Traffic Safety Improvement Research Articles

Factors Affecting Rear-End Collisions in Underground Road Junctions Using VISSIM

Due to urban overcrowding, available land is limited and traffic congestion has increased. Underground roads are being built to mitigate traffic congestion as an alternative. Studies associated with underground roads are needed because these roads are dark and closed and have a high risk of accidents compared to surface roads. In particular, there is limited study on junctions that connect two or more underground roads. In this study, an underground road network including junctions was constructed to analyze the factors behind rear-end collisions at underground road connections. To reflect the driving behavior on underground roads, the scenario analysis was conducted by applying the speed distribution of underground roads in Korea. The results of the analysis showed that variables such as acceleration standard deviation and lateral position standard deviation are crucial for accidents on underground roads. Thus, this study can be used as a basis for traffic management and safety improvement in the operation of underground road junctions in the future.

Paediatric maxillofacial fractures have increased in incidence and their nature and aetiology have changed during three decades

The safety of children’s living environment is affected by several factors. Safer living environments have been offered as one explanation to decreases in children’s fractures. Earlier studies provide evidence of a decreasing trend in children’s fractures in the past decades. The objective of this study was to investigate demographic and clinical features of paediatric maxillofacial fractures during three time periods. A retrospective cross-sectional single-centre study was designed. The study included 474 patients aged ≤ 15 years admitted to Helsinki University Hospital in Finland with maxillofacial fractures during 1980–1989, 1993–2002 and 2013–2018. Maxillofacial fractures increased by 25% during the study period. The increase was greater in boys (28%) than in girls (19%), and significant in age groups 0–5 years (71%) and 13–15 years (32%). Exclusively mandibular fractures decreased by 20%, while exclusively midfacial fractures increased more than four-fold and exclusively upper-third fractures five-fold. Being hit by object and falls from height increased more than two-fold. A temporary increase in assaults and decrease in bicycle accidents in the middle period of the study was observed. During the three decades, paediatric maxillofacial fractures have increased and both fracture type and underlying aetiology have changed. These findings reflect improvements in diagnostics, traffic safety, regulations and technology. The role of factors such as interpersonal violence and economic fluctuation on the incidence of childhood maxillofacial fractures is discussed.

Willingness to pay for private and public traffic safety improvements: the importance of the underlying good

ABSTRACT Findings in the traffic safety literature suggest that people value traffic risk reductions less when framed as a public good (e.g. infrastructure improvements) compared to when framed as a private good (e.g. personal safety equipment). This study contributes to this literature by reporting empirical evidence for the importance of controlling for the attributes of the goods used in such valuations. We focus on risks faced by vulnerable road users such as cyclists and pedestrians and compare valuations across private and public goods that vary in their attributes. When the goods are of an identical nature, we find no significant difference in valuations, resolving the controversy in previous findings. We find significant effects on valuations from using private or public provision, from offering the good as voluntary or mandated in use, and from changing the framing of the good between the private and the public versions. This adds further weight to the importance of controlling for many attributes.

Verification of the AIS service availability model based on dynamic data streams recorded from three receiving stations in the Polish coastal area

The reliability aspects of the operation of radio navigation systems constitute a crucial element for the safety of maritime navigation.. Technological progress in ship traffic monitoring is achieved through the design of ship systems and shore infrastructure equipped with Automatic Identification System (AIS) devices. One of the issues with AIS operation is the limited availability of the service in the form of data streams with an extended data age recorded on the receiving side. Another problem is the emission and reception by ships of incomplete positional reports without navigational parameters. Such situations render the system operationally unfit in terms of processed information. Therefore, it is essential to investigate the operational characteristics of radio navigation systems and develop tools to monitor the AIS service status on the receiving side. This article presents the development of a model for the availability of an AIS for vessels based on the determined mean time of the occurrence of incomplete navigation parameter values in AIS messages and the results of research in the domain of time and frequency using a mathematical method of the Fast Fourier Transform (FFT). The study results refer to six basic navigation parameters and show a varying service availability factor for the navigation parameters under study, i.e. the latitude (LAT), longitude (LON), speed over ground (SOG), course over ground (COG), heading (HDT), and the rate of turn (ROT). The data recorded by three receiving AIS stations on the Polish coast, i.e. PLKOL, PLSZZ, and PLSWI, were used as a key source of practical knowledge on the limitations of the AIS service availability. The experiment observed interruptions in the regular transmission of data from navigation equipment in the AIS service operational zone. As a result, the functional relationship was described based on the spectral analysis of the frequency of occurrence of times between the service repair (Time To Repair, TTR), and the model was proposed to be applied to the study of other variables. The presented model is a tool that allows for improving the monitoring of vessel traffic in terms of reliability, which directly affects the improvement of maritime traffic safety.

Text and Voice Message Distraction Detection: A Machine Learning Approach Using Vehicle Trajectory Data

Cellphone usage is often considered to be one of the major causes of distracted driving. Similarly, voice messaging is identified as a potential cause of distracted driving but has received limited attention in the literature. Thus, this study aims to develop supervised machine learning (ML) methods to detect distracted driving events caused by texting and voice messaging using vehicle trajectory data. Vehicle trajectory data was collected from 92 participants who drove a simulated network of the Baltimore metropolitan area using a driving simulator. Different key variables were extracted from the data to construct the features for developing the ML methods, including speed, brake usage, throttle, steering velocity, brake light, and offset from the road center. Several methods, including the support vector machine, k-nearest neighbor, decision tree, neural network, and adaptive boosting (AdaBoost), were examined on the data to achieve the best model. In addition, several metrics were used to assess the performance of the ML models, such as accuracy, sensitivity, precision, the Matthews correlation coefficient, and the area under the receiver operating characteristic curve (AUC-ROC). The results indicated that the AdaBoost algorithm had the best yields, with an accuracy of 74.67% and AUC-ROC of 82.5% on the independent test set. The findings of this study can be directly leveraged to develop in-vehicle driver warning systems to alert drivers with respect to distracted behavior, which lead to the reduction of distracted driving events and an improvement in traffic safety.

Traffic safety comprehensive evaluation of urban tunnel visual guiding system based on extension matter-element model: a case study in tunnel curves

Objective The visual guiding system, as a tunnel traffic safety improvement method by using visual guiding facilities to actively guide driving safely, has been widely used in countries with many tunnels, in recent years. This paper aims to quantitatively study the comprehensive evaluation of traffic safety of the visual guiding system in tunnels, which has certain engineering application value and can provide support for the quantitative evaluation and optimal design of tunnel traffic safety. Methods Based on the analysis of the relevant factors of urban tunnel traffic safety, a multi-factor comprehensive evaluation system with 5 upper-level indicators and 12 basic-level indicators was proposed. Considering the independent and incompatible indicators, a comprehensive evaluation method of traffic safety of the visual guiding system in urban tunnels was constructed by using the extension matter-element model. Taking the scene of 4 types of tunnel curves, such as no facilities, horizontal stripe, chevron alignment sign, and LED arch, as examples, the comprehensive evaluation of various schemes were carried out by using simulation tests. Results The traffic safety comprehensive evaluation system of visual guiding system in urban tunnels can be analyzed from five aspects: perception reaction, guidance ability, driver factor, driving task, and facility appearance. The results demonstrated significant the comprehensive evaluation result of the target level of scene 1 was L4, scene 2 was L3, scene 3 was L2, and scene 4 was L1. That is, the final results of the comprehensive evaluation of the four scenes were poor, medium, good, and very good, respectively. Conclusions In the scheme of visual guiding system for urban tunnel curves, the effectiveness of the three types of designs, from high to low, was the LED arch, chevron alignment sign, and horizontal stripe, and the safety of the scene without facilities was the lowest. Hence, setting the LED arch in the urban tunnel curve has a good effect in the aspects of guidance ability, sight distance, and sight zone, and is conducive to the driver’s perception reaction and driving task.

Calibrated confidence learning for large-scale real-time crash and severity prediction

Real-time crash and severity prediction is a complex task, and there is no existing framework to predict crash likelihood and severity together. Creating such a framework poses numerous challenges, particularly not independent and identically distributed (non-IID) data, large model sizes with high computational costs, missing data, sensitivity vs. false alarm rate (FAR) trade-offs, and real-world deployment strategies. This study introduces a novel modeling technique to address these challenges and develops a deployable real-world framework. We used extensive real-time traffic and weather data to develop a crash likelihood prediction modeling prototype, leveraging our preliminary work of spatial ensemble modeling. Next, we equipped this spatial ensemble model with local model regularization to calibrate model confidence training. The investigated regularizations include weight decay, label smoothing and knowledge distillation. Furthermore, post-calibration on model outputs was conducted to improve severity rating identification. We tested the framework to predict crashes and severity in real-time, categorizing crashes into four levels. Results were compared with benchmark models, real-world deployment mechanisms were explained, traffic safety improvement potential and sustainability aspects of the study were discussed. Modeling results demonstrated excellent performance, and fatal, severe, minor and PDO crash severities were predicted with 91.7%, 83.3%, 85.6%, and 87.7% sensitivity, respectively, and with very low FAR. Similarly, the viability of our model to predict different severity levels for specific crash types, i.e., all-crash types, rear-end crashes, and sideswipe/angle crashes, were examined, and it showed excellent performance. Our modeling technique showed great potential for reducing model size, lowering computational costs, improving sensitivity, and, most importantly, reducing FAR. Finally, the deployment strategy for the proposed crash and severity prediction technique is discussed, and its potential to predict crashes with severity levels in real-time will make a substantial contribution to tailoring specific strategies to prevent crashes.

Examining the impact of different LED road stud layouts on driving performance and gaze behaviour at night-time

The risks associated with night-time driving on dimly lit roads are substantial and are attributable to the limitations of human visual abilities. As a result, drivers often struggle to distinguish road geometry from ordinary road markings, thus increasing the likelihood of mistakes. These circumstances contribute to a 60 % higher likelihood of road crashes compared to daytime conditions. To mitigate these risks, active LED road studs, which previous studies have shown to have a positive influence on driving performance, can be used. However, there remains a gap in research regarding the optimal arrangement of these studs along road markings for an improvement in driver behaviour and traffic safety. In this study, we assessed the influence of five different LED road stud layouts (unlit, edge, centre, edge-centre, and lane) during night-time driving on two-lane rural highways with curves of different radii (120, 210, 300, 440 m) and directions (left, right). Following a within subject design, thirty-five participants drove in a simulator along a road track with 8 spiralled curves (4 radii × 2 directions) linked to straights. We monitored the longitudinal (i.e., speed), transversal (i.e., lateral position and standard deviation of lateral position) and gaze behaviours.Our findings indicate that the presence of LED road studs promotes safer driving, by helping drivers to adjust their speed when negotiating curves. Transversal behaviour analysis revealed layout-dependent effects on lateral position. The presence of road studs both at the lane centreline and edge allows drivers to maintain centred trajectories and improve steering control. Gaze behaviour analysis uncovered interesting patterns, demonstrating a strong correlation between road stud layout and the driver's focus on specific road targets. Illuminated markings prompt drivers to concentrate their gaze on distinct points, subsequently altering their transversal behaviour.

Study on Integrated Evaluation and Development Strategy of Pedestrian Protection in 2024 Edition of C-NCAP

This paper discusses the comprehensive assessment of pedestrian protection in the 2024 edition of China-New Car Assessment Program (C-NCAP) and its development strategies. The assessment plan has been adjusted according to the actual conditions of Chinese road traffic and vehicle characteristics, emphasizing comprehensive testing of vehicle safety performance. Improvement in structural crash safety significantly enhances a vehicle's ability to protect occupants in collision accidents and reduces injury risks. This article particularly focuses on pedestrian protection assessment and proposes a series of development strategies by integrating AEB VRU system testing with head and leg testing. These strategies aim to further enhance the level of pedestrian protection provided by vehicles and contribute to overall traffic safety improvement.

Exploring the challenges faced by Dutch truck drivers in the era of technological advancement.

Despite their important role in the economy, truck drivers face several challenges, including adapting to advancing technology. The current study investigated the occupational experiences of Dutch truck drivers to detect common patterns. A questionnaire was distributed to professional drivers in order to collect data on public image, traffic safety, work pressure, transport crime, driver shortage, and sector improvements. The findings based on 3,708 respondents revealed a general dissatisfaction with the image of the industry and reluctance to recommend the profession. A factor analysis of the questionnaire items identified two primary factors: 'Work Pressure', more common among national drivers, and 'Safety & Security Concerns', more common among international drivers. A ChatGPT-assisted analysis of textbox comments indicated that vehicle technology received mixed feedback, with praise for safety and fuel-efficiency improvements, but concerns about reliability and intrusiveness. In conclusion, Dutch professional truck drivers indicate a need for industry improvements. While the work pressure for truck drivers in general may not be high relative to certain other occupational groups, truck drivers appear to face a deficit of support and respect.

A variable speed limit control approach for freeway tunnels based on the model-based reinforcement learning framework with safety perception

To improve the traffic safety and efficiency of freeway tunnels, this study proposes a novel variable speed limit (VSL) control strategy based on the model-based reinforcement learning framework (MBRL) with safety perception. The MBRL framework is designed by developing a multi-lane cell transmission model for freeway tunnels as an environment model, which is built so that agents can interact with the environment model while interacting with the real environment to improve the sampling efficiency of reinforcement learning. Based on a real-time crash risk prediction model for freeway tunnels that uses random deep and cross networks, the safety perception function inside the MBRL framework is developed. The reinforcement learning components fully account for most current tunnels' application conditions, and the VSL control agent is trained using a deep dyna-Q method. The control process uses a safety trigger mechanism to reduce the likelihood of crashes caused by frequent changes in speed. The efficacy of the proposed VSL strategies is validated through simulation experiments. The results show that the proposed VSL strategies significantly increase traffic safety performance by between 16.00% and 20.00% and traffic efficiency by between 3.00% and 6.50% compared to a fixed speed limit approach. Notably, the proposed strategies outperform traditional VSL strategy based on the traffic flow prediction model in terms of traffic safety and efficiency improvement, and they also outperform the VSL strategy based on model-free reinforcement learning framework when sampling efficiency is considered together. In addition, the proposed strategies with safety triggers are safer than those without safety triggers. These findings demonstrate the potential for MBRL-based VSL strategies to improve traffic safety and efficiency within freeway tunnels.

Enhancing Urban Intersection Efficiency: Utilizing Visible Light Communication and Learning-Driven Control for Improved Traffic Signal Performance

This paper introduces an approach to enhance the efficiency of urban intersections by integrating Visible Light Communication (VLC) into a multi-intersection traffic control system. The main objectives include the reduction in waiting times for vehicles and pedestrians, the improvement of overall traffic safety, and the accommodation of diverse traffic movements during multiple signal phases. The proposed system utilizes VLC to facilitate communication among interconnected vehicles and infrastructure. This is achieved by utilizing streetlights, headlamps, and traffic signals for transmitting information. By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established. A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively. Agents placed at each intersection control traffic lights by incorporating information from VLC-ready cars, including their positions, destinations, and intended routes. The agents devise optimal strategies to improve traffic flow and engage in communication to optimize the collective traffic performance. An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times. The reinforcement learning approach effectively schedules traffic signals, and the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios. The discussion emphasizes the possibility of applying reinforcement learning in everyday traffic scenarios, showcasing the potential for the dynamic identification of control actions and improved traffic management.

Dynamic Lane Assignment and Signal-Timing Collaborative Optimization Considering the Effect of Lane Switching

Dynamic lane assignment (DLA) is an effective method to improve traffic flow and optimize urban road resources. However, lane switching can disrupt traffic under dynamic control due to clearance time and driving characteristics. Traditional rule-based methods hinder the selection of optimal schemes for DLA systems. On-site data analysis shows that lane switching increases saturated headway by 18.4% and reduces lane group utilization by 17.8% compared to fixed lanes in the same direction. To address these challenges, this study proposes an optimization method that integrates DLA and signal timing, considering the impact of lane switching. The proposed model aims to minimize total delay at intersections by optimizing the number and timing of lane switches within a given interval. Case studies and extensive numerical analyses demonstrate that the proposed method outperforms the rule-based approach, reducing total intersection delay by 1.91%. Additionally, the average number of lane switches decreases by 13.8%, reducing safety risks associated with frequent lane switching. Sensitivity analysis reveals the superiority of the proposed method when the utilization rate of variable lanes is below 0.4 and the saturated headway caused by lane switching exceeds 2.9 seconds. Moreover, the proposed method outperforms inductive and adaptive control schemes when the traffic flow deviation coefficient is less than 0.3. This makes it suitable for implementation in cities where drivers have limited experience with variable lanes during the initial stage, when historical and real-time data are more consistent. Overall, the proposed method offers significant improvements in traffic flow efficiency and safety in DLA systems.

Competency of Rural Roads Achieving Targets 3.6 and 11.2 of the Sustainable Development Goals in Bangladesh

Road Transportation is the major mode of transportation and is recognized as a significant contributor to the socio-economic development in many low-and-middle-income countries like in Bangladesh. In Bangladesh, this transport system is now responsible for carrying about 70% of passengers and 60% of freights and increasing the share of modalities day by day with the development of road transport systems. The responsibility for the improvement and maintenance of the total road network of the country is assigned mainly to the Roads and Highways Department (RHD) and the Local Government Engineering Department (LGED). But the road network belongs to LGED termed as “Rural Road” which is about 94% of the total road length and in the meantime, more than 40% is improved by mainly flexible pavements. However, the state of road traffic safety on many improved rural roads is poor due to narrow carriageways and shoulders along with roadside hazards. On the other hand, the overall road traffic safety in Bangladesh is poor compared to the developed and motorized countries. However, according to the police report, about 4000 people die and another 5000 people become injured by road traffic crashes in each year in Bangladesh but the World Health Organization estimates the fatalities are more than 25,000. However, Bangladesh has committed to achieving the targets 3.6 and 11.2 of the Sustainable Development Goals (SDG) assigned by the United Nations (UN) in 2015 concerning the improvement of road traffic safety by halving 50% of fatalities by 2030. Therefore, the achievement of the above two targets depends on the improvement of road traffic safety on highways as well as rural roads. This research highlights the barriers to improving the safety of rural roads to achieve the above two targets by 2030.

Mining the Micro-Trajectory of Two-Wheeled Non-Motorized Vehicles Based on the Improved YOLOx

Two-wheeled non-motorized vehicles (TNVs) have become the primary mode of transportation for short-distance travel among residents in many underdeveloped cities in China due to their convenience and low cost. However, this trend also brings corresponding risks of traffic accidents. Therefore, it is necessary to analyze the driving behavior characteristics of TNVs through their trajectory data in order to provide guidance for traffic safety. Nevertheless, the compact size, agile steering, and high maneuverability of these TNVs pose substantial challenges in acquiring high-precision trajectories. These characteristics complicate the tracking and analysis processes essential for understanding their movement patterns. To tackle this challenge, we propose an enhanced You Only Look Once Version X (YOLOx) model, which incorporates a median pooling-Convolutional Block Attention Mechanism (M-CBAM). This model is specifically designed for the detection of TNVs, and aims to improve accuracy and efficiency in trajectory tracking. Furthermore, based on this enhanced YOLOx model, we have developed a micro-trajectory data mining framework specifically for TNVs. Initially, the paper establishes an aerial dataset dedicated to the detection of TNVs, which then serves as a foundational resource for training the detection model. Subsequently, an augmentation of the Convolutional Block Attention Mechanism (CBAM) is introduced, integrating median pooling to amplify the model’s feature extraction capabilities. Subsequently, additional detection heads are integrated into the YOLOx model to elevate the detection rate of small-scale targets, particularly focusing on TNVs. Concurrently, the Deep Sort algorithm is utilized for the precise tracking of vehicle targets. The process culminates with the reconstruction of trajectories, which is achieved through a combination of video stabilization, coordinate mapping, and filtering denoising techniques. The experimental results derived from our self-constructed dataset reveal that the enhanced YOLOx model demonstrates superior detection performance in comparison to other analogous methods. The comprehensive framework accomplishes an average trajectory recall rate of 85% across three test videos. This significant achievement provides a reliable method for data acquisition, which is essential for investigating the micro-level operational mechanisms of TNVs. The results of this study can further contribute to the understanding and improvement of traffic safety on mixed-use roads.

The Correspondence between Large Pressure Fluctuations and Runway Wind Shear: The Event on 12 December 2019 at Songshan Airport, Taipei

In this study, the association of large pressure fluctuations (LPFs) ≥ 0.2 hPa and runway wind shear (RWS) ≥ 12 kt at the Songshan Airport in Taipei, Taiwan, during the event on 12 December 2019 with strong northeasterly winds are analyzed. The goal of the study is to demonstrate that the two phenomena exhibited close correspondence, and the former (LPFs) measured using a single barometer can be useful to detect the latter (RWS), which relies on the low-level wind-shear alert system (LLWAS) at the present time. Concentrated before 1200 UTC and especially during 0100-0800 UTC, both LPFs (52 times) and RWS (62 times) over the runway exhibited close association, and one rarely occurred more than 15–20 min apart in time from the other. Using the 2 × 2 contingency table and categorical scores, our results for LPFs and RWS to both occur at least once or five times within the same hour also suggest high accuracy rates of ≥80% and low miss rate and false alarm ratio of both < 10%, respectively. The two variables are also tested to be statistically dependent on each other to a high confidence level of 95–97.5%. Thus, using LPFs as an auxiliary or additional method to detect RWS at airports appears to be reasonable and feasible. At small and remote airports where the LLWAS is not yet available, this method also provides a good and less expensive alternative and can be helpful to the overall improvement of air traffic safety around the world.

Critical safety management driver identification based upon temporal variation characteristics of driving behavior

Identifying critical safety management drivers with high driver-level risks is essential for traffic safety improvement. Previous studies commonly evaluated driver-level risks based upon aggregated statistical characteristics (e.g., driving exposure and driving behavior), which were obtained from long-period driving monitoring data. However, given the great advancements of the connected vehicle and in-vehicle data instrumentation technologies, there has been a notable increase in the collection of short-period driving data, which has emerged as a prominent data source for analysis. In this data environment, traditionally employed aggregated behavior characteristics are unstable due to the time-varying feature of driving behavior coupled with insufficient data sampling periods. Thus, traditional modeling methods based upon aggregated statistical characteristics are no longer feasible. Instead of utilizing such unreliable statistical information to represent driver-level risks, this study employed temporal variation characteristics of driving behavior to identify critical safety management drivers in the short-period driving data environment. Specifically, the relationships between driving behavior temporal variation characteristics and individual crash occurrence probability were developed. To eliminate the impacts of drivers’ driving behavior heterogeneity on model performance, “traffic entropy” index that could quantify the abnormal degrees of driving behavior was proposed. Deep learning models including convolutional neural network (CNN) and long short-term memory (LSTM) were employed to conduct the temporal variation feature mining. Empirical analyses were conducted using data obtained from online ride-hailing services. Experiment results showed that temporal variation characteristics based models outperformed traditional aggregated statistical characteristics based models. The area under the curve (AUC) index was improved by 4.1%. And the proposed traffic entropy index further enhanced the model performance by 5.3%. The best model achieved an AUC of 0.754, comparable to existing approaches utilizing long-period driving data. Finally, applications of the proposed method in driver management program development and its further investigations have been discussed.

EMERGENT HORIZONS: THE CONVERGENCE OF AUTONOMOUS VEHICLES AND ADVANCED LEARNING IN POST-PANDEMIC TRANSPORT RESILIENCE

The unprecedented challenges posed by the COVID-19 pandemic to the road and transport sectors necessitated an in-depth exploration of innovative solutions to address disruptions. Through a quantitative approach involving a purposive sampling of professionals from a webinar series, we assessed the pandemic's impact and the potential of emergent technologies, chiefly Connected and Autonomous Vehicles (CAVs) and reinforcement learning. The structured questionnaire, focusing particularly on the pandemic’s ramifications, unveiled considerable operational disturbances across the sector. Notably, the integration of CAVs revealed improvements in traffic management, safety, and environmental sustainability. Additionally, the adoption of reinforcement learning, simulated using Python and the PyTorch framework, demonstrated significant enhancements in ambulance dispatch efficiency. In simulations, the model adeptly reduced response times, especially during high-traffic periods, and showcased improved decision-making accuracy when integrated with CAV capabilities. However, alongside these promising findings, challenges related to the broad-scale implementation of CAVs and potential biases in reinforcement learning models emerged as areas requiring further investigation. In essence, this research delineates a promising technological landscape for the transport sector, emphasizing the balance between innovation and practical, holistic considerations in a post-pandemic world.

Traffic safety and public health in China – Past knowledge, current status, and future directions

Transportation-related harms have developed into a social disease, threatening public safety and health in China. We aimed to increase the global understanding of traffic safety and public health in China from past knowledge, current status, and future directions by collecting, collating, and analyzing the Chinese traffic incidents reported in the published literature. A systematic search of China National Knowledge Infrastructure, Weipu, and published articles referenced in PubMed, Web of Science and ProQuest between January 1, 1988 and April 30, 2023 was performed. China encountered the first recorded traffic accident as early as three thousand years ago in the Shang Dynasty. An increase in vehicle capacity and velocity increased the traffic risks during the transition from rickshaws and livestock to motor vehicles in varying traffic environments. Humans are not only the decisive factor of a large number of vehicles, traffic routes, and environmental variables, but also the victims at the end and starting point of traffic accidents. Injuries (mechanical force, burns) and diseases (traffic-related air pollution, noise) caused by traffic activities not only threaten public health, but also cause risks to safe driving. Analysis of traffic activities and biomarkers promotes the treatment of traffic injuries in ethology and medicine. China prepared for the construction of healthy transportation in the “decade of road safety” toward an estimation of worldwide road traffic injuries in 2030. Improvement of traffic safety concerning public health under the “Outline of the National Comprehensive Three-dimensional Transportation Network Planning” in China will propel the realization of worldwide traffic environmental advancement.

Investigating surrogate safety measures at midblock pedestrian crossings using multivariate models with roadside camera data

This study aims to evaluate and compare Surrogate Safety Measures (SSMs) at five midblock Rectangular Rapid Flashing Beacons (RRFB) and two midblock Pedestrian Hybrid Beacons (PHB) sites in Florida using extensive video data collected over the study period of July to November 2021. Computer vision and data processing resulted in four pedestrian SSMs, namely spatial gap, temporal gap, relative time to collision (RTTC) and Post Encroachment Time (PET). An initial investigation of the SSMs using Mann-Whitney-Wilcoxon tests revealed significant differences in the SSM values across different treatment types and hours of the day. Additionally, univariate regression of spatial gap, and multivariate regression of temporal gap, RTTC and PET revealed significant differences of SSMs across RRFB and PHB sites. The study considered both linear and non-linear (gamma, inverse Gaussian and lognormal) regression models. After considering various traffic and operational parameters, the data were aggregated for each pedestrian-vehicle interaction on each lane to create a total of 395 observations. The SSMs included average spatial gap, temporal gap, RTTC and PET for each interaction of pedestrian and vehicle on each lane. The results indicated that non-linear models performed better than the linear models. Moreover, the presence of the PHB, weekday, signal activation, lane count, pedestrian speed, vehicle speed, land use mix, morning period and pedestrian starting position from the sidewalk have been found to be significant determinants of the SSMs. Results also suggest temporal SSMs increase at the PHB sites compared to the RRFB sites, indicating an improvement of traffic safety at PHB sites. However, the spatial gap decreased for PHB sites compared to the RRFB sites, which suggests that pedestrians tend to start to cross the RRFB sites when they perceive vehicles to be further away than at the PHB sites.