ORIGINAL_ARTICLE
An Experimental and Numerical Study on the Effect of Different Types of Sleepers on Track Lateral Resistance
Lateral resistance of railway track is one of the most important parameters in lateral stability. This parameter depends!on the conditions of different components of ballasted railway track (such as density of ballast layer, sleeper spacing, type of sleeper, etc.). From this perspective, type of sleeper has an important effect on lateral resistance. However in some conditions, in technical and economical investigations, using a special type of sleeper is not avoidable. In this research, concrete, wooden, and steel sleepers are studied using experimental and numerical analysis by finite element method. According to the experimental results, concrete sleeper B- 70 with 2.06 tons has the most lateral resistance among three types of sleepers. Steel and wooden sleepers with the amounts of 1.32 and 1.10 tons are in the next ranking. On the other hand, numerical analysis (modeled according to field conditions) shows that the lateral resistance of concrete, steel, and wooden sleepers is equal to 2.10, 1.36, and 1.15 tons, respectively.
http://www.ijte.ir/article_13359_01b1f3eede6b44e2d1f6501e62ef4878.pdf
2015-07-01
7
15
10.22119/ijte.2015.13359
lateral resistance
ballasted railway track
concrete sleeper
wooden sleeper
steel Sleepers
Arash
Bakhtiary
1
Department of Railway Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Jabbar Ali
Zakeri
zakeri@iust.ac.ir
2
Department of Railway Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Hun Je
Fang
3
College of Civil Engineering and Architecture, Jiaotong University, Beijing, China
AUTHOR
Ahmad
Kasraiee
4
Department of Railway Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
- Bakhtiary, A. (2011) “Investigation of parameters affecting the lateral resistance of ballasted railway track”, MSc seminar, Iran University of Science and Technology, Tehran, Iran. (In Farsi langiage)
1
- European Railway Research Institute (1995) “Theory of CWR track stability”, Utrecht, the Netherlands, Final Report ERRI, D 202/rp3.
2
- European Railway Research Institute (1998)”Measurement of lateral resistance characteristics for ballast trackutrechtthen etherlands”, Final Report ERRI, D 202/DT 361.
3
- Kabo E. (2006) ”A numerical study of the lateral ballast resistance in railway tracks”, Rail and Rapid Transit Journal, Vol. 220, July, pp. 425- 433.
4
- Kasraei, A. (2013) “A numerical study of lateral resistance of ballasted railway track”, MSc dissertation, Iran University of Science and Technology, Tehran, Iran. (In Farsi language)
5
- Technical-General Specifications for Railway Track Superstructure ( 2005) “ Leaflet No. 310”, published by Iran Planning and Strategic Bureau of Presidential Office, Islamic Republic of Iran.
6
- Le Pen, L. M. and Powrie, W. (2011) “Contribution of base, crib, and shoulder ballastto the lateral sliding resistance of railway track: a geotechnical perspective”, Journal of Rail and Rapid Transit, Vol. 225, pp.113-128.
7
- Lichtberger, B. (2005) “Track compendium, formation, permanent way, maintenance, economics”, Hamburg, Germany.
8
- Lim, N. H., Park, N. H. and Kang, Y. J. (2003) “Stability of continuous welded rail track”, Computers and Structures, 81 (22-23), pp. 2219– 2236.
9
- Mirfattahi, B. (2009) “Field investigation on lateral resistance of railway track with frictional sleepers”, MSc dissertation, Iran University of Science and Technology, Tehran, Iran. (In Farsi language)
10
- Perpinya, X. and Zakeri, J. A. (2012)”Reliability and Safety in Railway”, Chapter 13: Lateral resistance of Railway track.
11
- Zakeri, J. A. and Barati, M. (2013) “Utilizing the track panel displacement method for estimating vertical load effects on the lateral resistance of continuously welded railway track”, Rail and Rapid Transit Journal.
12
- Zakeri, A. [n.d] “Frictional concrete sleeper and its influence on the lateral resistance of track”, Iran University of Science and Technology, Tehran, Iran, Research Report 160/5776.
13
- Zakeri, J. A., Mirfattahi B. and Fakhari, M. (2012) “Lateral resistance of railway track with frictional sleepers”, Proceedings of the Institution of Civil Engineering, pp. 151-155.
14
ORIGINAL_ARTICLE
Estimation Model of Two-Lane Rural Roads Safety Index According to Characteristics of the Road and Drivers’ Behavior
Vehicle crashes are amongst the major causes of mortality and results in losses of lives and properties. A large number of the vehicle crashes occur on rural roads. Accidents become more noteworthy in two-lane roads due to going and coming traffic. Therefore, prediction of crashes and their causes are considerably important to reduce the number and severity of the accidents. The safety index is a suitable quantity for determination of road safety degree. It informs us to study the number of accidents in a specific road and time. In this study, safety index of two-lane rural roads is predicted by Artificial Neural Network (ANN), Radial Basis Function Neural Networks (RBFNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithms using MATLAB software. The number of causes which ends to an accident is related to some parameters. We chose seven new parameters as inputs to the ANN, RBFNN and ANFIS methods that are geometric and statistical values of the roads and one output variable that is the safety index of segments of two-lane rural roads. 5 roads in Ilam Province, Iran, were selected for the case study to train, validate and test the proposed estimation models. Finally, the results show that, it is possible to predict the safety index of two-lane rural roads with a high correlation coefficient and a low mean square error (MSE) in relation to real values. The ANN method has a higher correlation coefficient and lower MSE in comparison to RBFNN and ANFIS methods. The achieved correlation coefficient and MSE for validation of the ANN approach are 0.94 and 0.0086 respectively, and correlation coefficient of 0.845 and MSE of 0.019 for all data.
http://www.ijte.ir/article_13360_56fdc9b39190e95ec98b652269c6bdd6.pdf
2015-07-01
17
29
10.22119/ijte.2015.13360
Safety index
crashes
Artificial Neural Networks
two-lane rural roads
Amin Mirza
Broujerdian
1
Department of Civil and Environmental Engineering, Tarbiat Modarres University, Tehran, Iran
LEAD_AUTHOR
Seyed Peyman
Dehqani
dehghani.peyman@gmail.com
2
Department of Civil Engineering, Islamic Azad University of South Tehran, Tehran, Iran
AUTHOR
Masoud
Fetanat
masoud.fetanat@gmail.com
3
Department Of Electrical Engineering, Sharif University of Technology, Tehran, Iran
AUTHOR
- Abdel-At, M. A and Pemmanaboina, R. (2006) "Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data", IEEE Transportations on Intelligent Transportation Systems, Vol. 7, No. 2, pp. 167- 174.
1
- Abd-ol-manafi, Seyed Ibrahim, Ahmadi Nejad, Mahmood and Afandi Zade, Shahriyar. (2007) "Designing a model for predicting the number of accidents in intra-city intersections according to statistical models and neural network" M.S Dissertation, Iran University of Science and Technology, Tehran, Iran (In Farsi language)
2
- Akgüngor, A. P. and Dogan, E. (2008) "Estimating road accidents of Turkey based on regression analysis and artificial neural network approach", Advances in Transportation Studies, an International Journal, Vol. 4, No.9, pp. 906- 913.
3
- Ayati, Ismaeel (2002) "Vehicle crashes costs in Iran", Publication of University of Ferdousi Mashhad. (In Farsi language)
4
- Ayati, Ismaeel (2000) "Comprehensive study on vehicle crashes in Mashhad", Publication of University of Ferdousi, Mashhad. (In Farsi language)
5
- Bayata, H. F., Hattatoglu, F. and Karsli, N. (2011) "Modeling of monthly traffic accidents with the artificial neural network method", International Journal of the Physical Sciences Vol. 6, No.2, pp. 244-254.
6
- Chen, S., Cowan, C.F.N. and Grant, P.M. (1991) "Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks", IEEE Transactions on Neural Networks, Vol. 2, No. 2, March, pp. 302–309.
7
- Dougherty, M. (1995) "A review of neural networks applied to transport", Transportation Research Part C, Vol. 3, No. 4, pp.247–260.
8
- Han, J. and Kamber, M. (2006) "Data mining: concepts and techniques", Morgan Kaufmann.
9
- Haykin, S. (2009) "Neural networks and learning machines", London: Prentice Hall.
10
- Fetanat, M., Shamshiry, R. and Kazemi, M. H. (2013) "Mid-term prediction of wind turbine power generation using Artificial Neural Networks", 5th Conference on Electric Power Generation (EPGC 2013).
11
- Fielding, Gordon J., Mary, E. and Brenner, Katherine Faust (1985) "Typology for bus transit",Transportation Research, Part A, Vol 40, No. 4, pp. 1257-1266.
12
- Hagan, M. and Menhaj, M. (1994) "Training feed-forward networks with the Marquardt algorithm", IEEE Transactions on Neural Networks, Vol.5, No.6, 989–993.
13
- Haleem, K. and Abdel-Aty, M. (2010) "Examining traffic crash injury severity at unsignalized intersections”, Journal of Safety Research, Vol. 41, No. 4, pp. 347-357.
14
- Iran Road Maintenance and Transportation Organization [RMTO] (2008) “Annual Report”, Tehran, RMTO
15
- Jang, J. (1993) "ANFIS: adaptive-network-based fuzzy interference system", IEEE Transaction on Systems, Man and Cybernetics, Vol. 23(3), pp. 665–685.
16
- Kashani, T.A. and Mohaymany, S.A. (2011) "Analysis of the traffic injury severity on twolane two-way rural roads based on classification tree models", Safety Science, Vol. 49, pp. 1314– 1320.
17
- Kaveh, Ali and Servati, Homayoon (2001) "Artificial neural networks in analyzing and designing the structures", Publication of BHRC.
18
- Knuiman, M.W., Council, F.M. and Reinfurt, D.W. (1993) "Association of median width and highway accident rates", Transport Reseasch, Rec.,1401.
19
- Mahmoudabadi, A. (2010) "Comparison of weighted and simple linear regression and artificial neural network models in freeway accidents prediction (Case study: Qom & Qazvin Freeways in Iran)", Second International Conference on Computer and Network Technology, Thailand, Bangkok, 23-25, Part 7: Traffic and Logistic Management, pp. 392-396.
20
- Mahmood Abadi, Abbas and Safi Samg Abadi, Azam Dokhy (2008) " Estimation of daily road accidents using neural network relying on traffic status", Second Conference on phased and Smart Systems, Tehran : Technical University of Malek Ashtar.
21
- Mussone, L., Ferrari, A. and Oneta, M. (1999) "An analysis of urban collisions using on artificial intelligence model, accident analysis and prevention", Vol. 31, pp 705-718.
22
- Takagi, T. and Sugeno, T. (1985) "Fuzzy identification of system and its applications to modeling and control". IEEE Transaction on Systems, Man and Cybernetics, Vol. 15, pp. 116– 132.
23
- Vogt, A. and Bared, J. (1998) "Accident models for two-lane rural segments and intersections", In Transportation Research Record 1635, TRB, National Research Council, Washington, D.C. pp. 18-22.
24
ORIGINAL_ARTICLE
Application of Artificial Neural Networks for Analysis of Flexible Pavements under Static Loading of Standard Axle
In this study, an artificial neural network was developed in order to analyze flexible pavement structure and determine its critical responses under the influence of standard axle loading. In doing so, more than 10000 four-layered flexible pavement sections composed of asphalt concrete layer, base layer, subbase layer, and subgrade soil were analyzed under the impact of standard axle loading. Pavement sections were analyzed by means of multilayered elastic analysis theory and critical responses of pavement including maximum horizontal principal tensile strain at the bottom of asphalt layer and maximum vertical compressive strain on the top of subgrade were computed in each case. Then, a Feed-Forward back propagation neural network was served to predict these responses. The results show that the artificial neural network can be used as a powerful and accurate tool to predict the critical response of flexible pavements. Application of artificial neural networks for pavement analysis reduces the analysis time and can be used as a quick tool for predicting fatigue and rutting lives of different pavement sections and so in optimum design of pavement structure.
http://www.ijte.ir/article_13361_b428fe496a4d537d07b787220971f8a0.pdf
2015-07-01
31
43
10.22119/ijte.2015.13361
pavement analysis
Artificial Neural Network
critical responses
standard axle
Ali Reza
Ghanizadeh
ghanizadeh@sirjantech.ac.ir
1
Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran
LEAD_AUTHOR
Mohammad Reza
Ahadi
ahadireza@yahoo.com
2
Transportation Research Institute, Tehran, Iran
AUTHOR
- Abu-Lebdeh, G. & Ahmed, K. (2013) “A Neural Network Approach for Mechanistic Analysis of Jointed Concrete Pavement”, Proceedings of the Eastern Asia Society for Transportation Studies, Vol. 9.
1
-Ahmed, M., Tarefder, R. & Islam, M. (2013) “Effect of cross-anisotropy of hot-mix asphalt modulus on falling weight deflections and embedded sensor stress-strain”. Transportation
2
Research Record: Journal of the Transportation Research Board, Vol. 2369, pp.. 20-29.
3
-Al-Hadidy, A. I. & Tan, Y. Q. (2009) “Mechanistic analysis of st and sbs-modified flexible pavements”. Construction and Building Materials, Vol. 23, No. 8, pp. 2941-2950.
4
-Ameri, M. & Molayem, M. (2006) “Application of Artificial Neural Networks for the Analysis of Flexible Pavements”. International Journal of Engineering Science, Vol. 17, No. 5, pp. 60-54.
5
-Attoh-Okine, N. O. (2005) “modeling incremental pavement roughness using functional network”. Canadian Journal of Civil Engineering, Vol. 32, No. 5, pp. 899-905.
6
-Austroads. (2010) “Guide to pavement technology (Apt-02/10) – Part 2: Pavement structural design”. Sydney, Australia: Austroads.
7
-Beale, M. H., M.T., H. & Demuth, H. B. (2011)”Neural Network Toolbox. for Use with Matlab”, Themathworks, Natick.
8
-Boussinesq, M. J. (1885) “Applications des potentiels à l'étule de l'équilibreet du mouvement des solidesélastiques”, Gauthier Villars, Paris.
9
-Burmister, D. M. (1945) “The general theory of stresses and displacements in layered systems”.
10
International Journal of Applied Physics, Vol. 16, No. 2, pp..89-94.
11
-Ceylan, H. (2002) “Analysis and design of concrete pavement systems using artificial neural networks”, (Ph.D Dissertation), University of Illinois at Urbana-Champaign.
12
-Ceylan, H., Guclu, A., Tutumluer, E. & Thompson, M. R. (2005) ”Backcalculation of full-depth asphalt pavement layer moduli considering nonlinear stress-dependent subgrade behavior”. International Journal of Pavement Engineering, Vol. 6, No. 3, pp..171-182.
13
-Duncan, J. M., Monismith, C. L. & Wilson, E. L. (1968) “Finite element analyses of pavements”. Highway Research Record, Vol. 228, Pp.18-33.
14
-Fakhri, M. & Ghanizadeh, A. (2012) “Program Development for the Nonlinear Analysis of Flexible Pavements. Quarterly Journal of Transportation Engineering”, Vol. 3, No. 3, pp.257-245.
15
-Ghanizadeh, A. R. & Fakhri, M. (2014) “Prediction of frequency for simulation of asphalt mix fatigue tests using Mars and Ann”, the Scientific World Journal, pp..1-16.
16
-Hagan, M. T., Demuth, H. B. & Beale, M. H. (1996) “Neural Network Design”. PWS Publishing, Boston.
17
- Harichandran, R. S., Yeh, M.-S., & Baladi, G. Y. (1990). “MICH-PAVE: A nonlinear finite element program for analysis of flexible pavements”. Transportation research record, Vol. 1286., pp. 123-131.
18
- Hayhoe, G. F. (2002) “LEAF: A new layered elastic computational program for FAA pavement design and evaluation procedures”, Federal Aviation Administration.
19
- Haykin, S. (2001)”Neural networks: a comprehensive foundation”, New Jersey, Prentice Hall.
20
- Hornik, K. (1991) "Approximation capabilities of multilayer feedforward networks", Neural Networks, Vol. 4, No. 2, pp. 251–257
21
-Huang, Y. H. (2004) “Pavement analysis and design”, New Jersey, Prentice Hall.
22
-Huang, C. W., Abu Al-Rub, R. K., Masad, E. A., & Little, D. N. (2010) “Three-dimensional simulations of asphalt pavement permanent deformation using a nonlinear viscoelastic and Viscoplastic Model”. Journal of Materials in Civil Engineering, Vol. 23, No. 1, pp. 56-68.
23
-Indian Road Congress - IRC (2012) “Guidelines for the Design of Flexible Pavements”, (3rd Ed.), Indian Road Congress.
24
-Jong, D. D., Peutz, M., &Korswagen, A. (1979) “Computer program bisar, layered systems under normal and tangential surface loads”. Koninklijke/Shell Laboratorium, Amsterdam, Shell Research BV.
25
-Khazanovich, L., & Wang, Q. C. (2007) “Mnlayer: High-Performance Layered Elastic Analysis Program”. Transportation Research Record, Vol. 2037, Pp.63-75.
26
-Khazanovich, L., Selezneva, O. I., Thomas Yu, H. & Darter, M. I. (2001) “development of rapid solutions for prediction of critical continuously reinforced concrete pavement stresses”, Transportation Research Record: Journal of the Transportation Research Board, Vol. 1778, Pp. 64-72.
27
-Maher, A. & Bennert, T. A. (2008) “Evaluation of Poisson’s ratio for use in the mechanistic empirical pavement design guide (MEPDG)”, Final Report: FHWA-NJ-2008-004. Federal Highway Administration U.S. Department of Transportation Washington, D.C.
28
-NCHRP (2004) “Guide for mechanistic–empirical design of new and rehabilitated pavement structures”, Final Report for Project 1-37a. Washington, Dc: National Cooperative Research Program.
29
-Newmark, N. M. (1947) “influence charts for computation of vertical displacements in elastic foundations”, University of Illinois.
30
-Odemark, N. (1949) “Investigations as to the elastic properties of soils and design of pavements according to the theory of elasticity”, Meddelande, 77.
31
-Ozgan, E. (2011) “Artificial neural network based modeling of the Marshall stability of asphalt concrete”. Expert Systems with Applications, Vol. 38, No. 5, pp.6025-6030.
32
- Ozturk, H. I., & Kutay, M. E. (2014) “An artificial neural network model for virtual superpave asphalt mixture design”, International Journal of Pavement Engineering, Vol. 15, No. 2, pp.151-162.
33
-Pekcan, O., Tutumluer, E. & Thompson, M. (2008)”Artificial neural network based backcalculation of conventional flexible pavements on lime stabilized soils”. The 12th. International Conference Of Iinternational Association for Computer Methods And Advances in Geomechanics (IACMAG), Goa, India.
34
- Raad, L. & Figueroa, J. L. (1980) “Load response of transportation support systems”, Journal of Transportation Engineering, Vol. 106, No. 1, pp. 111-128.
35
- Rumelhart, D. E., Hintont, G. E. & Williams, R. J. (1986). “Learning Representations by Back- Propagating Errors”. Cambridge, MIT Press.
36
-Saltan, M. (2008)”Modeling deflection basin using artificial neural networks with crossvalidation technique in backcalculating flexible pavement layer moduli.advances in engineering software”, Vol. 39, No. 7, Pp. 588-592.
37
- Sanborn, J. L., & Yoder, E. J. (1967) ”Stress and displacements in an elastic mass under semiellipsoidal loads”. 2nd International Conference of Structural Design of Asphalt Pavements, An Arbor, Michigan.
38
- Schiffman, R. L. (1962) “General Analysis of Stresses and Displacements in Layered Elastic Systems”. 1st International Conference on the Structural Design of Asphalt Pavements, An Arbor, Michigan.
39
- Tapkin, S., Çevik, A. &Usar, Ü. (2009) “accumulated strain prediction of polypropylene modified marshall specimens in repeated creep test using artificial neural networks”, Expert Systems with Applications, Vol. 36, No. 8, Pp. 11186-11197.
40
- Uzan, J. (1994) “Advanced backcalculation techniques”, ASTM Special Technical Publication, 1198, 3-3.
41
- Werbos, P. (1974) “Beyond regression: new tools for prediction and analysis in the behavioral sciences”. (PHD Dissertation), Harvard University, Cambridge.
42
-Yang, C. F. & Liu, L. (2010) “Dynamic response analysis of cement concrete pavement under different vehicle speed”, Hebei Gongye Daxue Xuebao, Vol. 39, No. 3, pp. 112-115.
43
ORIGINAL_ARTICLE
Estimating the Safety Benefits of Red Light Cameras at Signalized Intersections in Urban Areas Case Study: The City of Virginia Beach
The Highway Safety Manual [HSM, 2010] recommends safety evaluations be performed before implementing any roadway treatment to predict the expected safety consequences. Safety consequences can be measured using crash prediction models, Crash Modification Factor (CMFs), or both. This paper develops a CMF to show the expected impact of red-light cameras (RLCs) on safety at signalized intersections. A CMF is a multiplicative factor used to compute the expected number of crashes after implementing a given countermeasure at a specific roadway site. RLCs are intended to improve driver’s alertness to avoid causing accidents. This paper analyzes accident data reported at thirteen signalized intersections in Virginia Beach in 2008 before the RLCs were installed and 2010 after the RLCs were installed. Safety performance functions (SPFs) and the empirical Bayes (EB) before-after methodologies are used to develop a CMF for this countermeasure. The result shows an overall CMF of 0.846, which is a 15.4% safety improvement. This result is not absolute; however, but sets a starting point for further investigations and potential inclusion in the future editions of the HSM.
http://www.ijte.ir/article_13362_18630b574e029ec798edfaaac2b50466.pdf
2015-07-01
45
54
10.22119/ijte.2015.13362
safety
Crash modification factors
Red-light cameras
Signalized Intersections
empirical Bayes
Eugene Vida
Maina
1
Department of Civil and Environmental Engineering, Postdoctoral Assistant, Virginia Modeling, Analysis and Simulation Center , Old Dominion University, Virginia, USA
LEAD_AUTHOR
Albert
Ford
2
Department of Civil and Environmental Engineering, New Jersey Institute of Technology, Newark, NJ, USA
AUTHOR
Michael
Robinson
rmrobins@odu.edu
3
Center for Innovative Transportation Solutions Virginia Modelling, Analysis and Simulation Center Old Dominion University, Virginai, USA
AUTHOR
- Elvik, R. (2008) “The predictive validity of empirical Bayes estimates of road safety”, Accident Analysis & Prevention, 40(6), pp.1964- 1969.
1
- Erke, A. (2009) “Red light for red-light cameras? A meta-analysis of the effects of red-light cameras on crashes”. Accident Analysis & Prevention, 41(5), 897-905.
2
- Garber, N. J., Miller, J. S., Eslambolchi, S., Khandelwal, R., Mattingly, K. M., Sprinkle, K. M. and Wachendorf, P. L. (2005) “Evaluation of red light camera (photo-red) enforcement programs in Virginia: a report in response to a request by Virginia’s secretary of transportation (No. VTRC 05-R21,)”.
3
- Gross, F., Persaud, B. and Lyon, C. (2010) “A guide to developing quality crash modification factors” (No. FHWA-SA-10-032).
4
- Hauer, E. (1997) “Observational before—after studies in road safety: estimating the effect of highway and traffic engineering measures on road safety”, Elsevier Science Ltd, 6, 7.
5
- Hauer, E., Harwood, D., Council, F. and Griffith, M. (2002) “Estimating safety by the empirical Bayes method: a tutorial”, Transportation Research Record: Journal of the Transportation Research Board, (1784), pp.126-131.
6
- Hieatt, K. (2011) “Study: Virginia Beach redlight cameras offer mixed results” , The Virginian-Pilot. http://hamptonroads.com/2011/04/study-virginiabeach- redlight-cameras-offer-mixed-results#
7
- Hu, W., McCartt, A. T. and Teoh, E. R. (2011) “Effects of red light camera enforcement on fatal crashes in large us cities”. Journal of safety research, 42(4), pp.277-282.
8
- Insurance Institute for Highway Safety, IIHS. (2007) “Status Report”, Vol. 42, No. 1. http://www.iihs.org/externaldata/srdata/docs/sr42 01.pdf
9
- Lum, K. M. and Wong, Y. D. (2003) “A beforeand- after study of driver stopping propensity at red light camera intersections”, Accident Analysis & Prevention, 35(1), pp.111-120.
10
- Manual, H. S. (2010) AASHTO. Washington, DC, 529.
11
- Martinez, K. L. H. and Porter, B. E. (2006) “Characterizing red light runners following implementation of a photo enforcement program”, Accident Analysis & Prevention, 38(5), pp.862-870.
12
- McGee, H. W. and Eccles, K. A. (2003) “Impact of red light camera enforcement on crash experience – a synthesis of highway practice (NCHRP Synthesis 310)”, Transportation Research Board. Washington, DC.
13
- National Highway Traffic Safety Administration, NTHSA. (2009) “Fatality analysis reporting system (FARS)”. http://wwwfars. nhtsa.dot.gov/Main/index.aspx
14
- National Highway Traffic Safety Administration, NTHSA. (2009) “Traffic safety facts 2008 report.” http://safety.fhwa.dot.gov/intersection/redlight/
15
- Patel, R., Council, F. and Griffith, M. (2007) “Estimating safety benefits of shoulder rumble strips on two-lane rural highways in Minnesota: empirical Bayes observational before-and-after study” ,Transportation Research Record: Journal of the Transportation Research Board, (2019), pp. 205-211.
16
- Persaud, B. and Lyon, C. (2007) “Empirical Bayes before–after safety studies: lessons learned from two decades of experience and future directions” , Accident Analysis & Prevention, 39(3), pp.546-555.
17
- Poch, M. and Mannering, F. (1996) “Negative binomial analysis of intersection-accident frequencies.” Journal of Transportation Engineering, 122(2), pp.105-113.
18
- Porter, B. E. and Berry, T. D. (2001) “A nationwide survey of self-reported red light running: measuring prevalence, predictors, and perceived consequences”, Accident Analysis & Prevention, 33(6), pp.735-741
19
- Porter, B. E. and England, K. J. (2000) “Predicting red-light running behavior: a traffic safety study in three urban settings.” Journal of Safety Research, 31(1), pp.1-8.
20
- Porter, B. E., Luckett Jr, S. and Martinez, K. H. (2008) “The relationship between red light running and traffic volume: a research brief”, New Transportation Research Progress, 169.
21
- Retting, R. A. and Kyrychenko, S. Y. (2002) “Reductions in injury crashes associated with red light camera enforcement in Oxnard, California”, American Journal of Public Health, 92(11), pp.1822-1825.
22
- Retting, R. A., Chapline, J. F. and Williams, A. F. (2002) “Changes in crash risk following retiming of traffic signal change intervals”, Accident Analysis & Prevention, 34(2), pp.215- 220.
23
- Retting, R. A., Ferguson, S. A. and Hakkert, A. S. (2003) “Effects of red light cameras on violations and crashes: A review of the international literature”, Traffic injury prevention, 4(1), 17-23.
24
- Retting, R. A., Ulmer, R. G. and Williams, A. F. (1999) “Prevalence and characteristics of red light running crashes in the United States”, Accident Analysis & Prevention, 31(6), pp.687- 694.
25
- Tegge, R. A., Jo, J. H. and Ouyang, Y. (2010) “Development and application of safety performance functions for Illinois”, Research Report ICT-10-066.
26
ORIGINAL_ARTICLE
Performance Evaluation of Nano-silica Modified Bitumen
In this study, different contents of Nano-silica, 2 wt.%, 4 wt.% and 6 wt.%, have been added to bitumen to modify the physical, mechanical and rheological properties of warm mix asphalt (WMA). WMA is containing 2 wt.% of Sasobit (mixture of long-chain hydrocarbons). Various quality control tests have been carried out to characterize the modified bitumen and WMA. The rheological investigations showed that the complex modulus of base bitumen increases by increasing the percentage of Nano-silica from 2 to 6 wt%. Phase angle and rut factor for the Nano-silica modified bitumen have also decreased significantly. From rheological analysis, 6 wt% Nano-silica has been selected as the optimum content. Results of investigations on the asphalt mixtures demonstrated the fact that by increasing Nano-silica content, the quality of the warm mix asphalt has been improved. By increasing the Nano-silica content, the resilient modulus of WMA has increased; as such, the pavement response under the traffic loading has decreased. By adding Nano-silica to the Sasobit WMA, the depth of cracking has decreased dramatically. So, fatigue life of WMA has been extended in the presence of Nano-silica. Also, the results of wheel tracking test demonstrated that the rutting depth of modified samples have been reduced. The results on WMA showed that 6 wt.% of Nano-silica is the optimum content. This result was in compliance with the rheological investigations.
http://www.ijte.ir/article_13377_2266d10314905ac1f3d631385d072836.pdf
2015-07-01
55
66
10.22119/ijte.2015.13377
WMA
Sasobit
Nano-silica Modified Bitumen
Rheological properties
Resilient Modulus
fatigue
rut depth
Saeed
Sadeghpour Galooyak
s.sadeghpour@aut.ac.ir
1
Refining Technology Development Division, Research Institute of Petroleum Industry, Tehran, Iran
LEAD_AUTHOR
Masoud
Palassi
2
Department of Civil Engineering, Tehran University, Tehran, Iran
AUTHOR
Ahmad
Goli
a.goli@trn.ui.ac.ir
3
Assistant Professor, Department of Transportation Engineering, University of Isfahan, Isfahan, Iran
AUTHOR
Hossein
Zanjirani Farahani
4
University of Tehran, Tehran, Iran
AUTHOR
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29
ORIGINAL_ARTICLE
Comparison between Combined and Separate Approaches for Solving a Location-Routing Problem in Hazardous Materials Transportation
In the case of hazardous materials management, selected routes for carrying hazardous materials (i.e., hazmat) have significant effects on locating hazmat distribution centers. Since, risk and cost are usually considered as two main attributes to determine the best routes, optimized locations are sequentially outlined depending on selected routes. In the present paper, two different approaches of developing separate and combined models of routing and locating problems have been utilized to determine hazmat transport routes together with optimized locations for distribution centers. While mathematical models are developed to carry out the above concepts, a three-stage procedure has also been developed to determine the routes and hazmat quantities should be required to transport for each origin destination pairs. An experimental network consists of eighty-nine nodes and one hundred and one links has been used as case study for analytical process and model validation. Results revealed that, although two different models have been developed following the above approaches, but results are the same. Therefore, decision makers who are dealing with hazardous material management should not be worried about the approach which is better to be utilized for solving routing-locating problem.
http://www.ijte.ir/article_13378_73406ce6b8f37ee91f21a11566f9e086.pdf
2015-07-01
67
77
10.22119/ijte.2015.13378
hazardous materials
Transportation
simultaneous location-routing
separate location-routing
risk-cost trade-off
Reza
Tavakkoli-Moghaddam
tavakoli@ut.ac.ir
1
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Azadeh
Abolghasem
azadeh.abolghasem@gmail.com
2
School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Abbas
Mahmoudabadi
mahmoudabadi@mehrastan.ac.ir
3
Department of Industrial Engineering, Mehrastan University, Gilan, Iran
AUTHOR
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