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Urbanization and Road Safety Management

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: closed (15 July 2021) | Viewed by 28278

Special Issue Editors


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Guest Editor
6-265, Donadeo Innovation Centre for Engineering, University of Alberta, 9211 116th Street NW, Edmonton, AB T6G 1H9, Canada
Interests: road safety; speed management; bayesian statistic; remote sensing

E-Mail Website
Guest Editor
6-281, Donadeo Innovation Centre for Engineering, University of Alberta, 9211 116th Street NW, Edmonton, AB T6G 1H9, Canada
Interests: ITS; transportation system/facility location optimizations; winter road maintenance; geostatistics; geomatics

Special Issue Information

Dear Colleagues,

Since 1950, the world’s total population has nearly tripled, with the number of people in urban centres more than doubling. By 2050, it is expected that 70% of the global population could be urbanized. This population growth and shift will significantly increase the strain on our urban transportation system, where approximately 70% of all injury collisions occur. The reality is that the greater share of injury collisions happens in urban environments, particularly at intersections and with vulnerable road users. Sustainable road safety implies that the road environment is designed to eliminate serious collisions and to mitigate the severity of their outcome if they do occur. Consequently, there is a need to establish a sound scientific knowledgebase of sustainable road safety and urbanization, since mobility demand and associated problems have increased proportionally, and for some cities exponentially. This Special Issue solicits papers with novel contributions that address emerging research challenges in sustainable road safety in the context of urban transportation. Potential topics include but are not limited to: engineering principles of sustainable road safety, advanced road safety management, safety measures for road and roadside design, safeguarding vulnerable road users, creating a road safety culture, role of enforcement in crash prevention, safety impacts of CASE (connected, automated, shared, and electric) and other ITS (intelligent transportation systems). All of the above topics need to focus on the urban environment.

Dr. Karim El-Basyouny
Dr. Tae J. Kwon
Guest Editors

Manuscript Submission Information

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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. Sustainability 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 2400 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

  • sustainable road safety
  • safety management
  • urbanization
  • safety culture
  • intelligent transportation systems
  • urban transportation systems
  • geometric and roadside design
  • vulnerable road users and behavior
  • enforcement

Published Papers (11 papers)

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Research

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16 pages, 2479 KiB  
Article
Safety Impact Assessment of Optimal RWIS Networks—An Empirical Examination
by Simita Biswas, Davesh Sharma and Tae J. Kwon
Sustainability 2023, 15(1), 327; https://doi.org/10.3390/su15010327 - 25 Dec 2022
Viewed by 1379
Abstract
Optimal RWIS network can be defined as an RWIS configuration where the total number of stations (RWIS density) are determined based on a well-established guideline and the locations are allocated systematically assuming that it will provide the maximum monitoring coverage of the network. [...] Read more.
Optimal RWIS network can be defined as an RWIS configuration where the total number of stations (RWIS density) are determined based on a well-established guideline and the locations are allocated systematically assuming that it will provide the maximum monitoring coverage of the network. This paper examines and quantifies the benefit of an optimized RWIS network and how these benefits impact traffic safety. The methodological framework presented herein builds upon our previous efforts in RWIS location-allocation, where the kriging variance is used as a performance indicator for monitoring coverage. In this study, the network coverage index (NCI) parameter is proposed to gauge RWIS network performance and quantitatively evaluate its impact on traffic safety. The findings of this study reveal a strong dependency between the NCI and the RWIS network configuration. In terms of traffic safety, the relationship between NCI and safety effectiveness can be expressed as a polynomial function, where the two are proportional to one another. In the state of Iowa, an RWIS network with 80% monitoring coverage (NCI = 0.8) can reduce additional 40 collisions per site annually compared to a network without RWIS stations. Based on the findings obtained in this study, road agencies and RWIS planners can now be assisted with conceptualizing the capabilities of an optimized RWIS network, which will help them increase monitoring coverage, and in the process, gain a quantitative understanding on its potential impact on traffic safety. Full article
(This article belongs to the Special Issue Urbanization and Road Safety Management)
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20 pages, 3388 KiB  
Article
Investigating Wi-Fi, Bluetooth, and Bluetooth Low-Energy Signal Characteristics for Integration in Vehicle–Pedestrian Collision Warning Systems
by Shahriar Mohammadi, Karim Ismail and Amir H. Ghods
Sustainability 2021, 13(19), 10823; https://doi.org/10.3390/su131910823 - 29 Sep 2021
Cited by 8 | Viewed by 2326
Abstract
The purpose of the study is to investigate the comparative field performance of Wi-Fi, Bluetooth Classic (Bluetooth) and Bluetooth Low Energy (BLE) signal modes for integration in vehicle–pedestrian collision warning systems. The study compares these wireless signal modes to find out which one [...] Read more.
The purpose of the study is to investigate the comparative field performance of Wi-Fi, Bluetooth Classic (Bluetooth) and Bluetooth Low Energy (BLE) signal modes for integration in vehicle–pedestrian collision warning systems. The study compares these wireless signal modes to find out which one is most appropriate to be utilized in these systems and provides better results in terms of accuracy and functionality. Five factors including received signal strength indicator (RSSI)-distance relationship, rainfall effects on the signals, motion effects, non-line of sight effects and signal transmission rates were selected for evaluation. These factors were selected considering the requirements of vehicle–pedestrian collision warning systems and compared with each other based on experimental outcomes. The results of the experiments indicated the overall superiority of BLE mode over Wi-Fi and Bluetooth modes to be utilized in these systems. Application of this mode may provide the possibility of fast collision warnings thanks to low signal transmission intervals and high probability of simultaneous signal detections by multiple signals scanners. Moreover, the capability of this mode to accurately estimate distance and position is higher than Wi-Fi mode and not significantly different from Bluetooth mode. Full article
(This article belongs to the Special Issue Urbanization and Road Safety Management)
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13 pages, 3775 KiB  
Article
Active Signage of Pedestrian Crossings as a Tool in Road Safety Management
by Piotr Szagała, Piotr Olszewski, Witold Czajewski and Paweł Dąbkowski
Sustainability 2021, 13(16), 9405; https://doi.org/10.3390/su13169405 - 21 Aug 2021
Cited by 3 | Viewed by 2652
Abstract
The main objective of the study was to verify the effectiveness of active pedestrian crossings equipped with flashing lights activated automatically by detected pedestrians. A pilot study was conducted in two sites, where speed profiles of vehicles over the distance of 30 m [...] Read more.
The main objective of the study was to verify the effectiveness of active pedestrian crossings equipped with flashing lights activated automatically by detected pedestrians. A pilot study was conducted in two sites, where speed profiles of vehicles over the distance of 30 m before the crossing were analyzed. The study produced promising results in terms of reducing vehicle speeds so the next study investigated four other unsignalized pedestrian crossings. They were video-recorded for 48 h each, before, after and a year after installation. The ANOVA test was used to check the statistical significance of changes in selected indicators. Even after a year from the installation, the effect of the active signage remained significant. The average percentage of drivers yielding to pedestrians was 77.4% higher and the average waiting time 25.2% lower than before the installation. The average speeds of vehicles were 3.53 km/h lower on collector and 2.60 km/h lower on arterial streets. A decline in the probability of a pedestrian being killed or severely injured (KSI) ranged from 6.3 pp (9.4%) on the arterial streets immediately after the installation up to 12.9 pp (31.7%) on the collector streets one year after. Full article
(This article belongs to the Special Issue Urbanization and Road Safety Management)
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22 pages, 19957 KiB  
Article
Safety Assessment of Urban Intersection Sight Distance Using Mobile LiDAR Data
by Omar Kilani, Maged Gouda, Jonas Weiß and Karim El-Basyouny
Sustainability 2021, 13(16), 9259; https://doi.org/10.3390/su13169259 - 18 Aug 2021
Cited by 10 | Viewed by 2768
Abstract
This paper proposes an automated framework that utilizes Light Detection and Ranging (LiDAR) point cloud data to map and detect road obstacles that impact drivers’ field of view at urban intersections. The framework facilitates the simulation of a driver’s field of vision to [...] Read more.
This paper proposes an automated framework that utilizes Light Detection and Ranging (LiDAR) point cloud data to map and detect road obstacles that impact drivers’ field of view at urban intersections. The framework facilitates the simulation of a driver’s field of vision to estimate the blockage percentage as they approach an intersection. Furthermore, a collision analysis is conducted to examine the relationship between poor visibility and safety. The visibility assessment was used to determine the blockage percentage as a function of intersection control type. The safety assessment indicated that intersections with limited available sight distances (ASD) exhibited an increased risk of collisions. The research also conducted a sensitivity analysis to understand the impact of the voxel size on the extraction of intersection obstacles from LiDAR datasets. The findings from this research can be used to assess the intersection without the burden of manual intervention. This would effectively support transportation agencies in identifying hazardous intersections with poor visibility and adopt policies to enhance urban intersections’ operation and safety. Full article
(This article belongs to the Special Issue Urbanization and Road Safety Management)
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9 pages, 265 KiB  
Article
Using Bayesian Tobit Models to Understand the Impact of Mobile Automated Enforcement on Collision and Crime Rates
by Shewkar Ibrahim and Tarek Sayed
Sustainability 2021, 13(11), 6422; https://doi.org/10.3390/su13116422 - 5 Jun 2021
Viewed by 1978
Abstract
The Data Driven Approaches to Crime and Traffic Safety approach identifies opportunities where a single enforcement deployment can achieve multiple objectives: reduce collision and crime rates. Previous research focused on modeling both events separately despite evidence suggesting a high correlation. Additionally, there is [...] Read more.
The Data Driven Approaches to Crime and Traffic Safety approach identifies opportunities where a single enforcement deployment can achieve multiple objectives: reduce collision and crime rates. Previous research focused on modeling both events separately despite evidence suggesting a high correlation. Additionally, there is a limited understanding of the impact of Mobile Automated Enforcement (MAE) on crime or the impact of changing a deployment strategy on collision and crime dates. For this reason, this study categorized MAE deployment into three different clusters. A random-parameter multivariate Tobit model was developed under the Bayesian framework to understand the impact of changing the deployment on collision and crime rates in a neighborhood. Firstly, the results of the analysis quantified the high correlation between collision and crime rates (0.86) which suggest that locations with high collision rates also coincide with locations with high crime rates. The results also demonstrated the safety effectiveness (i.e., reduced crime and collision rates) increased for the clusters that are associated with an increased enforcement duration at a neighborhood level. Understanding how changing the deployment strategy at a macro-level affects collision and crime rates provides enforcement agencies with the opportunity to maximize the efficiency of their existing resources. Full article
(This article belongs to the Special Issue Urbanization and Road Safety Management)
20 pages, 5318 KiB  
Article
Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization
by Rawan Shabbar, Anemone Kasasbeh and Mohamed M. Ahmed
Sustainability 2021, 13(6), 3314; https://doi.org/10.3390/su13063314 - 17 Mar 2021
Cited by 15 | Viewed by 3158
Abstract
Optimal placement of Charging stations (CSs) and infrastructure planning are one of the most critical challenges that face the Electric Vehicles (EV) industry nowadays. A variety of approaches have been proposed to address the problem of demand uncertainty versus the optimal number of [...] Read more.
Optimal placement of Charging stations (CSs) and infrastructure planning are one of the most critical challenges that face the Electric Vehicles (EV) industry nowadays. A variety of approaches have been proposed to address the problem of demand uncertainty versus the optimal number of CSs required to build the EV infrastructure. In this paper, a Markov-chain network model is designed to study the estimated demand on a CS by using the birth and death process model. An investigation on the desired number of electric sockets in each CS and the average number of electric vehicles in both queue and waiting times is presented. Furthermore, a CS allocation algorithm based on the Markov-chain model is proposed. Grey Wolf Optimization (GWO) algorithm is used to select the best CS locations with the objective of maximizing the net profit under both budget and routing constraints. Additionally, the model was applied to Washington D.C. transportation network. Experimental results have shown that to achieve the highest net profit, Level 2 chargers need to be installed in low demand areas of infrastructure implementation. On the other hand, Level 3 chargers attain higher net profit when the number of EVs increases in the transportation network or/and in locations with high charging demands. Full article
(This article belongs to the Special Issue Urbanization and Road Safety Management)
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18 pages, 128179 KiB  
Article
Driver Behavior Classification at Stop-Controlled Intersections Using Video-Based Trajectory Data
by Xiamei Wen, Liping Fu, Ting Fu, Jessica Keung and Ming Zhong
Sustainability 2021, 13(3), 1404; https://doi.org/10.3390/su13031404 - 29 Jan 2021
Cited by 9 | Viewed by 2973
Abstract
Understanding how drivers behave at stop-controlled intersection is of critical importance for the control and management of an urban traffic system. It is also a critical element of consideration in the burgeoning field of smart infrastructure and connected and autonomous vehicles (CAV). A [...] Read more.
Understanding how drivers behave at stop-controlled intersection is of critical importance for the control and management of an urban traffic system. It is also a critical element of consideration in the burgeoning field of smart infrastructure and connected and autonomous vehicles (CAV). A number of past efforts have been devoted to investigating the driver behavioral patterns when they pass through stop-controlled intersections. However, the majority of these studies have been limited to qualitative descriptions and analyses of driver behavior due to the unavailability of high-resolution vehicle data and sound methodology for classifying various driver behaviors. In this paper, we introduce a methodology that uses computer-vision vehicle trajectory data and unsupervised clustering techniques to classify different types of driver behaviors, infer the underlying mechanism and compare their impacts on safety. Two major types of behaviors are investigated, including vehicle stopping behavior and vehicle approaching patterns, using two clustering algorithms: a bisecting K-means algorithm for classifying stopping behavior, and the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm for classifying vehicle approaching patterns. The methodology is demonstrated using a case study involving five stop-controlled intersections in Montreal, Canada. The results from the analysis show that there exist five distinctive classes of driver behaviors representing different levels of risk in both vehicle stopping and approaching processes. This finding suggests that the proposed methodology could be applied to develop new safety surrogate measures and risk analysis methods for network screening and countermeasure analyses of stop-controlled intersections. Full article
(This article belongs to the Special Issue Urbanization and Road Safety Management)
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18 pages, 20114 KiB  
Article
Development of Models for Children—Pedestrian Crossing Speed at Signalized Crosswalks
by Irena Ištoka Otković, Aleksandra Deluka-Tibljaš, Sanja Šurdonja and Tiziana Campisi
Sustainability 2021, 13(2), 777; https://doi.org/10.3390/su13020777 - 14 Jan 2021
Cited by 22 | Viewed by 2612
Abstract
Modeling the behavior of pedestrians is an important tool in the analysis of their behavior and consequently ensuring the safety of pedestrian traffic. Children pedestrians show specific traffic behavior which is related to cognitive development, and the parameters that affect their traffic behavior [...] Read more.
Modeling the behavior of pedestrians is an important tool in the analysis of their behavior and consequently ensuring the safety of pedestrian traffic. Children pedestrians show specific traffic behavior which is related to cognitive development, and the parameters that affect their traffic behavior are very different. The aim of this paper is to develop a model of the children-pedestrian’s speed at a signalized pedestrian crosswalk. For the same set of data collected in the city of Osijek—Croatia, two models were developed based on neural network and multiple linear regression. In both cases the models are based on 300 data of measured children speed at signalized pedestrian crosswalks on primary city roads located near a primary school. As parameters, both models include the selected traffic infrastructure features and children’s characteristics and their movements. The models are validated on data collected on the same type of pedestrian crosswalks, using the same methodology in two other urban environments—the city of Rijeka, Croatia and Enna in Italy. It was shown that the neural network model, developed for Osijek, can be applied with sufficient reliability to the other two cities, while the multiple linear regression model is applicable with relatively satisfactory reliability only in Rijeka. A comparative analysis of the statistical indicators of reliability of these two models showed that better results are achieved by the neural network model. Full article
(This article belongs to the Special Issue Urbanization and Road Safety Management)
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20 pages, 11518 KiB  
Article
A Citywide Location-Allocation Framework for Driver Feedback Signs: Optimizing Safety and Coverage of Vulnerable Road Users
by Mingjian Wu, Tae J. Kwon and Karim El-Basyouny
Sustainability 2020, 12(24), 10415; https://doi.org/10.3390/su122410415 - 12 Dec 2020
Cited by 1 | Viewed by 1957
Abstract
Driver feedback signs (DFSs) are being adopted increasingly by municipalities around the world, as they have been proven to be a cost-effective countermeasure that improves road safety. However, research is still needed on developing a location-allocation framework to determine the optimal implementation strategies [...] Read more.
Driver feedback signs (DFSs) are being adopted increasingly by municipalities around the world, as they have been proven to be a cost-effective countermeasure that improves road safety. However, research is still needed on developing a location-allocation framework to determine the optimal implementation strategies for DFS placement. Hence, the main aim of this paper is to formulate a location-allocation optimization problem with the objective of reducing vehicular collisions (ΔC) while enhancing spatial coverage for vulnerable road users and facilities (Cov). Two distinct planning scenarios, namely, an all-new and expansion scenario, were proposed in the framework. It was found that ΔC and Cov can be improved by up to 149.44% and 69.27%, respectively, in the all-new scenario. Two expansion scenarios were done with 10 and 20 additional units into the system. It was found that ΔC can be improved by up to 30.22% and 51.61% for the additional 10 and 20 DFSs, respectively. Likewise, the Cov can be improved by up to 14.64% and 29.27%, respectively. This framework provides decision makers with the freedom to simulate and optimize their DFS network by balancing the needs of the road users, vulnerable facilities, and traffic safety in locating DFSs over an urban road network. Full article
(This article belongs to the Special Issue Urbanization and Road Safety Management)
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15 pages, 2978 KiB  
Article
Discovering Spatio-Temporal Clusters of Road Collisions Using the Method of Fast Bayesian Model-Based Cluster Detection
by Yeran Sun, Yu Wang, Ke Yuan, Ting On Chan and Ying Huang
Sustainability 2020, 12(20), 8681; https://doi.org/10.3390/su12208681 - 20 Oct 2020
Cited by 4 | Viewed by 1873
Abstract
Public availability of geo-coded or geo-referenced road collisions (crashes) makes it possible to perform geovisualisation and spatio-temporal analysis of road collisions across a city. This study aims to detect spatio-temporal clusters of road collisions across Greater London between 2010 and 2014. We implemented [...] Read more.
Public availability of geo-coded or geo-referenced road collisions (crashes) makes it possible to perform geovisualisation and spatio-temporal analysis of road collisions across a city. This study aims to detect spatio-temporal clusters of road collisions across Greater London between 2010 and 2014. We implemented a fast Bayesian model-based cluster detection method with no covariates and after adjusting for potential covariates respectively. As empirical evidence on the association of street connectivity measures and the occurrence of road collisions had been found, we selected street connectivity measures as the potential covariates in our cluster detection. Results of the most significant cluster and the second most significant cluster during five consecutive years are located around the central areas. Moreover, after adjusting the covariates, the most significant cluster moves from the central areas of London to its peripheral areas, while the second most significant cluster remains unchanged. Additionally, one potential covariate used in this study, length-based road density, exhibits a positive association with the number of road collisions; meanwhile count-based intersection density displays a negative association. Although the covariates (i.e., road density and intersection density) exhibit potential impact on the clusters of road collisions, they are unlikely to contribute to the majority of clusters. Furthermore, the method of fast Bayesian model-based cluster detection is developed to discover spatio-temporal clusters of serious injury collisions. Most of the areas at risk of serious injury collisions overlay those at risk of road collisions. Although not being identified as areas at risk of road collisions, some districts, e.g., City of London, are regarded as areas at risk of serious injury collisions. Full article
(This article belongs to the Special Issue Urbanization and Road Safety Management)
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Review

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19 pages, 2313 KiB  
Review
Analysis and Evaluation of Ramp Metering: From Historical Evolution to the Application of New Algorithms and Engineering Principles
by Salvatore Trubia, Salvatore Curto, Salvatore Barberi, Alessandro Severino, Fabio Arena and Giovanni Pau
Sustainability 2021, 13(2), 850; https://doi.org/10.3390/su13020850 - 16 Jan 2021
Cited by 6 | Viewed by 3217
Abstract
In the modern era, characterized by intense urbanization and frequent travel between interconnected communities, the constant expansion of cities, associated with high densities and growing need for traveling, has led to a significant increase in road traffic volumes. More than ever, road traffic [...] Read more.
In the modern era, characterized by intense urbanization and frequent travel between interconnected communities, the constant expansion of cities, associated with high densities and growing need for traveling, has led to a significant increase in road traffic volumes. More than ever, road traffic today requires effort to be managed effectively in order to improve performance and safety conditions, given the greater probability of unpleasant events such as accidents or road congestion with related delays and the increased stress levels of the user and infrastructure. Fortunately, there are already various engineering tools, such as ramp metering, that can be used for this purpose. Ramp metering allows for achieving the aforementioned desired benefits, including improving mobility, reliability, efficiency, and safety, and even reducing environmental impact. It also has been shown to be cost-effective from the existing literature. Further research will be necessary to strengthen the quality, efficacy, and efficiency of ramp metering, especially considering the fast-paced progress in technology (e.g., connected autonomous vehicles and drones used for surveys) and new challenging scenarios (e.g., congested industrial areas and emergency vehicles). This review’s scope is to present a general overview of principal ramp metering solutions, focusing on current research studies in the last couple of years and highlighting some of the main algorithms used for this purpose, depending on diverse scenarios. With this article, the authors desire to present the subject of ramp metering, providing a general overview of its story, evolution, and recent analytical models. Full article
(This article belongs to the Special Issue Urbanization and Road Safety Management)
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