Intelligent urban transport tracking and management system

dc.AffiliationOctober University for modern sciences and Arts (MSA)
dc.contributor.authorMohamed S.I.
dc.contributor.otherUniversity for Modern Sciences and Arts MSA
dc.contributor.otherEgypt
dc.date.accessioned2020-01-09T20:40:30Z
dc.date.available2020-01-09T20:40:30Z
dc.date.issued2019
dc.descriptionScopus
dc.description.abstractEgypt�s current bus system is large enough to satisfya significant portion of the populations demand, yet fails to do sodue t o mismanaged resources. Static lines and unclear schedulescreate a confusing and unappealing user experience which pushesmore of the population to cars for their transportation needs. Thisclearly leads to more congested streets which result in a net lossof productivity as well as an inc rease in stress, unnecessary fuelconsumption, and harmful emissions. An intelligent bus solutionis multi-faceted. It consists of (1) connected buses which arecapable of providing their geo-location data, feedback aboutdriving behavior, and health data to detect fail ures before theyoccur; (2) cashless payment through RFID cards to ensure muchtighter control over pricing; (3) a processing server or cloud, inwhich all of the incoming data would be handled; (4) knowledgesystems which dynamically optimize bus schedules and routesthrough learning algorithms; (5) and a mobile application tocapture demand and inform passengers of bus arrival times.The main functions and algorithms of the proposed systemare achieved based on machine learning algorithms and webtechnologies, whilst the hardware component is implementedbased on System-on-Chip technology with custom hardware tointerface with the vehicle. This paper will focus onthe software component of the proposed solution. It is shown that byapplying the proposed system to a previously static bus systemthat fuel consumption, maintenance costs, and carbon emissionscan be reduced by 10-20% while overall passenger satisfactionis increased. �2019.en_US
dc.description.urihttps://www.scimagojr.com/journalsearch.php?q=21100894501&tip=sid&clean=0
dc.identifier.issn22778616
dc.identifier.urihttps://t.ly/b29YG
dc.language.isoEnglishen_US
dc.publisherInternational Journal of Scientific and Technology Researchen_US
dc.relation.ispartofseriesInternational Journal of Scientific and Technology Research
dc.relation.ispartofseries8
dc.subjectNeural networksen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectUrban transportationen_US
dc.subjectVehicle routing problemen_US
dc.subjectWeb applicationen_US
dc.titleIntelligent urban transport tracking and management systemen_US
dc.typeArticleen_US
dcterms.isReferencedByNunes, A., Dias, T.G., Cunha, J.F., Passenger Journey Destination Estimation from AutomatedFare Collection System Data Using Spatial Validation IEEE Transactions on Intelligenttransportation Systems. Forthcoming; (2018) 'Egyptian Start-Up Swvl Raises USD 8 Million in Series Afunding Round''; (2018) ''KPIT � On-Bus Intelligent Transport System'', , https://www.kpit.com/what-wedo/products/on-bus-its, KPITTechnologies Ltd, Available; Hamilton, B., (2016) History of Intelligent Transportation Systems, , U.S. Departmentof Transportation; Matters, T., Facts & figures (2018) Transport for London, , https://tfl.gov.uk/corporate/about-tfl/what-we-do/londonunderground/facts-and-figures, Available; (2018) ''Seoul Transport Operation & Information Service, , http://topis.seoul.go.kr/eng/page/transInfo11.jsp, Topis.seoul.go.kr, Accessed: 02-Dec-2018; Angelakis, V., Tragos, E., Pohls, H., Kapovits, A., Bassi, A., (2017) Designing, Developing, and Faciliating Smart Cities; Bhusiri, N., Qureshi, A.G., Taniguchi, E., The trade-offbetween fixed vehicle costs and time-dependent arrival penalties in arouting problem (2014) Transportation Research Part E, 62, pp. 1-22; Bogdanov, V., (2018) ''How to Choose the Right Tech Stack for Yoursoftware Development Project, , https://intersog.com/blog/tech-tips/how-to-choose-the-right-tech-stackfor-your-software-development-project/, Welcome to Intersog YourApp Development Partner in Chicago; Pirson, C., (2017) Optimisation of a Demand-Responsive Transit System, , MasterThesis, University of Lige; Czarnowski, I., Caballero, A., Howlett, R., Jain, L., (2017) Intelligentdecision Technologies 2017, , Springer; Yahyaoui, H., Kaabachi, I., Krichen, S., Dekdouk, A., Two metaheuristicapproaches for solving the multi-compartment vehicle routingproblem (2018) Operational Research; Landwehr, N., Hall, M., Frank, E., Logistic Model Trees (2005) Machinelearning, 59 (1-2), pp. 161-205; Pornsing, C., (2014) ''A PARTICLE SWARM OPTIMIZATION FOR THEVEHICLE ROUTING, , Ph.D, UNIVERSITY OF RHODE ISLAND; Samaras, P., Fachantidis, A., Tsoumakas, G., Vlahavas, I., A predictionmodel of passenger demand using AVL and APC data from a bus fleet (2015) Proceedings of the 19Th Panhellenic Conference on Informatics � PCI�15; Rohrer, B., (2017) ''Brandon Rohrer-Instructor-End-To-Endmachine Learning, , https://brohrer.github.io/blog.html, Brohrer.github.io, Online; Kachitvichyanukul, V., Sombuntham, P., Kunnapapdeelert, S., Twosolution representations for solving multi-depot vehicle routing problemwith multiple pickup and delivery requests via PSO (2015) '', Computers& Industrial Engineering, 89, pp. 125-136. , Available:10.1016/j.cie.2015.04.011; Fan, W., Machemehl, R., Using a Simulated Annealing Algorithmto Solve the Transit Route Network Design Problem (2006) Journal of Transportationengineering, 132 (2), pp. 122-132; Karpathy, (2015) ''The Unreasonable Effectiveness of Recurrentneural Networks'', , https://karpathy.github.io/2015/05/21/rnn-effectiveness/, Karpathy.github.io; Umrao, P., (2015) Intelligent Transportation System, , Institute of Engineering & Technology, Lucknow; Yang, Q., Wang, L., Xia, W., Wu, Y., Shen, L., Development of on-board unit in vehicular adhoc network for highways (2014) International Conference on Connected Vehicles and Expo(Iccve), 2014, pp. 457-462. , Vienna; Matteo, P., Pagano, P., Riccardo, P., Marco, G., Claudio, S., Nastasi, C., On-Board Unit hardware and software design for Vehicular Ad-hoc Networks (2012) Roadside Networks for Vehicular Communications: Architectures, Applications, and Test Fields; Beyer, G., (1999) Kontroll�berzeugung Im Umgang Mit Technik, 9, pp. 648-693. , Report Psychologie; Karim, N.A., Nwagboso, C., ``Assistive technologies in public transport: Meeting the needs of elderly and disabled passengers (2004) Proceedings of the International Conference on Information and Communication Technologies: From Theory to Applications, p. 69. , New York: IEEE Press; May, A.J., Ross, T., Bayer, S.H., Tarkiainen, M.J., ``Pedestrian navigation aids: Information requirements and design implications (2003) Personal and Ubiquitous Computing, 7 (6), pp. 331-338; Lyons, G., The role of information in decision-making with regard to travel, (2006) Intelligent Transport Systems, 153 (2), pp. 199-212; Foth, M., Schroeter, R., Enhancing the experience of public transport users with urban screens and mobile applications (2010) Proceedings of the 14Th International Academic Mindtrek Conference, pp. 33-40. , New York: ACM; Schmidt, T., Philipsen, R., Ziefle, M., From v2x to control2trust-Why trust and control are major attributes in vehicle2x technologies International Conference on Human Computer Interaction; van Heek, J., Arning, K., Ziefle, M., (2014) `` Safety and Privacy Perceptions in Public Spaces: An Empirical Study on User Requirements for City Mobility,'' Internet of Things Summit, International Conference on Mobility and Smart Cities, , Berlin Heidelberg: Springer; Tarapiah, S.,
dcterms.sourceScopus

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