Predicting Travel Behavior by Analyzing Mobility Transactions

Urban planning can benefit tremendously from a better understanding of where, when, why, and how people travel. Through advances in technology, detailed data on the travel behavior of individuals has become available. This data can be leveraged to understand why one prefers one mode of transportation over another one. In this paper, we analyze a unique dataset through which we can address this question. We show that the travel behavior in our dataset is highly predictable, with an accuracy of 97%. The main predictors are reachability features, more so than specific travel times. Moreover, the travel type (commute or personal) has a considerable influence on travel mode choice

Transaction-Driven Mobility Analysis for Travel Mode Choices

Urban planning can benefit tremendously from a better understanding of where, when, why, how people travel. Through advances in technology, detailed data on the travel behavior of individuals has become available. This data can be leveraged to understand why one prefers one mode of transportation over another one. In this paper, we analyze a unique dataset through which we can address this question. We show that the travel behavior in our dataset is highly predictable, with an accuracy of 97%. The main predictors are reachability features, more so than specific travel times. Moreover, the travel type (commute or personal) has a considerable influence on travel mode choice

On the relation between COVID-19, mobility, and the stock market

The Covid-19 pandemic has brought forth a major landscape shock in the mobility sector. Due to its recentness, researchers have just started studying and understanding the implications of this crisis on mobility. We contribute by combining mobility data from various sources to bring a novel angle to understanding mobility patterns during Covid-19. The goal is to expose relations between mobility and Covid-19 variables and understand them by using our data. This is crucial information for governments to understand and address the underlying root causes of the impact

Understanding Human Mobility for Data-Driven Policy Making

This study aims to identify the patterns of behavior which underlie human mobility. More specifically, we compare commuters who drive in a car with those who use the train in the same geographic region of the Netherlands. We try to understand the mode choices of the commuters based on three factors: the cost of the transport mode, the CO2 emissions, and the travel time. The analysis has been based on data consisting of travel transactions in the Netherlands during 2018 containing over half a million records. We show how this raw data can be transformed into relevant insights on the three factors. A large difference is observed in terms of CO2 emissions and cost, a minor difference in speed. Besides, the computation of congestion shows intuitive results. These results can be used to stimulate behavioral change proactively and to improve trip planners

Benefits of Social Learning in Physical Robots

Robot-to-robot learning, a specific case of social learning in robotics, enables the ability to transfer robot controllers directly from one robot to another. Previous studies showed that the exchange of controller information can increase learning speed and performance. However, most of these studies have been performed in simulation, where robots are identical. Therefore, the results do not necessarily transfer to a real environment, where each robot is unique per definition due to the random differences in hardware. In this paper, we investigate the effect of exchanging controller information, on top of individual learning, in a group of Thymio II robots for two tasks: obstacle avoidance and foraging. The controllers of the robots are neural networks that evolve using a modified version of the state-of-the-art NEAT algorithm, called cNEAT, which allows the conversion of innovations numbers from other robots. This paper shows that robot-to-robot learning seems to at least parallelise the search, reducing wall clock time. Additionally, controllers are less complex, resulting in a smaller search space