Event Prediction Using Spatial–Temporal Data for a Predictive Traffic Accident Approach Through Categorical Logic
An event is an occurrence that takes place at a specific time and location that can be either weather-related (snowfall), social (crime), natural (earthquake), political (political unrest), or medical (pandemic) in nature. These events do not belong to the “normal” or “usual” spectrum and result in...
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MDPI AG
2025-06-01
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| author | Eleftheria Koutsaki George Vardakis Nikos Papadakis |
| author_facet | Eleftheria Koutsaki George Vardakis Nikos Papadakis |
| author_sort | Eleftheria Koutsaki |
| collection | DOAJ |
| description | An event is an occurrence that takes place at a specific time and location that can be either weather-related (snowfall), social (crime), natural (earthquake), political (political unrest), or medical (pandemic) in nature. These events do not belong to the “normal” or “usual” spectrum and result in a change in a given situation; thus, their prediction would be very beneficial, both in terms of timely response to them and for their prevention, for example, the prevention of traffic accidents. However, this is currently challenging for researchers, who are called upon to manage and analyze a huge volume of data in order to design applications for predicting events using artificial intelligence and high computing power. Although significant progress has been made in this area, the heterogeneity in the input data that a forecasting application needs to process—in terms of their nature (spatial, temporal, and semantic)—and the corresponding complex dependencies between them constitute the greatest challenge for researchers. For this reason, the initial forecasting applications process data for specific situations, in terms of number and characteristics, while, at the same time, having the possibility to respond to different situations, e.g., an application that predicts a pandemic can also predict a central phenomenon, simply by using different data types. In this work, we present the forecasting applications that have been designed to date. We also present a model for predicting traffic accidents using categorical logic, creating a Knowledge Base using the Resolution algorithm as a proof of concept. We study and analyze all possible scenarios that arise under different conditions. Finally, we implement the traffic accident prediction model using the Prolog language with the corresponding Queries in JPL. |
| format | Article |
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| institution | Kabale University |
| issn | 2306-5729 |
| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-9100daeb07574ff793057d421e98cfc52025-08-20T03:27:06ZengMDPI AGData2306-57292025-06-011068510.3390/data10060085Event Prediction Using Spatial–Temporal Data for a Predictive Traffic Accident Approach Through Categorical LogicEleftheria Koutsaki0George Vardakis1Nikos Papadakis2Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, GreeceDepartment of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, GreeceDepartment of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, GreeceAn event is an occurrence that takes place at a specific time and location that can be either weather-related (snowfall), social (crime), natural (earthquake), political (political unrest), or medical (pandemic) in nature. These events do not belong to the “normal” or “usual” spectrum and result in a change in a given situation; thus, their prediction would be very beneficial, both in terms of timely response to them and for their prevention, for example, the prevention of traffic accidents. However, this is currently challenging for researchers, who are called upon to manage and analyze a huge volume of data in order to design applications for predicting events using artificial intelligence and high computing power. Although significant progress has been made in this area, the heterogeneity in the input data that a forecasting application needs to process—in terms of their nature (spatial, temporal, and semantic)—and the corresponding complex dependencies between them constitute the greatest challenge for researchers. For this reason, the initial forecasting applications process data for specific situations, in terms of number and characteristics, while, at the same time, having the possibility to respond to different situations, e.g., an application that predicts a pandemic can also predict a central phenomenon, simply by using different data types. In this work, we present the forecasting applications that have been designed to date. We also present a model for predicting traffic accidents using categorical logic, creating a Knowledge Base using the Resolution algorithm as a proof of concept. We study and analyze all possible scenarios that arise under different conditions. Finally, we implement the traffic accident prediction model using the Prolog language with the corresponding Queries in JPL.https://www.mdpi.com/2306-5729/10/6/85eventsevent predictiontraffic accidentspatiotemporal dataartificial intelligencemachine learning |
| spellingShingle | Eleftheria Koutsaki George Vardakis Nikos Papadakis Event Prediction Using Spatial–Temporal Data for a Predictive Traffic Accident Approach Through Categorical Logic Data events event prediction traffic accident spatiotemporal data artificial intelligence machine learning |
| title | Event Prediction Using Spatial–Temporal Data for a Predictive Traffic Accident Approach Through Categorical Logic |
| title_full | Event Prediction Using Spatial–Temporal Data for a Predictive Traffic Accident Approach Through Categorical Logic |
| title_fullStr | Event Prediction Using Spatial–Temporal Data for a Predictive Traffic Accident Approach Through Categorical Logic |
| title_full_unstemmed | Event Prediction Using Spatial–Temporal Data for a Predictive Traffic Accident Approach Through Categorical Logic |
| title_short | Event Prediction Using Spatial–Temporal Data for a Predictive Traffic Accident Approach Through Categorical Logic |
| title_sort | event prediction using spatial temporal data for a predictive traffic accident approach through categorical logic |
| topic | events event prediction traffic accident spatiotemporal data artificial intelligence machine learning |
| url | https://www.mdpi.com/2306-5729/10/6/85 |
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