can find the buses via the satellite, and also the GPRS module can collect all knowledge and send it to the web site . The buses are monitored live exploitation coordinates with this method. additionally by implementing geo-fence, user get inform once bus arrived in his/her predefined space. we have a tendency to square measure developing an Android-application which can provide the time period schedule of buses. Also, it provides fast and time period replay for inquiry, via the server. additionally just in case of any sudden activities or breakdown, the alert are sent to the system, with Bus location. KEYWORDS - GPRS, GPS, google map, geofence, bus failure. 1. Introduction The transportation system provide as the heart in the social and economic-growth of the country. because the population in Asian country is increasing, speedy explode in rate of vehicles which ends in an exceedingly overload on trafficmanagement. . Public transport is changing into a very important a part of transport system in urban areas, advance in simply obtainable technology may be enforced that facilitate the traveller who compute between a rural and concrete to-get the travel data and it helps the passengers to comfort them with the ultimate real time location. Public transport principally the bus sluice has been properly developed in several elements of the globe. For reducing the fuel-usage, snobbish usage of automobile and luxury traffic situation we are able to use the bus services. Passengers needs the precise schedule of buse. The anxiety of passengers will increase whereas watching for a long-time at the stop and alter their mind to select the buses. several passengers square measure typically ontime for workplace and plenty of students restarted to their categories as they confirm to prevent for
the buses instead-of taking associate alternative mode of transportation. Goal of system is to decrease the complexness and price of content these services by implementing Easy-Tracker, associate automotive vehicle system for the transittracking and advent time prediction While going out for the public transportation, time-patience are of more concern. Many passengers travelling via public-transport buses realizes time-loss due to waiting at the bus-stops. We implementing a realtime vehicle tracking-system using a GPS technology to obtain the location of the buses and to connect it to internet by a general-packet-radio service (GPRS) technology for displaying a realtime update on the web-map by Google which allows all time tracking of buses. The GPS-module will detect the buses using satellite, and the GPRSmodule will collect the data and transfer it to the website. The buses will be monitored live with real time coordinates with this system. We are developing an android-application which will give real time schedule of buses. . Also in the case of any unexpected activities or breakdown, the alert will be given to the system, with Bus location. A geofence is a virtual-perimeter used on a real-world geographic area.[1] A geo-fence which is dynamically generated, as in a radius around a person or a point on the map, like school attendance-zones or neighborhood boundaries. This activity could trigger an notification to the device's user as-well-as alert to the passenger. 2. Literature survey A] The work projected by Zehra Naqvi. [1] titled “Smart Public-Transport System mistreatment mobile primarily based sensing” . per her the great circle and latitude values area unit analyzed for the gap of bus from destination and for estimating schedule of bus[1]. This data is shipped to the server that informs the passengers having the good phones to access this knowledge from anyplace. The limitation of their work is that, it'll not create user’s participation mandatory within the system. this method incurs high value of installation of GSMGPS parts within all buses. And this method is complicated additionally, because it needs different-different hardware modules .Moreover, it doesn’t need further location trailing devices to be put in on every bus. it's quite potential that there might not be several users willing to participate during this system because it will cause security threats. So, it 13 | P a g e
Proceedings of 1st Shri Chhatrapati Shivaji Maharaj QIP Conference on Engineering Innovations Organized by Shri. Chhatrapati Shivaji Maharaj College of Engineering, Nepti, Ahmednagar In Association with JournalNX - A Multidisciplinary Peer Reviewed Journal, ISSN No: 2581-4230 21st - 22nd February, 2018 depends on the group, that may be a major pair of.5 times higher than ANN regression limiting issue. model and a pair of times higher than SVM regression model and achieving the accuracy B] The work projected by Qiang Zhang1 et.al. [2] seventy five.65%. titled “EasyComeEasyGo: Predicting bus point with sensible phone”. In transit system time D] The work projected by B. Dhivyabharathi period is most generally used variable as a result et.al. [4] titled.” Actual Bus point in time of it are often simply understood by the Prediction-System below Indian Traffic individuals. From the transport company’s facet Condition”. The recent study projected a model time period info is beneficial in routing and based mostly prediction algorithmic program programming of buses. Hence, there's would like that used particle filtering technique, whose to develop a period of time bus point prediction inputs area unit obtained by k-NN algorithmic algorithms that may offer a lot of correct info program, to predict bus travel times and beneath now-a-days conditions. The bus point schedule below completely different traffic prediction may be a terribly advanced conditions that exist in India. The results downside, that involves several factors touch received were compared with the present random. the essential knowledge we tend to spatially discretized model based mostly accustomed predict bus point as well as bus approach and actual time period obtaining from numbers, real time, traveler count, longitude, the GPS fitted buses. conjointly the developed latitude and next station variety. The back-end model was capable to investigate the high server receives the data from all sensible phones variability condition of Indian traffic. Hence, it that are already put in in bus so provides the bus may be complete that developed model may be point prediction. once travelers use sensible viable one to implement for prediction of arrival phone log in ECEG system and sends its GPS times below extremely variable heterogeneous references and therefore the route variety traffic condition. The estimation accuracy is wished to the back-end server, back-end server found to be higher than the present technique can sends back point of the bus wished. By with MAPE values around 17 November with victimization this technique, travelers will the accuracy of two minutes. modify their travel plans supported these info, and that they will save their time and head to E] The work planned by Santa Maiti, et.al. [5] their destination as quickly as potential. The GPS titled.” Historical information based mostly Real references of discovered stop, that significantly Time Prediction of auto Arrival Time”. during reduces the initial construction overhead. this paper we've got self-addressed business Querying user. Our system implementation is transportation connected vehicle point that there exist a backend server associate prediction downside, specially applicable for degreed a wise phone put in in bus and an developing countries wherever convenience of robot-app for traveler The limitation of their adequate and correct information may be a work is that the android app put in within the massive challenge To predict bus point, we've smartphone can’t offer the live info of the got planned an easy, light-weight historical unconditional activities or factors which can information based mostly model. Analyzing the influence the bus schedule self-discipline. performed result we tend to understand that the historical information based mostly model C] The work planned by Rubina Choudhary et.al. retains prediction accuracy in restricted dataset [3] titled” period of time Predicting Bus regular by considering each location and time element. arrival-Time A Review” Prediction of point of The developing model, location element bus by exploitation a synthetic neural captures location wise vehicle speed which network(ANN) and kernel filter.Time index, bus incorporates road condition, dwell time and delay, point, travel time is taken into account the time element captures time wise vehicle speed most parameters with these models for which incorporates traffic congestion the prediction of schedule point. By finding out current model is capable to predict point of a information assortment has done through vehicle in a very stop once point of the vehicle in historical-global-positioning (GPS) and to avoid previous stop is givenThe planned models missing information assortment through GPS, conjointly need longer coaching time to Automatic-fare assortment (AFC) system. It reinforce the prediction accuracy.Though the works in 2 elements that ar location and time experimental result's quite promising, we'd like wise elements. Location options capture the to contemplate a extended vary of information placement wise vehicle speed that thought(throughout a year) to look at the performance about the road conditions, dwell time. of the planned model over weather modification. Timestamp feature capture the time wise F] The work planned by Wenping Liu, et.al. [6] vehicle speed, includeing the traffic conjointly. titled.” WiLocator: WiFi-sensing primarily based The works establish that HD model performs a period Bus Live chase and point in time 14 | P a g e
Proceedings of 1st Shri Chhatrapati Shivaji Maharaj QIP Conference on Engineering Innovations Organized by Shri. Chhatrapati Shivaji Maharaj College of Engineering, Nepti, Ahmednagar In Association with JournalNX - A Multidisciplinary Peer Reviewed Journal, ISSN No: 2581-4230 21st - 22nd February, 2018 Prediction in railway system Environments".We the ETA efficiently.limitation of their system is style a theme to leverage the quality constraint that the buses don't seem to be connected to of a bus, and also the travel-time consistency of every alternative since they can’t show the live buses on an equivalent road phase. we have a tarffic update.For achieving the higher results of tendency to instantly track a bus by their system they have to create Associate in victimisation the scanned WiFi data accessible Nursing androide application to produce the for bus riders WiLocator is by no means that important time schedule of the buses exclusive; it will proper integrate with GPS or Cell-ID primarily based location systems. for example, once a smartphone scans no WiFi data for a short while, the GPS-module is activated so the system will adaptively work from WiFicoverage areas to GPS viable environments.In addition to the applications for bus arrival-time prediction and traffic map generation, we have a tendency to additionally envision that the planned SVD has the potential for facilitating navigation in railway system environments wherever associate degree inaccurate positioning of the vehicle may result in a wrong flip instruction. G] The work projected by Mathieu Sinn, et.al. [7] titled. " Predicting arrival times of buses exploitation period GPS measurements".This paper demonstrate the sturdy performance of Kernel Regression algorithms for the prediction of bus arrival times, that clearly shell regression or K-Nearest Neighbor approaches. this can be the first paper learning the selection of interpolation points to scale back the dimensions of historical knowledge sets, and predictions supported period updates at on an irregular basis spaced locations. In our future work, we tend to decide to investigate more methods to scale back the procedure burden, each in terms of memory and procedure time. Note that computing Kernel Regression predictions needs linear time within the size of the coaching set. Hence, we tend to decide to study however coaching knowledge will be efficiently compressed to find a decent trade-off between computation time and accuracy of the predictions. more queries square measure a way to assess the uncertainty of the time of arrival predictions (e.g., by computing confidence intervals), and the way to integrate extra regression variables. H] The work planned by Sharad S, et.al. [8] titled. " The sensible Bus for a sensible town - A time period implementation “a model to create buses communicate to the commuters in an exceedingly sensible town system has been planned and developed. this method uses net of Things to make the system to attach the bus to the web and therefore, to the commuter and transport managers. With Artificial Neural Networks and applied mathematics Modeling techniques, algorithms and models area unit designed from historical knowledge to predict
Figure 1:Working structure of system I] The work planned by Pengfei Chou dynasty, et.al. [9] titled. " however Long to Wait? Predicting Bus point in time With transportable primarily based democratic Sensing ". during this paper, we tend to gift a crowd-participated bus arrivaltime prediction system. Primarily hoping on inexpensiveand wide out there cellular signals, the planned sys-tem provides efficient solutions to the matter. Wecomprehensively valuate the system through associate Androidprototype system. Over a 7week experiment amount, theevaluation results demonstrate that our system will accu-rately predict the bus point in time. Being freelance ofany support from transit agencies and site services, theproposed theme provides a versatile framework for par-ticipatory contribution of the community. Limitation is that the variety of sharing passengers affects the accuracy of predicting time in our system. J] The work planned by Ta-Sheng Kuan et.al. [10] titled. " A extremely economical technique of Mobile Positioning supported industrial Vehicle Operation knowledge "A extremely economical technique that deals with mobile positioning of vehicles is planned to collect and analyze the cellular-network signals of CVO knowledge. The planned includes style of parallel computing Associate in Nursingd cloud computing techniques for mobile positioning technique to quickly confirm the placement of an OBU for CVO. A case study determined that 15 | P a g e
Proceedings of 1st Shri Chhatrapati Shivaji Maharaj QIP Conference on Engineering Innovations Organized by Shri. Chhatrapati Shivaji Maharaj College of Engineering, Nepti, Ahmednagar In Association with JournalNX - A Multidisciplinary Peer Reviewed Journal, ISSN No: 2581-4230 21st - 22nd February, 2018 the final location errors mistreatment the (traffic, breakdown, unhealthy weather) is verify planned technique and also the ancient cell-IDimpromptu from straightforward GPS traces. based technique were 163.7 m and 521.2 m, victimisation Geofencing technology , defines a severally. The signals of neighboring cells will virtual boundary around a real-world geographical be analyzed at the same time to enhance mobile area. Geofencing allows automatic notification to be positioning. generated based on the defined coordinates of a geographic area. Passenger can determine exactly 3.Limitation of existing system when to leave the home or the office and As per the survey meted out the data uses the eliminating their waiting time. latitude and meridian parameters of the GPS to The methodologies or functionalities if the get the bus location, they involve the installation modules in the system architecture of many modules within the buses[1,2] that diagram are as fallows: results in a fancy and expensive a. Android app: This component of the implementation. Prediction of bus point is system which enable the user and achieved by many algorithmic rules like kNN driver to set their location ,identity and algorithm [4], kernel regression[7],linear the routes they are travelling on. regression[8].They have used historical Driver app will update the regular data knowledge that is obtained by machine learning on the server and in turn the user also. algorithmic rule to predict the vehicle point and At the same time user finds all the site accurately. however this historical available bus on his desired route at his knowledge based mostly model retains convenient schedule via the same app prediction accuracy for restricted dataset that itself. considers the situation and time b. Admin portal :- Access to the database component[5][3].In a number of the system they of the system to make any required need U.S.A.ed Artificial Neural Network changes for the live time update and Modeling technique {in that|during route of the buses as per the which|within which} buses aren't connected to information provided by the driver and every alternative attributable to which it doesn't send the appropriate information to the offer us with live traffic update. Some system user app. that involves crowd participation [9] provides c. Web server :- It accepts all the U.S.A. with versatile framework to community requests from both ends i.e. user and ,but the amount of sharing passengers will driver app. It in turn provide response increase can have an effect on the accuracy to the user and driver app as per the within the system. instruction provided by the admin portal. Admin portal 4. Proposed system d. Database :-Initially it store static time The limitations that features sudden bus point, table of the buses running on the unknown information regarding the breakdown respective routes. Also store android’s has given United States the intuition for developing app data. the reliable system which will inform the passengers regarding the point of their destination bus. By victimisation the Geo-fence in system that determines the nearby-location of the bus so the traveller don't have to be compelled to wait Associate in Nursing undetermined quantity of your time at probably unpleasant or unsafe stop. traveller will simply get the arrival info remotely on causing a SMS to the server or the prediction system. System can respond the estimation of bus live schedule or failure or breakdown at the side of location to traveller in SMS .In our advise system we tend to gift effective approach of predicting bus schedule supported user location victimisation GPS technology. The design diagram fig:4.1 contain 5 modules having severally vital role within the economical operating of the system The advise system eliminate overhead drawback at the side of providing platform operator to watch ,bus standing and update latest info to the user. we've got establish however lofty knowledge like route, Figure 2: Architecture diagram of system stops, transmit schedule and blooming factors 16 | P a g e
Proceedings of 1st Shri Chhatrapati Shivaji Maharaj QIP Conference on Engineering Innovations Organized by Shri. Chhatrapati Shivaji Maharaj College of Engineering, Nepti, Ahmednagar In Association with JournalNX - A Multidisciplinary Peer Reviewed Journal, ISSN No: 2581-4230 21st - 22nd February, 2018 5. CONCLUSION We are developing an android application for the passengers as well as for the bus driver. The system dynamically create geofence which notifies the passengers about live location of bus. This system is self-calibrating and functions everywhere and doesn’t require any lab or manmade environment. Having a GPS is truly an advantage you can determine your location. REFERENCES 1) Zehra Naqvi “Smart Public Transport System Using Mobile phone based sensing” IEEE INDICON 2015 1570187213. 2) Qiang Zhang1, Yanhu Zhang2.” EasyComeEasyGo: Predicting bus arrival time with smart phone” 2015 Ninth International Conference on Frontier of Computer Science and Technology. 3) Rubina Choudhary, Aditya Khamparia.” Real Time Prediction of Bus Arrival Time A Review”. 2016 2nd International Conference on Next Generation Computing Technologies (NGCT-2016) Dehradun, India 14-16 October 2016. 4) B. Dhivyabharathi, B. Anil Kumar. " Real Time Bus Arrival Time Prediction System under Indian Traffic Condition." 2016 IEEE International Conference on Intelligent Transportation Engineering. 5) Santa Maiti, Arpan Pal." Historical Data based Real Time Prediction of Vehicle Arrival Time". 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC) October 8-11, 2014. Qingdao, China 6) Wenping Liu∗†‡, Jiangchuan Liu. " WiLocator: WiFi-sensing based Real-time Bus Tracking and Arrival Time Prediction in Urban Environments" 2016 IEEE 36th International Conference on Distributed Computing Systems. 7) Mathieu Sinn, Ji Won Yoon. " Predicting arrival times of buses using real-time GPS measurements" 2012 15th International IEEEConference on Intelligent Transportation Systems Anchorage, Alaska, USA, September 16-19, 2012 8) Sharad S, Bagavathi Sivakumar P,." The Smart Bus for a Smart City - A real-time imple'mentation" sharad.sriram@gmail.com, pbsk@cb.amrita.edu,v ananthanarayanan@cb.amrita.edu 9) Pengfei Zhou, Student Member, IEEE," How Long to Wait? Predicting Bus Arrival Time With Mobile Phone-Based ParticipatorySensing" IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 13, NO. 6, JUNE 2014 10) Ta-Sheng Kuan , Kuen-Rong Lo," A HighEfficiency Method of Mobile PositioningBased on Commercial Vehicle-Operation Data",Journal 17 | P a g e