Stochastic modelling of spatial collective adaptive systems

Collective Adaptive Systems (CAS) are composed of individual agents with internal knowledge and rules which organize themselves into ensembles. These ensembles can often be observed to exhibit behaviour resembling that of a single entity with a clear goal and a consistent internal knowledge, even when the individual agents within the ensemble are not managed by any outside, globally-accessible entity. Because of their lack of a need for centralized control which results in high robustness, CAS are commonly observed in nature – and for similar reasons are often reflected in human engineered systems. Researching the patterns of operation observed in such systems provides meaningful insight into how to design and optimise stable multiagent systems capable of withstanding adverse conditions. Formal modelling provides valuable intellectual tools which can be applied to the problem of analysis of systems by means of modelling and simulation. In this thesis we explore the modelling of CAS in which space (topology and distances) plays a significant role. Working with CARMA (Collective Adaptive Resource-sharing Markovian Agents) a formal feature-rich language for modelling stochastic CAS, we investigate a number of spatial CAS scenarios from the realm of urban planning. When components operate in a spatial context, their behaviour can be affected by where they are located in that space. For example, their location can influence the speed at which they move, and their ability to communicate with other components. Components in CARMA have internal store, and behaviour expressed by Markov processes. They can communicate with each other through sending messages on state transitions in a unicast or broadcast fashion. Simulation with pseudo-random events can be used to obtain values of measures applied to CARMA models, providing a basis for analysis and optimisation. The CARMA models developed in the case studies are data-driven and the results of simulating these models are compared with real-world data. In particular, we explore two scenarios: crowd-routing and city transportation systems. Building on top of CARMA, we also introduce CGP (CARMA Graphical Plugin), a novel graphical software tool for graphically specifying spatial CAS systems with the feature of automatic translation into CARMA models. We also supply CARMA with additional syntax structures for expressing spatial constructs

Statistical analysis of CARMA models: an advanced tutorial

CARMA (Collective Adaptive Resource-sharing Markovian Agents) is a process-algebra-based quantitative language developed for the modeling of collective adaptive systems. A CARMA model consists of an environment in which a collective of components with attribute stores interact via unicast and broadcast communication, providing a rich modeling formalism. The semantics of a CARMA model are given by a continuous-time Markov chain which can be simulated using the CARMA Eclipse Plug-in. Furthermore, statistical model checking can be applied to the trajectories generated through simulation using the MultiVeStA tool. This advanced tutorial will introduce some of the theory behind CARMA and MultiVeStA as well as demonstrate its application to collective adaptive system modeling

Exploratory Analysis of Ramp Metering on Efficiency, and Safety of Freeways Using Microsimulation

The microscopic Verkehr In Städten – SIMulations Model (VISSIM) stochastic simulator program was used to explore the effectiveness of ramp metering on efficiency, Level of Service, and safety of freeways. Three different geometric configurations of ramp-freeway junctions were evaluated using different traffic volume conditions of the ramp and the freeway. Different signal timing scenarios were designed for the different traffic volume and geometric configuration scenarios. Calibration process was conducted for the collected traffic data that were obtained from cameras and detectors. Two-hundred-eighty models were built and run to explore the effectiveness of the performance and safety of the ramp meters on freeways. Average speed and average travel time of the vehicles passing a 3,000-ft long freeway segment were used as measures of effectiveness of the freeway efficiency evaluation. Average density in the ramp influence area was used to obtain the freeway level of service as a measure of effectiveness of the freeway capacity evaluation. Frequency, types, and severity of vehicle conflicts, which occurred on the 3,000-ft freeway segment, were used as measures of effectiveness of the freeway safety evaluation. The Surrogate Safety Assessment Model (SSAM) program, which was developed by the Federal Highway Administration (FHWA), was used to find the frequency and types of vehicle conflicts, while the severity of vehicle conflicts was separated by a designed method that was retrieved from the previous literature studies. Minitab statistical software was used for some tests such as normality test to determine the appropriate number of samples, and F-tests. A sensitivity analysis was also conducted for better understanding the effectiveness of two assumption changes on the results that were obtained from running the models. The assumptions were car following headway in the ramp influence area and traffic composition on the freeway. The findings of the study provided different results related to the different geometric configurations, signal timing designs, and traffic volumes. Ramp metering at the Type I geometric configuration provided positive effects on the efficiency and safety of the freeway when using the two designed signal timing scenarios when the freeway traffic volume was equal to or greater than 1,250 vehicle per hour per lane (vphpl) and the ramp traffic volume was equal to or greater than 800 vphpl. Ramp metering provided negative effects on the efficiency and safety of the freeway when using it for the Type II geometric configuration. In the geometric configuration of Type III, ramp metering using the signal timing of 2 seconds green and 4 seconds red provided the best efficiency and safety increases when the freeway traffic volume was equal to or greater than 1,250 vphpl and the ramp traffic volume was equal to or greater than 800 vphpl. Conclusively, ramp metering increases efficiency and improves safety of freeways only at specific situations regarding geometric configuration of the ramp-freeway junction type, traffic volume of the freeway and the ramp, and the designed traffic signal of the ramp meters

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios