Personalizing Walkability: A Concept for Pedestrian Needs Profiling Based on Movement Trajectories

Jonietz, David

doi:10.1007/978-3-319-33783-8_16

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…Jonietz focused on the user's physical ability and preferences, similar to our study, and extracted environmental attribute data (aesthetic buildings, number of stairs, road surface conditions, green space, etc.) in the area visited by the user from walking history data, and proposed a route recommendation method using this (Jonietz, 2016). They do not use the "subjective evaluation by users," which is taken into account in our study.…”

Section: Related Workmentioning

confidence: 99%

"User-Adaptive Navigation for Elderly Pedestrians Based on Preference Information"

2023

Proceedings of the International Conferences on E-Society 2023 and Mobile Learning 2023

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The aim of our project is to develop a route planning method that can provide each user with an appropriate route, taking into consideration the walkability, safety, and pleasantness peculiar to the elderly. To achieve this goal, we constructed a quantitative model that expresses the relationship between the attributes of elderly users and their preference for route selection and proposed a method of individual adaptation of the model using post-walking preference evaluations of route factors. We used "acceptable detour time" to quantify user preferences. A paper survey and a walking experiment were conducted to examine the feasibility of the proposed method. It is confirmed that the targeted user attributes are useful in estimating the preferences of the elderly for the routes. In some situations, we were able to identify improvements in the participants' evaluation of the routes. The result indicates the feasibility of the user adaptation method. By adjusting the update parameters and using more data, it is expected to plan routes that are adapted to individuals with high accuracy.

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Section: Related Workmentioning

confidence: 99%

"User-Adaptive Navigation for Elderly Pedestrians Based on Preference Information"

2023

Proceedings of the International Conferences on E-Society 2023 and Mobile Learning 2023

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“…Although our movement is always set in and influenced by its spatio-temporal context, which can involve the underlying physical space, the location of important places, the time, static or dynamic objects, and temporal events [29], the vast majority of work on trajectory data mining has so far focused on analyzing the geometry of the trajectories, while ignoring their contextual setting [30]. Notable examples include [31], who enrich pedestrian trajectories with information about the underlying urban infrastructure [32], who propose a framework for movement and context data integration [33], who annotate trajectories with points of interest (POIs) to identify significant places and movement patterns of people, or [29], who propose an event-based conceptual model for context-aware movement analysis based on spatio-temporal relations between movement events and the context. A further type of context which is particularly important when analyzing human movement is activity.…”

Section: Trajectory Data Mining and Contextmentioning

confidence: 99%

“…Thus, Ref. [35], for instance, proposes a hierarchical approach to model activities at different granularities based on movement data, while [31] uses a hierarchical model of the action of walking to infer personalized infrastructural needs for pedestrians based on their trajectories.…”

Section: Trajectory Data Mining and Contextmentioning

confidence: 99%

Assessing the Influence of Spatio-Temporal Context for Next Place Prediction using Different Machine Learning Approaches

Urner

Bucher

Yang

et al. 2018

IJGI

Self Cite

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For next place prediction, machine learning methods which incorporate contextual data are frequently used. However, previous studies often do not allow deriving generalizable methodological recommendations, since they use different datasets, methods for discretizing space, scales of prediction, prediction algorithms, and context data, and therefore lack comparability. Additionally, the cold start problem for new users is an issue. In this study, we predict next places based on one trajectory dataset but with systematically varying prediction algorithms, methods for space discretization, scales of prediction (based on a novel hierarchical approach), and incorporated context data. This allows to evaluate the relative influence of these factors on the overall prediction accuracy. Moreover, in order to tackle the cold start problem prevalent in recommender and prediction systems, we test the effect of training the predictor on all users instead of each individual one. We find that the prediction accuracy shows a varying dependency on the method of space discretization and the incorporated contextual factors at different spatial scales. Moreover, our user-independent approach reaches a prediction accuracy of around 75%, and is therefore an alternative to existing user-specific models. This research provides valuable insights into the individual and combinatory effects of model parameters and algorithms on the next place prediction accuracy. The results presented in this paper can be used to determine the influence of various contextual factors and to help researchers building more accurate prediction models. It is also a starting point for future work creating a comprehensive framework to guide the building of prediction models.

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A Heuristic for Multi-modal Route Planning

Bucher

Jonietz

Raubal

2016

Lecture Notes in Geoinformation and Cartography

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Personalizing Walkability: A Concept for Pedestrian Needs Profiling Based on Movement Trajectories

Cited by 4 publications

References 22 publications

"User-Adaptive Navigation for Elderly Pedestrians Based on Preference Information"

"User-Adaptive Navigation for Elderly Pedestrians Based on Preference Information"

Assessing the Influence of Spatio-Temporal Context for Next Place Prediction using Different Machine Learning Approaches

A Heuristic for Multi-modal Route Planning

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