Data Science in Transportation Industry

 Transportation Analysis

Data Science in Transport Industry

The application of data science in the transport industry benefits quick decision-making in certain key areas such as fuel consumption, customers, routes and distance covered. The application of data analytics in transportation can determine a significant amount of cost reduction. During the planning and strategizing phase, it is important to know where most customers are available and which routes are more profitable than others. In the transportation industry, it is unwise to cover all the routes. The relationship of source of energy consumption, customer capacity, vehicles and distance covered managing it all well together will make any transportation company survive in the presence of a fuel crisis. The transportation industry is always by virtue of disclosure during any given crisis. Since transportation is a need of every business and individual, it comes with a lot of cons as well, the effects of inflation have a direct impact on transportation system. It is the first line where any given effect of inflation is absorbed. The liability issue is that all the assets are on the road, unlike any other company, the value of assets in terms of vehicles. It is always deprecated. The vehicle has a time frame under which it has to perform and recover from its expenses. In terms of investment. The main goal of transportation is always to make huge profits in the shortest time possible and to search for routes. The simplest rule for transportation is not to fish where the big fish are, but always fish where there are plenty of them. 

Business Problem:

Because of an increase in fuel prices, a transportation company has made a quick decision based on the data over 24 hours. What routes should be taken for sustainable growth in the future and current role?

Background on Data

The data set for a company was created over the period of 24 hours. To measure business performance and find out the differences between short routes and long routes. To measure the difference in speed on long routes and short routes. The data set contains three columns includes drivers ID, mean distance as mean_dist_day and mean speed as mean_over_speed_perc.

Data 

 

Analytics

describe()

From the above we can measure some statistical figures for the variables mainly mean distance and mean over speed. The minmum distance is 15.52 kilometers and maximum distance is 244.79 while the average maximum speed of 100 mph. While there have been over 4000  thausands trips over the period of 24 hours.

 

info()

Total number of entries are 4000 and there are two data type float and integers both can be processed for mathematical and statistical computations. The mean_dist_day column is type of float.

isnull().sum()

This shows there are no null values for any column and all the data ready to process. The data type is integer.

Visualizations

 

From the above graph it is confirmed that the minimum distance covered is 15 kilometers while the maximum in the given graph is 80 kilometers. This is a bar chart for hundred drivers.

                                                           The correlation of speed and distance with respect to drivers                      

The co-relation of mean speed vs distance with respect drivers clearly show there are two significant amounts of market divisions. Those who have completed short distances are, 3200 which is 80% in total as calculated above and those who have completed long distance with high speed are 800 rides that are 20% in total as calculated above.

The speed of cars increases as the distance increases on long routes. The average of fuel consumption is better than short routes or in city. hence the fuel consumption is less on long distances and hustle free with minimal traffic on the free ways.

Conclusion.

From the above analytical view provided, it is suggested to sustain more presence in on short routes rather than long routes. Since the 80% of the business is conducted in a city and only 20% of the business is on long routes. The effective business optimization strategy is to look for new ideas and solutions. To capture 100% market share is to look for new ways of transportation with in city for more potential customers' hence resulting in business growth. Speed and fuel average may provide less fuel consumption, but this practice cannot be done with in the city.

Importance

Applying Data Science techniques ultimately helped turned the this piece of information with meaningful insight and a proof to guide towards the right decision, Only with the help of three variables. The power of analytics in transportation planning is an integral part since the customer market is massive.

Note: This is a fictional scenario and does not tend to provide any company or business any harm. This for educational purposes only from beginner to professionals in the field of analytics to provide them with the idea of data science in transportation.

 

 

Tags: Data Science