AN APPROACH TO ANALYZE ISSUES AND CHALLENGES OF ROAD ACCIDENT SCENARIOS
Sumit Kumar Research Scholar
Pankaj Agarwal
Eklavya University, Damoh (M.P.)
Abstract - The road accident situation in India, like in many developing countries, involves a diverse mix of traffic, including human-powered vehicles like bicycles and tricycles (cyclerickshaws), animal-drawn carts, and motor vehicles of different sizes and speeds.
Existing models used in various countries to study the accident scene have been examined and found to lack comprehensiveness in considering all the relevant variables associated with traffic accidents. It has been demonstrated that a multitude of variables need to be considered to effectively analyze the accident scene on urban roads in India. These variables encompass factors such as handrail index, congestion index, bicycle lane index, pedestrian violators, disturbance index, interactions between different modes of transportation, and road features.
The Multiple Linear Regression approach has been deemed suitable for constructing a model that takes into account all these variables. In this study, Anna Salai and Periyar Salai, two major urban arterial roads in the city (now Chennai), were selected for detailed analysis. Data spanning from 1985 to 1990 were collected to build the model and assess its effectiveness. Statistical techniques were employed to evaluate the model's reliability. The recommended model for estimating road traffic accidents within a six-month period incorporates fifteen independent variables. The model was found to estimate the accident scenes with an average variation of 10% in most cases.
Moreover, the model was utilized to project the future accident scene, and the potential impact of implementing bicycle tracks and pedestrian guard handrails along the road curbs was demonstrated. These measures have the potential to alter the accident scene positively by improving safety conditions.
1 ROAD ACCIDENTS ABROAD
In 1985, India experienced a significantly higher number of accidents per 1000 vehicles compared to several other countries. The rate stood at 23.1 in India, whereas it was 6.7 in France, 10.7 in West Germany, 3.6 in Sweden, and 8.6 in Japan. Moreover, India had a comparatively higher fatality rate of 4.33 per 1000 vehicles, while the rates were much lower in the UK (0.26), France (0.37), West Germany (0.27), Sweden (0.17), and Japan (0.14) [Srinivasan, 1991]. Among 15 developing countries, road accidents accounted for approximately 17% of total deaths from all causes, with deaths from tuberculosis and malaria making up 16% and 2%
respectively [Jacobs and Bardsley, 1977].
Furthermore, in 1985, India had a traffic fatality rate of 5.2 per 100,000 population, whereas France recorded 21 in 1984, and Japan reported 10 [Victor, 1989 and IATSS, 1986].
1.1 Trends of Urbanization in India In 1991, the estimated population of India reached 843.9 million, accounting for approximately 16% of the world's population. With a density of 267 persons per square kilometer, India has a significant population concentration.
Between 1981 and 1991, around 160 million people were added, which surpasses the entire population of Japan.
The urban population in India experienced substantial growth, rising from 26 million in 1901 to 156 million in 1981, and further to 232 million in 1991.
Consequently, the proportion of urban population to the total population increased from 11% in 1901 to 23% in 1981, and ultimately to 27% in 1991 [Subramaniam, 1988 and Mahendra, 1991]. Currently, India is home to 23 metropolitan cities with a population of over 1 million. Tamil Nadu ranks as the second-largest urbanized state, with 33%
of its population residing in urban areas.
Maharashtra takes the lead with 35% of its population living in urban regions. It is
projected that by the year 2001, 50% of Tamil Nadu's population will reside in urban areas. The five largest cities in the state, namely Madras, Madurai, Coimbatore, Trichy, and Salem, accommodate approximately 46% of the urban population [Metropolitan Development Authority, 1991a]. The population of the Metropolitan Area exhibits a faster growth rate (3.49%
annually) compared to the overall rate for urban population in all urban centers (2.90%) [Metropolitan Development Authority, 1991a].
1.2 Growth of Vehicles in India
The number of vehicles in India has experienced a significant surge over the past four decades. In 1951, there were only 306,313 vehicles, which increased to 1.865 million in 1971, and further escalated to 16.693 million in 1989. This translates to a more than fifty-five-fold increase in the number of motor vehicles during this period. Among various types of vehicles, two-wheelers have exhibited the highest growth rate. In 1951, India had a mere 26,890 registered motorcycles and scooters. By 1977, this number had swelled to 0.576 million, and by 1989, it reached a staggering 10.617 million. This remarkable increase in two-wheelers may be one of the contributing factors to the surge in traffic accidents in India [Srinivasan, 1991]. Para-transit modes, including taxis, cars, and autorickshaws, have witnessed a moderate growth rate.
The number of taxis, cars, and jeeps increased from 0.159 million in 1951 to 2.481 million in 1989. Additionally, between 1966 and 1986, bicycles increased from 5.1 million to 33.8 million, cyclerickshaws increased from 0.32 million to 0.67 million, and bullock carts increased from 11.9 million to 15 million [Srinivasan, 1991].
1.3 Road System in India
The road length per 100 square kilometers area in India is 46.7 km, whereas it is 294.7 km in Japan, 101.6 km in Sri Lanka, 274.5 km in France, 195.1 km in West Germany, 142.8 km in the UK, 152.8 km in Switzerland, 66.8 km in the USA, 34.3 km in New Zealand, and 11.3 km in Australia. Similarly, the road length per million population in India is
2,290 km, compared to 9,470 km in Japan, 4,606 km in Sri Lanka, 28,080 km in France, 7,894 km in West Germany, 6,258 km in the UK, 9,918 km in Switzerland, 28,400 km in the USA, 29,703 km in New Zealand, and 60,052 km in Australia [Borcar and Ramakrishnan, 1985].
2 MODELS BASED ON POPULATION AND VEHICLE OWNERSHIP
Srinivasan and Mahesh Chand (1986) conducted a study that explored the relationship between accidents and population. They proposed a model where the number of accidents (A) in a year, measured in thousands, is related to the population (P) in millions using the equation A = 12.45 *(0.003803 * P).
On the other hand, Victor (1990) employed a simple regression approach to analyze available data for India. His findings indicated a linear relationship between the number of road accident casualties (C) and population (P). The equation proposed was C = 0.221 + 0.000546 *P, where C represents the total casualties in millions and P represents the population in millions.
3 METHODOLOGY
The complexity of the traffic accident scene is increasing rapidly due to factors such as urban expansion, population growth, and the rise of motorized and non-motorized vehicles. The introduction of various traffic management measures also poses challenges. Chapters 1 and 2 provide a comprehensive discussion on these issues. In order to analyze the road traffic accident scene in selected urban arterials the following operations are conducted:
A detailed review is conducted on various accident studies and models developed in different countries to gain insights into accident scenes.
Through a critical analysis of existing models, their deficiencies are identified. This establishes the need for a comprehensive model that suits the mixed traffic conditions prevalent in Indian urban centers.
The contributing factors of the urban accident scene are identified and extensively discussed.
A comprehensive model form is determined, which is found to be a multiple linear regression model capable of accounting for a large number of independent variables associated with the accident scene.
Based on road geometries and traffic flow conditions, specific road sections are identified in two urban arterials namely Anna Salai and Periyar Salai.
Data related to all the variables required for building, calibrating, and evaluating the model's behavior and properties are collected or estimated. This includes data from both secondary and primary sources.
4 FACTORS INFLUENCING ACCIDENT SCENES
Road accidents are not random events, but rather have underlying causes.
Therefore, if we can identify the agents responsible for these accidents, we can develop and implement appropriate measures to prevent them to the best of our abilities. There are several factors that directly or indirectly contribute to the occurrence of accidents, including the road itself, the vehicle, the driver, other road users besides the driver, and the overall traffic environment [Kadiyali, 1987a].
4.1 Traffic Volume
Studies conducted on two-lane rural roads have revealed a pattern where the accident rate initially increases with traffic volume up to approximately 7,000 vehicles per day. However, beyond this threshold, the accident rate starts to decrease with further increases in volume. A comparison between Kenya, Jamaica, and developed countries demonstrated that at a flow level of 100 vehicles per hour, the accident rate in Kenya was more than three times higher than in developed countries, while in Jamaica, it was nearly five times higher.
This difference was attributed to factors such as road user behavior, vehicle condition, and maintenance [Jacobs, 1976; Srinivasan, 1991b].
The number of accidents, all other factors being equal, is influenced by the volume or intensity of traffic. This volume
determines the speed of vehicles, the flow dynamics of traffic, and the emotional stress experienced by drivers. Analyzing statistical data from different countries allows us to draw conclusions about how traffic volume affects the number of accidents. The number of accidents tends to increase gradually in proportion to the traffic volume corresponding to a normal level of service, which is typically around half the road's traffic capacity. However, when the volume surpasses this level, there is a sharp rise in the number of accidents. Furthermore, a wider range of speeds within the traffic stream leads to a higher number of accidents.
Consequently, mixed traffic, where different types of vehicles coexist, tends to have a significantly higher accident rate compared to homogeneous traffic.
5 COMPREHENSIVE MODEL
The traffic situation in cities of developing countries differs significantly from that of developed countries. Analyzing and modeling traffic accident scenes in these contexts requires a comprehensive understanding of various factors, including road conditions, environmental influences, road user behavior, and traffic flow. Existing accident prediction models often fail to account for mixed traffic flow, road conditions, and interactions between different types of vehicles.
In order to address this gap and develop an improved model that considers all relevant factors, a multiple linear regression approach is deemed appropriate. Specifically, a stepwise multiple linear regression approach allows for the inclusion of variables that contribute significantly to the model while eliminating those that do not. The selection of variables follows certain criteria, such as the magnitude of the partial correlation with the dependent variable and the calculated F-value, which tests the hypothesis that the regression coefficients are zero.
This comprehensive modeling approach, as described by Olive Jean Dunn and Virginia A. Clark in their work from 1987, enables the examination of each variable's influence on accident estimation in mixed traffic conditions. By employing this method, it becomes possible to develop a robust model that
encompasses all relevant factors and enhances our understanding of traffic accidents in these complex scenarios.
6 FORECASTING TRAFFIC VOLUME 6.1 Background
In order to project traffic volume for different modes in the arterials, suitable models need to be developed. Figure 6.1 illustrates the approach to estimate mode-wise traffic. Several independent variables can be utilized in building these models, such as the population residing within a 1 km radius on both sides of the roads, the number of workers in the zone, and the number of workplaces in the zone.
To validate the accuracy of these models, they can be used to estimate the traffic volumes for three half-yearly periods from 1989 to 1990. The estimated values can then be compared with the observed traffic volumes in the arterials during the same period. It has been observed that these models provide satisfactory estimations for the three periods from 1989 to 1990. Therefore, it can be concluded that these models are reliable for estimating future traffic volumes in different roads.
Burke et al. [1972] proposed a model to forecast volume in terms of vehicle miles of travel (VMT) = MVC * P * AAMV, where MVC represents Motor Vehicles per Capita, P represents Population, and AAMV represents Annual Average Miles per Vehicle. It was suggested to develop separate equations for different vehicle types, such as cars, buses, two-wheelers, freight vehicles, etc., and then aggregate the final forecasts at a later stage [Chari et al., 1986]. Sarna and Agarval [1990] reviewed traffic growth on the road system and recommended an annual growth rate of 3% for fast traffic and 1% for slow traffic in urban roads of metropolitan areas.
7 SELECTION OF A MODEL TO STUDY ACCIDENT SCENE
7.1 Background
A total of five multiple linear regression models have been developed to estimate the number of accidents occurring in a given section over a six-month period.
These models were created by progressively truncating the stepwise regression process, resulting in varying numbers of independent variables ranging from 4 to 22. Data from eight half-yearly periods between 1985 and 1988 were utilized for this purpose. The objective is to select a model that accurately represents the accident scene while using the minimum number of independent variables. To achieve this, the estimated accidents from these five models are compared with the actual accidents recorded at 23 sections in the arterials during three subsequent half-yearly periods (1989 to 1990). Various statistical measures, such as R2, adjusted R2, Theil's U-statistic, standard error of estimate, and chi-square statistic, are employed to evaluate the models' performance. It should be noted that the five models discussed are truncated versions of the same stepwise regression.
The next step is to identify the most appropriate model that closely reflects the reality. To accomplish this, all five models are employed to estimate the number of accidents for the following three half- yearly periods: January to June 1989, July to December 1989, and January to June 1990. The corresponding estimated traffic volumes for each period are utilized. For estimating truck traffic, the observed data from February 1990 is directly used, as there was minimal variation due to certain wholesale activities being relocated outside the city.
7.2 Comparison of Characteristics of Different Models
To assess and compare the explanatory accuracy of the forecasting models, Theil's U-statistic is employed. Theil's U-statistic is calculated as the square root of the ratio between the sum of squared differences between the observed and estimated values, and the sum of squared observed values. A Theil's U-statistic value of zero indicates a perfect explanation, while a value of 1 indicates a very poor explanation. Therefore, the model with the lowest Theil's U-statistic value will have a higher level of explanatory accuracy.
Table 7.1 Comparison of Accidents Recorded and Accidents Estimated
Table 7.2 Comparison of Accidents Recorded and Accidents Estimated
Table 7.3 Comparison of Accidents Recorded and Accidents Estimated
Table 7.4 Comparison of Accidents Recorded and Accidents Estimated
Table 7.5 Comparison of Accidents Recorded and Accidents Estimated
Table 7.6 Comparison of Accidents Recorded and Accidents Estimated
The Chi-square statistic, proposed by Makridakis et al. in 1983, is calculated as the sum of the ratios of squared deviations of observed frequencies from the expected frequencies to the expected frequencies. The values of R2, adjusted R2, standard error of estimate, Chi- square statistic.
Among the models considered, Model 4, which is truncated with the entry of 15 variables, can be selected.
This choice is justified by its reasonably agreeable R2 value of 0.8467.
Additionally, the addition of more variables does not significantly improve the R2 value. Model 4 also exhibits the highest adjusted R2 value and the lowest calculated chi-square value (2.97).
Moreover, Model 4 has the lowest Theils U-statistic.
When considering these statistics, both Model 4 and Model 5 are deemed acceptable. However, Model 4 is preferred as it achieves the same level of reliability with only 15 variables, whereas Model 5 would require 22 variables.
The final selected model, with 15 variables, yields an R2 of 0.8467 and an adjusted R2 of 0.8330. The t-statistic, which measures the ratio of the difference between estimated and hypothesized values to the standard error of the estimated value, is employed to assess the significance of the regression coefficients.
In Table 8.8, the regression coefficients marked with an asterisk (*) are significant at the 5% level when tested using the t- statistic.
The estimated regression coefficients indicate that variables representing two wheelers, pedestrian interaction, length of road section, width of road section, and disturbance index have positive and significant effects. On the other hand, variables representing bus interaction and deflections have negative and significant effects, as determined by the t-statistic at the 5%
significance level. The combined effect of all variables in the model is significant at the 5% level when tested using the F- statistic.
8 NEED FOR IN-DEPTH ANALYSIS OF ACCIDENT SCENE
Road accidents are influenced by a multitude of factors encompassing road conditions, traffic flow, environmental state, and road user behavior. However, existing models often oversimplify the complexities of accident occurrence by considering only a limited number of variables such as population, vehicle ownership, and road length. Such models fail to capture the intricate nature of accident incidence.
Several authors have attempted to study the accident scene by incorporating econometric and social variables such as real earned income, alcohol consumption, vehicle speed, the percentage of male drivers, the ratio of motor cycles to cars, industrial activity, and safety regulations.
These models offer an improvement over the previous group as they consider a set of variables that indirectly influence the occurrence of road accidents. However, even these models may not be suitable for the Indian context, where numerous contributing factors associated with road conditions, road user behavior, and traffic flow characteristics exist.
8.1 Estimation of Future Accident Scene
One of the key advantages of this model is its ability to analyze the future accident scene by implementing various management measures on the road sections, such as introducing cycle tracks, pedestrian guard handrails, and more (A). Additionally, models have been developed specifically to estimate traffic flow in different sections, taking into account variables like land use characteristics, number of workplaces, and width of road sections (B). By considering the expected development patterns along these road sections, traffic volumes have been projected.
Using the data from (A) and (B), the model has been utilized to forecast the accident scene up to the year 2001 for both Anna Salai and Periyar Salai. The results demonstrate that implementing pedestrian guard handrails and exclusive cycle tracks leads to a significant reduction in accidents. Similarly, prohibiting two-wheelers on major city
roads also proves to be effective in reducing accidents.
9 CONCLUSION
The model developed using 15 variables can be considered a reliable and comprehensive approach to estimate the accident scene on urban roads in the Indian context. It takes into account the majority of variables that directly influence road accidents in the multi-modal traffic flow conditions commonly observed in Indian urban roads.
Furthermore, this model can be used to examine the impact of implementing specific improvements as part of traffic management measures on the road accident scene.
The model has been constructed and tested using data from the city (now Chennai), which typically reflects conditions found in similar metropolitan cities across India. It is possible to study and evaluate the same model for similar urban centers in developing countries.
Gathering data for the 22 variables spanning the period from 1985 to 1990 was a challenging task, mainly due to the lack of well-maintained data by the relevant agencies and organizations. Since this investigation focuses on past variable behaviors, significant time and effort were dedicated to reviewing numerous records and reports from various sources to extract the necessary data. Rigorous cross-checks were conducted at every stage, comparing the findings with other reports and publications to ensure their reliability for model development.
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