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Tangramob: an agent-based simulation framework for validating urban smart mobility solutions

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abstract

Estimating the effects of introducing a range of smart mobility solutions within an urban area is a crucial concern in urban planning. The lack of a Decision Support System (DSS) for the assessment of mobility initiatives, forces local public authorities and mobility service providers to base their decisions on guidelines derived from common heuristics and best practices. These approaches can help planners in shaping mobility solutions, but given the high number of variables to consider the effects are not guaranteed. Therefore, a solution conceived respecting the available guidelines can result in a failure in a different context. In particular, difficult aspects to consider are the interactions between different mobility services available in a given urban area, and the acceptance of a given mobility initiative by the inhabitants of the area. In order to fill this gap, we introduce Tangramob, an agent-based simulation framework capable of assessing the impacts of a Smart Mobility Initiative (SMI) within an urban area of interest. Tangramob simulates how urban traffic is expected to evolve as citizens start experiencing the newly offered traveling solutions. This allows decision makers to evaluate the efficacy of their initiatives taking into account the current urban system. In this paper we provide an overview of the simulation framework along with its design. To show the potential of Tangramob, 3 mobility initiatives are simulated and compared on the same scenario. This shows how it is possible to perform comparative experiments so as to align mobility initiatives to the user goals.

fields

cs.CY 1

years

2019 1

verdicts

CONDITIONAL 1

representative citing papers

BISTRO: Berkeley Integrated System for Transportation Optimization

cs.CY · 2019-08-10 · conditional · novelty 5.0

The paper introduces BISTRO, an open-source simulation-optimization platform for transportation planning, and reports how a machine-learning competition uncovered an unbounded scoring function that was later fixed and re-evaluated.

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  • BISTRO: Berkeley Integrated System for Transportation Optimization cs.CY · 2019-08-10 · conditional · none · ref 20 · internal anchor

    The paper introduces BISTRO, an open-source simulation-optimization platform for transportation planning, and reports how a machine-learning competition uncovered an unbounded scoring function that was later fixed and re-evaluated.