A low-cost system for supporting drivers in traffic jams

• Autorzy: Hołubowski Waldemar , Frankiewicz Ryszard , Kusiński Sławomir , Sikora Andrzej , Zielonka Adam

Identyfikatory

DOI

10.20858/tp.2023.18.1.09

• Języki publikacji: EN

Abstrakty

EN

Cases of heavy traffic congestion (commonly referred to as traffic jams) have negative influences on the economy, public health, and social life. This makes it important to assist drivers in these situations with Internet of Things (IoT) systems. The aim of this article is to present a low-cost device that supports driving in traffic jams. The system, which is based on data from a laser sensor with a range of up to 40 m, controls the distances to vehicles in front of and behind a moving car. Through audio and video signals, it warns the driver whether the vehicle in front of or behind the equipped vehicle is too close. This allows the driver to control the distance and avoid minor collisions.

Słowa kluczowe

EN

IoT; distance sensor; low cost system

PL

IoT; czujnik odległości; system niskokosztowy

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Transport Problems
• Rocznik: 2023
• Tom: T. 18, z. 1
• Strony: 103--115
• Opis fizyczny: Bibliogr. 36 poz.

Twórcy

autor

Hołubowski Waldemar

• Silesian University of Technology, Faculty of Applied Mathematics; Kaszubska 23, 44-100 Gliwice, Poland

• https://orcid.org/0000-0003-1815-7105

autor

Frankiewicz Ryszard

• Silesian University of Technology, Faculty of Applied Mathematics; Kaszubska 23, 44-100 Gliwice, Poland

• https://orcid.org/0000-0003-2053-183X

autor

Kusiński Sławomir

• Silesian University of Technology, Faculty of Applied Mathematics; Kaszubska 23, 44-100 Gliwice, Poland

• https://orcid.org/0000-0002-7801-2752

autor

Sikora Andrzej

• Silesian University of Technology, Faculty of Electrical Engineering; Bolesłwa Krzywoustego 2, 44-100 Gliwice, Poland

• https://orcid.org/0000-0003-3498-5621

autor

Zielonka Adam

• Silesian University of Technology, Faculty of Applied Mathematics; Kaszubska 23, 44-100 Gliwice, Poland

• https://orcid.org/0000-0002-3850-8577

Bibliografia

• 1. Caves, R.W. Encyclopedia of the City. Routledge. 2004. 141 p.
• 2. Moya-Gómez, B. & García-Palomares, J.C. The impacts of congestion on automobile accessibility. What happens in large European cities? J. Transp. Geogr. 2017. Vol. 62. P. 148-159.
• 3. Cavallo, E.A. & Powell, A. & Serebrisky, T. From Structures to Services: The Path to Better Infrastructure in Latin America and the Caribbean. Inter-American Development Bank: Washington, DC. USA. 2020.
• 4. European Commission. Handbook on the External Costs of Transport. European Commission: Brussels. Belgium. 2020.
• 5. Treiber, M. & Kesting, A. Traffic Flow Dynamics: Data, Models and Simulation. Springer Science & Business Media. 2012. ISBN: 978-3-642-32459-8.
• 6. May, A.D. Traffic Flow Fundamentals. Prentice Hall. 1990. ISBN: 9780139260728.
• 7. Essien, A. & Petrounias, I. & Sampaio, P. & Sampaio, S. the impact of rainfall and temperature on peak and off-peak urban traffic. Lecture Notes in Computer Science. Springer International Publishing. 2018. Vol. 11030. P. 399-407. DOI: 10.1007/978-3-319-98812-2_36.
• 8. Duranton, G. & Turner, M.A. The fundamental law of road congestion: evidence from US cities. American Economic Review. 2011. Vol. 101. No. 6. P. 2616-52. DOI: 10.1257/aer.101.6.2616.
• 9. Roess, R.P. & Prassas, E.S. & McShane, W.S. Traffic Engineering. Fourth Edition. Pearson 2009.
• 10.Kumar, N. & Raubal, M. Applications of deep learning in congestion detection, prediction and alleviation: A survey. Transportation Research Part C: Emerging Technologies. 2021. Vol. 133. 103432. DOI: 10.1016/j.trc.2021.103432.
• 11.Peden, M. & Richard, S. & Sleet, D. & Mohan, D. & Hyder, A.A. & Jarawan, E. & Mathers, C. World Report on Road Traffic Injury Prevention. World Health Organization: Geneva, Switzerland. 2004.
• 12.Wang, C. & Quddus, M.A. & Ison, S.G. Analysis of the M25 motorway in England impact of traffic congestion on road safety: a spatial analysis of the M25 motorway in England. Accident Analysis and Prevention. 2009. Vol. 41(4). P. 798-808. DOI: 10.1016/j.aap.2009.04.002.
• 13.Sánchez González, S. & Bedoya-Maya, F. & Calatayud, A. Understanding the effect of traffic congestion on accidents using Big Data. Sustainability. 2021. Vol. 13. Paper No. 7500. P. 1-19. DOI: 10.3390/su13137500.
• 14.Retallack, A.E. &, Ostendorf B. Current understanding of the effects of congestion on traffic accidents. International Journal of Environmental Research and Public Health. 2019. Vol. 16. No. 18. Paper No. 3400. P. 1-13. DOI: 10.3390/ijerph16183400.
• 15.Woźniak, M. & Zielonka, A. & Sikora A. Driving support by type-2 fuzzy logic control model. Expert Systems with Applications. 2022. Vol. 207. P. 117798.
• 16.Nellore, K. & Hancke, G.P. Traffic management for emergency vehicle priority based on visual sensing. Sensors. 2016. Vol. 16. No. 11. P. 1892.
• 17.Fontaine, M.D. & Smith, B.L. Probe-based traffic monitoring systems with wireless location technology: An investigation of the relationship between system design and effectiveness. Transp. Res. Rec. 2005. Vol. 1925(1). P. 2-11.
• 18.Wasson, J.S. & Sturdevant, J.R. & Bullock, D.M. Real-time travel time estimates using media access control address matching. ITE J. 2008. Vol. 78. P. 20-23.
• 19.Haseman, R.J. & Wasson, J.S. & Bullock, D.M. Real-time measurement of travel time delay in work zones and evaluation metrics using Bluetooth probe tracking. Transp. Res. Rec. 2010. No. 2169. P. 40-53.
• 20.Schaefer, M.C. License plate matching surveys: Practical issues and statistical considerations. ITE J. 1988. Vol. 58. P. 37-42.
• 21.Garcia, C. & Huebschman, R. & Abraham, D.M. & Bullock, D.M. Using GPS to measure the impact of construction activities on rural interstates. J. Constr. Eng. Manag. 2006. Vol. 132. P. 508-515.
• 22.Quiroga, C.A. & Bullock, D. Travel time studies with global positioning and geographic information systems: An integrated methodology. Transp. Res. Part C: Emerg. Technol. 1998. Vol. 6. P. 101- 127.
• 23.Oh, C. & Oh, J.-S. & Ritchie, S. & Chang, M. Real-time estimation of freeway accident likelihood. J. Transp. Eng. 2001. Vol. 131. P. 358-363.
• 24.Golob, T.F. & Recker, W.W. & Alvarez, V.M. Freeway safety as a function of traffic flow. Accid. Anal. Prev. 2004. Vol. 36. P. 933-946.
• 25.Olmstead, T. Freeway management systems and motor vehicle crashes: A case study of phoenix, arizona. Accid. Anal. Prev. 2001. Vol. 33. P. 433-447.
• 26.Ahmed, M.M. & Abdel-Aty, M.A. The viability of using automatic vehicle identification data for real-time crash prediction. IEEE Trans. Intell. Transp. Syst. 2012. Vol. 13. P. 459-468.
• 27.Ahmed, M.M. & Abdel-Aty, M.A. A data fusion framework for real-time risk assessment on freeways. Transp. Res. Part C: Emerg. Technol. 2013. Vol. 26. P. 203-213.
• 28.Theofilatos, A. Incorporating real-time traffic and weather data to explore road accident likelihood and severity in urban arterials. J. Saf. Res. 2017. Vol. 61. P. 9-21.
• 29.Hanbali, R. & Fornal, C. Methodology for evaluating effectiveness of traffic-responsive systems on intersection congestion and traffic safety. Transportation Research Record: Journal of the Transportation Research Board. 1997. Vol. 1603. P. 137-149.
• 30.Hauer, E. On the estimation of the expected number of accident. Accident Analysis and Prevention. 1986. Vol. 18. No. 1. P. 1-12.
• 31.Gwynn, D.W. Relationship of accident rates and accident involvements with hourly volumes Traffic Quarterly. 1967. Vol. 21. No 3. P. 407-418.
• 32.Kopits, E. & Cropper, M. Traffic fatalities and economic growth. Accident Analysis and Prevention. 2005. Vol. 37. No. 1. P. 169-178.
• 33.Noland, R.B. Medical treatment and traffic fatality reductions in industrialized countries Accident Analysis and Prevention. 2003. Vol. 35. No. 6. P. 877-883.
• 34.Noland, R.B. Traffic fatalities and injuries: the effect of changes in infrastructure and other trends. Accident Analysis and Prevention. 2003. Vol.
• 35. No 4. P. 599-611. 35.Kalkulator drogi reakcji, hamowania, zatrzymania czasu i prędkości w kolizji. Available at: https://www.prawko-kwartnik.com/kalkulator-drogi-reakcji-hamowania-zatrzymania.php. [In Polish: Calculator of reaction distance, braking, stopping time and speed in a collision].
• 36.Calculator for Stopping (Braking) Distance, Acceleration/Deceleration, Speed, Time. Available at: https://www. johannes-strommer.com/en/calculators/stopping-distance-acceleration-speed/.