Quantifying the Impact of Transportation Network Companies on Urban Congestion in a Medium Sized City

Salman, Furat; Sisiopiku, Virginia P.; Khalil, Jalal; Jafarzadehfadaki, None Mostafa; Yan, Da

doi:10.17265/2328-2142/2023.01.001

Cited by 3 publications

(10 citation statements)

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“…The model selection in this study was based on MATSim's effectiveness in simulating TNC operations as demonstrated in the literature and confirmed by our earlier research efforts [7,8,38]. In particular, MATSim's Demand Responsive Transport (DRT) extension is a key software feature that gave MATSim a unique advantage over other available transportation software options considered.…”

Section: Simulation Model Selectionmentioning

confidence: 62%

“…By combining the different options, 6 ride-pooling scenarios were developed for further analysis. In addition, we considered the baseline scenario where we simulated no ride-pooling operations [7] and used it to facilitate comparisons.…”

Section: Simulation Study Experimental Designmentioning

confidence: 99%

“…Building on our earlier work [6,7], we adopted the Birmingham MATSim simulation model and made necessary modifications to the model to meet the needs of the current study. More specifically, in our previous research, we utilized MATSim's Taxi extension to simulate individual ride requests using the Birmingham MATSim model.…”

Section: Birmingham Matsim Simulation Modelmentioning

confidence: 99%

“…In our previous work [6][7][8][9], we addressed some of these limitations by (a) collecting TNC trip data from a survey of Uber/Lyft drivers in the Birmingham, AL region, and (b) showcasing the feasibility of modeling TNC services using the MATSim (Multi-Agent Transport Simulation) platform and the Birmingham, AL transportation network. To address the limitation of acquiring TNC trip data, the survey of Uber/Lyft drivers in the Birmingham metro area acted as a seed for generating population plans in the study area using the synthetic population technique.…”

Section: Introductionmentioning

confidence: 99%

“…More details on this effort are available in [6]. It should be noted that our earlier examination of the impact of ride-hailing TNC services on traffic operations was limited to individual ride requests, since ride-pooling services were not available in the Birmingham region [7]. However, it is of interest to understand how availability of various types of ride-pooling TNC services can affect operations and the likely impacts that such services can have on traffic congestion in the service area.…”

Section: Introductionmentioning

confidence: 99%

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Operational Impacts of On-Demand Ride-Pooling Service Options in Birmingham, AL

Salman

Sisiopiku

Khalil

et al. 2023

Future Transportation

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Transportation Network Companies (TNCs) use online-enabled apps to provide on-demand transportation services. TNCs facilitate travelers to connect with drivers that can offer them rides for compensation using driver-owned vehicles. The ride requests can be for (a) individual or (b) shared rides. The latter, also known as ride-pooling services, accommodates requests of unrelated parties with origins and destinations along the same route who agree to share the same vehicle, usually at a discounted fare. Uber and Lyft offer ride-pooling services in select markets. Compared to individual ride requests, ride-pooling services hold better promise toward easing urban congestion by reducing the number of automobiles on the road. However, their impact on traffic operations is still not fully understood. Using Birmingham, AL as a case study, this research evaluated the impact that ride-pooling services have on traffic operations using a Multi-Agent Transport Simulation (MATSim) model of the Birmingham metro area. Scenarios were developed to simulate baseline conditions (no TNC service) and ride-pooling availability with two types of ride-pooling services, namely door-to-door (d2d) and stop-based (sB) service and three fleet sizes (200, 400, and 800 vehicles). The results indicate that when TNC vehicles are added to the network, the Vehicle Kilometers Traveled (VKT) decrease by up to 5.78% for the door-to-door (d2d) service, and up to 2.71% for stop-based (sB) services, as compared to the baseline scenario (no TNC service). The findings also suggest that an increase in the size of the ride-pooling fleet results in a rise in total ride-pooling service VKT, network-wide total VKT, and detour distance. However, increasing the size of the ride-pooling fleet also results in a decrease in the ride request rejection rates, thus benefiting the customers and decreasing the vehicle empty ratio which, in turn, benefits the TNC drivers. The results further suggest that a fleet of 200 ride-pooling vehicles can meet the current demand for service in the Birmingham region at all times, thus it is the optimal ride-pooling TNC fleet size for a medium-sized city such as Birmingham.

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Section: Simulation Model Selectionmentioning

confidence: 62%

Section: Simulation Study Experimental Designmentioning

confidence: 99%

Section: Birmingham Matsim Simulation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Operational Impacts of On-Demand Ride-Pooling Service Options in Birmingham, AL

Salman

Sisiopiku

Khalil

et al. 2023

Future Transportation

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A Survey of Machine Learning-Based Ride-Hailing Planning

Wen,

Li,

Lau

2024

IEEE Trans. Intell. Transport. Syst.

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Urban Traffic Simulation with Shared Mobility Services: An Approach Using Spatiotemporal Network Kernel Density Estimation and MATSim

Khalil,

Yan,

Yuan

et al. 2024

ACM Trans. Spatial Algorithms Syst.

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The popularity of smartphones and 4G/5G network have enabled various novel transportation modes based on shared mobility, such as app-based ride-hailing services (e.g., Uber and Lyft) and shared micromobility services (e.g., Veo and Gotcha). However, little is known about to what degree their operations impact urban traffic, which is important for transportation planning and policy making. These companies seldom share their ride data due to business and user privacy reasons, and they are young and still exploring and growing their market to new locations. Recently, transportation engineering researchers began to collect data in large cities trying to understand the transportation impacts of shared mobility services, but (1) there does not exist a general analytics framework applicable to any city, and (2) the studies were based on historical data and cannot project the future easily to catch up with the rapid development of shared mobility services. In this paper, we introduce a general framework for multi-modal urban traffic analytics. The goal is to build a digital twin of the transportation in a city, i.e., an accurate agent-based transportation simulation model, based on a medium-sized dataset of the interested transportation modes collected by the research group combined with other open data sources such as US Census Bureau. With this digital twin, transportation engineering researchers can flexibly analyze the impact of shared mobility services under different scenarios, such as “if the number of Uber drivers doubles” or “if the number of deployed e-scooters doubles”. The digital twin may also enable new opportunities, such as being an environment for learning policies with reinforcement learning. Our framework consists of three steps: (1) fitting the spatiotemporal distribution of the shared mobility rides on the underlying road network, (2) generating the travel day-plans of the entire urban population based on the learned spatiotemporal ride distribution and user-specified parameters, and (3) configuring an agent-based simulation software such as MATSim to execute the generated realistic day-plans, which provides detailed transportation data for analytics. At the core of our approach is a new spatiotemporal network kernel density estimation (KDE) method that fixes the flaw of prior methods where the contributions of different data samples are not equal. We also propose a crowdsourcing method to collect app-based ride data that is easy to carry out in any city. Using Birmingham, AL as an example, we demonstrate how our framework can be applied to help transportation engineering researchers analyze the impacts of Uber ride-hailing and Veo e-scooter services.

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Quantifying the Impact of Transportation Network Companies on Urban Congestion in a Medium Sized City

Cited by 3 publications

References 0 publications

Operational Impacts of On-Demand Ride-Pooling Service Options in Birmingham, AL

Operational Impacts of On-Demand Ride-Pooling Service Options in Birmingham, AL

A Survey of Machine Learning-Based Ride-Hailing Planning

Urban Traffic Simulation with Shared Mobility Services: An Approach Using Spatiotemporal Network Kernel Density Estimation and MATSim

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