Presentatios will be uploaded in the next days!
Presentations and abstracts
Dilemma of transport policy, the e-bike city and can we model all of the changes? - Kay Axhausen (ETH Zurich) Download
Transport policy is in a dead end, as most acceptable policies are likely to be counterproductive given their induced demand effects: capacity expansion; e-cars; automated vehicles; the effective policies are politically unacceptable: mobility pricing; CO2 pricing. The talk will discuss this dilemma and will argue that we need new visions to approach the dilemma in the search for a new compromise. The e-bike city is such a vision: a city, where 50% of road space is dedicated to the slow modes, but with a base load public transport system to move large number of persons. The second part of the talk will highlight the modelling challenges which such a radical redesign would bring. It will ask if we have the tools and highlight the missing pieces.
Beyond Forecasting: Transport Models for Science and Policy - Greg Erhardt (University of Kentucky) Download
For many decades, planners have used transport models to forecast the long-term effects of proposed projects and evaluate alternatives. However, many important decisions are made outside the bounds of this traditional “rational planning model”. Using a series of examples—including growing congestion, decreasing transit ridership and induced demand--this talk explores the role of transport models in understanding recent trends and informing policy. It goes on to consider the implications for model design.
Activities while traveling or trade-off between in-person and online activities - Aruna Sivakumar (Imperial College London)
Activity-travel behaviour of individuals has evolved rapidly due to developments in information and communication technologies (ICTs) in recent years. In addition, we have seen a large-scale shift towards in-home and digital activities (e.g., teleworking, e-shopping, home entertainment) in the wake of the Covid-19 pandemic. In this talk, I will present two ongoing research threads.
The first piece of research examines travel time use, looking at specific mobile work tasks and their interaction with trip planning, expectations, ICT, and travel conditions. The second research thread is focused on developing a time allocation model that allocates the agent’s time to activity episodes, where each episode contains attributes of activity type, location (in home, out of home), modality (physical, virtual/digital), time-of-day and duration. These research threads will jointly contribute toward the development of an agent-based activity-travel model which is compatible with the increasingly digitalised lifestyles.
The interplay between in-person and virtual activity participations for shopping - Chandra Bhat (University of Texas at Austin)
In this presentation, we examine non-domestically cooked meal (NDCM) preferences for dinner meals by studying the monthly count of NDCMs by channel type: eat-out, eat-in takeout, and eat-in delivery. Data from a 2022 online survey collected in Texas is employed to estimate a multivariate joint model. The results highlight the impact of workplace location on dining choice. The results also indicate complementary and substitution effects; the delivery channel complements eating-out but substitutes takeout. Similarly, eat-out substitutes takeout. The developed models may be embedded within larger agent-based activity-travel systems by modeling additional location and mode dimensions in downstream models for each forecasted NDCM activity occasion
Consistency across time, space, and vehicle allocation in practical activity-based travel models - David Ory (WSP) Download
All practical activity-based travel models used in the United States make simplifications when constructing individual travel itineraries that result in inconsistencies in time, space, and/or vehicle allocation. In this presentation, I first describe typical use cases; motivate the need for consistency with an emphasis on the role automated vehicles are likely to play in lifting mobility constraints; and suggest directions for future research to make practitioners and academics more aware of the problem in the hope of motivating robust solutions.
The lost art of forecasting with activity-based models - Rick Donnelly: (Semi-retired consultant, formerly WSP) Download
Transport planners seek to inform decision-makers about likely impacts of policies and investments that will irrevocably shape our cities and regions. In the past, we've assumed that tomorrow will simply be a more crowded, prosperous, and congested version of our existing world. Such an approach increasingly lacks credibility with politicians and investors. Our commendable advances in activity-based modelling have mostly focused on the tools rather than outcomes. A vision of the future will be presented where scenario thinking is used with data-driven models, machine learning, and big data analytics to complement the advances in activity-based models.
Enhancing ADAPTS agent-based framework - Kouros Mohammadian (University of Chicago) Download
The Agent-based Dynamic Activity Planning and Travel Simulation (ADAPTS) is an activity-based model that is formulated as a dynamic model of how the activity planning and scheduling process is implemented for an individual over time. The ADAPTS model components have been reorganized to more closely fit the agent-based paradigm and have been implemented using the POLARIS framework. This presentation will discuss ADAPTS new modules that were developed and implemented since its initial development, as well as several components that are being extended or upgraded to deliver more realistic simulation outcomes. These include impacts of telework, e-shopping, e-learning during the COVID pandemic and beyond on travel demand, as well as new models for conflict resolution and mode choice involving TNC, micro-mobility, and parking effects.
ABIT - Activity-Based Incremental Travel Demand Model - Carlos Llorca & Joanna Ji (Technical University of Munich) Download
The Activity-Based Incremental Travel Demand Model (ABIT) is an activity-based model that generates travel demand individually for every person in the study area and updates it incrementally over time. The demand generated are tour plans consisting of chains of activities and trips over a weekly period. ABIT will be integrated with the land use model SILO, and instead of ordinarily regenerating the travel plans from year to year, the plans will be partially kept as long as the person, the household or the traffic situation remains stable. We posit the incremental approach will keep travel plans mostly similar and shorten runtimes. Our aim is to develop an activity-based travel demand model as a fully integrated component of our already existing agent-based and open-sourced modeling suite. This presentation will go over the current development and vision for ABIT.
An optimization framework for activity-based models - Janody Pougala (EPFL) Download
We propose an integrated framework for the simulation of daily activity schedules based on mixedinteger optimization: individuals derive a utility from performing activities, and they schedule them as to maximize the total utility. The parameters of the model are estimated by defining a discrete choice model where the alternatives for each individual are full daily schedules. The maximum likelihood estimators of the parameters (e.g. scheduling penalties, desired start times and durations, constants…) are evaluated on a choice set of daily schedules sampled using the Metropolis-Hastings algorithm. The estimation and simulation methodologies are applied on a sample of individuals from the 2015 Swiss Mobility and Transport Microcensus
8 Days a Week: Moving Beyond Single-Day Activity-Travel Models - Eric Miller (University of Toronto) Download
This presentation discusses the motivation for developing practical week-long agent-based microsimulation models of urban activity and travel. It first presents the case for developing such models from both behavioural representation and policy analysis perspectives. It then briefly reviews the (limited) literature on multi-day/week-long modelling. Next, it sketches how the current TASHA (Travel/Activity Scheduler for Household Agents) conceptual framework can be extended to a week-long application. Practical challenges in modelling at the weekly level are then discussed, leading to brief concluding comments on next research steps.
Integration between activity-based demand modelling and person centric traffic assignment - Kai Nagel (Technical University Berlin)
When doing activity-based modelling, it is useful at some point to assign the resulting travel to the network. Static assignment is not a good match here, since much of the expressivity of the person-centric modelling then gets lost. A better match is obtained when using a person-centric assignment, where all persons (and vehicles etc.) are individually resolved, and they follow daily plan and/or can make within-day decisions. Our own MATSim (Multi-Agent Transport Simulation) software is a possible platform here, which does not only do route assignment, but also (departure) time choice, mode choice, and (secondary) activity location choice. An issue then becomes which of these decisions are part of the assignment, and which are rather done upstream. I will report on practical experiences of integrating MATSim with CEMDAP, with FEATHERS, with ActiTopp, and with SILO/MITO.
Examining the treatment effect of teleworking on vehicle-miles driven: A close look into the role of travel stress - Patricia Mokhtarian (Georgia Institute of Technology) Download
We apply endogenous switching regression models to data from the Dallas-Ft. Worth and Washington, DC regions (N = 1,584), to identify factors that influence teleworking adoption and weekly vehicle-miles driven (VMD) while accounting for self-selection biases. We find that, on average, teleworking reduces VMD; however, it does so much more for workers who are travel-stressed than for those who are not. For non-travel-stressed teleworkers, teleworking may have a limited impact on reducing their pre-existing VMD, and/or may generate more new VMD due to teleworking. The paper also includes visualizations of factual and counterfactual effects.
Use and Time Poverty in a Post-Pandemic Era: An Activity-Based Approach Focused on Human Well-being - Ram Pendyala (Arizona State University)
The COVID-19 pandemic has brought about considerable changes in activity-travel and time use patterns, with important implications for human well-being. Using data from the American Time Use Survey (ATUS) series of 2019 and 2020, changes in activity-travel and time use patterns are assessed. The analysis employs two methods – a well-being scoring method and a time poverty based method – to evaluate the impacts of activity and time use changes on people’s lives. The results show that individuals experienced diminished well-being during the pandemic even though their time poverty statistics showed an improvement. The presentation will describe an activity-based model of well-being, together with a time poverty analysis, to explain the how and why of post-pandemic activity-travel patterns.
Applying activity-based models to explore the effects of urban systems on health - James Woodcock & Corin Staves (University of Cambridge) Download
Transport is a key determinant of urban population health. As people travel around cities they breathe in air pollution, hear noise, and risk traffic injuries. They also generate physical activity — a health benefit — especially if their travels involve walking and cycling. The effects of transport on health are complex and vary over space, time, and demographic. We present an activity-based open-source microsimulation modelling framework for assessing transport health exposures impacts at high resolution.
Activity-based models are especially advantageous as they allow health exposures to be evaluated not only during transport but rather throughout the entire day (e.g. air pollution at the workplace, physical activity at the park). We present our progress toward expanding our trip-based model into an agent-based one and discuss future directions for modelling health with activity-based models, from momentary assessment to the exposome.
A Large-Scale Real-Life Experiment: Effects of the 9-Euro Flat Rate Ticket for Public Transport - Klaus Bogenberger (Technical University of Munich)
In spring 2022, the German federal government agreed on a set of measures that aimed at reducing households' financial burden resulting from a recent price increase. These measures included among others, a nation-wide public transport ticket for 9,- EUR per month and a fuel tax cut that reduced fuel prices by more than 15%. We observe this natural experiment with a three-wave survey and an app-based travel diary on a sample of 1.000 participants as well as an analysis of traffic counts. In this presentation I will provide first findings from the surveys and the travel diary data.
Opportunities and challenges in representing the use of non-motorized modes in activity-based transport models - Kelly Clifton (University of British Columbia) Download
With the improvement and availability of spatially-explicit walking behavior data and information about the built and natural environment, research on walking behaviors and pedestrian demand has increased. Travel demand models have been relatively slow to incorporate this knowledge and improve the representation of pedestrians. This presentation will show a pedestrian-centric framework – called the Model of Pedestrian Demand (MoPeD)- and its incorporation pathway into transport models. First, we will present the success of integrating MoPeD with a trip-based travel demand model (MITO). Then we will discuss some opportunities and challenges in representing the use of non-motorized modes in activity-based transport models.