A study on Commercial Truck Driver’s Vulnerability in India – Mitigations and Strategies for enrichment of Truck Drivers lifestyle By Dr. P. Senthilkumar* and N. Rajkumar Abstract



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Root cause for the accidents on highway is presented in table 23 and major accidents happen due to opponent’s mistake and drowsiness which comprises of 27% and 28% respectively. When interviewed with the truck drivers it is understood from them that the accidents happen mostly during the early hours of morning between 3.00 AM and 6.00 AM where the truckers feel drowsy and a slip of the eye contact causes a major catastrophic accidents and this is in correlation to the safety on highways which is presented in table 24.



Table 23: Accident Root Cause




Table 24: Highway Safety

Accident Root Cause

Percentage




Highway Safety

Percentage

Poor Maintenance

24%




Highly Unsafe

23%

Drug Consumption

21%




Unsafe

24%

Opponents Mistake

27%




Occasionally Unsafe

30%

Drowsiness

28%




No fear While Driving

23%

Total

100%




Total

100%

The relationship between the truckers and cops were studied and the harassment by cops to truckers were surveyed and the results are presented in table 25. The survey reveals that 73% of the truckers claim that the cops harass them demanding money even if all the regulated papers are in hand. This is one critical factor which needs to be addressed and escalated. One of the trucker while interview revealed that one of the cops collected 50000 INR in 45 minutes as bribe in on the dense populated traffic signal in Chennai located near the Port.





Table 25: Police Harassment




Table 26 : Relationship with Logistics Company

Police Harassment

Percentage




Degrees of Happiness

Percentage

Yes

73%




Overwhelming Happy

25%

No

27%




Happy

42%

Total

100%




Not Happy

33%










Total

100%


7. Survey Analysis

In this section the observations are statistically tested to find out the association and significance between one factor and the other. To analyse statistically Chi Square test and two way anova methods have been used to check the association and significance between each factor.

The Significance level considered in this analysis is α = 0.05. Chi-Square test was done for 10 associations to check the dependencies between categories as shown in Table 27. The “p” value is calculated based on the observed frequency and expected frequency. Later the “p” value is compared to the “α” value and the inferences are measured/noted. For every associations, a null hypothesis (H0) and alternative hypothesis (H1) is predetermined.

Now referring to table 27, it is observed that the associations which are dependent are between age of the truck drivers and the ownership levels where the p = 0.01 which is less than α = 0.05. Hence the H0 is rejected and H1 is accepted.



Next the association between age of the truck drivers and type of trucking (inter and intra state) is found to be dependent where p = 0.001 which is less than α = 0.05. Hence the H0 is rejected and H1 is accepted. An Association between recreational facilities on highway and change in lifestyle / habits was compared and it is observed that p = 0.02 and less than α = 0.05.Hence the H0 is rejected and H1 is accepted. This shows that these two factors are dependent where there will be a change in the truck driver’s lifestyle and habits when there are more recreational facilities on highway which will help them to distress their tiredness after several kms of journey. Another association between AC and Non AC cabin with government influencing Truck manufacturers to provide mandate AC cabins was studied. The responses were significant with p = 0.02 and less than α = 0.05. Hence the H0 is rejected and H1 is accepted. However, 44 truck drivers out of the sample population claimed that they would not like to have AC cabins as the fuel efficiency of the vehicles will come down and they have to show a cause to their logistics owner for drop in fuel efficiency. This reveals that the low grade logistics owners need to be addressed about the consequences and vulnerabilities these truck drivers undergo and they must prepare a road map in order to reduce the stress levels of the truck drivers.


Table 27 : Chi Square Test Inference between Categories and Inferences

Sl.No

Categories

p Value

α Value

Inference

Dependent / Not Dependent

1

H0: No dependency between age and ownership profile
H1: Dependency between age and ownership profile

Age of the truck Drivers

Ownership Level

0.015

0.05

p < α

Dependent

2

H0: No dependency between age and trucking experience
H1: Dependency between age and trucking experience

Age of the truck Drivers

Trucking Experience

0.72

0.05

p > α

Not Dependent

3

H0: No dependency between education and monthly salary
H1: Dependency between education and monthly salary

Education of the Truck Drivers

Monthly Salary

0.1

0.05

p > α

Not Dependent

4

H0: No dependency between age and trucking range
H1: Dependency between age and trucking range

Age of the truck Drivers

Interstate and Intra State Driving

0.001

0.05

p < α

Dependent

5

H0: No dependency between trucking experience and trucking range
H1: Dependency between trucking experience and trucking range

Trucking Experience

Interstate and Intra State Driving

0.29

0.05

p > α

Not Dependent

6

H0: No dependency between monthly salary and trucking experience
H1: Dependency between monthly salary and trucking experience

Monthly Salary

Trucking Experience

0.96

0.05

p > α

Not Dependent

7

H0: No dependency between Rest per day and trucking range
H1: Dependency between Rest per day and trucking range

Resting per day

Interstate and Intra State Driving

0.58

0.05

p > α

Not Dependent

8

H0: No dependency between Rest per day and off days per month
H1: Dependency between Rest per day and off days per month

Resting per day

Off days per month

0.54

0.05

p > α

Not Dependent

9

H0: No dependency between trucking range and visit to home / month
H1: Dependency between trucking range and visit to home / month

Interstate and Intra State Driving

No. of times visiting home per month

0.75

0.05

p > α

Not Dependent

10

H0: No dependency between accidents and alcohol consumption
H1: Dependency between accidents and alcohol consumption

No.of Accidents

Alcohol Consumption

0.72

0.05

p > α

Not Dependent

11

H0: No dependency between recreational facilities and lifestyle
H1: Dependency between recreational facilities and lifestyle

Recreational Facilities on Highway

Change in Lifestyle

0.02

0.05

p < α

Dependent

12

H0: No dependency between AC/Non-AC cabin and mandate AC Cabin
H1: Dependency between AC/Non-AC cabin and mandate AC Cabin

AC/Non AC cabin

Mandate AC Cabin Happy or Not Happy

0.02

0.05

p < α

Dependent

13

H0: No dependency between usage of condom and awareness on AIDS
H1: Dependency between usage of condom and awareness on AIDS

Usage of Condom

Awareness on AIDS

0.21

0.05

p > α

Not Dependent

Rest of the 8 associations are not dependent with the selected categories like age and trucking experience, education level and monthly salary, trucking experience and type of trucking, monthly salary and trucking experience, rest taken per day and type of driving and No.of off days per month and awareness on AIDS to usage of condoms. The most critical and surprise association is that between the no. of accidents and alcohol consumption was observed to be not dependent between the categories. However it was thought vice-versa. It is evident that irrespective of alcohol consumption, accidents take place in a way or other due to negligence, high speed, opponent’s mistake and due to drowsiness as well.

Some of the factors were tested using two way anova method and is presented in table 28. It is observed from the two way anova that certain factors have significant difference between the treatments like Age factor of the truck drivers, no. of accidents occurred. However, Vehicle loading condition, alcohol consumption, multiple sexual affairs, and awareness of AIDS are with no significance between the treatments for the given frequency of truck drivers.

The occurrence of accidents with respect to vehicle loading condition and alcohol consumption are not dependent. Irrespective of vehicle loading condition and alcohol consumption the accidents occur. Second, on the age factor when compared with co-relation with awareness of AIDS and multiple sexual affairs does not dependent with each other. Irrespective of age the truck drivers tend to sexual affairs.

The reason behind sexual affairs being interrelated with all ages of interval of truck drivers is that because of the high stress which are being undergone by these drivers. Moreover, these truck drivers spend hours in night traffic near highways and ports where there are possibilities for these truck drivers get mingled with sexual high risk behaviour. As these drivers are high in mobility they do not have sexual affair with single sex worker and indulge in multiple sexual relationships.



As for as the survey on usage of condoms more than 50 % has confirmed that they use condoms and rest of the population had claimed that do not use condom and the awareness of AIDS is very pure and the understanding of AIDS is varied and they were not aware how HIV spreads among the truckers community. The Percentage distribution is shown in Fig 1 and Fig 2.

Fig 1: Awareness in AIDS

Fig 2: Usage of Condoms




















































































































































































































































































































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