Trajectory Length Prediction for Intelligent Traffic Signaling: A Data-Driven Approach

Gan, Shaojun; Liang, Shan; Li, Kang; Deng, Jiankang; Cheng, Tingli

doi:10.1109/tits.2017.2700209

Cited by 26 publications

(15 citation statements)

References 15 publications

(16 reference statements)

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“…The NYRB is located in the Nanjing section of the lower reaches of the Yangtze River, which has been the world’s busiest inland waterways since 2010 (Gan et al 2017 ). The bridge is China’s first double-deck railway and highway bridge across the Yangtze River, connecting the Nanjing City and the Pukou District (Huang et al 2019 ).…”

Section: Study Areamentioning

confidence: 99%

Navigational risk analysis based on GIS spatiotemporal trajectory mining: a case study in Nanjing Yangtze River Bridge waters

Huiyu

2021

Arab J Geosci

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With the continuous growth of the quantity, scale, and speed of vessels in recent years, maritime accidents are posing increasing risks to societies and individuals, especially in narrow inland waterways. Therefore, it is of great significance to analyze navigational risks to ensure the safety of waterborne transportation. In this paper, the navigational risks of Nanjing Yangtze River Bridge (NYRB) waters are investigated based on spatiotemporal mining on massive automatic identification system (AIS) trajectories by using geographic information system (GIS) techniques. A time-series-oriented trajectory processing method is proposed to deal with the historical AIS data in the whole year of 2019. The method adopts a periodic processing strategy to produce traffic density estimation products in multiple temporal scales for supporting spatiotemporal analysis. The proposed method greatly improves the data-processing efficiency and provides a flexible way to deeply understand the vessel behavior patterns in NYRB waters. Then the complete characteristics of the spatial distribution and temporal variation of AIS trajectories are revealed. Based on that, three types of critical navigational risks are discovered, which include the safety distance risk, the pier collision risk, and the traffic congestion risk. Moreover, we find that the greatest risk is existed in small vessels in the flood season, which is worth the most concern.

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Section: Study Areamentioning

confidence: 99%

Navigational risk analysis based on GIS spatiotemporal trajectory mining: a case study in Nanjing Yangtze River Bridge waters

Huiyu

2021

Arab J Geosci

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“…Neural networks are systems in which computational units analogous to the human brain are interconnected [Kang and Isik (2005); Gan, Liang, Li et al (2018)]. The idea is to input the historical behavior state and current behavior state of the ship in the sea as a network input, and to map the future ship behavior characterization data as a network, and train the network to establish historical ship behavior by comparing with the actual value [Zegers and Sundareshan (2003)].…”

Section: Ship Trajectory Prediction Model Based On Bp Neural Networkmentioning

confidence: 99%

Ship Trajectory Prediction Based on BP Neural Network

Zhou¹,

Chen²,

Zhang³

2019

Journal on Artificial Intelligence

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In recent years, with the prosperity of world trade, the water transport industry has developed rapidly, the number of ships has surged, and ship safety accidents in busy waters and complex waterways have become more frequent. Predicting the movement of the ship and analyzing the trajectory of the ship are of great significance for improving the safety level of the ship. Aiming at the multi-dimensional characteristics of ship navigation behavior and the accuracy and real-time requirements of ship traffic service system for ship trajectory prediction, a ship navigation trajectory prediction method combining ship automatic identification system information and Back Propagation (BP) neural network are proposed. According to the basic principle of BP neural network structure, the BP neural network is trained by taking the characteristic values of ship navigation behavior at three consecutive moments as input and the characteristic values of ship navigation behavior at the fourth moment as output to predict the future ship navigation trajectory. Based on the Automatic Identification System (AIS) information of the waters near the Nanpu Bridge in Pudong New Area, Shanghai, the results show that the method is used to predict the ship's navigational behavior eigenvalues accurately and in real time. Compared with the traditional kinematics prediction trajectory method, the model can effectively predict ship navigation. The trajectory improves the accuracy of the ship's motion situation prediction, and has the advantages of high computational efficiency and strong versatility, and the error is within an acceptable range.

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“…Without incorporating motion patterns, directly applying statistical or machine learning approaches and neural networks may lead to large error. The pattern of vessels can be extracted by clustering the trajectories, Latticebased DBSCAN and fuzzy c-means algorithms were proposed in [7,24]. With the assistance of automatic identification system (AIS), the authors in [25] were able to predict future trajectories of surrounding vessels in a recursively way.…”

Section: Related Workmentioning

confidence: 99%

LSTM-based Flight Trajectory Prediction

Shi

Pan

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

104

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Safety ranks the first in Air Traffic Management (ATM). Accurate trajectory prediction can help ATM to forecast potential dangers and effectively provide instructions for safely traveling. Most trajectory prediction algorithms work for land traffic, which rely on points of interest (POIs) and are only suitable for stationary road condition. Compared with land traffic prediction, flight trajectory prediction is very difficult because way-points are sparse and the flight envelopes are heavily affected by external factors. In this paper, we propose a flight trajectory prediction model based on a Long Short-Term Memory (LSTM) network. The four interacting layers of a repeating module in an LSTM enables it to connect the long-term dependencies to present predicting task. Applying sliding windows in LSTM maintains the continuity and avoids compromising the dynamic dependencies of adjacent states in the long-term sequences, which helps to improve accuracy of trajectory prediction. Taking time dimension into consideration, both 3-D (time stamp, latitude and longitude) and 4-D (time stamp, latitude, longitude and altitude) trajectories are predicted to prove the efficiency of our approach. The dataset we use was collected by ADS-B ground stations. We evaluate our model by widely used measurements, such as the mean absolute error (MAE), the mean relative error (MRE), the root mean square error (RMSE) and the dynamic warping time (DWT) methods. As Markov Model is the most popular in time series processing, comparisons among Markov Model (MM), weighted Markov Model (wMM) and our model are presented. Our model outperforms the existing models (MM and wMM) and provides a strong basis for abnormal detection and decision-making.

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Trajectory Length Prediction for Intelligent Traffic Signaling: A Data-Driven Approach

Cited by 26 publications

References 15 publications

Navigational risk analysis based on GIS spatiotemporal trajectory mining: a case study in Nanjing Yangtze River Bridge waters

Navigational risk analysis based on GIS spatiotemporal trajectory mining: a case study in Nanjing Yangtze River Bridge waters

Ship Trajectory Prediction Based on BP Neural Network

LSTM-based Flight Trajectory Prediction

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