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Promotion of Big Data and Intelligent Transportation to Traffic Safety and Environment

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

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 62277

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Dr. Feng Chen

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Guest Editor

Key Laboratory of Infrastructure Durability and Operation Safety in Airfield of CAAC, College of Transportation Engineering, Tongji University, Shanghai 1239, China
Interests: transportation safety, aviation meteorology; civil aviation safety; statistical modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Kun Xie

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Department of Civil and Environmental Engineering, Old Dominion University, Norfolk, VA 23529, USA
Interests: transportation safety; connected and autonomous vehicles; big data analytics, statistics and machine learning; resilience in multimodal transportation systems
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaoxiang Ma

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College of Transportation Engineering, Tongji University, 4800 Cao'an Road, Shanghai, China

Special Issue Information

Dear Colleagues,

Metropolitan areas face serious traffic-related problems. Road traffic accidents cause a large number of deaths and disabilities every day. Moreover, traffic congestion has been increasingly severe around the world, causing enormous pollutant emissions to degrade air quality. Both traffic accidents and vehicle pollutions have become major public health issues. The recent development of new technologies such as big data, automated driving, and connected vehicle and cooperative vehicle infrastructure systems show great potential to enhance traffic safety and mitigate traffic congestion. By harnessing the power of these emerging technologies, a better understanding of data-driven traffic systems can be achieved, which is of practical importance to traffic safety and traffic operation.

This Special Issue aims to report on recent advances in interdisciplinary research related to understanding associated risks and the improvement of traffic safety and environment problems 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 big data, ITS, automated driving, connected vehicle, cooperative vehicle infrastructure systems, etc., are particularly welcome. The International Journal of Environmental Research and Public Health is indexed by SCI-E, PubMed, and other databases.

Dr. Feng Chen
Dr. Kun Xie
Dr. Xiaoxiang Ma
Guest Editors

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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. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly 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 2500 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

big data and traffic safety
big data and traffic environment
intelligent transportation and traffic safety
intelligent transportation and traffic environment
automatic driving
cooperative vehicle infrastructure system
connected car

Related Special Issue

The 2nd Edition: Promotion of Big Data and Intelligent Transportation to Traffic Safety and the Environment in International Journal of Environmental Research and Public Health (33 articles)

Published Papers (14 papers)

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Research

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25 pages, 1789 KiB

Open AccessArticle

Causation Analysis of Hazardous Material Road Transportation Accidents Based on the Ordered Logit Regression Model

by Changxi Ma, Jibiao Zhou and Dong Yang

Int. J. Environ. Res. Public Health 2020, 17(4), 1259; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17041259 - 15 Feb 2020

Cited by 38 | Viewed by 5705

Abstract

Understanding the influence factors and related causation of hazardous materials can improve hazardous materials drivers’ safety awareness and help traffic professionals to develop effective countermeasures. This study investigates the statistical distribution characteristics, such as types of hazardous materials transportation accidents, driver properties, vehicle properties, environmental properties, road properties. In total, 343 data regarding hazardous materials accidents were collected from the chemical accident information network of China. An ordered logit regression (OLR) model is proposed to account for the unobserved heterogeneity across observations. Four independent variables, such as hazardous materials drivers’ properties, vehicle properties, environmental properties, and road properties are employed based on the OLR model, an ordered multinomial logistic regression (MLR) is estimated the OLR model parameters. Both parameter estimates and odds ratio (OR) are employed to interpret the impact of influence factors on the severity of hazardous materials accidents. The model estimation results show that 10 factors such as violations, unsafe driving behaviors, vehicle faults, and so on are closely related to accidents severity of hazardous materials transportation. Furthermore, three enforcement countermeasures are proposed to prevent accidents when transporting hazardous materials. Full article

(This article belongs to the Special Issue Promotion of Big Data and Intelligent Transportation to Traffic Safety and Environment)

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14 pages, 1731 KiB

Open AccessFeature PaperArticle

Design of a Network Optimization Platform for the Multivehicle Transportation of Hazardous Materials

by Sheng Dong, Jibiao Zhou and Changxi Ma

Int. J. Environ. Res. Public Health 2020, 17(3), 1104; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17031104 - 10 Feb 2020

Cited by 10 | Viewed by 2917

Abstract

With economic development, the volume of hazardous materials is increasing, and the potential risks to human beings and the natural environment are expanding. Road transportation has become the main mode of transportation for hazardous materials. Because of the specific characteristics of hazardous materials, if an accident occurs in the transportation process, it often causes mass casualties, serious property and socioeconomic damage, and damage to the ecological environment. Hence, transportation is an important part of the life cycle of hazardous materials. This paper designs an optimization platform for multidestination, multiterminal, and multivehicle networks that transport hazardous materials. The logistics module in TransCAD software is used to construct this platform. By identifying the effective transportation routes considering the transportation risk, sensitive target population, and transportation time of each road section, the entropy method can be used to fuse and obtain the comprehensive impedance value of each road section. Finally, the optimal transportation network of hazardous materials was obtained by the transportation network optimization algorithm in TransCAD. The platform can display the optimal transport program with data windows, text, and maps. The research results provide a reference for relevant departments to scientifically manage the transport of hazardous materials. Full article

(This article belongs to the Special Issue Promotion of Big Data and Intelligent Transportation to Traffic Safety and Environment)

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18 pages, 366 KiB

Open AccessArticle

A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions

by Xiaojun Shao, Xiaoxiang Ma, Feng Chen, Mingtao Song, Xiaodong Pan and Kesi You

Int. J. Environ. Res. Public Health 2020, 17(2), 395; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17020395 - 07 Jan 2020

Cited by 32 | Viewed by 2856

Abstract

Social and economic burdens caused by truck-involved rear-end collisions are of great concern to public health and the environment. However, few efforts focused on identifying the difference of impacting factors on injury severity between car-strike-truck and truck-strike-car in rear-end collisions. In light of the above, this study focuses on illustrating the impact of variables associated with injury severity in truck-related rear-end crashes. To this end, truck involved rear-end crashes between 2006 and 2015 in the U.S. were obtained. Three random parameters ordered probit models were developed: two separate models for the car-strike-truck crashes and the truck-strike-car crashes, respectively, and one for the combined dataset. The likelihood ratio test was conducted to evaluate the significance of the difference between the models. The results show that there is a significant difference between car-strike-truck and truck-strike-car crashes in terms of contributing factors towards injury severity. In addition, indicators reflecting male, truck, starting or stopped in the road before a crash, and other vehicles stopped in lane show a mixed impact on injury severity. Corresponding implications were discussed according to the findings to reduce the possibility of severe injury in truck-involved rear-end collisions. Full article

(This article belongs to the Special Issue Promotion of Big Data and Intelligent Transportation to Traffic Safety and Environment)

13 pages, 3733 KiB

Open AccessArticle

The Impact of Activity-Based Mobility Pattern on Assessing Fine-Grained Traffic-Induced Air Pollution Exposure

by Yizheng Wu and Guohua Song

Int. J. Environ. Res. Public Health 2019, 16(18), 3291; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16183291 - 07 Sep 2019

Cited by 9 | Viewed by 2319

Abstract

Quantifying the air pollution and health impacts of transportation plans provides decision makers with valuable information that can help to target interventions. However, a large number of environmental epidemiological research assumes exposures of static populations at residential locations and does not consider the human activity patterns, which may lead to significant estimation errors. This study uses an integrated modeling framework to predict fine-grained air pollution exposures occurring throughout residents’ activity spaces. We evaluate concentrations of fine particulate matter (PM_2.5) under a regional transportation plan for Sacramento, California, using activity-based travel demand model outputs, vehicle emission, and air dispersion models. We use predicted air pollution exposures at the traffic analysis zone (TAZ) level to estimate residents’ exposure accounting for their movements throughout the day to assess the impact of activity-based mobility pattern on air pollution exposure. Results of PM_2.5 exposures estimated statically (at residential locations) versus dynamically (over residents’ activity-based mobility) demonstrates that the two methods yield statistically significant different results (p < 0.05). In addition, the comparison conducted in different age groups shows that the difference between these two approaches is greater among youth and working age residents, whereas seniors show a similar pattern using both approaches due to their lower rates of travel activity. Full article

(This article belongs to the Special Issue Promotion of Big Data and Intelligent Transportation to Traffic Safety and Environment)

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28 pages, 1632 KiB

Open AccessArticle

Risk Assessment in Urban Large-Scale Public Spaces Using Dempster-Shafer Theory: An Empirical Study in Ningbo, China

by Jibiao Zhou, Xinhua Mao, Yiting Wang, Minjie Zhang and Sheng Dong

Int. J. Environ. Res. Public Health 2019, 16(16), 2942; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16162942 - 16 Aug 2019

Cited by 10 | Viewed by 2931

Abstract

Urban Large-scale Public Spaces (ULPS) are important areas of urban culture and economic development, which are also places of the potential safety hazard. ULPS safety assessment has played a crucial role in the theory and practice of urban sustainable development. The primary objective of this study is to explore the interaction between ULPS safety risk and its influencing factors. In the first stage, an index sensitivity analysis method was applied to calculate and identify the safety risk assessment index system. Next, a Delphi method and information entropy method were also applied to collect and calculate the weight of risk assessment indicators. In the second stage, a Dempster-Shafer Theory (DST) method with evidence fusion technique was utilized to analyze the interaction between the ULPS safety risk level and the multiple-index variables, measured by four observed performance indicators, i.e., environmental factor, human factor, equipment factor, and management factor. Finally, an empirical study of DST approach for ULPS safety performance analysis was presented. Full article

(This article belongs to the Special Issue Promotion of Big Data and Intelligent Transportation to Traffic Safety and Environment)

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12 pages, 784 KiB

Open AccessArticle

Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model

by Feng Chen, Mingtao Song and Xiaoxiang Ma

Int. J. Environ. Res. Public Health 2019, 16(14), 2632; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16142632 - 23 Jul 2019

Cited by 135 | Viewed by 5029

Abstract

The existing studies on drivers’ injury severity include numerous statistical models that assess potential factors affecting the level of injury. These models should address specific concerns tailored to different crash characteristics. For rear-end crashes, potential correlation in injury severity may present between the two drivers involved in the same crash. Moreover, there may exist unobserved heterogeneity considering parameter effects, which may vary across both crashes and individuals. To address these concerns, a random parameters bivariate ordered probit model has been developed to examine factors affecting injury sustained by two drivers involved in the same rear-end crash between passenger cars. Taking both the within-crash correlation and unobserved heterogeneity into consideration, the proposed model outperforms the two separate ordered probit models with fixed parameters. The value of the correlation parameter demonstrates that there indeed exists significant correlation between two drivers’ injuries. Driver age, gender, vehicle, airbag or seat belt use, traffic flow, etc., are found to affect injury severity for both the two drivers. Some differences can also be found between the two drivers, such as the effect of light condition, crash season, crash position, etc. The approach utilized provides a possible use for dealing with similar injury severity analysis in future work. Full article

(This article belongs to the Special Issue Promotion of Big Data and Intelligent Transportation to Traffic Safety and Environment)

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17 pages, 2644 KiB

Open AccessArticle

Classification of Fatigued and Drunk Driving Based on Decision Tree Methods: A Simulator Study

by Ying Yao, Xiaohua Zhao, Hongji Du, Yunlong Zhang, Guohui Zhang and Jian Rong

Int. J. Environ. Res. Public Health 2019, 16(11), 1935; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16111935 - 31 May 2019

Cited by 18 | Viewed by 3830

Abstract

It is a commonly known fact that both alcohol and fatigue impair driving performance. Therefore, the identification of fatigue and drinking status is very important. In this study, each of the 22 participants finished five driving tests in total. The control condition, serving as the benchmark in the five driving tests, refers to alert driving. The other four test conditions include driving with three blood alcohol content (BAC) levels (0.02%, 0.05%, and 0.08%) and driving in a fatigued state. The driving scenario included straight and curved roads. The straight roads connected the curved ones with radii of 200 m, 500 m, and 800 m with two turning directions (left and right). Driving performance indicators such as the average and standard deviation of longitudinal speed and lane position were selected to identify drunk driving and fatigued driving. In the process of identification, road geometry (straight segments, radius, and direction of curves) was also taken into account. Alert vs. abnormal and fatigued vs. drunk driving with various BAC levels were analyzed separately using the Classification and Regression Tree (CART) model, and the significance of the variables on the binary response variable was determined. The results showed that the decision tree could be used to distinguish normal driving from abnormal driving, fatigued driving, and drunk driving based on the indexes of vehicle speed and lane position at curves with different radii. The overall accuracy of classification of “alert” and “abnormal” driving was 90.9%, and that of “fatigued” and “drunk” driving was 94.4%. The accuracy was relatively low in identifying different BAC degrees. This experiment is designed to provide a reference for detecting dangerous driving states. Full article

(This article belongs to the Special Issue Promotion of Big Data and Intelligent Transportation to Traffic Safety and Environment)

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13 pages, 2067 KiB

Open AccessArticle

Effect of Using Mobile Phones on Driver’s Control Behavior Based on Naturalistic Driving Data

by Lanfang Zhang, Boyu Cui, Minhao Yang, Feng Guo and Junhua Wang

Int. J. Environ. Res. Public Health 2019, 16(8), 1464; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16081464 - 25 Apr 2019

Cited by 31 | Viewed by 5503

Abstract

Distracted driving behaviors are closely related to crash risk, with the use of mobile phones during driving being one of the leading causes of accidents. This paper attempts to investigate the impact of cell phone use while driving on drivers’ control behaviors. Given the limitation of driving simulators in an unnatural setting, a sample of 134 cases related to cell phone use during driving were extracted from Shanghai naturalistic driving study data, which provided massive unobtrusive data to observe actual driving process. The process of using mobile phones was categorized into five operations, including dialing, answering, talking and listening, hanging up, and viewing information. Based on the concept of moving time window, the variation of the intensity of control activity, the sensitivity of control operation, and the stability of control state in each operation were analyzed. The empirical results show strong correlation between distracted operations and driving control behavior. The findings contribute to a better understanding of drivers’ natural behavior changes with using mobiles, and can provide useful information for transport safety management. Full article

(This article belongs to the Special Issue Promotion of Big Data and Intelligent Transportation to Traffic Safety and Environment)

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17 pages, 4505 KiB

Open AccessArticle

Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method

by Tianpei Tang, Senlai Zhu, Yuntao Guo, Xizhao Zhou and Yang Cao

Int. J. Environ. Res. Public Health 2019, 16(7), 1166; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16071166 - 01 Apr 2019

Cited by 8 | Viewed by 2725

Abstract

Evaluating the safety risk of rural roadsides is critical for achieving reasonable allocation of a limited budget and avoiding excessive installation of safety facilities. To assess the safety risk of rural roadsides when the crash data are unavailable or missing, this study proposed a Bayesian Network (BN) method that uses the experts’ judgments on the conditional probability of different safety risk factors to evaluate the safety risk of rural roadsides. Eight factors were considered, including seven factors identified in the literature and a new factor named access point density. To validate the effectiveness of the proposed method, a case study was conducted using 19.42 km long road networks in the rural area of Nantong, China. By comparing the results of the proposed method and run-off-road (ROR) crash data from 2015–2016 in the study area, the road segments with higher safety risk levels identified by the proposed method were found to be statistically significantly correlated with higher crash severity based on the crash data. In addition, by comparing the respective results evaluated by eight factors and seven factors (a new factor removed), we also found that access point density significantly contributed to the safety risk of rural roadsides. These results show that the proposed method can be considered as a low-cost solution to evaluating the safety risk of rural roadsides with relatively high accuracy, especially for areas with large rural road networks and incomplete ROR crash data due to budget limitation, human errors, negligence, or inconsistent crash recordings. Full article

(This article belongs to the Special Issue Promotion of Big Data and Intelligent Transportation to Traffic Safety and Environment)

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18 pages, 5470 KiB

Open AccessArticle

Time-of-Day Control Double-Order Optimization of Traffic Safety and Data-Driven Intersections

by Chen Xu, Decun Dong, Dongxiu Ou and Changxi Ma

Int. J. Environ. Res. Public Health 2019, 16(5), 870; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16050870 - 09 Mar 2019

Cited by 5 | Viewed by 3104

Abstract

This paper proposes a novel two-order optimization model of the division of time-of-day control segmented points of road intersection to address the limitations of the randomness of artificial experience, avoid the complex multi-factor division calculation, and optimize the traditional model over traffic safety and data-driven methods. For the first-order optimization—that is, deep optimization of the model input data—we first increase the dimension of traditional traffic flow data by data-driven and traffic safety methods, and develop a vector quantity to represent the size, direction, and time frequency with conflict point traffic of the total traffic flow at a certain intersection for a period by introducing a 3D vector of intersection traffic flow. Then, a time-series segmentation algorithm is used to recurse the distance amongst adjacent vectors to obtain the initial scheme of segmented points, and the segmentation points are finally divided by the combination of the preliminary scheme. For the second-order optimization—that is, model adaptability analysis—the traffic flow data at intersections are subjected to standardised processing by five-number summary. The different traffic flow characteristics of the intersection are categorised by the K central point clustering algorithm of big data, and an applicability analysis of each type of intersection is conducted by using an innovated piecewise point division model. The actual traffic flow data of 155 intersections in Yuecheng District, Shaoxing, China, in 2016 are tested. Four types of intersections in the tested range are evaluated separately by the innovated piecewise point division model and the traditional total flow segmentation model on the basis of Synchro 7 simulation software. It is shown that when the innovated double-order optimization model is used in the intersection according to the ‘hump-type’ traffic flow characteristic, its control is more accurate and efficient than that of the traditional total flow segmentation model. The total delay time is reduced by approximately 5.6%. In particular, the delay time in the near-peak-flow buffer period is significantly reduced by approximately 17%. At the same time, the traffic accident rate has also dropped significantly, effectively improving traffic safety at intersections. Full article

(This article belongs to the Special Issue Promotion of Big Data and Intelligent Transportation to Traffic Safety and Environment)

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18 pages, 3900 KiB

Open AccessArticle

Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework

by Chen Wang, Lin Liu, Chengcheng Xu and Weitao Lv

Int. J. Environ. Res. Public Health 2019, 16(3), 334; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16030334 - 25 Jan 2019

Cited by 26 | Viewed by 3950

Abstract

The objective of this paper is to predict the future driving risk of crash-involved drivers in Kunshan, China. A systematic machine learning framework is proposed to deal with three critical technical issues: 1. defining driving risk; 2. developing risky driving factors; 3. developing a reliable and explicable machine learning model. High-risk (HR) and low-risk (LR) drivers were defined by five different scenarios. A number of features were extracted from seven-year crash/violation records. Drivers’ two-year prior crash/violation information was used to predict their driving risk in the subsequent two years. Using a one-year rolling time window, prediction models were developed for four consecutive time periods: 2013–2014, 2014–2015, 2015–2016, and 2016–2017. Four tree-based ensemble learning techniques were attempted, including random forest (RF), Adaboost with decision tree, gradient boosting decision tree (GBDT), and extreme gradient boosting decision tree (XGboost). A temporal transferability test and a follow-up study were applied to validate the trained models. The best scenario defining driving risk was multi-dimensional, encompassing crash recurrence, severity, and fault commitment. GBDT appeared to be the best model choice across all time periods, with an acceptable average precision (AP) of 0.68 on the most recent datasets (i.e., 2016–2017). Seven of nine top features were related to risky driving behaviors, which presented non-linear relationships with driving risk. Model transferability held within relatively short time intervals (1–2 years). Appropriate risk definition, complicated violation/crash features, and advanced machine learning techniques need to be considered for risk prediction task. The proposed machine learning approach is promising, so that safety interventions can be launched more effectively. Full article

(This article belongs to the Special Issue Promotion of Big Data and Intelligent Transportation to Traffic Safety and Environment)

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12 pages, 309 KiB

Open AccessArticle

Investigating Spatial Autocorrelation and Spillover Effects in Freeway Crash-Frequency Data

by Huiying Wen, Xuan Zhang, Qiang Zeng, Jaeyoung Lee and Quan Yuan

Int. J. Environ. Res. Public Health 2019, 16(2), 219; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16020219 - 14 Jan 2019

Cited by 21 | Viewed by 3344

Abstract

This study attempts to investigate spatial autocorrelation and spillover effects in micro traffic safety analysis. To achieve the objective, a Poisson-based count regression with consideration of these spatial effects is proposed for modeling crash frequency on freeway segments. In the proposed hybrid model, the spatial autocorrelation and the spillover effects are formulated as the conditional autoregressive (CAR) prior and the exogenous variables of adjacent segments, respectively. The proposed model is demonstrated and compared to the models with only one kind of spatial effect, using one-year crash data collected from Kaiyang Freeway, China. The results of Bayesian estimation conducted in WinBUGS show that significant spatial autocorrelation and spillover effects simultaneously exist in the freeway crash-frequency data. The lower value of deviance information criterion (DIC) and more significant exogenous variables for the hybrid model compared to the other alternatives, indicate the strength of accounting for both spatial autocorrelation and spillover effects on improving model fit and identifying crash contributing factors. Moreover, the model results highlight the importance of daily vehicle kilometers traveled, and horizontal and vertical alignments of targeted segments and adjacent segments on freeway crash occurrences. Full article

(This article belongs to the Special Issue Promotion of Big Data and Intelligent Transportation to Traffic Safety and Environment)

21 pages, 34113 KiB

Open AccessArticle

Investigation of the Contributory Factors to the Guessability of Traffic Signs

by Jing Liu, Huiying Wen, Dianchen Zhu and Wesley Kumfer

Int. J. Environ. Res. Public Health 2019, 16(1), 162; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16010162 - 08 Jan 2019

Cited by 9 | Viewed by 4758

Abstract

Traffic signs play an important role in traffic management systems. A variety of studies have focused on drivers’ comprehension of traffic signs. However, the travel safety of prospective users, which has been rarely mentioned in previous studies, has attracted considerable attention from relevant departments in China. With the growth of international and interregional travel demand, traffic signs should be designed more universally to reduce the potential risks to drivers. To identify key factors that improve prospective users’ sign comprehension, this study investigated eight factors that may affect users’ performance regarding sign guessing. Two hundred and one Chinese students, all of whom intended to be drivers and none of whom had experience with daily driving after obtaining a license or visits to Germany, guessed the meanings and rated the sign features of 54 signs. We investigated the effects of selected user factors on their sign guessing performance. Additionally, the contributions of four cognitive design features to the guessability of traffic signs were examined. Based on an analysis of the relationships between the cognitive features and the guessability score of signs, the contributions of four sign features to the guessability of traffic signs were examined. Moreover, by exploring Chinese users’ differences in guessing performance between Chinese signs and German signs, cultural issues in sign design were identified. The results showed that vehicle ownership and attention to traffic signs exerted a significant influence on guessing performance. As expected, driver’s license training and the number of years in college were dominant factors for guessing performance. With regard to design features, semantic distance and confidence in guessing were two dominant factors for the guessability of signs. We suggest improving the design of signs by including vivid, universal symbols. Thus, we provide several suggestions for designing more user-friendly signs. Full article

(This article belongs to the Special Issue Promotion of Big Data and Intelligent Transportation to Traffic Safety and Environment)

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Review

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18 pages, 3449 KiB

Open AccessReview

Risk Riding Behaviors of Urban E-Bikes: A Literature Review

by Changxi Ma, Dong Yang, Jibiao Zhou, Zhongxiang Feng and Quan Yuan

Int. J. Environ. Res. Public Health 2019, 16(13), 2308; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16132308 - 28 Jun 2019

Cited by 95 | Viewed by 12051

Abstract

In order to clearly understand the risky riding behaviors of electric bicycles (e-bikes) and analyze the riding characteristics, we review the research results of the e-bike risky riding behavior from three aspects: the characteristics and causes of e-bike accidents, the characteristics of users’ traffic behavior, and the prevention and intervention of traffic accidents. The analysis results show that the existing research methods on risky riding behavior of e-bikes mainly involve questionnaire survey methods, structural equation models, and binary probability models. The illegal occupation of motor vehicle lanes, over-speed cycling, red-light running, and illegal manned and reverse cycling are the main risky riding behaviors seen with e-bikes. Due to the difference in physiological and psychological characteristics such as gender, age, audiovisual ability, responsiveness, patience when waiting for a red light, congregation, etc., there are differences in risky cycling behaviors of different users. Accident prevention measures, such as uniform registration of licenses, the implementation of quasi-drive systems, improvements of the riding environment, enhancements of safety awareness and training, are considered effective measures for preventing e-bike accidents and protecting the traffic safety of users. Finally, in view of the shortcomings of the current research, the authors point out three research directions that can be further explored in the future. The strong association rules between risky riding behavior and traffic accidents should be explored using big data analysis. The relationships between risk awareness, risky cycling, and traffic accidents should be studied using the scales of risk perception, risk attitude, and risk tolerance. In a variety of complex mixed scenes, the risk degree, coupling characteristics, interventions, and the coupling effects of various combination intervention measures of e-bike riding behaviors should be researched using coupling theory in the future. Full article

(This article belongs to the Special Issue Promotion of Big Data and Intelligent Transportation to Traffic Safety and Environment)

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