Serge Hoogendoorn’s research while affiliated with Delft University of Technology and other places

To promote urban sustainability, many cities are adopting bicycle‐friendly policies, leveraging GPS trajectories as a vital data source. However, the inherent errors in GPS data necessitate a critical preprocessing step known as map‐matching. Due to GPS device malfunction, road network ambiguity for cyclists, and inaccuracies in publicly accessible streetmaps, existing map‐matching methods face challenges in accurately selecting the best‐mapped route. In urban settings, these challenges are exacerbated by high buildings, which tend to attenuate GPS accuracy, and by the increased complexity of the road network. To resolve this issue, this work introduces a map‐matching method tailored for cycling travel data in urban areas. The approach introduces two main innovations: a reliable classification of road availability for cyclists, with a particular focus on the main road network, and an extended multi‐objective map‐matching scoring system. This system integrates penalty, geometric, topology, and temporal scores to optimize the selection of mapped road segments, collectively forming a complete route. Rotterdam, the second‐largest city in the Netherlands, is selected as the case study city, and real‐world data is used for method implementation and evaluation. Hundred trajectories were manually labelled to assess the model performance and its sensitivity to parameter settings, GPS sampling interval, and travel time. The method is able to unveil variations in cyclist travel behavior, providing municipalities with insights to optimize cycling infrastructure and improve traffic management, such as by identifying high‐traffic areas for targeted infrastructure upgrades and optimizing traffic light settings based on cyclist waiting times.

For car traffic it was found that a more crowded area leads to a lower speed and a lower arrival rate. The relation between crowdedness and speed (or arrival rate) can be expressed in a network fundamental diagram, or macroscopic fundamental diagram (MFD). Similar concepts have been shown for pedestrian and train traffic. In this paper, we extend the concept to three spatial dimensions. While simulations have explored some concepts, we present for the first time empirical results of the relation between the crowdedness in the air and the performance of the “network.” We base our results on several months of data of airplanes around Amsterdam Schiphol Airport. Similar to car traffic, we observe a reduction in speeds as the number of airplanes in the area increases. However, even at the highest observed densities, we do not see a reduction in flows. This is because of active and intensive management (based on departure/landing possibilities), comparable to perimeter control in traffic, as well as a minimum airplane speed. This paper introduces an interesting concept of applying a MFD to three-dimensional (3D) spaces. We also show to what extent the performance reduction is caused by speed reduction and to what extent it is caused by less efficient routes. The MFD concept can eventually be used to also manage 3D airspaces for applications with less strict microscopic air traffic management than the current management around airports.

Data-driven approaches are helpful for quantitative justification and performance evaluation. The Netherlands has made notable strides in establishing a national protocol for bicycle traffic counting and collecting GPS cycling data through initiatives such as the Talking Bikes program. This article addresses the need for a generic framework to harness cycling data and extract relevant insights. Specifically, it focuses on the application of estimating average bicycle delays at signalized intersections, as this is an essential variable in assessing the performance of the transportation system. This study evaluates machine learning (ML)-based approaches using GPS cycling data. The dataset provides comprehensive yet incomplete information regarding one million bicycle rides annually across The Netherlands. These ML models, including random forest, k-nearest neighbor, support vector regression, extreme gradient boosting, and neural networks, are developed to estimate bicycle delays. The study demonstrates the feasibility of estimating bicycle delays using sparse GPS cycling data combined with publicly accessible information, such as weather information and intersection complexity, leveraging the burden of understanding local traffic conditions. It emphasizes the potential of data-driven approaches to inform traffic management, bicycle policy, and infrastructure development.

In mobility panels, respondents may use a strategy of soft-refusal to lower their response burden, e.g. by claiming they did not leave their house even though they actually did. Soft-refusal leads to poor data quality and may complicate research, e.g. focused on people with actual low mobility. In this study we develop three methods to detect the presence of soft-refusal in mobility panels, based on respectively (observed and predicted) out-of-home activity, straightlining and speeding. For each indicator, we explore the relation with reported immobility and panel attrition. The results show that speeding and straightlining in a questionnaire is strongly related to reported immobility in a (self-reported) travel diary. Using a binary logit model, respondents who are predicted to leave their home but report no trips are identified as possible soft refusers. To reveal different patterns of soft-refusal and assess how these patterns influence the probability to drop out of the panel, a latent transition model is estimated. The results show four behavioral patterns with respect to soft-refusal ranging from a large class of reliable respondents who score positive on all three soft-refusal indicators, to a small ‘high-risk’ class of respondents who score poorly on all indicators. This ‘high-risk’ group also reports the highest immobility and has the highest attrition rate. The model also shows that respondents who do not drop out of the panel, tend to stay in the same behavioral pattern over time. The amount of soft-refusal expressed by a respondent therefore seems to be a stable behavioral trait.