scholarly journals Fuzzy Ontology and LSTM-based Text Mining: A Transportation Network Monitoring System for Assisting Travel

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 234 ◽  
Author(s):  
Farman Ali ◽  
Shaker El-Sappagh ◽  
Daehan Kwak
Keyword(s):  
Social Media ◽  
Intelligent Transportation Systems ◽  
Short Term Memory ◽  
Transportation Network ◽  
Semantic Knowledge ◽  
Transportation Systems ◽  
Traffic Information ◽  
Fuzzy Ontology ◽  
Transport Services ◽  
Ontology Language

Intelligent Transportation Systems (ITSs) utilize a sensor network-based system to gather and interpret traffic information. In addition, mobility users utilize mobile applications to collect transport information for safe traveling. However, these types of information are not sufficient to examine all aspects of the transportation networks. Therefore, both ITSs and mobility users need a smart approach and social media data, which can help ITSs examine transport services, support traffic and control management, and help mobility users travel safely. People utilize social networks to share their thoughts and opinions regarding transportation, which are useful for ITSs and travelers. However, user-generated text on social media is short in length, unstructured, and covers a broad range of dynamic topics. The application of recent Machine Learning (ML) approach is inefficient for extracting relevant features from unstructured data, detecting word polarity of features, and classifying the sentiment of features correctly. In addition, ML classifiers consistently miss the semantic feature of the word meaning. A novel fuzzy ontology-based semantic knowledge with Word2vec model is proposed to improve the task of transportation features extraction and text classification using the Bi-directional Long Short-Term Memory (Bi-LSTM) approach. The proposed fuzzy ontology describes semantic knowledge about entities and features and their relation in the transportation domain. Fuzzy ontology and smart methodology are developed in Web Ontology Language and Java, respectively. By utilizing word embedding with fuzzy ontology as a representation of text, Bi-LSTM shows satisfactory improvement in both the extraction of features and the classification of the unstructured text of social media.

scholarly journals Using Deep Learning to Forecast Maritime Vessel Flows

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1761 ◽  
Author(s):  
Xiangyu Zhou ◽  
Zhengjiang Liu ◽  
Fengwu Wang ◽  
Yajuan Xie ◽  
Xuexi Zhang
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Short Term Memory ◽  
Absolute Error ◽  
Transportation Systems ◽  
Traffic Information ◽  
Automatic Identification ◽  
Identification System ◽  
System Data ◽  
Lstm Network

Forecasting vessel flows is important to the development of intelligent transportation systems in the maritime field, as real-time and accurate traffic information has favorable potential in helping a maritime authority to alleviate congestion, mitigate emission of GHG (greenhouse gases) and enhance public safety, as well as assisting individual vessel users to plan better routes and reduce additional costs due to delays. In this paper, we propose three deep learning-based solutions to forecast the inflow and outflow of vessels within a given region, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and the integration of a bidirectional LSTM network with a CNN (BDLSTM-CNN). To apply those solutions, we first divide the given maritime region into M × N grids, then we forecast the inflow and outflow for all the grids. Experimental results based on the real AIS (Automatic Identification System) data of marine vessels in Singapore demonstrate that the three deep learning-based solutions significantly outperform the conventional method in terms of mean absolute error and root mean square error, with the performance of the BDLSTM-CNN-based hybrid solution being the best.

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

scholarly journals Blockchain-based Reputation for Intelligent Transportation Systems

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 791 ◽  
Author(s):  
Liviu-Adrian Hîrţan ◽  
Ciprian Dobre ◽  
Horacio González-Vélez
Keyword(s):  
Internet Of Things ◽  
San Francisco ◽  
Intelligent Transportation Systems ◽  
Routing Algorithm ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Information ◽  
Disruptive Technology ◽  
Average Speed ◽  
The Impact

A disruptive technology often used in finance, Internet of Things (IoT) and healthcare, blockchain can reach consensus within a decentralised network—potentially composed of large amounts of unreliable nodes—and to permanently and irreversibly store data in a tamper-proof manner. In this paper, we present a reputation system for Intelligent Transportation Systems (ITS). It considers the users interested in traffic information as the main actors of the architecture. They securely share their data which are collectively validated by other users. Users can choose to employ either such crowd-sourced validated data or data generated by the system to travel between two locations. The data saved is reliable, based on the providers’ reputation and cannot be modified. We present results with a simulation for three cities: San Francisco, Rome and Beijing. We have demonstrated the impact of malicious attacks as the average speed decreased if erroneous information was stored in the blockchain as an implemented routing algorithm guides the honest cars on other free routes, and thus crowds other intersections.

scholarly journals Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
M. Benalla ◽  
B. Achchab ◽  
H. Hrimech
Keyword(s):  
Intelligent Transportation Systems ◽  
Contextual Information ◽  
Sensory Information ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Evidential Reasoning ◽  
Traffic Information ◽  
Driver Assistance ◽  
Agent Based ◽  
Reasoning System

Providing accurate real-time traffic information is an inherent problem for intelligent transportation systems (ITS). In order to improve the knowledge base of advanced driver assistance systems (ADAS), ITS are strongly concerned with data fusion techniques of all kinds of sensors deployed over the traffic network. Driver assistance is devoid of a comprehensive evidential reasoning system on contextual information, more specifically when a combination involves inside and outside sensory information of the driving environment. In this paper, we propose a novel agent-based evidential reasoning system using contextual information. Based on a series of information handling techniques, specifically, the belief functions theory and heuristic inference operations to achieve a consensus about daily driving activity in automatically inferring. That is quite different from other existing proposals, as it deals jointly with the driving behavior and the driving environment conditions. A case study including various scenarios of experiments is introduced to estimate behavioral information based on synthetic data for prediction, prescription, and policy analysis. Our experiments show promising, thought-provoking results encouraging further research.

scholarly journals Predicting Future Locations of Moving Objects by Recurrent Mixture Density Network

2020 ◽  
Vol 9 (2) ◽  
pp. 116
Author(s):  
Rui Chen ◽  
Mingjian Chen ◽  
Wanli Li ◽  
Naikun Guo
Keyword(s):  
Prediction Model ◽  
Intelligent Transportation Systems ◽  
Moving Objects ◽  
Short Term Memory ◽  
Transportation Systems ◽  
Performance Comparison ◽  
Parameter Analysis ◽  
Location Prediction ◽  
Time Step ◽  
Mixture Density

Accurate and timely location prediction of moving objects is crucial for intelligent transportation systems and traffic management. In recent years, ubiquitous location acquisition technologies have provided the opportunity for mining knowledge from trajectories, making location prediction and real-time decisions more feasible. Previous location prediction methods have mostly developed on the basis of shallow models whereas shallow models are not competent for some tricky challenges such as multi-time-step location coordinates prediction. Motivated by the current study status, we are dedicated to a deep-learning-based approach to predict the coordinates of several future locations of moving objects based on recent trajectory records. The method of this work consists of three successive parts: trajectory preprocessing, prediction model construction, and post-processing. In this work, a prediction model named the bidirectional recurrent mixture density network (BiRMDN) was constructed by integrating the long short-term memory (LSTM) and mixture density network (MDN) together. This model has the ability to learn long-term contextual information from recent trajectory and model real-valued location coordinates. We employed a vessel trajectory dataset for the implementation of this approach and determined the optimal model configuration after several parameter analysis experiments. Experimental results involving a performance comparison with other widely used methods demonstrate the superiority of the BiRMDN model.

scholarly journals A New Distributed and Scalable Network Protocol Targeting Intelligent Transportation Systems

2011 ◽  
Vol 2011 ◽  
pp. 1-7
Author(s):  
M. Meribout
Keyword(s):  
Intelligent Transportation Systems ◽  
Road Traffic ◽  
Response Times ◽  
Cluster Head ◽  
Data Communication ◽  
Intelligent Transportation ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Traffic Information ◽  
Scalable Network

Vehicular networks are the major ingredients of the envisioned Intelligent Transportation Systems (ITS) concept. An important component of ITS which is currently attracting wider research focus is road traffic monitoring. The actual approaches for traffic road monitoring are characterized by longer response times and are also subject to higher processing requirements and possess high deployment costs. In this paper, we propose a completely distributed and scalable mechanism for wireless sensor network-based road traffic monitoring. The approach relies on the distributed and bidirectional exchange of traffic information between the vehicles traversing the routes and a miniature cluster head and takes into consideration both the security and reliability of data communication. In addition, the communication between nodes is collision-free since the underlined data link layer protocol relies on a heuristic time multiplexed-based protocol. The performance analysis shows that the proposed mechanism usually outperforms other algorithms for different traffic densities.

Road Traffic Congestion (TraCo) Estimation Using Multi-Layer Continuous Virtual Loop (MCVL)

2021 ◽  
Vol 17 (2) ◽  
pp. 46-71
Author(s):  
Manipriya Sankaranarayanan ◽  
Mala C. ◽  
Samson Mathew
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Vehicle Detection ◽  
Transportation Systems ◽  
Traffic Information ◽  
Traffic Characteristics ◽  
Traffic Video ◽  
Virtual Loop

Any road traffic management application of intelligent transportation systems (ITS) requires traffic characteristics data such as vehicle density, speed, etc. This paper proposes a robust and novel vehicle detection framework known as multi-layer continuous virtual loop (MCVL) that uses computer vision techniques on road traffic video to estimate traffic characteristics. Estimations of traffic data such as speed, area occupancy and an exclusive spatial feature named as corner detail value (CDV) acquired using MCVL are proposed. Further, the estimation of traffic congestion (TraCo) level using these parameters is also presented. The performances of the entire framework and TraCo estimation are evaluated using several benchmark traffic video datasets and the results are presented. The results show that the improved accuracy in vehicle detection process using MCVL subsequently improves the precision of TraCo estimation. This also means that the proposed framework is well suited to applications that need traffic characteristics to update their traffic information system in real time.

Analysis of the Influence of Traffic Information Messages on Route Selection

1995 ◽  
Vol 39 (17) ◽  
pp. 1082-1086
Author(s):  
W. Bradley Fain
Keyword(s):  
Situation Awareness ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Information ◽  
Route Selection ◽  
Variable Message Signs ◽  
Message Format ◽  
Message Signs

Intelligent Transportation Systems (ITS) can reduce traffic congestion by displaying congestion-related delay information on roadside variable message signs or in-vehicle displays. Message format and content may have a significant impact on the percentage of drivers who decide to make a route diversion. In this study, the effect of various traffic information message types on driver routing decisions was evaluated. Results suggest that messages including both an advisory and a descriptive component promote situation awareness and rapid decision making, both of which are critical for this application.

Determination of Routing Velocity with GPS Floating Car Data and WebGIS-Based Instantaneous Traffic Information Dissemination

2008 ◽  
Vol 61 (2) ◽  
pp. 337-353 ◽  
Author(s):  
Chun Liu ◽  
Xiaolin Meng ◽  
Yeming Fan
Keyword(s):  
Intelligent Transportation Systems ◽  
Information Dissemination ◽  
Field Testing ◽  
Transportation Systems ◽  
Geographical Information ◽  
Road Segment ◽  
Traffic Information ◽  
Map Matching ◽  
Floating Car Data ◽  
Traffic Information Dissemination

The acquisition of accurate and timely traffic information is a vital precondition to rational traffic decision making. Intelligent Transportation Systems (ITS) are bound to be the outcome when modern traffic systems develop to a high degree. In ITS, instantaneous traffic information can be collected by the Floating Car Data (FCD) method. Based on the establishment of the Shenzhen Urban Transportation Simulation System (SUTSS) in China, the authors explored how to use 4000 taxis as the data collection sensors in Shenzhen, a southern city in China which borders Hong Kong. The authors introduce the procedures and algorithms for the computation and map-matching of road segment velocities to a digital road network. To superimpose the near real-time traffic information onto a digital map, coordinate transformation is required and the transformation precision is analyzed using field testing data. Due to the nature of FCD, continuous GPS data such as routing velocities and coordinates can be collected by any GPS equipped vehicle. Therefore, relevant algorithms are developed and utilized for the map-matching according to probability and statistical theories. To evaluate the reliability of proposed map-matching method, the confidence levels are calculated statistically, from which it can be determined whether the positioning data is valid or not with predefined threshold values. Furthermore, road segment velocity matching methods based on the Metropolis criteria is extended and relevant validation is carried out through the comparison of estimated and measured results. The major objective of this method is to obtain more accurate road segment travel time through the combination of those estimated by FCD and historical ones. This can significantly improve the reliability of instantaneous traffic information before its web publication. The final part of this paper introduces the architecture and the realization of a web Geographical Information System (GIS) and FCD-based instantaneous traffic information dissemination system for the whole of Shenzhen City.

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