Revisiting freeway single tunnel crash characteristics analysis: A six-zone analytic approach

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.

Risk field modeling of urban tunnel based on APF

OBJECTIVE

The purpose of this paper is to explore the changing laws of driving safety in the complex and changing driving environment in urban tunnels, to analyze the evolution of driving risk fields caused by changes in adjacent vehicles, driving behavior characteristics and road environment, and to reveal the formation mechanism of tunnel driving danger zones.

METHODS

The kinetic field, behavioral field and potential field models are constructed according to the APF theory. The driving safety risks arising from the surrounding vehicles, driving behavior characteristics and changes in the tunnel environment are analyzed in the process of driving from the open section to the exit of the tunnel.

RESULTS

The magnitude of the risk field force is inversely proportional to the spacing of the vehicles and the distance between the tunnel sidewalls, and is proportional to the relative speed between the vehicles and the slope of the longitudinal slope. Under the same conditions, the vehicle at the entrance and exit of the tunnel is subjected to a greater force of travel risk than inside the tunnel, and the effect of speed on the force of the risk field is greater than the distance.

CONCLUSIONS

The established model better describes the trend of driving risk during the driving of vehicles in urban tunnels, and the research findings can provide theoretical support for the design and traffic management of urban tunnels.

Lessons learned from characteristics of extraordinarily severe traffic crashes in China, 2004-2019

China has experienced remarkable achievements in terms of reducing the number of extraordinarily severe traffic crashes (ESTCs) that cause more than 10 deaths each crash. However, ESTCs still occur occasionally and result in extremely adverse social impacts. This study aims at investigating the common characteristics, characteristic patterns, and changes of characteristics of ESTCs in China with the expectation to learn from the past and act for the future. A total of 373 ESTCs occurred in 2004-2019 were collected, and characteristics of driver factors, road factors, vehicle factors, environment factors, and other factors were analyzed through the multiple correspondence analysis (MCA). The results show that run off road crashes, not qualified drivers, improper driving, large bus, overload, class II highway, and straight road sections are the most common categories of characteristics. In addition, four underlying characteristic patterns are identified through the MCA. Significant changes in characteristics and characteristic patterns are also found, and these changes are the results of various law enforcement, safety policies, educational interventions, and engineering interventions. It is also inferred that the specific law enforcement targeting to certain category of characteristics is more effective than the corresponding safety campaigns or policies in terms of ESTC prevention.

The impact of different service states of tunnel lighting on traffic safety

The service states of tunnel lighting will directly affect the lighting conditions, which affect traffic safety. Therefore, it is imperative to evaluate and predict traffic safety accurately in different lighting states. In this research, three hundred experimental scenarios of the service states of tunnel lighting were designed and implemented to evaluate the impact of different service states of tunnel lighting on traffic safety. The evaluation was achieved through a visual identification experiment in a physical tunnel. The experimental results show higher simulated vehicle speeds pose a greater threat to traffic safety. The severity of lighting attenuation contributes to an increased risk to traffic safety. An increase in the number of luminaires failure also poses a greater threat to traffic safety. The newly proposed traffic safety factor was employed to evaluate traffic safety quantitatively in road tunnels. To improve the accuracy and comprehensiveness of the traffic safety factor prediction in different lighting service states, an advanced neural network prediction system was developed. The prediction system was constructed using the Sparrow Search Algorithm (SSA) to optimize Extreme Learning Machine (ELM) neural network, and the dataset from the experiment was used for the prediction model. The SSA-ELM neural network model is a reliable model that can predict the traffic safety factor comprehensively and accurately. The recommended threshold value for the traffic safety factor is 0.6. When the value falls below 0.6, it shows that the service states of tunnel lighting pose a threat to traffic safety in the tunnel. These findings can provide insights into the safe and energy-efficient maintenance of road tunnels.

A method for evaluating the safety of freeway tunnel sections based on driving comfort - a naturalistic driving study

OBJECTIVE

Driving comfort is crucial for tunnel safety because tunnel sections on freeways often introduce significant environmental changes that can compromise comfort and increase the risk of traffic accidents. This study aimed to quantitatively evaluate the driving comfort in tunnel sections and its implications for safety management.

METHODS

Four indicators were used to assess the driving comfort: heart rate growth rate (Hrgr), skin conductance response (SCR), speed, and acceleration. The CRITIC weighting method was employed to calculate a quantitative driving comfort score, and the presence and severity of discomfort were used to evaluate the safety of each tunnel area. In addition, the evaluation was based on a naturalistic test consisting of Hrgr, SCR, speed, and acceleration data. A total of 32 participants were recruited based on a web-based questionnaire screening process, after which they were tested while driving through 30 tunnel sections on the roadway. These 30 tunnels included 14 short (< 500 m), 12 medium (500-1,000 m), and 4 long (1,000-3,000 m) tunnels.

RESULTS

The results revealed that the four selected indicators exhibited minimal multicollinearity and effectively captured the driving comfort. Among the indicators, SCR had the most significant contribution to the driving comfort score. Most drivers did not experience substantial discomfort while driving through tunnels. The area where drivers were most susceptible to discomfort was the middle zones of tunnels. However, drivers were more likely to experience strong discomfort in the outside exit, entrance, and middle zones of short, medium, and long tunnels, respectively.

CONCLUSIONS

This study provides a comprehensive set of safety evaluation methods for tunnel sections on freeways, with a focus on quantifying the driving comfort. The findings provide theoretical support for freeway management personnel in implementing personalized controls in different tunnel areas with the aim of enhancing tunnel safety and mitigating the occurrence of traffic accidents.

Exploring Factors Affecting Crash Injury Severity with Consideration of Secondary Collisions in Freeway Tunnels

Although there have been several studies conducted exploring the factors affecting injury severity in tunnel crashes, most studies have focused on identifying factors that directly influence injury severity. In particular, variables related to crash characteristics and tunnel characteristics affect the injury severity, but the inconvenient driving environment in a tunnel space, characterized by narrow space and dark lighting, can affect crash characteristics such as secondary collisions, which in turn can affect the injury severity. Moreover, studies on secondary collisions in freeway tunnels are very limited. The objective of this study was to explore factors affecting injury severity with the consideration of secondary collisions in freeway tunnel crashes. To account for complex relationships between multiple exogenous variables and endogenous variables by considering the direct and indirect relationships between them, this study used a structural equation modeling with tunnel crash data obtained from Korean freeway tunnels from 2013 to 2017. Moreover, based on high-definition closed-circuit televisions installed every 250 m to monitor incidents in Korean freeway tunnels, this study utilized unique crash characteristics such as secondary collisions. As a result, we found that tunnel characteristics indirectly affected injury severity through crash characteristics. In addition, one variable regarding crashes involving drivers younger than 40 years old was associated with decreased injury severity. By contrast, ten variables exhibited a higher likelihood of severe injuries: crashes by male drivers, crashes by trucks, crashes in March, crashes under sunny weather conditions, crashes on dry surface conditions, crashes in interior zones, crashes in wider tunnels, crashes in longer tunnels, rear-end collisions, and secondary collisions with other vehicles.

Exploring factors affecting the injury severity of freeway tunnel crashes: A random parameters approach with heterogeneity in means and variances

Generally, freeway tunnels are built to overcome the complex driving environments in mountainous terrains. However, crashes in these tunnels can be more severe than those on the open road sections due to their closed driving environment. Despite the higher crash severity, very few studies have attempted to investigate the severity of injuries in freeway tunnel crashes. Also, the existing studies on the injury severity analysis of tunnels did not fully consider the unobserved heterogeneity and its interactive effects. To address these issues, the present study first collected a comprehensive dataset containing five-year of police-reported tunnel crashes from Hunan province, China. A random parameters model with heterogeneity in means and variances was then developed to explore the influence of different variables related to the environment, drivers, crashes, vehicles, and tunnels. The study observed that the presence of curves and speeding indicators produce random parameters with heterogeneity in means and variances for freeway tunnels, which is influenced by the young drivers and outside exit zone variables. Also, the results reveal that factors, including weekdays, daytime, speeding, fatigue driving, rear-end collisions, collisions with fixtures, large passenger vehicles, and downgrades increase, while rain reduces the probability of severe injury outcomes in freeway tunnel crashes. More importantly, considering the unique tunnel driving environment, the summer, young drivers, novice drivers, presence of curves, and different tunnel sections (access, entrance, and outside exit zones) also significantly affect the risk of severe injury outcomes. Finally, the study's findings could be used as a basis for developing plans and technologies to minimize the severity of crash injuries in freeway tunnels.

Understanding drivers' awareness, habits and intentions inside road tunnels for effective safety policies

Tunnels have a unique driving environment; thus, a small incident in a tunnel may result in severe consequences and a high probability of secondary crashes. Fortunately, studies have found that adopting safe driving behavior in a tunnel minimizes the severe outcomes of an incident. Therefore, implementing driver-oriented safety policies and conducting public awareness campaigns that emphasize safe behavior when driving through tunnels are essential. However, before devising policies and campaigns on the right issues, it is necessary to understand drivers' current level of knowledge regarding tunnel safety, their habits, behavioral intentions, and psychological condition while driving through tunnels. To achieve this objective, a sample of 841 responses was collected from China using a questionnaire survey consisting of fifty-two items. The results showed that several gaps exist in drivers' knowledge regarding tunnel safety and equipment. Drivers often adopt inappropriate habits and behaviors while driving through tunnels. Also, the tunnel environment has a significant influence on the psychological condition of the drivers. Moreover, drivers' demographic characteristics significantly affect their knowledge, reported habits and behavioral intentions, and psychological condition. The authorities and safety analysts could employ the suggestions highlighted in the present study for improving tunnel safety.

Impact of Light Environment on Driver's Physiology and Psychology in Interior Zone of Long Tunnel

In tunnels, lighting not only affects visual performance, but also non-visual aspects such as drivers' physiological fatigue and mental stress. The non-visual impacts in the interior zone of long tunnels are particularly prominent as drivers are confined for a long time. To alleviate this problem, this study aims to investigate the relationship between drivers' physiological and psychological states and lighting environments. The physiological signal test system (MP150) breathing belt was used to record the changes of heart rate variability (HRV) of drivers when passing through the interior zone of a long tunnel under various lighting conditions. In particular, sympathetic indicators of physiological fatigues and the ratio of low frequency and high frequency (LF/HF) representing mental load were obtained. By analyzing the temporal variation in these two indicators, it is found that environmental luminance perception can more accurately reflect drivers' physiological and psychological states in the long tunnel than road luminance. An increase in road luminance or background luminance will result in a decrease in the mental stress, thereby reducing fatigue sense. Compared to simply increasing road luminance, mental stress of drivers decreased more obviously when the background luminance of long tunnel increased. Based on this, this paper proposed a method to regulate non-visual effect by adding contour markers without increasing light source intensity for the improvement in lighting performance, driving safety, and energy efficiency in long tunnels.

Temporal stability of associations between crash characteristics: A multiple correspondence analysis

Understanding the associations between crash characteristics facilitates the development of traffic safety policies for improving traffic safety. This study investigates the temporal stability of associations between crash characteristics at different temporal levels using multiple correspondence analysis (MCA). For each date in 2020, crash data from the previous week, month, season, half year, one year, two years, three years, and four years are collected respectively as eight temporal levels. MCA plots and chi-square distance analysis are used to assess the temporal stability of associations between crash characteristics across dates in 2020 with data from various temporal levels. The key findings of this study demonstrate that associations between crash characteristics at lower temporal levels show notable and potential cyclical variations across dates, while more stable and long-term trend of associations between crash characteristics may be identified as the temporal level increases, especially at the two-year level and higher temporal levels at which temporal stability may be expected. The study contributes to the literature by presenting a challenge for traffic analysts in that both temporally stable and unstable associations between crash characteristics may be observed at any point in time when different temporal levels are considered as study periods. Therefore, it may serve as a foundation for future research and practical works to identify traffic safety issues and optimal policies as well as facilitate the interpretation of statistical modeling in the presence of temporally unstable data.

A data-driven approach to strengthening policies to prevent freeway tunnel strikes by motor vehicles

Freeway tunnel strikes by motor vehicles inflict serious damages to the infrastructure, cause personal injuries, and create traffic congestions. Freeway tunnel hits are a constant threat in South Korea due to its mostly mountainous terrain. Despite efforts by public agencies to include disaster remedial or preventative facilities in the development of freeway tunnels, these facilities are designed mainly to reduce the number of collisions instead of also mitigate the consequences of a crash. Hence, the Korea Ministry of Land, Infrastructure and Transport (KMOLIT) recently presented a plan to modify the list of risk factors affecting tunnel traffic safety, and it recommended several strategies for tunnel traffic safety management. The study presented here took a data-driven approach to quantitatively confirming the strategies recommended by KMOLIT through the random forest-based binomial regression. The following factors were found to be significantly associated with serious injury crashes involving freeway tunnel strikes: adverse weather, fatigued and distracted drivers, collision type (i.e., head-on/angle/rear-end), tunnel exit, tunnel width, curve radius (radius less than 1800 m), and heavy vehicle. This study compares specifications of each government strategy with the effects of the identified risk factors on injuries involved in tunnel crashes to quantitatively support recommendations to modify the government strategies.