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Sensor Technologies and Machine Learning for Intelligent Transportation Systems
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Sensor Technologies and Machine Learning for Intelligent Transportation Systems

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

Deadline for manuscript submissions: closed (25 August 2024) | Viewed by 7978

Special Issue Editors

Prof. Dr. Cheng Siong Chin

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Guest Editor
Intelligent Systems Design, Newcastle University, Singapore 038986, Singapore
Interests: intelligent systems design of complex systems in uncertain environments (underwater/electric vehicle, battery, PV system, acoustic enclosure, and water distribution network) involving predictive analytics (data mining, predictive modeling, and machine learning)
Special Issues, Collections and Topics in MDPI journals
Dr. Dariush Ebrahimi

E-Mail Website
Guest Editor
Department of Physics and Computer Science, Wilfrid Laurier University, 75 University Ave. W, Waterloo, ON N2L 3C5, Canada
Interests: internet of things; intelligent transportation systems; wireless communications; cloud and edge computing; data mining and machine learning; algorithm design and optimization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jean Chamberlain Chedjou

E-Mail Website
Guest Editor
Associate Professor, Department of Smart System Technologies, University of Klagenfurt, 9020 Klagenfurt, Austria
Interests: analog computing; dynamical systems; neuro-computing with applications in systems simulation and ultra-fast differential equations solving; nonlinear oscillatory theory with applications; traffic modeling and simulation; traffic telematics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent transportation systems (ITSs) are dedicated to enhancing transportation safety, efficiency, and mobility by seamlessly integrating cutting-edge technologies into both transportation infrastructure and vehicles. This Special Issue is specifically centered on the pivotal role played by sensor technologies within the realm of ITSs and their collaboration with machine learning techniques. Sensors, encompassing devices such as cameras, radar, LiDAR, and GPS, are fundamental in collecting crucial data pertaining to road and traffic conditions, vehicle performance, and driver behavior. Simultaneously, machine learning empowers the processing and analysis of vast, diverse datasets derived from transportation systems, yielding valuable insights. This Special Issue serves as a platform for consolidating groundbreaking research that employs sensor technologies and machine learning algorithms to address critical challenges within ITSs, including autonomous driving, traffic forecasting and management, public transportation optimization, infrastructure monitoring, vehicular network communication, and human–vehicle interactions.

We welcome contributions that span a wide spectrum, encompassing fundamental insights into sensor technologies, machine learning theories for ITSs, as well as applied research aimed at ITS applications. Submissions focused on issues related to safety, simulation, forecasting, efficiency, accessibility, and sustainability in the domain of transportation are particularly encouraged.  We also encourage contributions in the field of smart-home monitoring and control, remote monitoring in transportation and communication applications. 

Prof. Dr. Cheng Siong Chin
Dr. Dariush Ebrahimi
Prof. Dr. Jean Chamberlain Chedjou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent transportation systems (ITS)
  • sensors and sensor technologies for ITS
  • machine learning
  • autonomous driving
  • traffic forecasting and management

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Published Papers (4 papers)

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Research

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22 pages, 27460 KiB  
Article
Towards Efficient Risky Driving Detection: A Benchmark and a Semi-Supervised Model
by Qimin Cheng, Huanying Li, Yunfei Yang, Jiajun Ling and Xiao Huang
Sensors 2024, 24(5), 1386; https://doi.org/10.3390/s24051386 - 21 Feb 2024
Viewed by 1561
Abstract
Risky driving is a major factor in traffic incidents, necessitating constant monitoring and prevention through Intelligent Transportation Systems (ITS). Despite recent progress, a lack of suitable data for detecting risky driving in traffic surveillance settings remains a significant challenge. To address this issue, [...] Read more.
Risky driving is a major factor in traffic incidents, necessitating constant monitoring and prevention through Intelligent Transportation Systems (ITS). Despite recent progress, a lack of suitable data for detecting risky driving in traffic surveillance settings remains a significant challenge. To address this issue, Bayonet-Drivers, a pioneering benchmark for risky driving detection, is proposed. The unique challenge posed by Bayonet-Drivers arises from the nature of the original data obtained from intelligent monitoring and recording systems, rather than in-vehicle cameras. Bayonet-Drivers encompasses a broad spectrum of challenging scenarios, thereby enhancing the resilience and generalizability of algorithms for detecting risky driving. Further, to address the scarcity of labeled data without compromising detection accuracy, a novel semi-supervised network architecture, named DGMB-Net, is proposed. Within DGMB-Net, an enhanced semi-supervised method founded on a teacher–student model is introduced, aiming at bypassing the time-consuming and labor-intensive tasks associated with data labeling. Additionally, DGMB-Net has engineered an Adaptive Perceptual Learning (APL) Module and a Hierarchical Feature Pyramid Network (HFPN) to amplify spatial perception capabilities and amalgamate features at varying scales and levels, thus boosting detection precision. Extensive experiments on widely utilized datasets, including the State Farm dataset and Bayonet-Drivers, demonstrated the remarkable performance of the proposed DGMB-Net. Full article
(This article belongs to the Special Issue Sensor Technologies and Machine Learning for Intelligent Transportation Systems)
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19 pages, 4884 KiB  
Article
A Unified Spatio-Temporal Inference Network for Car-Sharing Serial Prediction
by Nihad Brahimi, Huaping Zhang, Syed Danial Asghar Zaidi and Lin Dai
Sensors 2024, 24(4), 1266; https://doi.org/10.3390/s24041266 - 16 Feb 2024
Cited by 2 | Viewed by 1917
Abstract
Car-sharing systems require accurate demand prediction to ensure efficient resource allocation and scheduling decisions. However, developing precise predictive models for vehicle demand remains a challenging problem due to the complex spatio-temporal relationships. This paper introduces USTIN, the Unified Spatio-Temporal Inference Prediction Network, a [...] Read more.
Car-sharing systems require accurate demand prediction to ensure efficient resource allocation and scheduling decisions. However, developing precise predictive models for vehicle demand remains a challenging problem due to the complex spatio-temporal relationships. This paper introduces USTIN, the Unified Spatio-Temporal Inference Prediction Network, a novel neural network architecture for demand prediction. The model consists of three key components: a temporal feature unit, a spatial feature unit, and a spatio-temporal feature unit. The temporal unit utilizes historical demand data and comprises four layers, each corresponding to a different time scale (hourly, daily, weekly, and monthly). Meanwhile, the spatial unit incorporates contextual points of interest data to capture geographic demand factors around parking stations. Additionally, the spatio-temporal unit incorporates weather data to model the meteorological impacts across locations and time. We conducted extensive experiments on real-world car-sharing data. The proposed USTIN model demonstrated its ability to effectively learn intricate temporal, spatial, and spatiotemporal relationships, and outperformed existing state-of-the-art approaches. Moreover, we employed negative binomial regression with uncertainty to identify the most influential factors affecting car usage. Full article
(This article belongs to the Special Issue Sensor Technologies and Machine Learning for Intelligent Transportation Systems)
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19 pages, 1047 KiB  
Article
Assessment of Drivers’ Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios
by Jiaqi Huang, Qiliang Zhang, Tingru Zhang, Tieyan Wang and Da Tao
Sensors 2024, 24(3), 1041; https://doi.org/10.3390/s24031041 - 5 Feb 2024
Cited by 4 | Viewed by 2781
Abstract
Assessing drivers’ mental workload is crucial for reducing road accidents. This study examined drivers’ mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels [...] Read more.
Assessing drivers’ mental workload is crucial for reducing road accidents. This study examined drivers’ mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels of mental workload (i.e., low, medium, high) were manipulated by varying the difficulty levels of the secondary task (i.e., no presence of secondary task, 1-back, 2-back). Multimodal measures, including a set of subjective measures, physiological measures, and behavioral performance measures, were collected during the experiment. The results showed that an increase in task difficulty led to increased subjective ratings of mental workload and a decrease in task performance for the secondary N-back tasks. Significant differences were observed across the different levels of mental workload in multimodal physiological measures, such as delta waves in EEG signals, fixation distance in eye movement signals, time- and frequency-domain measures in ECG signals, and skin conductance in EDA signals. In addition, four driving performance measures related to vehicle velocity and the deviation of pedal input and vehicle position also showed sensitivity to the changes in drivers’ mental workload. The findings from this study can contribute to a comprehensive understanding of effective measures for mental workload assessment in driving scenarios and to the development of smart driving systems for the accurate recognition of drivers’ mental states. Full article
(This article belongs to the Special Issue Sensor Technologies and Machine Learning for Intelligent Transportation Systems)
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Review

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22 pages, 8169 KiB  
Review
Suggestions and Comparisons of Two Algorithms for the Simplification of Bluetooth Sensor Data in Traffic Cordons
by Beylun Özlü and Mustafa Sinan Yardım
Sensors 2024, 24(13), 4375; https://doi.org/10.3390/s24134375 - 5 Jul 2024
Viewed by 819
Abstract
Bluetooth sensors in intelligent transportation systems possess extensive coverage and access to a large number of identity (ID) data, but they cannot distinguish between vehicles and persons. This study aims to classify and differentiate raw data collected from Bluetooth sensors positioned between various [...] Read more.
Bluetooth sensors in intelligent transportation systems possess extensive coverage and access to a large number of identity (ID) data, but they cannot distinguish between vehicles and persons. This study aims to classify and differentiate raw data collected from Bluetooth sensors positioned between various origin–destination (i–j) points into vehicles and persons and to determine their distribution ratios. To reduce data noise, two different filtering algorithms are proposed. The first algorithm employs time series simplification based on Simple Moving Average (SMA) and threshold models, which are tools of statistical analysis. The second algorithm is rule-based, using speed data of Bluetooth devices derived from sensor data to provide a simplification algorithm. The study area was the Historic Peninsula Traffic Cord Region of Istanbul, utilizing data from 39 sensors in the region. As a result of time-based filtering, the ratio of person ID addresses for Bluetooth devices participating in circulation in the region was found to be 65.57% (397,799 person IDs), while the ratio of vehicle ID addresses was 34.43% (208,941 vehicle IDs). In contrast, the rule-based algorithm based on speed data found that the ratio of vehicle ID addresses was 35.82% (389,392 vehicle IDs), while the ratio of person ID addresses was 64.17% (217,348 person IDs). The Jaccard similarity coefficient was utilized to identify similarities in the data obtained from the applied filtering approaches, yielding a coefficient (J) of 0.628. The identity addresses of the vehicles common throughout the two date sets which are obtained represent the sampling size for traffic measurements. Full article
(This article belongs to the Special Issue Sensor Technologies and Machine Learning for Intelligent Transportation Systems)
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