Prediction of transportation specialized views of median safety

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  • 1. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), INTERNATIONAL JOURNAL OF CIVIL ENGINEERING AND ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEME TECHNOLOGY (IJCIET)ISSN 0976 – 6308 (Print)ISSN 0976 – 6316(Online)Volume 4, Issue 1, January- February (2013), pp. 38-44 IJCIET© IAEME: www.iaeme.com/ijciet.aspJournal Impact Factor (2012): 3.1861 (Calculated by GISI)www.jifactor.com © IAEME PREDICTION OF TRANSPORTATION SPECIALIZED VIEWS OF MEDIAN SAFETY BY USING FUZZY LOGIC APPROACH S.G.Uma Sankara, Dr.G.Kalivarathanb a Research Scholar, CMJ University, Meghalaya, Shillong. b Principal/ PSN Institute of Technology and Science, Tirunelveli, Tamilnadu, Supervisor, CMJ university, Shillong. Email:sakthi_eswar@yahoo.com ABSTRACT A transportation expert may be asked to support a decision, determine a preference, rank influencing factors, or assess alternatives through various methods including surveys, interviews, panel meetings, and expert analyses. In many of these cases, before the experts render their opinion they formulate it through the use of linguistic information and their own subjective decision criteria. An efficient method to analyze subjective and linguistic information employed by people, whether expert or layman is to apply a fuzzy set concept. The primary strength of a fuzzy approach is that it is applicable for the analysis of human knowledge and subjective human perception, which are represented by linguistic terms rather than numerical terms, and the deductive process. Various applications of fuzzy sets have been applied to analyze many types of information, such as fuzzy decision making analyses, fuzzy aggregation methods, and fuzzy inference systems. The fuzzy inference system, which mimics the human perception and decision making processes, is a deductive process of mapping given inputs to certain outputs based on fuzzy membership functions and fuzzy rules. It has been widely applied in various analysis of subjective and ambiguous information. 1.0 INTRODUCTION Fuzzy inference systems have been applied in various areas. However, many studies reported limitations of the conventional fuzzy inference system when dealing with multiple variables .The number of rules in a conventional fuzzy system increases exponentially with the number of variables involved. Normally, three or four variables are the maximum number that can be considered as part of a conventional fuzzy inference system. One of the ways to solve this “rule-explosion problem” is to use a fuzzy inference system with a hierarchical structure called a hierarchical fuzzy system. The hierarchical fuzzy system, proposed by Roju 38
  • 2. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEMEet al. (1991), can reduce the computational complexity of a multivariable fuzzy system and thenumber of rules. This rule explosion problem is more complex when fuzzy logic is applied tostudy transportation user perception. This is because transportation user perception regardingtransportation service or safety is usually affected by many factors, such as roadway geometry,traffic flows, driver characteristics, and other driving conditions. It may not be able to bedetermined by only a few factors. Also, each driving condition has many sub elements. Forexample, geometric conditions consist of many measures of cross section elements, horizontaland vertical alignment, and roadside environments. To select variables to be used for the fuzzyinference system, the five highest ranked variables and the variables for which data wereavailable in the median safety database were considered. Since the crash data were used forcorroborating the results of the proposed fuzzy inference system, the availability of each variablein the database was also a critical issue in this variable selection procedure. Additionally, currentdesign manuals were reviewed to select relevant variables for the fuzzy inference system. For thecurrent median barrier warrant in the AASHTO Roadside Design Guide, ADT and median widthare employed. These two variables have been known as the most critical factors in assessingmedian safety. From these reviews, five variables to evaluate geometric conditions and onevariable to evaluate traffic flow conditions were selected. The five geometric variables weremedian width, horizontal curvature, operating speed, median cross-slope, and shoulder width.ADT was used to describe the traffic flow condition.2.0 CONSTRUCTION OF FUZZY MEMBERSHIP FUNCTIONS There are various ways to determine fuzzy membership functions. Generally, the methodsof formulating fuzzy membership functions can be classified into three approaches: constructingthe membership functions through the analyst’s judgment, constructing the membership functionsthrough experiments, or constructing the membership functions from a given numerical data set.Selecting a method to determine the membership functions depends on many conditionsincluding the characteristics of the study and the available data set associated with the study.In this study, the method based on analyst’s judgment was used to determine the fuzzymembership functions. It is the most common means used to construct fuzzy membershipfunctions because of its simplicity and wide applicability. In this method, an analyst employstheir own knowledge and information gleaned from relevant literature to compose themembership functions. In the proposed study, fuzzy membership functions for the selectedvariables were constructed through four resources: the authors’ own knowledge; a review of theexperts’ opinion; a review of the literature and the state of the practice related to transportationsafety, typically median safety; and a basic review of the roadways for which crash data werecollected. A review of relevant literature and associated practice is usually the most significantresource for determining reasonable and appropriate fuzzy membership functions in the analystintuition method. Since there are few studies that have investigated the relationship betweencontrolling factors and CMC, the general safety effects of the selected variables were alsoreviewed. Through a review of the experts’ opinion regarding the influence of the various factorson median safety, the relative importance of each factor was investigated. This relativeimportance was used to determine the weight of each variable. A basic review of the roadwayswithin the crash database was conducted without any statistical analysis. The variable type (e.g.,binary, continuous), the number of classes for each variable, and the range of values were mainlyconsidered in this review. Using these resources, two types of fuzzy membership functions weredetermined. The first fuzzy membership functions represented how significantly each factorinfluences median safety. The second fuzzy membership functions represented the relativeimportance of each geometric factor. 39
  • 3. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEME3.0 FUZZY MEMBERSHIP FUNCTION FOR THE FACTORS CONTROLLINGMEDIAN SAFETY The initial construction of the fuzzy membership function for the six factorsinfluencing median safety was accomplished through the review of references andcommon engineering judgment. They were then slightly modified to reflect PennsylvaniaInterstate highways or expressways because the review results represented general oruniversal information regarding driving environments and not specifically the drivingenvironments of Pennsylvania. The review results, which allowed for the construction ofthe preliminary fuzzy membership function, are explained in the paragraphs below. Thefirst variable, average daily traffic (ADT) represents the traffic condition. ADT is knownas a significant factor influencing median safety, and it is used as one of two criteria ofthe median barrier warrant. The median barrier warrant in the AASHTO Roadside DesignGuide uses two categories to determine the barrier installation guideline. For ADTs lessthan 20,000, barrier is optional, but for ADTs greater than 20,000, barrier is warranted,depending on the median width. This category is also applied in the PennDOT designmanual. In the expert survey, four categories: 15, 000 to 30,000, 30,000 to 50,000, 50,000to 75,000, and greater than 75,000, were considered to investigate the safety effects of thetraffic flow condition. The second variable is median width which represents a geometriccondition. Median width is one of the most significant factors used to evaluate mediansafety in conjunction with ADT. To determine the fuzzy membership function for medianwidth, AASHTO’s A Policy on Geometric Design of Highways and Streets AASHTO’sRoadside Design Guide, and other references were reviewed. The 2001 Green Bookindicates that median widths of 50 to 100ft are common on rural freeways. In AASHTO’sRoadside Design Guide, median barrier warrant is based on three categories of medianwidth. Barrier is warranted for medians less than 30ft, and barrier is not considered formedians greater than 50ft. Barrier is optional for medians between 30 and 50ft. However,the creation of the fuzzy membership function for horizontal curvature and median crossslope was restricted to reflect the review results. The previous studies emphasized thatvarious features of horizontal curvature can affect roadway safety as mentioned above.Given their findings, the fuzzy membership functions representing the effect of horizontalcurvature on median safety should be determined by taking into consideration variousfeatures of a horizontal curve. Three condition levels, poor, fair, and good, havecommonly been used for evaluating the effect of horizontal curves on safety in previousstudies. However, the median crash data used for comparison with the safety index, whichis based on the proposed fuzzy inference system, included only the presence of horizontalcurvature as binary information, such as 0 for no curve and 1 for a curved alignment. Dueto this limitation of the database, the fuzzy membership functions for horizontal curvaturewere determined with just two levels in this study even though it is not as desirable as themulti-condition level described above. The median cross slope data in the crash databasewas also binary data with 0 indicating flatter than 6:1 and 1 indicating steeper than 6:1.This limitation of the median crash database necessitated the creation of two levels offuzzy membership functions, such as poor and acceptable or steeper and flatter formedian cross slopes steeper than 6:1 or flatter than 6:1, respectively. 40
  • 4. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEME4.0 APPLICATION OF PROPOSED THE HIERARCHICAL FUZZY INFERENCESYSTEM The results of the developed HFIS were compared with observed crash data for thepurpose of validation. For this application, the Pennsylvania median safety database was usedper the previous discussion. This database included various elements, such as crash type,severity, and roadway inventory data. Out of those data, the number of CMC crashes and theinventory data of the six variables used in the fuzzy inference system described above wereapplied. Five years of data on traffic volumes (ADTs) and the geometric features covered bythe five variables discussed above for 12,781 roadway segments were used as input values forthe lower level or the upper level fuzzy inference system. However, since the safety databasedid not include an operating speed but posted speed, posted speed was used as a surrogateinput variable in place of operating speed. Through the developed HFIS and a defuzzificationprocedure, FMSIs for each roadway segment were produced and compared with the observedmedian crash data of the same roadway segments. First, the relationship between ADT andFGI of the given roadway segments was examined. Through this procedure, the fuzzypartition and rule mapping conducted for the upper level fuzzy inference system wereverified. The data for the roadway segments reflect the results of the partition and rule-mapping relatively well. Most of the roadway segments FGI ranged from 0.3 to 0.7 and theirADTs vary widely. The minimum ADT is 481 and maximum ADT is 84,939 vehicles perday. However, most (84.6 percent) of the roadway segments have less than 20,000 ADT.Therefore, most of the given roadway segments have relatively acceptable geometricconditions and uncongested traffic flow conditions. There were many other median safetyfactors that were not used in this study due to the limited availability of data, such as weather,radius of horizontal curves, and factors regarding drivers. However, the proposed HFIS canproduce an indicator, FMSI, which explains well the degree of median safety on Interstatehighways or expressways. It is one of the advantages of the fuzzy approach to analyze orinfer well with ambiguous or incomplete information. Therefore, it can be concluded that theproposed HFIS, in terms of FMSI values, reflect well the real median crash problem, and theincorporated transportation expert opinions appear to be valid. FIGURE 1. Fuzzy Median Safety Index & CMC Frequency 41
  • 5. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEME FIGURE 2. The Crossover Median Crash and Fuzzy Median Safety Index5.0 CONCLUSION In this paper, a new approach to incorporate transportation experts’ opinions using theHFIS was developed. Generally, transportation experts use linguistic information and theirown subjective decision criteria to formulate and express their opinion. However, it isdifficult to aggregate those linguistic and subjective experts’ opinions using conventionalmethods. The proposed method allows for the analysis and aggregation of the subjective andlinguistic expert opinions taking into consideration the unique characteristics and decisioncriteria of an individual expert.To apply this method, variables in the HFIS were selected through the transportation expertsurvey results of a previous study. The fuzzy membership functions for the selected sixvariables were constructed using common engineering knowledge garnered from a review ofthe experts’ opinions, a review of the references related to transportation safety, and theauthors’ own knowledge. A hierarchical structure for the fuzzy system was applied to reducethe complexity of a multivariable fuzzy system, which can lead to the fuzzy rule explosionproblem. The fuzzy weighted average method was used in the process of formulating the 42
  • 6. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEMEfuzzy inference system to avoid the difficulty of fuzzy rule mapping with a large number ofvariables. The incorporated experts’ opinions regarding median safety were finally expressedby FMSI as an indicator of the degree of median safety. Then, the values of FMSI computedusing roadway inventory data were compared with observed median crashes to validate thefuzzy results. Since the roadway type used in this study was the Interstate highway andexpressway, most of roadway segments in the database have relatively favorable drivingconditions. For this reason, most of the roadway segments were less than 0.5 FMSI. To avoida biased interpretation of the results from the unbalanced data, the mean of CMC crashfrequency and the CMC crash rate were used for the validation process. The mean of CMCfrequency increases exponentially with an increase of FMSI. Typically, a roadway segmentwith an FMSI equal to or greater than 0.7 included more CMC crashes than those segmentswith an FMSI less than 0.7. Through these comparisons, the developed HFIS based onexperts’ opinions was evaluated as the system that can explain relatively well the degree ofmedian safety for Interstate highways and expressways.REFERENCES[1]Donnell, E.T., Cost-Effective Approach to Improve Median Safety on PennsylvaniaInterstates. Ph.D Dissertation of Department of Civil and Environmental Engineering, ThePennsylvania State University, 2003[2]Dubois, D. and H. Prade. Fundamentals of Fuzzy Sets, Kluwer Academic publishers,Boston, MA, 2000[3]Fang, F.C., L. Elefteriadou, K.K. Pecheux, and M.T. Pietrucha, Using Fuzzy Clustering ofUser Perception to Define Levels of Service at Signalized Intersections, Journal ofTransportation Engineering. Vol.129, Issue 6, 2003, pp657-663.[4]Favilla, J., a. Machion, and F. Gomide. Fuzzy Traffic Control: Adaptive Strategies. Inproceedings of the IEEE International Conference on Fuzzy Systems, San Francisco, CA,1993.[5]Fitzpatrick, K., P. Carlson, M.A. Brewer, M.D. Wooldridge, and S-P Miaou. NCHRPReport 504: Design Speed, Operating Speed, and Posted Speed Practices, TransportationResearch Board, National Research Council, Washington, D.C. 2003.[6]Flanner, A., K. Wochinger, and K.K. Pecheux, Research approaches to assess automobiledrivers’ perception of quality of service. In proceeding of 83rd Annual Meeting of theTransportation Research Board, Transportation Research Board, Washington D.C., 2004.[7]Flannery, A. and N. Pedersen. Incorporating Customer Perceptions and Satisfaction intoDetermination of Level of Service, In proceeding of 84 Annual Meeting of theTransportation Research Board, Transportation Research Board, Washington D.C, 2005[8]Flannery, A., K. Wochinger, and A. Martin, Driver Assessment of Service Quality onUrban Streets. In proceeding of 84th Annual Meeting of the Transportation ResearchBoard, Transportation Research Board, Washington D.C., 2005.[9]Ishbushi, H. and T. Yamamoto, Trade-off between the Number of Fuzzy Rules and TheirClassification Performance, Accuracy Improvements in Linguistic Fuzzy Modeling, 2003.[10]Jang, J.R., ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEETransactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665-684, 1993.[11]Juang, C. H., J. E. Clark, and P. Ghosh. Representation, Processing, and Interpretation ofFuzzy Information in Civil Engineering, Transportation Research Record 1399, TRB,National Research Council, Washington, D.C., 1993, pp. 20-26. 43
  • 7. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEME[12]Juang, C. H., X. H. Huang, and S. D. Shiff. Determination of weight of criteria fordecision making by the fuzzy eigenvector method. Civil Engineering System, Vol. 9,1992, pp. 1-16.[13]Kita, Hideyuki. Level-of-Service Measure of Road Traffic Based on the Driver’sPerception. Transportation Research Circular E-C018: 4th International Symposium onHighway Capacity, 2000, pp53-62.[14]Knuiman, M.W., F.M. Council, and D.W. Reinfurt. Association of Median Width andHighway Accident Rates. Transportation Research Record 1401, TRB, NationalResearch Council, Washington, D.C. 1993, pp. 70-82.[15]Kochen, M. and A.N. Badre, On the Precision of Adjectives which denote fuzzy sets,Journal of Cyberbetics, Vol. 4, pp. 49-59, 1974.[16] Anand Handa and Ganesh Wayal, “Software Quality Enhancement Using Fuzzy LogicWith Object Oriented Metrics In Design” International journal of Computer Engineering &Technology (IJCET), Volume 3, Issue 1, 2012, pp. 169 - 179, Published by IAEME 44