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Sensory Data Supported Traffic Safety Analysis for Smart City Era

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 6463

Special Issue Editors


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Guest Editor
1. Instituto de Telecomunicações, 1049-001 Lisboa, Portugal
2. Escola de Tecnologias e Arquitetura (ISTA), ISCTE-Instituto Universitário de Lisboa, 1600-077 Lisboa, Portugal
3. DCTI-Departamento de Ciências e Tecnologias da Informação, ISCTE-Instituto Universitário de Lisboa, 1600-077 Lisboa, Portugal
Interests: smart sensors; automated measurement systems; artificial intelligence; biomedical sensors; intelligent transportation
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Shanghai Maritime University, No 1550 Haigang Avenue,Pudong New Area, Shanghai, 201306, China
Interests: Sensors and IoT for Smart Ports and Logistics; Indoor and Outdor localization, Algorithms for Smart Sensor Networks, IoT System Design Methodologies
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Insitute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: video data-driven intelligent transportation environment perception and understanding; large-scale transportation data analysis (traffic flow data, AIS, etc.); smart ship/autonomous port
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensor deployment provides kinematic and static information in real-world applications, which benefit intelligent transportation systems, smart ships, autonomous port, etc. It is of great importance that smart ship (and autonomous port) management and control requires a lot of on-spot surveillance data (e.g., surveillance videos, historical trajectory, port machinery status). We can employ computer vision techniques, automatic identification systems (AIS), GPS trajectory, radar and infrared data to identify potential traffic collisions at sea, port, railway and freeway, etc. For instance, we can predict maritime traffic collisions by forecasting ship trajectories with the help of AIS data. Besides, we can analyze the responsibility for a real-world maritime accident by tracing back ship historical trajectories from AIS databases. Similarly, the roadway traffic collision in the port area can be predicted via various sensing data, such as GPS, surveillance videos, etc.

The Special Issue invites research that analyzes traffic safety in the smart city era by exploiting various sensory data, which include surveillance video, GPS, AIS, radar, infrared, inertial navigation systems, etc. More specifically, we aim to obtain spatial-temporal traffic information from both visual (videos, infrared, satellite images) and non-visual data (e.g., GPS, AIS) sources. The Special Issue will publish work that develops novel theories and techniques to obtain on-site information from various data, which can be further employed to identify potentially dangerous events. We invite full paper submissions focusing on the theme of “sensory data-supported traffic safety analysis for smart city era”. We also encourage submissions from a broad range of research fields related to the relevant issues. Some potential topics this issue will cover (but is not limited to) are as follows:

  • Traffic behavior recognition and analysis via video data (maritime videos, roadway videos)
  • Remote sensing for traffic monitoring
  • Large-scale trajectory data supported traffic safety analysis
  • Traffic safety modeling and analysis by sensor data fusion
  • Improvement of maritime traffic management with AIS, surveillance video, ship-borne sensors, GPS, radar
  • Data quality control on multiple traffic-relevant data sources
  • Traffic accident prevention and early-warning with various kinematic and static data

Dr. Octavian Postolache
Prof. Yongsheng Yang
Dr. Xinqiang Chen
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.

Published Papers (2 papers)

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Research

15 pages, 4534 KiB  
Article
A Spatial Autoregressive Quantile Regression to Examine Quantile Effects of Regional Factors on Crash Rates
by Tianjian Yu, Fan Gao, Xinyuan Liu and Jinjun Tang
Sensors 2022, 22(1), 5; https://doi.org/10.3390/s22010005 - 21 Dec 2021
Cited by 4 | Viewed by 2560
Abstract
Spatial autocorrelation and skewed distribution are the most frequent issues in crash rate modelling analysis. Previous studies commonly focus on the spatial autocorrelation between adjacent regions or the relationships between crash rate and potentially risky factors across different quantiles of crash rate distribution, [...] Read more.
Spatial autocorrelation and skewed distribution are the most frequent issues in crash rate modelling analysis. Previous studies commonly focus on the spatial autocorrelation between adjacent regions or the relationships between crash rate and potentially risky factors across different quantiles of crash rate distribution, but rarely both. To overcome the research gap, this study utilizes the spatial autoregressive quantile (SARQ) model to estimate how contributing factors influence the total and fatal-plus-injury crash rates and how modelling relationships change across the distribution of crash rates considering the effects of spatial autocorrelation. Three types of explanatory variables, i.e., demographic, traffic networks and volumes, and land-use patterns, were considered. Using data collected in New York City from 2017 to 2019, the results show that: (1) the SARQ model outperforms the traditional quantile regression model in prediction and fitting performance; (2) the effects of variables vary with the quantiles, mainly classifying three types: increasing, unchanged, and U-shaped; (3) at the high tail of crash rate distribution, the effects commonly have sudden increases/decrease. The findings are expected to provide strategies for reducing the crash rate and improving road traffic safety. Full article
(This article belongs to the Special Issue Sensory Data Supported Traffic Safety Analysis for Smart City Era)
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19 pages, 2118 KiB  
Article
A Novel Acceleration Signal Processing Procedure for Cycling Safety Assessment
by Emanuele Murgano, Riccardo Caponetto, Giuseppina Pappalardo, Salvatore Damiano Cafiso and Alessandro Severino
Sensors 2021, 21(12), 4183; https://doi.org/10.3390/s21124183 - 18 Jun 2021
Cited by 21 | Viewed by 2938
Abstract
With the growing rate of urban population and transport congestion, it is important for a city to have bike riding as an attractive travel choice but one of its biggest barriers for people is the perceived lack of safety. To improve the safety [...] Read more.
With the growing rate of urban population and transport congestion, it is important for a city to have bike riding as an attractive travel choice but one of its biggest barriers for people is the perceived lack of safety. To improve the safety of urban cycling, identification of high-risk location and routes are major obstacles for safety countermeasures. Risk assessment is performed by crash data analysis, but the lack of data makes that approach less effective when applied to cyclist safety. Furthermore, the availability of data collected with the modern technologies opens the way to different approaches. This research aim is to analyse data needs and capability to identify critical cycling safety events for urban context where bicyclist behaviour can be recorded with different equipment and bicycle used as a probe vehicle to collect data. More specifically, three different sampling frequencies have been investigated to define the minimum one able to detect and recognize hard breaking. In details, a novel signal processing procedure has been proposed to correctly deal with speed and acceleration signals. Besides common signal filtering approaches, wavelet transformation and Dynamic Time Warping (DTW) techniques have been applied to remove more efficiently the instrument noise and align the signals with respect to the reference. The Euclidean distance of the DTW has been introduced as index to get the best filter parameters configuration. Obtained results, both during the calibration and the investigated real scenario, confirm that at least a GPS signal with a sampling frequency of 1Hz is needed to track the rider’s behaviour to detect events. In conclusion, with a very cheap hardware setup is possible to monitor riders’ speed and acceleration. Full article
(This article belongs to the Special Issue Sensory Data Supported Traffic Safety Analysis for Smart City Era)
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