van Eggermond, Michael
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van Eggermond, Michael
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- PublikationBegleitstudie zum Tier-Pilotprojekt in Riehen(Institut Bauingenieurwesen, Hochschule für Architektur, Bau und Geomatik FHNW, 16.01.2023) Erath, Alexander; van Eggermond, Michael05 - Forschungs- oder Arbeitsbericht
- PublikationForecasting district-wide pedestrian volumes in multi-level networks in high-density mixed-use areas(Western Norway University of Applied Sciences (HVL), 06/2022) Mavros, Panos; van Eggermond, Michael; Erath, Alexander; Helle, Veera; Acebillo, Pablo; Xu, Shuchen; van Nees, Akkelies; de Koning, Remco Elric; Jacobsen Åsli, Thale [in: 13th International Space Syntax Symposium]This paper is concerned with improvements in the forecasting of pedestrian flows in multilevel pedestrian networks in high-density urban environments. 3D network topology measures are combined with land-use data, and validated against extensive pedestrian counts, to provide both evidence for the applicability of network analysis in tropical metropolises, as well as a calibrated tool for urban planners. The research focuses on four area in Singapore. These areas have in common that they all are prominent transport hubs, but differ in surrounding land-use types and dominant network topology (e.g. indoor, outdoor, above ground, below ground, at grade). Multi-level pedestrian networks were drawn based on OpenStreetMap, include sidewalks on both sides of major roads for a radius up to 2 kilometres from the site centroids. Spatial network analysis was performed using sDNA which allows vertical networks to generate measures describing the spatial configuration of the network. Subsequently, pedestrian counts were conducted during three consecutive days. In total, counts were conducted at more than 250 locations in 2018 and 2019, well before the global COVID19 pandemic. Pedestrian flows are set against a series of variables, including pedestrian attractors and generators (e.g. shops, offices, hotels, dwellings), and variables describing the spatial configuration of the network, using advanced regression models. Our results show that betweenness metrics (i.e. space syntax choice) combined with land-use yield high predictive power. Dependent on the study site, network metrics based on angular distance outperform those based on metric distance or perceived link distance. This research demonstrates that is necessary to account for the multi-level nature of networks, and that indoor flows through private developments cannot be neglected, in particular when planning for integrated transport developments. The paper concludes with recommendations and implications for practice.04B - Beitrag Konferenzschrift
- PublikationHuman navigation in a multilevel travelling salesperson problem(PsyArXiv, 22.01.2022) Mavros, Panagiotis; van Eggermond, Michael; Hölscher, ChristophFinding the optimal tour that visits a series of locations sequentially, such as going for errands, is an everyday task formally known as the travelling salesperson problem (TSP). In this article we focus on the understudied type of multilevel or M-TSP, which take place in a multilevel environment, like a building. In a TSP, the number of alternative tours the decision-maker needs to consider is given by the factorial of the locations to visit; hence a 3-target TSP has 6 alternatives and a 12- target TSP has 479 million. Considerable research has focused on combinatorial optimisation algorithms for TSPs, and in the cognitive sciences there has equally been a sustained interest on how various foraging species and humans achieve remarkably optimal performance. However, research has primarily studied planar environments, and it is unclear how people will combine horizontal and vertical spatial information to make navigational decisions in a multilevel TSP. In this study, we asked 41 participants to first learn the locations of 12 shops (targets) in a multilevel building, and then complete a structure mapping task and two open 8-target M-TSP tasks (more than 40.000 alternatives). Using bayesian methods for mixed effects modelling, we show that human performance in navigational M-TSPs is lower than this of Euclidean TSPs, and we differentiate between the choice of tour (visit sequence) and transitions (local wayfinding). Our results show an effect of horizontal versus vertical learning. We also found that performance in navigational TSP are a composite of global and local decision making, and the people adaptively employ a path-based, rather than euclidean, measure of distance when this is ecologically relevant. Overall we provide multiple sources of evidence for the horizontal bias theory both in mental representations and wayfinding behaviour. This study contributes to current knowledge of mental representations 3D space and is the first huto provide human data on an multilevel TSP. More generally, these findings have implications for our understanding of wayfinding and navigational behaviour in multilevel environments.05 - Forschungs- oder Arbeitsbericht
- PublikationUsing backcasting to support corporate mobility management(2021) van Eggermond, Michael; Erath, AlexanderThe paper at hand describes a research project conducted in collaboration with a major employer based in Basel, Switzerland. The company employs innovative mobility policies, such as a strict parking regime, with lots only available to employees who have to travel more than 45 minutes by public transport, offers bike sharing and public transport bonuses, but would like to further reduce parking lots and desires to reduce greenhouse emissions resulting from commuting while remaining an attractive employer. The aim of the project was the to better understand the impact of exogenous developments (e.g. new train lines, road pricing, infrastructure improvements, safer cycling routes) and endogenous mobility policies (e.g. bike sharing, parking fees, charging stations). These developments and policies were identified in a series of workshops with stakeholders. At the same, key performance indicators were formulated. Instead of forecasting the impact of these policy measures, the project set out to describe a desirable future (e.g., less emissions, attractive employer), reason backwards from the desired situation and formulate a package of policy measures that could in this future, whilst taking into account exogenous developments. This process is also known as backcasting and has been applied in several studies (e.g. Banister et al., 2000; Barandier 2015) To quantify the impact of the policy measures several data sets were available and newly collected. Travel times and distances for motorized private transport, walking and cycling were calculated using the Google travel time API for all employees. As Google’s API only offers limited coverage for public transport in Germany and France, use was made of publicly available public transport schedules and the open-source routing engine R5. A survey was conducted among employees, resulting in over 6000 responses. Based on the survey data, choice models were estimated and applied. Exogenous and endogenous developments for over 10 policy measures were quantified using simplified assumptions, whilst taking into account the spatial differences, and used to forecast the impact of each individual measure and combinations of measures. Measures include the impact of e-bike provision, the impact of improved cycling infrastructure, new train stations and the differentiated parking fees. The project resulted in a set of mobility policies and recommendations to monitor these mobility policies, and the methodology has been applied at other stakeholders to support sustainable mobility policies.06 - Präsentation
