ROAD ACCIDENT MODELLING FOR INDIAN NATIONAL HIGHWAYS

Transcription

1 International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 1, January 2017, pp Article ID: IJCIET_08_01_093 Available online at &IType=1 ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed PREDICTION OF ROAD ACCIDENT MODELLING FOR INDIAN NATIONAL HIGHWAYS B.Naga Kiran Research Scholar, School of Civil Engineering, Rajeev Gandhi Memorial college of Engineering and Technology, Nandyal, Andhrapradesh, India Dr. N. Kumara Swamy Professor, Department of Civil Engineering, Vasi Reddy Venkataadri Institute of Technology, Guntur, Andhrapradesh, India Dr. C. Sashidhar Professor, Department of Civil Engineering, Jawaharlal Nehru Technological University, Anantapur, Andhra Pradesh, India ABSTRACT The objective of this research article is to identify the most critical safety influencing variables of a section of four-lane National Highway-18(old)/40(New) through statistical models that explains the relationship between frequency of accident count and highway safety variables. The Highway traverses mainly through a plain terrain of mostly agricultural areas. The study is for newly constructing Four-Lane road between chainage (Chagalamarri) to 359.9(Kurnool) to identify all safety deficiencies responsible for road accidents. The predictive ability using Multiple linear regression model is under two categories: First for the 2 lane sections and second for 4 lane sections separately. The validation tools were applied to examine the ability of models to predict accidents. Key words: Accident Prediction Model, Multiple Linear Regression, Model Validation. Cite this Article B.Naga Kiran, Dr. N. Kumara Swamy and Dr. C. Sashidhar, Prediction of Road Accident Modelling For Indian National Highways. International Journal of Civil Engineering and Technology, 8(1), 2017, pp /issues.asp?jtype=ijciet&vtype=8&itype= =

2 Prediction of Road Accident Modelling For Indian National Highways 1. INTRODUCTION As per the official records 1,41,526 persons were died and 4,77,731 were injured in road traffic accidents in India in 2014(NCRB 2015).Hence Road safety Management has emerged as a topic of discussion for researchers all over the world. The traffic police are the source for giving information relating to road traffic injuries in India, based on the cases reported them. The burden of road traffic injuries has increased over the last 20 years in India. National Safety policies are to be promoted to ensure improvements in traffic safety. As per the reports of MORTH and NCRB (National crime Record Bureau, the data can be useful up to 20% only and the remaining 80% is unreliable information which should not be used for analysis. The faults in the reports probably occur from a wrong coding of the victims status which needs to be reviewed &revised. The fatalities are more than doubled between 1991 and 2014 in big cities of India. In Rajastan, Maharastra, Orissa and Tripura fatalities increased by 4 to 6 times and in Assam, Gujarat, Punjab and Haryana 8-10 times increased in the same period. Much concentration need to be put to street and highways designs which influence on vulnerable road user safety,because present policies do not appear to be giving needed effect. Number of deaths were increased between 1996 to 2014 in almost all cities in India Survey reports suggested that in Agra and Ludhiana lower volumes vehicle velocities can be higher at night because sufficient lighting system is not available and there is limited checking s of drivers under the influence of alcohol. In India, national highways are only 15% of the total length but account for 33% of the fatalities. The expressway are only to a length of 1000kms in 2014 in the country but a high death rate of 1.8 per Km per year.68% of persons are getting killed on highways in India by the vulnerable road users. Rear end crashes including with parked vehicles are high on all types of highways. Safety should be enhanced by separating roads for slow and fast moving vehicles on the same roads. An independent road safety Agency is to be established in India to set standards, to monitor and implements. Multi disciplinary research centers are to be established to get more innovative results. Highway designs must be adequate with safe facilities for slow traffic and separate paths are to be provided for bicycle lanes and disabled pedestrians. 2. LITERATURE REVIEW Earlier Research Model Parameters Conclusion Shankar et al(1994) Negative binomial model They concluded that rainfall Road geometric, weather and played or significant positive role seasonal effects in accident occurrences Persaud et al(2000) Generalized linear model Accidents per year increases with Traffic flow, Road geometry ADT Golob and Recker(2003) Linear and non linear Traffic flow, Weather and Significant variables are original multivariate statistical analysis lighting traffic flow variables Wong et al(2007) Degree of curve more significant Poisson and Negative Traffic flow, geometric role in crash risk binomial regression design, road environment Quddus et al(2010) Ordered Response model Traffic congestion, road geometry Xiugang Lietal (2011) Generalized additive models Geometric elements Anitha Jacob et al(2013) Passion regression, NB regression model Geometrics elements Traffic congestion did not affect the severity of road crashes Lane and shoulder widths significant role in crash risk Crashes effectively reduced by widening roads

3 B.Naga Kiran, Dr. N. Kumara Swamy and Dr. C. Sashidhar 3. STUDY AREA Government of India through NHAI has taken up the development, maintenance and management of national highways under NHDP phase-iii programme of 4/6 lining of 10,000 km length of highways, where the intensity of traffic has increased significantly and to augment the capacity for safe and efficient movement of traffic. National highway number 18 starts from Chittoor (NH-4 jn.) and ends at Kurnool (NH-7 jn.).total length of highway is located in Andhra Pradesh and it passes via Cuddapah (167.7), Maidukur (194.0), Allagadda (247.7), Nandya l (282.2), Panyam (303.0).The present project study passes through only Kurnool district. Most of this national highway study segments falls in rural areas (approximately 85%).The study area for this project is newly constructing four lane road between chainage Chagalamarri ( ) to Kurnool ( ) ALIGNMENT The present project study stretch have both 2 lane as well as 4 lane because this National Highway (NH-18) is under during construction. Hence the alignment has contains number of sharp horizontal and vertical curves. This stretch passes through plain terrain. The existing alignment in the rolling and mountainous terrain stretches from km to km This alignment runs bisecting the existing irrigation tank at km near Thandrapadu village for approximately 200m length on about 3.0m high embakment. The alignment does not run in high embankment except on approaches to major bridges, where the height of embankment is upto 5m..Generally the existing road is on 0.5m to 2.5m high embankment. Figure 1 Google Road Map Chagalamarri To Kurnool, A.P 3.1. DATA COLLECTION Accident data was collected from the National Highway Authority of India (NHAI). Road geometry data was collected through field studies. For the purpose of collecting road geometry data, the road was divided into two type of segments i.e 2 lane segments and 4 lane segments. A five years (2011 July to 2015 November) accident data was collected from the National Highway Authority of India (NHAI) at Kurnool district. And also km length road geometric characteristics was collected such as carriage way, shoulder width, number of curves..etc., 3.2. ROAD GEOMETRIC DATA The total length of selected study area is km consisting both 2 lane and 4 lane. Total length of 2 lane is 63.5 km is divided into 16 segments called A1,A2,A3 A16 and Total length of 4 lane is 72 km is divided into 14 segmentss called B1,B2,B3 B14. The lane details consisting of each individual chainage, road geometric characteristics such as Carriageway (CW), Shoulder width (SW), Number of curves (NC), Number of bridge (NB),Number of minor culverts (NMC), Number of canals 791

4 Prediction of Road Accident Modelling For Indian National Highways (NCS),Number of junctions(nj),number of minor roads (MR),Width at minor roads (WMR),Roadmarkings(RM),Roadsigns(RS),Roadcondition(RC),Shouldertype(ST),Shoulder condition(sc),land use(lu). Majority of these two lane and four lane study stretch was straight. The total number of curves in entire two lane stretch are 46.And the total number of curves in only 4 lane segments are 62. The carriageway for two lane segments varies 8.7m,8.5m,8.0m.etc., While in case of four lane carriageway varies 16.0m,14.0m,14.2m etc., The shoulder width varies in two lane segments is 0.9m,1.2m..etc., The shoulder width varies in four lane segments 1.4m,1.5m etc., 4. MODL DEVELOPMENT Multiple linear Regression: The Normal linear regression model having (Y) as Response variable and X. The Normal MRM (Multiple Regression model) developed to understand the relationship b/w a set of variables that shows in a data set. Y=β O +β 1 X+u... 1 Y=f(X) +U Where X1, X2---= explanatory variables β 1 β 2 =estimated model coefficients U=Random error term (Assumed to be distributed Normally with variance and mean zero) β O =Regression Constant f(x)=population regression function This shows that Y has to be distributed normally with mean population regression function and variance. Even though the development of model and Interpretation is so simple: its use in Accident Analysis is restricted due to the following drawbacks Some variables are not follow normal distribution Response variable(y) cannot have a negative value and, Accidents are countable events. Even with above drawbacks this Multiple Regression model has been Implemented (Or) tried in this Research paper. The model can be Implemented (Or) tried whenever a set of data from bigger area is used for Modelling because the presence of a huge no of small effects acting Independently and additively can be assumed to follow normal distribution.(central Limittheore) 5. VALIDATION OF MODEL The validity of model is carried out as follows: By finding the Coefficient of Determination (R 2 ). By the comparison of results of total number of accidents found by model with the data obtained from NHAI. Coefficient of Determination (R 2 ): It is defined as a ratio of the explained variance to the total variance of the independent variable y. The value ofr 2 lies between 0 and 1, the closer it is to 1, The better is the model. The following fig 2 shows Sixteen two lane segments road geometric characteristics on Minitab(17.0)software contains C1 as a dependent variable(number of accidents) and C2,C3..C15 as a independent variables. In two lane road geometric segments only fourteen independent variabls was selected for regression input

5 B.Naga Kiran, Dr. N. Kumara Swamy and Dr. C. Sashidhar Figure 2 Accidents Along with Geometric Charcteristics on Two Lane Segments Figure 3 Two Lane Segment Output Summary (Analysis) 793

6 Prediction of Road Accident Modelling For Indian National Highways Figure 4 Two Lane Segment Output Summary (Coeeficents) The above figures 3&4 indicates regression data output on Minitab 17.0 version. In this data C1 refers to Response(Number of accidents NA ).and C2, C3,C4,C5, C6.. are refers to Predictors(CW,SW,NC,NB etc). 6. MULTIPLE REGRESSION MODEL (FOR TWO LANE SEGMENTS) The following model was selected as the best fit model, satisfying both statistical (R 2 = 0.912) as well as practical considerations. Sensitivity studies were conducted to calibrate the model and also study the effect of each variable on the urban road accidents. Figure 5 Normal Probability Plot for Two Lane Segments 794

7 B.Naga Kiran, Dr. N. Kumara Swamy and Dr. C. Sashidhar Figure 6 Residuals Versus Fits For Two Lane Segments Figure 7 Residuals Versus Order For Two Lane Segments EQUATION: Y = CW-20.63SW+2.94NC-19.5NB-5.5NMC+7.7NCS- 16.6NJ+13.7NMR+1.93WMR+2.4RM30.7RS+8.69RC+21.9ST+4.6SC

8 Prediction of Road Accident Modelling For Indian National Highways Table 1 Comparision of Observed and Predicted Number of Accidents SL OBSERVED VALUES PREDICTD VALUES DIFFERENCE * The following fig 8 shows fourteen four lane segments road geometric characteristics on Minitab(17.0)software contains C1 as a dependent variable(numberr of accidents) and C2,C3..C15 as a independent variables. In two lane road geometric segments only twelve independent variabls was selected for regression input. Figure 8 Accidents Along With Geometric Charcteristics On Four Lane Segments 796

9 B.Naga Kiran, Dr. N. Kumara Swamy and Dr. C. Sashidhar Figure 9 Four Lane Segment Out Put Summary (Analysis) Figure 10 Four Lane Segment Output Summary (Coefficents) 797

10 Prediction of Road Accident Modelling For Indian National Highways Figure 11 Normal Probability Plot For Four Lane Segments Figure 12 Residuals versus Fits For Four Lane Segments 798

11 B.Naga Kiran, Dr. N. Kumara Swamy and Dr. C. Sashidhar Figure 13 Residuals Versus Order For Four Lane Segments EQUATION Y= CW-82.7SW+11.26NC27.3NB+3.44NMC25.5NMR+1.24WMR+56.9RM- -5.6RS-43.1RC-13.1ST- 21.2SC Table 2 Comparision of Observed Predicted Accidents SL NO OBSERVED VALUES PREDICTED VALUES ERROR

12 Prediction of Road Accident Modelling For Indian National Highways A1 A2 A3 A5 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 OBSERVED VALUES PREDICTED VALUES Figure 14 Comparision of Observed and Predicted Number of Accidents of Two Lane Segments B1 B2 B3 B4 B5 B6 B7 B8 B9 B10B11B12B13B14 OBSERVED VALUES PREDICTED VALUES Figure 15 Comparision of Observed and Predicted Number of Accidents of Four Lane Segments 7. DISCUSSIONS 1. The model developed can be used for the highways having conditions similar to the study and can help to take right decision in the direction of accidents management i.e. to decide and implement remedial measures in the field of traffic safety. 2. For safety diagnosis and specially, identification of dangerous zones in network by ranking the sites by their accident rates, the model can be very helpful. 3. The model can be used for evaluation of the effectiveness of a safety measure by comparing the accident rates of two compatible samples of sites before and after the implementation and to predict accidents, their nature, causes and pattern. 4. Also, the effect of various parameters like carriage width, shoulder width, number of minor roads, number of curves etc; on the road traffic accidents can be studied with the help of model

13 B.Naga Kiran, Dr. N. Kumara Swamy and Dr. C. Sashidhar 5. More over, the results can act as a quick guideline for road network planning and the authorities concerned with accident mitigation measures. 8. CONCLUSIONS Accident prediction model (APM) is developed by using multiple regression analysis for NH-18 From chainage 224.7(at Chagalamarri) To 359.9(at Kurnool) based on the factors influencing road accident. For two lane segments the dependent variable used in the model is number of accidents(y). The independent variables used in the as Carriageway (CW),Shoulder width(sw),number of curves(nc), Number of bridges(nb),number of minor culverts (NMC), Number of canals (NCS), Number of junctions(nj), Number of minor roads (MR),Width at minor roads (WMR), Road markings (RM),Road signs (RS), Road condition (RC),Shoulder type(st),shoulder condition(sc), Land use(lu). The model developed from the above variables is Y= CW-20.63SW+2.94NC-19.5NB-5.5NMC+7.7NCS- 16.6NJ+13.7NMR+1.93WMR+2.4RM30.7RS+8.69RC+21.9ST+4.6SC. The coefficient of determination (R 2 ) obtained is For four lane segments the dependent variable used in the model is number of accidents(y). The independent variables used in the as Carriageway (CW),Shoulder width(sw),number of curves(nc), Number of bridges(nb),number of minor culverts (NMC),Number of minor roads (MR),Width at minor roads (WMR), Road markings (RM),Road signs (RS), Road condition (RC), Shoulder type(st), Shoulder condition (SC),Land use(lu). The model developed from the above variables is Y= CW82.7SW+11.26NC27.3NB+3.44NMC25.5NMR+1.24WMR+56.9RM-5.6RS-43.1RC-13.1ST- 21.2SC. The coefficient of determination (R 2 ) obtained is It has been clearly demonstrated that regression analysis has been successfully applied to formulate a prediction model for system testing defects. By using statistical approach such as regression analysis, the research can justify the reasons and significance of metrics from requirement, design and coding phase in predicting defects for system testing. Moreover, it is also explained that in order to have a good model, the prediction must fall between a defined minimum and maximum range so that it is feasible to incorporate and implement defect prediction as part of software development process, particularly test process. 2. Accident data from NHAI suggestion that there is a lack of proper in design of road and education to road way safety. These weaknesses can be minimized through comprehensive corrective measures. Local community initiatives to improve the conditions are very sparse and it is also conducted that much greater effort, desirably with the support from international agencies and specialized institutes is needed in combating the problem. Importantly, such effort would require considerable resources particularly trained local personal, safety specialization and researchers so as to buildup indigenous capacity and attain sustainable safety program. 3. It is suggested to further refine the model reported in this study using more number of variables (eg: Traffic volume, Spot speed..etc) to get a more realistic picture in predicting or forecasting accidents, though accidents occurrence is random phenomenon and therefore we can not exactly predict future trends by using any model or theory, but it is a very handy tool in the hands of planners and decision makes to take remedial measures in advance by studying future trends using such models, to take mitigation measures to minimize the accident rate to certain extent and to take other safety measures

14 Prediction of Road Accident Modelling For Indian National Highways REFERENCES [1] Shankar, V., Mannering, F. and Woodrow, B., Effect of roadway geometrics and environmental factors on rural freeway accident frequencies, Accident Analysis and Prevention, Volume 27, pp ,1994. [2] Golob, T.F.,Recker, W.W.,2003,Relationship among urban freeway accidents, traffic Flow, Weather, and lighting conditions J.Transp. Engg 129, [3] Wong, S.C., Sze, N.N. and Li Y.C., Contributory factors to traffic crashes at signalized intersections in Hong Kong, Accident Analysis and Prevention, Vol.39, pp , [4] Quddus, A.M., Chao Wang and Stephen, G. I., Road traffic congestion and crash severity: Econometric analysis using ordered response models, Journal of Transportation Engineering, ASCE, Vol. 136, No.5, pp , [5] Xiugang Li, Dominique, L., and Yunlong, Z., Development of accident modification factors for rural frontage road segments in Texas using Generalized Additive Models, Journal of Transportation Engineering, ASCE, Vol pp , [6] Gëzim Hoxha and Nijazi Ibrahimi, Implementation of Graphical - Analytical Method In Determination of Speed of Vehicle In Case of Road Accident and Comparing of Results with The Software Method. International Journal of Civil Engineering and Technology, 4(6), 2013, pp [7] Y. P. Raiwani and Pragya Baluni, Extraction of Road Accident Patterns In Uttarakhand Using Neural Network. International Journal of Civil Engineering and Technology, 5(8), 2014, pp [8] Anitha Jacob, M.V.R.Anjaneyulu., Development of cras prediction models for Two lane Rural highways using Regression Analysis, Highway Research journal, January-June 2013 [9] National Highway Authority of India, Nandyal. A.P [10] http//data.gov.in./road accidents [11] IRC for road accident form 802