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Sensors to Improve Road Safety and Sustainable Mobility

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 18645

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Special Issue Editors

Prof. Dr. Francisco Alonso

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Guest Editor

INTRAS (Research Institute on Traffic and Road Safety), University of Valencia, 46022 Valencia, Spain
Interests: public transit; sustainable transportation; urban safety; safe mobility; emerging countries; pedestrian behavior; cyclist behavior; preventive measures
Special Issues, Collections and Topics in MDPI journals

Dr. Armando Ortuño

E-Mail Website
Guest Editor

Civil Engineer Department, Transport Planning Area, University of Alicante, Alicante, Spain
Interests: road accidentes; traffic systems; public transport; sustainable urban and metropolitan mobility; transit oriented development; cost–benefit analysis; applied transport economy; environmental impact

Special Issue Information

Dear Colleagues,

Reducing traffic accidents is one of the main challenges facing modern societies, as is improving mobility, especially through improving public transportation and promoting soft modes of transportation.

Undoubtedly, the development of new technologies is extremely useful in addressing these challenges. Intelligent transport systems (ITS) in urban, interurban, on-road, and in-vehicle settings for logistics, emissions reduction, environmental improvement, etc., are fields in which significant advances are being made, but more are still needed.

Specifically, cooperative intelligent transport systems (C-ITS), as an area of ITS that deals with real-time information exchange between users and infrastructure, enables the implementation of advanced applications and services with the potential to address the abovementioned challenges. In this context, the possibility of establishing communication between the different actors (vehicle to everything or V2X, X being a vehicle, the infrastructure, a pedestrian, or the network itself), is dependent upon the installation and operation of a multitude of sensors in the vehicles, the roadside, personal devices, and the traffic control centre.

Accordingly, the C-ITS platform, having been organized by the European Commission, uses mature ad hoc short-range (like ETSI ITS G5) and complementing wide-area communication technologies (like 3G, 4G, 5G and hybrid communications) that allow road vehicles to share data from sensors and communicate with other vehicles, traffic signals, roadside infrastructure, and other road users.

Sensors are one of the fundamental components of these systems, since they allow for the support of multiple functionalities by obtaining information about user behaviors (braking, lane changes, etc.), vehicle characteristics (typology, gauges, etc.), flow dynamics (vehicle position, intensities, speeds, etc.), meteorological phenomena, environmental parameters (emissions, air quality, noise, etc.), and risk detection (fires in tunnels, stationary vehicles), among many others.

Consequently, the present Special Issue deals with the development and application of sensors aimed at increasing road safety and improving mobility to make it more efficient, sustainable and ultimately resilient. All this regardless of whether they are focused on roads and highways, on-board vehicles, UAVs, or other types of devices that can be carried by road actors.

We also wish to deal with sensors for smart cities in terms of smart mobility, traffic management, planners, information systems, development of sustainable transportation, resilient transport, low emission zones, autonomous vehicles, and everything related to future mobility and future transportation.

Sensor systems applied to phenomena such as the explosion in the use of scooters or the interaction of vehicles with vulnerable users such as pedestrians and cyclists, which as modes of travel are being encouraged by the authorities but are not without problems, are welcome.

Additionally, articles on sensors used in pilot tests and experimental developments in the field are welcome.

We are calling for review articles, theoretical and experimental research, etc., related to technological solutions without necessarily requiring them to be close to market deployment, evaluation, user perception, etc.

Topics of Interest:

Sensors to Improve Road Safety
Intelligent Transportation Systems (ITS)
Transport Systems and Networks
Sensors and Human Factors

Prof. Dr. Francisco Alonso
Dr. Armando Ortuño
Guest Editors

Manuscript Submission Information

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Keywords

road safety
sustainable mobility and transportation
traffic management
ITS/C-ITS

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Published Papers (8 papers)

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Research

20 pages, 4377 KiB

Open AccessArticle

Electric Bus Pedal Misapplication Detection Based on Phase Space Reconstruction Method

by Aihong Lyu, Kunchen Li, Yali Zhang, Kai Mu and Wenbin Luo

Sensors 2023, 23(18), 7883; https://doi.org/10.3390/s23187883 - 14 Sep 2023

Viewed by 1319

Abstract

Due to the environmental protection of electric buses, they are gradually replacing traditional fuel buses. Several previous studies have found that accidents related to electric vehicles are linked to Unintended Acceleration (UA), which is mostly caused by the driver pressing the wrong pedal. Therefore, this study proposed a Model for Detecting Pedal Misapplication in Electric Buses (MDPMEB). In this work, natural driving experiments for urban electric buses and pedal misapplication simulation experiments were carried out in a closed field; furthermore, a phase space reconstruction method was introduced, based on chaos theory, to map sequence data to a high-dimensional space in order to produce normal braking and pedal misapplication image datasets. Based on these findings, a modified Swin Transformer network was built. To prevent the model from overfitting when considering small sample data and to improve the generalization ability of the model, it was pre-trained using a publicly available dataset; moreover, the weights of the prior knowledge model were loaded into the model for training. The proposed model was also compared to machine learning and Convolutional Neural Networks (CNN) algorithms. This study showed that this model was able to detect normal braking and pedal misapplication behavior accurately and quickly, and the accuracy rate on the test dataset is 97.58%, which is 9.17% and 4.5% higher than the machine learning algorithm and CNN algorithm, respectively. Full article

(This article belongs to the Special Issue Sensors to Improve Road Safety and Sustainable Mobility)

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14 pages, 1791 KiB

Open AccessArticle

Traffic Sign Recognition with Deep Learning: Vegetation Occlusion Detection in Brazilian Environments

by Vanessa Dalborgo, Thiago B. Murari, Vinicius S. Madureira, João Gabriel L. Moraes, Vitor Magno O. S. Bezerra, Filipe Q. Santos, Alexandre Silva and Roberto L. S. Monteiro

Sensors 2023, 23(13), 5919; https://doi.org/10.3390/s23135919 - 26 Jun 2023

Cited by 7 | Viewed by 2728

Abstract

Traffic Sign Recognition (TSR) is one of the many utilities made possible by embedded systems with internet connections. Through the usage of vehicular cameras, it’s possible to capture and classify traffic signs in real time with Artificial Intelligence (AI), more specifically, Convolutional Neural Networks (CNNs) based techniques. This article discusses the implementation of such TSR systems, and the building process of datasets for AI training. Such datasets include a brand new class to be used in TSR, vegetation occlusion. The results show that this approach is useful in making traffic sign maintenance faster since this application turns vehicles into moving sensors in that context. Leaning on the proposed technique, identified irregularities in traffic signs can be reported to a responsible body so they will eventually be fixed, contributing to a safer traffic environment. This paper also discusses the usage and performance of different YOLO models according to our case studies. Full article

(This article belongs to the Special Issue Sensors to Improve Road Safety and Sustainable Mobility)

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16 pages, 5873 KiB

Open AccessArticle

Utilizing High Resolution Satellite Imagery for Automated Road Infrastructure Safety Assessments

by Ivan Brkić, Marko Ševrović, Damir Medak and Mario Miler

Sensors 2023, 23(9), 4405; https://doi.org/10.3390/s23094405 - 30 Apr 2023

Cited by 1 | Viewed by 2162

Abstract

The European Commission (EC) has published a European Union (EU) Road Safety Framework for the period 2021 to 2030 to reduce road fatalities. In addition, the EC with the EU Directive 2019/1936 requires a much more detailed recording of road attributes. Therefore, automatic detection of school routes, four classes of crosswalks, and divided carriageways were performed in this paper. The study integrated satellite imagery as a data source and the Yolo object detector. The satellite Pleiades Neo 3 with a spatial resolution of 0.3 m was used as the source for the satellite images. In addition, the study was divided into three phases: vector processing, satellite imagery processing, and training and evaluation of the You Only Look Once (Yolo) object detector. The training process was performed on 1951 images with 2515 samples, while the evaluation was performed on 651 images with 862 samples. For school zones and divided carriageways, this study achieved accuracies of 0.988 and 0.950, respectively. For crosswalks, this study also achieved similar or better results than similar work, with accuracies ranging from 0.957 to 0.988. The study also provided the standard performance measure for object recognition, mean average precision (mAP), as well as the values for the confusion matrix, precision, recall, and f1 score for each class as benchmark values for future studies. Full article

(This article belongs to the Special Issue Sensors to Improve Road Safety and Sustainable Mobility)

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25 pages, 1472 KiB

Open AccessArticle

Understanding Autonomous Shuttle Adoption Intention: Predictive Power of Pre-Trial Perceptions and Attitudes

by Fahimeh Golbabaei, Tan Yigitcanlar, Alexander Paz and Jonathan Bunker

Sensors 2022, 22(23), 9193; https://doi.org/10.3390/s22239193 - 26 Nov 2022

Cited by 6 | Viewed by 2541

Abstract

The capability of ‘demand-responsive transport’, particularly in autonomous shared form, to better facilitate road-based mobility is considered a significant advantage because improved mobility leads to enhanced quality of life and wellbeing. A central point in implementing a demand-responsive transit system in a new area is adapting the operational concept to the respective structural and socioeconomic conditions. This requires an extensive analysis of the users’ needs. There is presently limited understanding of public perceptions and attitudes toward the adoption of autonomous demand-responsive transport. To address this gap, a theory-based conceptual framework is proposed to provide detailed empirical insights into the public’s adoption intention of ‘autonomous shuttle buses’ as a form of autonomous demand-responsive transport. South East Queensland, Australia, was selected as the testbed. In this case study, relationships between perceptions, attitudes, and usage intention were examined by employing a partial least squares structural equation modeling method. The results support the basic technology acceptance model casual relationships that correspond with previous studies. Although the direct effects of perceived relative advantages and perceived service quality on usage intention are not significant, they could still affect usage intention indirectly through the attitude factor. Conversely, perceived risks are shown to have no association with perceived usefulness but can negatively impact travelers’ attitudes and usage intention toward autonomous shuttle buses. The research findings provide implications to assist policymakers, transport planners, and engineers in their policy decisions and system plans as well as achieving higher public acknowledgment and wider uptake of autonomous demand-responsive transport technology solutions. Full article

(This article belongs to the Special Issue Sensors to Improve Road Safety and Sustainable Mobility)

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18 pages, 6445 KiB

Open AccessArticle

Implementation of a Low-Cost Data Acquisition System on an E-Scooter for Micromobility Research

by Ana María Pérez-Zuriaga, David Llopis-Castelló, Víctor Just-Martínez, Alejandra Sofía Fonseca-Cabrera, Carlos Alonso-Troyano and Alfredo García

Sensors 2022, 22(21), 8215; https://doi.org/10.3390/s22218215 - 26 Oct 2022

Cited by 9 | Viewed by 2823

Abstract

In recent years, cities are experiencing changes in the ways of moving around, increasing the use of micromobility vehicles. Bicycles are the most widespread transport mode and, therefore, cyclists’ behaviour, safety, and comfort have been widely studied. However, the use of other personal mobility vehicles is increasing, especially e-scooters, and related studies are scarce. This paper proposes a low-cost open-source data acquisition system to be installed on an e-scooter. This system is based on Raspberry Pi and allows collecting speed, acceleration, and position of the e-scooter, the lateral clearance during meeting and overtaking manoeuvres, and the vibrations experienced by the micromobility users when riding on a bike lane. The system has been evaluated and tested on a bike lane segment to ensure the accuracy and reliability of the collected data. As a result, the use of the proposed system allows highway engineers and urban mobility planners to analyse the behaviour, safety, and comfort of the users of e-scooters. Additionally, the system can be easily adapted to another micromobility vehicle and used to assess pavement condition and micromobility users’ riding comfort on a cycling network when the budget is limited. Full article

(This article belongs to the Special Issue Sensors to Improve Road Safety and Sustainable Mobility)

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15 pages, 3274 KiB

Open AccessArticle

A Case Study on Vestibular Sensations in Driving Simulators

by Jose V. Riera, Sergio Casas, Francisco Alonso and Marcos Fernández

Sensors 2022, 22(15), 5837; https://doi.org/10.3390/s22155837 - 4 Aug 2022

Viewed by 2137

Abstract

Motion platforms have been used in simulators of all types for several decades. Since it is impossible to reproduce the accelerations of a vehicle without limitations through a physically limited system (platform), it is common to use washout filters and motion cueing algorithms (MCA) to select which accelerations are reproduced and which are not. Despite the time that has passed since their development, most of these algorithms still use the classical washout algorithm. In the use of these MCAs, there is always information that is lost and, if that information is important for the purpose of the simulator (the training simulators), the result obtained by the users of that simulator will not be satisfactory. This paper shows a case study where a BMW 325Xi AUT fitted with a sensor, recorded the accelerations produced in all degrees of freedom (DOF) during several runs, and data have been introduced in mathematical simulation software (washout + kinematics + actuator simulation) of a 6DOF motion platform. The input to the system has been qualitatively compared with the output, observing that most of the simulation adequately reflects the input to the system. Still, there are three events where the accelerations are lost. These events are considered by experts to be of vital importance for the outcome of a learning process in the simulator to be adequate. Full article

(This article belongs to the Special Issue Sensors to Improve Road Safety and Sustainable Mobility)

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16 pages, 8354 KiB

Open AccessArticle

Calculation of an Average Vehicle’s Sideways Acceleration on Small Roundabouts

by Juraj Jagelčák, Jozef Gnap, Mariusz Kostrzewski, Ondrej Kuba and Jaroslav Frnda

Sensors 2022, 22(13), 4978; https://doi.org/10.3390/s22134978 - 1 Jul 2022

Cited by 8 | Viewed by 2049

Abstract

The calculation of the average sideways acceleration, based on speed and angular velocity on small roundabouts for a vehicle of up to 3.5 t gross vehicle mass, is described in this paper. Calculations of the turning radius are derived from angular velocity and an automatic selection of events, based on the lateral acceleration of the coefficient of variation within a defined time window. The calculation of the turning radius based on speed and angular velocity yields almost identical results to the calculation of the turning radius by the three-point method using GPS coordinates, as described in previous research. This means that the calculation of the turning radius, derived from the speed of GNSS/INS dual-antenna sensor and gyroscope data, yields similar results to those from the computation of the turning radius derived from the coordinates of a GNSS/INS dual-antenna sensor. The research results can be used in the development of sensors to improve road safety. Full article

(This article belongs to the Special Issue Sensors to Improve Road Safety and Sustainable Mobility)

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14 pages, 1427 KiB

Open AccessArticle

Analysis of Extended Information Provided by Bluetooth Traffic Monitoring Systems to Enhance Short-Term Level of Service Prediction

by Rubén Fernández Pozo, Ana Belén Rodríguez González, Mark Richard Wilby and Juan José Vinagre Díaz

Sensors 2022, 22(12), 4565; https://doi.org/10.3390/s22124565 - 17 Jun 2022

Viewed by 1497

Abstract

Bluetooth monitoring systems (BTMS) have opened a new era in traffic sensing, providing a reliable, economical, and easy-to-deploy solution to uniquely identify vehicles. Raw data from BTMS have traditionally been used to calculate travel time and origin–destination matrices. However, we could extend this to include other information like the number of vehicles or their residence times. This information, together with their temporal components, can be applied to the complex task of forecasting traffic. Level of service (LOS) prediction has opened a novel research line that fulfills the need to anticipate future traffic states, based on a standard link-based variable, accepted for both researchers and practitioners. In this paper, we incorporate BTMS’s extended variables and temporal information to an LOS classifier based on a Random Undersampling Boost algorithm, which is proven to efficiently respond to the data unbalance intrinsic to this problem. By using this approach, we achieve an overall recall of

87.2 %

for up to 15-min prediction horizons, reaching

96.6 %

predicting congestion, and improving the results for the intermediate traffic states, especially complex given their intrinsic instability. Additionally, we provide detailed analyses on the impact of temporal information on the LOS predictor’s performance, observing improvements up to a separation of 50 min between last features and prediction horizons. Furthermore, we study the predictor importance resulting from the classifiers to highlight those features contributing the most to the final achievements. Full article

(This article belongs to the Special Issue Sensors to Improve Road Safety and Sustainable Mobility)

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