ORIGINAL_ARTICLE
Estimating the Cost of Road Traffic Accidents in Iran using Human Capital Method
Road traffic accidents and the effects they subsequently incur are increasing dramatically, and estimating the cost of road traffic crashes could be a vital step in improving the recognition of this widespread problem. The main objective of this paper is to estimate the cost of road traffic crashes in Iran using the Human Capital (HC) Method. Results of this study indicate that the cost of road traffic crashes in Iran for the year 2009 was approximately 114,455 billion Iranian Rials (about $US 11.458 billion), which accounted for 1.41% of Iran’s Gross National Product (GNP) in that year. This study shows that lost output and property damage account for the largest proportion of this cost, whereas human, administration and medical costs are the next highest contributors. In addition, this study estimates the cost of accidents in Iran for the year 2009, depending on their status as fatal, causing serious injury, slight injury and propertydamageonly.
http://www.ijte.ir/article_9601_a769c3b8a7932e44bc54cde540894886.pdf
20150101
163
178
10.22119/ijte.2015.9601
Human Capital Method
crashes
costs
Developing countries
Mohammad Reza
Ahadi
ahadireza@yahoo.com
1
Road, Housing & Urban Development Research Center
LEAD_AUTHOR
Hesamoddin
RaziArdakani
hesam.razi@gmail.com
2
Department of Civil Engineering, Sharif University of Technology
AUTHOR
ORIGINAL_ARTICLE
ProbitBased Traffic Assignment: A Comparative Study between LinkBased Simulation Algorithm and PathBased Assignment and Generalization to RandomCoefficient Approach
Probabilistic approach of traffic assignment has been primarily developed to provide a more realistic and flexible theoretical framework to represent traveler’s route choice behavior in a transportation network. The problem of path overlapping in network modelling has been one of the main issues to be tackled. Due to its flexible covariance structure, probit model can adequately address the problem. Despite that probit is one of the most appealing choice models, due to the lack of closed form expressions for evaluating choice probabilities; it has not received extensive attention by network modeling researchers. This study is set out to focus on this approach of traffic assignment. Computational difficulty of application of probit model in the largescale network equilibrium problem has triggered development of some linkbased probit network loading methods which exempt the analyst from generating and maintaining pathflow variables explicitly. To the best of our knowledge, however, the bias of these heuristic linkbased methods has not been studied so far. This contribution primarily focuses on investigation of such potential bias in linkbased probit assignment methods. In this research, this bias for a certain simulated linkbased method proposed in the literature is empirically considered and investigated through comparison with pathbased probit equilibrium solution. Our findings indicate considerable level of bias for the examined linkbased algorithm. Capable of representing utility correlation and heteroscedasticity, probit model has always been one of the most theoretically attractive models for representing route choice behavior. However, this soundness of theory could further be enhanced through combining the ideas of probit and randomcoefficient modeling which enables the analyst to capture random taste heterogeneity over travelers as well. To do this, the notion of mixed probit model, as a generalization to classical fixedcoefficient probit, is introduced and applied to an illustrative network in this study, in addition to the main contribution of the article.
http://www.ijte.ir/article_9602_c9a6be22f55f963058e645a62d490bdf.pdf
20150101
179
198
10.22119/ijte.2015.9602
Probit Model
Multivariate Normal Distribution
Monte Carlo simulation
randomcoefficient choice models
LinkBased and PathBased Traffic Assignment
Milad
Haghani
milad.haghani@monash.edu
1
Department of Civil Engineering, Institute of Transport Studies, Monash University
LEAD_AUTHOR
Zahra
Shahhoseini
zahra.shahhoseini@ymail.com
2
Department of Civil Engineering, Institute of Transport Studies, Monash University
AUTHOR
Majid
Sarvi
majid.sarvi@monash.edu
3
Department of Civil Engineering, Institute of Transport Studies, Monash University
AUTHOR
 Akamatsu, T. (1996) “Cyclic flows, Markov process and stochastic traffic assignment”, Transportation Research 30B(5), pp.369386.
1
 Bekhor S., T. Toledo, and L. Reznikova. (2008) “A PathBased Algorithm for the CrossNested Logit Stochastic User Equilibrium Traffic Assignment”, ComputerAided Civil and Infrastructure Engineering, Vol 24, pp. 1525.
2

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 Bekhor, S., M. E. BenAkiva, and M. S. Ramming. (2006) “Evaluation of Choice Set Generation Algorithms for Route Choice Models”, Ann Oper Res, 144, pp. 235247.
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5
 Bekhor, S., M. S. BenAkiva, and S. M. Ramming. (2002) “Adaptation of Logit Kernel to Route Choice Situation”, In Transportation Research Record: Journal of the Transportation Research Board, No. 1805, TRB, National Research Council, Washington, D.C., pp. 78–85.
6
 Bell, M. G. H. (1995) “Alternatives to Dial's Logit Assignment Algorithm”, Transportation Research 29B(4), pp.287296.
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 BenAkiva, M. and Bierlaire, M. (1999) “Discrete choice methods and their applications to short term travel decisions”, in: W. Hall (Ed.) “Handbook of Transportation Science”, pp. 5–33 (Dordrecht: Kluwer).
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 Cascetta, E., Nuzzolo, A., Russo, F. and Vitetta, A. (1996) “A modified logit route choice model overcoming path overlapping problems: Specification and some calibration results for Interurban Networks”, In Proc., 13th International Symposium on Transportation and Traffic Theory, Pergamon, Lyon, France, pp. 697–711.
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15
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16
 Eaton, M. L. (1983) “Multivariate statistics: a vector space approach”, John Wiley and Sons..
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 Fisk, C. (1980) “Some developments in equilibrium traffic assignment”, Transportation Research, Vol. 14B, pp. 243–255.
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 Florian, M., and B. Fox. (1976) “On the Probabilistic Origin of Dialʼs Multipath Traffic Assignment Model”, Transportation Research, Vol. 10, pp. 339341.
21
 Haghani, M., Shahhoseini, Z., Samimi, A. and Aashtiani, A. Z. (2013) “On calibration and application of logitbased stochastic traffic assignment models”, International Journal of Transportation Engineering (IJTE), Vol.13, pp. 151172.
22
 Horn, R. A. and Johnson, C. R. (1985) “Matrix analysis”, Cambridge University Press.
23
 Horowitz, J. L., Sparmann, J. M and. Daganzo, C. F (1982) “An investigation of the accuracy of the Clark approximation for the multinomial probit model”, Transportation Science, Vol. 16 (3), pp. 382401.
24
 Haghani, M., Shahhoseini, Z. and Sarvi, M. (2014) “Pathbased stochastic traffic assignment: an investigation on the effect of choicesets size, model specification and model calibration on prediction of static flow,93rd Annual Meeting of Transportation Research Board (TRB), Washington, DC.
25
 Maher, M. (1992) “SAM—A stochastic assignment model”, In Mathematics in Transport Planning and Control (J. D. Griffiths, ed.), Oxford University Press, pp. 121–132.
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 Maher, M. and Hughes, P. C (1997) “A Probit based stochastic user equilibrium assignment model”, Transportation Research, Vol. 31B, pp. 341–355.
27
 Nguyen, S. and Dupuis, D. (1984) “An efficient method for computing traffic equilibria in networks with asymmetric transportation costs”, Transportation Science, Vol.18, pp. 185202.
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 Prashker, J. N. and Bekhor, S. (1998) “Investigation of stochastic network loading procedures”, Transportation Research Record, 1645, pp. 94–102.
29
 Prashker, J. N. and Bekhor, S. (2000) “Congestion, stochastic, and similarity effects in stochastic userequilibrium models”. In Transportation Research Record: Journal of the Transportation Research Board, No. 1733, TRB, National Research Council, Washington, D.C., pp. 80–87.
30
 Prato, C. G., and Bekhor, S. (2006) “Applying branchandbound technique to route choice set generation. in transportation research record”, Journal of Transportation Research Board, No. 1985, Transportation Research Board of the National Academies, Washington, D.C., pp. 19–28.
31
 Prato, C. G., and Bekhor, S. (2007) “Modeling route choice behavior: how relevant is the composition of choice set?”. In Transportation Research Record, Vol. 2003, pp. 6473.
32
 Rice, J. (1995) “Mathematical statistics and data analysis(second ed.)”, Duxbury Press.
33
 Rosa, A. (2003) “Probit based methods in traffic assignment and discrete choice modeling”, Ph.D. Dissertation, Napier University.
34
 Sheffi, Y. and Powell, W.B. (1981) “A comparison of stochastic and deterministic traffic assignment over congested networks”, Transportation Research, Vol.15B, pp. 5364.
35
 Sheffi, Y. and Powell, W. B. (1982) “An algorithm for the equilibrium assignment problem for random link times”, Networks, 12(2), pp. 191207.
36
 Train, K. E. (2009) “Discrete choice methods with simulation”, Cambridge University Press, New York.
37
 Von Falkenhausen, H. (1966) “Traffic assignment by a stochastic model”, Proceedings, 4th International Conference on Operational Science, pp. 415421.
38
 Yai, T., Iwakura, S.and Morichi, S. (1997) “Multinomial probit with structured covariance for route choice behavior”, Transportation Research, Vol. 31B, pp. 195–207.
39
 Zhang, K., Mahmassani, H. S. and Lu, C. C. (2008) “Probitbased timedependent Stochastic user equilibrium traffic assignment model”, Transportation Research Record, 2085, pp. 8694.
40
ORIGINAL_ARTICLE
Rock Slope Stability Analysis Using Discrete Element Method
Rock slope stability depends very much on the strength features of the rock and the geometrical and strength characteristics of the discontinuities (e.g., roughness, wall strength and persistence). Since a rock mass is not a continuum, its behavior is dominated by such discontinuities as faults, joints and bedding planes. Also, Rock slope instability is a major hazard for human activities and often causes economic losses, property damage (maintenance costs), as well as injuries or fatalities. A computer program has been developed in this research study to perform the stability analysis of a rock slope using the Discrete Element Method (DEM). The rock in the present model is treated as some blocks connected together by elastoplastic Winkler springs. This method, the formulation of which satisfies all equilibrium and compatibility conditions, considers the progressive failure and is able to find the slip surface or unstable blocks. To demonstrate the applicability and usefulness of the method, several examples have been presented for the analysis and optimization of the rock slope stabilization.
http://www.ijte.ir/article_9603_def6a10a2dd44cd4fd9005eabd3da00e.pdf
20150101
199
212
10.22119/ijte.2015.9603
rock slope stability
discrete element method
Limit equilibrium
Mohammad Reza
Maleki Javan
malkijavan@ut.ac.ir
1
University of Tehran
AUTHOR
Fouad
Kilanehei
2
Department of Civil Engineering, Imam Khomeini International University
AUTHOR
Amir
Mahjoob
amahjoob@ut.ac.ir
3
Road, Housing & Urban Development Research Center
LEAD_AUTHOR
 Chang, C. S. (1991) "Discrete element method for bearing capacity analysis", Computers and Geotechnics, 12, pp. 273288.
1
 Chang, C. S. (1992) "Discrete element method for slope stability analysis", Journal of Geotechnical Engineering, 118(12), pp. 18891905.
2
 Chang, C. S. (1994) "Discrete element analysis for active and passive pressure distribution on retaining walls", Computers and Geotechnics, 16, pp. 291310.
3
 Cundall, P.A. (1987) "Distinct element models of rock and soil structure", Analytical and Computational Methods in Engineering Rock Mechanics. George Allen and Unwin, London, pp. 129– 163.
4
 Easki, T., Jiang, Y., Bhattarai, T.N., Maeda, T., Nozaki, A. and Mizokami, T. (1999) "Modeling jointed rock masses and prediction of slope stabilities by DEM", Proceedings of the 37th U.S. Rock Mech. Symp., Vail, Colorado, June 1999, pp. 83– 90.
5
 Heuze, F.E., Walton, O.R., Maddix, D.M., Shaffer, R.J. and Butkovich, T.R. (1990) "Analysis of explosion in hard rocks: the power of the discrete element modeling”, Mechanics of Jointed and Faulted Rock., Proc. Int. Conf. Vienna Balkema, Rotterdam, pp. 21–28.
6
 Kainthola, A., Singh, P. K., Wasnik, A. B. and Singh, T. N. (2012) "Distinct element modelling of Mahabaleshwar Road cut hill slope", Geomaterials, 2, pp. 105 113
7
 Kim, J. S., Lee, S. R. and Kim, J. Y (1997) "Analysis of soil nailed earth slope by discrete element method", Computers and Geotechnics, 19(1), pp. 114.
8
 Kveldsvik, V., Kaynia, A. M., Nadim, F., Bhasin,R., Bjrn N., Einstein, H. H., (2009), "Dynamic distinct element analysis of the 800 m high Aknes rock slope", International Journal of Rock Mechanics and Mining Sciences, 46(4), pp. 686698.
9
 Lin, Y., Zhu, D., Deng, Q., and He, Q. (2012) "Collapse analysis of jointed rock slope based on UDEC software and practical seismic load", International Conference on Advances in Computational Modelling and Simulation, 31, pp. 441416.
10
 Pal, S., Kaynia, A., Bhasin, A. M. and Paul, R. K. (2012) "Earthquake stability analysis of rock slopes: A case study", Rock Mechanics and Rock Engineering, 45(2), pp. 205–215.
11
 Rathod, G. W., Shrivastava, A. K. and Rao, K. S. (2011) "Distinct element modelling for high rock slopes in static and dynamic conditions: A case study", GeoRisk, ASCE, pp. 484492.
12
 Shen, H., Abbas, S.M. (2013) "Rock slope reliability analysis based on distinct element method and random set theory", International Journal of Rock Mechanics and Mining Sciences, 61, pp. 1522.
13
 Zhang, C., Pekau, O.K., Feng, J. and Guanglun, W. (1997) "Application of distinct element method in dynamic analysis of high rock slopes and blocky structures", Soil Dynamic Earthquake Engineering, 16, pp. 385–394.
14
ORIGINAL_ARTICLE
A Monte Carlo Simulation of Chain Reaction Rear End Potential Collisions on Freeways
In recent research on modelling road collisions very little attention has been paid to rearend chain reaction collisions, which is characterized by more than two vehicles involved in a collision at the same time. The core aim of the present research is to develop a methodology to estimate such potential collision probabilities based on a proactive perspective, where deceleration rate to avoid collision is used as a surrogate safety measure. In a rearend chain reaction collision the following driver’s response time and the vehicle’s maximum available deceleration rate are both assumed as stochastic causes of collision. To consider the uncertainty of variables in estimating the Nvehicle rearend collision, a methodology based on Monte Carlo simulation is proposed. To show the applicability of the proposed methodology, the NGSIM trajectory database of I80 interstate freeway is used. The probability density function for drivers’ response time is developed through the analysis of 1534 car following situations detected in 45 minutes of movement. The potential risk of two to five vehicle reaction collisions in a five vehicle platoon is estimated by running the simulation through 20 thousand substitutions of randomized generation values drawn from probability density function of response time and maximum available deceleration rate in a following outcome function. Results show that avoiding rearend collision should be considered as a shared responsibility among the drivers in the platoon. As expected, the methodology considers probability of N vehicles colliding at the same time decreasing as N increases. Nvehicle collision is shown to be directly related to the clearance between the following vehicles within the platoon and the speed of individual vehicles as well as the drivers’ reaction time and the maximum deceleration rate available in individual vehicles. The proposed methodology considers all vehicles in the platoon to estimate the risk of potential chain reaction collisions, rather than just simply focusing only on the two leading and following vehicles. Therefore, the proposed methodology is believed to act more effectively than the ordinary methods, particularly if it is used to alarm drivers of vehicles synchronized based on vehicular ad hoc network (VANET) methodologies.
http://www.ijte.ir/article_9604_2622fff9da11c2ccdba7390d092f77d8.pdf
20150101
213
230
10.22119/ijte.2015.9604
Chain reaction rear end collision
Response time
maximum available deceleration rate
Monte Carlo simulation
Amir Reza
Mamdoohi
armamdoohi@modares.ac.ir,
1
Department of Civil and Environmental Engineering, Tarbiat Modares University
LEAD_AUTHOR
Mohsen
Fallah Zavareh
2
Department of Civil and Environmental Engineering, Tarbiat Modares University
AUTHOR
Abolfazl
Hassani
3
Department of Civil and Environmental Engineering, Tarbiat Modares University
AUTHOR
Trond
Nordfjærn
4
Research Scientist, Norwegian Institute for Alcohol and Drug Research, Oslo, Norway
AUTHOR
American Association of State Highway and Transportation Officials [AASHTO] (2004) "AASHTO Green Book, Policy on Geometric Design of Highways and Streets 2004", 5th ed., Washington: American Association of State Highway & Transportation.
1
 Amundsen, F.H, and C. Hydén (1977) “Proceedings of The First Workshop on Traffic Conflicts”, In Oslo, TTI, Oslo, Norway and LTH Lund, Sweden.
2
 Brackstone, Mark and Mike McDonald, Mike (1999) “Carfollowing: A historical review”, Transportation Research Part F: Traffic Psychology and Behaviour, 2 (4): pp.181–96.
3
Cunto, Flavio, Craveiro, Jose and Saccomanno, Frank F. (2007) “Microlevel traffic simulation method for assessing crash potential at intersections”, In . http://trid.trb.org/view.aspx?id=802073.
4
 Cunto, Flávio and Saccomanno, Frank F. (2008) “Calibration and validation of simulated vehicle safety performance at signalized intersections”, Accident Analysis & Prevention, 40 (3): pp.1171–79.
5
 Davis, Gary A., Hourdos, John, Xiong, Hui and Chatterjee, Indrajit (2011) “Outline for a causal model of traffic conflicts and crashes”, Accident Analysis & Prevention 43 (6): pp.1907–19.
6
 Davis, Gary A. and Swenson, Tait (2006) “Collective responsibility for freeway rearending accidents?: An application of probabilistic causal models”, Accident Analysis & Prevention 38 (4): pp.728–36.
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 Del Castillo, J. M., Pintado, P. and Benitez, F. G. (1994) “The reaction time of drivers and the stability of traffic flow”, Transportation Research Part B: Methodological 28 (1): pp.35–60.
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 Guido, Giuseppe, Saccomanno, Frank; Vitale, Alessandro; Astarita, Vittorio and Festa, Demetrio (2010) “Comparing safety performance measures obtained from video capture data”, Journal of Transportation Engineering 1 (1): pp.165–165.
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Laureshyn, Aliaksei (2010) “Application of automated video analysis to road user behaviour”, Ph.D. dissertation, Sweden: Lund University.
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Laureshyn, Aliaksei, Åse Svensson, and Christer Hydén (2010) “Evaluation of traffic safety, based on microlevel behavioural data: Theoretical framework and first implementation”, Accident Analysis & Prevention, 42 (6): pp.1637–46.
14
Lord, Dominique and Fred Mannering (2010) “The statistical analysis of crashfrequency data: A review and assessment of methodological alternatives”, Transportation Research Part A: Policy and Practice 44 (5): pp.291–305.
15
Mamdoohi, Amir Reza, Mohsen Fallah Zavareh, Christer Hydén, and Trond Nordfjærn (2014) “Comparative analysis of safety performance indicators based on inductive loop detector data”, PROMET  Traffic&Transportation 26 (2): pp.139–49.
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17
 Oh, Cheol and Taejin, Kim (2010) “Estimation of rearend crash potential using vehicle trajectory data”, Accident Analysis & Prevention 42 (6): pp.1888–93.
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Oh, Cheol, Park, Seri and. Ritchie, Stephen G. (2006) “A method for identifying rearend collision risks using inductive loop detectors.” Accident Analysis & Prevention 38 (2): pp.295–301.
19
Ozaki, H. [ed.] (1993) “Reaction and anticipation in the carfollowing behavior”, In Transportation and Traffic Theory, Berkeley, California.
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Punzo, Vincenzo, Borzacchiello, Maria Teresa and Ciuffo, Biagio (2011) “On the assessment of vehicle trajectory data accuracy and application to the Next Generation SIMulation (NGSIM) Program Data”, Transportation Research Part C: Emerging Technologies 19 (6): 1243–62.
21
Ranjitkar, Prakash, and Nakatsuji, Takashi (2010) “A Trajectory based analysis of drivers’ response in carfollowing situations”, In . http://trid.trb.org/view.aspx?id=910337.
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Ratick, S. and G. Schwarz (2009) “Monte Carlo Simulation”, In International Encyclopaedia of Human Geography, edited by Rob Kitchin and Nigel Thrift, pp.175–84. Oxford: Elsevier. http://www.sciencedirect.com/science/article/pii/B9780080449104004764.
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25
Wu, Yihu, Yuan, Huihua , Chen, Haitao and Li, Jigjig (2009) “A study on reaction time distribution of Group Drivers at CarFollowing.” In Second International Conference on Intelligent Computation Technology and Automation, 2009. ICICTA ’09, 3:452–55.
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Xu, Cheng, Tarko, Andrew, P., Wang, Wei and Liu, Pan (2013) “Predicting crash likelihood and severity on freeways with realtime loop detector data”, Accident Analysis & Prevention 57 (August): pp.30–39.
27
Yu, Rongjie and AbdelAty. Mohamed (2014) “Analyzing crash injury severity for a mountainous freeway incorporating realtime traffic and weather data”, Safety Science 63 (March): pp.50–56.
28
ORIGINAL_ARTICLE
A Fuzzy BiObjective Mathematical Model for Sustainable Hazmat Transportation
Today, transportation of hazardous materials (i.e., hazmat) is a very important issue for researchers due to the risk of this transit, which should be considered for development of industries and transportation. Therefore, a model that is useful should consider the risk and damage to humanitarian and environmental issues due to transit of hazmat materials. By considering the related cost, the reality and applicability of the model are also very important. In this paper, a biobjective model is presented for routing railtruck intermodal terminals with the cost and risk as objective functions by considering three sets of the effective factors that lead to hazardous materials transportation accidents in favor of sustainability. Additionally, a fuzziness concept is considered in the presented model. The model is first validated by the prevalent software using the authoritative solver to solve a numerical example. Furthermore, to help managers, an efficient fuzzy approach proposed in the literature is used. Finally, it is concluded that in this work a reality and sustainable model is suitable for hazmat transportation.
http://www.ijte.ir/article_9607_19dd69f57d7a92ef854c201fde3ba98f.pdf
20150101
231
243
10.22119/ijte.2015.9607
Railtruck intermodal
Hazmat transportation
sustainable
fuzzy multiobjective
method
Zohreh
ZahedianTejenaki
z.zahedian@ut.ac.ir
1
Department of Industrial Engineering, University of Tehran
AUTHOR
Reza
TavakkoliMoghaddam
tavakoli@ut.ac.ir
2
Department of Industrial Engineering, University of Tehran
LEAD_AUTHOR
Agrawal, A. W., Dill, J. and Nixon, H.
1
(2010) “Green transportation taxes and fees:
2
A survey of public preferences in California”,
3
Transportation Research  Part D, Vol. 15, pp.
4
189–196.
5
Akgün, V., Parekh, A., Batta, R. and Rump,
6
C.M. (2007) “Routing of a hazmat truck in the
7
presence of weather systems”, Computers and
8
Operations Research, Vol. 34, pp. 1351–1373.
9
Berman, O., Verter, V. Y. and Kara, B. (2007)
10
“Designing emergency response networks for
11
hazardous materials transportation”, Computers
12
and Operations Research, Vol. 34, pp.
13
1374–1388.
14
Bianco, L., Caramia, M. and Giordani, S.
15
(2009) “A bilevel flow model for hazmat
16
transportation network design”, Transportation
17
Research  Part C, Vol. 17, pp. 175–196.
18
Bonvicini, S. and Spadoni, G. (2008) “A
19
hazmat multicommodity routing model satisfying
20
risk criteria: A case study”, Journal of
21
Loss Prevention in the Process Industries, Vol.
22
21, pp. 345–358.
23
Dadkar, Y., Nozick, L. and Jones, D. (2010)
24
“Optimizing facility use restrictions for the
25
movement of hazardous materials”, Transportation
26
Research  Part B, Vol. 44, pp.267–281.
27
Erkut, E. and Alp, O. (2007) “Designing
28
a road network for dangerous goods shipments”,
29
Computers and Operations Research,
30
Vol. 34, No. 5, pp. 1241–1242.
31
Erkut, E. and Gzara, F. (2008) “Solving the
32
hazmat transport network design problem”,
33
Computers and Operations Research, Vol. 35,
34
pp. 2234–2247.
35
Liang, T. F. (2006) “Distribution planning
36
decisions using interactive fuzzy multiobjective
37
linear programming”, Fuzzy Sets and
38
Systems, Vol. 157, pp. 1303–1316.
39
Lozano, A., Muñoz, A., Macías, L., Pablo
40
Antún, J., Granados, F. and Guarneros, L.
41
(2010) “Analysis of hazmat transportation accidents
42
in congested urbanareas, based on actual
43
accidents in Mexico”, Procedia Social and
44
Behavioral Sciences, Vol. 2, pp. 6053–6064.
45
A Fuzzy BiObjective Mathematical Model for Sustainable Hazmat Transportation
46
243 International Journal of Transpotation Engineering,
47
Vol.2, No.3, Winter 2014
48
Lozano, A., Muñoz, A., Macías, L. and Pablo
49
Antún, J. (2011) “Hazardous materials transportation
50
in Mexico City: Chlorine and gasoline
51
cases”, Transportation Research  Part C,
52
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53
Raemdonck, K. V., Macharis, C. and
54
Mairesse, O. (2013) “Risk analysis system for
55
the transport of hazardous materials”, Journal
56
of Safety Research, Vol. 45, pp. 55–63.
57
Schweitzer, L. (2006) “Environmental justice
58
and hazmat transport: A spatial analysis in
59
southern California”, Transportation Research
60
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61
TavakkoliMoghaddam, R., MahmoudSoltani,
62
F. and Mahmoudabadi, A. (2013) “Bicriteria
63
hazmat routing problem under uncertainty
64
– A case study of fuel shipment in
65
Mazandaran province”, Journal of Transportation
66
Research, Vol. 4, No. 3, pp. 209–220.
67
(in Farsi)
68
TavakkoliMoghaddam, R., Salamatbakhsh,
69
A., Norouzi, N. and Alinaghian, M. (2011)
70
“Solving a new vehicle routing problem considering
71
safety in hazardous materials transportation”,
72
Journal of Transportation Research,
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Vol. 2, No. 3, pp. 223–237. (in Farsi).
74
Torabi, S. A. and Hassini, E. (2008) “An interactive
75
possibilistic programming approach
76
for multiple objective supply chain master
77
planning”, Fuzzy Sets and Systems, Vol. 159,
78
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79
Verma, M. and Verter V. (2007) “Railroad
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transportation of dangerous goods: Population
81
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ORIGINAL_ARTICLE
Evaluation of Long Term Ageing of Asphalt Mixtures Containing EAF and BOF Steel Slags
This study was conducted in order to evaluate the effects of long term ageing on toughness and resilient modulus of asphalt concrete mixtures containing Electric Arc and Basic Oxygen Furnace steel slags. After initial evaluation of the properties of steel slags using Xray Diffraction and Scanning Electric Microscope, eleven sets of laboratory mixtures were prepared. Each set was treated replacing various portions of limestone coarse aggregates of the mixture (≥ 2.36 mm) with steel slags. The main laboratory program consisted of the determination of resilient modulus at three testing temperatures of 5, 20 and 40˚C (ASTM D4123) and indirect tensile strength of the samples at 20˚C. In order to evaluate the long term performance of mixtures containing slags, the specimens were subjected to ageing according to AASHTO PP2 standard method. Results showed that the peak tensile strength, area up to peak tensile strength and total dissipated energy density of the specimens containing Electric arc furnace slag were greater than the control mixtures. Fracture energy was almost the same for both mixes containing basic oxygen furnace slag and limestone. Results also indicated that the resilient modulus of mixtures increased along with an increase in slag contents in asphalt mixtures. The ratio of aged to unaged resilient modulus of the specimens decreased upon increasing slag contents. It was concluded that mixtures containing electric arc furnace slag exhibited less susceptibility to ageing compared with mixtures containing basic oxygen furnace slag and limestone. At 5oC, the highest ratio belonged to control mixtures, which might indicate that at lower temperatures, the susceptibility to ageing of the control mixtures were more pronounced
http://www.ijte.ir/article_9608_60969d780204d599cdb01337a912903c.pdf
20150101
245
265
10.22119/ijte.2015.9608
EAF and BOF slag
Resilient Modulus
IDT
fracture energy
ageing
Seyed Ali
Ziaee
saziaee@um.ac.ir
1
Department of Civil Engineering, Ferdowsi University of Mashhad
AUTHOR
Amir
Kavussi
kavussi@yahoo.co.uk
2
Dept of Civil and Environmental Engineering, Tarbiat Modares University
LEAD_AUTHOR
Morteza
Jalili Qazizadeh
morteza.jalili@modares.ac.ir
3
Quchan University of Advanced Technology, Department of Engineering
AUTHOR
Abolfazl
Mohammadzadeh Moghadam
4
Department of Civil Engineering, Ferdowsi University of Mashhad
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