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Transport Simulation for Sustainable Cities
Simulation is the art of simplifying a real world situation to its most relevant components, such that it can be believably reproduced by a computer. The simplified parameters can then be varied to test different scenarios of interest. For instance, in transportation, questions of policy, design or operations may all be explored in simulated experiments.

Urban transportation systems are particularly complex, and they are dynamic (changing with time). As one research team put it, "The key concept in Dynamic Systems Modelling is that of process; a process transforms a time stream of input flows of materials, energy or information into a time stream of output flows." At any moment in time, a simulated dynamic system may be defined by its particular state, or the set of variables that it has been reduced to. As the simulation advances in time, the state changes in a way prescribed by the current state and by the stochastic or deterministic definitions of its variables.

To be properly managed, they need to be examined at different scales. Traffic trends must be understood at the network level and the corridor level, in a context of multi-modal transport that is affected by land use patterns and human behavior. Also, the impacts of the urban transportation system, environmental, social and economic, are much higher than in non-urban regions simply because of the density of people and travel demand that is found in cities.This article will provide an overview of the approaches used to transport modeling in these various areas.

Traffic Simulation
Microscopic

Microscopic simulation or "microsimulation" for traffic relies on the modelling of individual vehicle movements. Microsimulation has proved useful at the corridor level to find possible causes of congestion. As with all simulation applications, microsimulation is based on a simplification of reality. It makes assumptions about driver behavior, for instance using car following models that describe drivers as in constant reaction to the movements of vehicles ahead of them. This is clearly a simplification, however, as real drivers have other concerns in mind, such as vehicles beside and behind, not to mention the goal of reaching a destination within the urban network.

Macroscopic

Just as patterns have been seen to merge in traffic at a corridor level, resulting in the "fundamental diagrams" of traffic engineering , an urban traffic system can be described at the aggregate level using a "Macroscopic Fundamental Diagram." The purpose of macroscopic modeling, that is modeling city traffic at an aggregate level, is to remove the chaotic considerations of individual vehicle movement (Daganzo 2007) and develop appropriate network control policies that are independent of driver behavior. The macroscopic traffic modeling paradigm has been evolved by several researchers over teh last few decades, including Herman and Prigogine (1979), Ardekani and Herman (1987) , and Mahmassani et al. (1987).

Multimodal Analysis
Cities have a lot going on in their traffic networks, including the interaction of multiple modes of transport operating simultaneously. For instance, cars, trucks, bicycles, buses, and pedestrians may all be moving in a single corridor. Traffic modelers are beginning to address issues of multimodal and intermodal systems, and of heterogeneous traffic mix in a variety of simulation environments.

Land Use Analysis
The transportation system does not operate without a context. The physical context is the surrounding land uses. Land use and transport are closely linked because the the types and locations of land uses within a city, such as residential, commercial, industrial, etc., will dictate trip origins and destinations on the transport network. An example urban transport problem related to land-use might be: "will the effects of a policy instrument in the transport market be couteracted or amplified by the relocation of households and workplaces in the land market?" Land-Use-Transport planning models have been under development since the 1960s, but simulation has only been used to study land-use recently. Micro-simulation can be used to project changes in land use in a spatial network, and these models can be linked to transportation generation models.

Demand and Behavioral Modeling
The population and employment densities in cities and metropolitan regions lead to high travel demands on the transport network. Transportation planners need to know how people are using the network, and how they will use it in the future in order to design and plan to meet demand. For this, simulation can be used for discrete choice modeling, network routing, and transportation demand forecasting.. Traditional microsimulation approaches usually model the travel demand and transport supply separately, and they formulate nested feedback loops in order to combine them for routing assignment. In order to streamline this process, work has been done to attempt to combine the two into higher level simulation logic, leading to "mesoscopic" simulation frameworks. As with integrated land-use and transportation demand, demand and the supply provided by infrastructure are closely related and interdependent.

Environmental Impacts
More than one quarter of human-made greenhouse gas emissions come from the transportation sector, through the burning of fossil fuels. Land use decisions and building processes contribute to the preponderance of the rest. Increasing concerns about GHG effects on global climate health make it an imperative to be able to account for and forecast the emissions from various sectors. This is not a problem unique to cities, but it is especially important in a city context because of the density of transport and land use. Simulation can be used to address these problems, and to make policy recommendations that link land use, transport, and emissions. Simulation can also be used to model the dispersion of pollutants that affect human health, many of which can be attributed to the transportation sector.