How to measure clutch riding with an OBD device?

This article is the 3rd part of the series, where we focus on how driving impacts vehicles and costs of ownership. Earlier articles focused on harsh driving and gear usage and their effect on fuel economy. In this blog, we focus on clutch riding and how to measure it using Yatis OBD IoT device. What is Clutch Riding Clutch riding is defined […]

How Speed and Gears affect Fuel Economy

This post is 2nd in the series that focuses impact of driving style on vehicles and its usage. An earlier post explored the relationship between Aggressive Driving and fuel consumption. This post will explain the importance of using the right gears and speed to maximize fuel economy. How we did it Again we use data from an OBD […]

Analyse Impact of Aggressive Driving on Fuel Efficiency with IoT OBD

In this report, we will analyse the impact of harsh driving on fuel efficiency. A harsh driver uses his brake and accelerator abruptly and regularly. Intuitively, it is obvious that aggressive driving behaviour, like above will impact fuel efficiency negatively. Harsher the driving, worse will be the fuel efficiency. Using the OBD devices installed in […]

A few months we started partnering with logistics companies, providing them GPS tracking solutions. Most of our device are on trucks that service inter-city logistics. Our platform has been collecting data from several trucks, that travel throughout the country.  With this information, we have been able to generate a speed map of Indian roads. The color of a road stretch is the average speed of a truck along that stretch.

india-speed

Blue stretches are the fastest (>30kms/hr), red the slowest (<20kms/hr), orange (>20kms/hr, < 25kms/hr), and green (>25kms/hr, < 30kms/hr).

Procedure:

  1. Our entire serviced area was divided into grids. Each grid is about 1.2 sq. km
  2. Since the total volume of data is large, all the information cannot be read just at once, due to insufficient memory. Data for each day for a few devices is read one at a time.
  3. For each grid, GPS speeds on moving vehicles is read. The average speed for that day is calculated and count for that day is kept track of.
  4. The average speed and count of all previous days has also been saved. We calculate the weighted mean speed using data for previous days and the new day.
  5. Steps 2-4 are done for all devices and days to come up with the mean speed of that grid across all device and chose time period.
  6. The color of each grid is chosen, based on the average speed across all vehicles and days, it has been traversed. The color configuration in the decreasing order of speed is Blue, Green, Orange, Red.

The average speed on Indian roads (30-40 kms/hr) is about half of the worldwide average. Please note that the trucks are among the slowest moving vehicle on the road. Large loaded trucks can not move very fast. Besides, most of these trucks are quite old. The government has been considering laws to mandate lifetime of commercial vehicles primarily for controlling pollution. However, it seems GST has thrown a spanner in the works, requiring the government to re-evaluate its options.

A logistics veteran had once told me that in India, trucks are rarely scrapped. They are usually driven to point where the owner thinks it cannot be repaired further. After that the vehicle is abandoned and the driver/owner walks away. You can find corpses of these vehicle rusting away on the roads, more so near transport hubs. The lifetime of the vehicle is determined by owners perception of incremental ROI.

The picture above also shows a deep red around the cities, indicating that the speeds are least in and near the cities. That should not surprise anybody. The congestion around Indian cities is legendary. India has one of lowest road and highest traffic densities in the world.

The environmental and economic impact of slow logistics is quite bad. Slower moving vehicles, mean a higher fuel consumption and a much higher emission problem.