Hierarchical IPF: Generating a synthetic population for Switzerland

Agent-based microsimulation models for land use or transportation simulate the behavior of agents over time, although at different time scales and with different goals. For both kinds of models, the initial step is the definition of agents and their relationships. Synthesizing the population of agents often is the only solution, due to privacy and cost constraints. In this paper, we assume that the model simulates persons grouped into households, and a person/household population needs to be synthesized. However, the methodology presented here can be applied to other kinds of agent relationships as well, e.g. persons and jobs/workplaces or persons and activity chains. Generating a synthetic population requires (a) reweighting of an initial population, taken from census or other survey data, with respect to current constraints, and (b) choosing the households that belong to the generated population. The reweighting task can be performed using an Iterative Proportional Fitting (IPF) procedure; however, IPF cannot control for attributes at both person and household levels. A frequently applied pattern is to estimate household-level weights using IPF, so that they match the control totals for the households, and then, using these weights, to generate a population of households that best fits the person-level control totals. We propose an algorithm that estimates household-level weights that fit the control totals at both person and household levels. This eliminates the need to account for person-level control during the generation of synthetic households. The algorithm essentially performs a proportional fitting in the domains of both households and persons, and introduces an entropy-minimizing fitting step to switch between these two domains. We evaluate the performance of our algorithm by generating a synthetic population for Switzerland and checking it against the complete Swiss census.

Agent-Based Demand-Modeling Framework for Large-Scale Microsimulations

Microsimulation is becoming increasingly important in traffic demand modeling. The major advantage over traditional four-step models is the ability to simulate each traveler individually. Decision-making processes can be included for each individual. Traffic demand is the result of the different decisions made by individuals; these decisions lead to plans that the individuals then try to optimize. Therefore, such microsimulation models need appropriate initial demand patterns for all given individuals. The challenge is to create individual demand patterns out of general input data. In practice, there is a large variety of input data, which can differ in quality, spatial resolution, purpose, and other characteristics. The challenge for a flexible demand-modeling framework is to combine the various data types to produce individual demand patterns. In addition, the modeling framework has to define precise interfaces to provide portability to other models, programs, and frameworks, and it should be suitable for large-scale applications that use many millions of individuals. Because the model has to be adaptable to the given input data, the framework needs to be easily extensible with new algorithms and models. The presented demand-modeling framework for large-scale scenarios fulfils all these requirements. By modeling the demand for two different scenarios (Zurich, Switzerland, and the German states of Berlin and Brandenburg), the framework shows its flexibility in aspects of diverse input data, interfaces to third-party products, spatial resolution, and last but not least, the modeling process itself

Efficient detection of contagious outbreaks in massive metropolitan encounter networks

Physical contact remains difficult to trace in large metropolitan networks, though it is a key vehicle for the transmission of contagious outbreaks. Co-presence encounters during daily transit use provide us with a city-scale time-resolved physical contact network, consisting of 1 billion contacts among 3 million transit users. Here, we study the advantage that knowledge of such co-presence structures may provide for early detection of contagious outbreaks. We first examine the "friend sensor" scheme --- a simple, but universal strategy requiring only local information --- and demonstrate that it provides significant early detection of simulated outbreaks. Taking advantage of the full network structure, we then identify advanced "global sensor sets", obtaining substantial early warning times savings over the friends sensor scheme. Individuals with highest number of encounters are the most efficient sensors, with performance comparable to individuals with the highest travel frequency, exploratory behavior and structural centrality. An efficiency balance emerges when testing the dependency on sensor size and evaluating sensor reliability; we find that substantial and reliable lead-time could be attained by monitoring only 0.01% of the population with the highest degree.Comment: 4 figure

A simultaneous two-dimensionally constraint disaggregate trip generation, distribution and mode choice model - Theory and application for a Swiss national model

The Swiss federal government has asked the IVT, ETH Zürich in collaboration with the TU Dresden and Emch+Berger, Zürich to estimate origin-destination matrices by mode and purpose for the year 2000. The zoning system employing about 3’000 zones of very uneven size required a solution algorithm which is fast, but also able to model generation, distribution and mode choice simultaneously, while addressing the different data availability for traffic within, destined for and passing through the country. The EVA algorithm developed by Lohse (1997) was adapted for this purpose. The key proper-ties of the algorithm are its disaggregate description of demand, its use of appropriate logit-type models for the demand distribution, while maintaining the known marginal distributions of the matrices generated. This last point is of particular importance in a large scale planning applica-tion such as the one at hand. The algorithm calculates trip production and attractions by zone using activity pairs. The 17 ac-tivity pairs distinguished are the combinations of two activities, such as home-work or work-leisure. The relevant daily rates are derived for each of the 17 activity pairs from the 2000 Swiss National Travel Survey (Bundesamt für Statistik and Bundesamt für Raumentwicklung, 2001). The zonal attractivity is defined separately for each trip purpose. In addition to the common variables, such as employment or population, detailed descriptions of education places, shop-ping or leisure facilities, overnight accommodations, shopping centres etc. are employed (see Tschopp, Keller and Axhausen, 2003 for the data). The combined destination and mode choice models estimated for the different traveller types and activity pairs are based on the Swiss National Travel survey (RP data), but incorporates re-sults from a prior SP study on mode and route choice (Vrtic and Axhausen, 2004). The different zone sizes and the different levels of data available required the formulation of new additional models for the transit traffic passing through Switzerland and the traffic originat-ing outside, respectively leaving the country The matching network models for public transport and road traffic were implemented using VISUM 9.0 of PTV AG, Karlsruhe. The timetable based assignment considers all scheduled train services plus the relevant interurban bus services, in particular in rural areas. The paper has three main parts: the first main part derives and describes for the first time the EVA algorithm in English, including the solution method used. The second part summarizes the results of choice model estimation using the generalised cost elasticities of demand by purpose and traveller type. The third part assesses the quality of the results. These assessments are based on two independently derived matrices, which are available for rail-travel from on board - counts and for commuters from the 2000 national census. In addition, we compare the assign-ment results with the available cross section counts. The conclusions discuss computing times, accuracy and issues for further research.

Income and distance elasticities of values of travel time savings: New Swiss results

This paper presents the findings of a study looking into the valuation of travel time savings (VTTS) in Switzerland, across modes as well as across purpose groups. The study makes several departures from the usual practice in VTTS studies, with the main one being a direct representation of the income and distance elasticity of the VTTS measures. Here, important gains in model performance and significantly different results are obtained through this approach. Additionally, the analysis shows that the estimation of robust coefficients for congested car travel time is hampered by the low share of congested time in the overall travel time, and the use of an additional rate-of-congestion coefficient, in addition to a generic car travel time coefficient, is preferable. Finally, the analysis demonstrates that the population mean of the indicators calculated is quite different from the sample means and presents methods to calculate those, along with the associated variances. These variances are of great interest as they allow the generation of confidence intervals, which can be extremely useful in cost-benefit analyses

Interactions between Travel Behaviour, Accessibility and Personal Characteristics

This paper explores the impacts of personal characteristics and the spatial structure on travel behaviour, especially mode choice. The spatial structure is described among other things by accessibility measures. The models are estimated using structural equation modelling (SEM). The models are based on the 1992 Upper Austrian travel survey and the Upper Austrian transport model.   The results highlight the key roles of car ownership, gender and work status in explaining the observed level and intensity of travel. The most important spatial variable is the number of facilities which can be reached by a household. The municipality based variables and the accessibility measures have rather little explanatory power. The reasons for this low explanatory power are considered. Although the findings in this study indicate that the spatial structure is not a decisive determinant of traffic, the results provide useful hints for possible policy alternatives

The Zurich case study of UrbanSim

Abstract-- UrbanSim is an open-source software being developed by Waddell and colleagues(Waddell and Ulfarsson, 2004), simulating land use-development in cities based on the choices of households, businesses, land owners and developers, interacting in urban Real Estate markets and with the option to be connected to a transportation simulation. SustainCity is an EU-funded project with twelve European research-institutions1, coordinated by the IVT of the Swiss Federal Institute of Technology Zurich (ETHZ). Within the project of SustainCity2, UrbanSim is being adapted to European conditions by creation of a European version (UrbanSimE) with new calibration of choice-models and additional models for households, demographics and firmographics. Focus will be on the data-structure in Europe as well as the different behaviour of companies, residents and developers. For this UrbanSim will be used in three case studies: Brussels, Paris and Zurich. Although previous studies have been implemented in all of those region, the previous study in Zurich can be considered as a new set up as it uses another version of UrbanSim. This paper will report on the implementation of this parcel-based version of UrbanSim within the Zurich case study of SustainCity. It will refer to the data acquired and necessary as basis for the simulation, discuss the approach of data preparation through PostGIS and report on the new structure of the data-models defined within UrbanSim. Finally the first results of the UrbanSim runs of the Zurich case study will be presented and compared to the runs of previous versions. Keywords: UrbanSim; Urban Simulation; SustainCity; Zurich case study 02.03.2011

Surveying energy efficiency in housing and transport using a Priority Evaluator

This paper presents a survey combining a stated choice experiment and a priority evaluator. The survey addresses ways that people would invest in energy efficiency and differences between energy efficiency in housing and private transport. The survey sample consists of 500 homeowners (owner occupiers) owning at least one car and is divided in two parts: a paper and pen questionnaire with Stated Preference experiments followed by an Internet-based Priority Evaluator. Both choice experiments are personalized to present the candidates with meaningful choice sets. In the stated preference experiments, respondents are asked to choose between four alternatives as a reaction to hypothetically increasing fuel prices: insulating the house, buying a heat pump, buying a new, more efficient car and selling the car and switching to public transport. In the second part of the survey, the Priority Evaluator, respondents interactively optimize their CO2 output in an Internet application, selecting among long-term investments as well as short-term measures. Data collected in the survey will be processed using statistical models, such as multinomial logit models, to derive parameters for different efficiency measures used to predict long-term investment behavior of homeowners