A large scale combined private car and commercial vehicle-based traffic simulation

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 agentbased approach to generate 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 generated 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.the field is provided in Section 2, along with an introduction to MATSim.Initial demand generation, the first step in the simulation process, is discussed in Section 3. The results, presented in Section 4, are discussed mainly from the commercial vehicle point of view.Finally, we end the paper with a conclusion and a brief research agenda. FREIGHT TRANSPORT MODELINGThe majority of freight transport models are derived from the classical four-step model originally developed for passenger transport.A detailed review of freight transport models, all derived from the classical approach, can be found in De Jong et al. (10).We cast our work against the framework proposed by Tavasszy (11).The framework is illustrated in Figure 1 and extends the four-step modeling approach to account for decisons and issues relevant to freight modeling.Production and consumption volumes (in tons) are generated for each location, typically a zone, using land-use and transport interaction; trip generation; facility location; and various freighteconomy coupling approaches.Tapio (12) warns that traditional coupling of freight traffic volumes and Gross Domestic Product (GDP) should be considered with caution.Trade values are converted to volume to establish commodity flows, linking locations.Logistics involve choices regarding inventory location and supply chain management.Transportation is challenged with modal choice, intermodal and transshipment decisions.Discrete choice techniques and trip conversion factors are often employed.At this stage, tons are often converted to ton-kilometers, or even to the individual consignment level.Kveiborg and Fosgerau (13) suggest a distinction between freight traffic, when vehicle-kilometer is the unit of measure, and freight transport for ton-kilometer.The most disaggregate level, network and routing, often seeks decision support regarding congestion, tour planning, and city access.Here, chosen techniques include network assignment models and simulation.Although fairly sophisticated models can be used throughout, many of the approaches only handle flows at an aggregate level (zones) and the detail movement of the freight carriers are not considered (14).Urban tours, where vehicles make multiple stops, are usually completely ignored.Friedrich et al. (5) report on recent models where activity chain generation is addressed but non of the models, unfortunately, models the behavior at the individual carrier level.Wisetjindawat et al. (15) consider commodity flow following a top-down approach that explains commodity movement through the interaction among several freight agents in the supply chain.The spatial discrete choice model distinguishes between shippers, receivers and the relationships among them.Their model addresses the commodity movement (generation and distribution), shipment sizing, carrier choice, and the routing and traffic assignment of the carrier vehicles.Hunt and Stefan (16) present a tour-based microsimulation model of urban commercial movements using data from an extensive survey of 37 000 activity chains in Calgary, Canada.Agent-based modeling techniques, as opposed to four-step variants, allow one to incorporate and embed the decision making across the trade, logistics, transportation, and network levels of the framework.Instead of a top-down approach, the individual stakeholders (vehicles, firms, commodities, or industries) have its own autonomous decision-making mandate.All agents are simulated simultaneously, and their interactions with one another and with the environment allows for emergent phenomena that is otherwise lost if a top-down system description approach was followed.Liedtke (17) notes three advantages that agent-based simulation models present in the freight context: 1) through statistical modeling one is able to represent the heterogeneous nature of Percentage of maximum activities 0 -5 6 -20 21 -40 41 -60 61 -80 81 -100 (a) Actual observed activities.

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