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Context-Aware Online Spatiotemporal Traffic Prediction
2014
2014 IEEE International Conference on Data Mining Workshop
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As real-time traffic data arrives, the context space is adaptively partitioned in order to efficiently estimate the effectiveness of each predictor in different contexts. ...
One key challenge in predicting traffic congestion is how much to rely on the historical data v.s. the real-time data. ...
CONTEXT-AWARE ADAPTIVE TRAFFIC PREDICTION
A. Algorithm description First we introduce several useful concepts for describing the proposed algorithm: i) Context subspace. ...
doi:10.1109/icdmw.2014.102
dblp:conf/icdm/XuDDSS14
fatcat:65queqbucbf4bj4galkwogpigu
CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting
2024
Proceedings of the 17th ACM International Conference on Web Search and Data Mining
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In this paper, we exploit the informative traffic-related context factors to jointly tackle the dynamic regional heterogeneity and explain the stochasticity, towards a credible uncertainty-aware traffic ...
However, we argue that previous traffic predictions are still unreliable due to two aspects. ...
Definition 3 (Context factors). Context factors are traffic covariates, related with traffics but not for predictions. ...
doi:10.1145/3616855.3635759
fatcat:brgy6qgkdrhpvfedo5bb27uzjm
On how to incorporate public sources of situational context in descriptive and predictive models of traffic data
2021
European Transport Research Review
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, triggering new opportunities for context-aware traffic data analysis. ...
and predictive models of urban traffic data? ...
We tackle the problem of context-augmented traffic data analysis in accordance with three major research needs: 1. how to perform context-aware analysis for different spatiotemporal traffic data structures ...
doi:10.1186/s12544-021-00519-w
pmid:38624925
pmcid:PMC8613527
fatcat:u3y4vk33orbsjeualot2kqawjm
Mining the Situation: Spatiotemporal Traffic Prediction With Big Data
2015
IEEE Journal on Selected Topics in Signal Processing
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In this paper, we propose a novel online framework that could learn from the current traffic situation (or context) in real-time and predict the future traffic by matching the current situation to the ...
Using the proposed framework, the context dimension that is the most relevant to traffic prediction can also be revealed, which can further reduce the implementation complexity as well as inform traffic ...
Algorithm Description In this subsection, we describe the proposed online context-aware traffic prediction algorithm (CA-Traffic). ...
doi:10.1109/jstsp.2015.2389196
fatcat:y3lo3vk4avdqlftva26spot4fu
Spatiotemporal Traffic Analysis using Big Data
2020
International journal of advanced information and communication technology
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In collaborated online technique traffic prediction methods is proposed with distributed context aware random forest learning algorithm .The random forest is ensemble classifier which learns different ...
The proposed algorithm is online predictor of real-time traffic, the global prediction is achieved with less convergence time .The distributed scenarios (traffic data and context data) are collected together ...
Context-aware adaptive traffic prediction is used for predict traffic pattern. ...
doi:10.46532/ijaict-2020010
fatcat:jai2bptqcfc35azckrls23ck24
Consolidation of Massive Medical Emergency Events With Heterogeneous Situational Context Data Sources
2022
International Conference on Extending Database Technology
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In this work, we implement best practices in multidimensional database modelling to consolidate emergency event data with public sources of situational context for context-aware data analysis. ...
Emergency occurrences and responses are intricately dependent on contextual factors, including weather, epidemic context, urban traffic, large-scale events, and demographics. ...
The context-aware predictive modelling of emergency events is essential to support resource allocation and assist in vehicle allocation at large gatherings. ...
dblp:conf/edbt/Tiam-LeeHCMG22
fatcat:3g7cacjgvrhmhns5n3xsmxhmne
Integrative analysis of multimodal traffic data: addressing open challenges using big data analytics in the city of Lisbon
2021
European Transport Research Review
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The manuscript is focused on the context-aware analysis of multimodal traffic data with a focus on public transportation, offering four major contributions. ...
Finally, we instantiate some principles by conducting a spatiotemporal analysis of multimodality indices in the city against available context. ...
In fact, state-of-the-art contributions for context-aware descriptive and predictive tasks generally fail to model the joint impact that these multiple sources of context exert on urban mobility. ...
doi:10.1186/s12544-021-00520-3
fatcat:atdack4iwzbfdp22r5nlmw4lhm
BASM: A Bottom-up Adaptive Spatiotemporal Model for Online Food Ordering Service
[article]
2022
arXiv
pre-print
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Specifically, a spatiotemporal-aware embedding layer performs weight adaptation on field granularity in feature embedding, to achieve the purpose of dynamically perceiving spatiotemporal contexts. ...
representation under different spatiotemporal contexts. ...
traffic scales. ...
arXiv:2211.12033v1
fatcat:7opcba76hrdxdht7neyy5fmhdm
Contextualizing MLP-Mixers Spatiotemporally for Urban Data Forecast at Scale
[article]
2024
arXiv
pre-print
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Spatiotemporal urban data (STUD) displays complex correlational patterns. Extensive advanced techniques have been designed to capture these patterns for effective forecasting. ...
Furthermore, it was deployed in a collaborative urban congestion project with Baidu, specifically evaluating its ability to forecast traffic states in megacities like Beijing and Shanghai. ...
The spatiotemporal context makes them distinguishable across location and time. ...
arXiv:2307.01482v5
fatcat:5hm3xydfrfb4ljv36aij5iwrym
Mining future spatiotemporal events and their sentiment from online news articles for location-aware recommendation system
2012
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems - MobiGIS '12
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Such spatiotemporal future events can be utilized by a recommender system on a location-aware device to provide localized future event suggestions. ...
In our application context, a valid event is defined both spatially and temporally. The mining procedure consists of two main steps: recognition and matching. ...
[10] proposed a system for extracting events and their corresponding spatiotemporal context from photo images. Ye et al. ...
doi:10.1145/2442810.2442816
dblp:conf/gis/HoLWS12
fatcat:2h3cyaa5gvdnvfg4rdokiudv7e
Introduction to spatio-temporal data driven urban computing
2020
Distributed and parallel databases
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Matching of spatio-temporal data is a fundamental research problem in spatiotemporal data analytics. ...
group query in their paper "Social-aware spatial keyword top-k group query." ...
doi:10.1007/s10619-020-07300-3
fatcat:e7js4tbxuneg7nzqakksvdtlum
GIN: Graph-based Interaction-aware Constraint Policy Optimization for Autonomous Driving
[article]
2022
arXiv
pre-print
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In this framework, the motion prediction and control modules are trained simultaneously while sharing a latent representation that contains a social context. ...
This paper proposes a new policy optimization method for safe driving using graph-based interaction-aware constraints. ...
single RL framework by allowing the two modules to share spatiotemporally compressed social context for robust prediction and response to dynamic risks. ...
arXiv:2206.01488v3
fatcat:p4zer3afd5fqrhogywlmf3ctvy
Knowledge discovery from sensor data (SensorKDD)
2008
SIGKDD Explorations
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dynamic data streams or events require real-time analysis methodologies and systems, while on the other hand centralized processing through high end computing is also required for generating offline predictive ...
His research interests include data stream mining, wireless sensor networks and context-aware computing. ...
Mohamed has served in the program committees of several international and local conferences and workshops in the area of data mining and context-aware computing. ...
doi:10.1145/1540276.1540297
fatcat:72jirtrxibbrpmpfcwrivmedjm
Knowledge discovery from sensor data (SensorKDD)
2010
SIGKDD Explorations
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dynamic data streams or events require real-time analysis methodologies and systems, while on the other hand centralized processing through high end computing is also required for generating offline predictive ...
His research interests include data stream mining, wireless sensor networks and context-aware computing. ...
Mohamed has served in the program committees of several international and local conferences and workshops in the area of data mining and context-aware computing. ...
doi:10.1145/1809400.1809417
fatcat:jrtrixjfzzgo3bdlelcyldihom
From Raw Pedestrian Trajectories to Semantic Graph Structured Model—Towards an end-to-end spatiotemporal analytics framework
2021
Procedia Computer Science
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The conditions and context in which pedestrians are exposed to the risk of road accidents, air pollution or epidemy are important for improving safety. ...
The abstract model provides a high semantic expressiveness through spatiotemporal queries and a high-level graph representation of the data. ...
Works given in [13, 14] present a model of semantic trajectories that apply in indoor space, which adapt to context-aware mobility data mining and analytics methods. ...
doi:10.1016/j.procs.2021.03.018
fatcat:f5fina36vzcrpljqgoyfbta4be
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