System Dynamics on Ride Sharing Services for Smarter Cities
Background
The focus of the causal loop diagram (CLD) stems from exploring the issues of the ride sharing applications in effort to answering one question: How might ride sharing applications such as Uber and Lyft affect transport and its environmental costs in Toronto? Below demonstrates a list of quantitative research findings according to Statista.com.
Revenue and Growth:
Revenue in the Ride Hailing segment amounts to US$18,405m in 2019. (Statista.com)
Revenue is expected to show an annual growth rate (CAGR 2019-2023) of 9.3%, resulting in a market volume of US$26,315m by 2023. (Statista.com)
User Demographics:The number of users is expected to amount to 61.3m by 2023. (Statista.com)
User penetration is 16.2% in 2019 and is expected to hit 18.1% by 2023.
(Statista.com)
In the year 2017 a share of 50.9% of users is 25-34 years old. (Statista.com)
In the year 2017 a share of 43.5% of users is in the high income group.
(Statista.com)
Card holders tend to live in urban settings and are wealthier than the population at large, so they would likely have more exposure to ride-sharing apps, which are based in cities and require card payments. (recode.net)
The Boston study found that the main reason people opted for ride-hailing was speed. Even those with a public transit pass would drop it for ride-hailing despite the higher cost. (appnews.com)
According to the statistics, there will be an increased trend of more people starting to use the ride sharing apps in order to save time and money on owning cars and purchasing insurance. If this trend persists, the biggest issue will rise from the exponential growth of cars on the streets which will yield greater traffic congestions. In summary, there is an interesting correlation between the goal of the ride sharing companies (to increase revenue and market share), to the goal of the users (to reach a desired convenience level, driven by income), which will be explored in the CLD.
In Relation to Sidewalk Toronto’s Vision
Sidewalk Toronto states that “A sustainable transit strategy for the Eastern Waterfront must compete with private vehicles even at a site with excellent highway access and nearby parking options. To realize this objective, Sidewalk will deploy private car restrictions, traditional transit, and digital mobility tools to provide the most effective choices at lower cost for trips in and around Quayside, and usher in a new era of urban mobility in the Eastern Waterfront anchored around shared, electric, self-driving transit.” (Doctoroff, 27)
As the city of Toronto witnesses the implementation of Sidewalk Toronto, will visions regarding the transportation system be challenged by the limitations and the dependencies that failed to surface as it pursues to support ride sharing services within the project?
Affinity Mapping
As a first step, an affinity map was used to explore as many variables and to narrow down the relationships. As the photo indicates, many variables, drivers and dependencies emerged. Some examples include ‘user income’, ‘laws and regulations’ and ‘5G network’.
Figure 1: Affinity mapping brainstorming to find groupings and relationships
After a further investigation, “Ride sharing services and its usage” was chosen to be a good starting point to discover hidden relationships. Notable themes include transportation medium impacting the transportation fare, and desired convenience level preference based on user income.
Causal Loop Diagram
Below diagram indicates how services like Uber and Lyft are affecting Toronto’s public transportation usages by consumers, and another around increased ride sharing application usages yielding greater traffic congestion based on user income. Both of the loops derive from the common variable, ’Ride Sharing Services & Usage’.
CLD left is a reinforcing loop. As user income increases, the ride sharing services and usage also increases, which fulfills and increases the desired convenience level that will increase the demand to better support the convenience level.
CLD right is also a reinforcing loop. As the ride sharing services & usage increases, the ride fares will decrease, which will cause more consumers to continue using the service, which will then decrease overall public transportation usage. Once the transportation usage decreases, the transportation fare will increase to fill the gap to support unionized jobs. When the transportation fares increase, the general consumers will turn to Uber and Lyft to save time and money.
Figure 2: CLD left: illustrating the demand for ride sharing services based on user income; CLD right: Ride sharing service fares impacting the public transportation fare
Behaviour Over Time
Further analyzing the CLD, below diagram illustrates a behaviour over time patterns based on the first CLD diagram in Figure 2:
Figure 3: Behaviour over time diagram illustrating the left CLD from Figure 2.
The assumption has been made that the desired convenience level is consistent depending on individual preferences. As the user income slowly progresses to increase, the ride sharing services and usages will grow exponentially to fulfill the desired convenience level. If this pattern persists, the demand for ride sharing services will also increase exponentially to support the convenience level.
References
Statista. “Ride Hailing”, https://www.statista.com/outlook/368/109/ride-hailing/ united-states
Molla, Rani. "Americans seem to like ride-sharing services like Uber and Lyft. But it’s hard to say exactly how many use them.” https://www.recode.net/ 2018/6/24/17493338/ride-sharing-services-uber-lyft-how-many-people-use
LeBlanc, Steve. “Studies are increasingly clear: Uber, Lyft congest cities”, https:// apnews.com/e47ebfaa1b184130984e2f3501bd125d
Doctoroff, Daniel L. “A. Project Vision”, https://sidewalktoronto.ca/wp-content/ uploads/2018/05/Sidewalk-Labs-Vision-Sections-of-RFP-Submission.pdf
