Congestion charging systems have emerged as a promising policy tool for mitigating traffic congestion and reducing emissions in urban areas. This study applies a dynamic activity scheduling model to assess the effects of congestion pricing in the Greater Helsinki region. By simulating daily activity patterns and travel behaviour, we analyse the impacts of congestion charges on mode choice, destination selection, and departure time adjustments. Our findings reveal a 10% reduction in car use and a 27% decrease in total car kilometres travelled, demonstrating the effectiveness of congestion pricing in alleviating traffic congestion. However, the analysis also highlights the potential equity concerns, with impacts varying across locations and commuting patterns. These insights contribute to the growing body of evidence on the behavioural and distributional consequences of congestion pricing, offering valuable guidance for policymakers.

Responding to climate change and sustainability goals, many cities and countries have renewed efforts to upgrade active transportation infrastructure, such as cycling. Supportive built environment conditions, such as dedicated cycling lanes, safe intersections, and low-traffic streetsare a necessary prerequisite to promote cycling and so is the understanding of behavioral variations in travel, with travel time being particularly crucial. Although travel time and value of travel time has been widely studied in transportation research cycling travel time is less explored, particularly how they vary across cities. Moreover, external factors such as wind speed and bike type (e.g., e-bike vs. conventional bike) can significantly influence cycling travel time. This study addresses these gaps by examining the effects of trip characteristics and socio-demographic factors on cycling travel time across three European cities: Tallinn, Braga, and Istanbulwhich differ in cultural and urban contexts but have each recently initiated policies to foster cycling. Using a Latent Class Accelerated Failure Time (LCAFT) model, the analysis reveals that, as expected, distance consistently increases travel time and e-bikes have low to significant effects. However, the influence of trip purpose and bike type varies depending on socio-demographic and cultural contexts. For instance, in Tallinn and Braga, age and gender significantly shape travel time, whereas in Istanbul, age and educational attainment are more influential. These findings highlight the importance of incorporating contextual and demographic differences when assessing cycling behavior across diverse urban settings.

In travel behaviour modelling, latent class models are used to represent underlying discrete groupings of behavioural preferences. The paper presents a latent class extension of a dynamic discrete choice model (DDCM) and applies the model to the problem of activity demand generation and scheduling. The DDCM is a recursive multinomial logit model where agents make sequential decisions in time, maximizing the expected future utility of their decisions in a random utility maximization framework. It generates activities and their associated travel within a full day schedule, endogenously respecting agents' inherent time-space constraints. The latent class DDCM builds on the base model by representing heterogeneous lifestyle preferences. A specification of the model is estimated on a Stockholm travel survey and uses age, income level, gender, car ownership and presence of children in the household as classifying variables. The models result in classes which primarily represent modality styles, finding car-, transit- and bike-primary behavioural groups as well as a multimodal group, each linked with different socio-demographic characteristics. The models improve over non-latent class reference models and provide insight into the structure of heterogeneity in travel behaviour preferences in Stockholm.

We present the use of duration-dependent activity utility within the dynamic scheduling model Scaper, which simulates individuals' full-day activity and travel schedules. In Scaper, agents make sequential choices in time which maximize expected future utility and respect time-space constraints. Using Swedish travel survey data, we estimate a new version of the model including piecewise linear utility functions for marginal activity duration by activity purpose. Our model reveals a strong duration dependence for work, leisure, and visit activities with differing functional shapes for each purpose. In simulation, the duration-dependent model better reproduces observed distributions of activity duration and performs as well across other metrics as the model without duration dependence. We illustrate the potential policy applications of the model using a scenario of shortened work days. The duration-dependent model offers useful predictions for the effects of the scenario on commute timing, nonwork activities, time spent at home, and trip chaining.

This study addresses the challenge of estimating traffic states for road links. We propose an innovative approach that leverages partial trajectory data captured by camera-equipped probe vehicles traveling in the opposite lane. The methodology combines state-of-the-art computer vision algorithms for extracting vehicle trajectories from street-view video sequences with a novel estimation technique based on the Cell Transmission Model (CTM) and Genetic Algorithms (GA). Our approach first calibrates Fundamental Diagram (FD) parameters using observed cell densities, then estimates boundary conditions for all space-time diagrams. We validate the method using simulated traffic data from three different types of links and parameter settings. Results show that the proposed methodology can estimate traffic densities in unobserved regions, even with limited data availability. This research contributes to the field by introducing a cost-effective, high-resolution traffic data collection method and a robust estimation technique for comprehensive traffic state information. While the study shows promising results, it also identifies areas for improvement, including refining models, optimizing processes, and testing with real-world data to enhance accuracy and scalability.

This paper presents a population synthesis model based on the Wasserstein Generative-Adversarial Network with Gradient Penalty (WGAN-GP) for training on incomplete microsamples. The proposed method aims to address the challenge of missing information in microsamples on one or more attributes due to privacy concerns or data collection constraints. By using a mask matrix to represent missing values, the study proposes a WGAN-GP training algorithm that lets the model learn from a training dataset that has some missing information. The paper compares the ability of WGAN-GP models trained on incomplete microsamples to those trained on complete microsamples to create a synthetic population. We conducted a series of evaluations of the proposed method using a Swedish national travel survey. We validate the efficacy of the proposed method by generating synthetic populations from all the models and comparing them to the actual population dataset. The results from the experiments showed that the proposed methodology successfully generates synthetic data that closely resembles a model trained with complete data as well as the actual population. The paper makes a contribution to the field by giving a strong solution for population synthesis using incomplete microsamples. It also opens up new research areas and shows how deep generative models can be used to improve population synthesis.

This paper applies recursive logit (RL) to model activity-trip chaining behaviour. We present a comparison between two approaches to applying the RL model in this context. In the first ‘sequential’ approach, agents form a trip chain by making a sequence of joint choices of activity location (i.e. trip destination) and travel mode, ending the chain by choosing to return home. The second ‘dynamic’ approach adds a time variable. Its agents form a full-day activity/travel schedule by making a sequence of choices either to continue the current activity for a fixed timestep or make a joint choice of new activity location and travel mode. We estimate parameters for both models using data from a Stockholm travel survey and validate model simulations against observed data. The models reproduce patterns of observed behaviour beyond their estimated parameters, including different types of trip chains and the spatial distribution of activities. While the dynamic model is advantageous in its ability to predict agent schedules, reflect time-varying travel conditions and endogenously represent space–time constraints, it does not surpass the simpler sequential model on mutual areas of trip chaining behaviour. We conclude that the RL model is well-suited to model trip chaining behaviour, and that the simpler sequential approach may be appropriate for many modelling purposes.

This chapter discusses the use of neo-classical welfare economics in appraisal, i.e. cost-benefit analysis, using random utility choice modelling. The central role of the logsum formula is explained, generalisations are given for cases where utility is random, and approximations are discussed. Alternative approaches are also introduced.

The City of Stockholm is conducting a scenario planning exercise to explore where potential future office development should be planned: closer to the city centre as in the status quo, in peripheral hubs on the outskirts of the city, or dispersed throughout multiple neighbourhoods. To support this exercise, this paper models these three scenarios using a nested work location and dynamic activity-based scheduling model. Our model predicts that high-income individuals have the highest consumer welfare benefits and are over-represented as workers in all scenarios. Developing more central office space will likely reinforce existing geographical patterns of income inequality in Stockholm; developing peripheral or dispersed office space, especially in the south of the city, will challenge these patterns. However, the model also illustrates a tension between the goals of equity and the environment. By taking advantage of existing transit infrastructure and congestion patterns, more central office development will result in lower vehicle kilometers travelled and lower car mode share for commuting than more peripheral or dispersed development.

This paper aims to investigate whether increasing non-linear opportunities in a neural network-based scheduling model improves its predictive performance. More specifically, this paper experiments on a trip distribution model that is part of an activity-based scheduling model called Skyline-seqNN from the ongoing thesis A neural network scheduling model. The motivation behind that model s proposed structure is to lay the groundwork for a neural network discrete choice model (DCM) that achieves to model travel demand on a detailed level while also being suitable for experimental analysis. Similar to a four-step model framework in the sequential aspect, the model system from the referenced paper utilizes the three sub-models; trip generation, trip distribution, and mode choice using a utility-maximizing micro-simulation approach. The trip generation model first decides whether, at every 10-minute interval between 05:00 am and 11:00 pm, an individual in the next time step should stay and continue the current activity or take an activity-defined trip. The distribution and mode choice models are used whenever a trip is selected. The trip distribution model decides the trip s destination by evaluating travel times and land use descriptions of each zone. The mode choice model learns the probability distribution of modes given each mode s travel time to the selected destination zone. Tests performed in this paper show how successive non-linear opportunities between input features in the trip distribution model increase its predictive performance. The data used for training and evaluation comes from a travel questionnaire from 2015 per-formed in Stockholm containing 10819 individuals and days.