Hindawi Publishing Corporation Journal of Engineering Volume 2013, Article ID 245293, 5 pages http://dx.doi.org/10.1155/2013/245293 Research Article Application of Chaos Theory in Trucks’ Overloading Enforcement Abbas Mahmoudabadi, 1 and Arezoo Abolghasem 2 1 Department of Industrial Engineering, Payame Noor University (PNU), Shahnaz Alley, Nourian Street, North Dibagi Avenue, Tehran, Iran 2 Road Maintenance and Transportation Organization, Number 12 Dameshq Street, Vali-e-Asr Avenue, Tehran, Iran Correspondence should be addressed to Abbas Mahmoudabadi; mahmoudabadi@phd.pnu.ac.ir Received 14 August 2012; Accepted 30 October 2012 Academic Editor: Sang-Min Han Copyright © 2013 A. Mahmoudabadi and A. Abolghasem. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Trucks’ overloading is considered as one of the most substantial concerns in road transport due to a possible road surface damage, as well as, are less reliable performance of trucks’ braking system. Sufficient human resource and adequate time scheduling are to be planned for surveying trucks’ overloading; hence, it seems required to prepare an all-around model to be able to predict the number of overloaded vehicles. In the present research work, the concept of chaos theory has been utilized to predict the ratio of trucks which might be guessed overloaded. e largest Lyapunov exponent is utilized to determine the presence of chaos using experimental data and concluded that the ratio of overloaded trucks re�ects chaotic behavior. e prediction based on chaos theory is compared with the results of simple smoothing and moving average methods according to the well-known criterion of mean square errors. e results have also revealed that the chaotic prediction model would act more capably comparing the analogous methods including simple smoothing and moving average to predict the ratio of passing trucks to be possibly overloaded. 1. Introduction Road transportation is a dominant mode of freight trans- portation in Iran that accounts for about 80% of the freight movement [1]. e number of heavy vehicles is continuously increasing on the road network. Overloading is growing up, and it is a ma�or cause for signi�cantly accelerating the rate of pavement deterioration [2]. In addition to damage road surface, overloaded trucks endanger other vehicles because of predesigning of vehicles’ braking systems particularly in curves and slopes. Having promoted road safety and minimizing road maintenance costs, ordinary road measures imposed on road pavement by axles’ load of trucks are made as part of the enforcement process laws [3] is legally controlled by weighing stations. Relevant studies in the �eld of pavement designs based on truck axle loads are referred in the literature. Turochy et al. [4] developed truck factors for pavement design and axle load distribution models for mechanistic-empirical pave- ment design using information from weigh-in-motion sites on arterial roads in Alabama. Till also developed a detailed method of wheel load modeling from overload trucks for bridge decks [5]. Chia-pei calculated average load factors for combined heavy vehicles and axle load ratios for various types of heavy vehicles, to design bridge standard speci�cation [6]. Although some location allocation techniques can be utilized to improve the efficiency of overloading enforcement [7], in order to minimize overloading, scheduling human resources who are employed staff in the process of checking trucks’ axles load in weighing stations should be consid- ered in road enforcement. Improving the efficiency, human resources scheduling would require the utilization of predic- tion methods. Recent techniques of prediction are observed in the literature by applying chaos theory in the �eld of road traffic. Frazier and �ockelman analyzed traffic �ow data and found it chaotic. eir studies showed that predictions based on chaos theory would have greater predictive power than a nonlinear least-squares method [8]. Disbro and Frame demonstrated how the theoretically derived Gazis-Herman- Rothery traffic model [9] is highly chaotic, even though