Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data

Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on predicting taxi demand in the near future by learning patterns from historical data. However, they fail in case of abnormality because dispersal events with abnormally high demand are non-repetitive and violate common assumptions such as smoothness in demand change over time. Instead, in this paper we argue that dispersal events follow a complex pattern of trips and other related features in the past, which can be used to predict such events. Therefore, we formulate the dispersal event prediction problem as a survival analysis problem. We propose a two-stage framework (DILSA), where a deep learning model combined with survival analysis is developed to predict the probability of a dispersal event and its demand volume. We conduct extensive case studies and experiments on the NYC Yellow taxi dataset from 20142016. Results show that DILSA can predict events in the next 5 hours with F1-score of 0:7 and with average time error of 18 minutes. It is orders of magnitude better than the state-of-the-art deep learning approaches for taxi demand prediction.

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DILSA+: Predicting Urban Dispersal Events through Deep Survival Analysis with Enhanced Urban Features

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3469085 ◽

2021 ◽

Vol 12 (4) ◽

pp. 1-25

Author(s):

Amin Vahedian Khezerlou ◽

Xun Zhou ◽

Xinyi Li ◽

W. Nick Street ◽

Yanhua Li

Keyword(s):

Survival Analysis ◽

Deep Learning ◽

Survival Function ◽

City Management ◽

Learning Approaches ◽

Mobility Patterns ◽

Demand Prediction ◽

Short Period ◽

The Future ◽

Dispersal Events

Urban dispersal events occur when an unexpectedly large number of people leave an area in a relatively short period of time. It is beneficial for the city authorities, such as law enforcement and city management, to have an advance knowledge of such events, as it can help them mitigate the safety risks and handle important challenges such as managing traffic, and so forth. Predicting dispersal events is also beneficial to Taxi drivers and/or ride-sharing services, as it will help them respond to an unexpected demand and gain competitive advantage. Large urban datasets such as detailed trip records and point of interest ( POI ) data make such predictions achievable. The related literature mainly focused on taxi demand prediction. The pattern of the demand was assumed to be repetitive and proposed methods aimed at capturing those patterns. However, dispersal events are, by definition, violations of those patterns and are, understandably, missed by the methods in the literature. We proposed a different approach in our prior work [32]. We showed that dispersal events can be predicted by learning the complex patterns of arrival and other features that precede them in time. We proposed a survival analysis formulation of this problem and proposed a two-stage framework (DILSA), where a deep learning model predicted the survival function at each point in time in the future. We used that prediction to determine the time of the dispersal event in the future, or its non-occurrence. However, DILSA is subject to a few limitations. First, based on evidence from the data, mobility patterns can vary through time at a given location. DILSA does not distinguish between different mobility patterns through time. Second, mobility patterns are also different for different locations. DILSA does not have the capability to directly distinguish between different locations based on their mobility patterns. In this article, we address these limitations by proposing a method to capture the interaction between POIs and mobility patterns and we create vector representations of locations based on their mobility patterns. We call our new method DILSA+. We conduct extensive case studies and experiments on the NYC Yellow taxi dataset from 2014 to 2016. Results show that DILSA+ can predict events in the next 5 hours with an F1-score of 0.66. It is significantly better than DILSA and the state-of-the-art deep learning approaches for taxi demand prediction.

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Deep Learning-Based Approaches to Optimize the Electricity Contract Capacity Problem for Commercial Customers

Energies ◽

10.3390/en14082181 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2181

Author(s):

Rafik Nafkha ◽

Tomasz Ząbkowski ◽

Krzysztof Gajowniczek

Keyword(s):

Deep Learning ◽

Power Grid ◽

Hybrid Approach ◽

Electricity Consumption ◽

Maximum Power ◽

Learning Approaches ◽

Two Stage ◽

Load Optimization ◽

Optimal Capacity ◽

Maximal Load

The electricity tariffs available to customers in Poland depend on the connection voltage level and contracted capacity, which reflect the customer demand profile. Therefore, before connecting to the power grid, each consumer declares the demand for maximum power. This amount, referred to as the contracted capacity, is used by the electricity provider to assign the proper connection type to the power grid, including the size of the security breaker. Maximum power is also the basis for calculating fixed charges for electricity consumption, which is controlled and metered through peak meters. If the peak demand exceeds the contracted capacity, a penalty charge is applied to the exceeded amount, which is up to ten times the basic rate. In this article, we present several solutions for entrepreneurs based on the implementation of two-stage and deep learning approaches to predict maximal load values and the moments of exceeding the contracted capacity in the short term, i.e., up to one month ahead. The forecast is further used to optimize the capacity volume to be contracted in the following month to minimize network charge for exceeding the contracted level. As confirmed experimentally with two datasets, the application of a multiple output forecast artificial neural network model and a genetic algorithm (two-stage approach) for load optimization delivers significant benefits to customers. As an alternative, the same benefit is delivered with a deep learning architecture (hybrid approach) to predict the maximal capacity demands and, simultaneously, to determine the optimal capacity contract.

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Multi-source information based short-term taxi pick-up demand prediction using deep-learning approaches

10.17760/d20382824 ◽

2020 ◽

Author(s):

Ziyan Chen

Keyword(s):

Deep Learning ◽

Learning Approaches ◽

Source Information ◽

Short Term ◽

Demand Prediction

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A Survey on Deep Learning for Human Mobility

ACM Computing Surveys ◽

10.1145/3485125 ◽

2023 ◽

Vol 55 (1) ◽

pp. 1-44

Author(s):

Massimiliano Luca ◽

Gianni Barlacchi ◽

Bruno Lepri ◽

Luca Pappalardo

Keyword(s):

Deep Learning ◽

Predictive Power ◽

Human Mobility ◽

Well Being ◽

Learning Approaches ◽

Comprehensive Description ◽

Mobility Data ◽

Flow Generation ◽

Disease Spreading ◽

Gps Traces

The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides a taxonomy of mobility tasks, a discussion on the challenges related to each task and how deep learning may overcome the limitations of traditional models, a description of the most relevant solutions to the mobility tasks described above, and the relevant challenges for the future. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.

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Survival Prediction of Lung Cancer Using Small-Size Clinical Data with a Multiple Task Variational Autoencoder

Electronics ◽

10.3390/electronics10121396 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1396

Author(s):

Thanh-Hung Vo ◽

Guee-Sang Lee ◽

Hyung-Jeong Yang ◽

In-Jae Oh ◽

Soo-Hyung Kim ◽

...

Keyword(s):

Lung Cancer ◽

Survival Analysis ◽

Deep Learning ◽

Language Processing ◽

Network Architecture ◽

Survival Prediction ◽

Learning Approaches ◽

Process Data ◽

Analysis Task ◽

Variational Autoencoder

Due to the increase of lung cancer globally, and particularly in Korea, survival analysis for this type of cancer has gained prominence in recent years. For this task, mathematical and traditional machine learning approaches are commonly used by medical doctors. While the deep learning approach has had proven success in computer vision tasks, natural language processing and other AI techniques are also adopted for this task. Due to the privacy issues and management process, data in medicine are difficult to collect, which leads to a paucity of samples. The small number of samples makes it difficult to use deep learning and renders this approach unusable. In this investigation, we propose a network architecture that combines a variational autoencoder (VAE) with the typical DNN architecture to solve the survival analysis task. With a training size of n = 4107, MVAESA achieves a C-index of 0.722 while CoxCC, CoxPH, and CoxTime achieved scores of 0.713, 0.703, and 0.710, respectively. With a small training size of n = 379, MVAESA achieves a C-index of 0.707, compared with 0.689, 0.688 and 0.690 for CoxCC, CoxPH, and CoxTime, respectively. The results show that the combination of a VAE with a target task makes the network more stable and that the network could be trained using a small-sized sample.

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Multi-Source Information Based Short-term Taxi Demand Prediction Using Deep-Learning Approaches

Journal of Physics Conference Series ◽

10.1088/1742-6596/2033/1/012167 ◽

2021 ◽

Vol 2033 (1) ◽

pp. 012167

Author(s):

Ziyan Chen

Keyword(s):

Deep Learning ◽

Learning Approaches ◽

Source Information ◽

Short Term ◽

Demand Prediction

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Review of Statistical Methods for Evaluating the Performance of Survival or Other Time-to-Event Prediction Models (from Conventional to Deep Learning Approaches)

Korean Journal of Radiology ◽

10.3348/kjr.2021.0223 ◽

2021 ◽

Vol 22 ◽

Author(s):

Seo Young Park ◽

Ji Eun Park ◽

Hyungjin Kim ◽

Seong Ho Park

Keyword(s):

Deep Learning ◽

Statistical Methods ◽

Prediction Models ◽

Learning Approaches ◽

Time To Event ◽

Event Prediction

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Deep Learning Approaches for Whiteboard Image Quality Enhancement

Color and Imaging Conference ◽

10.2352/j.imagingsci.technol.2019.63.4.040404 ◽

2019 ◽

Vol 2019 (1) ◽

pp. 360-368

Author(s):

Mekides Assefa Abebe ◽

Jon Yngve Hardeberg

Keyword(s):

Deep Learning ◽

Image Quality ◽

Image Data ◽

Quality Enhancement ◽

Network Architectures ◽

Learning Approaches ◽

Data Set ◽

Image Quality Enhancement ◽

Processing Techniques ◽

White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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