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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 simulator 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; however, 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 within an urban area of interest. Tangramob simulates how urban traffic is expected to evolve as citizens start experiencing 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, three mobility initiatives are simulated and compared in the same scenario. This demonstrates how it is possible to perform comparative experiments so as to align mobility initiatives to the user goals.

Track-based flow management systems like transportation systems and traffic control systems play a crucial role in our daily life. Safety and performance are among the most important quality requirements for these systems. This paper presents AdaptiveFlow as a framework for modeling, safety checking and performance analysis of track-based flow management systems. AdaptiveFlow is based on the Hewitt actors computation model. In particular, tracks are modeled as actors and moving objects as messages. Timed Rebeca is used for modeling, and the model checking tool Afra is used for safety verification and performance evaluation in AdaptiveFlow. To react to dynamic changes in the environment, AdaptiveFlow provides support for three adaptive policies, which can be examined and compared in different scenarios. To demonstrate the applicability of AdaptiveFlow, we consider the Electric Site Research Project of Volvo Construction Equipment as a case study. In this project, a fleet of autonomous haulers is utilized to transport materials in a quarry site. Furthermore, to show the reusability of the framework for other flow management scenarios, an experiment on an urban garbage collection system is presented. © 2020 Information Processing Society of Japan.

In this paper we present AdaptiveFlow as a platform for designing system of systems. A model-based development approach is proposed and tools are provided for formal verification and performance evaluation. The actor-based language, Timed Rebeca, is used for modelling, and the model checking tool Afra is used for checking the safety properties and also for performance evaluation. We investigate the efficiency of our approach and the applicability of the developed platform by conducting experiments on a case study based on the Electric Site Research Project of Volvo Construction Equipment. In this project, a fleet of autonomous haulers is utilised to transport materials in a quarry site. We used three adaptive policies as plugins to our platform and examined these policies in different scenarios., ITS-EASY Post Graduate School for Embedded Software and Systems

Assessing the impacts of a mobility initiative prior to deployment is a complex task for both urban planners and transport companies. Computational models like Tangramob offer an agent-based framework for simulating the evolution of urban traffic after the introduction of new mobility services. However, simulations can be computationally expensive to perform due to their iterative nature and the microscopic representation of traffic. To address this issue, we designed a simplified model architecture of Tangramob in Timed Rebeca (TRebeca) and we developed a tool-chain for the generation runnable instances of this model starting from the same input files of Tangramob. Running TRebeca models allows users to get an idea of how the mobility initiatives under study affect the traveling experience of commuters, in a short time and without the need to use the simulator during this first experimental step. Then, once a subset of these initiatives is identified according to user's criteria, it is reasonable to simulate them with Tangramob in order to get more detailed results. To validate this approach, we compared the output of both the simulator and the TRebeca model on a collection of mobility initiatives. The correlation between the results demonstrates the usefulness of using TRebeca models for unconventional contexts of application.