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Internet of Things and Artificial Intelligence in Transportation Revolution

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 58766

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Special Issue Editors

Prof. Dr. Miltiadis D. Lytras

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Computer Science Department, College of Engineering, Effat University, Jeddah, Saudi Arabia
Interests: cognitive computing; artificial intelligence; data science; bioinformatics; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; knowledge management; semantic web
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Dr. Kwok Tai Chui

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Guest Editor

Department of Technology, School of Science and Technology, The Open University of Hong Kong, Ho Man Tin, Hong Kong
Interests: big data; bioinformatics; computational intelligence; data science; energy monitoring and management; intelligent transportation; optimization; semantic web
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Dr. Ryan Wen Liu

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Guest Editor

School of Navigation, Wuhan University of Technology, Wuhan 430063, China
Interests: computer vision (unmanned vehicle); ship trajectory data mining; maritime intelligent transportation system
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Special Issue Information

Dear Colleagues,

Internet of Things (IoT) has become the leading infrastructure for our modern and smart transport. Connected sensing devices provide crucial information for further analytics via artificial intelligence (AI) which aims at optimizing the performance of transportation systems and applications. One of the best examples is the autonomous vehicle which is supported by numerous sensors like GPS, sonar, Lidar, radar, camera, inertial and odometry modules.

There are many use cases of IoT applications in intelligent transportation system (ITS) named geo-fencing, asset utilization, inventory management, public transport management, traffic monitoring, situational awareness, urban planning, fleet management, predictive maintenance, and traffic safety enhancement. However, similar to other applications, the adoption of IoT in transportation also experiences technical challenges, for instance, security, privacy, standard, regulation, connectivity, network infrastructure and investment cost.

This special issue is intended to report high-quality research on recent advances towards IoT and AI in transportation revolution, more specifically to the state-of-the-art theories, methodologies and systems for the design, development, deployment and innovative use of those convergence technologies for providing insights into the theoretical and technological revolution in transportation science and engineering. The topics of interest include, but are not limited to the following:

Sensor-based traffic data acquisition in transportation revolution
IoT big data storage and mining techniques
Large scale traffic data mining and visualization
Multi-sensor data fusion for IoT applications
AI techniques for IoT big data in ITS systems
Signal, image and video processing technologies in ITS systems
Autonomous, semi-autonomous and IoT control
New theories and applications of AI techniques in transport IoT
Unmanned aerial vehicles-assisted communications in transport IoT
IoT-based situational awareness framework for ITS systems
5G-enabled IoT sensors and techniques in transportation revolution

Prof. Miltiadis D. Lytras
Dr. Kwok Tai Chui
Dr. Ryan Wen Liu
Guest Editors

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Published Papers (11 papers)

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Editorial

Jump to: Research

4 pages, 163 KiB

Open AccessEditorial

Moving Towards Intelligent Transportation via Artificial Intelligence and Internet-of-Things

by Miltiadis D. Lytras, Kwok Tai Chui and Ryan Wen Liu

Sensors 2020, 20(23), 6945; https://doi.org/10.3390/s20236945 - 4 Dec 2020

Cited by 8 | Viewed by 2893

Abstract

One of the key smart city visions is to bring smarter transport networks, specifically intelligent/smart transportation [...] Full article

(This article belongs to the Special Issue Internet of Things and Artificial Intelligence in Transportation Revolution)

Research

Jump to: Editorial

24 pages, 9825 KiB

Open AccessArticle

On the Application of Time Frequency Convolutional Neural Networks to Road Anomalies’ Identification with Accelerometers and Gyroscopes

by Gianmarco Baldini, Raimondo Giuliani and Filip Geib

Sensors 2020, 20(22), 6425; https://doi.org/10.3390/s20226425 - 10 Nov 2020

Cited by 24 | Viewed by 3020

Abstract

The detection and identification of road anomalies and obstacles in the road infrastructure has been investigated by the research community using different types of sensors. This paper evaluates the detection and identification of road anomalies/obstacles using the data collected from the Inertial Measurement Unit (IMU) installed in a vehicle and in particular from the data generated by the accelerometers’ and gyroscopes’ components. Inspired by the successes of the application of deep learning to various identification problems, this paper investigates the application of Convolutional Neural Network (CNN) to this specific problem. In particular, we propose a novel approach in this context where the time-frequency representation (i.e., spectrogram) is used as an input to the CNN rather than the original time domain data. This approach is evaluated on an experimental dataset collected using 12 different vehicles driving for more than 40 km of road. The results show that the proposed approach outperforms significantly and across different sampling rates both the application of CNN to the original time domain representation and the application of shallow machine learning algorithms. The approach achieves an identification accuracy of 97.2%. The results presented in this paper are based on an extensive optimization both of the CNN algorithm and the spectrogram implementation in terms of window size, type of window, and overlapping ratio. The accurate detection of road anomalies/obstacles could be useful to road infrastructure managers to monitor the quality of the road surface and to improve the accurate positioning of autonomous vehicles because road anomalies/obstacles could be used as landmarks. Full article

(This article belongs to the Special Issue Internet of Things and Artificial Intelligence in Transportation Revolution)

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18 pages, 3406 KiB

Open AccessArticle

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

by Kh Tohidul Islam, Ram Gopal Raj, Syed Mohammed Shamsul Islam, Sudanthi Wijewickrema, Md Sazzad Hossain, Tayla Razmovski and Stephen O’Leary

Sensors 2020, 20(12), 3578; https://doi.org/10.3390/s20123578 - 24 Jun 2020

Cited by 11 | Viewed by 8008

Abstract

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications. Full article

(This article belongs to the Special Issue Internet of Things and Artificial Intelligence in Transportation Revolution)

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17 pages, 1001 KiB

Open AccessArticle

Using Deep Learning to Forecast Maritime Vessel Flows

by Xiangyu Zhou, Zhengjiang Liu, Fengwu Wang, Yajuan Xie and Xuexi Zhang

Sensors 2020, 20(6), 1761; https://doi.org/10.3390/s20061761 - 22 Mar 2020

Cited by 33 | Viewed by 5916

Abstract

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 \times 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. Full article

(This article belongs to the Special Issue Internet of Things and Artificial Intelligence in Transportation Revolution)

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15 pages, 3401 KiB

Open AccessArticle

Hybrid Dynamic Traffic Model for Freeway Flow Analysis Using a Switched Reduced-Order Unknown-Input State Observer

by Yuqi Guo, Bin Li, Matthew Daniel Christie, Zongzhi Li, Miguel Angel Sotelo, Yulin Ma, Dongmei Liu and Zhixiong Li

Sensors 2020, 20(6), 1609; https://doi.org/10.3390/s20061609 - 13 Mar 2020

Cited by 5 | Viewed by 2606

Abstract

This paper introduces a new methodology for reconstructing vehicle densities of freeway segments by utilizing the limited data collected by traffic-counting sensors and developing a macroscopic traffic stream model formulated as a switched reduced-order state observer design problem with unknown or partially known inputs. Specifically, the traffic network is modeled as a hybrid dynamic system in a state space that incorporates unknown inputs. For freeway segments with traffic-counting sensors installed, vehicle densities are directly computed using field traffic count data. A reduced-order state observer is designed to analyze traffic state transitions for freeway segments without field traffic count data to indirectly estimate the vehicle densities for each freeway segment. A simulation-based experiment is performed applying the methodology and using data of a segment of Beijing Jingtong freeway in Beijing, China. The model execution results are compared with the field data associated with the same freeway segment, and highly consistent results are achieved. The proposed methodology is expected to be adopted by traffic engineers to evaluate freeway operations and develop effective management strategies. Full article

(This article belongs to the Special Issue Internet of Things and Artificial Intelligence in Transportation Revolution)

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20 pages, 3674 KiB

Open AccessArticle

A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM

by Kwok Tai Chui, Miltiadis D. Lytras and Ryan Wen Liu

Sensors 2020, 20(5), 1474; https://doi.org/10.3390/s20051474 - 7 Mar 2020

Cited by 34 | Viewed by 3659

Abstract

Driver drowsiness and stress are major causes of traffic deaths and injuries, which ultimately wreak havoc on world economic loss. Researchers are in full swing to develop various algorithms for both drowsiness and stress recognition. In contrast to existing works, this paper proposes a generic model using multiple-objective genetic algorithm optimized deep multiple kernel learning support vector machine that is capable to recognize both driver drowsiness and stress. This algorithm simplifies the research formulations and model complexity that one model fits two applications. Results reveal that the proposed algorithm achieves an average sensitivity of 99%, specificity of 98.3% and area under the receiver operating characteristic curve (AUC) of 97.1% for driver drowsiness recognition. For driver stress recognition, the best performance is yielded with average sensitivity of 98.7%, specificity of 98.4% and AUC of 96.9%. Analysis also indicates that the proposed algorithm using multiple-objective genetic algorithm has better performance compared to the grid search method. Multiple kernel learning enhances the performance significantly compared to single typical kernel. Compared with existing works, the proposed algorithm not only achieves higher accuracy but also addressing the typical issues of dataset in simulated environment, no cross-validation and unreliable measurement stability of input signals. Full article

(This article belongs to the Special Issue Internet of Things and Artificial Intelligence in Transportation Revolution)

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35 pages, 11554 KiB

Open AccessArticle

An Autonomous Path Planning Model for Unmanned Ships Based on Deep Reinforcement Learning

by Siyu Guo, Xiuguo Zhang, Yisong Zheng and Yiquan Du

Sensors 2020, 20(2), 426; https://doi.org/10.3390/s20020426 - 11 Jan 2020

Cited by 146 | Viewed by 12582

Abstract

Deep reinforcement learning (DRL) has excellent performance in continuous control problems and it is widely used in path planning and other fields. An autonomous path planning model based on DRL is proposed to realize the intelligent path planning of unmanned ships in the unknown environment. The model utilizes the deep deterministic policy gradient (DDPG) algorithm, through the continuous interaction with the environment and the use of historical experience data; the agent learns the optimal action strategy in a simulation environment. The navigation rules and the ship’s encounter situation are transformed into a navigation restricted area, so as to achieve the purpose of planned path safety in order to ensure the validity and accuracy of the model. Ship data provided by ship automatic identification system (AIS) are used to train this path planning model. Subsequently, the improved DRL is obtained by combining DDPG with the artificial potential field. Finally, the path planning model is integrated into the electronic chart platform for experiments. Through the establishment of comparative experiments, the results show that the improved model can achieve autonomous path planning, and it has good convergence speed and stability. Full article

(This article belongs to the Special Issue Internet of Things and Artificial Intelligence in Transportation Revolution)

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17 pages, 1974 KiB

Open AccessArticle

Simultaneous Optimization of Vehicle Arrival Time and Signal Timings within a Connected Vehicle Environment

by Wei Wu, Ling Huang and Ronghua Du

Sensors 2020, 20(1), 191; https://doi.org/10.3390/s20010191 - 29 Dec 2019

Cited by 39 | Viewed by 3887

Abstract

Most existing signal timing plans are optimized given vehicles’ arrival time (i.e., the time for the upcoming vehicles to arrive at the stop line) as exogenous input. In this paper, based on the connected vehicle (CV) technique, vehicles can be regarded as moving sensors, and their arrival time can be dynamically adjusted by speed guidance according to the current signal status and traffic conditions. Therefore, an integrated traffic control model is proposed in this study to optimize vehicle arrival time (or travel speed) and signal timing simultaneously. “Speed guidance model at a red light” and “speed guidance model at a green light” are presented to model the influences between travel speed and signal timing. Then, the methods to model the vehicle arrival time, vehicle delay, and number of stops are proposed. The total delay, which includes the control delay, queuing delay, and signal delay, is used as the objective of the proposed model. The decision variables consist of vehicle arrival time, starting time of green, and duration of green for each phase. The sliding time window is adopted to dynamically tackle the problems. Compared with the results optimized by the classical actuated signal control method and the fixed-time-based speed guidance model, the proposed model can significantly decrease travel delays as well as improve the flexibility and mobility of traffic control. The sensitivity analysis with the communication distance, the market penetration of connected vehicles, and the compliance rate of speed guidance further demonstrates the potential of the proposed model to be applied in various traffic conditions. Full article

(This article belongs to the Special Issue Internet of Things and Artificial Intelligence in Transportation Revolution)

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29 pages, 18971 KiB

Open AccessArticle

Continuous Authentication of Automotive Vehicles Using Inertial Measurement Units

by Gianmarco Baldini, Filip Geib and Raimondo Giuliani

Sensors 2019, 19(23), 5283; https://doi.org/10.3390/s19235283 - 30 Nov 2019

Cited by 2 | Viewed by 3428

Abstract

The concept of Continuous Authentication is to authenticate an entity on the basis of a digital output generated in a continuous way by the entity itself. This concept has recently been applied in the literature for the continuous authentication of persons on the basis of intrinsic features extracted from the analysis of the digital output generated by wearable sensors worn by the subjects during their daily routine. This paper investigates the application of this concept to the continuous authentication of automotive vehicles, which is a novel concept in the literature and which could be used where conventional solutions based on cryptographic means could not be used. In this case, the Continuous Authentication concept is implemented using the digital output from Inertial Measurement Units (IMUs) mounted on the vehicle, while it is driving on a specific road path. Different analytical approaches based on the extraction of statistical features from the time domain representation or the use of frequency domain coefficients are compared and the results are presented for various conditions and road segments. The results show that it is possible to authenticate vehicles from the Inertial Measurement Unit (IMU) recordings with great accuracy for different road segments. Full article

(This article belongs to the Special Issue Internet of Things and Artificial Intelligence in Transportation Revolution)

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18 pages, 2137 KiB

Open AccessArticle

Start from Scratch: A Crowdsourcing-Based Data Fusion Approach to Support Location-Aware Applications

by Yonghang Jiang, Bingyi Liu, Ze Wang and Xiaoquan Yi

Sensors 2019, 19(20), 4518; https://doi.org/10.3390/s19204518 - 17 Oct 2019

Cited by 4 | Viewed by 2760

Abstract

As one of the most important breakthroughs for modern transportation, the indoor location-based technology has been gradually penetrating into our daily lives and underlines the foundation of the Internet of Things (IoT). To improve the positioning accuracy and efficiency, crowdsourcing has been widely applied in indoor localization in recent years. However, the crowdsourced data can hardly be fused easily to enable usable applications for the reason that the data are collected by different users, in different locations, at different times, with different noises and distortions. Although different data fusing methods have been implemented in different crowdsourcing services, we find that they may not fully leverage the data collected from multiple dimensions that can potentially lead to a better fusion results. In order to address this problem, we propose a more general solution, which can fuse the multi-dimensional crowdsourced data together and align them with the consistent time and location stamps, by using the features of the sensory data only, and thus build high quality crowdsourcing services from the raw data samplings collected from the environment. Finally, we conduct extensive evaluations and experiments using different commercial devices to validate the effectiveness of the method we proposed. Full article

(This article belongs to the Special Issue Internet of Things and Artificial Intelligence in Transportation Revolution)

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18 pages, 4636 KiB

Open AccessArticle

Decision-Making for the Autonomous Navigation of Maritime Autonomous Surface Ships Based on Scene Division and Deep Reinforcement Learning

by Xinyu Zhang, Chengbo Wang, Yuanchang Liu and Xiang Chen

Sensors 2019, 19(18), 4055; https://doi.org/10.3390/s19184055 - 19 Sep 2019

Cited by 80 | Viewed by 7757

Abstract

This research focuses on the adaptive navigation of maritime autonomous surface ships (MASSs) in an uncertain environment. To achieve intelligent obstacle avoidance of MASSs in a port, an autonomous navigation decision-making model based on hierarchical deep reinforcement learning is proposed. The model is mainly composed of two layers: the scene division layer and an autonomous navigation decision-making layer. The scene division layer mainly quantifies the sub-scenarios according to the International Regulations for Preventing Collisions at Sea (COLREG). This research divides the navigational situation of a ship into entities and attributes based on the ontology model and Protégé language. In the decision-making layer, we designed a deep Q-learning algorithm utilizing the environmental model, ship motion space, reward function, and search strategy to learn the environmental state in a quantized sub-scenario to train the navigation strategy. Finally, two sets of verification experiments of the deep reinforcement learning (DRL) and improved DRL algorithms were designed with Rizhao port as a study case. Moreover, the experimental data were analyzed in terms of the convergence trend, iterative path, and collision avoidance effect. The results indicate that the improved DRL algorithm could effectively improve the navigation safety and collision avoidance. Full article

(This article belongs to the Special Issue Internet of Things and Artificial Intelligence in Transportation Revolution)

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