Addressing spatial service provision equity for pooled ride‐hailing services through rebalancing

Schlenther, Tilmann; Leich, Gregor; Maciejewski, Michał; Nagel, Kai

doi:10.1049/itr2.12279

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…For relations that are found to be highly suitable for pooling, it remains unclear whether those could or should rather be served with small fixed-route buses. Simulations as well as real-world applications show that profit-oriented service operators are mainly interested in areas of high demand density (Bischoff et al 2018, Schlenther et al 2023a. In urban contexts, these areas are commonly well served by conventional public transport, which leads to undesirable competition and the aforementioned cannibalisation effects.…”

Section: Transport Modeling: Exploring User Behavior Gauged With Empi...mentioning

confidence: 99%

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Shared pooled mobility: expert review from nine disciplines and implications for an emerging transdisciplinary research agenda

Creutzig,

Schmaus,

Ayaragarnchanakul

et al. 2024

Environ. Res. Lett.

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Shared pooled mobility has been hailed as a sustainable mobility solution that uses digital innovation to efficiently bundle rides. Multiple disciplines have started investigating and analyzing shared pooled mobility systems. However, there is a lack of cross-community communication making it hard to build upon knowledge from other fields or know which open questions may be of interest to other fields. Here, we identify and review 9 perspectives: transdisciplinary social sciences, social physics, transport simulations, urban and energy economics, psychology, climate change solutions, and the Global South research and provide a common terminology. We identify more than 25,000 papers, with more than 100 fold variation in terms of literature count between research perspectives. Our review demonstrates the intellectual attractivity of this as a novel perceived mode of transportation, but also highlights that real world economics may limit its viability, if not supported with concordant incentives and regulation. We then sketch out cross-disciplinary open questions centered around 1) optimal configuration of ride-pooling systems, 2) empirical studies, and 3) market drivers and implications for the economics of ride-pooling. We call for researchers of different disciplines to actively exchange results and views to advance a transdisciplinary research agenda.

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Section: Transport Modeling: Exploring User Behavior Gauged With Empi...mentioning

confidence: 99%

“…Electric charging adds another aspect (Bischoff and Maciejewski 2014). Fleet rebalancing improves service quality, but consumes more resources (Winter et al 2017, Schlenther et al 2023a. In contrast, pre-booking allows significant savings (Lu et al 2023).…”

Section: Interactions Between Dispatch Algorithms Rebalancing Service...mentioning

confidence: 99%

Shared pooled mobility: expert review from nine disciplines and implications for an emerging transdisciplinary research agenda

Creutzig,

Schmaus,

Ayaragarnchanakul

et al. 2024

Environ. Res. Lett.

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“…MATSim's DRT extension is based on Maciejewski (25) and was introduced by Bischoff et al (26). It has been used extensively to study the impact of on-demand mobility services (27)(28)(29)(30).…”

Section: Drt Extensionmentioning

confidence: 99%

Flow-Inflated Selective Sampling for Efficient Agent-Based Dynamic Ride-Pooling Simulations

Kuehnel¹,

Rewald

Axer

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Agent-based simulations are powerful tools for simulating emergent mobility modes, but they often require significant memory and computing power. To address this issue, researchers have previously used sampling techniques, where only a fraction of agents are explicitly simulated while others are simulated through teleportation. However, recent studies have highlighted the challenges of scaling ride-pooling simulations because of their reliance on demand density, which does not scale linearly. In this study, we introduce a new methodology for simulating dynamic ride-pooling services called flow-inflated selective sampling (FISS). Unlike previous approaches, FISS considers all agents but—for selected modes—only explicitly simulates a fraction of their trips while simulating the remaining trips through teleportation. Here, we explicitly simulate all public transport and ride-pooling trips and sample private car trips. The capacity consumption of explicitly simulated cars is scaled up to obtain realistic traffic flows, rather than adjusting the network capacity as in previous approaches. We implement FISS in the MATSim simulation environment for a large scenario in Munich, Germany, and show that it preserves traffic flows while keeping key performance indicators of ride-pooling services stable and unbiased. Mode choice decisions based on FISS also remain stable, and runtimes of the assignment can be almost halved. Overall, FISS is a simple yet effective approach that can significantly reduce the computational burden of agent-based simulations while maintaining the accuracy of the results. It can be particularly useful for simulating ride-pooling services, which can be challenging to scale because of their dependence on demand density.

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“…When the public sector operates SAVs, their goals extend beyond mere operational costs to include broader sustainability and equity objectives [20]. Public operators aim to prioritize service quality for users with limited access to PT to address urban mobility challenges, reduce transportation-related inequalities, and support sustainable transportation goals [21]. This paper moves one step in this direction to ensure that transportation services are accessible and equitable for all users, aligning with public policy objectives that promote sustainable and inclusive urban mobility.…”

Section: Introductionmentioning

confidence: 99%

Smart Insertion Strategies for Sustainable Operation of Shared Autonomous Vehicles

Tiwari,

Nassir,

Lavieri

2024

Sustainability

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As shared autonomous vehicles (SAV) emerge as an economical and feasible mode of transportation in modern cities, effective optimization models are essential to simulate their service. Traditional optimization approaches, based on first-come-first-served principles, often result in sub-optimal outcomes and, more notably, can impact public transport (PT) operations by creating unnecessary competition. This study introduces four insertion strategies within the MATSim model of the Melbourne Metropolitan Area, addressing these challenges. Two strategies optimize SAV operations by considering overall network costs, and the other two make insertion decisions based on the available PT service in the network. The findings show that strategic insertions of the requests can significantly enhance SAV service quality by improving the vehicle load and decreasing vehicle and empty kilometers traveled per ride. The analysis indicates that these strategies are particularly effective for smaller fleet sizes, leading to an increased number of served rides and a more equitable distribution of wait times across the network, reflected in an improved Gini Index. The findings suggest that prioritization-based insertions significantly enhance service quality by prioritizing users with limited access to PT, ensuring that those with fewer PT options are served first, and encouraging a more integrated and sustainable urban transportation system.

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Addressing spatial service provision equity for pooled ride‐hailing services through rebalancing

Cited by 7 publications

References 30 publications

Shared pooled mobility: expert review from nine disciplines and implications for an emerging transdisciplinary research agenda

Shared pooled mobility: expert review from nine disciplines and implications for an emerging transdisciplinary research agenda

Flow-Inflated Selective Sampling for Efficient Agent-Based Dynamic Ride-Pooling Simulations

Smart Insertion Strategies for Sustainable Operation of Shared Autonomous Vehicles

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