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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Furat Salman1, Virginia P. Sisiopiku1, Jalal Khalil2, Mostafa Jafarzadehfadaki1 and Da Yan2
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DOI:10.17265/2328-2142/2023.01.001
1. Department of Civil, Construction, and Environmental Engineering, The University of Alabama at Birmingham, Birmingham 35294, Alabama, United States
2. Department of Computer Science, The University of Alabama at Birmingham, Birmingham 35294, Alabama, United States
In the recent years, TNCs (transportation network companies) and on-demand ridesharing services have grown rapidly. Given conflicting reports on TNC impacts, a need exists to study mode choice shifts in the presence of TNC services and their effects on urban congestion. Using Birmingham, AL (Alabama) as a case study, this paper showcases the feasibility of modeling TNC services using the MATSim (Multi-Agent Transport Simulation) platform, and evaluating the impact of such services on traffic operations. Data used for the study were gathered from Uber drivers and riders through surveys, as well as the US Census. The results indicate that when 200, 400, and 800 TNC vehicles are added to the network, the VKT (vehicle kilometers traveled) increase by 22%, 23.6%, and 23.2%, respectively, compared to the baseline scenario (no TNC service). Analysis of hourly average speeds, hourly average travel times, and hourly volumes along study corridors further indicate that TNC services increase traffic congestion, in particular, during the AM/PM peak periods. Moreover, the study shows that the optimal TNC fleet size for the Birmingham region is 400 to 500 active TNC vehicles per day. Such fleet size minimizes idle time and the number of TNC vehicles hovering, which have adverse impacts on TNC drivers, and the environment while ensuring TNC service availability and reasonable waiting times for TNC customers.
TNC, Uber, Lyft, on-demand ridesourcing, MATSim.
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