Using passively collected data to investigate social travel

In the last decades, a growing body of evidences of the influence of the need for social contacts on mobility, and in particular leisure mobility, has been accumulated. The idea that explicitly considering those motives in transport models could improve forecasts is making progress. However, the actual implementation of those ideas is still difficult, in particular due to the lack of data on how do individuals plan joint activities. This paper will report on analysis performed in this context, using data from the SensibleDTU data collection effort (Stopczynski et al., 2014), where smartphones with GPS tracking were provided to 1000 bachelor students over one year. This dataset is unique in the sense that it tracks mobility of a densely connected social network of substantial size over a long period of time. Using a anonymized extract of this data, the paper will focus on the properties of travel for meeting friends, focusing on the properties of the travel patterns of the participants. In particular, the hypothesis that party composition have a significant impact on the properties of travel will be tested

Using a synthetic social network to improve leisure destination choice simulation

In developed countries, in the last years, a continuous increase of the share of trips that are performed for leisure purposes could be observed. Various data sources further indicate that the most important motivation behind out-of-home leisure activities is social contact. On the other side, leisure remains very hard to model in simulation models, mainly because it depends highly on characteristics that are difficult to observe, such as heterogeneity of taste and the characteristics of leisure locations. As such, the best such models can do is to calibrate the level of noise added on top of classical distance and cost based utility of travel, such that simulated travel distances fit the data adequately. This paper makes a first step in using the social nature of leisure travel to decrease the level of noise needed to simulate leisure travel. Using a realistic synthetic population with a realistic social network for Switzerland, it implements a simple model where agents have individual preferences over activity locations and social contacts, and shows how this can help reproduce the observed travel distances. In particular, the traveled distances for visiting social contacts, one of the most important leisure types in Switzerland, are pretty insensitive to the scale of the error terms and come out of the structure of the social network

Using a realistic social network to improve leisure destination choice simulation

In developed countries, in the last years, a continuous increase of the share of trips that are performed for leisure purposes could be observed. Various data sources further indicate that the most important motivation behind out-of-home leisure activities is social contact. On the other side, leisure remains very hard to model in simulation models, mainly because it depends highly on characteristics that are difficult to observe, such as heterogeneity of taste and the characteristics of leisure locations. As such, the best such models can do is to calibrate the level of noise added on top of classical distance and cost based utility of travel, such that simulated travel distances fit the data adequately. This paper makes a first step in using the social nature of leisure travel to decrease the level of noise needed to simulate leisure travel. Using a realistic synthetic population with a realistic social network for Switzerland, it implements a simple model where agents have individual preferences over activity locations and social contacts, and shows how this can help reproduce the observed travel distances. In particular, the traveled distances for visiting social contacts, one of the most important leisure types in Switzerland, are pretty insensitive to the scale of the error terms and come out of the structure of the social network

Comparing short- and long-term values of travel time savings derived from a joint modelling framework

The value of travel time is an important element of cost-benefit analysis for appraisal of trans- portation project, by encapsulating the willingness to pay of the population for improvements in the transport system. Those values are typically obtained from mobility choice data, in the form of revealed or stated preference surveys. Although short term decisions, such as route and mode choice, are typically used for this purpose, a growing number of authors is arguing that long term decisions might provide more meaningful values for the evaluation of transportation projects. This paper uses the German Value of Time Study, that contains both short and long term choice experiments, to investigate the impact of different time horizons on the valuation of time. In particular, the availability in the dataset of two different long term experiments (residential and workplace choice) allow to evaluate not only the impact of the time horizon, but of the type of long term decision. Using a joint model including all relevant choice situations, this paper investigates the difference in the valuation of time coming from different kind of choice experiments. The results show that the chosen time horizon does have a significant effect on the valuation of travel time and cost. Another finding is that the type of long term decision and the structure of the choice experiment itself also influence the valuation

Epidemic spreading in urban areas using agent-based transportation models

Human mobility is a key element in the understanding of epidemic spreading. Thus, correctly modeling and quantifying human mobility is critical for studying large-scale spatial transmission of infectious diseases and improving epidemic control. In this study, a large-scale agent-based transport simulation (MATSim) is linked with a generic epidemic spread model to simulate the spread of communicable diseases in an urban environment. The use of an agent-based model allows reproduction of the real-world behavior of individuals’ daily path in an urban setting and allows the capture of interactions among them, in the form of a spatial-temporal social network. This model is used to study seasonal influenza outbreaks in the metropolitan area of Zurich, Switzerland. The observations of the agent-based models are compared with results from classical SIR models. The model presented is a prototype that can be used to analyze multiple scenarios in the case of a disease spread at an urban scale, considering variations of different model parameters settings. The results of this simulation can help to improve comprehension of the disease spread dynamics and to take better steps towards the prevention and control of an epidemic

Comparing values of travel time obtained from residential, workplace and short-term decisions

The value of travel time (VTT) is an important element cost-benefit analysis of transportation projects, by encapsulating the willingness to pay of the population for improvements in the transport system. Those values are typically obtained from mobility behaviour data, in form of revealed or stated preference survey data. Although short-term decisions are typically used for this purpose, a growing number of authors is arguing that long-term decisions might provide more meaningful values for the evaluation of transportation projects, as those decisions have a longer-lasting effect on the experienced travel times. This paper uses data, which contains both, short- and long-term experiments, to investigate the impact of different time horizons on the valuation of time. In particular, two different long-term experiments (residential and workplace choice) in the dataset allow to evaluate not only the impact of the time horizon, but also the type of long-term decision. Using a joint model including all relevant choice situations, this paper investigates the difference in the valuation of time coming from different kind of choice experiments. The results show that the chosen time horizon does have a significant effect on the valuation of travel time and cost. Another finding is that the type of long-term decision and the structure of the choice experiment itself also influence the valuation. The resulting VTTs with a sharp decline by about a half for commute trips show an opposite effect to previous work. Thus this paper demonstrates the need for refinement of the definition of such a VTT