In metropolitan city Chennai there has seen a significant increase in the number of vehicles in city roads due to the rapid urban growth over the past few years. As the city is also developing infrastructural there is lot of roads, bridges and metro construction going on .As a result, it has become more difficult for daily commuters to manage transportation. Two-wheelers remain the most preferred mode of travel because they are economical and easy to ride in crowded streets. However, delays caused by signal waiting, road diversions and construction activities affect the efficiency of travel. Often, these conditions cause fuel to be used excessively and commute times to be longer. Understanding these issues is important for improving urban mobility and reducing fuel wastage. Hence, the purpose of this study is to study two wheeler riders perception towards traffic issues and their impact on fuel consumption among two-wheeler users in Chennai.

THEORETICAL BACKGROUND OF THE STUDY

The theoretical foundation of this study is based on concepts related to urban transportation systems, traffic congestion, and fuel consumption. Existing road infrastructure is under significant pressure due to a considerable growth in vehicle ownership caused by rapid urban expansion and population growth. The theory of traffic flow states that congestion occurs when the road capacity exceeds the volume of vehicles, leading to slower movement and an increase in travel delay. Frequent stopping and prolonged idling of vehicles are often the result of these conditions, leading to higher fuel consumption. Moreover, road design, traffic signal coordination, and overall traffic management practices are all factors that affect transportation efficiency. Ineffective management of these elements can lead to reduced mobility and increased operational costs for commuters. Understanding these theoretical perspectives helps in examining the relationship between traffic congestion and fuel consumption among two-wheeler users in Chennai.

REVIEW OF LITERATURE

Recent studies have revealed that traffic congestion is having an increasingly significant impact on fuel consumption and urban mobility. A study done by Parkavi (2025) has examined that high traffic volumes at major junctions like Kathipara and T. Nagar, leading to severe delays and controlled speed. Similarly, Agrawal (2025) highlighted that road transport in India contributes significantly to energy consumption and carbon emissions, mainly due to the increasing number of vehicles. Rishikesh  (2024) analysed the concept of Mobility as a Service (MAAS) and found that its adoption could decrease the use of private vehicles to enhance fuel efficiency.  According to Desai (2024), switching to public transportation is highly influenced by traffic congestion, fuel costs, and travel time. Furthermore, Halder (2024) highlighted the economic effects of congestion, acknowledging that a substantial amount of fuel is discarded each year because of idling vehicles and major cities. In summary, these studies suggest that fuel consumption and commuting patterns are greatly influenced by traffic congestion, increasing vehicle usage, and infrastructure challenges, supporting the relevance of the present study.

RESEARCH METHODOLOGY:

The study’s approach is based on a descriptive research design to examine the effect of traffic problems on fuel consumption among two-wheeler users in Chennai. By utilising the descriptive approach, one can comprehend the characteristics, behaviour, and opinions of commuters regarding traffic congestion and fuel usage. Primary data is collected through a structured questionnaire and secondary data was gathered from the research articles and related sources to back up the study. A sample of 120 respondents using two-wheelers were selected from Chennai for the purpose of the study. Responses from the respondents were collected through Google forms and convenience sampling technique is used for the purpose of the study. The study was carried out over a period of three months. ANOVA Correlation and Regression ism used by the researcher to analyse the data statistically.

DATA INTERPRETATION AND FINDINGS

Table 1: Demographic profile of the respondents

Chart 1: Demographic profile of the respondents

Inference:

The demographic analysis shows that there is a close ratio of male and female respondents, with 51.7% being male and 48.3% being female. A large proportion of respondents (45.8%) belong to the 18-28 years age group, suggesting that younger commuters constitute a major segment of two-wheeler users in Chennai. Regarding travel distance,45% of respondents travel between 10-20 km daily, which stipulates moderate commuting distances within the city. As for traffic exposure,46.7% of the respondents spend approximately 30-60 minutes indoors every day, highlighting the significant effect of congestion on daily commuters. By examining these demographic characteristics, we can clearly understand the respondent profile used to examine traffic problems and their impact and influence on fuel consumption among two-wheeler users.

Table 2: Reasons for Traffic Jam in Chennai

Chart 2: Reasons for Traffic Jam in Chennai

Inference:

According to the table, 50.8% of the respondents agree, and 16.7% are strongly in agreement, that the increasing number of vehicles is the primary cause of traffic congestion. In addition, a majority of 50% and a majority of 22.5% agree that road maintenance is a cause of congestion. A significant number of people (38.3%) agree and (39.2%) strongly agree that the construction of metro and flyover construction temporarily elevates traffic levels. Regarding inefficient signal timing, 44.2% agree, and 20.8% strongly agree that it increases delays. Additionally, 35.8% agree, and 25% strongly agree, that lack of traffic discipline causes congestion, while 37.5% agree and 29.2% strongly agree that encroachments and roadside parking contribute to traffic problems. Finally, 45% agree, and 30% strongly agree, that traffic congestion increases stress among commuters.

Table 3: Impact of traffic on fuel consumption

Chart 3: Impact of traffic on fuel consumption

Inference

From the above table, it is seen that the traffic diversions (22.5%), more time in traffic (21.7%), increase fuel use, damaged roads (16.7%), better traffic management 915%), metro work (12.5%), and congestion (11.7%) also affect fuel congestion. The majority (12.5%) of the respondents say traffic diversions are the main reason for increased fuel consumption.

Table 4: Fuel Saving Practices adopted by the two-wheeler respondents

Chart 4: Fuel Saving Practices

Inference:

It is seen that the planning routes in advance (22.5%) and turning off the engine at signals (20%) are the most preferred fuel-saving methods, followed by avoiding peak hours (14.2%) and improving public transport (14.2%), while tyre maintenance, fuel-efficient vehicles, electric vehicles, awareness programs (12.5% each) also help, and carpooling (5.8%) is least preferred. The majority (22.5%) of the respondents prefer route planning as the most effective way to save fuel, followed by reducing idle time (20%).

STATISTICAL ANALYSIS

ANOVA Analysis

Hypothesis 1

• H₀ (Null Hypothesis): There is no significant association in the age of respondents and their opinions regarding whether traffic congestion in Chennai is severe.
• H₁ (Alternative Hypothesis): There is significant association in the age of respondents and their opinions regarding whether traffic congestion in Chennai is severe

Inference:

Since P value (.002) is less than 0.05 we reject the null hypothesis and accept the alternate hypothesis. Hence there is significant association in the age of respondents and their opinions regarding whether traffic congestion in Chennai is severe

Hypothesis 2

• H₀ (Null Hypothesis): There is no significant association in the age of respondents and their opinions regarding metro and flyover construction increases traffic problems.
• H₁ (Alternative Hypothesis): There is significant association in the age of respondents and their opinions regarding metro and flyover construction increases traffic problems.

Inference

Since P value (0.12) is less than 0.05 we reject the null hypothesis and accept the alternate hypothesis. Hence, there is significant association in the age of respondents and their opinions regarding metro and flyover construction increases traffic problems.

Hypothesis 3

• H₀ (Null Hypothesis): There is no significant association in the age of respondents and their opinions that traffic congestion increases their stress level.
• H₁ (Alternative Hypothesis): There is no significant association in the age of respondents and their opinions that traffic congestion increases their stress level.

Since P value (0.40) is less than 0.05 we reject the null hypothesis and accept the alternate hypothesis. Hence, there is significant association in the age of respondents and opinions that traffic congestion increases their stress level.

REGRESSION ANALYSIS

Hypothesis 4

• H₀ (Null Hypothesis): There is no significant relationship between traffic-related factors and the perceived impact of traffic on fuel consumption among two-wheeler users in Chennai.
• H₁ (Alternative Hypothesis): There is significant relationship between traffic-related factors and the perceived impact of traffic on fuel consumption among two-wheeler users in Chennai.

Inference:

The regression model shows a moderate positive relationship between the independent variables and fuel consumption. The regression analysis indicates that traffic-related factors such as the impact of metro construction, average time spent in traffic, and average distance travelled per day significantly influence the fuel consumption among two-wheeler users in Chennai. The model shows a moderate positive relationship (R = 0.375), suggesting that as traffic-related issues increase, fuel consumption tends to increase as well. However, the explanatory power of the model is relatively modest, with an R² value of 0.140, meaning that only 14% of the variation in fuel consumption is explained by these variables, while the remaining variation may be due to other factors not included in the study. The adjusted R² (0.118) further supports this, indicating limited but meaningful predictive ability. Overall, the findings imply that while traffic conditions do significantly affect fuel consumption, additional variables should be considered for a more comprehensive understanding of the issue. The ANOVA results (F = 6.315, p < 0.001) also indicate that the overall regression model is statistically significant, meaning that the selected traffic variables jointly have a significant effect on fuel consumption. Therefore, the null hypothesis is rejected, and it can be inferred that traffic conditions significantly influence fuel consumption among two-wheeler users, although additional factors may also play a role in explaining the full extent of this impact.

CORRELATION ANALYSIS

Hypothesis 5

Relationship between traffic conditions, user perceptions, and fuel consumption among two-wheeler users in Chennai

• H0: There is no significant relationship between perceived traffic congestion and fuel consumption among two-wheeler users.
• H1: There is a significant relationship between perceived traffic congestion and fuel consumption among two-wheeler users.

Inference

The correlation analysis indicates that the major traffic problems in Chennai are closely interconnected, with both infrastructural issues and driver behaviour playing significant roles. Strong positive relationships are observed between encroachment and lack of discipline among road users (r = .519), as well as between improper road maintenance and encroachment (r = .449), suggesting that poor infrastructure and unregulated road usage often occur together. Additionally, lack of discipline is moderately associated with daily stress levels (r = .406), indicating that behavioural factors contribute substantially to commuter stress. Traffic signal inefficiencies and population congestion also show meaningful correlations with other variables, reinforcing the idea that traffic issues are multi-dimensional rather than isolated. Overall, the findings suggest that improving road discipline, managing encroachments, and enhancing infrastructure could collectively reduce traffic congestion and its associated stress.

CONCLUSION

The study on traffic problems in Chennai and their impact on fuel consumption, along with the perceptions of two-wheeler users, reveals that traffic congestion is a significant factor influencing fuel usage. The findings from the correlation and regression analyses indicate that key traffic-related issues such as metro construction, increased time spent in traffic, travel distance, traffic signals, parking constraints, and lack of lane discipline are positively associated with higher fuel consumption. Users’ perceptions also align with these findings, as most respondents acknowledge that traffic conditions contribute to increased fuel usage in their daily commute. Overall, the study concludes that traffic congestion in Chennai not only affects travel time and commuter experience but also leads to economic and environmental consequences through increased fuel consumption. The inclusion of user perceptions strengthens the findings, highlighting the need for effective traffic management strategies, improved infrastructure planning, and policy interventions to reduce congestion and promote fuel efficiency.

REFFERENCES

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