927 research outputs found
Predictive spatio-temporal modelling with neural networks
Hongbin Liu studied the predictive spatio-temporal modelling using Neural Networks. Predictive spatio-temporal modelling is a challenge task due to the complex non-linear spatio-temporal dependencies, data sparsity and uncertainty.
Hongbin Liu investigated the modelling difficulties and proposed three novel models to tackle the difficulties for three common spatio-temporal datasets. He also conducted extensive experiments on several real-world datasets for various spatio-temporal prediction tasks, such as travel mode classification, next-location prediction, weather forecasting and meteorological imagery prediction. The results show our proposed models consistently achieve exceptional improvements over state-of-the-art baselines
Unify Change Point Detection and Segment Classification in a Regression Task for Transportation Mode Identification
Identifying travelers' transportation modes is important in transportation
science and location-based services. It's appealing for researchers to leverage
GPS trajectory data to infer transportation modes with the popularity of
GPS-enabled devices, e.g., smart phones. Existing studies frame this problem as
classification task. The dominant two-stage studies divide the trip into
single-one mode segments first and then categorize these segments. The over
segmentation strategy and inevitable error propagation bring difficulties to
classification stage and make optimizing the whole system hard. The recent
one-stage works throw out trajectory segmentation entirely to avoid these by
directly conducting point-wise classification for the trip, whereas leaving
predictions dis-continuous. To solve above-mentioned problems, inspired by YOLO
and SSD in object detection, we propose to reframe change point detection and
segment classification as a unified regression task instead of the existing
classification task. We directly regress coordinates of change points and
classify associated segments. In this way, our method divides the trip into
segments under a supervised manner and leverage more contextual information,
obtaining predictions with high accuracy and continuity. Two frameworks,
TrajYOLO and TrajSSD, are proposed to solve the regression task and various
feature extraction backbones are exploited. Exhaustive experiments on GeoLife
dataset show that the proposed method has competitive overall identification
accuracy of 0.853 when distinguishing five modes: walk, bike, bus, car, train.
As for change point detection, our method increases precision at the cost of
drop in recall. All codes are available at
https://github.com/RadetzkyLi/TrajYOLO-SSD
Estimator: An Effective and Scalable Framework for Transportation Mode Classification over Trajectories
Transportation mode classification, the process of predicting the class
labels of moving objects transportation modes, has been widely applied to a
variety of real world applications, such as traffic management, urban
computing, and behavior study. However, existing studies of transportation mode
classification typically extract the explicit features of trajectory data but
fail to capture the implicit features that affect the classification
performance. In addition, most of the existing studies also prefer to apply
RNN-based models to embed trajectories, which is only suitable for classifying
small-scale data. To tackle the above challenges, we propose an effective and
scalable framework for transportation mode classification over GPS
trajectories, abbreviated Estimator. Estimator is established on a developed
CNN-TCN architecture, which is capable of leveraging the spatial and temporal
hidden features of trajectories to achieve high effectiveness and efficiency.
Estimator partitions the entire traffic space into disjointed spatial regions
according to traffic conditions, which enhances the scalability significantly
and thus enables parallel transportation classification. Extensive experiments
using eight public real-life datasets offer evidence that Estimator i) achieves
superior model effectiveness (i.e., 99% Accuracy and 0.98 F1-score), which
outperforms state-of-the-arts substantially; ii) exhibits prominent model
efficiency, and obtains 7-40x speedups up over state-of-the-arts learning-based
methods; and iii) shows high model scalability and robustness that enables
large-scale classification analytics.Comment: 12 pages, 8 figure
Traveler Mobility and Activity Pattern Inference UsingPersonal Smartphone Applications and ArtificialIntelligence Methods
Recent advances in communication technologies have enabled researchers to collect travel data from location-aware smartphones. These advances hold out the promise of allowing the automatic detection of the critical aspects (mode, purpose, etc.) of people’s travel. This thesis investigates the application of artificial intelligence methods to infer mode of transport, trip purpose and transit itinerary from traveler trajectories gathered by smartphones. Supervised, Random Forest models are used to detect mode, purpose and transit itinerary of trips. Deep learning models, in particular, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), are also employed to infer mode of transport and trip purpose. The research also explores the use of Generative Adversarial Networks (GANs), as a semi-supervised learning approach, to classify trip mode. Moreover, we investigate the application of multi-task learning to simultaneously infer mode and purpose.
The research uses several different data sources. Trip trajectory data was collected by the MTL Trajet smartphone Travel Survey App, in 2016. Also, other complementary datasets, such as locational
data from social media, land-use, General Transit Feed Specification (GTFS), and elevation data are exploited to infer trip information.
Mode of transport can be inferred with Random Forest models, ensemble CNN models, and RNN approaches with an accuracy of 87%, 91%, and 86%, respectively. The Random Forest and
multi-task RNN models to infer trip purpose achieve an accuracy of 71% and 78%, respectively. Also, the Random Forest transit itinerary inference model can predict used transit itineraries with an accuracy of 81%. While further improvement is required to enhance the performance of the developed artificial intelligence models on smartphone data, the results of the research indicate the capability of smartphone-based travel surveys as a complementary (and potentially replacement) surveying tool to household travel surveys
Travel Mode Recognition from GPS Data Based on LSTM
A large amount of GPS data contains valuable hidden information. With GPS trajectory data, a Long Short-Term Memory model (LSTM) is used to identify passengers' travel modes, i.e., walking, riding buses, or driving cars. Moreover, the Quantum Genetic Algorithm (QGA) is used to optimize the LSTM model parameters, and the optimized model is used to identify the travel mode. Compared with the state-of-the-art studies, the contributions are: 1. We designed a method of data processing. We process the GPS data by pixelating, get grayscale images, and import them into the LSTM model. Finally, we use the QGA to optimize four parameters of the model, including the number of neurons and the number of hidden layers, the learning rate, and the number of iterations. LSTM is used as the classification method where QGA is adopted to optimize the parameters of the model. 2. Experimental results show that the proposed approach has higher accuracy than BP Neural Network, Random Forest and Convolutional Neural Networks (CNN), and the QGA parameter optimization method can further improve the recognition accuracy
Dynamic Switching State Systems for Visual Tracking
This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together
Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion
which represents a critical problem globally, resulting in negative
consequences such as lost hours of additional travel time and increased fuel
consumption. Integrating emerging technologies into transportation systems
provides opportunities for improving traffic prediction significantly and
brings about new research problems. In order to lay the foundation for
understanding the open research challenges in traffic prediction, this survey
aims to provide a comprehensive overview of traffic prediction methodologies.
Specifically, we focus on the recent advances and emerging research
opportunities in Artificial Intelligence (AI)-based traffic prediction methods,
due to their recent success and potential in traffic prediction, with an
emphasis on multivariate traffic time series modeling. We first provide a list
and explanation of the various data types and resources used in the literature.
Next, the essential data preprocessing methods within the traffic prediction
context are categorized, and the prediction methods and applications are
subsequently summarized. Lastly, we present primary research challenges in
traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies
(TR_C), Volume 145, 202
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