A context-sensitive conceptual framework for activity modeling

Das, Rahul Deb; Winter, Stephan

doi:10.5311/josis.2016.12.260

Cited by 8 publications

(11 citation statements)

References 67 publications

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“…Several studies diagnose the number of long-lasting PPGIS solutions to be surprisingly low [48,116,117] Scopus: 513 articles identified, cited 4890 times in total, 209 papers mention "place", most cited paper [118] Affordances Jordan et al [54] conceptually propose a methodology including the affordances 'agent', 'environment' and 'task requirements' for modeling places, stating that the interplay of these factors results in different place definitions for each individual. Most of the approaches that try to implement affordances seem to use agent-based modeling software paradigm [119,120] Few prototypical examples, e.g., as [56] approximate places to a special kind of media, which are bounded by a surface, and can be traversed, located, and move themselves, given that the affordance constituting them appears or disappears at some location Scopus: 39 articles identified, cited 584 times in total, 24 mention "place", most cited paper [121]…”

Section: Ppgis/pgismentioning

confidence: 99%

Revisiting the Role of Place in Geographic Information Science

Merschdorf

Blaschke

2018

IJGI

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Although place-based investigations into human phenomena have been widely conducted in the social sciences over the last decades, this notion has only recently transgressed into Geographic Information Science (GIScience). Such a place-based GIS comprises research from computational place modeling on one end of the spectrum, to purely theoretical discussions on the other end. Central to all research that is concerned with place-based GIS is the notion of placing the individual at the center of the investigation, in order to assess human-environment relationships. This requires the formalization of place, which poses a number of challenges. The first challenge is unambiguously defining place, to subsequently be able to translate it into binary code, which computers and geographic information systems can handle. This formalization poses the next challenge, due to the inherent vagueness and subjectivity of human data. The last challenge is ensuring the transferability of results, requiring large samples of subjective data. In this paper, we re-examine the meaning of place in GIScience from a 2018 perspective, determine what is special about place, and how place is handled both in GIScience and in neighboring disciplines. We, therefore, adopt the view that space is a purely geographic notion, reflecting the dimensions of height, depth, and width in which all things occur and move, while place reflects the subjective human perception of segments of space based on context and experience. Our main research questions are whether place is or should be a significant (sub)topic in GIScience, whether it can be adequately addressed and handled with established GIScience methods, and, if not, which other disciplines must be considered to sufficiently account for place-based analyses. Our aim is to conflate findings from a vast and dynamic field in an attempt to position place-based GIS within the broader framework of GIScience.

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Section: Ppgis/pgismentioning

confidence: 99%

Revisiting the Role of Place in Geographic Information Science

Merschdorf

Blaschke

2018

IJGI

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“…Unlike a crisp set theory where an element is either present or absent in a given set, fuzzy set theory assigns a membership value to an element and, thus, introduces the concept of a partial membership of that element in a number of different set(s). If A is a fuzzy set defined on a universe of discourse U, then the membership of an element y in A can be defined by a membership function (MF) µ A (y) within an interval of [0,1]. This can be mathematically expressed as follows.…”

Section: Fuzzy Expert Systemmentioning

confidence: 99%

“…This paper focuses on detecting travel modes in urban mobility in near-real time, where the context relates to urban mobility based activities [1,2]. Traditionally, travel behaviour information is collected from memory by paper-based or telephone surveys.…”

Section: Introductionmentioning

confidence: 99%

Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory

Das

Winter

2016

IJGI

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Transport mode information is essential for understanding people's movement behavior and travel demand estimation. Current approaches extract travel information once the travel is complete. Such approaches are limited in terms of generating just-in-time information for a number of mobility based applications, e.g., real time mode specific patronage estimation. In order to detect the transport modalities from GPS trajectories, various machine learning approaches have already been explored. However, the majority of them produce only a single conclusion from a given set of evidences, ignoring the uncertainty of any mode classification. Also, the existing machine learning approaches fall short in explaining their reasoning scheme. In contrast, a fuzzy expert system can explain its reasoning scheme in a human readable format along with a provision of inferring different outcome possibilities, but lacks the adaptivity and learning ability of machine learning. In this paper, a novel hybrid knowledge driven framework is developed by integrating a fuzzy logic and a neural network to complement each other's limitations. Thus the aim of this paper is to automate the tuning process in order to generate an intelligent hybrid model that can perform effectively in near-real time mode detection using GPS trajectory. Tests demonstrate that a hybrid knowledge driven model works better than a purely knowledge driven model and at per the machine learning models in the context of transport mode detection.

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“…Travel can be viewed as an activity —a temporally extended process—or an action —a not further expanded event—depending on the context of the travel analysis [1]. Furthermore, this notion of travel is open across a range of spatial scales.…”

Section: Preliminariesmentioning

confidence: 99%

“…This paper focuses on the automatic interpretation of a travel as a sequence of activities, i.e., segments travelled in a single mode of travelling, from sensor traces collected on smartphones. In particular, the role of granularity will be highlighted (e.g., between getting on board of a bus , taking the bus , or going to work ), along with the ambiguity about the mode that comes along with it [1]. The elementary trips of a travel are connected by transfers, at certain granularities.…”

Section: Introductionmentioning

confidence: 99%

Automated Urban Travel Interpretation: A Bottom-up Approach for Trajectory Segmentation

Das

Winter

2016

Sensors

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Understanding travel behavior is critical for an effective urban planning as well as for enabling various context-aware service provisions to support mobility as a service (MaaS). Both applications rely on the sensor traces generated by travellers’ smartphones. These traces can be used to interpret travel modes, both for generating automated travel diaries as well as for real-time travel mode detection. Current approaches segment a trajectory by certain criteria, e.g., drop in speed. However, these criteria are heuristic, and, thus, existing approaches are subjective and involve significant vagueness and uncertainty in activity transitions in space and time. Also, segmentation approaches are not suited for real time interpretation of open-ended segments, and cannot cope with the frequent gaps in the location traces. In order to address all these challenges a novel, state based bottom-up approach is proposed. This approach assumes a fixed atomic segment of a homogeneous state, instead of an event-based segment, and a progressive iteration until a new state is found. The research investigates how an atomic state-based approach can be developed in such a way that can work in real time, near-real time and offline mode and in different environmental conditions with their varying quality of sensor traces. The results show the proposed bottom-up model outperforms the existing event-based segmentation models in terms of adaptivity, flexibility, accuracy and richness in information delivery pertinent to automated travel behavior interpretation.

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A context-sensitive conceptual framework for activity modeling

Cited by 8 publications

References 67 publications

Revisiting the Role of Place in Geographic Information Science

Revisiting the Role of Place in Geographic Information Science

Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory

Automated Urban Travel Interpretation: A Bottom-up Approach for Trajectory Segmentation

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