Authors : Niraj Patel

Volume/Issue : Volume 10 - 2025, Issue 2 - February

Google Scholar : https://tinyurl.com/2p5cnjx7

Scribd : https://tinyurl.com/4c99794h

DOI : https://doi.org/10.38124/ijisrt/25feb803

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Abstract : This study provides a detailed analysis of the evolving impact of ride-sourcing platforms such as Uber and Lyft on travel behavior and mobility patterns within Toronto. Utilizing origindestination data from approximately 100 million trips recorded between 2016 and 2019, we examine spatial and temporal trends in ride-sourcing activities. Our methodology integrates these data with car travel times, public transportation networks, and city regulations to assess the influence of ride-sourcing on overall traffic flow, public transit usage, and cyclist safety. Through route analysis and trip linkage techniques, we estimate that ride-sourcing vehicles contributed to 5–8% of the total daily vehicle kilometers traveled (VKT) in September 2018—approximately double the figures from October 2016. While ride-sourcing activity surged, our findings indicate that downtown travel times remained largely stable. Additionally, curbside pick-up and drop-off patterns highlight the necessity for improved curb management strategies to enhance safety and efficiency. These insights provide a foundation for future policy decisions regarding urban mobility and ride-sourcing regulation.

References :

1. T. W. Schneider, “Taxi and ridehailing usage in new york city,” https://toddwschneider.com/dashboards/ nyc-taxi-ridehailing-uber-lyft-data, ac- cessed: July 17,  2019.
2. City of Chicago, “Transportation network providers-trips, ”https://data.cityofchicago.org/Transportation/ Transportation-Network-Providers-Trips/m6dm-c72p, accessed: July  17, 2019
3. D. Cooper, J. Castiglione, A. Mislove, and C. Wilson, “Profiling trans- port network company activity using big data,” Transportation Research Record, vol. 2672, no. 42, pp. 192–202,   2018.
4. A. Henao and W. E. Marshall, “The impact of ride-hailing on vehicle miles traveled,” Transportation, pp. 1–22, 2018.
5. Ride Austin, Ride-austin-june6-april13, https://data.world/ride-austin/ride-austin-june-6-april-13, accessed: April 12, 2019.
6. New York City Taxi and Limousine Commission and New York City Department of Transportation, “Improving efficiency and managing growth in new york’s for-hire vehicle sector,” https://www1.nyc.gov/assets/tlc/downloads/pdf/fhv congestion study report.pdf, 2019.
7. City of Toronto, “Toronto municipal code chapter 546, licensing of vehicles-for-hire,” https://www.toronto.ca/legdocs/municode/toronto-code-546.pdf, 2019, accessed: July 30, 2019.
8. Big Data Innovation Team and University of Toronto Transportation Research Institute, “The transportation impacts of vehicle-for-hire in the city of toronto,” https://www.toronto.ca/wp-content/uploads/2019/06/ 96c7-Report v1.0 2019-06-21.pdf, 2019.
9. P. Loa, J. Hawkins, and K. N. Habib, “Use of ”sp-off-rp” experiment to understand the impacts and utilization of ride-hailing services and the determinants of mode choice: The case of the city of toronto,” Submitted to Transportation Research Record, 2019.
10. W. Li, A. Shalaby, and K. N. Habib, “Comparative analysis of the travel characteristics between ride-hailing and public transit in toronto,” Submitted to Transportation Research Record, 2019.
11. “Exploring the ridership impacts of ride-hailing on multimodal public transit in toronto,” Submitted to Transportation Research Record,2019.
12. F. Calder´on and E. J. Miller, “A prototype model of ridehailing service provision,” Submitted to Transportation Research Record, 2019.
13. G. D. Erhardt, S. Roy, D. Cooper, B. Sana, M. Chen, and J. Castiglione,“Do transportation network companies decrease or increase congestion?”Science Advances, vol. 5, no. 5, 2019.
14. NewYorkCityOfficeoftheMayor,“For-hirevehicletransportation study, https://www1.nyc.gov/assets/operations/downloads/pdf/ For-Hire-Vehicle-Transportation-Study.pdf, 2016.
15. “Intersection file - city of toronto,” https://open.toronto.ca/dataset/ intersection-file-city-of-toronto/, 2019,accessed: July 30, 2019.
16. SharedStreets, “Taxi and tnc activity,” https://sharedstreets.io/ taxi-tnc-activity/, 2019, accessed: July 30, 2019.
17. B. R. C. Reddy, A. Ganji, S. Abiola, and M. Hatzopoulou, “Analyzing the effects of transit-only corridors on traffic and emissions in the affected neighbourhoods: A before-after intervention analysis,” in Presented at 98th Annual Meeting of the Transportation Research Board, Washington, D.C., 2019.
18. The PostgreSQL Global Development Group, “PostgreSQL,”  https://www.postgresql.org, accessed: July 17, 2019.
19. PostGIS Project, “PostGIS,” https://postgis.net, accessed: July 16, 2019.
20. E. Eros, “How the sharedstreets referencing system works,” https://sharedstreets.io/how-the-sharedstreets-referencing-system-works/, accessed:July 30, 2019.
21. pgRouting Project, “pgRouting,” https://pgrouting.org, accessed: July 16,2019.
22. D. N. Anderson, “”not just a taxi”? for-profit ridesharing, driver strategies, and vmt,” Transportation, vol. 41, no. 5, pp. 1099–1117, 2014.
23. B. Gurley, “A deeper look at uber’s dynamic pricing model,” http://abovethecrowd.com/2014/03/11/ a-deeper-look-at-ubers-dynamic-pricing-model, accessed: July 19,2019.
24. Stanford University School of Engineering, “Dawn woodward: How uber matches riders and drivers to reduce waiting time,” https://www.youtube.com/watch?v=jZk04aqvzBU,  accessed: July 27, 2019.
25. M. M. Vazifeh, P. Santi, G. Resta, S. Strogatz, and C. Ratti, “Addressing the minimum fleet problem in on-demand urban mobility,” Nature, vol.557, no. 7706, p. 534, 2018.
26. J. P. Hanna, M. Albert, D. Chen, and P. Stone, “Minimum cost matching for autonomous carsharing,” IFAC-PapersOnLine, vol. 49, no. 15, pp.254–259, 2016.