scholarly journals A Hybrid Population Distribution Prediction Approach Integrating LSTM and CA Models with Micro-Spatiotemporal Granularity: A Case Study of Chongming District, Shanghai

2021 ◽  
Vol 10 (8) ◽  
pp. 544
Author(s):  
Pengyuan Wang ◽  
Xiao Huang ◽  
Joseph Mango ◽  
Di Zhang ◽  
Dong Xu ◽  
...  
Keyword(s):  
Traffic Management ◽  
Short Term Memory ◽  
Population Distribution ◽  
Absolute Error ◽  
Urban Traffic ◽  
Hybrid Population ◽  
Collecting Methods ◽  
Hybrid Data ◽  
Prediction Approach ◽  
Population Prediction

Studying population prediction under micro-spatiotemporal granularity is of great significance for modern and refined urban traffic management and emergency response to disasters. Existing population studies are mostly based on census and statistical yearbook data due to the limitation of data collecting methods. However, with the advent of techniques in this information age, new emerging data sources with fine granularity and large sample sizes have provided rich materials and unique venues for population research. This article presents a new population prediction model with micro-spatiotemporal granularity based on the long short-term memory (LSTM) and cellular automata (CA) models. We aim at designing a hybrid data-driven model with good adaptability and scalability, which can be used in more refined population prediction. We not only try to integrate these two models, aiming to fully mine the spatiotemporal characteristics, but also propose a method that fuses multi-source geographic data. We tested its functionality using the data from Chongming District, Shanghai, China. The results demonstrated that, among all scenarios, the model trained by three consecutive days (ordinary dates), with the granularity of one hour, incorporated with road networks, achieves the best performance (0.905 as the mean absolute error) and generalization capability.

scholarly journals Spatio-Temporal Visual Analysis for Urban Traffic Characters Based on Video Surveillance Camera Data

2021 ◽  
Vol 10 (3) ◽  
pp. 177
Author(s):  
Haochen Zou ◽  
Keyan Cao ◽  
Chong Jiang
Keyword(s):  
Video Surveillance ◽  
Traffic Management ◽  
Visual Analysis ◽  
Urban Traffic ◽  
License Plate ◽  
Road Vehicle ◽  
Urban Road ◽  
Surveillance Camera ◽  
Vehicle Density ◽  
Spatio Temporal

Urban road traffic spatio-temporal characters reflect how citizens move and how goods are transported, which is crucial for trip planning, traffic management, and urban design. Video surveillance camera plays an important role in intelligent transport systems (ITS) for recognizing license plate numbers. This paper proposes a spatio-temporal visualization method to discover urban road vehicle density, city-wide regional vehicle density, and hot routes using license plate number data recorded by video surveillance cameras. To improve the accuracy of the visualization effect, during data analysis and processing, this paper utilized Internet crawler technology and adopted an outlier detection algorithm based on the Dixon detection method. In the design of the visualization map, this paper established an urban road vehicle traffic index to intuitively and quantitatively reveal the traffic operation situation of the area. To verify the feasibility of the method, an experiment in Guiyang on data from road video surveillance camera system was conducted. Multiple urban traffic spatial and temporal characters are recognized concisely and efficiently from three visualization maps. The results show the satisfactory performance of the proposed framework in terms of visual analysis, which will facilitate traffic management and operation.

scholarly journals Interdisciplinary Evaluation of Intersection Performances—A Microsimulation-Based MCDA

2021 ◽  
Vol 13 (4) ◽  
pp. 1859
Author(s):  
Kadir Diler Alemdar ◽  
Ahmet Tortum ◽  
Ömer Kaya ◽  
Ahmet Atalay
Keyword(s):  
Traffic Engineering ◽  
Traffic Management ◽  
Evaluation Criteria ◽  
Urban Traffic ◽  
Suitable Alternative ◽  
Order Preference ◽  
Energy Environment ◽  
Vehicle Delay ◽  
Hierarchy Process ◽  
Co Emission

Intersections are the most important regions in terms of urban traffic management. The intersection areas on the corridor should be analyzed together for consistency in traffic engineering. To do so, three intersections on the Vatan Street corridor in İstanbul, the most crowded city of Turkey, were examined. Various geometric and signal designs were performed for intersections and the most suitable corridor design was analyzed. The corridor designs were modeled with the PTV VISSIM microsimulation software. The most suitable corridor design was evaluated by using the results obtained from the microsimulation via analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) from multi criteria decision analysis (MCDA) methods. The evaluation criteria in the study are vehicle delay, queue length, stopped delay, stops, travel time, vehicle safety, CO emission, fuel consumption, and construction cost. As a result, the current and the most suitable alternative corridors were compared according to the comparison parameters and up to 80% improvements were observed. Thus, some advantages were obtained in terms of energy, environment, time, and cost.

MDTP

2021 ◽  
Vol 14 (8) ◽  
pp. 1289-1297
Author(s):  
Ziquan Fang ◽  
Lu Pan ◽  
Lu Chen ◽  
Yuntao Du ◽  
Yunjun Gao
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Real Life ◽  
Feature Modeling ◽  
Traffic Prediction ◽  
Interactive System ◽  
Trajectory Data ◽  
Spatio Temporal ◽  
Prediction Approach

Traffic prediction has drawn increasing attention for its ubiquitous real-life applications in traffic management, urban computing, public safety, and so on. Recently, the availability of massive trajectory data and the success of deep learning motivate a plethora of deep traffic prediction studies. However, the existing neural-network-based approaches tend to ignore the correlations between multiple types of moving objects located in the same spatio-temporal traffic area, which is suboptimal for traffic prediction analytics. In this paper, we propose a multi-source deep traffic prediction framework over spatio-temporal trajectory data, termed as MDTP. The framework includes two phases: spatio-temporal feature modeling and multi-source bridging. We present an enhanced graph convolutional network (GCN) model combined with long short-term memory network (LSTM) to capture the spatial dependencies and temporal dynamics of traffic in the feature modeling phase. In the multi-source bridging phase, we propose two methods, Sum and Concat, to connect the learned features from different trajectory data sources. Extensive experiments on two real-life datasets show that MDTP i) has superior efficiency, compared with classical time-series methods, machine learning methods, and state-of-the-art neural-network-based approaches; ii) offers a significant performance improvement over the single-source traffic prediction approach; and iii) performs traffic predictions in seconds even on tens of millions of trajectory data. we develop MDTP + , a user-friendly interactive system to demonstrate traffic prediction analysis.

Forecasting the risk at infractions: an ensemble comparison of machine learning approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lei Li ◽  
Desheng Wu
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Short Term Memory ◽  
Model Performance ◽  
Large Data ◽  
Support Vector ◽  
Learning Approaches ◽  
Content Type ◽  
Day To Day Operations ◽  
Prediction Approach

PurposeThe infraction of securities regulations (ISRs) of listed firms in their day-to-day operations and management has become one of common problems. This paper proposed several machine learning approaches to forecast the risk at infractions of listed corporates to solve financial problems that are not effective and precise in supervision.Design/methodology/approachThe overall proposed research framework designed for forecasting the infractions (ISRs) include data collection and cleaning, feature engineering, data split, prediction approach application and model performance evaluation. We select Logistic Regression, Naïve Bayes, Random Forest, Support Vector Machines, Artificial Neural Network and Long Short-Term Memory Networks (LSTMs) as ISRs prediction models.FindingsThe research results show that prediction performance of proposed models with the prior infractions provides a significant improvement of the ISRs than those without prior, especially for large sample set. The results also indicate when judging whether a company has infractions, we should pay attention to novel artificial intelligence methods, previous infractions of the company, and large data sets.Originality/valueThe findings could be utilized to address the problems of identifying listed corporates' ISRs at hand to a certain degree. Overall, results elucidate the value of the prior infraction of securities regulations (ISRs). This shows the importance of including more data sources when constructing distress models and not only focus on building increasingly more complex models on the same data. This is also beneficial to the regulatory authorities.

scholarly journals A dynamic and deadline-oriented road pricing mechanism for urban traffic management

2022 ◽  
Vol 27 (1) ◽  
pp. 91-102
Author(s):  
Jiahui Jin ◽  
Xiaoxuan Zhu ◽  
Biwei Wu ◽  
Jinghui Zhang ◽  
Yuxiang Wang
Keyword(s):  
Traffic Management ◽  
Road Pricing ◽  
Urban Traffic ◽  
Pricing Mechanism

scholarly journals Data-Driven Anomaly Detection in High-Voltage Transformer Bushings with LSTM Auto-Encoder

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7426
Author(s):  
Imene Mitiche ◽  
Tony McGrail ◽  
Philip Boreham ◽  
Alan Nesbitt ◽  
Gordon Morison
Keyword(s):  
Anomaly Detection ◽  
High Voltage ◽  
Short Term Memory ◽  
Absolute Error ◽  
Real World Data ◽  
Voltage Transformer ◽  
Power Outage ◽  
Phase Measurements ◽  
High Voltage Transformer ◽  
Supply Industry

The reliability and health of bushings in high-voltage (HV) power transformers is essential in the power supply industry, as any unexpected failure can cause power outage leading to heavy financial losses. The challenge is to identify the point at which insulation deterioration puts the bushing at an unacceptable risk of failure. By monitoring relevant measurements we can trace any change that occurs and may indicate an anomaly in the equipment’s condition. In this work we propose a machine-learning-based method for real-time anomaly detection in current magnitude and phase angle from three bushing taps. The proposed method is fast, self-supervised and flexible. It consists of a Long Short-Term Memory Auto-Encoder (LSTMAE) network which learns the normal current and phase measurements of the bushing and detects any point when these measurements change based on the Mean Absolute Error (MAE) metric evaluation. This approach was successfully evaluated using real-world data measured from HV transformer bushings where anomalous events have been identified.

scholarly journals LAR: A User Behavior Prediction Model in Server Log Based on LSTM-Attention Network and RSC Algorithm

2020 ◽  
Author(s):  
Yingying Shang
Keyword(s):  
Clustering Algorithm ◽  
Short Term Memory ◽  
User Behavior ◽  
Absolute Error ◽  
Research Area ◽  
Behavior Prediction ◽  
Attention Network ◽  
Important Research Area ◽  
User Access ◽  
Single User

Using server log data to predict the URLs that a user is likely to visit is an important research area in user behavior prediction. In this paper, a predictive model (called LAR) based on the long short-term memory (LSTM) attention network and reciprocal-nearest-neighbors supported clustering algorithm (RSC) for predicting the URL is proposed. First, the LSTM-attention network is used to predict the URL categories a user might visit, and the RSC algorithm is then used to cluster users. Subsequently, the URLs belonging to the same category are determined from the user clusters to predict the URLs that the user might visit. The proposed LAR model considers the time sequence of the user access URL, and the relationship between a single user and group users, which effectively improves the prediction accuracy. The experimental results demonstrate that the LAR model is feasible and effective for user behavior prediction. The accuracy of the mean absolute error and root mean square error of the LAR model are better than those of the other models compared in this study.

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