smart traffic management system research paper

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Sustainable traffic management for smart cities using internet-of-things-oriented intelligent transportation systems (its): challenges and recommendations.

1. Introduction

1.1. background of the study, 1.2. traffic management in smart cities, 1.3. internet of things (iot)-based intelligent transportation system, 1.4. applications of the intelligent transportation system in smart cities, 1.4.1. detecting transportation incidences, 1.4.2. automated ramp control system, 1.4.3. traffic signal management, 1.4.4. effective parking management tools in smart cities, 1.4.5. demand-responsive transport management (drtm), 1.4.6. logistics management, 1.4.7. special provision to vulnerable road commuters, 1.4.8. route guidance, 1.4.9. cooperative perception, 1.5. platooning, 2. models and advanced ai algorithms for analysis of traffic in smart cities, 2.1. models for analysis of traffic in smart cities, 2.2. application of an advanced al algorithm to smart cities’ operation, 3. traffic management as a decision-making process, 3.1. installation of inductive loop detectors and short-range communication, 3.2. short-range communication, 3.3. pedestrian detection systems.

• Physical layer: This consists of physical parts of the systems, which are composed of smart devices and agents which are normally located within strategized locations along the roads for sensing, recording, and collecting information from roads, road users, and vehicles, and these data and information will be uploaded to the cloud with the help of a strong network connection.
• Network layer: Uploading and transmitting specified data of interest by the traffic officials is carried out by using a network layer; the uploaded data can be used to give a wider range of applications to road users.
• Application layer: This is usually a software which feeds with the information received from the first and second layers to assist road users with the real-time traffic condition of the cities.

4. Framework/Performance Measures for the Proper Management of Traffic in Smart Cities

4.1. land use visioning/scenario planning, 4.2. long-term transportation planning, 4.3. corridor studies programming, 4.4. environmental review and performance monitoring, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Musa, A.A.; Dulawat, S.; Saleh, K.T.; Alhassan, I.A. Applicability of Utilizing Blockchain Technology in Smart Cities Development Smart City Infrastructure: The Blockchain Perspective ; Scrivener Publishing LLC: Beverly, MA, USA, 2022; pp. 317–340. [ Google Scholar ]
• Moir, E.; Moonem, T.; Clark, G. What Are Future Cities? Origin Meaning and Uses Complied the Business of Cities for the Foresight Future of Cities Project and Future Cities Catapult ; Government Office of Science: London, UK, 2014.
• Simpson, P.; Rocque, M. Smart Cities: Understanding the Challenges and Opportunities Smart Cities World in Association with Philips. 2017. Available online: www.smartcitiesworld.net (accessed on 3 February 2023).
• United Nations Sustainable Development Goal. Transforming Our World: The 2030 Agenda for Sustainable Development. In Proceedings of the UN Sustainable Development Summit, New York, NY, USA, 25–27 September 2015; Available online: https://sdsg.un.org (accessed on 3 February 2023).
• Chakwizira, J. The question of road traffic congestion and decongestion in the greater Johannesburg area: Some perspectives. In Proceedings of the SATC 2007—26th Annual South African Transportation Conference, Pretoria, South Africa, 9–12 July 2007; Challenges Implementation Policy. pp. 499–511. [ Google Scholar ]
• World Population Prospects, The 2015 Vision: Key Findings and Advance Tables ; Department of Economics and Social Affairs (Population Division), United Nation: New York, NY, USA, 2015.
• World Bank. Available online: www.worldbank.org (accessed on 3 February 2023).
• IEA, World Energy Outlook 2, International. Energy Agency, 2018. Available online: https://www.iea.org/reports/world-energy-outlook-2018 (accessed on 7 February 2023).
• Abduljabbar, R.; Dia, H.; Liyanage, S.; Bagloee, S.A. Applications of artificial intelligence in transport: An overview. Sustainability 2019 , 11 , 189. [ Google Scholar ]
• World Health Organization. Global Status Report on Road Safety 2018 ; World Health Organization: Geneva, Switzerland, 2018.
• Dresner, P.S.K. A Multiagent approach to Autonomous Intersection Management. J. Artif. Intell. Res. 2008 , 31 , 591–656. [ Google Scholar ]
• Troutbeck, R. Modelling Signalized and Un-Signalized Junctions. In Handbook of Transport Modelling Article Information ; Emerald Group Publishing Limited: Bingley, UK, 2016; pp. 443–460. [ Google Scholar ]
• Khallouk, A.; Echab, H.; Ez-Zahraouy, H.; Lakouari, N. Traffic flow behaviour at Un-signalized intersection with crossing pedestrians. Phys. Lett. A 2018 , 382 , 566–573. [ Google Scholar ]
• Lee, R.; Blogg, M.; Myers, E.; Kyte, M.; Dixon, M.; List, G.; Flannery, A.; Troutbrck, R.; Brilon, W.; Werner, B.; et al. National Cooperative Highway Research Program (NCHRP) Report 572 ; Transportation Research Board of the National Academics: Washington, DC, USA, 2007; Volume 9. [ Google Scholar ]
• Ghasem-Aghaee, N. Tuncer Oren and Levent Yilmaz. In Simulation, Intelligence and Agent: Exploring the Synergy Current and Future Developments in Artificial Intelligence ; Bentham Science Books Publisher: Bussum, The Netherland, 2019; pp. 1–58. [ Google Scholar ]
• Ferrara, A.; Sacone, S.; Siri, S. Fundamental of traffic dynamics. In freeway traffic modelling and control. Adv. Ind. Control. Ser. 2018 , 1185 , 44. [ Google Scholar ]
• U.S. Energy Information Administration. International Energy Outlook ; U.S. Energy Information Administration: Washington, DC, USA, 2017.
• Othman, B.; De Nunzio, G.; Di Domenico, D.; Canudas-De-Wit, C. Ecological traffic management: A review of the modeling and control strategies for improving environmental sustainability of road transportation. Annu. Rev. Control. 2019 , 48 , 292–311. [ Google Scholar ] [ CrossRef ]
• Montella, A.; Turner, S.; Chiaradonna, S.; Aldridge, D. International overview of roundabout design practices and insights for improvement of the Italian standard. Can. J. Civ. Eng. 2013 , 40 , 1215–1226. [ Google Scholar ] [ CrossRef ]
• Ma, C.; He, R.; Zhang, W. Path optimization of taxi carpooling. PLoS ONE 2018 , 13 , e020322. [ Google Scholar ]
• Schrank, D.; Eisele, B. “Annual Urban Mobility Report”, Texas A&M University. 2021. Available online: http://mobility.tamu.edu/ums/ (accessed on 23 February 2023).
• Pu, H.; Li, Y.; Ma, C.; Mu, H. Analysis of the projective synchronization of the urban public transportation super network. Adv. Mech. Eng. 2017 , 9 , 1687814017702808. [ Google Scholar ]
• Misbahuddin, S.; Zubairi, J.A.; Saggaf, A.; Basuni, J.; Sulaiman, A.; Al-Sofi, A. IoT Based Dynamic Road Traffic Management for Smart Cities. In Proceedings of the 2015 12th International Conference on High-Capacity Optical Networks and Enabling/Emerging Technologies (HONET), Islamabad, Pakistan, 21–23 December 2015; pp. 142–146. [ Google Scholar ]
• Traffic Signaling Timing Manual. Publication Number: FHWA-HOP-08-024, 2008. Available online: https://ops.fhwa.dot.gov (accessed on 10 January 2023).
• Cai, C. Adaptive Traffic Signal Control Using Approximate Dynamic Programming. Ph.D. Thesis, University College, London, UK, 2009. [ Google Scholar ]
• Wen, W. A dynamic and automatic traffic light control expert system for solving the road congestion problem. Expert Syst. Appl. 2008 , 34 , 2370–2381. [ Google Scholar ]
• Collotta, M.; Sun, Y.; Di Persio, L.; Ebeid, E.S.M.; Muradore, R. Smart Green Applications: From Renewable Energy Management to Intelligent Transportation Systems. Energies 2018 , 11 , 1317. [ Google Scholar ]
• Sharma, V.; You, I.; Pau, G.; Collotta, M.; Lim, J.D.; Kim, J.N. LoRaWAN-Based Energy-Efficient Surveillance by Drones for Intelligent Transportation Systems. Energies 2018 , 11 , 573. [ Google Scholar ]
• Sun, Y.; Song, H. Secure and Trustworthy Transportation Cyber-Physical Systems ; Springer: Berlin/Heidelberg, Germany, 2017. [ Google Scholar ]
• Jacobsen, R.H.; Aliu, D.; Ebeid, E. A Low-Cost Vehicle Tracking Platform Using Secure SMS. In Proceedings of the Porto: 2nd International Conference on Internet of Things, Big Data and Security, Porto, Portugal, 24–26 April 2017. [ Google Scholar ]
• Mukti, I.Y.; Prambudia, Y. Challenges in Governing the Digital Transportation Ecosystem in Jakarta: A Research Direction in Smart City Frameworks. Challenges 2018 , 9 , 14. [ Google Scholar ]
• Jacobsen, R.H.; Gabioud, D.; Basso, G.; Alet, P.J.; Azar, A.G.; Ebeid, E. SEMIAH An Aggregator Framework for European Demand Response Programs. In Proceedings of the Euromicro Conference on Digital System Design (DSD), Madeira, Portugal, 26–28 August 2015; pp. 470–477. [ Google Scholar ]
• Ersoy, P.; Börühan, G. Intelligent Transportation Systems and Their Applications in Road Transportation Industry in Turkey. In Proceedings of the 12th International Conference on Logistics & Sustainable Transport, Celje, Slovenia, 11–13 June 2015. [ Google Scholar ]
• Pau, G.; Campisi, T.; Canale, A.; Severino, A.; Collotta, M.; Tesoriere, G. Smart Pedestrian Crossing Management at Traffic Light Junctions through a Fuzzy-Based Approach. Futur. Internet 2018 , 10 , 15. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Intelligent Transport Systems Reference Material for Competence, Steer Program of the EU, Intelligent Energy; 2006. Available online: https://elitis.org (accessed on 13 March 2023).
• Liu, J.; Li, J.; Niu, X.; Cui, X.; Sun, Y. GreenOCR: An Energy-Efficient Optimal Clustering Routing Protocol. Comput. J. 2014 , 58 , 1344–1359. [ Google Scholar ] [ CrossRef ]
• Lara, T.; Yáñez, A.; Céspedes, S.; Hafid, A.S. Impact of Safety Message Generation Rules on the Awareness of Vulnerable Road Users. Sensors 2021 , 21 , 3375. [ Google Scholar ] [ CrossRef ]
• Gu, B.; Liu, J.; Xiong, H.; Li, T.; Pan, Y. ECPC-ICP: A 6D Vehicle Pose Estimation Method by Fusing the Roadside Lidar Point Cloud and Road Feature. Sensors 2021 , 21 , 3489. [ Google Scholar ] [ CrossRef ]
• Fukatsu, R.; Sakaguchi, K. Automated Driving with Cooperative Perception Using Millimeter-Wave V2V Communications for Safe Overtaking. Sensors 2021 , 21 , 2659. [ Google Scholar ] [ CrossRef ]
• Shan, M.; Narula, K.; Wong, Y.F.; Worrall, S.; Khan, M.; Alexander, P.; Nebot, E. Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations. Sensors 2020 , 21 , 200. [ Google Scholar ] [ CrossRef ]
• Baek, M.; Mun, J.; Kim, W.; Choi, D.; Yim, J.; Lee, S. Driving Environment Perception Based on the Fusion of Vehicular Wireless Communications and Automotive Remote Sensors. Sensors 2021 , 21 , 1860. [ Google Scholar ] [ CrossRef ]
• Jiang, C.; Zhao, D.; Zhang, Q.; Liu, W. A Multi-GNSS/IMU Data Fusion Algorithm Based on the Mixed Norms for Land Vehicle Applications. Remote. Sens. 2023 , 15 , 2439. [ Google Scholar ] [ CrossRef ]
• Huang, S.; Tan, K.K.; Lee, T.H. Fault Diagnosis and Fault-Tolerant Control in Linear Drives Using the Kalman Filter. IEEE Trans. Ind. Electron. 2012 , 59 , 4285–4292. [ Google Scholar ] [ CrossRef ]
• Zhao, Y. Performance evaluation of Cubature Kalman filter in a GPS/IMU tightly-coupled navigation system. Signal Process. 2016 , 119 , 67–79. [ Google Scholar ] [ CrossRef ]
• Impraimakis, M.; Smyth, A.W. An unscented Kalman filter method for real time input-parameter-state estimation. Mech. Syst. Signal Process. 2022 , 162 , 108026. [ Google Scholar ] [ CrossRef ]
• Lu, F. HRegNet: A hierarchical network for large-scale outdoor LiDAR point cloud registration. In Proceedings of the IEEE International Conference on Computer Vision, Montreal, BC, Canada, 11–17 October 2021; pp. 15994–16003. [ Google Scholar ]
• Papaioannidis, C.; Mademlis, I.; Pitas, I. (2021) Autonomous UAV safety by visual human crowd detection using multi-task deep neural networks. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, 30 May–5 June 2021; pp. 11074–11080. [ Google Scholar ]
• Liu, W.; Xia, X.; Xiong, L.; Lu, Y.; Gao, L.; Yu, Z. Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic. IEEE Sensors J. 2021 , 21 , 21675–21687. [ Google Scholar ] [ CrossRef ]
• Xia, X.; Hashemi, E.; Xiong, L.; Khajepour, A. Autonomous Vehicle Kinematics and Dynamics Synthesis for Sideslip Angle Estimation Based on Consensus Kalman Filter. IEEE Trans. Control Syst. Technol. 2022 , 31 , 179–192. [ Google Scholar ] [ CrossRef ]
• Liu, W.; Quijano, K.; Crawford, M.M. YOLOv5-Tassel: Detecting Tassels in RGB UAV Imagery With Improved YOLOv5 Based on Transfer Learning. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 2022 , 15 , 8085–8094. [ Google Scholar ] [ CrossRef ]
• IEA. The Future of Trucks ; IEA: Paris, France, 2017; p. 167. [ Google Scholar ]
• Ling, T.; Deng, L.; He, W.; Wu, H.; Deng, J. Load Effect of Automated Truck Platooning on Highway Bridges and Loading Strategy. Sensors 2022 , 22 , 7704. [ Google Scholar ] [ CrossRef ]
• Birgisson, B.; Morgan, C.; Yarnold, M.; Warner, J.; Glover, B.; Steadman, M.; Srinivasa, S.; Cai, S.; Lee, D. Evaluate Potential Impacts, Benefits, Impediments, and Solutions of Automated Trucks and Truck Platooning on Texas Highway Infrastructure: Technical Report ; Texas A&M Transportation Institute: Austin, TX, USA, 2020. [ Google Scholar ]
• Robertson, F.H.; Bourriez, F.; He, M.; Soper, D.; Baker, C.; Hemida, H.; Sterling, M. An experimental investigation of the aerodynamic flows created by lorries travelling in a long platoon. J. Wind. Eng. Ind. Aerodyn. 2019 , 193 , 103966. [ Google Scholar ] [ CrossRef ]
• Janssen, R.; Zwijnenberg, H.; Blankers, I.; de Kruijff, J. Truck Platooning Driving the Future of Transportation ; The Neighbourhood Organization (TNO): Delft, The Netherlands, 2015. [ Google Scholar ]
• Correia, A.G.; Cortez, P.; Tinoco, J.; Marques, R. Artificial Intelligence Applications in Transportation Geotechnics. Geotech. Geol. Eng. 2013 , 31 , 861–879. [ Google Scholar ]
• Chapman, P.; Clinton, J.; Kerber, R.; Khabaza, T.; Reinartz, T.; Shearer, C.; Wirth, R. CRISP-DM 1.0: “Step-by-Step Data Mining Guide ; CRISP-DM Consortium; SPSS Inc.: Chicago, IL, USA, 2000. [ Google Scholar ]
• Fayyad, U.; Piattetsky-Shapiro, G.; Smyth, P. The KDD process for extracting useful knowledge from volumes of data. Commun. ACM 1996 , 39 , 27–34. [ Google Scholar ]
• Ferrara, A.; Sacone, S.; Siri, S. Microscopic and Mesoscopic Traffic Models ; Freeway Traffic Modelling and Control. Advances in Industrial Control Series, Freeway Traffic Modeling and Control; Springer: Cham, Switzerland, 2008; pp. 113–143. [ Google Scholar ]
• Sheikh, M.S.; Peng, Y. A Comprehensive Review on Traffic Control Modeling for Obtaining Sustainable Objectives in a Freeway Traffic Environment. J. Adv. Transp. 2022 , 1–22. [ Google Scholar ]
• Bando, M.; Hasebe, K.; Nakayama, A.; Shibata, A.; Sugiyama, Y. Dynamical model of traffic congestion and numerical simulation. Phys. Rev. A 1995 , 51 , 1035–1042. [ Google Scholar ]
• Gazis, D.C.; Herman, R.; Rothery, R.W.; Ni, D.; Leonard, J.D.; Jia, C.; Wang, J.; Mahmassani, H.S.; Sun, D.; Elefteriadou, L.; et al. Nonlinear Follow-the-Leader Models of Traffic Flow. Oper. Res. 1961 , 9 , 545–567. [ Google Scholar ] [ CrossRef ]
• Newell, G. A simplified car-following theory: A lower order model. Transp. Res. Part B Methodol. 2002 , 36 , 195–205. [ Google Scholar ] [ CrossRef ]
• Wu, Y.; Lin, Y.; Hu, R.; Wang, Z.; Zhao, B.; Yao, Z. Flow with Connected Automated Vehicles: A Cell Transmission Model Approach. J. Adv. Transp. 2022 . [ Google Scholar ]
• Qian, Z.; Li, J.; Li, X.; Zhang, M.; Wang, H. Modeling heterogeneous traffic flow: A pragmatic approach. Transp. Res. Part B Methodol. 2017 , 99 , 183–204. [ Google Scholar ] [ CrossRef ]
• Zheng, F.; Liu, C.; Liu, X.; Jabari, S.E.; Lu, L. Analyzing the impact of automated vehicles on uncertainty and stability of the mixed traffic flow. Transp. Res. Part C Emerg. Technol. 2020 , 112 , 203–219. [ Google Scholar ] [ CrossRef ]
• Wu, Y.; Lin, Y.; Hu, R.; Wang, Z.; Zhao, B.; Yao, Z. Modeling and Simulation of Traffic Congestion for Mixed Traffic Flow with Connected Automated Vehicles: A Cell Transmission Model Approach. J. Adv. Transp. 2022 . [ Google Scholar ]
• Carey, M.; Balijepalli, C.; Watling, D. Extending the Cell Transmission Model to Multiple Lanes and Lane-Changing. Networks Spat. Econ. 2013 , 15 , 507–535. [ Google Scholar ] [ CrossRef ]
• Canudas-De-Wit, C.; Ferrara, A. A variable-length Cell Transmission Model for road traffic systems. Transp. Res. Part C Emerg. Technol. 2018 , 97 , 428–455. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Xu, T.; Yao, Z.; Jiang, Y.; Yang, T. Fundamental Diagram Model of Considering Reaction Time in Environment of Intelligent Connected Vehicles. Gonglu Jiaotong Keji/J. Highw. Transp. Res. Dev. 2020 , 37 , 108–117. [ Google Scholar ]
• Yang, H.; Jin, W.L. A control theoretic formulation of green driving strategies based on inter-vehicle communications. Transp. Res. Part C Emerg. Technol. 2014 , 41 , 48–60. [ Google Scholar ]
• Wu, C.; Bayen, A.M.; Mehta, A. Stabilizing traffic with autonomous vehicles. In Proceedings of the IEEE International Conference on Robotics and Automation, Brisbane, QLD, Australia, 21–25 May 2018; pp. 1–7. [ Google Scholar ]
• Jiang, Y.; Wang, S.; Yao, Z.; Zhao, B.; Wang, Y. A cellular automata model for mixed traffic flow considering the driving behavior of connected automated vehicle platoons. Phys. A Stat. Mech. Its Appl. 2021 , 582 , 126262. [ Google Scholar ] [ CrossRef ]
• Jiang, Y.; Zhao, B.; Liu, M.; Yao, Z. A Two-Level Model for Traffic Signal Timing and Trajectories Planning of Multiple CAVs in a Random Environment. J. Adv. Transp. 2021 , 2021 , 1–13. [ Google Scholar ] [ CrossRef ]
• Hegyi, A.; Hoogendoorn, S.P.; Schreuder, M.; Stoelhorst, H.; Viti, F. Specialist: A dynamic speed limit control algorithm based on shock wave theory. In Proceedings of the International IEEE Conference on Intelligent Transportation Systems, Beijing, China, 12–15 October 2008; pp. 827–832. [ Google Scholar ]
• Zegeye, S.K.; De Schutter, B.; Hellendoorn, J.; Breunesse, E.A.; Hegyi, A. A Predictive Traffic Controller for Sustainable Mobility Using Parameterized Control Policies. IEEE Trans. Intell. Transp. Syst. 2012 , 13 , 1420–1429. [ Google Scholar ] [ CrossRef ]
• Zhu, F.; Ukkusuri, S.V. Accounting for dynamic speed limit control in a stochastic traffic environment: A reinforcement learning approach. Transp. Res. Part C Emerg. Technol. 2014 , 41 , 30–47. [ Google Scholar ]
• Kavitha, D.; Ravikumar, S. Retracted article: Designing an IoT based autonomous vehicle meant for detecting speed bumps and lanes on roads. J. Ambient. Intell. Humaniz. Comput. 2021 , 12 , 7417–7426. [ Google Scholar ]
• Park, J.H.; Salim, M.; Jo, J.H.; Sicato, J.C.S.; Rathore, S.; Park, J.H. CIoT-Net: A scalable cognitive IoT based smart city network architecture. Hum.-Cent. Comput. Inf. Sci. 2019 , 19 , 1–20. [ Google Scholar ]
• Kuppusamy, P.; Kalpana, R.; Rao, P.V.V. Optimized traffic control and data processing using IoT. Clust. Comput. 2018 , 22 , 2169–2178. [ Google Scholar ] [ CrossRef ]
• Bierwirth, C.; Meisel, F. A follow-Up survey of berth allocation and quay crane scheduling problems in container terminals. Eur. J. Oper. Res. 2014 , 244 , 675–689. [ Google Scholar ]
• Santos, A.; Gomes, A.; Mendes, A.J. Integrating New Technologies and Existing Tools to Promote Programming Learning. Algorithms 2010 , 3 , 183–196. [ Google Scholar ] [ CrossRef ]
• Dulebenets, M.A.; Kavoosi, M.; Abioye, O.; Pasha, J. A Self-Adaptive Evolutionary Algorithm for the Berth Scheduling Problem: Towards Efficient Parameter Control. Algorithms 2018 , 11 , 100. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kavoosi, M.; Dulebenets, M.A.; Abioye, O.; Pasha, J.; Theophilus, O.; Wang, H.; Kampmann, R.; Mikijeljevi, M. Berth scheduling at marine container terminals A universal island-based metaheuristic approach. Marit. Bus. Rev. 2019 , 5 , 30–66. [ Google Scholar ] [ CrossRef ]
• Theophilus, O.; Dulebenets, M.A.; Pasha, J.; Abioye, O.F.; Kavoosi, M. Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review. Sustainability 2019 , 11 , 5245. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Dulebenets, M.A. A Diploid Evolutionary Algorithm for Sustainable Truck Scheduling at a Cross-Docking Facility. Sustainability 2018 , 10 , 1333. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Dulebenets, M.A. An Adaptive Polyploid Memetic Algorithm for scheduling trucks at a cross-docking terminal. Inf. Sci. 2021 , 565 , 390–421. [ Google Scholar ] [ CrossRef ]
• Okwu, M.O.; Chukwu, V.U.; Oguoma, O.N. Application of Artificial Neural Network Model for Cost Optimization in a Single-source, Multi-destination System with Non-Deterministic Inputs. IWANN 2019 Adv. Comput. Intell. 2019 , 11507 , 539–554. [ Google Scholar ]
• Walraven, E.; Spaan, M.T.; Bakker, B. Traffic flow optimization: A reinforcement learning approach. Eng. Appl. Artif. Intell. 2016 , 52 , 203–212. [ Google Scholar ] [ CrossRef ]
• Olayode, I.O.; Tartibu, L.K.; Okwu, M.O.; Uchechi, U.F. Intelligent transportation systems, un-signalized road intersections and traffic congestion in Johannesburg: A systematic review. Procedia CIRP 2020 , 91 , 844–850. [ Google Scholar ]
• Humayun, M.; Afsar, S.; Almufareh, M.F.; Jhanjhi, N.Z.; AlSuwailem, M. Smart Traffic Management System for Metropolitan Cities of Kingdom Using Cutting Edge Technologies. Hindawi J. Adv. Transp. 2022 , 2022 . [ Google Scholar ]
• Liu, Z.; Wu, Y.; Cao, S.; Zhu, L.; Shen, G. A Ramp Metering Method Based on Congestion Status in the Urban Freeway. IEEE Access 2020 , 8 , 76823–76831. [ Google Scholar ] [ CrossRef ]
• Guide to Sustainable Transportation Performance Measures United. States Environmental Protection Agency EPA by ICF International EPA 231-K-10-004 August 2011. Available online: www.epa.gov/smartgrowth (accessed on 2 March 2023).

Click here to enlarge figure

Share and Cite

Musa, A.A.; Malami, S.I.; Alanazi, F.; Ounaies, W.; Alshammari, M.; Haruna, S.I. Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations. Sustainability 2023 , 15 , 9859. https://doi.org/10.3390/su15139859

Musa AA, Malami SI, Alanazi F, Ounaies W, Alshammari M, Haruna SI. Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations. Sustainability . 2023; 15(13):9859. https://doi.org/10.3390/su15139859

Musa, Auwal Alhassan, Salim Idris Malami, Fayez Alanazi, Wassef Ounaies, Mohammed Alshammari, and Sadi Ibrahim Haruna. 2023. "Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations" Sustainability 15, no. 13: 9859. https://doi.org/10.3390/su15139859

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

IEEE Account

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

• DOI: 10.22214/ijraset.2020.32219
• Corpus ID: 229402580

Smart Traffic Management System using IoT

• Aniket Tiwari
• Published in International Journal for… 30 November 2020
• Engineering, Computer Science, Environmental Science

Figures from this paper

5 Citations

Iot based smart traffic controller system, a framework for iot-enabled environment aware traffic management, traffic control, congestion management and smart parking through vanet, ml, and iot: a review, smart traffic monitoring and controlling, smart vehicle signal breaking prevention system using rfid in iot, 23 references, reducing traffic congestion in modern cities, a challenge based on physico-mathematical theories and wireless sensor network, implementation on priority based signal management in traffic system, traffic flow optimization and vehicle safety in smart cities, an intelligent v2i-based traffic management system, optimizing traffic signal settings in smart cities, time optimization for traffic signal control using genetic algorithm, djikstra algorithm based approach to shortest path model in public bus transportation, study on saturation flow rates for signalized intersections, sigcap: a computer program for assessing the traffic capacity of signal-controlled road junctions, can a sustainable transportation system be developed for san antonio, texas, related papers.

Showing 1 through 3 of 0 Related Papers

Accessibility Links

• Skip to content
• Skip to search IOPscience
• Skip to Journals list
• Accessibility help
• Accessibility Help

Click here to close this panel.

Purpose-led Publishing is a coalition of three not-for-profit publishers in the field of physical sciences: AIP Publishing, the American Physical Society and IOP Publishing.

Together, as publishers that will always put purpose above profit, we have defined a set of industry standards that underpin high-quality, ethical scholarly communications.

We are proudly declaring that science is our only shareholder.

Smart Traffic Management System for Traffic Control using Automated Mechanical and Electronic Devices

Mamata Rath 1

Published under licence by IOP Publishing Ltd IOP Conference Series: Materials Science and Engineering , Volume 377 , International Conference on Mechanical, Materials and Renewable Energy 8–10 December 2017, Sikkim, India Citation Mamata Rath 2018 IOP Conf. Ser.: Mater. Sci. Eng. 377 012201 DOI 10.1088/1757-899X/377/1/012201

Article metrics

24713 Total downloads

Share this article

Author e-mails.

[email protected]

Author affiliations

1 Dept. of I.T, C.V.Raman College of Engineering, Bhubaneswar, India

Buy this article in print

In the current context of smart city, specifically in the industrial and market zones, the traffic scenario is very congested most of the time particularly at the peak time of business hours. Due to increasing growth of population and vehicles in smart and metropolitan cities people face lot of problem at the major traffic points of the business towns. Not only it causes travelling delays, it also contributes to environmental pollution as well as health hazards due to pollution caused by vehicle fuels.To keep away from such severe issues many radiant urban communities are right now implementing smart traffic control frameworks that work on the standards of traffic automation with prevention of the previously mentioned issues. The fundamental concept lies in collection of traffic congestion information quickly and passing the alternate strategy to vehicles as well as passengers with on-line traffic information system and effectively applying it to specific traffic stream. In this context, an enhanced traffic control and monitoring framework has been proposed in the present article that performs quick information transmission and their corresponding action. In the projected approach, under a Vehicular Ad-hoc Network (VANET) scenario, the mobile agent based controller executes a congestion control algorithm to uniformly organize the traffic flow by avoiding the congestion at the smart traffic zone. It exhibits other unique features such as prevention of accidents, crime, driver flexibility and security of the passengers. Simulation carried out using Ns2 simulator shows encouraging results in terms of better performance to control the delay and prevent any accident due to profound congestion up to a greater extent.

Export citation and abstract BibTeX RIS

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Smart Traffic Management System using IoT

2020, International Journal For Research In Applied Science & Engineering Technology

Our country (India) is the second largest population of world; according to that vehicles are increased day to day life. Here, the questions arise! how to avoid the congestion in the road; that means traffic management. Traffic management has since quite a while ago existed in some frame, from the beginning of railroad flagging or movement lights on city lanes, yet the improvement and execution of modern coordinated applications in light of Intelligent Transport Systems (ITS) has developed apace lately, because of effective research and technological advances. Develop a system which can be used to predict high level of traffic congestion using data collected from live video stream analysis sensors image processing traffic congestion system can utilize the power of cloud computing and the strength of artificial neural networks traffic is increasing in every major city, which raises an average commute time. Los Angeles has the highest time lost in traffic congestion and parking search while minimizing implementation costs and requirements of maintenance.

Related Papers

Editor IJMTER

DOMINIC ABUGA

Over the last two decades, there has been an exponential increase in population in many cities globally, posing a challenge in managing and controlling traffic. According to the National Transport and Safety Authority of Kenya, in the year 2020, 134,000 crashes led to 3600 fatalities. During peak hours of traffic, Kenya's average city resident wastes two litres of fuel in traffic daily. Traffic jams result in wastage of time with an average motorist using almost an hour daily. They cause fuel wastage and environmental pollution as a result of the emission of greenhouse gases that cause global warming. Pollution has a devastating effect on human beings, animals, and plants around us. IoT technology is best suited for tackling the problem of traffic management and control in the city. This paper will focus on the design and development of IoT based real-time monitoring framework for the city that also incorporates the use of remote sensing technology. The proposed system has an ad...

IJSES Editor

In recent years, traffic congestion is a major problem in Indian cities as well along with other countries. Traffic congestion is because of increased use of vehicles by the tremendous growing population of country. Another reason is the infrastructure that exists cannot be expanded so as the need for better management of the traffic. Traffic congestion affects day today life in terms of pollution of environment, fuel wastage, increased travelling time etc. Traffic monitoring system is used for monitoring of traffic. Intelligent traffic systems are developed to monitor the traffic keeping the goal as improving transportation safety, mobility and efficiency of transporting. It can be used to detect traffic congestion, Vehicle violating traffic rules, provision for smart parking of vehicles, automatic toll collection charges speed limit violation detection etc. Internet of things (IOT) can play a vital role in Traffic Management, by using various availed methods for traffic management such as video analysis, Infrared sensors, Inductive loop detection, wireless sensor network, RFID etc. are effective methods for traffic Management. Different types of sensors are used for data collection.

International Journal of Scientific Research in Science, Engineering and Technology

International Journal of Scientific Research in Science, Engineering and Technology IJSRSET

India is a developing country. Increase in personal vehicles comes with the development of a country parallely. This has led to rise in congestion in large cities. So, we need a better traffic management system. The purpose of this project is to create a traffic system which is adaptive to the present traffic scenario in a lane. Usually, we have fixed average waiting time for all lanes. This project suggests to change the average waiting time by monitoring the number of vehicles in a lane. The data will be sent to central system through internet, which will decide the timing for signal according to the dumped program. This project also, suggests implementing congestion lights at previous intersections, so that drivers can change lanes at the situation of congestion. The system is useful in emergencies and it also, helps in reducing pollution and traffic congestion.

IAEME Publication

Arul Xavier

The utilization of private vehicles increased in the past few years due to the advancement of technology and life style of the civilians causes hectic traffic complexities in the cities as well as rural areas across the globe. Huge traffic leads to an environmental pollution, unavoidable accidents and wastage of time due to traffic blocks and congestions. In recent years, the concept of Internet of Things (IoT) provides an excellent automated solution to all major applications. Thes technology provides an efficient traffic management system by means of its automated tracking, monitoring, processing and management. This research paper gives the complete overview of various intelligent traffic management system developed based on IoT to reduce the traffic congestions.

Last few Decades the traffic management it's the vital issues in a big cities. With the help of Internet of Things(IoT) we can improve the traffic efficiency. In this paper we describe the things using Internet to control the traffic well..Last few dacades the major problem is increasing number of vehicles as same as growth of population because of it causes major traffic congestion ,noise and increase travelling time due to this congestion of traffic is increases along with increase the pollution every day on road traffic is jammed. In this we can manage the traffic singals by monitoring the traffic density to avoid traffic congestion on road using network communication between server and hardware module. The traffic awareness it is most key problem now a days. Basically the architecture is divided into modules such as wireless sensor network, RFID, GSM-GPS

مجلة النيل للعلوم التجارية والقانونية ونظم المعلومات

Hesham Arafat Ali

IJAERS Journal

A smart traffic management is a wide topic of research. Many modifications can be made to make the urban traffic flow smoothly on the roads. The increasing utilization of private vehicles and public transportation due to advancement of technology causes hectic traffic complexities for the civilians across the globe. The problem of traffic congestion is an everyday problem for human resource and therefore hinders the growth of the country by affecting its productivity as well as economy. Moreover, the traffic signaling systems have predetermined fixed operational time which fails to manage the traffic density changing with time and thus, long traffic queues are formed at the road crossings resulting in increased pollution and waiting time. In this paper, we tried to provide solution to reduce the waiting time at road crossings while keeping in mind the importance of time of the citizens as well as the emergency service providers (such as EMS i.e. Emergency Medical Services, Fire and Rescue Services, etc.). The presented system in this paper is based on smart traffic congestion control system that will automatically set the signal time based on the measured values of vehicle density on road lanes. However, the manual changes can also be made to traffic signals for efficient traffic management in case of emergencies. This paper presents an idea of traffic management using internet of things (IOT). The Internet of Things (IoT) refers to a system of internet-connected objects that are able to collect and transfer data over a wireless network without human intervention. This technology provides an effective communication between different signals and helps in collection of data thereby providing an IoT based smart traffic management system in terms of its automated tracking, monitoring and controlling of vehicles and its data processing.

International Journal for Research in Applied Science & Engineering Technology (IJRASET)

IJRASET Publication

This research paper explores the potential of the Internet of Things (IoT) in transportation. The paper proposes an algorithm that uses sensors and cameras to monitor traffic flow in real-time and suggests alternative routes to reduce congestion. Additionally, the paper suggests a smart parking allocation system (SPAS) that allows vehicle owners to locate available parking spaces and reserve them in advance. The use of IoT in transportation can lead to a more efficient and sustainable transportation system that enhances the travel experience for commuters.

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

IAEME PUBLICATION

INTERNATIONAL JOURNAL OF ADVANCE RESEARCH, IDEAS AND INNOVATIONS IN TECHNOLOGY

Ijariit Journal

Ijetrm Journal

IRJET Journal

International Journal of Engineering Research and Technology (IJERT)

IJERT Journal

Rodolfo Meneguette

International Journal of Recent Technology and Engineering

TUSHAR DEB NATH

Dr. Hussam Elbehiery

International Journal of Advance Research Ideas and Innovations in Technology

subhendu pani

Electronics

Vassilis Gerogiannis

Aman Maheshwari

International Journal of Scientific Research in Science and Technology

International Journal of Scientific Research in Science and Technology IJSRST

IJSRD Journal

Journal of Physics: Conference Series

Suparth Jain

Swapnil Jain

2018 20th International Conference on Advanced Communication Technology (ICACT)

Mehak Tanveer

Review of Computer Engineering Studies Vol. 7, No. 3, 55-59

Dr. Charles Okunbor

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Research on Vehicle Monitoring and Management System Based on Intelligent Perception

• Conference paper
• First Online: 11 August 2024
• Cite this conference paper

• Wenwei Zhou 6 ,
• Sanjun Wang 7 &
• Xiaobo Wu 8  

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 391))

Included in the following conference series:

• IEEE International Conference on Advanced Infocomm Technology

The construction and application of smart highway accelerate the development of digital transformation of highway infrastructure, and provide rich data basis and technical support for vehicle monitoring and management. Based on the intelligent perception ability of highway facilities, this paper designs and develops a vehicle monitoring and management system to realize functions such as vehicle monitoring, trajectory tracking, event detection, driving analysis and credit evaluation, to improve the operation monitoring and digital control capabilities of road transport vehicles.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Durable hardcover edition
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Tu, Y.F., Li, J.Z., Yang, X.J., et al.: Status quo and development trend analysis of smart highway construction at the present stage. Transport. Manager World 689 (07), 61–63 (2023)

Google Scholar  

Vehicle Video surveillance Analysis System for expressway (2021) J. Xidian Univer. 48 (05), 178–189 (2021) (In Chinese)

Dong, X.S., Zhu, W., Liu, Y., Shi, L.: Millimeter wave radar and vision fusion vehicle target detection system. Command Inform. Syst. Technol. 12 (01), 91–96 (2019)

Kang, G.Q., Lin, J., Liu, S.W., Yue, W., Xiong, Q.F., Tong, H.: Real-time detection and tracking of vehicles operating in complex mine. Environ. Control and Inform. Technol. 05 , 68–74 (2022)

Ca,i Z.T.: Research on forward vehicle detection system based on millimeter wave radar and vision fusion. East China Normal Univer. (2022)

Yang, M., Dong, B., Wang, H., et al.: Research on real-time pose estimation for mobile robots based on LiDAR. Acta Automatica Sinica 30 (5), 679–687 (2004)

He, X.Z., Qian, L.B., Fu, P.M. et al.: Road transportation monitoring system based on transportation management information platform: a case study of Jiangsu Province. J. Transport. Res. 7 (02), 28–37 (2019)

Yang, M., Dang, Q., Wan, J.: Design of key vehicle supervision system for road transportation. Times Automobile 378 (06), 184–185 (2022)

Dong, Y.R.: Research on operation monitoring of gate frame segment billing system for ETC vehicles in expressway. Transp. Manager World 25 , 55–57 (2022)

Download references

Acknowledgements

This work has been supported by Basal Research Fund of Central Public Research Institute of China (Grant No.20220203 and Grant No.20230205), the research project of China Construction Infrastructure Corporation for developing China's transportation strength (YLZJ2022-12).

Author information

Authors and affiliations.

Hubei Highway ETC Center, Wuhan, 430051, Hubei, China

Wenwei Zhou

Hubei Transport Telecommunications and Information Center, Wuhan, 430051, Hubei, China

Sanjun Wang

China Academy of Transportation Sciences, Chaoyang District, Beijing, 100029, China

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Sanjun Wang .

Editor information

Editors and affiliations.

University of Hyogo, Kobe, Japan

Kazumi Nakamatsu

Interscience Institute of Management and Technology, Bhubaneswar, India

Srikanta Patnaik

TK Engineering, Sofia, Bulgaria

Roumiana Kountcheva

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper.

Zhou, W., Wang, S., Wu, X. (2024). Research on Vehicle Monitoring and Management System Based on Intelligent Perception. In: Nakamatsu, K., Patnaik, S., Kountcheva, R. (eds) Advanced Intelligent Technologies and Sustainable Society. ICAIT 2023. Smart Innovation, Systems and Technologies, vol 391. Springer, Singapore. https://doi.org/10.1007/978-981-97-3210-4_39

Download citation

DOI : https://doi.org/10.1007/978-981-97-3210-4_39

Published : 11 August 2024

Publisher Name : Springer, Singapore

Print ISBN : 978-981-97-3209-8

Online ISBN : 978-981-97-3210-4

eBook Packages : Intelligent Technologies and Robotics Intelligent Technologies and Robotics (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research