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

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

Size Matters: The Use and Misuse of Statistical Significance in Discrete Choice Models in the Transportation Academic Literature

In this paper we review the academic transportation literature published between 2014 and 2018 to evaluate where the field stands regarding the use and misuse of statistical significance in empirical analysis, with a focus on discrete choice models. Our results show that 39% of studies explained model results exclusively based on the sign of the coefficient, 67% of studies did not distinguish statistical significance from economic, policy or scientific significance in their conclusions, and none of the reviewed studies considered the statistical power of the tests. Based on these results we put forth a set of recommendations aimed at shifting the focus away from statistical significance towards proper and comprehensive assessment of effect magnitudes and other policy relevant quantities.Comment: 14 pages, 1 table, 0 figure

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

Subsidized ridesourcing for the first/last mile: how valuable for whom?

The first/last mile is a long known deterrent to public transportation use, yet difficult to solve with fixed route transit. Many transit agencies are exploring partnerships with ridesourcing companies to offer subsidized feeder services. Ridership, however, has been surprisingly low. We explore two conceptual explanations. First, ridesourcing fares are found to exceed travel time savings for all distances below 1 mile and annual household incomes below USD 30,000 (i.e., the majority of US bus-using households). Subsidies are thus necessary, yet common schemes (flat fees, flat value or percentage discounts) are inequitable as they particularly benefit high-income households (thus miss their main target group). Second, the disutility of the additional transfer (‘transfer penalty’) and wait times exceed travel time savings assuming modest values for all distances below 0.45 miles. Subsidized ridesourcing for the first/last mile is thus not the panacea often portrayed, particularly not for short first/last miles. Where first/last miles are longer, investments in first/last mile services only might miss their purpose as the private car often remains the faster, more convenient and cheaper option. A much more holistic set of policy changes is hence required. Where transit agencies decide to proceed with first/last mile subsidies, they are advised to integrate them into existing fares (offering first/last mile rides for free) as this is the most equitable approach

An interactive stated adaptation survey of activity scheduling decisions

The paper reports on current research in a project exploring new approaches for analyzing travel demand induced by changes in generalized costs of travel and activity participation. A sample of respondents were administered a five-day travel diary, from which one day was selected for further analysis. The conditions of that day were changed using predefined heuristics based on the household characteristics, to attain significant changes in the generalized costs of the reported trips. The households were then faced with these hypothetical scenarios in face-to-face interviews. All household members are asked to state how the implied changes would have affected their activity scheduling on the specified day, that is to adapt their reported schedule to the new conditions. The data will allow the computation of discrete choice models of activity scheduling. The results are expected to reflect the effects of the changes in generalized costs on activity generation. The results will be applied in MATSim, an agent-based micro-simulation. The application will allow the validation of the model results and the evaluation of aggregated effects of measures changing generalized costs, as well as their repercussions on the transport system and the resulting feedback effects, thus allowing the assessment of total induced demand and a comparison to the results from earlier aggregate models. The paper focuses on the description of the survey approach, which to our best knowledge is novel in its application, and reports preliminary analyses of the respondents’ reactions to the changes implied in the household interviews