Development of New Generation Microsimulation Models for Transport Demand

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Over the last 20 years, a large part of my research has been in the development of computer models that simulate land use and transportation processes in urban areas. Land use processes refer to the way we organize our lives over space, that is, where we live, work, shop and pursue other activity types. Transportation processes refer to the way transport demand/travel is generated by the needs of individuals to participate in such activities. Empirical research has demonstrated that a bi-directional relationship exists between transport demand and land use. The simulation models I developed, known as Integrated Transport and Land Use Models (ITLUM), attempt to capture this relationship. Such models can be used as decision support tools by planners for they allow the investigation of potential consequences of policies. While the field of ITLUM has evolved over four decades, in the 1990s there was a noticeable shift from aggregate to disaggregate models. In the former, a city is conceptualized as an aggregation of zones that constitute the observables or units of analysis. Based on traits of these zones, a set of equations estimate trips that are generated between zones and the allocation of those trips on the transportation network. In contrast, disaggregate models view a city as a collection of individuals, households and firms (collectively "agents") that make decisions on their daily activities. These decisions often result in travel. While the shift towards bottom-up or agent-based models attempts to capture the behaviour of agents, there remain three aspects that hamper progress in the field. The first is that the behavior of agents is often studied in isolation from the environment within which decisions are made. The second is that this environment is dynamic and decision-making must adapt to it. Third is the lack of a widely acceptable and flexible open-source platform within which research teams from around the world and from a variety of disciplines can contribute. At the moment, efforts are fragmented and disconnected. I contend that if significant progress is to be made, what is required is nothing short of a paradigm shift. In the last three years, I started work with Complex Adaptive Systems (CAS), a field that witnessed an explosion over the last 20 years but not in ITLUM. CAS refers to natural and artificial systems composed of populations of adaptive agents whose interactions lead to emergent behaviour that may bear some resemblance to the real-world. Finding a set of rules that are relevant to these agents, will help to simulate real-world behaviour and help us predict, control, or explain complex systems. I believe that research into CAS in the ITLUM context has the potential to overcome the three obstacles mentioned above. Accordingly, the overall objective of this research is to develop a CAS framework for an ITLUM and to compare its performance against prior models. To this end, our sub-objectives are to: 1. Perform a thorough review of the literature on CAS along with the open software platforms that can be used to develop such systems. 2. Develop a synthetic population for the Hamilton Census Metropolitan Area at the household, individual and firm level for 2001 in a Geographical Information System platform to be used as a test bed. 3. Develop the components of a final ITLUM using CAS and the synthetic population, and perform extensive experiments with it. 4. Design and implement an ITLUM for Hamilton using CAS. 5. Thoroughly evaluate the performance of the developed system vis-a-vis more conventional models. The development of an open platform, as part of this research, and dedicated to the use of CAS in the ITLUM context, offers the possibility to be nothing short of a revolution in the field.