Creating an integrated agent-based travel demand model by combining mobiTopp and MATSim

Briem, Lars; Mallig, Nicolai; Vortisch, Peter

doi:10.1016/j.procs.2019.04.105

Cited by 19 publications

(9 citation statements)

References 10 publications

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“…Under certain conditions MATSim's queue model leads to false congestion patterns and therefore misleading vehicle dynamics especially on short links or in sample runs 6 . The flow capacity basically acts like a batch system: A flow capacity of 600 cars/h means that only every sixth second a vehicle is allowed to leave a link.…”

Section: Matsim's Capabilities and Limits In Drive Cycle Deductionmentioning

confidence: 99%

Pre-study and insights to a sequential MATSim-SUMO tool-coupling to deduce 24h driving profiles for SAEVs

Triebke

Vortisch

2022

SUMO Conf Proc

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New mobility concepts such as shared, autonomous, electric vehicle (SAEV) fleets raise questions to the vehicles’ technical design. Compared to privately owned human driven cars, SAEVs are expected to exhibit different load profiles that entail the need for newly dimensioned powertrain and battery components. Since vehicle architecture is very sensitive to operating characteristics, detailed SAEV driving cycles are crucial for requirement engineering. As real world measurements reach their limit with new mobility concepts, this contribution seeks to evaluate three different traffic simulation approaches in their ability to model detailed SAEV driving profiles. (i) The mesoscopic traffic simulation framework MATSim is analyzed as it is predestined for large-scale fleet simulation and allows the tracking of individual vehicles. (ii) To improve driving dynamics, MATSim’s simplified velocity profiles are enhanced with real-world driving cycles. (iii) A sequential tool-coupling of MATSim with the microscopic traffic simulation tool SUMO is pursued. All three approaches are compared and evaluated by means of a comprehensive test case study. The simulation results are compared in terms of driving dynamics and energy related key performance indicators (KPI) and then benchmarked against real driving cycles. The sequential tool-coupling approach shows the greatest potential to generate reliable SAEV driving profiles.

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Section: Matsim's Capabilities and Limits In Drive Cycle Deductionmentioning

confidence: 99%

Pre-study and insights to a sequential MATSim-SUMO tool-coupling to deduce 24h driving profiles for SAEVs

Triebke

Vortisch

2022

SUMO Conf Proc

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“…Route choice is currently only available for public transport (44). For other modes, external tools such as Visum and MATSim can be used (45).…”

Section: Approachmentioning

confidence: 99%

Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

Wilkes

Engelhardt

Briem

et al. 2021

Transportation Research Record

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This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.

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“…Overall, these two contributions improve the accuracy and scalability of synthetic population generation, ultimately benefiting agent-based simulation models and their applications. Despite being able to share common fitting methods in population synthesis and workplace assignment, the two are typically performed as completely independent processes due to computational tractability or proprietary program scope (Briem et al, 2019). To clearly discuss the two, the following background discussion is divided into two main sections of population synthesis and workplace assignment.…”

Section: Contributionsmentioning

confidence: 99%

Integrated population synthesis and workplace assignment using an efficient optimization-based person-household matching method

et al. 2020

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Creating an integrated agent-based travel demand model by combining mobiTopp and MATSim

Cited by 19 publications

References 10 publications

Pre-study and insights to a sequential MATSim-SUMO tool-coupling to deduce 24h driving profiles for SAEVs

Pre-study and insights to a sequential MATSim-SUMO tool-coupling to deduce 24h driving profiles for SAEVs

Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

Integrated population synthesis and workplace assignment using an efficient optimization-based person-household matching method

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