Statistics reveals that the visual problems are the prime
reasons for a larger number of road accidents. The blind spot
is the major problem related to vision. The aim of this study
is to develop a fuzzy-based multi criteria decision-making
model for optimizing the area of the blind spot in the front
and sides of a heavy transport vehicle. To achieve this, the
statistical tool ANOVA (Analysis of Variance) and multi criteria
optimization techniques like TOPSIS (Technique for Order
of Preference by Similarity to Ideal Solution), FAHP (Fuzzy
Analytical Hierarchy Process) and GRA (Grey Relational
Analysis) were also used in this problem This paper consists
of three modules: first, the blind spots of the existing body
structure dimension used in heavy vehicles were studied
and the optimal design parameters were determined by using
ANOVA and TOPSIS methodologies; next, the weights of
the design parameters were calculated using FAHP method.
Finally, GRA-based Multi Criteria Decision Making (MCDM)
approach has been used to rank the vehicle body structures.
The proposed model has been implemented in a transport
corporation to compare four different types of body structures
and concluded that the body structure which was built
by an outsourced body builder is having a smaller area of
blind spot and optimal design parameters as well.
Hatamleh H (Moh’d Said), Sharadqeh AAM, Alnaser AM, Alheyasat O, Abu-Ein AA-K. Computer simulation to detect the blind spots in automobiles. Int J Comput Sci Issues. 2013;10 (1):453-456.
Olejnik K. Estimation of the need for harmonization of technical demands for vehicles used in transit countries to decrease threats made by the accidents, presented in the selected examples. Transport. 2008;23(1):78-81.
Sanchez-Alejo FJ, Alvarez MA, Holgado NF, Lopez JM. Defining the ergonomic parameters of the driver’s seat in a competition single-seater. Int J Vehicle Des. 2011;55(2/3/4):139-161.
Ahmadian M, Boggs C. Safety effects of operator seat design in large commercial vehicles. Final Report Safety IDEA Project; 2005.
Carrier R. Ergonomic study of the driver’s work station in urban buses. Canadian Urban Transit Association Publishers; 1992.
Shen W, Vertiz A. Redefining seat comfort, SAE Paper. 1997;970597.
Park S, Lee Y, Nahm Y, Lee J, Kim J. Seating physical characteristics and subjective comfort: Design considerations. SAE Paper. 1998;980653.
Gundogdu O. Optimal seat and suspension design for a quarter car with driver model using genetic algorithms. Int J Ind Ergon. 2007;37(4):327–332.
Burger W. Evaluation of innovative passenger car and truck rear vision system. SAE paper. 1974;740965.
Ayres T, Li L, Trachtman D, Youn D. Passenger-side rear-view mirrors: driver behavior and safety. Int J Ind Ergon. 2005;35:157-162.
Huang S-J, Chao S-T. A new lateral impact warning system with grey prediction. P I Mech Eng D-J Aut. 2010;224(3):285-297.
van Erp JB, Padmos P. Image parameters for driving with indirect viewing systems. Ergonomics. 2003;46(15):1471-1499.
Dyakov I. The problems of optimal design in the automotive industry. Transport. 2013;28(3):290-294.
Hughes C, Glavin M, Jones E, Denny P. Wide-angle camera technology for automotive applications: A review. Intell Transport Sys. 2009;3(1):19-31.
Lakshmi S, Wahida Banu RSD. Efficient realisation and rendering of images in blind zone. J Comput Eng. 2010;1:1-5.
Cho YH, Han BK. Application of slim a-pillar to improve driver’s field of vision. Int J Auto Techn. 2010;11(4):517-524.
Tideman M, van der Voort MC, van Arem B. A new scenario based approach for designing driver support systems applied to the design of a lane change support system. Transport Res C. 2010;18:247-258.
Kim JH, Park BH, Han YO. Surface flow and wake characteristics of automotive external rear-view mirror. P I Mech Eng D-J Aut. 2011;225(12):1605-1613.
Bosurgi G, D’Andrea A, Pellegrino O. What variables affect to a greater extent the driver’s vision while driving?, Transport. 2013;28(4):331-340.
Wu L, Yang Y , Jing G. Technique for order preference by similarity to Ideal solution (TOPSIS) for safety synthetic evaluation on coal mine transportation system. Prog Saf Sci Technol 2006;6:87-91.
Bao Q, Ruan D, Shen Y, Hermans E, Janssens D. Improved hierarchical fuzzy TOPSIS for road safety performance evaluation. Know Based Syst. 2012;32:84-90.
Mahmoud M, Hine J. Using AHP to measure the perception gap between current and potential users of bus services. Transport Plan Techn. 2013;36(1):4-23.
Pitchipoo P, Vincent DS, Rajini N, Rajakarunakaran S. COPRAS decision model to optimize blind spot in heavy vehicles: A comparative perspective. Proce Engg.2014;97:1049-1059.
Arias-Castro E, Candes EJ, Plan Y. Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism. Ann Stat. 2011;39(5):2533-2556.
Hwang CL, Yoon K. Multiple attribute decision making: Methods and applications. New York: Springer-Verlag; 1981.
Deng JL. Introduction to grey system. J Grey Sys. 1989;1(1):1-24.
Thomas L. Saaty. How to make a decision: the analytic hierarchy process. Eur J Oper Res. 1990;48(1):9-26.
Vincent DS, Pitchipoo P, Rajakarunakaran S. Elimination of blind Spots for heavy transport vehicles by driver seat design. Int Conf Adv Manuf Auto, Kalasalingam University, India, March 28-30, 2013.
Mohamad Ashari Alias, Siti Zaiton Mohd Hashim, Supiah Samsudin. Using fuzzy analytic hierarchy process for southern Johor river ranking, Int J Adv Soft Comput Appl. 2009;1(1):62-76.
Guest Editor: Eleonora Papadimitriou, PhD
Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.
Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal