Field and Analytical Investigation of Accidents Data on the Egyptian Road Network

Transcription

1 J. Civil Eng. Architect. Res. Vol. 4, No. 2, 2017, pp Received: January 23, 2016; Published: February 25, 2017 Journal of Civil Engineering and Architecture Research Field and Analytical Investigation of Accidents Data on the Egyptian Road Network Mohamed M. Elshafey Department of Civil Engineering, Canadian International College, Cairo, Egypt Corresponding author: Mohamed M. Elshafey Abstract: The Road Traffic safety aims at reducing the rate of accidents and crashes that cause human agony, death, injuries, and property damage caused by crashes of road vehicles. Human and economic losses resulting from road traffic is greater than that resulting from all other transportation modes air, sea, train, etc. Many developing countries invest huge sums of money to develop road networks with acceptable level of service and safety. The traffic mix and road usage in developing countries are very different from those in developed countries. Road crashes can be reduced by better planning and more safety conscious design of road networks. This paper presents an analytical approach for evaluating traffic accidents on the Egyptian road network using actual field data. Two major roads have been considered in the study, which are Cairo-Ismailia and Cairo-Suez roads that are located east of Greater Cairo. Collected data included Average Annual Daily Traffic (AADT), road characteristics, as well as accidents characteristics. Statistical models have been developed using data obtained from 23 Egyptian major highways to predict number and rate of accidents. The independent variable used in the model include on pavement condition index (P.C.I) Average annual daily traffic (AADT), lane width, shoulder width, and type of highway facility. The models were used to evaluate two major highways between Cairo and Ismailia and Suez. Key words: Traffic safety, accident rate, statistical analysis, Egyptian highways, drivers behavior. Nomenclature: R C N AADT L LW SHW DN PCI NOL Crash rate for the road segment expressed as crashes per 100 million vehicle-miles of travel (VMT). Total number of crashes in the study period Number of years of data Average Annual Daily Traffic Length of the roadway segment (miles) Lane Width (m) Shoulder Width (m) Day or Night Pavement Condition Index Number of Lanes 1. Introduction Over the last 30 years, rapid growth in population, urbanization, and industrialization, along with advanced technologies in the automobile industry have increased traffic volumes and operating speeds on the roads and highways. Such factors have increased the risk of automobile accidents; therefore, more attention towards road safety standards is highly required. Improving road safety requires first the understanding of factors that contribute to accident occurrence, and then controlling the conditions under which those factors lead to the accidents. Traffic accidents are considered to be a primary cause of accidental death, injuries and related property losses. Research shows that the death toll on roads exceeds the total number of fatalities of major medical diseases and military victims of World Wars combined [1]. On 2010, The U.S. Department of Transportation s National Highway Traffic Safety Administration (NHTSA) released a study showing that the crashes economic impact is around $ 871 billion compared to $US 453 billion on 1997 and

2 1924 Field and Analytical Investigation of Accidents Data on the Egyptian Road Network Societal Impact is 32,999 fatalities, 3.9 million non-fatal injuries, and 24 million damaged vehicles [2, 3]. Thus, the cost of such road accidents can be presented as an impact on the Gross Domestic Product (GDP) of each country. For example, developing countries has approximately 1 to 3% of GDP impact resulted from road accidents only [3]. Despite the fact of the high number of accidents and their consequences either economical or societal, the road networks are considered the corner stone and main indicator of all aspects for countries development, such as political, administrative, economic, security and military. The first road was constructed as early as 4000 BC, which consists of stone paved roads. On 1824, the first asphalt road was laid in Champs-Elysees, Paris, France. More improvement of the asphalt mix was carried out and on 1872 a modern well-graded asphalt mix was laid down on Fifth Avenue in New York City. As of today, almost all roads are paved taking into consideration the road classification (local street through Freeways) to use the optimum asphalt mix for such category [4]. The accidents number mainly depends on complex interaction between human behavior, vehicle characteristics, road condition and environmental conditions. Recent statistics show that the fatalities resulted from road accidents represent more than 99% compared to other mode of transportation either land, sea or air [5]. The following table shows the reasons of accidents as percentage of total number of accidents in Egypt. As shown in Table 1, the human behavior is accounted for most of the accidents percentage on Egyptian roads [5]. Moreover, huge number of global fatalities resulted from road accidents when compared to infectious diseases as shown in a study that was conducted in 2004, where, 1.27 million people died as a result of such accidents. The age group between 15 and 44 years old represents over 50 percent of all fatalities resulting from road traffic accidents [6]. Table 1 Statistics of road accidents in Egypt. Reason of accidents Percentage (%) Exceeding speed limit 50 Lack of driver attention 14 Wrong maneuver 11 Tire explosion 16 Environmental condition 4 Distresses of roads 5 A study was conducted on data collection and analysis of 23 Egypt s highways to identify the main causes of traffic accidents. However, this paper presents the data collection and analysis of 2 Egypt s highways only. The collected data includes number of traffic accidents, time of accidents, day of accidents, accidents locations, highway sections (straight, curved and/or intersection) and finally number of fatalities and injuries for the years 2001 through The data were collected through the General Authority of Roads, Bridges and Land Transport [7]. 2. Data Collection The data were collected through General Authority of Roads, Bridges and Land Transport [7]. The data includes Average Annual Daily Traffic (AADT), road characteristics, number of traffic accidents, time of accidents, day of accidents, accidents locations, highway sections (straight, curved and/or intersection) and finally number of fatalities and injuries for the years 2001 through Table 2 shows the AADT, road characteristics and number of accidents for the two Egyptian highways. Fig. 1 presents an output traffic counts data for the national road network as obtained from the General Authority for Roads and Bridges and land Transport (GARBLT) [7]. 3. Field Investigation This section represents the analysis of accident data among various parameters for two main highways of Egyptian roads. These two Highways are Cairo-Ismailia and Cairo-Suez. This paper represents the accident frequency as well as rate of accidents. The accident frequency is used to identify the day of the week with

3 Field and Analytical Investigation of Accidents Data on the Egyptian Road Network 1925 the maximum number of accidents and the spot location along the highway with the highest frequency. However, the rate of accidents is used to compare the relative safety of the above mentioned highways. Table 2 Collected data for sample of Egyptian highways. Road Cairo/Ismailia Cairo/Suez AADT PCI No. of Lanes L.W ACC. D/N Total No. of acc Fig. 1 Permanent traffic counts aadt on main Egyptian rural road network [7].

4 1926 Field and Analytical Investigation of Accidents Data on the Egyptian Road Network 3.1 Cairo-Ismailia Road Fig. 2(a) shows the number of accidents versus days of week for the Cairo-Ismailia Road. It is clear that the maximum number of accidents occurred on Sundays because of the large traffic volume as it is the first day of the week in Egypt. In addition, the traffic mix varies, where; more trucks are on the road compared to passenger vehicles. Fig. 2(b) and 2(c) show the number of injuries and fatalities. Although Saturday was not the highest number of accidents, it has the highest number of fatalities. That might be because the severity of accidents since it is a weekend with low traffic volume. Such low traffic volume encourages the drivers to exceed the speed limit. Fig. 2(d) shows that the maximum number of accidents is located at 70 km from Cairo due to the existence of a main market where heavy truck volume occurs. 3.2 Cairo-Suez Road Fig. 3(a) shows the number of accidents versus days of week for the Cairo-Suez Road. It is clear that the maximum number of accidents occurred on Wednesday at which the maximum number of injuries occur as shown in Fig. 3(b). Fig. 3(c) shows the number fatalities. Although Monday was not the highest number of accidents, it has the highest number of fatalities. Fig. 3(d) shows that the maximum number of accidents is sited at two locations. First location is at 10 kilometers from Cairo by the intersection of the ring road at which high traffic volume occurs. Second location is 90 km from Cairo by the entrance of new development area (Shorouk City). 4. Rate of Accidents The accident frequency might be misleading when used to determining the relative safety of the two highways included in this paper. Thus, accident rate is used to represent accident frequency in relation to the AADT as shown in Eq. (1) [8]. R = (100,000,000 C)/(365 N AADT L) (1) Where: R = Crash rate for the road segment expressed as crashes per 100 million vehicle-miles of travel (VMT); C = Total number of crashes in the study period; N = Number of years of data; AADT = Average Annual Daily Traffic; L = Length of the roadway segment in miles. Fig. 2 Cairo-Ismailia road.

5 Field and Analytical Investigation of Accidents Data on the Egyptian Road Network 1927 Fig. 3 Cairo-Suez road. Table 3 shows that Cairo-Ismailia highway has experienced accidents per 100 million vehicle-miles traveled. However, Cairo-Suez has experienced accidents per 100 million vehicle-miles traveled. By comparing the two accident rates of both highways, it is clear that Cairo-Suez is more susceptible to accidents despite the fact it has lower AADT compared to Cairo-Ismailia highway. Thus, more attention is needed for Cairo-Suez highway to improve safety and reduce accident frequency. 5. Statistical Analysis Linear regression analysis was used to estimate the expected value of one variable (y) given the values of some other variable or variables (x) as presented in Eq. (2). The (y) variable is called dependent variable, Table 3 Highway accident rate calculation by vehicle miles traveled. Length of Accidents Crash Years of Highway AADT segment frequency rate data (N) (Km) (C) (R) Cairo/Ismailia 39, Cairo/Suez 13, while the (x) variables are called independent variables. Linear regression is called linear because the relation of the dependent and independent variables is a linear function. The typical linear regression is expressed as follows: [9, 10]. y = α + βx + ε (2) The right hand side generally comprises a linear combination of parameters, here denoted as α, β. In addition the term ε represents the unpredicted variation in the dependent variable; it is conventionally called the error. 6. Accident Number Prediction The research studied various analytical models to predict number of accidents based on different Independent variables. Such variables included lane width (LW) shoulder width (SHW), day or night (DN), pavement condition index (P.C.I), Average annual daily traffic (AADT) and finally the number of lanes (NOL) as presented in Table 2. Linear regression analysis was used utilizing the statistical software SPSS version 23.0 Many transformation and combination of Independent variables were performed

6 1928 Field and Analytical Investigation of Accidents Data on the Egyptian Road Network and included in the model, the model was developed using many models including enter stepwise, remove, backward and forward. The model with the highest coefficient of determination (R 2 = 0.684) and Significance equals to value is presented below as shown in Eq. (3). The independent variables of such model are as follows, DN, SHW, LW, NOL, PCI, and AADT. ACC = DN SHW LW NOL PCI E 5 AADT (3) The above mentioned model was chosen to predict number of accidents as it has the highest coefficient of determination (R 2 ) value of 0.648, which indicated that a large amount of variation in number of accidents could be explained by the independent variables included in the model. The sign of all terms are rational where there is an inverse relationship between number of accidents and shoulder width and PCI respectively. In addition, there is a direct relationship between the number of accident and A.A.D.T, lane width, number of lanes respectively. The negative sign in the first variable (DN) suggest that the higher number of accidents happen at the day time may be explained by the relatively higher traffic volume at the day time. The negative sign in the second variable (SHW) suggests that; when the shoulder width increases the number of accident decreases and vice versa clearly the shoulder provides enough space for car stopping in emergency situations. The positive sign in the third variable (LW) suggest that; when the lane width increases, the number of accidents increases. That is because of the excessive maneuvering at higher speeds of the drivers in Egypt. Same can be noticed when investigate the positive sign of fourth variable (NOL) which suggests that, when the number of lanes increases the number of accidents increases. This happens because of excessive maneuvering due to reliability of the drivers on the higher number of lanes at maneuvering, also drivers don t respect the various lane speeds. The negative sign in PCI suggest that when the PCI increases the number of accident decreases. This is because the surface of the highways with higher PCI value is better than those with lower PCI values. Finally, the positive sign of the last variable indicate the higher the traffic volume, the more accidents are expected. 7. Accident Rate Prediction Prior developing the linear regression models of the rate of accident data, one must test the linearity of the available data. Fig. 4 shows the Normal P-P plot, where it can be concluded that the points is scattered almost around the zero value and the scores is distributed above and below zero. Thus, the relationship is considered linear relationship. Table 4 shows the models that were developed to predict the accident rates. The significance values for all the independent variables were tested to make sure they are less than 0.05, which means that the independent variable are significance and it is recommended to reject the null hypothesis and accept Fig. 4 Normal P-P plot of regression standardized residual.

7 Field and Analytical Investigation of Accidents Data on the Egyptian Road Network 1929 Table 4 Rate of accidents regression models. Model type Model R 2 Enter AR = E-5 AADT PCI NO.OF.LANES L.W SHW D.N Stepwise AR = PCI AR = E-5 AADT PCI NO.OF.LANES L.W SHW D.N AR = E-5 AADT PCI NO.OF.LANES L.W SHW Backward AR = E-5 AADT NO.OF.LANES L.W SHW AR = NO.OF.LANES L.W SHW 0.640* the alternative hypothesis. Therefore, the slopes for these variables are not equal to zero. It was found that the significance of almost all the variables except number of lanes (NOL), lane width (LW) and shoulder width (SHW) were greater than 0.05, which means that these variables are insignificant and it is recommended accept the null hypothesis and reject the alternative hypothesis. Therefore, the slopes for these variables are equal to zero. Hence, these variables have to be excluded from the model. Knowing that, the best model to predict the rate of accidents is the forth backward model with coefficient of determination (R 2 ) value was 0.640, which indicated that a large amount of variation in accident rates could be explained by the independent variables with significance less than The negative sign of the (SHW) variable suggests that; when the shoulder width increases the rate of accidents decreases and vice versa. Clearly, the shoulder with enough width helps in providing enough space for car to stop in emergency situations. On the other hand, the positive sign (LW) variable means that; when the lane width increases, the rate of accidents increases. That is because of the excessive maneuvering at higher speeds. Same can be noticed when investigate the positive sign of (NOL) variable, which suggests that, when the number of lanes increases the rate of accidents increases. This happens because of excessive maneuvering due to reliability of the drivers on the higher number of lanes at maneuvering, also drivers don t respect the various lane speed limits. Fig. 5 represents the actual accident rates versus the predicted accident rates. Fig. 5 Actual accident rates versus predicted accident rates. 8. Conclusions and Recommendations The analysis of the analytical and statistical models shows that the human behavior is the main causes of road accidents. It was clear from the study that the number of accidents increases as the number of lanes increase as well as the increase in the width of the lanes. Moreover, most of the accidents occurred on straight sections, where, less alignment effect occurs. Multiple regression analysis showed that linear backward model was the best model represents the highest correlation between accident rate and factors affecting it. The result of regression analysis shows that accident rate is inversely related to shoulder width. The wider the shoulder width, the more area for errant vehicles and allows certain amount of flexibility. More investigations need to be done on the same highways included in the study after upgrading. The above mentioned highways are planned for restoration with respect to number of lanes, shoulder width and pavement condition. Such research would help in studying the effect of restoration on road safety with respect to number and rate of accidents.

8 1930 Field and Analytical Investigation of Accidents Data on the Egyptian Road Network Acknowledgment The author would like to acknowledge Eng. Ayman Fahim for providing the required data and his cooperation in preparing this paper. Moreover, the author would like to express his gratitude and appreciation to the Ministry of Transportation in Egypt for their support and continuous cooperation. References [1] L. Evans, Traffic Safety and the driver, standard developments-an overview of ANSI Z115, safety requirements for motor vehicle fleet operations, Professional Safety 47 (9) (1991) 9. [2] NHTSA, The economic and societal impact of motor vehicle crashes, The U.S. Department of Transportation s National Highway Traffic Safety Administration, Report No. DOT HS , Washington, DC., [3] M.A. Ismail, S.M.M. Abdelmageed, Cost of road traffic accidents in Egypt, World Academy of Science, Engineering and Technology, International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering 4 (6) (2010) [4] J.L. Pline, Institute of Transportation Engineers, Traffic Engineering Handbook, [5] Y.A. Hassan, M.O. Ayman, A.M. Wahaballa, Traffic Accident Analysis & Modelling for Upper Egypt Rural Roads, Department of Civil Engineering, Assuit University, [6] P. Puvanachandra, H. Connie, H.F. El-Sayed, R. Saad, N. Al-Gasseer, M. Bakr, Road traffic injuries and data systems in Egypt: Addressing the challenges, Traffic Injury Prevention 13 (1) (2012) [7] GARBLT, Egyptian General Authority for Roads, Bridges, and Land Transport, [8] Roadway Departure Safety: A Manual for Local Rural Road Owners, Appendix C: Crash Rate Calculations, U.S. Department of Transportation, Federal Highway Administration, 1200 New Jersey Avenue, SE, Washington, D.C., [9] P.G. Hoel, Elementary Statistics, Fourth Edition, John Wiley, Sons, Inc, New York, USA, [10] A.M. Ismail, H.Y. Ahmed, M.A. Owais, Analysis and modelling of traffic accidents causes for main rural roads in Egypt, Journal of Engineering Sciences 38 (4) (2010)