Simulation Study of Miami Beach Network Revealed a 4.1 Percent Modal Shift to Transit with Combined Light Rail and Road Pricing Strategy, Compared to 0.5 Percent with Light Rail Alone.

Demand Management Strategies Coupled with Transportation Network Company Services Simulated for Miami Beach Area Led to Mobility Benefits.

Date Posted

Mitigating Network Congestion by Integrating Transportation Network Companies and Urban Transit

Summary Information

Transportation Network Companies (TNCs) offer on-demand transportation services via online platforms, connecting drivers using their own vehicles with passengers. While TNCs are convenient and reliable, they may promote car-dependent travel which leads to an increase in urban congestion and reduces reliance on public transit and taxis. The objective of this study was twofold: (a) to investigate the impact of transit and TNC operations on individual travelers' mode choices and (b) to evaluate the impact of these mode choices on transportation network performance. The study conducted four interrelated case studies, using Birmingham, AL, and/or Miami Beach, FL, as test beds. The analyses were conducted using traffic simulation tools.


The research team developed various models to accomplish the intended goals of this study: 

  1. A base microsimulation model of the Miami Beach network was calibrated using a mode split behavioral model and real-world traffic counts. The calibrated model was then used to estimate modal shifts between the passenger car and transit services due to the introduction of an enhanced transit alternative and road pricing. Southeast Florida Regional Planning Model (SERPM 7.0) developed by Florida DOT (FDOT) in 2015 was used as the travel demand model. Traffic count data from 50 traffic count stations in the area were also utilized.
  2. In addition, this study examined potential impacts of expanding public transit options in the Birmingham region on traffic volumes, speeds, and travel times, using a comprehensive activity-based simulation model of Birmingham to simulate traffic operations under various transit ridership scenarios ranging from zero percent (base case) to 10.1 percent. 
  3. Finally, this study also demonstrated the feasibility of modeling TNC services using an agent-based simulation platform and evaluated the impact of such services on traffic operations in the Birmingham region, using TNC trip data in Birmingham in 2019 and 2021, before and after the COVID pandemic. 



  • Microsimulation results in Miami Beach revealed that modal shift towards transit was greater when the new transit option (light rail addition) was introduced in combination with a road pricing strategy (4.1 percent shift towards transit), rather than light rail alone (0.5 percent shift towards transit).
  • The activity-based simulation results from Birmingham were only partially statistically significant for traffic volumes, and insignificant for speed and travel time improvements. The statistically significant results revealed that increasing the transit ridership from 1.1 percent to 5.7 percent resulted in up to 20 percent traffic volume reductions based on the time of day. Further increase of the public transit ridership from 1.1 percent to 10.1 percent reduced the traffic volumes even further, with the highest reduction percentage of 36.6 percent occurring between 3 PM to 4 PM. Finally, the results of the agent-based simulation confirmed that TNC operations result in small improvements in network performance in terms of reducing congestion in Birmingham (a moderate-sized city) during peak hours. 
Results Type
Deployment Locations