Influence of Traffic Congestion on Fuel Consumption & Emissions

Introduction & Objective

The key objective of this brief report was to analyse real time traffic situations on a section of a city road/highway based on which fuel wastage and CO2 emission estimates are calculated.

The road section that we have analysed is on NH160 from Saravali to Thane. The origin and destination address along with their coordinates are as follows,

Kalwa, Thane, Maharashtra 400605, India

Saravali, Bhiwandi, Maharashtra 421311, India

The total distance of this patch is 12.5 kilometers. Also, our analysis for number of vehicles on this patch is based on assumption of 2-lanes, moving from origin to destination at average speed.

We have analysed the data for different parts of the day viz. Early Morning, Morning, Afternoon, Evening and Night. In these time periods we have then calculated the amount of time taken to travel through the patch, fuel wasted due to idling of vehicles and the resulting CO2 emissions.

Assumptions

We have classified different parts of the day for the available traffic data as follows,

Part of the day

The following assumptions have been made for fuel wastage of the traffic,

• Fuel wastage in parking, idling and in slow traffic may vary depending on type and age of vehicle.
• For each hour above average duration, fuel consumption has been assumed to be 0.6 litres.
• Fuel wastage have been calculated for all vehicles on the route which exceeded average time taken.
• All vehicles, fuel type and vehicle have been considered similar. Further bifurcation of vehicle type, ageing of the vehicle, and type of fuel used can provide more accurate results.
• Assumptions have been made after considering several research papers (1,2)

For calculating the CO2 emissions, the assumptions made were,

• CO2 emission from fuel is considered as 2.4 kgs per litre.
• All fuel type have been considered similar for all vehicle type.
• Effect of vehicle ageing on fuel burnt and incomplete consumption have been normalized for the sake of computing.
• Assumption have been made after scrutinizing several research papers and empirical results. (3,4,5)

For detailed underlying base data, please refer here.

Data Management

We sourced the data from Google Maps API with a pre-defined latitude-longitude coordinate and fixed frequency. We then used Google Sheets to stage and store the data. We have been able to manage data streaming in this case.

Data Modeling & Dashboarding

The transformation was then done using Power Query module of Power BI. Appropriate relationships were prepared based on KPIs that were planned. This data model aided in calculating the average route duration at various time intervals of the day i.e. early morning, morning, afternoon, evening, night.

KPIs were defined to calculate the fuel wastage due to idling of vehicles on the patch of road based on certain assumptions. The fuel wastage then aided in calculating the CO2 emissions. The data can be sliced for the past 3 days, week, fortnight, month and three months.

Reference:

Niraj Sharma, PV Pradeep Kumar, Rajni Dhyani, Ch Ravisekhar, K. Ravinder, Idling fuel consumption and emissions of air pollutants at selected signalized intersections in Delhi, Journal of Cleaner Production, Volume 212, 2019, Pages 8-21, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2018.11.275

Arora, S., Vyas, A. and Johnson, L.R. (2011), Projections of highway vehicle population, energy demand, and CO2 emissions in India to 2040. Natural Resources Forum, 35: 49-62. https://doi.org/10.1111/j.1477-8947.2011.01341.x

Sharma R, Kumar R, Singh PK, Raboaca MS, Felseghi R-A. A Systematic Study on the Analysis of the Emission of CO, CO2 and HC for Four-Wheelers and Its Impact on the Sustainable Ecosystem. Sustainability. 2020; 12(17):6707. https://doi.org/10.3390/su12176707