Large-scale agent-based combined traffic simulation of private cars and commercial vehicles (original) (raw)
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A large scale combined private car and commercial vehicle-based traffic simulation
2009
The number of independent and interdependent freight actors (firms), the complex supply chain structures among them, and the sensitivity of shipment data are but a few reasons why modeling freight traffic is lagging its public and private transit counterparts. In this paper we used an agent-based approach to reconstruct commercial activity chains, and simulated them—along with private vehicles— for a large-scale scenario in Gauteng, South Africa. The simulated activities are compared to the actual observed activities of 5196 vehicles that were inferred from GPS logs covering approximately six months. The results show that the activity chains reconstructed are both spatially and temporally accurate, especially in areas of high activity density. With freight vehicles being a major contributor to traffic congestion and emissions, our contribution is significant in bridging the gap between the person and commercial transport modeling state-of-the-art.
Computers, Environment and Urban Systems, 2014
Urban road traffic dynamics are the product of the behaviours and interactions of thousands, often millions of individuals. Traditionally, models of these phenomena have incorporated simplistic representations of individual behaviour, ensuring the maximisation of simulation scale under given computational constraints. Yet, by simplifying representations of behaviour, the overall predictive capability of the model inevitably reduces. In this work a hybrid agent-based modelling framework is introduced that aims to balance the demands of behavioural realism and computational capacity, integrating a descriptive representation of driver behaviour with a simplified, collective model of traffic flow. The hybridisation of these approaches within an agent-based modelling framework yields a representation of urban traffic flow that is driven by individual behaviour, yet, in reducing the computational intensity of simulated physical interaction, enables the scalable expansion to large numbers of agents. A real-world proofof-concept case study is presented, demonstrating the application of this approach, and showing the gains in computational efficiency made in utilising this approach against traditional agent-based approaches. The paper concludes in addressing how this model might be extended, and exploring the role hybrid agent-based modelling approaches may hold in the simulation of other complex urban phenomena.
A multi-scale agent-based modelling framework for urban freight distribution
Transportation Research Procedia, 2017
Comprehensive modelling of urban freight operations remains a challenge in transportation research. This is partly due to the diversity of commodities transported, shipment units, vehicle types used, stakeholders' objectives (e.g. suppliers, carriers, receivers), and to the limited availability of data. Thus, existing modelling efforts require several assumptions yet have limited behavioral foundations and minimal interaction between agents. This paper proposes a new agent-based modelling framework, which considers the heterogeneity of urban freight agents and their interactions. Agents include establishments (suppliers, carriers, and receivers) and freight vehicle drivers. Agents' decisions are structured in three temporal resolutions: strategic, tactical, and operational. A single set of agents is represented throughout all modelling levels ensuring a consistent and sequential flow of information. At the strategic level, establishments' characteristics and strategic decisions are modelled. These include location choices, fleet constitution, annual production/consumption of commodities, and establishment-to-establishment commodity flows. At the tactical level, shipments are assigned to carriers, who in turn plan their operations in terms of vehicledriver-route assignments. Finally, at the operational level, the interactions between daily operational demands and transportation network supply are simulated. The supply representation has two different resolution levels (micro or meso) allowing for either detailed or computational efficient simulation. The simulation platform is distinct from previous works, as it explicitly considers planning horizons, replicates agent decision makings/interactions and involves a structure that allows for the propagation of influences bottom-up (e.g., prior simulation travel times on future route choice). The paper describes the simulation platform, constituent models, and illustrates its capabilities using an application of the modelling framework to the city of Singapore.
AN AGENT-BASED VEHICLE ROUTING SIMULATION TOOL FOR ROAD NETWORKS WITH TIME-VARIANT DATA
Simulation modeling is one of the analytic techniques commonly used for transportation management; it includes such activities as route planning and post-operation analysis. One of the simulation methods, agent-based simulation, has become increasingly popular due to the availability of good micro-level data collected through technologies such as GPS-enabled devices and road sensors. This paper presents the design and implementation of an agent-based simulation tool that can be used to analyse vehicle routing algorithms. We demonstrate how the tool can be used in practice by implementing two vehicle routing algorithms: shortest-path and LANTIME. LANTIME is an algorithm that can be used to minimize CO 2 emissions.
An Agent Based Model for the Simulation of Transport Demand and Land Use
International Symposium for Next Generation Infrastructure Conference Proceedings, 2015
Agent based modelling has emerged as a promising tool to provide planners with insights on social behaviour and the interdependencies characterising urban system, particularly with respect to transport and infrastructure planning. This paper presents an agent based model for the simulation of land use and transport demand of an urban area of Sydney, Australia. Each individual in the model has a travel diary which comprises a sequence of trips the person makes in a representative day as well as trip attributes such as travel mode, trip purpose, and departure time. Individuals are associated with each other by their household relationship, which helps define the interdependencies of their travel diary and constrains their mode choice. This allows the model to not only realistically reproduce how the current population uses existing transport infrastructure but more importantly provide comprehensive insight into future transport demands. The router of the traffic micro-simulator TRANSIMS is incorporated in the model to inform the actual travel time of each trip and changes of traffic density on the road network. Simulation results show very good agreement with survey data in terms of the distribution of trips done by transport modes and by trip purposes, as well as the traffic density along the main road in the study area.
Multi-Agent Based Simulation of Individual Traffic in Berlin
2005
Multi-agent simulations of traffic are widely expected to become an important tool for transportation planning in the mid-term future. This paper reports on the first steps of a project which aims to apply such a tool to a large real world scenario based on datasets create in the normal transportation planning process for use with established transportation planning tools. As the first steps of the implementation show, many problems related to different data semantics and the different modelling concepts can occur. In most cases, theses problems can be resolved by minor adoptions of the software or the data. The evaluation of a large scenario of several hundred thousand agents shows that performance issues do no longer hinder applications of this kind. Thereby, the implementation of this scenario helps to push multi-agent traffic simulation tools forward to real world applications.
Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems, 2018
The performance of urban transportation systems can be improved if travelers make better-informed decisions using advanced modeling techniques. However, modeling city-level transportation systems is challenging not only because of the network scale but also because they encompass multiple transportation modes. This paper introduces a novel simulation framework that efficiently supports large-scale agent-based multi-modal transportation system modeling. The proposed framework utilizes both microscopic and mesoscopic modeling techniques to take advantage of the strengths of each modeling approach. In order to increase the model scalability, decrease the complexity and achieve a reasonable simulation speed, the proposed framework utilizes parallel simulation through two partitioning techniques: spatial partitioning by separating the network geographically and vertical partitioning by separating the network by transportation mode for modes that interact minimally. The proposed framework creates multi-modal plans for each trip and tracks the travelers trips on a second-by-second basis across the different modes. We instantiate this framework in a system model of Los Angeles (LA) supporting our study of the impact on transportation decisions over a 5 hour period of the morning commute (7am-12pm). The results show that by modifying travel choices of only 10% of the trips a significant reduction in traffic congestion is achievable that results in better traffic flow and lower travel times.