An Empirical Activity Sequence Approach for Travel Behavior Analysis in Vilnius City

Abstract

The approach defines the process of conducting an empirical research of the travel behavior patterns of residents of Vilnius city. It defines survey methodology and important mobility parameters such as activity sequences and their probabilities of homogeneous urban population segments during the weekday. This empirical research is based on a travel diary survey that was planned and executed in cooperation with Vilnius Municipality during preparation of sustainable mobility plan. The following work describes the research object, the questionnaire design, sampling strategy and the analysis of results based on characteristics of respondents. An innovative activity sequence-focused travel behavior research approach designed to collect data for a tour-based travel demand model.

The impacts of the COVID-19 pandemic on surface passenger transport and related CO2 emissions during different waves

“…Studies show that risk perception of traveling is the most influential factor to choose a transport mode during COVID-19. Case studies in different cities such as Gdansk, Poland (Dumbliauskas and Grigonis, 2020), Vilnius city, Lithuania (Przybylowski et al 2021), and various cities in the US (Liu et al 2020) on travel behavior and mode choice also yields similar results. As a result, public transport use has declined rapidly, leaving authorities to operate at a very low capacity to adhere to social distancing measures (UITP, 2020a).…”

Activity-Based Demand Modeling for a Future Urban District

“…In this study, the synthetic population is adapted specifically to the mobility case study: The model generates for each person from the household an activity chain, including the activity type, the transport mode between an activity pair and the activity location. Second, the activities in our model are richer than state-of-the-art proposals (e.g., [7]). Finally, our model is detailed and reproducible.…”

“…Except when specific disaggregated data on mobility pattern are available such as transport mode at the spatial zoning level (TAZ = Traffic Analysis Zone) [6], or tailored census in the area of interest by experts consultant which are not reproducible. For instance, the study of [7] design a travel diary survey that was executed with the Vilnius Municipality, to devise a sustainable mobility plan. They had access to a fine-grained information on important mobility parameters of activity sequences over a sample of urban population during the weekday.…”

“…The quality of the generated population depends essentially on the quality and details' level of the (sample) data [9]. Synthetic populations can be used as input in several models such as activity-based models (demand models) [7,10] and disease transmission models [11].…”