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01 A Cooperative Map Matching Algorithm Applied in Intelligent and Connected Vehicle Positioning
02 Indoor Sign-based Visual Localization Method
03 Intelligent Vehicles Localization Based on Semantic Map Representation from 3D Point Clouds
04 Companion Relationship Discovering Algorithm for Passengers in the Cruise Based on UWB Positioning
05 An Overview of Traffic Management in "Automatic+Manual" Driving Environment
06 An Analysis of Injury Severities in School Bus Accidents Based on Random Parameter Logit Models
07 An Image Generation Method for Automated Driving Based on Improved GAN
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2023, 41(4): 1-13.
doi: 10.3963/j.jssn.1674-4861.2023.04.001
Abstract:
Safety of lane change decision-makings for connected automated vehicles (CAVs) is a key task to improve traffic safety and enhance road mobility. In this paper, the safety issues related to lane changing of CAVs are investigated. From the perspective of driving safety, the adverse impacts of extreme lane-changing behavior and emergency lane-changing behavior on traffic safety are analyzed, emphasizing the importance of risk assessment. The various risk assessment methods of lane changing are reviewed, including the use of environmental sensors, traffic conflict indicators, and vehicle-level micro-trajectory data. Identifying risks through risk assessment and taking corresponding measures can significantly reduce traffic accidents caused by dangerous lane-changing behavior. Furthermore, the methods for CAVs to make lane-changing decisions by obtaining environmental information in both traditional and vehicle to everything (V2X) environments are elaborated. Particularly focusing on the CAVs in V2X environment, the decision-making through the environment perception and recognition, targets detection, and data processing is analyzed. Reasonable recommendations are proposed for achieving safe decision-making by CAVs in V2X environment in the future. Then the existing models for decision-making of lane-changing are analyzed and categorized into four types: rule-based models, discrete choice models, artificial intelligence models, and game theory models. The status of research and application, existing problems, and prospects of decision-making models in the field of road traffic safety are systematically summarized, both domestically and internationally. In summary, despite significant research achievements in lane-changing technologies for CAVs, there are still many challenges ahead. To tackle the existing problems in research, such as ensuring safe and reliable decisions in low-level automated driving environments, making more efficient and intelligent driving decisions for CAVs in low-penetration scenarios, achieving safe decision-making in situations with incomplete information, and improving the optimization of the algorithms for lane-changing decision-making, feasible solutions are proposed accordingly.
Safety of lane change decision-makings for connected automated vehicles (CAVs) is a key task to improve traffic safety and enhance road mobility. In this paper, the safety issues related to lane changing of CAVs are investigated. From the perspective of driving safety, the adverse impacts of extreme lane-changing behavior and emergency lane-changing behavior on traffic safety are analyzed, emphasizing the importance of risk assessment. The various risk assessment methods of lane changing are reviewed, including the use of environmental sensors, traffic conflict indicators, and vehicle-level micro-trajectory data. Identifying risks through risk assessment and taking corresponding measures can significantly reduce traffic accidents caused by dangerous lane-changing behavior. Furthermore, the methods for CAVs to make lane-changing decisions by obtaining environmental information in both traditional and vehicle to everything (V2X) environments are elaborated. Particularly focusing on the CAVs in V2X environment, the decision-making through the environment perception and recognition, targets detection, and data processing is analyzed. Reasonable recommendations are proposed for achieving safe decision-making by CAVs in V2X environment in the future. Then the existing models for decision-making of lane-changing are analyzed and categorized into four types: rule-based models, discrete choice models, artificial intelligence models, and game theory models. The status of research and application, existing problems, and prospects of decision-making models in the field of road traffic safety are systematically summarized, both domestically and internationally. In summary, despite significant research achievements in lane-changing technologies for CAVs, there are still many challenges ahead. To tackle the existing problems in research, such as ensuring safe and reliable decisions in low-level automated driving environments, making more efficient and intelligent driving decisions for CAVs in low-penetration scenarios, achieving safe decision-making in situations with incomplete information, and improving the optimization of the algorithms for lane-changing decision-making, feasible solutions are proposed accordingly.
2023, 41(4): 14-23.
doi: 10.3963/j.jssn.1674-4861.2023.04.002
Abstract:
In the Level 3 autonomous driving stage, the driver needs to respond and take over the vehicle when the system sends a takeover request. Therefore, to accurately assess the safety of the takeover process of Level 3 autonomous vehicles, the safety evaluation index system of the takeover process of autonomous driving is constructed. In this paper, a 4×2×2 takeover scenario factor is used to design a driving simulation test, and a driving simulator is used to collect various types of driving data. Based on the coefficient of variation method and Spearman correlation discriminant method, 13 security evaluation indicators are obtained from the analysis of 3 aspects, such as risk perception, risk avoidance manipulation and takeover performance. The subjective weights of the indicators are obtained using an improved hierarchical analysis that characterizes the experience of the experts, and the subjective weights of the indicators are obtained using entropy weights that reflect the characteristics of the data. To combine the advantages of the two methods, a composite weight incorporating both subjective and objective weights is obtained using the grade maximization method. The combined weights of risk perception, risk avoidance manipulation, and takeover performance are calculated to be 0.259, 0.475, and 0.271, which are used to construct the security evaluation index system of the takeover process. In this paper, the system is applied to comprehensively evaluate 655 takeover processes obtained from driving simulation tests, and they are classified into 3 categories of A, B and C takeover processes according to the evaluation results. Comparing the scores of the 3 types of takeover processes in 3 aspects: risk perception, risk avoidance manipulation and takeover performance, it is found that the A-type takeover process performs well in three aspects, the C-type takeover process performs poorly in risk avoidance manipulation and takeover performance, and the B-type takeover process performs intermediary between the A-type and C-type. Different types of takeover process have a better degree of differentiation in each indicator. The indicator system is constructed that effectively combines expert experience and indicator characteristics. The evaluation index system constructed in this paper effectively combines expert experience and index characteristics. It can provide theoretical support for a more comprehensive, reasonable and scientific evaluation of the safety in the process of automatic driving takeover.
In the Level 3 autonomous driving stage, the driver needs to respond and take over the vehicle when the system sends a takeover request. Therefore, to accurately assess the safety of the takeover process of Level 3 autonomous vehicles, the safety evaluation index system of the takeover process of autonomous driving is constructed. In this paper, a 4×2×2 takeover scenario factor is used to design a driving simulation test, and a driving simulator is used to collect various types of driving data. Based on the coefficient of variation method and Spearman correlation discriminant method, 13 security evaluation indicators are obtained from the analysis of 3 aspects, such as risk perception, risk avoidance manipulation and takeover performance. The subjective weights of the indicators are obtained using an improved hierarchical analysis that characterizes the experience of the experts, and the subjective weights of the indicators are obtained using entropy weights that reflect the characteristics of the data. To combine the advantages of the two methods, a composite weight incorporating both subjective and objective weights is obtained using the grade maximization method. The combined weights of risk perception, risk avoidance manipulation, and takeover performance are calculated to be 0.259, 0.475, and 0.271, which are used to construct the security evaluation index system of the takeover process. In this paper, the system is applied to comprehensively evaluate 655 takeover processes obtained from driving simulation tests, and they are classified into 3 categories of A, B and C takeover processes according to the evaluation results. Comparing the scores of the 3 types of takeover processes in 3 aspects: risk perception, risk avoidance manipulation and takeover performance, it is found that the A-type takeover process performs well in three aspects, the C-type takeover process performs poorly in risk avoidance manipulation and takeover performance, and the B-type takeover process performs intermediary between the A-type and C-type. Different types of takeover process have a better degree of differentiation in each indicator. The indicator system is constructed that effectively combines expert experience and indicator characteristics. The evaluation index system constructed in this paper effectively combines expert experience and index characteristics. It can provide theoretical support for a more comprehensive, reasonable and scientific evaluation of the safety in the process of automatic driving takeover.
2023, 41(4): 24-32.
doi: 10.3963/j.jssn.1674-4861.2023.04.003
Abstract:
Studying longitudinal collision risk of paired approach on closely spaced parallel runways (CSPRs) is crucial for assessing its safety, where positioning errors directly influence the longitudinal collision risk during the process. Given the lack of consideration on actual data fitting for positioning error distribution in previous studies, this study aims investigate the longitudinal collision risk during paired approach under the influence of actual data-fitted positioning error. According to the implementation process of paired approach, a kinematic model for the longitudinal spacing between aircrafts before and after pairing is established. In terms of positioning error during flight, statistical data of actual aircraft positioning errors are utilized to fit the distribution. Next, utilizing Automatic Dependent Surveillance-Broadcast (ADS-B) data, the longitudinal positioning error during the final approach phase is analyzed and fitted to identify the best-fitting distribution, that is, normal distribution. The collision risk between the aircraft fuselages in paired approach and the collision risk between the wake turbulence of lead aircraft and the fuselage of trailing aircraft are studied separately, and integral intervals for each collision risk model are determined. Based on the normal distribution and the movements of the paired aircrafts during paired approach, an assessment model for the longitudinal collision risk is established. Finally, data about the B737-800 aircraft at Shanghai Hongqiao Airport in December 2020 are collected for a case study. Simulations are conducted to analyze the changes in collision risk of fuselage Px1 and collision risk of wake turbulence Px2 over time under the initial longitudinal separations of 926 m and 2 778 m. Further, the relationship between different initial longitudinal separations and Px1 / Px2 or the maximum value of overall longitudinal collision risk. The results indicate that: ①when the initial longitudinal separation is 926 m, Px1 gradually decreases while Px2 increases over time, and Px1 is significantly greater than Px2. ②When the initial longitudinal separation is 2 778 m, the results are the opposite. ③ Px1 decreases while Px2 increases as the initial longitudinal separation increases. ④The overall longitudinal collision risk between the lead and trailing aircrafts decreases first and then increases with increasing initial longitudinal separation; ⑤when the initial longitudinal separation is smaller than 2 136 m, the longitudinal collision risk is primarily determined by the collision risk between the fuselages of lead and trailing aircrafts; when the initial longitudinal separation is larger than 2 136 m, it is determined by the collision risk between the wake turbulence of lead aircraft and the fuselage of trailing aircraft.
Studying longitudinal collision risk of paired approach on closely spaced parallel runways (CSPRs) is crucial for assessing its safety, where positioning errors directly influence the longitudinal collision risk during the process. Given the lack of consideration on actual data fitting for positioning error distribution in previous studies, this study aims investigate the longitudinal collision risk during paired approach under the influence of actual data-fitted positioning error. According to the implementation process of paired approach, a kinematic model for the longitudinal spacing between aircrafts before and after pairing is established. In terms of positioning error during flight, statistical data of actual aircraft positioning errors are utilized to fit the distribution. Next, utilizing Automatic Dependent Surveillance-Broadcast (ADS-B) data, the longitudinal positioning error during the final approach phase is analyzed and fitted to identify the best-fitting distribution, that is, normal distribution. The collision risk between the aircraft fuselages in paired approach and the collision risk between the wake turbulence of lead aircraft and the fuselage of trailing aircraft are studied separately, and integral intervals for each collision risk model are determined. Based on the normal distribution and the movements of the paired aircrafts during paired approach, an assessment model for the longitudinal collision risk is established. Finally, data about the B737-800 aircraft at Shanghai Hongqiao Airport in December 2020 are collected for a case study. Simulations are conducted to analyze the changes in collision risk of fuselage Px1 and collision risk of wake turbulence Px2 over time under the initial longitudinal separations of 926 m and 2 778 m. Further, the relationship between different initial longitudinal separations and Px1 / Px2 or the maximum value of overall longitudinal collision risk. The results indicate that: ①when the initial longitudinal separation is 926 m, Px1 gradually decreases while Px2 increases over time, and Px1 is significantly greater than Px2. ②When the initial longitudinal separation is 2 778 m, the results are the opposite. ③ Px1 decreases while Px2 increases as the initial longitudinal separation increases. ④The overall longitudinal collision risk between the lead and trailing aircrafts decreases first and then increases with increasing initial longitudinal separation; ⑤when the initial longitudinal separation is smaller than 2 136 m, the longitudinal collision risk is primarily determined by the collision risk between the fuselages of lead and trailing aircrafts; when the initial longitudinal separation is larger than 2 136 m, it is determined by the collision risk between the wake turbulence of lead aircraft and the fuselage of trailing aircraft.
2023, 41(4): 33-43.
doi: 10.3963/j.jssn.1674-4861.2023.04.004
Abstract:
A short distance section is a section between the tunnel and the mainline exit of a mountainous highway whose length is lower than the normative value because of the limitation of geographical and investment factors. In order to analyze the driving characteristics of this area thus to enhance the theoretical base for mountain road design and traffic control, high-definition driving videos are collected by drones in 7 mountain roads (e.g., Qinling service area) with short distance sections in China. The high-precision speed and trajectory data of vehicles across the entire region have been extracted. The SIFT algorithm is used for video stabilization. The YOLOv5 and DeepSORT algorithms are adopted for vehicle detection and tracking. The Savitzky-Golay filter is utilized to filter the data. Finally, high precision driving data can be obtained based on the above methods. It is verified that the accuracy of speed can reach more than 95% and the error of trajectory is less than 20 cm. Next, the driving characteristics are analyzed from a variety of perspectives, such as clearance distance, vehicle type, lane distribution, and others. The results show that: ①the driving characteristics on short distance section are very different from those on usual road section that the speed distribution does not follow a normal distribution; ②generally the outgoing vehicles would be steady 10 to 20 m ahead of the commencement of the fading phase; ③the speed of trucks is smoother as truck drivers can identify the exit road conditions more quickly than the car drivers because of the larger angle of view; ④approximately 20 meters after the starting point of the transition section, the cars in the inner lane enter the deceleration lane with a lateral speed of 1.1 to 1.4 m/s, and when the mainline has a leftward curve it is most favorable to drive out; ⑤the clearance distance has the highest influence on driving behaviors, the traffic volume affects the most among traffic flow factors while the direction of curve deflection and deflection angle affect the most among the road geometry factors.
A short distance section is a section between the tunnel and the mainline exit of a mountainous highway whose length is lower than the normative value because of the limitation of geographical and investment factors. In order to analyze the driving characteristics of this area thus to enhance the theoretical base for mountain road design and traffic control, high-definition driving videos are collected by drones in 7 mountain roads (e.g., Qinling service area) with short distance sections in China. The high-precision speed and trajectory data of vehicles across the entire region have been extracted. The SIFT algorithm is used for video stabilization. The YOLOv5 and DeepSORT algorithms are adopted for vehicle detection and tracking. The Savitzky-Golay filter is utilized to filter the data. Finally, high precision driving data can be obtained based on the above methods. It is verified that the accuracy of speed can reach more than 95% and the error of trajectory is less than 20 cm. Next, the driving characteristics are analyzed from a variety of perspectives, such as clearance distance, vehicle type, lane distribution, and others. The results show that: ①the driving characteristics on short distance section are very different from those on usual road section that the speed distribution does not follow a normal distribution; ②generally the outgoing vehicles would be steady 10 to 20 m ahead of the commencement of the fading phase; ③the speed of trucks is smoother as truck drivers can identify the exit road conditions more quickly than the car drivers because of the larger angle of view; ④approximately 20 meters after the starting point of the transition section, the cars in the inner lane enter the deceleration lane with a lateral speed of 1.1 to 1.4 m/s, and when the mainline has a leftward curve it is most favorable to drive out; ⑤the clearance distance has the highest influence on driving behaviors, the traffic volume affects the most among traffic flow factors while the direction of curve deflection and deflection angle affect the most among the road geometry factors.
2023, 41(4): 44-54.
doi: 10.3963/j.jssn.1674-4861.2023.04.005
Abstract:
Road traffic accidents are one of the major problems causing large numbers of casualties and property losses worldwide. By classifying road traffic accidents and predicting risk levels, it becomes possible to identify high-risk vehicles and reduce the probability of accidents and casualties. Traffic accidents are often influenced by multiple factors such as environment, weather, road conditions, and infrastructure, but existing accident impact analysis methods lack comprehensive research on traffic accident data. Therefore, this paper proposes a traffic accident classification model that incorporates an improved dimensionality reduction algorithm called PCA-LPP, which measures the similarity between data of different levels to achieve secondary dimensionality reduction. The model utilizes a large-scale traffic accident dataset and applies the DBSCAN algorithm to partition the accident data into risk areas. By training the spatial representations of different risk levels iteratively, the model could assess the risk levels in simulated vehicle environments. Experimental results demonstrate the effectiveness of the proposed approach. Comparative experiments on large-scale traffic data reduced to different dimensions show that the PCA-LPP algorithm achieves higher correlation between the reduced features and sample categories compared to traditional PCA. Moreover, when handling complex and sporadic traffic accident data, the density-based DBSCAN clustering algorithm achieves a purity of 0.942 9, a Rand index of 0.946 2, and a mutual information index of 0.678 4. Comparing these results with traditional algorithms like K-means and spectral clustering, DBSCAN consistently outperforms them in various evaluation metrics. Additionally, visual analysis of the classification results indicates that the proposed model reduces the influence of noisy data. Finally, an ablation experiment confirms that the PCA-LPP algorithm with secondary dimensionality reduction achieves the highest evaluation metrics. The confusion matrix of the prediction results shows that the model achieves precision rates of 85.77%, 70.78%, and 80.65% for different risk levels, further validating its effectiveness and practicality.
Road traffic accidents are one of the major problems causing large numbers of casualties and property losses worldwide. By classifying road traffic accidents and predicting risk levels, it becomes possible to identify high-risk vehicles and reduce the probability of accidents and casualties. Traffic accidents are often influenced by multiple factors such as environment, weather, road conditions, and infrastructure, but existing accident impact analysis methods lack comprehensive research on traffic accident data. Therefore, this paper proposes a traffic accident classification model that incorporates an improved dimensionality reduction algorithm called PCA-LPP, which measures the similarity between data of different levels to achieve secondary dimensionality reduction. The model utilizes a large-scale traffic accident dataset and applies the DBSCAN algorithm to partition the accident data into risk areas. By training the spatial representations of different risk levels iteratively, the model could assess the risk levels in simulated vehicle environments. Experimental results demonstrate the effectiveness of the proposed approach. Comparative experiments on large-scale traffic data reduced to different dimensions show that the PCA-LPP algorithm achieves higher correlation between the reduced features and sample categories compared to traditional PCA. Moreover, when handling complex and sporadic traffic accident data, the density-based DBSCAN clustering algorithm achieves a purity of 0.942 9, a Rand index of 0.946 2, and a mutual information index of 0.678 4. Comparing these results with traditional algorithms like K-means and spectral clustering, DBSCAN consistently outperforms them in various evaluation metrics. Additionally, visual analysis of the classification results indicates that the proposed model reduces the influence of noisy data. Finally, an ablation experiment confirms that the PCA-LPP algorithm with secondary dimensionality reduction achieves the highest evaluation metrics. The confusion matrix of the prediction results shows that the model achieves precision rates of 85.77%, 70.78%, and 80.65% for different risk levels, further validating its effectiveness and practicality.
2023, 41(4): 55-63.
doi: 10.3963/j.jssn.1674-4861.2023.04.006
Abstract:
Freeway accidents are frequent, and previous studies have failed to adequately reveal the effect of dynamic traffic flow on accident type and severity. This study focuses on a prediction method for types and severity of freeway accidents based on real-time traffic flow data. Traffic flow characteristics, including volume, density, and speed, are extracted from freeway gantry data. Simultaneously, temporal features and spatiotemporal non-uniformity features are considered. These data are then matched with accident data to constitute the full dataset for modeling. The model based on the extreme gradient boosting tree (XGBoost) algorithm is developed to predict the occurrence of accidents and accident types, and also to assess accident severity. Two types of accidents (i.e., rear-end collisions and other types of accidents) are considered and two levels of accident severity (i.e., injury or fatal accidents and proper-ty-damage-only accidents) are distinguished. The results indicate that: ①a higher risk of traffic accidents is associated with significant speed difference between upstream and downstream traffic, low speeds, high traffic volumes with frequent merging and diverging conditions; ②rear-end accidents are more likely to occur in situations with lower speeds, high traffic volumes with merging and diverging flows, and significant speed difference between upstream and downstream traffic; ③accidents involving rear-end collisions may result in higher severity when they occur on road segments with lower traffic volumes or occur during weekends or nighttime. The Area Under Curve (AUC) of the XGBoost-based models for accident types prediction and accident severity prediction reached 0.76 and 0.88 respectively. Compared with other commonly used algorithms such as Sequential Logistic, Gaussian Naive Bayes, Linear Support Vector Machine (SVM), Random Forest, and Neural Network, the XGBoost-based model demonstrates an average improvement of 0.08 and 0.24 in AUC values for predictions of accident types and accident severity. These results indicate that the XGBoost-based model exhibits better prediction performance. The research findings provide a reliable way for state warning of real-time traffic flow on freeway segments, which could be useful for improving driving safety.
Freeway accidents are frequent, and previous studies have failed to adequately reveal the effect of dynamic traffic flow on accident type and severity. This study focuses on a prediction method for types and severity of freeway accidents based on real-time traffic flow data. Traffic flow characteristics, including volume, density, and speed, are extracted from freeway gantry data. Simultaneously, temporal features and spatiotemporal non-uniformity features are considered. These data are then matched with accident data to constitute the full dataset for modeling. The model based on the extreme gradient boosting tree (XGBoost) algorithm is developed to predict the occurrence of accidents and accident types, and also to assess accident severity. Two types of accidents (i.e., rear-end collisions and other types of accidents) are considered and two levels of accident severity (i.e., injury or fatal accidents and proper-ty-damage-only accidents) are distinguished. The results indicate that: ①a higher risk of traffic accidents is associated with significant speed difference between upstream and downstream traffic, low speeds, high traffic volumes with frequent merging and diverging conditions; ②rear-end accidents are more likely to occur in situations with lower speeds, high traffic volumes with merging and diverging flows, and significant speed difference between upstream and downstream traffic; ③accidents involving rear-end collisions may result in higher severity when they occur on road segments with lower traffic volumes or occur during weekends or nighttime. The Area Under Curve (AUC) of the XGBoost-based models for accident types prediction and accident severity prediction reached 0.76 and 0.88 respectively. Compared with other commonly used algorithms such as Sequential Logistic, Gaussian Naive Bayes, Linear Support Vector Machine (SVM), Random Forest, and Neural Network, the XGBoost-based model demonstrates an average improvement of 0.08 and 0.24 in AUC values for predictions of accident types and accident severity. These results indicate that the XGBoost-based model exhibits better prediction performance. The research findings provide a reliable way for state warning of real-time traffic flow on freeway segments, which could be useful for improving driving safety.
2023, 41(4): 64-71.
doi: 10.3963/j.jssn.1674-4861.2023.04.007
Abstract:
To address the uncertainty regarding the effectiveness of traffic safety slogans in enhancing public awareness of traffic safety, a quantitative analysis of Chinese traffic safety slogans for preventing drunk driving and their effectiveness is conducted, aiming to analyze the linguistic characteristics and their effectiveness. Using Python 3.7 software, a total of 1 828 traffic safety slogans about drunk driving prevention, collected from the Baidu search engine between September 2019 to September 2020, are analyzed. The linguistic characteristics of these slogans were categorized and analyzed from six aspects, including person expression, emotion, rhetoric, rhyme, context, and length. The primary linguistic characteristics found in these slogans include the use of the other person expression (87.7%), negative emotion (45.4%), lack of rhetoric (49.9%), presupposed states (52.4%), and medium length (65.2%). Through an online questionnaire survey, the effectiveness of slogans with different linguistic characteristics is evaluated in terms of public attention, understanding, and acceptance. By employing the Chi-square test, the differences in linguistic characteristics of the slogans are analyzed and a generalized linear regression model is developed to identify significant linguistic factors affecting public attention, understanding, and acceptance. The results of generalized linear regression show as follow. ①Public attention to slogans is primarily influenced by the person expression and length of the slogans. Specifically, the slogans using the first- and other-person expression attract greater public attention compared to slogans using the second person expression (b =0.24, 0.49). Longer slogans (18 words or more) aroused more public attention compared to shorter slogans (less than 12 words) (b =0.26). ② Public acceptance of slogans is mainly influenced by the use of person expression. Slogans using the first-person expression receive higher public acceptance than those using the second person expression (b =0.31). ③There is no statistically significant relationship between public understanding of slogans and six linguistic characteristics of slogans. The results indicate that Chinese traffic safety slogans designed to prevent drunk driving exhibit diverse linguistic characteristics. The different linguistic characteristics affects the public attention and acceptance of the slogans. In the future, when designing traffic safety slogans, it is recommended to use the first person or other person expression and to consider increasing the length of the slogans to enhance the effectiveness of slogans in enhancing public awareness of traffic safety.
To address the uncertainty regarding the effectiveness of traffic safety slogans in enhancing public awareness of traffic safety, a quantitative analysis of Chinese traffic safety slogans for preventing drunk driving and their effectiveness is conducted, aiming to analyze the linguistic characteristics and their effectiveness. Using Python 3.7 software, a total of 1 828 traffic safety slogans about drunk driving prevention, collected from the Baidu search engine between September 2019 to September 2020, are analyzed. The linguistic characteristics of these slogans were categorized and analyzed from six aspects, including person expression, emotion, rhetoric, rhyme, context, and length. The primary linguistic characteristics found in these slogans include the use of the other person expression (87.7%), negative emotion (45.4%), lack of rhetoric (49.9%), presupposed states (52.4%), and medium length (65.2%). Through an online questionnaire survey, the effectiveness of slogans with different linguistic characteristics is evaluated in terms of public attention, understanding, and acceptance. By employing the Chi-square test, the differences in linguistic characteristics of the slogans are analyzed and a generalized linear regression model is developed to identify significant linguistic factors affecting public attention, understanding, and acceptance. The results of generalized linear regression show as follow. ①Public attention to slogans is primarily influenced by the person expression and length of the slogans. Specifically, the slogans using the first- and other-person expression attract greater public attention compared to slogans using the second person expression (b =0.24, 0.49). Longer slogans (18 words or more) aroused more public attention compared to shorter slogans (less than 12 words) (b =0.26). ② Public acceptance of slogans is mainly influenced by the use of person expression. Slogans using the first-person expression receive higher public acceptance than those using the second person expression (b =0.31). ③There is no statistically significant relationship between public understanding of slogans and six linguistic characteristics of slogans. The results indicate that Chinese traffic safety slogans designed to prevent drunk driving exhibit diverse linguistic characteristics. The different linguistic characteristics affects the public attention and acceptance of the slogans. In the future, when designing traffic safety slogans, it is recommended to use the first person or other person expression and to consider increasing the length of the slogans to enhance the effectiveness of slogans in enhancing public awareness of traffic safety.
Journal of Transport Information and Safety
(Founded in 1983 bimonthly )
Former Name：Computer and Communications
Supervised by：Ministry of Education of P. R. CHINA
Sponsored by：Wuhan University of Technology
Network of Computer Application Information in Transportation
In Association With：Intelligent Transportation Committee of China Association of Artificial Intelligence
Editor-in-Chief：ZHONG Ming
Edited and Published by：Editorial Office of Transport Information and Safety
Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA
Postcode：430063
Tel：027-86580355
E-mail：jtjsj@vip.163.com
Website：http://www.jtxa.net/
Postal Code：38-94
Domestic Issue：
CN 42-1781/U
Publication No.：ISSN 1674-4861
Indexed In
- Chinese Core Journal in “Integrated Transportation” category
- Chinese Science Citation Database (CSCD)
- Core Science and Technology Journals
- Chinese Scientific and Technological Papers and Citations (CSTPCD)
- Class A of Research Center for Chinese Science Evaluation (RCCSE)
- Chinese Academic Journal Comprehensive Evaluation Database (CAJ-CED)
- Chinese Core Journals (Selection) Database
- Chinese Scientific and Technological Periodicals Database
- China National Knowledge Infrastructure (CNKI)
- Chinese Academic Journals (CAJ-CD)
- Chinese Lifelong Education Academic Research Database
- Japan Science and Technology Agency (JST)
- World Journal Clout Index Report (2020 STM)
