Investigating the effect of smartening traffic signs for traffic problems in cities

Volume 2, Issue 9 - Serial Number 19
December 2021
Pages 57-73

Document Type : Research Article

Authors

1 Department of Civil Engineering, Savadkuh Branch, Islamic Azad University, Savadkooh, Iran

2 Department of civil engineering, Faculty of Engineering, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran

Abstract
Traffic sign recognition plays a vital role in the intelligent transportation system, which increases traffic safety by providing safety and precautionary information about road hazards to drivers. Knowledge of how humans process the meaning of symptoms reduces the time it takes to respond to them and make decisions while driving. Although the expansion of the transportation network helps in the discussion of traffic, this expansion may not be feasible due to high financial costs, geographical and environmental constraints, as well as long-term improvements to transportation infrastructure. These limitations can be minimized with Traffic Management Systems (TMS). Efficient use of resources and waste reduction are key goals in managing a smart city, in which traffic and transportation activities have a significant impact. In smart city approaches, the use of intelligent processing methods in vehicles to increase safety is proposed without any interference in the driving process. This study addresses an innovative system that aims to assist councils in developing a road management plan and creating a framework for placing street signs and intelligent car systems using Google Images (GSV). Recent advances in machine memory detection technology have provided Google Automated Images with an automated approach to identifying and classifying street signs, allowing it to be used to produce a standalone image recognition system to improve traffic information monitoring and storage. This article reviews and compares studies and developments related to traffic signs and signs in smart cities and intelligent transportation systems. The results show a reduction in travel time, improved vehicle maintenance and transportation system logistics, better fleet utilization, reduced vehicle fuel consumption and CO2 emissions.

Keywords

Allen, H., Millard, K., & Stonehill, M. (2113). A summary of the proceedings from the united nations climate change
conference in doha qatar and their significance for the land transport sector. In copenhagen: Bridging the gap (btg) initiative.
Anguelov, D., Dulong, C., Filip, D., Frueh, C., Lafon, S., Lyon, R., ... & Weaver, J. (2111). Google street view: Capturing
the world at street level. Computer, 43(6), 32-32.
Balali, V., Rad, A. A., & Golparvar-Fard, M. (2115). Detection, classification, and mapping of US traffic signs using
google street view images for roadway inventory management. Visualization in Engineering, 3(1), 15.
Bastidas, V., Bezbradica, M., & Helfert, M. (2112, June). Cities as enterprises: a comparison of smart city frameworks
based on enterprise architecture requirements. In International Conference on Smart Cities (pp. 21-22). Springer, Cham.
Campbell, A., Both, A., & Sun, Q. C. (2112). Detecting and mapping traffic signs from Google Street View images using
deep learning and GIS. Computers, Environment and Urban Systems, 22, 111351.
Cintra, M. (2113). A crise do trânsito em São Paulo e seus custos. GV EXECUTIVO, 12(2), 52-61.
Cunha, F., Maia, G., Ramos, H. S., Perreira, B., Celes, C., Campolina, A., ... & Mini, R. (2112). Vehicular networks to
intelligent transportation systems. In Emerging Wireless Communication and Network Technologies (pp. 222-315).
Springer, Singapore.
De Souza, A. M., Brennand, C. A., Yokoyama, R. S., Donato, E. A., Madeira, E. R., & Villas, L. A. (2112). Traffic
management systems: A classification, review, challenges, and future perspectives. International Journal of Distributed
Sensor Networks, 13(4), 1551142216623612.
Du, R., Santi, P., Xiao, M., Vasilakos, A. V., & Fischione, C. (2112). The sensable city: A survey on the deployment and
management for smart city monitoring. IEEE Communications Surveys & Tutorials, 21(2), 1533-1561.
Junior, G. D., Frozza, R., & Molz, R. F. (2115). Simulação de controle adaptativo de tráfego urbano por meio de sistema
multiagentes e com base em dados reais. Revista Brasileira de Computação Aplicada, 2(3), 65-21.
Karthiga, P. L., Roomi, S. M. D. M., & Kowsalya, J. (2116). Traffic-sign recognition for an intelligent vehicle/driver
assistant system using HOG. Computer Science & Engineering: An International Journal, 6(1).
Kishore, A.N.N., & Sodhi, Z. (2115), Exploratory Research on Smart Cities. Theory, Policy and Practice, New Delhi: Pearl,
Available at: http://pearl.niua.org/content/exploratory-research-smart-cities.
Latorre-Biel, J. I., Faulin, J., Jiménez, E., & Juan, A. A. (2112, June). Simulation Model of Traffic in Smart Cities for
Decision-Making Support: Case Study in Tudela (Navarre, Spain). In International Conference on Smart Cities (pp. 144-
153). Springer, Cham.
Liu, Y., Weng, X., Wan, J., Yue, X., Song, H., & Vasilakos, A. V. (2112). Exploring data validity in transportation systems
for smart cities. IEEE Communications Magazine, 55(5), 26-33.
Mazurkiewicz, J. (2112). Intelligent Processing Methods Usage for Transport Systems Safety Improvement. Procedia
Engineering, 122, 162-121.
Meneguette, R. I., De Grande, R. E., & Loureiro, A. A. (2112). Intelligent transport system in smart cities. Springer: Cham,
Switzerland.
www.cpjournals.com ISSN:6262-155X )Civil & Project Journal)CPJ(( پروژه و عمران نشریه
ؾبَ زْٚ، زٚضٜ 2 ،قٕبضٜ 9 ،آشض 1399،قٕبضٜ پیبپی 19،نفحٝ 57 تب نفحٝ 73
53
Meneguette, R. I., Filho, G. P., Guidoni, D. L., Pessin, G., Villas, L. A., & Ueyama, J. (2116). Increasing intelligence in
inter-vehicle communications to reduce traffic congestions: experiments in urban and highway environments. PLoS one,
11(2), e1152111.
Parliament of Victoria (2114). Road management act 2114. [Online]. Available at
http://www.legislation.vic.gov.au/domino/Web_Notes/LDMS/LTObject_Store/ltobjst11.nsf/DDE311B246EED2C2CA252
16111A3521/ 2BFFD5DBDFECCB1DCA25211111211B22/$FILE/14-12aa153٪21authorised.pdf.
Pop, M. D., & Proștean, O. (2112). A comparison between smart city approaches in road traffic management. Procediasocial and behavioral sciences, 232, 22-36.
Rocha Filho, G. P., Meneguette, R. I., Neto, J. R. T., Valejo, A., Weigang, L., Ueyama, J., ... & Villas, L. A. (2121).
Enhancing intelligence in traffic management systems to aid in vehicle traffic congestion problems in smart cities. Ad Hoc
Networks, 112, 112265.
Schrank, D., Eisele, B., Lomax, T., & Bak, J. (2115). 2115 urban mobility scorecard.
Souza, A. M., Guidoni, D., Botega, L. C., & Villas, L. A. (2115). CO-OP: Uma solução para a detecção, classificação e
minimização de congestionamentos de veículos utilizando roteamento cooperativo. Simpósio Brasileiro de Redes de
Computadores e Sistemas Distribuídos-(SBRC).
Sun, Q. C., Xia, J. C., Li, Y., Foster, J., Falkmer, T., & Lee, H. (2112). Unpacking older drivers’ maneuver at intersections:
Their visual-motor coordination and underlying neuropsychological mechanisms. Transportation research part F: traffic
psychology and behaviour, 52, 11-12.
Vilchez, J. L. (2112). Mental representation of traffic signs and their classification: warning signs. Transportation research
part F: traffic psychology and behaviour, 64, 442-462.
  • Receive Date 27 October 2020
  • Revise Date 20 November 2020
  • Accept Date 20 November 2020
  • First Publish Date 21 November 2020
  • Publish Date 21 November 2020