ORIGINAL_ARTICLE
Identification of Pattern used in Determination of Critical Success Factors in ITS Projects, Case Study: Road Maintenance and Transportation Organization
One of the risks recognized by relevant authorities is the risk of outsourcing ITS projects. The purpose of this study was to design and explain the pattern of determining the critical success factors in outsourcing large-scale ITS projects in the Ministry of Roads and Urban Development (Road Maintenance and Transportation Organization). This study was performed using qualitative method. The participants in the research were the ITS experts experienced in large-scale projects, 25 of whom were selected purposefully as the sample. Theoretical coding method was used to analyze the information obtained by having experts’ opinion. The method included open coding and axial coding. The paper aims to develop a local model to recognize vital factors in outsourcing of large-scale ITS projects with regard to social and cultural characteristics of relevant organizations in Iran. Using in-depth interview with experts in relevant filed, a model was developed. The results obtained by theoretical coding methods showed that the critical success factors in outsourcing large-scale ITS projects in the Ministry of Roads and Urban Development are organizational factors, management factors, environmental factors and individual factors. Each of these factors includes a number of sub-sections that can be taken into account particularly. This local pattern can be applied for determining the critical factors in the Ministry of Roads and Urban Development (Road Maintenance and Transportation Organization). In addition to this, its outputs, including critical success factors and prioritization and preparation of outsourcing large-scale ITS projects can be exploited by the senior managers of the executive agencies for more accurate and consistent planning. The latter would prevent waste of resources and treasury.
http://www.ijte.ir/article_49729_d9e39b0629337b35138242de27b0dc71.pdf
2018-04-01
319
330
10.22119/ijte.2018.49729
Critical success factors
Outsourcing
intelligent transportation system (ITS)
information technology (IT)
large-scale project
Amir Masoud
Ataei Jafari
dr.ataee@gmail.com
1
Department of Industrial engineering, Faculty of Technical and Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
Ali Mohammad
Ahmadvand
alimohamad.ahmadvand@gmail.com
2
Department of Industrial engineering, Faculty of Technical and engineering, Imam Hosein University, Tehran, Iran
AUTHOR
-Alizadeh Khoshkhesal, I. (2012) “Presenting a pattern to evaluate the productivity of outsourcing cloth distribution system in corps logistics”, Dissertation, Imam Hossein University
1
-Barfield, W. and Dingus, T. A. (2014) “Human factors in intelligent transportation systems”, (ITS), Psychology Press.
2
-Blecher, M. (2007) “Outsourcing IT governance to deliver business value”, Information Systems Control Journal, Vol. 4, pp. 13-14
3
-Brown, D. and Wilson, S. (2005) “The black books of outsourcing: how to manage the changes, challenges, and opportunities”, John Wiley and Sons
4
-Campbell, J. D. (2010) “Outsourcing in maintenance management: A valid alternative to self-provision”, Journal of Quality in Maintenance Engineering, Vol. 3, pp.18-24.
5
-Chow, T. and Cao, D. B. (2008) “A survey study of critical success factors in agile software projects. Journal of Systems and Software”, V0l. 81, No. 6, pp. 961-971.
6
-Claver, E., González, R., Gascó, J. and Llopis, J. (2002) “Information systems outsourcing: reasons, reservations and success factors. Logistics Information Management”, Vol. 15, No. 4, pp. 294-308.
7
-Gonzalez, R., Gasco, J. and Llopis, J. (2005) “Information systems outsourcing risks: a study of large firms”, Industrial management and Data systems, Vol. 105, No.1, pp. 45-62.
8
-Gorla, N. and Somers, T. M. (2014) “The impact of ITS outsourcing on information systems success”, Information and Management, Vol. 51, No. 3, pp. 320-335.
9
-Grewal, C. S., Sareen, K. K. and Gill, S. (2008) “A multi criteria logistics-outsourcing decision making using the analytic hierarchy process”, International Journal of Services Technology and Management, Vol. 9, No. 1, pp.1-13.
10
-Harland, C., Knight, L., Lamming, R. and Walker, H. (2005) “Outsourcing: assessing the risks and benefits for organizations, sectors and nations”, International journal of Operation and Production Management, Vol. 25, No.9, pp. 831-850.
11
-Hazel, T. (2012) “The move to outsourced ITS projects: key risks from the provider perspective”, Proceedings of the 2010 ACM SIGMIS CPR conference on Computer personnel research Atlanta, Georgia, USA: ACM.
12
-Kremic, T., Icmeli Tukel, O. and Rom, W. O. (2006) “Outsourcing decision support: a survey of benefits, risks, and decision factors”, Supply Chain Management: an International Journal, Vol. 11, No.6, pp. 467-482.
13
-Lacity, M. C., Khan, S., Yan, A. and Willcocks, L. P. (2010) “A review of the IT outsourcing empirical literature and future research directions”, Journal of Information Technology, Vol. 25, No.4, pp. 395-433.
14
-Lau, Y. Y. and Ng, C. W. L. (2015) “Adoption of intelligent transportation system: Hong Kong bus”, Research at PolyU Speed, Issue 3, Working paper series No. 4
15
-Mallik, S. (2014) “Intelligent transportation system”, International Journal of Civil Engineering Research, Vol. 5, No. 4, pp.367-372
16
-McIvor, R. (2005) “The outsourcing process: strategies for evaluation and management”, Cambridge Universit
17
-Mikaeili, F. and Sedaghati, H. (2006) “Evaluation the risks of outsourcing IT projects – case study: Iranian Force and Water Resources Development Company (Water-Force)”, Industrial Management Studies, Vol. 13, pp. 19-40.
18
-Marchewka, J. T. and Oruganti, S. (2014) “A combined model of ITS outsourcing partnerships and success”, Communications of the IIMA, 13(2), 6.
19
-Power, M. (2006). “The outsourcing handbook: how to implement a successful outsourcing process”, Kogan Page Publishers Press
20
- Rahnavard, F. and Khavandkar, J. (2008) “The effect of knowledge sharing on the success of outsourcing information technology services”, Journal of IT Management, Vol.1, No.1, pp. 49-64.
21
-Ran, B., Jin, P. J., Boyce, D., Qiu, T. Z. and Cheng, Y. (2012) “Perspectives on future transportation research: impact of intelligent transportation system technologies on next-generation transportation modeling”. Journal of Intelligent Transportation Systems, 16(4), 226-242.
22
-RMTO (2016) Road Maintenance and Transportation Organization website
23
-Rivard, S. and Aubert, B. A. (2008) “Information technology outsourcing”, New York: ME Sharpe.
24
-Rockart, J. (1979) “Chief executives define their own information needs”, Harvard Business Review, March 79.
25
-Samantra, C., Datta, S. and Mahapatra, S. S. (2014) “Risk assessment in ITS outsourcing using fuzzy decision-making approach: An Indian perspective”, Expert Systems with Applications, Vol. 41, No. 8, pp.4010-4022.
26
-Sislian, E. and Satir, A. (2000) “Strategic sourcing: a framework and a case study”, Journal of Supply Chain Management, Vol. 36, No. 2, pp.4-11.
27
-Teichler, U. (2007) “Higher education in globalization world’, London: Springer Publisher.
28
ORIGINAL_ARTICLE
An Improved Particle Swarm Optimization for a Class of Capacitated Vehicle Routing Problems
Vehicle Routing Problem (VRP) is addressed to a class of problems for determining a set of vehicle routes, in which each vehicle departs from a given depot, serves a given set of customers, and returns back to the same depot. On the other hand, simultaneous delivery and pickup problems have drawn much attention in the past few years due to its high usage in real world cases. This study, therefore, considered a Vehicle Routing Problem with Time Windows and Simultaneous Delivery and Pickup (VRPTWSDP) and formulated it into a mixed binary integer programming. Due to the NP-hard nature of this problem, we proposed a variant of Particle Swarm Optimization (PSO) to solve VRPTWSDP. Moreover, in this paper we improve the basic PSO approach to solve the several variants of VRP including Vehicle Routing Problem with Time Windows and Simultaneous Delivery and Pickup (VRPTWSDP), Vehicle Routing Problem with Time Windows (VRPTW), Capacitated Vehicle Routing Problem (CVRP) as well as Open Vehicle Routing Problem (OVRP). In proposed algorithm, called Improved Particle Swarm Optimization (IPSO), we use some removal and insertion techniques and also combine PSO with Simulated Annealing (SA) to improve the searching ability of PSO and maintain the diversity of solutions. It is worth mentioning that these algorithms help to achieve a trade-off between exploration and exploitation abilities and converge to the global solution. Finally, for evaluating and analyzing the proposed solution algorithm, extensive computational tests on a class of popular benchmark instances, clearly show the high effectiveness of the proposed solution algorithm.
http://www.ijte.ir/article_47766_633c1e357d93cb2ba6aacaf6bca921d8.pdf
2018-04-01
331
347
10.22119/ijte.2018.47766
Improved particle swarm optimization
Simulated Annealing
vehicle routing problem
simultaneous delivery and pickup
time windows
Hamed
Alinezhad
halinezhad10@gmail.com
1
MSc. Student, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
ُSaeed
Yaghoubi
yaghoubi@iust.ac.ir
2
Assistant Professor, School of Industrial Engineering, Iran University if Science and Technology, Tehran, Iran
LEAD_AUTHOR
Seyed Mehdi
Hoseini Motlagh
motlagh@iust.ac.ir
3
Assistant Professor, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Somayeh
Allahyari
s_allahyari@ind.iust.ac.ir
4
Instructor, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Mojtaba
Saghafi Nia
5
Instructor, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
-Ai, T. J. and Kachitvichyanukul, V. (2009) “Particle swarm optimization and two solution representations for solving the capacitated vehicle routing problem”, Computers & Industrial Engineering,Vol. 56, No. 1, pp. 380-387.
1
-Allahyari, S., Salari, M., and Vigo, D. (2015) “A hybrid metaheuristic algorithm for the multi-depot covering tour vehicle routing problem”, European Journal of Operational Research, Vol. 242, No. 3, pp. 756-768.
2
-Belmecheri, F., Christian, P., Farouk, Y.and Lionel, A. (2013) “Particle swarm optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows”, Journal of intelligent manufacturing, Vol. 24, No. 4, pp. 775-789.
3
-Castro, J. P., Landa-Silva, D., and Pérez, J. A. M., (2009) “Exploring feasible and infeasible regions in the vehicle routing problem with time windows using a multi-objective particle swarm optimization approach”, Nature inspired cooperative strategies for optimization, Springer, Vol. 236, pp. 103-114.
4
-Chen, A., Yang, G., and Wu, Z., (2006) “Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem”, Journal of Zhejiang University Science A, Vol. 7, No. 4, pp. 607-614.
5
-Chen, C. Y. and Ye, F. (2012) “Particle swarm optimization algorithm and its application to clustering analysis”, Networking, Sensing and Control, IEEE International Conference.
6
-Cho, P., Cheung, S. W., Edwards, M. H., Fung, J., (2003) “An assessment of consecutively presenting orthokeratology patients in a Hong Kong based private practice”, Clinical and Experimental Optometry, Vol. 86, No. 5, pp. 331-338.
7
-Christofides, B., Mingozzi, A., and Toth, P. (1979) “The Vehicle Routing Problem, in Combinatorial optimization”, B. Christofides, Editors. 1979, Chichester: Wiley, pp. 313-338.
8
-Chun-Hua, L., Hong, Z., and Jian, Z. (2009) “Vehicle routing problem with time windows and simultaneous pickups and deliveries”, Industrial Engineering and Engineering Management, IE&EM 16th International Conference.
9
-Clerk, M. (2006) “Particle Swarm Optimization”, London: ISTE.
10
-Cheng, C. Y., Chen, Y. Y., Chen, T. L., Yoo, J. J. (2015) “Using a hybrid approach based on the particle swarm optimization and ant colony optimization to solve a joint order batching and picker routing problem”, International Journal of Production Economics, Vol. 170, No. 3, pp. 805-814.
11
-Chen, M. C., Hsiao, Y. H., Reddy, R. H., Tiwari, M. K. (2016) “The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks”, Transportation Research Part E: Logistics and Transportation Review, Vol. 91, pp. 208-226.
12
-Dantzig, G. B. and Ramser, J. H. (1959) “The truck dispatching problem”, Management Science, Vol. 6, No. 1, pp. 80-91.
13
-Diana, M. and Dessouky, M. M. (2004) “A new regret insertion heuristic for solving large-scale dial-a-ride problems with time windows”, Transportation Research Part B: Methodological, Vol. 38, No. 6, pp. 539-557.
14
-Figureueiredo, E., Ludermir, T. B., and Bastos-Filho, C. (2016) “Many Objective Particle Swarm Optimization”, Information Sciences, Vol. 374, pp. 115-134.
15
-Goksal, F. P., Karaoglan, I. and Altiparmak, F. (2013) “A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery” Computers & Industrial Engineering, Vol. 65, No. 1, pp. 39-53.
16
-Gong, Y. J., Zhang, J., Liu, O., Huang, R. Z., Chung, H. S. (2012) “Optimizing the vehicle routing problem with time windows: a discrete particle swarm optimization approach”, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions, Vol. 42, No. 2, pp. 254-267.
17
-Han, J., Zhang, G., Hu, Y., Lu, J. (2016) “A solution to bi/tri-level programming problems using particle swarm optimization”, Information Sciences, Vol. 370-371, pp. 519-537.
18
-Karaoglan, I., Altiparmak, F., Kara, I., Dengiz, B. (2012) “The location-routing problem with simultaneous pickup and delivery: Formulations and a heuristic approach”, Omega, Vol. 40, No. 4, pp. 465-477.
19
-Kennedy, J. and Eberhart, R. (1955) “particle swarm optimization”, proceedings of IEEE international conference on neural networks, Vol. 4, pp. 1942-1948.
20
-Kennedy, J. and Eberhart, R. (2001) “Swarm Intelligence”, San Francisco: Morgan Kaufmann Publishers.
21
-Kim, B. I. and Son, S. J. (2012) “A probability matrix based particle swarm optimization for the capacitated vehicle routing problem”, Journal of Intelligent Manufacturing, Vol. 23, No. 4, pp. 1119-1126.
22
-Kirkpatrick, S. C., Gelatt, D and Vecchi, M. P. (1984) “Optimization by simulated annealing: Quantitative studies”, Journal of statistical physics, Vol. 34, No. 5-6, pp. 975-986.
23
-Küçükoğlu, İ. and Öztürk, N. (2015) “An advanced hybrid meta-heuristic algorithm for the vehicle routing problem with backhauls and time windows”, Computers & Industrial Engineering, Vol. 86, pp. 60-68.
24
-Kumar, R. S., Kondaraneni, K., Dixit, V., Goswami, A. and Thakur, L. S. (2016) “Multi-objective modeling of production and pollution routing problem with time window: A self-learning particle swarm optimization approach”, Computers & Industrial Engineering, Vol. 99, pp. 29-40.
25
-Lichtblau, T. (2002) “Discrete Optimization using Mathematica, in World Multi Conference on Systemic and Informatics”, Callaos, N. Editors, International Institute of Informatics and Systemics, pp. 169-174.
26
-Liang, J. J., Qin, I. K., Suganthan, P. N., Baskar, S. (2006) “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions”, IEEE transactions on evolutionary computation, Vol. 10, No. 3, pp. 281-295.
27
-Marinakis, Y. and Marinaki, M. (2008) “A particle swarm optimization algorithm with path relinking for the location routing problem”, Journal of Mathematical Modelling and Algorithms, Vol. 7, No. 1, pp. 59-78.
28
-Marinakis, Y., Iordanidou, G. R. and Marinaki, M. (2013) “Particle swarm optimization for the vehicle routing problem with stochastic demands”, Applied Soft Computing, Vol. 13, No. 4, pp. 1693-1704.
29
-Mester, D. and Bräysy, O. (2007) “Active-guided evolution strategies for large-scale capacitated vehicle routing problems”, Computers & operations research, Vol. 34, No. 10, pp. 2964-2975.
30
-Mester, D., Bräysy, O. and Dullaert, W. (2007) “A multi-parametric evolution strategies algorithm for vehicle routing problems”, Expert Systems with Applications, Vol. 32, No. 2, pp. 508-517.
31
-Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N. and Teller A. H. (1953) “Equation of state calculations by fast computing machines”, The journal of chemical physics, Vol. 21, No. 6, pp. 1087-1092.
32
-MirHassani, S. and Abolghasemi, N. (2011) “A particle swarm optimization algorithm for open vehicle routing problem”, Expert Systems with Applications, Vol. 38, No. 9, pp. 11547-11551.
33
-Moghaddam, B. F., Ruiz, R. and Sadjadi, S. J. (2012) “Vehicle routing problem with uncertain demands: An advanced particle swarm algorithm”, Computers & Industrial Engineering, Vol. 62, No. 1, pp. 306-317.
34
-Montané, F. A. T. and Galvao, R. D. (2006) “A tabu search algorithm for the vehicle routing problem with simultaneous pick-up and delivery service”, Computers and Operations Research, Vol. 33, No. 3, pp. 595–619.
35
-Mokhtarimousavi, S., Rahami, H., Saffarzadeh, M. and Piri, S. (2014) “Determination of the aircraft landing sequence by two meta-heuristic algorithms”, International Journal of Transportation Engineering, Vol. 1, No. 4, pp. 271-284.
36
-Marinakis, Y., (2015) “An improved particle swarm optimization algorithm for the capacitated location routing problem and for the location routing problem with stochastic demands”, Applied Soft Computing, Vol. 37, pp. 680-701.
37
-Naderi, B., Zandieh, M., Balagh, A. K. G. and Roshanaei, V. (2009) “An improved simulated annealing for hybrid flowshops with sequence-dependent setup and transportation times to minimize total completion time and total tardiness”, Expert systems with Applications, Vol. 36, No. 6, pp. 9625-9633.
38
-Norouzi, N., Sadegh-Amalnick, M. and Alinaghiyan, M. (2015) “Evaluating of the particle swarm optimization in a periodic vehicle routing problem”, Measurement, Vol. 62, pp. 162-169.
39
-Pisinger, D. and Ropke, S. (2007) “A general heuristic for vehicle routing problems”, Computers & operations research, Vol. 34, No. 8, pp. 2403-2435.
40
-Planeta, D. S. (2007) “Priority Queue Based on Multilevel Prefix Tree”, eprint arXiv:0708.2936
41
-Poli, R., Kennedy, J. and Blackwell, T. (2007) “Particle swarm optimization”, Swarm intelligence, Vol. 1, No. 1, pp. 33-57.
42
-Romero, R., Gallego, R. and Monticelli, A. (1995) “Transmission system expansion planning by simulated annealing”, IEEE Transactions on Power Systems, Vol. 11, No. 1, pp. 364-369.
43
-Salari, M., Toth, P. and Tramontani, A. (2010) “An ILP improvement procedure for the Open Vehicle Routing Problem”, Computers & Operations Research, Vol. 37, No. 12, pp. 2106-2120.
44
-Shi, Y. and Eberhart, R. C. (1998) “A modified particle swarm optimizer”, IEEE international conference on evolutionary computation Proceedings, pp. 69–73.
45
-Solomon, M. M. and Desrosiers, J. (1988) “Survey paper-time window constrained routing and scheduling problems”, Transportation science, Vol. 22, No. 1, pp. 1-13.
46
-Solomon, M. M. (1987) “Algorithms for the vehicle routing and scheduling problems with time window constraints”, Operations research, Vol. 35, No. 2, pp. 254-265.
47
-Thoth, P. and Vigo, D. (2002) “The vehicle routing problem”, Philadelphia, PA: SIAM.
48
-Tian, J., Ma, W. Z., Wang, Y. L. and Wang, K. L. (2011) “Emergency supplies distributing and vehicle routes programming based on particle swarm optimization”, Systems Engineering-Theory & Practice, Vol. 5, pp. 0-16.
49
-Ursani, Z., Essam, D., Cornforth, D. and Stocker, R. (2011) “Localized genetic algorithm for vehicle routing problem with time windows”, Applied Soft Computing, Vol. 11, No. 8, pp. 5375-5390.
50
-Van Laarhoven, P. J. and Aarts, E. H. (1987) “Simulated annealing: theory and applications”, Springer Science & Business Media, Vol. 37, pp. 7-15.
51
-Vidal, T., Crainic, T. G., Gendreau, M., Lahrichi, N. and Rei, W. (2012) “A hybrid genetic algorithm for multidepot and periodic vehicle routing problems”, Operations Research, Vol. 60, No. 3, pp. 611-624.
52
-Wang, H. and Chen, Y. A (2012) “A genetic algorithm for the simultaneous delivery and pickup problems with time window”, Computers and Industrial Engineering, Vol. 62, No. 1, pp. 84-95.
53
-Wang, H., Sun, H., Li, C., Rahnamayan, S. and Pan, J. S. (2013) “Diversity enhanced particle swarm optimization with neighborhood search”, Information Sciences, Vol. 223, pp. 119-135.
54
-Wang, K. P., Huang, L., Zhou, C. G. and Pang, W. (2003) “Particle swarm optimization for traveling salesman problem”, International Conference on Machine Learning and Cybernetics, IEEE, Vol. 3, pp. 1583-1585.
55
-Wang, W., Wu, B., Zhao, Y. and Feng, D. (2006) “Particle swarm optimization for open vehicle routing problem”, Computational Intelligence, Springer, Volume 4114 of the book series, pp. 999-1007.
56
-Wu, C., Liang, Y., Lee, H. P. and Lu, C. (2004) “Generalized chromosome genetic algorithm for generalized traveling salesman problems and its applications for machining”, Physical Review E, Vol. 70, No. 1, pp. 016701.
57
-Xiao, J. M., Huang, Y. F., Li, J. J. and Wang, X. H. (2005) “Vehicle Routing Problem Based on Discrete Particle Swarm Optimization”, Systems Engineering, Vol. 4, pp. 97-100.
58
-Xiao, J. M., Li, J. J. and Wang, X. H. (2005) “particle swarm optimization algorithm for vehicle routing problem1”, Computer Integrated Manufacturing Systems, Vol. 11, No. 4, pp. 577-581.
59
-Xu, J. and Huang, D. (2007) “Hybrid particle swarm optimization for vehicle routing problem with multiple objectives”, Computer Integrated Manufacturing Systems-Beijing, Vol. 13, No. 3, pp. 573.
60
-Yannis, M. and Magdalene, M. A. (2010) “Hybrid genetic - particle swarm optimization algorithm for the vehicle routing problem”, Expert Systems with Applications, Vol. 37, No. 2, pp. 1446-1455.
61
-Zang, H., Jue, J.P. and Mukherjee, B. (2000) “Capacity allocation and contention resolution in a photonic slot routing all-optical WDM mesh network”, Journal of lightwave technology, Vol. 18, No. 12, pp. 1728.
62
-Zhang, J. (2012) “Particle Swarm Optimization Using Crossover Operator”, Journal of Convergence Information Technology, Vol. 7, No. 4, pp. 287-295.
63
-Zhang, L., Pang, X. H., Xia, W. and Wu, Z. (2006) “A hybrid particle swarm optimization algorithm for vehicle routing problem with time windows”, Journal of Shanghai Jiaotong University, Vol. 40, No. 11, pp. 1890.
64
-Zhu, Q., Qian, L., Li, Y. and Zhu, S. (2006) “An improved particle swarm optimization algorithm for vehicle routing problem with time windows”, IEEE Congress on Evolutionary Computation, pp. 1386-1390.
65
ORIGINAL_ARTICLE
Evaluating the Performance of Dowel in PCC Pavement of Roads using ABAQUS Finite Element Software
In Portland Cement Concrete (PCC) pavement of the roads, dowels bar transfers vehicle loading to the unloaded slab. Load Transfer Efficiency (LTE) is used to evaluate dowel bars in PCC pavement. This parameter is defined as the vertical displacement ratio of the loaded slab versus the unloaded slab. In this study, the impact of effective factors (friction coefficient between dowel and concrete slab, wheel loading, dowel diameter and dowel spacing) on load transfer efficiency was studied with modeling by using the ABAQUS finite element software. The verification process was presented to increase confidence in model results and the response data from the numerical simulation agrees well with analytical solution. The results show that with increasing the friction coefficient between slab and dowel, load transfer efficiency increases but the failure of concrete around dowel bars was found to initiate at the face of joint. Furthermore, if strains remain in elastic range, increasing in wheel loading magnitude does not lead to reduce load transfer efficiency but dowel diameter or its spacing have an important role on load transfer by dowels.
http://www.ijte.ir/article_47765_2f771c2e64f79b6286a0c35afb76fbd1.pdf
2018-04-01
349
365
10.22119/ijte.2018.47765
Three-dimensional (3D) modeling
load transfer efficiency
dowel
mahmoodreza
Keymanesh
mrkeymanesh@pnu.ac.ir
1
Assistant Professor, Department of Civil Engineering, Payam-e-Nour University, Tehran, Iran
LEAD_AUTHOR
Mehrdad
Mirshekarian Babaki
mhrdiut@gmail.com
2
Ph. D. Candidate, Department of Civil Engineering, Payam-e-Nour University, Tehran, Iran
AUTHOR
Noushin
Shahriari
mhrdiut@yahoo.com
3
MSc. Student, Department of Civil Engineering, Payam-e-Nour University, Tehran, Iran
AUTHOR
Ali
pirhadi
aptm80@yahoo.com
4
MSc. Grad., Department of Civil Engineering, Payam-e-Nour University, Tehran, Iran
AUTHOR
-ARA and ERES Consultants Division (2004) " Guide for the mechanistic-empirical design of new and rehabilitated pavement structures (NCHRP 1-37A)", Final Report Prepared for National Cooperative Highway Research Program (NCHRP), Transportation Research Board, National Research Council, Washington D.C, USA.
1
-Azadravesh, Ehsan (2010) "Generating a 3D model for evaluating the joint opening effects on load transfer efficiency in concrete pavements, using ABAQUS ", 5th. National Congress on Civil Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
2
-Crovetti, James and Khazanovich, Lev (2005) "Early opening of Portland Cement Concrete (PCC) pavements to traffic", Technical Report, Marquette University, Department of Civil, Construction and Environmental Engineering, Minnesota, USA.
3
-Demir, Serhat and Husem, Metin (2015) " Investigation of bond-slip modelling methods used in FE analysis of RC members", Structural Engineering and Mechanics, An Int'l Journal Vol. 56, No. 2, pp. 11-28
4
-Friedberg, B. F. (1940) "Design of dowels in transvers joints of concrete pavements", Transactions of the ASCE, Vol. 122, No. 2, pp. 146-154.
5
-Ghauch, Ziad (2013) "Finite element investigation of the deterioration of doweled rigid pavements", Department of Civil Engineering Lebanese American University
6
-Han, Jin and Seong-Min, Kim and Wonseok, Chung and Yong Hyeon, Lee (2014) "Effect of joint type on rigid airfield pavement behavior ", KSCE Journal of Civil Engineering, Vol. 18, No. 5, pp. 1389-1396.
7
-Huang, Yang (1993) "Pavement analysis and design", USA: Prentice Hall, New Jersey.
8
-Jankowiak, Tomasz and Lodygowski, Tomasz (2005) "Identification of parameters of concrete damage plasticity constitutive model", Foundation of Civil and Environmental Engineering, Vol. 1, No. 6, pp. 54-69.
9
-Karlsson, Hibbitt and Sorensen, Inc. (2015) “ABAQUS, Finite Element Computer Program, Version 14.1.0”, Karlsson Hibbitt and Sornsen Inc.
10
Luoke, L., Tan, Y., Xiangbing, G. and Yunliang, L. (2012) "Characterization of contact stresses between dowels and surrounding concrete in jointed concrete pavement", International Journal of Civil and Environmental Engineering, Vol.12, No. 05, pp. 16-24
11
-Mackiewicz, Pioter (2015) "Analysis of stresses in concrete pavement under a dowel according to its diameter and load transfer efficiency", Canadian Journal of Civil Engineering, Vol. 42, No. 11, pp. 845-853.
12
-Maitra, Swati Roy Reddy, K. S. and Ramachandra, L. S. (2015) "Estimation of joint and interface parameters for the finite element analysis of jointed concrete pavement using structural evaluation results", International Journal on Pavement Engineering and Asphalt Technology, Vol.16, No. 2, pp. 21-38.
13
-National Cooperative Highway Research Program (2009) "NCHRP Guidelines for dowel alignment in concrete pavement", Rep. No. 637, Transportation Research Board, National Research Council, Washington, D.C, USA.
14
-Sharif Tehrani, Saleh and Hosseini Lavasani, Seyed Hossein (2016) "The of concrete pavement mix design parameters on durability under freeze and thaw condition", International Journal of Transportation Engineering, Vol. 4, No.3, pp. 211-224.
15
-Sii, H. B., Chai, G. W., Staden, R. V. and Guan, H. (2014) "Development of prediction model for doweled joint concrete pavement using three-dimensional finite element analysis", Applied Mechanics and Materials, Vols. 587-589, pp. 1047–1057.
16
-Tabatabaie, A. M. and Barenberg, E. H. (1980) "Structural analysis of concrete pavement systems", Journal of Transportation Engineering, ASCE, Vol. 106, No. 5, pp. 493-506.
17
-Timoshenko, S. and Lessels, J. M. (1925) "Applied elasticity", Westinghouse Technical Night School Press, Pittsburgh, Pennsylvania, USA
18
-Wadkar, A., Mehta Y., Guo, E. and Kettleson, W. (2011) "Load-transfer efficiencies of rigid airfield pavement joints based on stresses and deflections", Journal of Materials in Civil Engineering, Vol. 23, No. 8, pp. 1171–1180.
19
-Yu, H. T., Smith, K. D., Darter, M.I., Jiang, J. and Khazanovich, L. (1993) "The performance of concrete pavements", Volume III, Report No. FHWA- RD-95-111. Federal Highway Administration, Washington, DC, USA.
20
-Zaghloul, Sameh and White, Thomas (1993) "أNonlinear dynamic analysis of concrete pavements", Fifth International Conference on Concrete Pavement Design and Rehabilitation, Vol. 1, Purdue University, West Lafayette, Indiana, USA.
21
ORIGINAL_ARTICLE
Effects of Aggregate Gradation on Resilient Modulus and CBR in Unbound Granular Materials
Resilient modulus and California Bearing Ratio (CBR) in unbound granular materials are the key technical characteristics of layers in a flexible pavement design. Among the factors affecting these two parameters, the aggregate gradation is the most important. Using particle size distribution curve developed by AASHTO, together with other considerations mentioned in the related regulations have yielded desirable results in many cases. However, many roads loaded by heavy vehicles, for which all technical instructions of standard regulations were observed, have undergone deformations caused by subsidence of layers. According to the related technical documents, one hypothesis could be the proximity of aggregate gradation to the boundary areas. Therefore, the aim of this study was to determine the effect of changes in the scope of aggregation in the border areas on strength parameters. For this purpose, effects of aggregate grading variation on two types of aggregates, i.e. limestone and quartzite (as determined by AASHTO) were investigated using specific gravity, CBR, and resilient modulus tests. The results showed that, in the gradation boundaries determined by AASHTO, the difference between specific gravity values was insignificant. In the CBR and resilient modulus tests, however, there was a significant difference between test results in the upper and lower limits of gradation. In addition, gradation variation had a lower impact on resistance parameters in quartzite aggregate compared to limestone aggregate. Therefore, under special utilization conditions, materials with highest values of technical specifications should be used, since even materials whose technical specifications are in the standard range may not behave as expected in real world situations.
http://www.ijte.ir/article_49727_6b1674c8152d25a4d44cc5f3be196533.pdf
2018-04-01
367
381
10.22119/ijte.2018.49727
Unbound granular materials
particle size distribution curve
specific gravity
CBR
Resilient Modulus
Mohsen
Aboutalebi Esfahani
m.aboutalebi.e@eng.ui.ac.ir
1
Assistant Professor, Faculty of Transportation, University of Isfahan, Isfahan, Iran
LEAD_AUTHOR
Ahmad
Goli
a.goli@trn.ui.ac.ir
2
Assistant Professor, Faculty of Transportation, University of Isfahan, Isfahan, Iran
AUTHOR
-AASHTO (1990) "Construction Manual for Highway Construction", Washington, AASHTO
1
-AASHTO® (1993)" AASHTO Guide for Design of Pavement Structures", Washington, AASHTO
2
-AASHTO (1993) "Guide Specification for Highway Construction", USA Washington, D.C.
3
-AASHTO (2003) "Determining the resilient modulus of soils and aggregate materials", 20th ed., Washington, AASHTO
4
-AASHTO (2014) "Standard Specifications for Transportation Materials and Methods of Sampling and Testing", 34th Edition and AASHTO Provisional Standards, 2014 Edition 4-Guide Specification for Highway Construction, Washington, AASHTO
5
-Barksdale R. (1991) "The aggregate handbook", USA Washington, D.C.
6
-Bilodeau, J. P., Dore, G. and Pierre, P. (2008) "Gradation influence on frost susceptibility of base granular materials", International Journal of Pavement Engineering, Vol. 9, No. 6, pp. 397-411.
7
-Bilodeau, J. P.; Plamondon C. O. and Dore G. (2016) "Estimation of resilient modulus of unbound granular materials used as pavement base: combined effect of grainsize distribution and aggregate source frictional properties", Materials and Structures, Vol. 49, No. 10, PP. 1-11.
8
-Cunninghama, C. N., Evansb, T. M. and Tayebalib, A. A. (2013) "Gradation effects on the mechanical response of crushed stone aggregate", International Journal of Pavement Engineering, Vol. 14, No. 3, pp. 231–241.
9
-Ekblad J. (2008) "Statistical evaluation of resilient models characterizing coarse granular materials", Materials and Structures, Vol. 41, No. 3, pp. 509–525.
10
-Fuller, W. and Thompson, S. (1907) "The laws of proportioning concrete", Trans ASCE, Vol. 59, pp. 67–143.
11
-Ghabchi, R., Zaman, M., Khoury, N., Kazmee, H. and Solanki, P. (2013) "Effect of gradation and source properties on stability and drainability of aggregate bases: a laboratory and field study", International Journal of Pavement Engineering, Vol. 14, No. 3, pp. 274-290.
12
-Golalipour, A., Jamshidi, E., Niazi, Y., Afsharikia, Z. and Khadem, M. (2012) "Effect of aggregate gradation on rutting of asphalt pavements", Procedia - Social and Behavioral Sciences, Vol. 53, pp. 440-449.
13
-Gu, F., Sahin, H., Luo, X., Luo, R. and Lytton, R. (2014) "Estimation of resilient modulus of unbound aggregates using performance-related base course properties", Journal of Materials in Civil Engineering, Vol. 27, No.6.
14
-Hamidi, A., Azini, E. and Masoudi, B. (2012) "Impact of gradation on the shear strength-dilation behavior of well graded sand-gravel mixtures", Scientia Iranica Transactions A: Civil Engineering, Vol. 19 No. 3, pp.. 393-402.
15
-Huang, Y. (2004) "Pavement analysis and design", second Ed. ed., U.S.A: Pearson Education Inc.
16
-Jiang, Y., Wong, L. N. Y. and Ren, J. (2015) “A numerical test method of California bearing ratio on graded crushed rocks using particle flow modeling”, Journal of Traffic and Transportation Engineering (English Edition), Vol. 2, No. 2, pp. 107–115
17
-Kim, D. and Kim, J. R. (2007) "Resilient behavior of compacted subgrade soils under the repeated triaxial test", Construction and Building Materials, Vol. 21, No. 7, pp. 1470-1479.
18
-Lavin, P. (2003) "A practical guide to design, production and maintenance for engineers and architects", London and New York: Spon Press.
19
-Lekarp F. and Isacsson U. (2001) "The effects of grading scale on repeated load triaxial test results", International Journal of Pavement Engineering, Vol. 2, No. 2, pp. 85-101.
20
-Pan, T. and Tutumluer, E. (2005) "Imaging based evaluation of coarse aggregate size and shape properties affecting pavement performance", In Geotechnical Special Publication, Austin, TX; United States.
21
-Sang and Kooh Company (2015) "Sang and Kooh Company’s technical report of projects of south of Iran".
22
-TRB (2013) "NCHRP Synthesis 445, Practices for Unbound Aggregate Pavement Layers", USA Washington, D.C.
23
-Uthus L. (2007) "Deformation properties of unbound granular materials", Doctoral dissertation, Norwegian University of Science and Technology.
24
-Xiao, Y. and Tutumluer, E. (2016) "Gradation and packing characteristics affecting stability of granular materials: Aggregate Imaging-Based Discrete Element Modeling Approach", International Journal of Geomechanics, Vol. 17, No. 3, http://dx.doi.org/10.1061/(ASCE) GM.1943-5622.0000735.
25
-Xiao, Y., Tutumluer, E., Qian, Y. and Siekmeier, J. (2012) "Gradation effects influencing mechanical properties of aggregate base-granular subbase materials in Minnesota", Transportation Research Record: Journal of the Transportation Research Board, No. 2267, PP. 14-26.
26
-Yildirim, B. and Gunaydin, O. (2011) “Estimation of California bearing ratio by using soft computing systems”, Expert Systems with Applications, Vol. 38, N0. 5, pp. 6381-6391.
27
ORIGINAL_ARTICLE
Analyzing Stop Time Phase Leading to Congestion Based on Drivers’ Behavior Patterns
Traffic oscillation, stop and go traffic, is created by different reasons such as: sudden speed drop of leader vehicle. Stop and go traffic commonly is observed in congested freeways results in traffic oscillation. Many theories had been presented to define congestion traffic based on laws of physics such as: thermodynamics and fluid. But, these theories could not explain the complexity of driving responses in different situations of traffic especially in traffic jams. Unfortunately, because trajectories data are very scarce, our understanding of this type of oscillations in congested traffic is still limited. When the leader vehicle of a platoon drops speed, deceleration waves are released from downstream to upstream. Follower vehicles reacts different behavioral reactions based on personal characteristics. In this paper, behavioral patterns of follower driver were classified based on asymmetric microscopic driving behavior theory and traffic hysteresis in NGSIM trajectories. They were four patterns in deceleration phase and two patterns in acceleration phase. Then, two parameters of last deceleration wave leading to congestion, time and space parameters, τ and δ, were calculated based on Newell’s car following model. Time of two phases, stop and congestion phases, were identified based on follower vehicle trajectory. In order to calculate time of two phases, two points were identified: point of receiving stop wave leading to congestion and point of entering to congestion. Artificial neural network models were developed to analyze the relationship between the microscopic parameters and time of two phases. Analysis results present spacing difference of follower between stop and congestion phase based on under reaction-timid pattern and spacing difference of follower between deceleration and congestion phase based on over reaction-timid pattern and spacing of leader vehicle at the wave diffusion point are most effective parameters in stop time leading to congestion. One of the main practical applications of this paper can be the addressing one of the main problems of micro simulation soft wares (like Aimsun) due to behavioral patterns.
http://www.ijte.ir/article_49734_978a95876f36b306d1675e5713e7cf7a.pdf
2018-04-01
383
400
10.22119/ijte.2018.49734
Stop time lead to congestion
stop and go traffic
NGSIM data trajectory
behavior patterns
Artificial Neural Networks
Babak
Mirbaha
bmirbaha@gmail.com
1
Assistant Professor, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran
LEAD_AUTHOR
Ali
Abdi Kordani
aliabdi001@yahoo.com
2
Assistant Professor, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran
AUTHOR
arsalan
salehikalam
salehikalam.arsalan@gmail.com
3
PhD candidate, Imam Khomeini International University, Qazvin, Iran
AUTHOR
Mohammad
Zareyi
mohammad.zareyi@gmail.com
4
PhD candidate, Imam Khomeini International University, Qazvin, Iran
AUTHOR
-Ahn, S. and Cassidy, M. (2006) “ Freeway traffic oscillations and vehicle lane-changemanoeuvres. In: Heydecker, B., Bell, M., Allsop, R. (Eds.)”, Forthcoming in 17thInternational Symposium on Transportation and Traffic Theory. Elsevier, NewYork.
1
-Ahn, S., Vadlamani, S. and Laval, J. A. (2011) “A method to account for non-steady state conditions in measuring traffic hysteresis." Transportation Research Part C: Emerging Technologies Vol. 34, pp. 138-147”
2
-Abdi, A. and Salehikalam, A. (2016) “Analyzing deceleration time lead to congestion based on behavior patterns”, Modares Civil Engineering Journal - Volume 16, Special Issue, Winter 1395, pp. 91-102.
3
-Arab Moghadam, M., Pahlavani, P., Naseralavi, S. (2016) “Prediction of car following behavior based on the instantaneous reaction time using an ANFIS-CART based model”, International Journal of Transportation Engineering , Article 4, Volume 4, Issue 2, pp.. 109-126.
4
-Bilbao-Ubillos, J. (2008) “The costs of urban congestion: estimation of welfare losses arising from congestion on cross-town link roads”, TransportationResearch Part A Vol. 42, No. 8, pp.1098–11082.
5
-Chen, D., Laval, J. A. Zheng, Z. and Ahn, S. (2012a) “Traffic oscillations: a behavioral car-following model”, Transportation Research Part B, Vol. 46, No. 6, pp. 744-761.
6
-Chen, D., Laval, J. A., Ahn, S. and Zheng, Z. (2012b) “Microscopic traffic hysteresis in traffic oscillations: A behavioral pespective”, Transportation Research Part B, Vol.43 A. pp.126-141.
7
-Del Castillo, J. M. (2001) “Propagation of perturbations in dense traffic flow: a model and its implications”, Transportation Research Part B Vol. 35, pp. 367-389.
8
-Edie, L. C. and Baverez, E. (1967) “Generation and propagation of stop-start traffic waves”, Proceedings of Third International Symposium on the Theory of Traffic Flow. American Elsevier Publishing Co. New York. pp. 26-37.
9
-Forbes, T. W., Zagorski, H.J. Holshouser, E. L. and Deterline, W. A. (1958) “Measurement of driver reactions to tunnel conditions”, Proceedings of Highway Research Board.Vol.37, pp. 345-357.
10
-Herman, R. and Potts, R. B. (1961) “Single-lane traffic theory and experiment”, Proceedings of Symposium on the Theory of Traffic Flow (R. Herman Ed.). Elsevier publishing Co. Amsterdam. pp. 120-146.
11
-Herman, R. and Rothery, R. (1967) “Propagation of disturbances in vehicular platoons”, Proceedings of Third International Symposium on the Theory of Traffic Flow (L.C. Edie, Ed.), American Elsevier publishing Co. New York. pp. 26-37.
12
-Hongfei, J., Zhicai, J. and Anning, N. (2003) “Develop a car-following model using data collected by ‘five-wheel system”. Proceedings of the IEEE Intelligent Transportation System, Vol. 1, China, pp. 346–351.
13
-Kim, T. and Zhang, H. M. (2004) “Gap time and stochastic wave propagation”, IEEE Intelligent Transportation Systems Conference, pp. 88-93.
14
-Koshi, M., Kuwahara, M. and Akahane, H. (1992) “Capacity of sags and tunnels injapanese motorways”, ITE Journal (May issue), pp.17–29.
15
-Karlaftis, M. G. and Vlahogianni, E. I. (2011) “Statistics versus neural networks in transportation research: Differences, similarities and some insights”, Transportation Research Part C: Emerging Technologies. Vol. 19, No. 3, pp. 387-399.
16
-Khodayari, A. and Ghaffari, A. (2011) “ Modify car following model human effects based on locally linear neuro fuzzy”, Intelligent Vehicles Symposium (IV), 2011 IEEE. pp. 661-666.
17
-Laval, J. A. and Daganzo, C. F. (2006) “Lane-changing in traffic streams”, Transportation Research Part B Vol. 40, No. 3, pp. 251–264.
18
-Laval, J. A. (2006) “Stochastic processes of moving bottlenecks: Approximate formulas for highway capacity”, Transportation Research Record, pp. 86–91.
19
-Laval, J. A. and Leclercq, L. (2010) “A mechanism to describe the formation and propagation of stop-and-go waves in congested freeway traffic”, Philosophical Transactions of The Royal Society A. 368, pp. 4519-4541.
20
-Laval A. J. (2010) “Hysteresis in traffic flow revisited: An improved measurement method, Transportation Research”, Part B. Vol. 45, No 2, pp. 385–391.
21
-Laval A. J. (2009) “Hysteresis in the fundamental diagram: impact of measurement methods”, 89th Annual Meeting of the Transportation Research Board, Washington, D.C.
22
-Mauch, M. and Cassidy, M. J. (2002) “Freeway traffic oscillations: observation and predictions”, The 15th International Symposium on Transportation and Traffic Flow Theory.
23
-Newell, G. F. (1962) “Theories of instability in dense highway traffic”, Journal of the Operations Research Society of Japan Vol. 5, pp.9–54.
24
-Newell, G. F. (2002) “A simplified car-following theory: a lower order model”, Transportation Research Part B Vol. 36, pp. 196-205.
25
-NGSIM. Accessed at: http://ngsim-community.org/
26
-Orfanou, F., Vlahogianni, E and Karlaftis, M. (2012) “Identifying features of traffic hystersis on freeways, Transportation Research, Part B.
27
-Panwai, S. and Dia, H. (2007) “ Neural agent car-following models, IEEE Transactions on Intelligent Transportation Systems, Vol. 8, No. 1, pp. 60–70.
28
-Trajectory Explorer. Accessed at: http://trafficlab.ce.gatech.edu/tools.html.
29
-Treiterer, J. and Myer, J. A (1974) “The hysteresis phenomenon in traffic flow”, Proceedings of the Sixth Symposium on Transportation and Traffic Flow Theory. D. J. Buckley (Ed.). pp. 213-219.
30
-Xiaoliang, Ma. (2006) “A neural-fuzzy framework for modeling car following behavior”, Systems, Man and Cybernetics, 2006. SMC'06. IEEE International Conference on. Vol. 2. IEEE, 2006, pp. 770-776.
31
-Yeo, H. and Skabardonis, A. (2009) “Understanding stop-and-go traffic in view of asymmetric traffic theory”, Transportation and Traffic Theory 2009: Golden Jubilee, Springer, pp. 99-115.
32
-Zheng, Z., Ahn, S., Chen, D. and Laval, J. A. (2011) “Freeway traffic oscillations: Microscopic analysis of formations and propagations using wavelet transform”, Transportation Research Part B, Vol. 45, No. 9, pp. 1378-1388.
33
-Zheng, Z., Ahn, S., Chen, D. and Laval, J. (2011a) “Applications of wavelet transform for analysis of freeway traffic: bottlenecks, transient traffic, and traffic oscillations. Transportation Research Part B Vol. 45 No. 2, pp.372–384.
34
-Zheng, Z., Ahn, S., Chen, D., Laval, J.A. (2011b) “Freeway traffic oscillations: microscopic analysis of formations and propagations using wavelet transform”. The 19th International Symposium on Transportation and Traffic flow Theory, pp.717–731.
35
-Zhang, H. M. (1999) “A mathematical theory of traffic hysteresis”, Transportation Research Part B, Vol. 33, pp. 1-23.
36
-Zhang, H. M. and Kim, T. (2005) “A car-following theory for multiphase vehicular traffic flow”, Transportation Research Part B, Vol. 39, pp. 385-399.
37
-Zheng, J., Suzuki, K. and Fujita., M. (2013) ” Car-following behavior with instaneous driver-vehicle reaction delay: a neural-network-based methodolghy”, Transportation Research Part B, Vol. 36, pp. 339-351.
38
ORIGINAL_ARTICLE
Assessing Behavioral Patterns of Motorcyclists Based on Traffic Control Device at City Intersections by Classification Tree Algorithm
According to the forensic statistics, in Iran, 26 percent of those killed in traffic accidents are motorcyclists in recent years. Thus, it is necessary to investigate the causes of motorcycle accidents because of the high number of motorcyclist casualties. Motorcyclists' dangerous behaviors are among the causes of events that are discussed in this study. Traffic signs have the important role of traffic controller, and road surface marking is a tool for traffic separation and has a significant effect on drivers' behaviors. The aim of this study is to investigate the effect of variables, including traffic conditions, motorcyclists' psychological conditions, and symptoms and function of traffic lights on the motorcyclists' dangerous behaviors. In this study, classification tree method is used to determine the effective factors in some motorcyclists' dangerous behaviors such as the amount of deviation from the center lane, lane changing, and running red lights. The classification tree is easy to understand and interpret because of the graphical display of results. The data classification tree is made based on the classification and regression tree algorithm (CRT) in this study. The data are collected from the 7 intersections in a city with the medium population by video-based observation method. Hand-held cameras randomly record the motorcyclists' motions and, then, these behaviors are investigated in the office by playing back the videos at slow motion. The obtained trees show that the variables of traffic volume have the greatest impact on the motorcyclists' diversion from the center lane and lane changing. Also, the clarity of the pavement marking is effective in reducing deviation from the middle lane by cyclists so that, in the streets with the line color contrast of more than 1.36, deviation from the center lane is reduced by 25 cm.
http://www.ijte.ir/article_49725_08ed37c0a76f7899ef01914ecba95ee7.pdf
2018-04-01
501
415
10.22119/ijte.2018.49725
Pavement Marking
classification trees
dangerous behavior
motorcyclist
color contrast
lateral deviation from center lane
Mohammad Mehdi
Khabiri
mkhabiri@yazd.ac.ir
1
Associate Professor, Department of Civil Engineering, Yazd University, Yazd, Iran
LEAD_AUTHOR
-Abdulhai, B. and Ritchie, S. G. (1999) “Enhancing the universality and transferability of freeway incident detection using a Bayesian-based neural network”, Transportation Research Part C 7, pp. 261–280.
1
-Adeli, H. and Karim, A. (2000) “Fuzzy-wavelet RBFNN model for freeway incident detection”, Journal of Transportation Engineering Vol. 126, No. 6, pp. 464–471.
2
-Ahmed, S. A. (1983) “Stochastic processes in freeway traffic”, Traffic Engineering Control, pp.306–310.
3
-Aultman-Hall, L., Hall, F.L., Shi, Y. and Lyall, B. (1991) “A catastrophe theory approach to freeway incident detection”, Proceedings of the Second International Conference on Applications of Advanced Technologies in Transportation
4
Engineering, The American Society of Civil Engineers, New York, NY, pp. 373–377.
5
-Chang, E. C.-P. and Wang, S.-H. (1995) “Improved freeway incident detection using fuzzy set theory”, Transportation Research Record Vol. 1453, 75–82.
6
-Cook, A. R. and Cleveland, D. E. (1974) “Detection of freeway capacity-reducing incidents by traffic-stream measurements”, Transportation Research Record, Vol. 495, pp.1–11.
7
-Daganzo, C. (1995) “The cell transmission model, Part II: Network traffic” Transportation Research,Part B, Vol. 29, No. 2, pp.79–93.
8
-Dudek, C. L. and Messer, C. J. (1974) “Incident detection on urban freeways” Transportation Research Record, Vol.495, pp. 12–24.
9
-Golob T. F., Will, Rocker and Yannis, Pavlis (2008) “ Probabilistic models of freeway safety performance using traffic flow data as predictors”, Safety Science, Vol.46 (2008) pp.1306-1333.
10
-Hoogendoorn, S. P. and Bovy, P. H. L. (2001) “State of the art of vehicular traffic flow modelling“, Jornal of Systems and Control engineering, Vol.215(4).
11
-Hsiao, C.-H., Lin, C.-T. and Cassidy, M. (1994) “Application of fuzzy logic and neural networks to automatically detect freeway traffic incidents” Journal of Transportation Engineering Vol.120 (5), pp.753–772.
12
-Ishak, S. and Al-Deek, H. (1999) “Performance of automatic ANN-based incident detection on freeways” Journal of Transportation Engineering, pp.281–290.
13
-Kerner, B. S. (2013) “Criticism of generally accepted fundamentals and methodologies of traffic and transportation theory”, A brief review Physic A: Statistical Mechanics and its Applications Vol. 392 (21), pp. 5261-5282.
14
-Kurzhanskiy, A. and Varaiyav, P. (2010) “Active traffic management on road networks: a macroscopic approach“. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 368(1928), pp. 4607-4626.
15
-Levin, M. and Krause, G. M. (1978) “Incident detection: a Bayesian approach” Transportation Research Record Vol.682, pp.52–58.
16
-Lin, W.-H. (1995) “Incident detection with data from loop surveillance systems: the role of wave analysis”, Dissertation, Institute of Transportation Studies, University of California at Berkeley.
17
-Payne, H. J. and Tignor, S. C. (1978) “Freeway incident detection algorithms based on decision trees with states”, Transportation Research Record, Vol. 682, pp.30–37.
18
-Porikli, F. and Li, X. (2004) “Traffic congestion estimation using HMM models without vehicle tracking”, Mitsubishi Electronic Reserch Labratories.
19
-Qiu, T.Z., Lu, X., Chow, A. H. F. and Shladover, S. E. (2010) “Estimation of freeway traffic density with loop detector and prob vehicle data“, Transportation Research Record, Jornal of Transportation Research Board, No. 2178, pp. 21-29.
20
-Stephanedes, Y. J. and Chassiakos, A. P. (1993) “Application of filtering techniques for incident detection”, Journal of Transportation Engineering, Vol. 119, No. 1, pp.13–26.
21
-Torfehnejad, H. (2011) “A practical dynamic speed limit control method using real-time traffic counting systems“, 18th ITS World Congress, 16-20 October, Orlando Florida, USA.
22
-Torfehnejad, H. and Adamnejad, Sh. (2014) “A practical symple technique to detect abnormal traffic flow in freeway“, 21th ITS World Congress, 7-11September, Detroit, USA.
23
-U.S. Department of Transportation. Federal Highway Administration (2006) “Traffic detector handbook“, Third edition- Volume 1.
24
-Whitson, R. H., Burr, J. H., Drew, D. R. and McCasland, W. R. (1969) “Real-time evaluation of freeway quality of traffic service” Highway Research Record, Vol. 289, pp.38–50.
25
-Willsky A. S., Chow E.Y.,. Gershwin, S. B, Greene, C. S., Houpt, P. K. and Kurkjian, A. L. (1980) “Dynamic model-based techniques for the detection of incidents on freeways“ , IEEE Transactions on automatic control, Vol. AC-25, No.3.
26
-Yang, L. and Sahli, H. [n.d.]“Motion-based traffic analysis and incident detection“ , IBBT/VUB-ETRO, FLEXYS
27