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Special Issue "Traffic Safety and Injury Prevention"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (31 August 2016)

Special Issue Editors

Guest Editor
Dr. Suren Chen

Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO 80523, USA
Website | E-Mail
Phone: +1 970 491 7722
Fax: +1 970 491 7727
Interests: traffic safety risk assessment; traffic injury prevention; traffic infrastructure system engineering
Guest Editor
Dr. Feng Chen

College of Transportation Engineering, Tongji University, 1239 Siping Road, Shanghai, China
E-Mail
Phone: +86 021 6958 5721
Fax: +86 021 6958 3813
Interests: traffic injury prevention; statistical analysis of Crash Data; vehicle dynamic model; reliability analysis; driving simulator

Special Issue Information

Dear Colleagues,

Vehicle crashes cause more injuries and casualties than any natural or man-made hazard in most countries around the world. To study traffic crashes and injury risk and prevention strategy under normal, and even adverse, conditions has become a long-term endeavor and commitment of society. To tackle these issues, researchers have been working on crash data, physical simulations, and cutting-edge experimental studies, by deciphering the complex interactions between people, environment, vehicles, and transportation infrastructures. With these exciting progresses, advanced technology has been employed in developing the next generation of Intelligent Transportation Systems (ITS).

This Special Issue aims to report on recent advances in interdisciplinary research related to understanding associated risks, crash avoidance, and the improvement of traffic safety and injury prevention in transportation networks around the world. It is open to any subject area of the related theme, and research articles encompassing multiple fields, such as traffic engineering, infrastructure engineering, hazard modeling and mitigation, public health, psychology, policy-making and management, etc., are particularly welcome. The International Journal of Environmental Research and Public Health is indexed by SCI-E, PubMed, and other databases.

Dr. Suren Chen
Dr. Feng Chen
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. International Journal of Environmental Research and Public Health 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 1800 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

  • traffic crash risk prediction
  • injury severity assessment
  • driving behavior characterization and advanced experimental techniques
  • active traffic management and ITS application
  • advanced statistical study on traffic safety
  • injury prevention policy and techniques
  • public health resource allocation and emergency response
  • traffic safety and response during hazard mitigation and evacuation

Published Papers (19 papers)

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Research

Open AccessArticle
Driver’s Cognitive Workload and Driving Performance under Traffic Sign Information Exposure in Complex Environments: A Case Study of the Highways in China
Int. J. Environ. Res. Public Health 2017, 14(2), 203; https://doi.org/10.3390/ijerph14020203
Received: 31 August 2016 / Accepted: 9 January 2017 / Published: 17 February 2017
Cited by 10 | PDF Full-text (4271 KB) | HTML Full-text | XML Full-text
Abstract
Complex traffic situations and high driving workload are the leading contributing factors to traffic crashes. There is a strong correlation between driving performance and driving workload, such as visual workload from traffic signs on highway off-ramps. This study aimed to evaluate traffic safety [...] Read more.
Complex traffic situations and high driving workload are the leading contributing factors to traffic crashes. There is a strong correlation between driving performance and driving workload, such as visual workload from traffic signs on highway off-ramps. This study aimed to evaluate traffic safety by analyzing drivers’ behavior and performance under the cognitive workload in complex environment areas. First, the driving workload of drivers was tested based on traffic signs with different quantities of information. Forty-four drivers were recruited to conduct a traffic sign cognition experiment under static controlled environment conditions. Different complex traffic signs were used for applying the cognitive workload. The static experiment results reveal that workload is highly related to the amount of information on traffic signs and reaction time increases with the information grade, while driving experience and gender effect are not significant. This shows that the cognitive workload of subsequent driving experiments can be controlled by the amount of information on traffic signs. Second, driving characteristics and driving performance were analyzed under different secondary task driving workload levels using a driving simulator. Drivers were required to drive at the required speed on a designed highway off-ramp scene. The cognitive workload was controlled by reading traffic signs with different information, which were divided into four levels. Drivers had to make choices by pushing buttons after reading traffic signs. Meanwhile, the driving performance information was recorded. Questionnaires on objective workload were collected right after each driving task. The results show that speed maintenance and lane deviations are significantly different under different levels of cognitive workload, and the effects of driving experience and gender groups are significant. The research results can be used to analyze traffic safety in highway environments, while considering more drivers’ cognitive and driving performance. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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Open AccessArticle
Driver Vision Based Perception-Response Time Prediction and Assistance Model on Mountain Highway Curve
Int. J. Environ. Res. Public Health 2017, 14(1), 31; https://doi.org/10.3390/ijerph14010031
Received: 30 August 2016 / Revised: 14 December 2016 / Accepted: 20 December 2016 / Published: 30 December 2016
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Abstract
To make driving assistance system more humanized, this study focused on the prediction and assistance of drivers’ perception-response time on mountain highway curves. Field tests were conducted to collect real-time driving data and driver vision information. A driver-vision lane model quantified curve elements [...] Read more.
To make driving assistance system more humanized, this study focused on the prediction and assistance of drivers’ perception-response time on mountain highway curves. Field tests were conducted to collect real-time driving data and driver vision information. A driver-vision lane model quantified curve elements in drivers’ vision. A multinomial log-linear model was established to predict perception-response time with traffic/road environment information, driver-vision lane model, and mechanical status (last second). A corresponding assistance model showed a positive impact on drivers’ perception-response times on mountain highway curves. Model results revealed that the driver-vision lane model and visual elements did have important influence on drivers’ perception-response time. Compared with roadside passive road safety infrastructure, proper visual geometry design, timely visual guidance, and visual information integrality of a curve are significant factors for drivers’ perception-response time. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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Open AccessArticle
Hotspot Identification for Shanghai Expressways Using the Quantitative Risk Assessment Method
Int. J. Environ. Res. Public Health 2017, 14(1), 20; https://doi.org/10.3390/ijerph14010020
Received: 31 August 2016 / Revised: 12 December 2016 / Accepted: 20 December 2016 / Published: 27 December 2016
Cited by 1 | PDF Full-text (2281 KB) | HTML Full-text | XML Full-text
Abstract
Hotspot identification (HSID) is the first and key step of the expressway safety management process. This study presents a new HSID method using the quantitative risk assessment (QRA) technique. Crashes that are likely to happen for a specific site are treated as the [...] Read more.
Hotspot identification (HSID) is the first and key step of the expressway safety management process. This study presents a new HSID method using the quantitative risk assessment (QRA) technique. Crashes that are likely to happen for a specific site are treated as the risk. The aggregation of the crash occurrence probability for all exposure vehicles is estimated based on the empirical Bayesian method. As for the consequences of crashes, crashes may not only cause direct losses (e.g., occupant injuries and property damages) but also result in indirect losses. The indirect losses are expressed by the extra delays calculated using the deterministic queuing diagram method. The direct losses and indirect losses are uniformly monetized to be considered as the consequences of this risk. The potential costs of crashes, as a criterion to rank high-risk sites, can be explicitly expressed as the sum of the crash probability for all passing vehicles and the corresponding consequences of crashes. A case study on the urban expressways of Shanghai is presented. The results show that the new QRA method for HSID enables the identification of a set of high-risk sites that truly reveal the potential crash costs to society. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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Open AccessArticle
Empirical Analysis and Modeling of Stop-Line Crossing Time and Speed at Signalized Intersections
Int. J. Environ. Res. Public Health 2017, 14(1), 9; https://doi.org/10.3390/ijerph14010009
Received: 25 August 2016 / Revised: 10 November 2016 / Accepted: 7 December 2016 / Published: 23 December 2016
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Abstract
In China, a flashing green (FG) indication of 3 s followed by a yellow (Y) indication of 3 s is commonly applied to end the green phase at signalized intersections. Stop-line crossing behavior of drivers during such a phase transition period significantly influences [...] Read more.
In China, a flashing green (FG) indication of 3 s followed by a yellow (Y) indication of 3 s is commonly applied to end the green phase at signalized intersections. Stop-line crossing behavior of drivers during such a phase transition period significantly influences safety performance of signalized intersections. The objective of this study is thus to empirically analyze and model drivers’ stop-line crossing time and speed in response to the specific phase transition period of FG and Y. High-resolution trajectories for 1465 vehicles were collected at three rural high-speed intersections with a speed limit of 80 km/h and two urban intersections with a speed limit of 50 km/h in Shanghai. With the vehicle trajectory data, statistical analyses were performed to look into the general characteristics of stop-line crossing time and speed at the two types of intersections. A multinomial logit model and a multiple linear regression model were then developed to predict the stop-line crossing patterns and speeds respectively. It was found that the percentage of stop-line crossings during the Y interval is remarkably higher and the stop-line crossing time is approximately 0.7 s longer at the urban intersections, as compared with the rural intersections. In addition, approaching speed and distance to the stop-line at the onset of FG as well as area type significantly affect the percentages of stop-line crossings during the FG and Y intervals. Vehicle type and stop-line crossing pattern were found to significantly influence the stop-line crossing speed, in addition to the above factors. The red-light-running seems to occur more frequently at the large intersections with a long cycle length. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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Open AccessArticle
Modeling Driver Behavior near Intersections in Hidden Markov Model
Int. J. Environ. Res. Public Health 2016, 13(12), 1265; https://doi.org/10.3390/ijerph13121265
Received: 31 August 2016 / Revised: 15 December 2016 / Accepted: 15 December 2016 / Published: 21 December 2016
Cited by 7 | PDF Full-text (1435 KB) | HTML Full-text | XML Full-text
Abstract
Intersections are one of the major locations where safety is a big concern to drivers. Inappropriate driver behaviors in response to frequent changes when approaching intersections often lead to intersection-related crashes or collisions. Thus to better understand driver behaviors at intersections, especially in [...] Read more.
Intersections are one of the major locations where safety is a big concern to drivers. Inappropriate driver behaviors in response to frequent changes when approaching intersections often lead to intersection-related crashes or collisions. Thus to better understand driver behaviors at intersections, especially in the dilemma zone, a Hidden Markov Model (HMM) is utilized in this study. With the discrete data processing, the observed dynamic data of vehicles are used for the inference of the Hidden Markov Model. The Baum-Welch (B-W) estimation algorithm is applied to calculate the vehicle state transition probability matrix and the observation probability matrix. When combined with the Forward algorithm, the most likely state of the driver can be obtained. Thus the model can be used to measure the stability and risk of driver behavior. It is found that drivers’ behaviors in the dilemma zone are of lower stability and higher risk compared with those in other regions around intersections. In addition to the B-W estimation algorithm, the Viterbi Algorithm is utilized to predict the potential dangers of vehicles. The results can be applied to driving assistance systems to warn drivers to avoid possible accidents. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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Open AccessArticle
Exploration of Pedestrian Head Injuries—Collision Parameter Relationships through a Combination of Retrospective Analysis and Finite Element Method
Int. J. Environ. Res. Public Health 2016, 13(12), 1250; https://doi.org/10.3390/ijerph13121250
Received: 30 August 2016 / Revised: 18 October 2016 / Accepted: 22 November 2016 / Published: 16 December 2016
Cited by 4 | PDF Full-text (3814 KB) | HTML Full-text | XML Full-text
Abstract
There are a very limited number of reports concerning the relationship between pedestrian head injuries and collision parameters through a combination of statistical analysis methods and finite element method (FEM). This study aims to explore the characteristics of pedestrian head injuries in car–pedestrian [...] Read more.
There are a very limited number of reports concerning the relationship between pedestrian head injuries and collision parameters through a combination of statistical analysis methods and finite element method (FEM). This study aims to explore the characteristics of pedestrian head injuries in car–pedestrian collisions at different parameters by using the two means above. A retrospective analysis of pedestrian head injuries was performed based on detailed investigation data of 61 car–pedestrian collision cases. The head damage assessment parameters (head injury criterion (HIC), peak stress on the skull, maximal principal strain for the brain) in car–pedestrian simulation experiments with four contact angles and three impact velocities were obtained by FEM. The characteristics of the pedestrian head injuries were discussed by comparing and analyzing the statistical analysis results and finite element analysis results. The statistical analysis results demonstrated a significant difference in skull fractures, contusion and laceration of brain and head injuries on the abbreviated injury scale (AIS)3+ was found at different velocities (p < 0.05) and angles (p < 0.05). The simulation results showed that, in pedestrian head-to-hood impacts, the values of head damage assessment parameters increased with impact velocities. At the same velocity, these values from the impact on the pedestrian’s back were successively greater than on the front or the side. Furthermore, head injury reconstruction and prediction results of two selected cases were consistent with the real injuries. Overall, it was further spelled out that, for shorter stature pedestrians, increased head impact velocity results in greater head injury severity in car–pedestrian collision, especially in pedestrian head-to-hood impacts. Under a back impact, the head has also been found to be at greater damage risk for shorter stature pedestrians, which may have implications on automotive design and pedestrian protection research if prevention and treatment of these injuries is to be prioritized over head injuries under a front or side impact. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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Open AccessArticle
Predicting Driver Behavior during the Yellow Interval Using Video Surveillance
Int. J. Environ. Res. Public Health 2016, 13(12), 1213; https://doi.org/10.3390/ijerph13121213
Received: 31 August 2016 / Revised: 21 November 2016 / Accepted: 29 November 2016 / Published: 6 December 2016
Cited by 1 | PDF Full-text (2297 KB) | HTML Full-text | XML Full-text
Abstract
At a signalized intersection, drivers must make a stop/go decision at the onset of the yellow signal. Incorrect decisions would lead to red light running (RLR) violations or crashes. This study aims to predict drivers’ stop/go decisions and RLR violations during yellow intervals. [...] Read more.
At a signalized intersection, drivers must make a stop/go decision at the onset of the yellow signal. Incorrect decisions would lead to red light running (RLR) violations or crashes. This study aims to predict drivers’ stop/go decisions and RLR violations during yellow intervals. Traffic data such as vehicle approaching speed, acceleration, distance to the intersection, and occurrence of RLR violations are gathered by a Vehicle Data Collection System (VDCS). An enhanced Gaussian Mixture Model (GMM) is used to extract moving vehicles from target lanes, and the Kalman Filter (KF) algorithm is utilized to acquire vehicle trajectories. The data collected from the VDCS are further analyzed by a sequential logit model, and the relationship between drivers’ stop/go decisions and RLR violations is identified. The results indicate that the distance of vehicles to the stop line at the onset of the yellow signal is an important predictor for both drivers’ stop/go decisions and RLR violations. In addition, vehicle approaching speed is a contributing factor for stop/go decisions. Furthermore, the accelerations of vehicles after the onset of the yellow signal are positively related to RLR violations. The findings of this study can be used to predict the probability of drivers’ RLR violations and improve traffic safety at signalized intersections. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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Open AccessArticle
Research on the Relationship between Reaction Ability and Mental State for Online Assessment of Driving Fatigue
Int. J. Environ. Res. Public Health 2016, 13(12), 1174; https://doi.org/10.3390/ijerph13121174
Received: 21 September 2016 / Revised: 3 November 2016 / Accepted: 18 November 2016 / Published: 24 November 2016
Cited by 7 | PDF Full-text (1373 KB) | HTML Full-text | XML Full-text
Abstract
Background: Driving fatigue affects the reaction ability of a driver. The aim of this research is to analyze the relationship between driving fatigue, physiological signals and driver’s reaction time. Methods: Twenty subjects were tested during driving. Data pertaining to reaction time and physiological [...] Read more.
Background: Driving fatigue affects the reaction ability of a driver. The aim of this research is to analyze the relationship between driving fatigue, physiological signals and driver’s reaction time. Methods: Twenty subjects were tested during driving. Data pertaining to reaction time and physiological signals including electroencephalograph (EEG) were collected from twenty simulation experiments. Grey correlation analysis was used to select the input variable of the classification model. A support vector machine was used to divide the mental state into three levels. The penalty factor for the model was optimized using a genetic algorithm. Results: The results show that α/β has the greatest correlation to reaction time. The classification results show an accuracy of 86%, a sensitivity of 87.5% and a specificity of 85.53%. The average increase of reaction time is 16.72% from alert state to fatigued state. Females have a faster decrease in reaction ability than males as driving fatigue accumulates. Elderly drivers have longer reaction times than the young. Conclusions: A grey correlation analysis can be used to improve the classification accuracy of the support vector machine (SVM) model. This paper provides basic research that online detection of fatigue can be performed using only a simple device, which is more comfortable for users. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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Open AccessArticle
Evaluation Research of the Effects of Longitudinal Speed Reduction Markings on Driving Behavior: A Driving Simulator Study
Int. J. Environ. Res. Public Health 2016, 13(11), 1170; https://doi.org/10.3390/ijerph13111170
Received: 31 August 2016 / Revised: 4 November 2016 / Accepted: 10 November 2016 / Published: 23 November 2016
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Abstract
The objective of this paper is to explore the effects of longitudinal speed reduction markings (LSRMs) on vehicle maneuvering and drivers’ operation performance on interchange connectors with different radii. Empirical data were collected in a driving simulator. Indicators—relative speed change, standard deviation of [...] Read more.
The objective of this paper is to explore the effects of longitudinal speed reduction markings (LSRMs) on vehicle maneuvering and drivers’ operation performance on interchange connectors with different radii. Empirical data were collected in a driving simulator. Indicators—relative speed change, standard deviation of acceleration, and gas/brake pedal power—were proposed to characterize driving behavior. Statistical results revealed that LSRMs could reduce vehicles’ travel speed and limit drivers’ willingness to increase speed in the entire connector. To probe the impacts of LSRMs, the connecter was split into four even sections. Effects of LSRMs on driving behavior were stronger in the second and the final sections of connectors. LSRMs also enhanced drivers’ adaptability in the first three quarters of a connector when the radius was 50 m. Drivers’ gas pedal operation would be impacted by LSRMs in the entire connector when the radius was 50 m. LSRMs could only make drivers press brake pedal more frequently in the second section with 80 m and 100 m radius. In the second quarter section of a connector—from the FQP (the first quartile point) to the MC (the middle point of curve)—LSRMs have better effects on influencing vehicle maneuvering and drivers’ operation performance. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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Open AccessArticle
Drivers’ Visual Search Patterns during Overtaking Maneuvers on Freeway
Int. J. Environ. Res. Public Health 2016, 13(11), 1159; https://doi.org/10.3390/ijerph13111159
Received: 31 August 2016 / Revised: 25 October 2016 / Accepted: 10 November 2016 / Published: 19 November 2016
Cited by 2 | PDF Full-text (1620 KB) | HTML Full-text | XML Full-text
Abstract
Drivers gather traffic information primarily by means of their vision. Especially during complicated maneuvers, such as overtaking, they need to perceive a variety of characteristics including the lateral and longitudinal distances with other vehicles, the speed of others vehicles, lane occupancy, and so [...] Read more.
Drivers gather traffic information primarily by means of their vision. Especially during complicated maneuvers, such as overtaking, they need to perceive a variety of characteristics including the lateral and longitudinal distances with other vehicles, the speed of others vehicles, lane occupancy, and so on, to avoid crashes. The primary object of this study is to examine the appropriate visual search patterns during overtaking maneuvers on freeways. We designed a series of driving simulating experiments in which the type and speed of the leading vehicle were considered as two influential factors. One hundred and forty participants took part in the study. The participants overtook the leading vehicles just like they would usually do so, and their eye movements were collected by use of the Eye Tracker. The results show that participants’ gaze durations and saccade durations followed normal distribution patterns and that saccade angles followed a log-normal distribution pattern. It was observed that the type of leading vehicle significantly impacted the drivers’ gaze duration and gaze frequency. As the speed of a leading vehicle increased, subjects’ saccade durations became longer and saccade angles became larger. In addition, the initial and destination lanes were found to be key areas with the highest visual allocating proportion, accounting for more than 65% of total visual allocation. Subjects tended to more frequently shift their viewpoints between the initial lane and destination lane in order to search for crucial traffic information. However, they seldom directly shifted their viewpoints between the two wing mirrors. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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Open AccessArticle
Crash Risk Prediction Modeling Based on the Traffic Conflict Technique and a Microscopic Simulation for Freeway Interchange Merging Areas
Int. J. Environ. Res. Public Health 2016, 13(11), 1157; https://doi.org/10.3390/ijerph13111157
Received: 31 August 2016 / Revised: 2 November 2016 / Accepted: 15 November 2016 / Published: 19 November 2016
Cited by 3 | PDF Full-text (2987 KB) | HTML Full-text | XML Full-text
Abstract
This paper evaluates the traffic safety of freeway interchange merging areas based on the traffic conflict technique. The hourly composite risk indexes (HCRI) was defined. By the use of unmanned aerial vehicle (UAV) photography and video processing techniques, the conflict type and severity [...] Read more.
This paper evaluates the traffic safety of freeway interchange merging areas based on the traffic conflict technique. The hourly composite risk indexes (HCRI) was defined. By the use of unmanned aerial vehicle (UAV) photography and video processing techniques, the conflict type and severity was judged. Time to collision (TTC) was determined with the traffic conflict evaluation index. Then, the TTC severity threshold was determined. Quantizing the weight of the conflict by direct losses of different severities of freeway traffic accidents, the calculated weight of the HCRI can be obtained. Calibration of the relevant parameters of the micro-simulation simulator VISSIM is conducted by the travel time according to the field data. Variables are placed into orthogonal tables at different levels. On the basis of this table, the trajectory file of every traffic condition is simulated, and then submitted into a surrogate safety assessment model (SSAM), identifying the number of hourly traffic conflicts in the merging area, a statistic of HCRI. Moreover, the multivariate linear regression model was presented and validated to study the relationship between HCRI and the influencing variables. A comparison between the HCRI model and the hourly conflicts ratio (HCR), without weight, shows that the HCRI model fitting degree was obviously higher than the HCR. This will be a reference to design and implement operational planners. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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Open AccessArticle
A Case Study of Dynamic Response Analysis and Safety Assessment for a Suspended Monorail System
Int. J. Environ. Res. Public Health 2016, 13(11), 1121; https://doi.org/10.3390/ijerph13111121
Received: 31 August 2016 / Revised: 3 November 2016 / Accepted: 7 November 2016 / Published: 10 November 2016
Cited by 8 | PDF Full-text (3844 KB) | HTML Full-text | XML Full-text
Abstract
A suspended monorail transit system is a category of urban rail transit, which is effective in alleviating traffic pressure and injury prevention. Meanwhile, with the advantages of low cost and short construction time, suspended monorail transit systems show vast potential for future development. [...] Read more.
A suspended monorail transit system is a category of urban rail transit, which is effective in alleviating traffic pressure and injury prevention. Meanwhile, with the advantages of low cost and short construction time, suspended monorail transit systems show vast potential for future development. However, the suspended monorail has not been systematically studied in China, and there is a lack of relevant knowledge and analytical methods. To ensure the health and reliability of a suspended monorail transit system, the driving safety of vehicles and structure dynamic behaviors when vehicles are running on the bridge should be analyzed and evaluated. Based on the method of vehicle-bridge coupling vibration theory, the finite element method (FEM) software ANSYS and multi-body dynamics software SIMPACK are adopted respectively to establish the finite element model for bridge and the multi-body vehicle. A co-simulation method is employed to investigate the vehicle-bridge coupling vibration for the transit system. The traffic operation factors, including train formation, track irregularity and tire stiffness, are incorporated into the models separately to analyze the bridge and vehicle responses. The results show that the coupling of dynamic effects of the suspended monorail system between vehicle and bridge are significant in the case studied, and it is strongly suggested to take necessary measures for vibration suppression. The simulation of track irregularity is a critical factor for its vibration safety, and the track irregularity of A-level road roughness negatively influences the system vibration safety. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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Open AccessArticle
Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data
Int. J. Environ. Res. Public Health 2016, 13(11), 1043; https://doi.org/10.3390/ijerph13111043
Received: 21 August 2016 / Revised: 13 October 2016 / Accepted: 19 October 2016 / Published: 26 October 2016
Cited by 1 | PDF Full-text (314 KB) | HTML Full-text | XML Full-text
Abstract
Random effect panel data hurdle models are established to research the daily crash frequency on a mountainous section of highway I-70 in Colorado. Road Weather Information System (RWIS) real-time traffic and weather and road surface conditions are merged into the models incorporating road [...] Read more.
Random effect panel data hurdle models are established to research the daily crash frequency on a mountainous section of highway I-70 in Colorado. Road Weather Information System (RWIS) real-time traffic and weather and road surface conditions are merged into the models incorporating road characteristics. The random effect hurdle negative binomial (REHNB) model is developed to study the daily crash frequency along with three other competing models. The proposed model considers the serial correlation of observations, the unbalanced panel-data structure, and dominating zeroes. Based on several statistical tests, the REHNB model is identified as the most appropriate one among four candidate models for a typical mountainous highway. The results show that: (1) the presence of over-dispersion in the short-term crash frequency data is due to both excess zeros and unobserved heterogeneity in the crash data; and (2) the REHNB model is suitable for this type of data. Moreover, time-varying variables including weather conditions, road surface conditions and traffic conditions are found to play importation roles in crash frequency. Besides the methodological advancements, the proposed technology bears great potential for engineering applications to develop short-term crash frequency models by utilizing detailed data from field monitoring data such as RWIS, which is becoming more accessible around the world. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
Open AccessArticle
Effects of Lane Width, Lane Position and Edge Shoulder Width on Driving Behavior in Underground Urban Expressways: A Driving Simulator Study
Int. J. Environ. Res. Public Health 2016, 13(10), 1010; https://doi.org/10.3390/ijerph13101010
Received: 17 July 2016 / Revised: 4 October 2016 / Accepted: 7 October 2016 / Published: 14 October 2016
Cited by 12 | PDF Full-text (2457 KB) | HTML Full-text | XML Full-text
Abstract
This study tested the effects of lane width, lane position and edge shoulder width on driving behavior for a three-lane underground urban expressway. A driving simulator was used with 24 volunteer test subjects. Five lane widths (2.85, 3.00, 3.25, 3.50, and 3.75 m) [...] Read more.
This study tested the effects of lane width, lane position and edge shoulder width on driving behavior for a three-lane underground urban expressway. A driving simulator was used with 24 volunteer test subjects. Five lane widths (2.85, 3.00, 3.25, 3.50, and 3.75 m) and three shoulder widths (0.50, 0.75, and 1.00 m) were studied. Driving speed, lane deviation and subjective perception of driving behavior were collected as performance measures. The results show that lane and shoulder width have significant effects on driving speed. Average driving speed increases from 60.01 km/h in the narrowest lane to 88.05 km/h in the widest lane. While both narrower lanes and shoulders result in reduced speed and lateral lane deviation, the effect of lane width is greater than that of shoulder width. When the lane and shoulder are narrow, drivers in the left or right lane tend to shy away from the tunnel wall, even encroaching into the neighboring middle lane. As the lane or shoulder gets wider, drivers tend to stay in the middle of the lane. An interesting finding is that although few participants acknowledged that lane position had any great bearing on their driving behaviors, the observed driving speed is statistically higher in the left lane than in the other two lanes when the lane width is narrow (in 2.85, 3 and 3.25 m lanes). These findings provided support for amending the current design specifications of urban underground roads, such as the relationship between design speed and lane width, speed limit, and combination form of lanes. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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Open AccessArticle
A Method for Formulizing Disaster Evacuation Demand Curves Based on SI Model
Int. J. Environ. Res. Public Health 2016, 13(10), 986; https://doi.org/10.3390/ijerph13100986
Received: 6 August 2016 / Revised: 15 September 2016 / Accepted: 27 September 2016 / Published: 10 October 2016
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Abstract
The prediction of evacuation demand curves is a crucial step in the disaster evacuation plan making, which directly affects the performance of the disaster evacuation. In this paper, we discuss the factors influencing individual evacuation decision making (whether and when to leave) and [...] Read more.
The prediction of evacuation demand curves is a crucial step in the disaster evacuation plan making, which directly affects the performance of the disaster evacuation. In this paper, we discuss the factors influencing individual evacuation decision making (whether and when to leave) and summarize them into four kinds: individual characteristics, social influence, geographic location, and warning degree. In the view of social contagion of decision making, a method based on Susceptible-Infective (SI) model is proposed to formulize the disaster evacuation demand curves to address both social influence and other factors’ effects. The disaster event of the “Tianjin Explosions” is used as a case study to illustrate the modeling results influenced by the four factors and perform the sensitivity analyses of the key parameters of the model. Some interesting phenomena are found and discussed, which is meaningful for authorities to make specific evacuation plans. For example, due to the lower social influence in isolated communities, extra actions might be taken to accelerate evacuation process in those communities. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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Open AccessArticle
Mortality from Unspecified Unintentional Injury among Individuals Aged 65 Years and Older by U.S. State, 1999–2013
Int. J. Environ. Res. Public Health 2016, 13(8), 763; https://doi.org/10.3390/ijerph13080763
Received: 24 June 2016 / Revised: 17 July 2016 / Accepted: 22 July 2016 / Published: 27 July 2016
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Abstract
Introduction: Recent changes in unspecified unintentional injury mortality for the elderly by U.S. state remain unreported. This study aims to examine U.S. state variations in mortality from unspecified unintentional injury among Americans aged 65+, 1999–2013; Methods: Using mortality rates from the U.S. CDC’s [...] Read more.
Introduction: Recent changes in unspecified unintentional injury mortality for the elderly by U.S. state remain unreported. This study aims to examine U.S. state variations in mortality from unspecified unintentional injury among Americans aged 65+, 1999–2013; Methods: Using mortality rates from the U.S. CDC’s Web-based Injury Statistics Query and Reporting System (WISQARS™), we examined unspecified unintentional injury mortality for older adults aged 65+ from 1999 to 2013 by state. Specifically, the proportion of unintentional injury deaths with unspecified external cause in the data was considered. Linear regression examined the statistical significance of changes in proportion of unspecified unintentional injury from 1999 to 2013; Results: Of the 36 U.S. states with stable mortality rates, over 8-fold differences were observed for both the mortality rates and the proportions of unspecified unintentional injury for Americans aged 65+ during 1999–2013. Twenty-nine of the 36 states showed reductions in the proportion of unspecified unintentional injury cause, with Oklahoma (−89%), Massachusetts (−86%) and Oregon (−81%) displaying the largest changes. As unspecified unintentional injury mortality decreased, mortality from falls in 28 states and poisoning in 3 states increased significantly. Mortality from suffocation in 15 states, motor vehicle traffic crashes in 12 states, and fire/burn in 8 states also decreased; Conclusions: The proportion of unintentional injuries among older adults with unspecified cause decreased significantly for many states in the United States from 1999 to 2013. The reduced proportion of unspecified injury has implications for research and practice. It should be considered in state-level trend analysis during 1999–2013. It also suggests comparisons between states for specific injury mortality should be conducted with caution, as large differences in unspecified injury mortality across states and over time could create bias for specified injury mortality comparisons. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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Open AccessArticle
Injury Severity of Motorcycle Riders Involved in Traffic Crashes in Hunan, China: A Mixed Ordered Logit Approach
Int. J. Environ. Res. Public Health 2016, 13(7), 714; https://doi.org/10.3390/ijerph13070714
Received: 17 May 2016 / Revised: 1 July 2016 / Accepted: 8 July 2016 / Published: 14 July 2016
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Abstract
Issues related to motorcycle safety in China have not received enough research attention. As such, the causal relationship between injury outcomes of motorcycle crashes and potential risk factors remains unknown. This study intended to investigate the injury risk of motorcyclists involved in road [...] Read more.
Issues related to motorcycle safety in China have not received enough research attention. As such, the causal relationship between injury outcomes of motorcycle crashes and potential risk factors remains unknown. This study intended to investigate the injury risk of motorcyclists involved in road traffic crashes in China. To account for the ordinal nature of response outcomes and unobserved heterogeneity, a mixed ordered logit model was employed. Given that the crash occurrence process is different between intersections and non-intersections, separate models were developed for these locations to independently estimate the impacts of various contributing factors on motorcycle riders’ injury severity. The analysis was based on the police-reported crash dataset obtained from the Traffic Administration Bureau of Hunan Provincial Public Security Ministry. Factors associated with a substantially higher probability of fatalities and severe injuries included motorcycle riders older than 60 years, the absence of helmets, motorcycle riders identified to be equal duty, and when a motorcycle collided with a heavy vehicle during the night time without lighting. Crashes occurred along county roads with curve and slope alignment or at regions with higher GDP were associated with an elevated risk of fatality of motorcycle riders, while unsignalized intersections were related to less severe injuries. Findings of this study are beneficial in forming several targeted countermeasures for motorcycle safety in China, including designing roads with appropriate road delineation and street lighting, strict enforcement for speeding and red light violations, promoting helmet usage, and improving the conspicuity of motorcyclists. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
Open AccessArticle
Effect of In-Vehicle Audio Warning System on Driver’s Speed Control Performance in Transition Zones from Rural Areas to Urban Areas
Int. J. Environ. Res. Public Health 2016, 13(7), 634; https://doi.org/10.3390/ijerph13070634
Received: 23 April 2016 / Revised: 20 June 2016 / Accepted: 21 June 2016 / Published: 25 June 2016
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Abstract
Speeding is a major contributing factor to traffic crashes and frequently happens in areas where there is a mutation in speed limits, such as the transition zones that connect urban areas from rural areas. The purpose of this study is to investigate the [...] Read more.
Speeding is a major contributing factor to traffic crashes and frequently happens in areas where there is a mutation in speed limits, such as the transition zones that connect urban areas from rural areas. The purpose of this study is to investigate the effects of an in-vehicle audio warning system and lit speed limit sign on preventing drivers’ speeding behavior in transition zones. A high-fidelity driving simulator was used to establish a roadway network with the transition zone. A total of 41 participants were recruited for this experiment, and the driving speed performance data were collected from the simulator. The experimental results display that the implementation of the audio warning system could significantly reduce drivers’ operating speed before they entered the urban area, while the lit speed limit sign had a minimal effect on improving the drivers’ speed control performance. Without consideration of different types of speed limit signs, it is found that male drivers generally had a higher operating speed both upstream and in the transition zones and have a larger maximum deceleration for speed reduction than female drivers. Moreover, the drivers who had medium-level driving experience had the higher operating speed and were more likely to have speeding behaviors in the transition zones than those who had low-level and high-level driving experience in the transition zones. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
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Open AccessArticle
Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models
Int. J. Environ. Res. Public Health 2016, 13(6), 609; https://doi.org/10.3390/ijerph13060609
Received: 3 April 2016 / Revised: 8 June 2016 / Accepted: 13 June 2016 / Published: 18 June 2016
Cited by 10 | PDF Full-text (320 KB) | HTML Full-text | XML Full-text
Abstract
Traffic and environmental conditions (e.g., weather conditions), which frequently change with time, have a significant impact on crash occurrence. Traditional crash frequency models with large temporal scales and aggregated variables are not sufficient to capture the time-varying nature of driving environmental factors, causing [...] Read more.
Traffic and environmental conditions (e.g., weather conditions), which frequently change with time, have a significant impact on crash occurrence. Traditional crash frequency models with large temporal scales and aggregated variables are not sufficient to capture the time-varying nature of driving environmental factors, causing significant loss of critical information on crash frequency modeling. This paper aims at developing crash frequency models with refined temporal scales for complex driving environments, with such an effort providing more detailed and accurate crash risk information which can allow for more effective and proactive traffic management and law enforcement intervention. Zero-inflated, negative binomial (ZINB) models with site-specific random effects are developed with unbalanced panel data to analyze hourly crash frequency on highway segments. The real-time driving environment information, including traffic, weather and road surface condition data, sourced primarily from the Road Weather Information System, is incorporated into the models along with site-specific road characteristics. The estimation results of unbalanced panel data ZINB models suggest there are a number of factors influencing crash frequency, including time-varying factors (e.g., visibility and hourly traffic volume) and site-varying factors (e.g., speed limit). The study confirms the unique significance of the real-time weather, road surface condition and traffic data to crash frequency modeling. Full article
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
Int. J. Environ. Res. Public Health EISSN 1660-4601 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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