AN OVERVIEW OF THE TRANSIMS MICROSIMULATION MODEL : APPLICATION POSSIBILITIES FOR SOUTH AFRICA

The Transportation Analysis and Simulation System (TRANSIMS) model was developed as a replacement of the traditional four-step travel demand model. The goal was to provide a simulation model that could analyze many of the issues facing transportation planners such as sustainable development, environmental impacts of proposed projects, and Intelligent Transportation Systems (ITS) deployment. While a simulation approach is powerful and potentially useful, a challenge for transportation modelers is that they will need to fully understand the new modeling paradigm so that the new model is not simply treated as a “black box”. The focus of this paper is on providing on overview of TRANSIMS based on lessons learned from research on real, calibrated transportation networks in Texas. An overview of the TRANSIMS model in terms of its main components along with a brief comparison to the four-step model will be provided. This is followed by an analysis of the highway link supply relationship in the TRANSIMS micro-simulation model with an emphasis on the implications for transportation planning practice. One of the benefits of a micro-simulation model is that the fundamental traffic flow properties are emergent from the model. This appears to eliminate the need for the modeler to assume a prior link flow-density-speed relationship. However, it is shown that the results from the model can be very sensitive to the calibration parameters chosen. Lastly, the paper examines implementation implications from a South African perspective and what transportation planners should be doing in order to prepare for the transition to micro-simulation planning models. INTRODUCTION It has long been recognized that the traditional four-step travel demand model is not robust enough to analyze adequately many of the issues facing transportation planners. Sustainable development, environmental impacts of proposed projects, and Intelligent Transportation Systems (ITS) deployment are examples of some current topics of interest. One potential solution has been to adopt stochastic, microscopic based models that can model individual demand responses to changes in supply. The most visible of these models is the Transportation Analysis and Simulation System (TRANSIMS) which was developed as part of the Travel Model Improvement Program (TMIP) (Weiner and Ducca, 1999). The focus of this paper is on providing some an overview of the TRANSIMS model using insight gained from research on calibrated transportation networks in Texas. This paper is broken down into three sections. The first section provides an overview of TRANSIMS in terms of its main components along with a brief comparison to the four-step model. Next the TRANSIMS traffic flow theory is described and the challenges for transportation modelers are highlighted using examples from Texas. Lastly, some implementation implications from a South African perspective are provided. Overview of the Transportation Analysis and Simulation System (TRANSIMS) Figure 1 provides a conceptualization of the TRANSIMS architecture. It may be seen that the system consists of five modules. The first module, the population synthesizer, is used to create a synthetic population of the households in the study area. It combines aggregate information from the census demographic tables (summarized by census tract or block group) and disaggregate data from the Public Use Microdata Samples (PUMS) census records in order to create a synthetic population base in which each individual is also assigned to a distinct household (Beckman et al. 1996). The aggregate statistics of these households, at the census tract and block group level, mimic the aggregate statistics of the true population contained within the census data. The synthetic household attributes used in the analysis are identified a priori and may include anything contained in the census data including gender, age, education, employment, income, vehicle information, etc. The second module, the household activity generator, identifies the set of “potential” daily activities of each synthetic individual in each of the synthetic households. The number of trips each individual is scheduled to complete can be ascertained by counting the number of location changes in their daily list of activities. In essence, the list of activities defines the daily trip chain(s) desired by each traveler in the population and would be analogous to the information contained in a traditional travel diary. The input to this module is information from household activity surveys, workplace surveys, and land use information. For illustration purposes a distinction is made in Figure 1 between the list of activities and the activity attributes. The attributes of the activities would normally includes such things as activity priority, start time, duration, mode preference, location, et cetera. The third module, the route planner, identifies the route for each trip output from the second module. Note that the route attributes in the TRANSIMS context includes not only what links a traveler would use but also information such as mode, changes in mode, parking locations, traveling companions, et cetera. The input to the process includes traveler information and activity information from Module 2 as well as network information. Network information would include link location, link travel times for each mode, mode accessibility, et cetera. Note that all of the information associated with each route (i.e. departure time, links used by mode, expected travel time, etc.) for each trip is explicitly enumerated and output. The fourth module, the micro-simulation uses the route plans from Module 2 as input and simulates the transportation network at a microscopic level of detail – albeit at a lower fidelity than most traffic operations models (LANL 1998). In effect, Module 4 simulates the interaction between demand (the synthetic populations desire to travel between activity locations) and supply (the ability of the transportation system to meet this demand). As would be expected, the network data for each mode is also required. The output of the micro-simulation module can include information on each traveler, information on each mode down to the sub link level, and summary information on the network as a whole. Once a TRANSIMS simulation is complete the output from the micro-simulation is used to analyze the network. Because the module is micro-simulation based and every traveler and vehicle is modeled explicitly the user has considerable flexibility in which metric(s) to use. It should be noted that while the vehicles are modeled at a microscopic level of detail their emissions are not estimated within the micro-simulation module. They are instead estimated from the aggregate output data as shown in Module 5 in Figure 1. This approach was adopted because the mircosimulation module is based on Cellular Automata (CA) rules and while TRANSIMS has been calibrated to macroscopic flow observations there is no guarantee that the microscopic speed profiles are accurate or even reasonable (Nagel et al. 1997, Williams et al. 1999, Zietsman and Rilett 2001 a). Because of the inherent complexity associated with demand estimation, TRANSIMS models the traveler’s decision making process, which is inherently simultaneous in nature, in a sequential manner with appropriate feedback loops. For example, in order to identify accurately a travelers’ activities or plans (i.e. in Module 2) the level of service (LOS) attributes of the different modes by time of day needs to be known – which will clearly not be known until the micro-simulation module (i.e. Module 4) is complete. Therefore, during the first iteration the values are estimated. If the estimated LOS values and the resulting output LOS values do not match then adjustments are made and the process is repeated as shown in Figure 1. That is, the simulated activities as defined by A, the activity attributes (location, time, etc.) as defined by B, and the routes (departure time, links used, etc.) as defined by C may be changed as a result of new level of service attributes estimated in the micro-simulation module. The optimal configuration of the iterations or feedback loops and the conditions, if any, under which this process converges are ongoing research topics (Smith et al., 2000). Comparison of TRANSIMS with the Four-Step Approach Because of the long history of the four-step model in transportation planning, there is a natural tendency to attempt to discuss TRANSIMS using traditional terminology. In one sense this is reasonable because the underlying conceptualization of the transportation demand-supply process for both approaches are essentially the same. In addition, while the underlying process is simultaneous in nature both approaches are iterative as evidenced by the feedback steps associated with them. However, a comparison is problematic because TRANSIMS represents a fundamental shift, rather than an incremental change, in the implementation of the underlying conceptualization. Obviously the key difference between the approaches is that TRANSIMS is microsimulation based and is therefore capable of modeling the stochastic and dynamic attributes of the transportation system. Each traveler’s activities, for example, are considered across the entire day as a single entity or chain. Thus all of the major lifestyle and travel decisions such as what activities to participate in, when to participate in the activities, where to participate, what mode to use, what route to choose, etc. can be made in a consistent manner at the individual traveler level, an ability which is not remotely possible with the macroscopic four-step model. In addition, because TRANSIMS is stochastic the LOS results, for example, can have confidence and/or tolerance intervals associated with them. In contrast, the four-step model tends to have varying levels of detail at each step. For example, trad