Simulation Modelling Practice and Theory最新文献

Tramways decrease time, cost, and environmental impact of urban transport, while requiring multimodal intersections where trams arriving with nominal periodic timetables may have right of way over road vehicles. Quantitative evaluation of stochastic models enables early exploration and online adaptation of design choices, identifying operational parameters that mitigate impact on road transport performance.

We present an efficient analytical approach for offline scheduling of traffic signals at multimodal intersections among road traffic flows and tram lines with right of way, minimizing the maximum expected percentage of queued vehicles of each flow with respect to sequence and duration of phases. To this end, we compute the expected queue size over time of each vehicle flow through a compositional approach, decoupling analyses of tram and road traffic. On the one hand, we define microscopic models of tram traffic, capturing periodic tram departures, bounded delays, and travel times with general (i.e., non-Exponential) distribution with bounded support, open to represent arrival and travel processes estimated from operational data. On the other hand, we define macroscopic models of road transport flows as finite-capacity vacation queues, with general vacation times determined by the transient probability that the intersection is available for vehicles, efficiently evaluating the exact expected queue size over time. We show that the distribution of the expected queue size of each flow at multiples of the hyperperiod, resulting from temporization of nominal tram arrivals and vehicle traffic signals, reaches a steady state within few hyper-periods. Therefore, transient analysis starting from this steady-state distribution and lasting for the hyper-period duration turns out to be sufficient to characterize road transport behavior over time intervals of arbitrary duration.

We implemented the proposed approach in the novel OMNIBUS Java library, and we compared against Simulation of Urban MObility (SUMO). Experimental results on case studies of real complexity with time-varying parameters show the approach effectiveness at identifying optimal traffic signal schedules, notably exploring in few minutes hundreds of schedules requiring tens of hours in SUMO.

Several scenarios that use the Internet of Drones (IoD) networks require a Fog paradigm, where the Fog devices, provide time-sensitive functionality such as task allocation, scheduling, and resource optimization. The problem of efficient task allocation/scheduling is critical for optimizing Fog-enabled Internet of Drones performance. In recent years, many articles have employed meta-heuristic approaches for task scheduling/allocation in Fog-enabled IoT-based scenarios, focusing on network usage and delay, but neglecting execution time. While promising in the academic area, metaheuristic have many limitations in real-time environments due to their high execution time, resource-intensive nature, increased time complexity, and inherent uncertainty in achieving optimal solutions, as supported by empirical studies, case studies, and benchmarking data. We propose a task allocation method named F-DTA that is used as the fitness function of two metaheuristic approaches: Particle Swarm Optimization (PSO) and The Krill Herd Algorithm (KHA). We compare our proposed method by simulation using the iFogSim2 simulator, keeping all the settings the same for a fair evaluation and only focus on the execution time. The results confirm its superior performance in execution time, compared to the metaheuristics.

Educational big data analysis is facilitated by the significant amount of unstructured data found in education institutions. Python has various toolkits for both structured and unstructured data processing. However, its ability for processing large-scale data is limited. On the other hand, Spark is a big data processing framework, but it does not have the needed toolkits for processing unstructured rich text documents, 3D model and image processing. In this study, we develop a generic framework that integrates Python toolkits and Spark based on service-oriented architecture. The framework automatically extends the serial algorithm written in Python to distributed algorithm to accomplish parallel processing tasks seamlessly. First, our focus is on achieving non-intrusive deployment to Spark servers and how to run Python codes in Spark environment to process rich text documents. Second, we propose a compression-based schema to address the poor performance of small sized files in HDFS. Finally, we design a generic model that can process different types of poly-structured data such as 3D models and images. We published the services used in the system for sharing them at https level for constructing different systems. It is evaluated through simulation experiments using large-scale rich text documents, 3D models and images. According to the results, the speedup is 49 times faster than the standalone Python-docx in the simulations of extracting 232 GB docx files when eight physical nodes with 128 cores are used. It reaches about 89 times after further compression schema is applied. In addition, simulations for 3D model descriptors' extraction show that the simulation achieves a speedup of about 116 times. In the large-scale image's HOG features extraction simulation task of up to 256.7 GB (6,861,024 images), a speedup of up to 110 times is achieved.

This study proposes a simulation model for allocating counterbalanced forklifts in a logistics distribution center (LDC) with aisle constraints. Modeling the case study for a consumer goods firm, the performance measures of the logistics operation were calculated and certain experimental scenarios were purposed for decision-making regarding the number of forklifts and their productivity. The relevance of this research is validated by the gap in existing literature on enhancing forklift assignments in massive storage systems with restrictions. The simulation scenarios contribute toward standardizing logistics operations with similar characteristics, starting from the layout stage of an LDC. The designed simulation model demonstrates that the simulated allocation incorporates technical and human resources in warehouse operations. Utilizing discrete-event simulation (DES) as a framework, this study assesses various scenarios in an LDC with restrictions on the forklift. The hypothesis of the problem was analyzed, and the simulation model was used to characterize the system behavior under different scenarios and guide the decision-making processes impacting operational costs and client service levels. This research employs DES to address performance indicators and operational costs, serving as a methodological guide for resource allocation in logistics operations at distribution centers.

Recent unprecedented trend towards novel vehicular network applications (e.g., lane change assistance, collision avoidance, accident reporting, and infotainment) led to research activities towards the novel design of vehicular networks. Such a novel design can leverage metaverse architecture among various possible schemes. However, enabling the metaverse for a vehicular network requires careful placement of meta spaces (i.e., virtual network functions based on virtual machines running a virtual model of the actual network) at the network edge. Furthermore, virtual network functions will use additional entities (e.g., memory storage) to better implement meta spaces. Therefore, in this work, we consider the efficient placement of meta spaces at the network edge. We formulate a cost function that accounts for latency (i.e., meta space computing latency and transmission latency for meta spaces signaling). To minimize this cost, an optimization problem is formulated that uses three variables: (a) meta spaces (i.e., based on virtual machines) operating frequency, (b) meta space placement, and (c) wireless resource allocation. A decomposition-based solution is used to solve the formulated problem due to its difficult nature. Finally, numerical results are provided and the paper is concluded.

Intelligent vehicles passing through intersections in platoons have the potential to reduce speed fluctuations and improve traffic efficiency. This paper centers on the cooperative optimization of platooning behavior in urban fixed-time signalized intersection scenarios. It delves into the spatial group distribution characteristics of platoons within an intersection entrance lane and employs modeling techniques to vividly portray and express group behaviors, including lane-changing and car-following, across various physical areas within a platoon. Based on real-world traffic data collected in Beijing, SUMO (Simulation of Urban Mobility) is used to build simulation scenarios in this research. Considering both low- and high-volume conditions, the influence of different platoon size combinations on entrance lane traffic efficiency improvement is explored by adjusting the platoon size to determine an ideal vehicle group organization form under a given fixed signal timing. Experimental results show that the optimal platoon sizes and crossing sequences are affected by traffic volume and the number of lanes. For example, when the volume of a single lane in the east entrance straight direction is large (500 or 600 pcu/h/lane), the observed optimal platoon size is 7 to 9 pcu/p (pcu/platoon). These findings indicate that at fixed-time signalized intersections, there exists a reasonable platoon size that optimizes the overall capacity of each entrance, which provides ideas for future vehicle group control.

Simulation of pedestrian motion in urban traffic networks is crucial for designing autonomous vehicle systems. In a mixed traffic system, a complex interaction occurs between a pedestrian and a vehicle. To understand this interaction pattern and evaluate traffic safety analysis, a simulation tool can be useful. It can help autonomous vehicle designers to visualize pedestrian and vehicle trajectory, extract velocity and acceleration profile of both agents, test different autonomous vehicle planning algorithms, and assess the traffic safety in severe traffic conflicts. This paper presents a rule-based social force model to simulate pedestrian trajectories during interaction with an autonomous vehicle. The social force model is then integrated with an autonomous vehicle control and planning algorithm for simulating the behavior of both pedestrian and vehicle in traffic conflicts by varying different parameters such as agent's initial speed, different vehicle sensor types (error percentage of pedestrian detection varies), different pedestrian types (risk-taking, cautious, and distracted), etc. This simulation tool provides minimum distance accepted by a pedestrian during a road crossing scenario as output. Additionally, the simulation illustrates the impact of vehicle initial speed on crossing decision and minimum distance accepted by pedestrians before crossing. The simulation tool can be useful to simulate risky interaction scenarios to understand the effectiveness of autonomous vehicle planning algorithm while interacting with different types of pedestrians.

We develop an agent-based simulator named METS-R SIM to support operational decisions for multi-modal shared autonomous vehicle (SAEV) services. Compared with existing traffic simulators, METS-R SIM offers several valuable features including: 1) A microscopic vehicle movement model for SAEV services, which allows us to explicitly model vehicular interactions and generate detailed speed and acceleration profiles for energy estimation. 2) An efficient implementation in which parallel computing is embedded in METS-R SIM which can update the state of different agents (e.g., vehicle locations in different links) simultaneously. 3) A modular and extensible framework as the simulator is built upon an agent-based modeling environment named Repast Simphony which is featured by its well-factored abstractions; in addition, a server-client structure is introduced to implement real-time operational algorithms such as energy-efficient routing and adaptive transit scheduling. 4) Open-source, reproducible with web-based visualization (METS-R SIM introduces these features to promote transparency). We validate METS-R SIM by matching the aggregated travel time and travel distance with the real observed ones obtained from New York City (NYC). We also compare the generated speed profiles qualitatively to the ones reported in published studies. We demonstrate the functionalities of our simulator by simulating SAEV services deployed to serve travel needs related to three main transportation hubs in NYC.

This paper presents a comparison literature study of two potential 3D robotic simulators i.e., Gazebo and Unity 3D to simulate a certain task. Apart of the literature review, the study also conducted a questionnaire survey collected from the robotics community and academia in the field of robotics. The objective of the literature study and the questionnaire is to gain an information related to the most appropriate 3D robotic simulator in creating virtual experience during a certain task which will be used for digital twin in the future study. The selected task is pick and place movement which is the basic part of the assembly process in the manufacturing industry. The questionnaire questions were focused on the user experience in using Gazebo and Unity 3D related to efficiency and other performance tests. To clarify the result obtained from the questionnaire, two identical 3D robotic simulation were built and created on the same assets to provide the objective performance comparison. The simulation is carried out by replicating the same standard 3D scene of UR3 in Gazebo and Unity 3D. According to the literature review, questionnaire, and the simulation result, it provides a new insight to enable a potential digital twin platform and to familiarise the user with the workflow on each platform.