Shifts in perspective: Operational aspects in (non-)autonomous ride-pooling simulations

Kuehnel, Nico; Zwick, Felix; Hörl, Sebastian

doi:10.1016/j.tra.2022.09.001

Cited by 7 publications

(7 citation statements)

References 36 publications

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“…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. This extension enables the simulation of ondemand ride-pooling services [11,15,41], and makes MATSim an excellent choice for a simulation platform for meeting the objectives of our study.…”

Section: Simulation Model Selectionmentioning

confidence: 99%

“…Specifically, we considered two types of ridepooling services, namely door-to-door (d2d) and stop-based (sB), and three fleet sizes (i.e., 200, 400, and 800 vehicles). The maximum acceptable waiting time was set to 5 min, as in other literature studies [15,41]. By combining the different options, 6 ride-pooling scenarios were developed for further analysis.…”

Section: Simulation Study Experimental Designmentioning

confidence: 99%

“…The DRT module configuration file, created specifically for this study, defined the DRT operational characteristics and selected reasonable parameter threshold values based on recommendations from earlier studies and local considerations. For example, studies conducted in Germany [10,11,41,42] used a 30 s stop time duration for pickup and drop off of passengers, Bischoff et al [43] assumed a stop duration of 60 s, and a study conducted by [17] in Wayne County, Michigan, set the stop duration to 105 s. In our study, we set a stop time duration of 60 s for passengers being picked up and dropped off at each stop. The maximum number of passengers per vehicle was set to 4 and the maximum detour time in our study was 8 min.…”

Section: Birmingham Matsim Simulation Modelmentioning

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: 99%

Section: Simulation Study Experimental Designmentioning

confidence: 99%

Section: Birmingham Matsim Simulation Modelmentioning

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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“…The MOIA service was studied extensively in simulation studies [17][18][19][20] and user (behavior) research [21,22]. Gilibert et al [21] and Kostorz et al [22] found that leisure activities and airport transfers were among the main travel purposes for MOIA trips.…”

Section: Research On Ride-pooling Systems In Europe and Moiamentioning

confidence: 99%

“…The MOIA service was studied extensively in simulation studies [17–20] and user (behavior) research [21, 22]. Gilibert et al.…”

Section: Introductionmentioning

confidence: 99%

Ride‐pooling in the light of COVID‐19: Determining spatiotemporal demand characteristics on the example of MOIA

Zwick

Fraedrich²,

Axhausen

2022

IET Intelligent Trans Sys

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The mobility provider MOIA operates Europe's largest contiguous electric ride-pooling service in Hamburg, representing a testbed of how shared and digitized transport can help foster the transformation of urban mobility. The on-demand service has been in operation since 2019 and was thus affected by the COVID-19 pandemic in 2020. This study shows real-world insights into travel behavior before and during the pandemic, contributing to the empirical evidence on recent mobility behavior. After the application of descriptive statistical analyses, several (spatial) regression models are estimated to understand the relationship between spatial variables and demand. MOIA trip data from three different time periods are used: (a) before the COVID-19 pandemic in summer and autumn 2019, (b) during the time of the first lockdown in Germany in spring 2020, and (c) after the first lockdown in summer and autumn 2020. A significant positive effect on ride-pooling demand is observed for number of inhabitants, workplaces, gastronomic facilities, and at the airport in all time periods. In the course of the pandemic, the main travel patterns remained stable. However, the positive influences of gastronomy and the airport on ride-pooling demand diminished in 2020. In contrast, the impact of hospitals on ride-pooling demand increased in the course of the pandemic. In areas with high car ownership, ride-pooling demand declined compared to pre-pandemic times.

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A shift scheduling model for ridepooling services

Berthold,

Fliedner,

Schulz

2024

OR Spectrum

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The planning of efficient shift schedules is a key challenge for many service companies whose economic success heavily relies on the efficient employment of personnel. In spite of the recent advances in autonomous driving, mobility services, such as ride pooling, still heavily rely on the use of human drivers and will presumably remain in this category in the near to midterm. As a consequence, shift scheduling of drivers is one of the key success factors in the current industry environment. Determining appropriate shifts that minimize an under- and oversupply of vehicles for all planning periods is a challenging task, since demand can vary heavily over time and the assignment flexibilities are limited due to driver preferences and regulations. In this work, we present a shift scheduling model for ridepooling services. Moreover, we introduce a data generator for instances with realistic properties of a ridepooling service. Using it, we study the effect of different kinds of flexibilities on solution quality.

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Shifts in perspective: Operational aspects in (non-)autonomous ride-pooling simulations

Cited by 7 publications

References 36 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

Ride‐pooling in the light of COVID‐19: Determining spatiotemporal demand characteristics on the example of MOIA

A shift scheduling model for ridepooling services

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