Posted: February 16th, 2022

A Case Study Report of Ride-sharing in The Transport Industry


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A Case Study Report of Ride-sharing in The Transport Industry

Ridesharing is an essential topic to a discussion as it is a matter that is not only open to the eye but also a significant characteristic in the modern transport industry across the globe. To start with, the taxi industry has been a significant economic player in the past centuries. The industry’s origin dates back to the early 1640s when horse-drawn vehicles were used on private commutation of people. The taxi industry gained fame in the 1890s when automobiles became more prevalent on city streets. At the eve of the 21st century, It is arguably true that the use of Taxis has drastically risen globally as most people have preferred it as a form of cheap and confutable transport that suits people, especially those living in the urban areas. These vehicles are used to ferry people regardless of the locations and time, contrary to the public service transport where people are forced to travel at specified durations and fixed locations. The Taxi industry has gone through a series of development, including the use of electric cars and the deregulation of the industry, which came to be a significant bone of contention among most economies in the world. It wasn’t until the invention of the mobile taxi apps that we saw the most prominent innovation that made a big dive into the taxi industry. The innovation has made it easy and flexible for commuters to use the cabs on their own time.

The Ridesharing platforms acting as significant players in the current economies are attached to the demand and supply economic theories of microeconomics. These forces work jointly to design ways of price determination that are suitable to the customers at specified periods. for instance, the supply and demand theory states the unit price for a good or service may vary until it settles at the point when the economy is in equilibrium. (Zhong et al. 2019) Additionally, the variation may result from situations when the quantity at which consumers demand a good equals the quantity the service is supplied. In this case, when  the supply of ridesharing services decreases when the demand for the services persists, the prices for these services may sky-rocket. In; In such situations, the demand is higher than the supply, or the number of customers exceeds the number of cars available for Uber services.


Primarily, Ridesharing has been used to define the current use of taxi cabs in ferrying passengers from one place to the other. The process has been facilitated by the latest advancement in the communication sector as mobile devices have the taxi website or app installed in them. This helps customers pinpoint their location when the need for a taxi arises. The compatibility of the communication sector with the taxi industry is new dawn since, in cases of emergencies, people can order taxi services easily. This paper gives a case study report accounting and critically justifies the causes of the steep rise of the ridesharing platforms such as Uber, Didi, and Ola. in the paper. We analyze the old economic theories of the ancient taxi industry while comparing them to the current industry. Attached to a specific jurisdiction, the paper takes a case study of New York City as one of the cities that had a better picture of the tremendous development in the taxi industry. 

The market structure under which the taxi industry falls is controversial as the industry’s operation varies with policymaking in different countries. From the most economical point of view, the industry falls under the oligopoly market structure as within the industry. There are few sellers (taxi owners) compared to the increasing number of customers .this this enables them to significantly influence the price and other market factors of the alternative forms of transport like Public Service Vehicles (PSV). With the innovation of Uber and other latest technologies in the Taxi industry, The innovation has broken into the market previously dominated by monopolistic taxi providers. In the context of cities, NYC in particular, the taxi industry has been entrenched in an oligopoly where one or two taxi companies provide medallions for willing drivers. This market structure has been characterized by high barriers of entry to the market, e.g., a $50000 driver medallion is needed from a driver needs to make them legible to operate in the industry. (Mathew,2008)

The best way to understand the consumer behavior resulting from the shift from taxi services to Ridesharing is first to understand the concept of sharing economy prevalent in Ridesharing. The companies involved, such as Uber Didi, employ an online rating system for their employees as well as its customers .since most of the communication between the duo is online, It is easier to implement a rating system where customers can place their ratings on the kind of ratings on the nature of services that were offered by the Uber service delivery. From these ratings, the Uber owners or companies can analyze the feedback and quickly improve their weaknesses while capitalizing on their strengths. By offering transport services at cut prices and providing high transparency in choosing of drivers and determination of fares, Ridesharing has undoubtedly proved a positive shift in consumer behavior (Alley,2016). There is a possible evasion of middlemen by the consumers, making it comfortable as a transport option in the world cities .although Ridesharing poses stiff competition to other transport methods, it has had a positive view on how customers perceive the services.

The demand on the availability of ride services has drastically risen as most people have significantly shifted to Ridesharing from the traditional taxi services. Despite the advantages of the taxi, such as professionalism and added amenities attached to them, most people have preferred this type of service. With increased demand, the advent of Uber services has had a corresponding increase in supply to meet the surge in the number of customers in need of Ridesharing .these impacts are well represented in the supply and demand curves below.



Fig 1.1 shows the demand curve for uber services.

In cases where there is a need to control the number of people demanding rides using ridesharing platforms. The Uber services use surge pricing to control the demand and supply of the services. When the demand for Uber services rises. surge pricing kicks in and as a result there is increased in prices of the services done to reallocate the cars to the respective places. based on the demand curve shown in fig 1.1. increase in demand of Uber from Q1 to Q2 leads to the increased price of the service from Price 1 to Price 2. this is called Surge pricing.


Fig 1.2 shows the supply curve of Ridesharing platforms 

From the supply curve above, it is undoubtedly true that the supply quantity of the Uber services has a direct effect on the pricing for the services. when the demand for the services shoots from 8500 to 12000, there is a corresponding increase in price from 27 to 30 pounds after the 5% rise in tax. this is evident that the less the number of cars available at a given time period, the higher the price is charged from the customers demanding for the Uber services.

Although Ridesharing provides a range of advantages to its customers such a slow prices and minimal taxation the increased consumer surplus forces the platforms to design ways for cutting off surge in number of customers as well as looking for ways that will help improving the supply of the vehicles needed.

Calculating the price elasticity of demand.

Price elasticity of demand =percent change in quantity /percentage change in price 

To calculate the percentage, we divide the change in quantity by the initial quantity 

Date  Number of passengers  Fare collected 
01/01/2021 29297 346863.05
01/02/2021 46368 557386.57


From the above data the increase in number of passengers led to a significant increase in the fare collected.

Percentage change in quantity = (46368-29297) =17071/29297 x100=58.268%

Percentage change in pricing = (557386.57-346863.05) =210523.52/346863.05×100=60.693%

PED=percentage change in quantity /percentage change in price 

=58.268/60.603= 0.9600

From the result obtained above price elasticity of demand is less than 1, justifying that the demand is inelastic. This implies that the change in the quantity demanded due to changes in price is price ,similarly quantity changes faster than price (Anderson et al ,1997).in cases of inelastic demand, the quantity changes slower than the price, and as seen from the Covid-excel spreadsheet ,the number of people gradually increase while the amount of fare steeply rises .the cause for this kind of PED in Ridesharing might be related to the fact that most customers demanding for Uber services have considered Ridesharing as a priority and necessity deviating from the taxi industry .this might result from the need to evade the restrictions and cons that are attached to substitute means of transport.

The price elasticity of taxi services (elastic) has been examined in several industry reports. based on my result it agrees to the prior research as in the taxi industry, the prices rely on the decisions made by the taxi owners. This brings the difference between the taxi and the advent of ridesharing platforms.

6.although Ridesharing has currently been subscribed to by most customers ,most countries across the globe, for instance : Bulgaria, Italy, Denmark ,Hungary, and Saudi Arabia have totally banned the use of ridesharing platforms in their countries .when allowed to operate they work on stringent rules that discriminate the operators of such services .in Denmark, Uber services were banned as a result of invisibility of fare and failure to adhere to seat regulations .the policies in Denmark require Uber owners to be transparent on pricing, something that the drivers cannot respond to considering that they drive their own vehicles (Clewlow ,2017).in Romania, Ridesharing was banned as a result of the fierce competition that arises from these services that tends to discriminate against those in the taxi sector .the hearing of cases filed by Romanian Cabbies association led to a ban of ridesharing services in Romania.

Apart from the above discussed reasons, Ridesharing has been banned as they are considered to violate most rules in countries such as overloading and exorbitant pricing which discriminates customers. although they look comfortable to most of them, most policies ban the platforms to shelter other service providers such as Taxis which not only play a part in revenue contribution but adhere to set rules. Based on the case study report. the ban on Ridesharing services among countries has a major impact on the consumers and businesses in the respective countries. this is because the Uber services were ideal for urgency like reporting to work and ensured that people commute at every location regardless of time. businesses are likely to drop as the employees are affected by such legislations undermining the use of Uber services. is arguably true that the outbreak of the novel coronavirus has served as a major challenge affecting most sectors of the economy .the transport sector is exceptional .the demand for ridesharing services and car-sharing services drastically dropped at the onset of the a result, COVID 19 led to a steep reduction in the reduction of profits from the transport services .the fatal pandemic not only lead to the loss of lives of potential Uber consumers but also resulted to loss of Jobs by most of the drivers in the industry.

We estimate a simple regression model as follows.

Y =a +bX where Y is the dependent variable, X is the total passenger ‘s count, a is the vertical intercept of the straight line and is the slope of the line. the regression obtained from the data in the excel spreadsheet shows that the independent variable is a good predictor of the dependent variable. with a high value of the X multiplier b, the Y variable is high and vice versa. The regression output is a powerful tool as it enables researchers to validate a hypothesis that a particular action will lead to a significant change in another variant based on the result of the regression between the dependent and independent variable .as for the data given in the excel spreadsheet .an increase in the independent variable affected by b leads to increase in the dependent variable Y.the data given supports my expectation as the number of positives tested for Coronavirus affects the passenger count implying that it is an explicit determinant of the X variable .

In conclusion , Ridesharing is a common platform in the 21st century .this study found out that it is one of the most preferred means of transport that best suits customers needs despite the fact that it has been banned by most countries after being alleged with unlawful operations .however the ridesharing sector should impact practices such as visibility of fares charged to passengers and maximum conformity to the laws underlying the Taxi sector in the respective countries .this will help in not only keeping them in the markets but also in enhancing the continued provision of services to the customers. Investors willing to step into this market should be knowledgeable of the prevalent rules and regulations that govern the ridesharing sector to ensure that they fit into the policies and requirements that dictate their operation as private cab operators.









Clewlow, R. R., & Mishra, G. S. (2017). Disruptive transportation: The adoption, utilization, and impacts of ride-hailing in the United States.

Zhong, Y., Lin, Z., Zhou, Y. W., Cheng, T. C. E., & Lin, X. (2019). Matching supply and demand on ridesharing platforms with permanent agents and competition. International Journal of Production Economics218, 363-374.

Zhong, Y., Lin, Z., Zhou, Y. W., Cheng, T. C. E., & Lin, X. (2019). Matching supply and demand on ridesharing platforms with permanent agents and competition. International Journal of Production Economics218, 363-374.

Mathew, B. (2008). Taxi!: cabs and capitalism in New York City. Cornell University Press.

Anderson, P. L., McLellan, R. D., Overton, J. P., & Wolfram, G. L. (1997). Price elasticity of demand. Mackinac Center for Public Policy. Accessed October13, 2010.

Alley, J. K. (2016). The impact of Uber Technologies on the New York City transportation industry.


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