Advances in Civil Engineering

Advances in Civil Engineering / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 4536414 |

Hao Sun, Qiuping Wang, Peng Zhang, Yujian Zhong, Xiabing Yue, "Spatialtemporal Characteristics of Tunnel Traffic Accidents in China from 2001 to Present", Advances in Civil Engineering, vol. 2019, Article ID 4536414, 12 pages, 2019.

Spatialtemporal Characteristics of Tunnel Traffic Accidents in China from 2001 to Present

Academic Editor: Emanuele Brunesi
Received02 Jan 2019
Accepted16 Jul 2019
Published15 Aug 2019


There are often greater risks in the process of tunnel construction, and because of the unpredictable tunnel features, the risks during tunnel operation are also great. Due to the substantial growth of TTAs during the recent years, the safety issues of the tunnel traffic accidents (TTAs) have become a widely discussed topic in China. This study examined 2703 TTAs that occurred in the expressway tunnels of China, from 2001 to the present. The datasets from these accidents were analyzed in detail to determine the temporal and spatial characteristics of the traffic accidents as well as the source of the traffic accident distributions in the expressway tunnels. According to the analysis results, 58% of TTAs took place in the entrance and exit zones, and the rear end accident is the most common accident. In addition, the special festivals are liable to traffic accidents, especially during the Chinese Spring Festival. In order to reduce the accidents, special traffic service needs to be taken in key periods, the expressway tunnel operation departments should improve the management rules, and all the drivers should be educated to be more careful when they enter the tunnel.

1. Introduction

With the booming development of transportation in China, the expressway tunnel has become an important component of the traffic system. Over the past decades, the number of tunnels in China had a massive increase, and it will continue to grow, as shown in Figure 1. According to the statistical results published at the Ministry of Transport of the People’s Republic of China at the end of 2016, the number of expressway tunnels in China has reached 15181 with a total length of 14039.7 km [1]. In addition, China has grown to have the largest construction scale and fastest construction speed for tunnel and underground engineering.

The operation of the tunnel can not only shorten the distance and increase the travel efficiency of vehicles but also play an important role in protecting the environment. Furthermore, the development of tunnels has resulted in great social and economic benefits. However, with the increase in the number of tunnels, the traffic accidents caused by tunnels increase correspondingly which should be paid attention [24]. Tunnels as one of the underground engineering, it will face enormous risks during tunnel construction and operation under the uncertainty of the natural environment. The number of TTAs has increased year by year, which results in great economic and spiritual loss to individuals and the state [5, 6]. Statistics indicate that during the period of 2012-2013, there were 469 TTAs in China, and among them, 198 caused deaths. The death rate per accident was 0.42, which is 1.44 times the annual average of traffic accidents of ordinary highways [7]. Some serious and fatal China TTAs in the past decades are described in Table 1 [8]. Therefore, it is of great importance to investigate the causes and occurrence rate of TTAs, and it is also important to put forward effective countermeasures to prevent TTAs. In addition, we should pay particular attention to the location distribution of the TTAs to enhance the management of the tunnel traffic system and to improve the traffic safety of expressway tunnels [911]. In recent years, many studies have explored the issue of traffic accidents in expressway tunnels. For instance, in the study of Amundsen and Ranes [2], 900 studies about tunnels in Norway were used to research the traffic safety. Existing studies have indicated that there was a correlation between the number of TTAs and the construction standard to which the expressway was constructed; the higher the construction standard is, the fewer the number of TTAs occur. We also learn that the accident rates were higher in the entrance zone of tunnels than in other zones. In addition, traffic safety issues in expressway tunnels cannot be treated the same as the traffic safety issues of an ordinary highway. Yeung and Wong [12] studied the characteristics of 608 traffic accidents occurred in expressway tunnels in Singapore during 2009–2011, and accorded to the location of traffic accidents, the location of tunnels is classified. Lu et al. [13] described the temporal and space distribution characteristics of traffic accidents occurred in river-crossing tunnels of Shanghai. Ma et al. [14] and Zhang et al. [15] explored the major distribution characteristics and rules of TTAs based on the statistical analyses of TTAs of the Shaoguan section of the Beijing-Zhuhai expressway, the causes of traffic accidents were determined, and the practical countermeasures were proposed. Zhang et al. [16] investigated 10 expressway tunnels in the Zhejiang province and explored the relationship between traffic accident rates and tunnel traffic volume, rear end accident occurrence, fire, rollover, and tunnel wall collisions. The Gray–Markov model method was applied to create a prediction model of traffic accidents in tunnel groups by Zhan [17], and the occurrence rate of traffic accidents in the tunnel groups was determined. Zhao et al. [18] analyzed data from 208 expressway TTAs in China and presented some basic characteristics of expressway tunnel accidents.

No.Accident siteTimeAccident descriptionDeath tollInjuries

1Shaanxi province
Qinling tunnel no. 1
A motorbus collided with tunnel opening wall3613
2Yunnan province
Yuanmo Expressway tunnel
A small SUV collided with tunnel wall33
3Jiangxi province
Shangrao county tunnel
A van rollover518
4Guizhou province
Jinjishan tunnel
A bus collided with two trucks525
5Shaanxi province
Liushililiang tunnel
A semitrailer truck collided with 3 cars and caught fire82
6Fujian province
Shenhai expressway tunnel
A car crashed tunnel wall32
7Guangdong province
Huangpu tunnel
A minibus collided with a truck41
8Shaanxi province Xianglushi tunnel entrance 500 m2011/6/22
A coach collided with a van82
9Yunnan province
Damakan tunnel
A truck collided with 2 buses and four minibus451
10Anhui province
Qinlaowu tunnel
For faster speed, a bus lost control and crashed tunnel wall415
11Guizhou province
Qinghuang expressway tunnel
A coach and a minivan rear-ended623
12Zhejiang province
Yuzhuang tunnel exit zone
A sleeper bus cracked on the wall of the tunnel entrance1036
13Fujian province
Wenshan tunnel
A coach rollover416
14Fujian province
Liannan tunnel
There were 21 vehicles in a chain collision87
15Guizhou province
Gaojiayan tunnel
Because the brake was out of control, a truck rear-ended with a car42
16Chongqing-Guizhou expressway
Zhenwushan tunnel near 400 meters
A bus knocked back a car which ran away38
17G302 Jilin province
Mijang tunnel
A truck collided with a coach86
18Anhui province
Shidaoshan tunnel
A coach and a truck rear-ended1323
19Liaoning province
Maling tunnel
A coach suddenly burst into flames136

However, the TTA datasets used in the previous studies are still limited. As a result, the empirical study is still insufficient to investigate the statistic regularity or basic law of TTAs in China, and a large-scale expressway tunnel traffic accident investigation and statistical analysis have not been carried out in China either. In this study, 2703 TTAs from 2001 up to now were collected by the transportation department (AQS2010). This study investigates the temporal and spatial distribution rules of traffic accidents, the distribution of accident types, and the distribution of accident vehicles types using a statistical analysis approach to objectively show the status quo of TTAs in China and determine the rules and characteristics of TTAs.

2. Data Basis and Statistics

2.1. Accident Causes

TTAs are caused by coordination imbalance between human, vehicle, road (tunnel), and environment. Therefore, it is important to analyze how these factors affect TTAs. The statistics of TTAs in China is analyzed, it can be shown that overspeeding, icy pavement, vehicles stopping in front of other vehicles, vehicle fault, tunnel collapse, pavement debris, and other less common factors can result in TTAs. Figure 2 shows the statistical results of the major causes of TTAs. It can be seen that overspeeding and icy pavement are the primary causes of TTAs, which account for 60.1% of all reasons. This is consistent with the statistical results of the literature of Cao et al. [19]. Additionally, the traffic accidents of expressway tunnels are little affected by the weather, but accidents may occur at the entrance and exit of the tunnel as the friction coefficient of pavement is low on rainy and snowy days. Many rear end collisions are a result of a vehicle stopping in front of another vehicle without warning. According to the statistics, many traffic accidents are caused by the negligent operation of drivers. In the three factors of the accident, human, vehicles and road (tunnel), and environment, the human factor is the main factor that causes expressway tunnel traffic accident.

2.2. Accident Types

Several typical TTAs types are shown in Figure 3. Specifically, in August 10th at 11 pm, a motorbus was involved in a bad traffic accident which occurred at the Qinling No.1 tunnel, killing 36 people. According to the statistics result of cases of our research, rear end accidents and tunnel wall collisions are the primary TTA types, accounting for 60% and 20% of all TTAs, respectively, and 17% are the result of vehicle rollover. Accidents resulting from fire and other causes (such as goods falling out of a vehicle) are relatively few, accounting for 1% and 2%, as shown in Figure 4. The results of this research are in line with the results of some scholars in China [2024]. During emergency situations or when drivers engage in excessive speeds, drivers cannot brake in time or lack of space is the main cause of rear end collision in the tunnel.

2.3. Accidents versus Vehicle Types

The vehicle types involved in TTAs include minibuses, minivans, motorbuses, and large trucks. Several accident datasets from four tunnels were examined to study the vehicle types involved in TTAs, the Shaoguan section of the Beijing-Zhuhai expressway, 510 traffic accidents in the Zhongliangshan tunnel of Chengdu-Chongqing expressway, traffic accidents in 31 tunnels of the Ningbo-Taizhou-Wenzhou expressway in the Zhejiang province, and 2703 TTAs in China [14, 17, 25]. The vehicle types involved in TTAs and vehicle type proportion are shown in Figure 5. It can be seen from the figure that minibuses and large trucks are involved in the most traffic accidents. In the traffic accident involving the minibuses, most of the sedans are made in China, and most of the vans are light chassis. Those traffic accidents involving minivans and minitrucks are most involved. Among the traffic accidents involving large trucks, heavy trucks account for the majority; in addition, traffic accidents caused by overloading account for the majority.

3. Spatial Distributions of Traffic Accidents

3.1. Accident Quantity and Tunnel Lengths

According to “JTG/T D70-2010, Guidelines for Design of Highway Tunnel,” 156 tunnels are divided into 4 types: short tunnels, medium tunnels, long tunnels, and super-long tunnels [26]. The number of TTAs and the length of the tunnel involved are shown in Table 2. The datasets indicate that TTAs occur more frequently in longer tunnels. The reason for this is that drivers easily get fatigue in a long tunnel with monotonous environment for a long time. Moreover, with the increase in the tunnel length, the traffic volume also increases in the tunnel which leads to the increase of smoke concentration and visibility reduction.

ItemsShort tunnelMedium tunnelLong tunnelSuper-long tunnel

Number of tunnels51364920
Number of accidents673602976452
Average number of accidents13161922

3.2. Traffic Accidents in Tunnel Zones

Measures can be taken to effectively prevent traffic accidents and to improve the traffic safety situations according to the understanding of the traffic accidents distribution at various tunnel sections and the analysis of the characteristics of TTAs’ spatial distributions [27, 28]. The traffic accident characteristics for each of the various tunnel sections were analyzed in this study. The division of tunnel zones is shown in Figure 6. Zone 1 consists of both the interior and exterior areas within 200 m of the tunnel portal; Zone 2 consists of the area from 200 m to 400 m inside the tunnel; and Zone 3 composes of the remainder of the tunnel length. It is worth noting that tunnels with a length of less than 200 m only include Zone 1.

Figure 7 shows the spatial distributions of TTAs. It can be seen from the statistical results that Zone 1 is the most accident-prone section, and the accident rate accounts for 58% of all tunnel accidents. The number of traffic accidents occurred in Zone 2 and Zone 3 is less, as each accounts for 21%. Additionally, the statistical data of the literature of Amundsen and Ranes [2], which includes data of 587 tunnels in Norway from 1992 to 1996, show that accidents 50 m away from the tunnel entrance accounted for 25% of all the tunnel traffic accidents and those within 150 m of the tunnel entrance accounted for 50% of the total traffic accidents. The statistical results of this study regarding tunnel accidents in China are basically consistent with those of Norway; that is, the probability of traffic accidents near the tunnel entrance is higher than elsewhere in the tunnel because of the special environmental characteristics of the tunnel and the effects of ray aberration, inside and outside the tunnel. On a sunny day, when vehicles enter the tunnel from the outside to the inside, the light suddenly changes from bright to dark, which generates the so-called “black hole effect.” Conversely, when vehicles drive away from the inside of the tunnel, the light changes from dark to bright, generating “white hole effect.” Both of these effects reduce the driver’s emergency response time and may lead to accidents [2931].

Great differences exist in traffic environment inside and outside the tunnel, especially in illumination and brightness, which seriously affects the extraction, analysis, and judgment of traffic information by driver visual organs. Therefore, it has a serious impact on safe driving in the tunnel [32]. Drivers would have a series of visual problems in the process of approaching, entering, passing, and driving out of tunnels from bright environment, as shown in Figure 8. That is, (1) Visual problems before entering the tunnel in the day: “black hole effect” for the long tunnel and “black frame effect” for the short tunnel, which are mainly due to the excessive brightness difference. (2) Visual problems immediately after entering the tunnel in the day: dark adaptation, which mainly refers to the lagging phenomenon of adaptation. (3) Visual Problems inside the tunnel in the daytime and at night: smoke from exhaust gases, which reduces the visibility of the tunnel. (4) Visual problems at the tunnel exit: “white hole effect” for the long tunnel in the daytime, which induces the driver glare, and “black hole effect” at night, which causes the alignment of the external road and the obstacles on the road to be indistinguishable.

During the driving process, the information of change of road traffic environment can be transmitted to the human brain decision-making system through senses, and the proportion of visual information is about 80–90%. When approaching the transitional section of tunnel entrance and exit, the traffic information and brightness around the tunnel change. Driver physiological load tends to approach the critical value, and they are prone to panic, vertigo, and physical disharmony. Meanwhile, vehicle lights have a certain impact on the brightness of the environment in the transitional zone of the tunnel entrance and exit, which will affect the driver handling stability, thus affecting the light and shade adaptation characteristics of the driver [33]. The purpose of tunnel lighting is to reduce or eliminate the visual differences caused by the light and shade of the road inside and outside the tunnel and to ensure that vehicles traveling in the daytime and at night can safely approach, cross, and pass through the tunnel at the designed speed. Moreover, the safety and comfort of drivers should be no less than that of the open zone adjacent to the tunnel. The illumination system of highway tunnel includes interior zone illumination, entrance zone illumination, transition zone illumination, exit zone illumination, access zone dimming facilities, emergency illumination, and approach illumination outside the tunnel. Highway tunnel lighting area can mainly be divided into five zones (Figure 9): access zone, entrance zone, transition zone, interior zone, and exit zone.

To reduce the rate of tunnel traffic accidents, the highway tunnel illumination system should not only have sufficient brightness but also meet the requirements of quality. In recent years, the tunnel illumination system has gradually developed from automation to intellectualization [34]. For the intelligent control method, advanced technologies such as artificial intelligence and neural network are adopted to simulate the brightness inside and outside the tunnel by collecting data. After constant debugging and testing, an efficient, accurate, and stable system is established, and dynamic dimming is carried out according to the actual demand of illumination brightness in the highway tunnel so as to realize the purpose of lighting according to local conditions and on demand.

4. Temporal Distributions of Traffic Accidents

The temporal distribution of traffic accidents is defined as the statistical characteristics of traffic accidents over time. Through analysis of the characteristics of the temporal distribution of TTAs, the traffic accidents trends can be revealed, and a basis can be provided to understand the causes of the TTAs. For convenience of study, the characteristics of the temporal distribution of TTAs can be grouped into distribution characteristics of month, week, and hour.

4.1. Monthly Distribution Characteristics of TTAs

The monthly distribution characteristics of traffic accidents in expressway tunnel sections are affected by numerous factors, including changes in geographic position, weather conditions, traffic volume, and human behavioral characteristics [3538]. Thus, the monthly distribution for traffic accidents can be analyzed to make the necessary monitoring of tunnel traffic safety during different months, conduct effective control over traffic accidents, reduce accident rates, and guarantee the safe operation of the tunnel. The monthly accident distributions of a certain tunnel in Hebei province, the Beijing-Zhuhai expressway tunnel section, the Ningbo-Wenzhou expressway tunnel section in Zhejiang province, and the monthly accident distributions of the 2703 traffic accidents in various expressway tunnels in China are shown in Figure 10 [17, 19, 21]. It can be seen from the statistical results of the 2703 TTAs that the monthly distribution trend of TTAs in various regions of China generally shows that most accidents occur in January, February, April, May, and July, and fewer accidents occur in March, June, August, September, October, November, and December. This trend shows that the number of tunnel traffic accidents dropped sharply from January to March, increased again from April to July, and remained stable from August to December. Traffic volume is generally high during the several months before and after Spring Festival, which includes January, February, and December, which proved that accident rates are directly proportional to traffic volume. However, there are fewer accidents in December than in January and February, which is related to the mental state of the drivers and an increase in the traffic department’s management efforts. After March, the weather gradually warmed up, the number of holiday travel trips increased, the traffic volume of expressway increased significantly, and the traffic accidents showed an upward trend after March. That is to say human factors are the main cause of traffic accidents [3942].

4.2. Weekly Distribution Characteristics of TTAs

Influenced by the pattern of people’s lives, traffic volume varies within a week on the road; therefore, the weekly distribution corresponding to traffic accidents varies with number of trips that people take. The distribution of TTAs by weekday in China is shown in Figure 11. It is obvious from the figure that the weekday distribution of TTAs is significantly different than the weekend distribution. Saturday and Sunday account for 43.24% of traffic accidents, while other days of the week show little fluctuation in the number of accidents per day which is because of the traffic volume from Monday to Friday is less and the volume is consistent. Conversely, the gross traffic volume sharply increases on the weekends, and the probability of traffic accidents correspondingly increases. The traffic structure of expressway also changes as weekend travelers generally use sedans, while large vans can make up a large component on weekday traffic. Sedan speed is far higher than that of large vans, which intensifies the occurrence of traffic accidents on the weekend.

4.3. Hourly Distribution Characteristics of TTAs

The hourly distribution of traffic accidents in expressway tunnels is shown in Figure 12. Influenced by climate, the environment, and the pattern of people’s lives, there is an obvious difference in the hourly distribution of TTAs throughout the day. From the figure, we can observe that there are three peak periods of traffic accidents, namely, 9:00-10:00, 11:00-12:00, and 13:00–15:00. The number of traffic accidents occurring in these three periods account for 48% of the total TTAs. The traffic accidents that happened in 9:00-10:00 are primarily the result of speeding. During the period from 11:00 to 12:00, the main cause of tunnel traffic accidents is the increase of traffic volume, especially due to an increase in the number of sedans which increases the complexity of the traffic structure. From 13:00–15:00, many drivers’ physiological function decreases after noon, and they are easily susceptible to somnolence. In addition, this is also the period during the day with most sunshine, which results in a large luminance difference between the inside and the outside of the tunnel. As a result, the ability of the driver to adapt to the transition from brightness to darkness at the entrance is reduced, and thus accidents occur.

4.4. Holiday Distribution Characteristics of TTAs

In July 24, 2012, China implemented the official holiday highway free passage policy, which led to the growth of passenger flow in holidays, and the traffic accidents were becoming more and more serious. These official holidays include the Spring Festival, Tomb-sweeping Day, May Day, and National Day. Tomb-sweeping Day and May Day are the most concentrated periods of the annual passenger flow before and after Spring Festival, but also is in a high rate of the traffic accident. According to the data analysis of traffic accidents during the past three years, the traffic accidents are mainly concentrated in the fourth and sixth days of the Spring Festival holidays, as shown in Figure 13. In order to prevent the occurrence of traffic accidents during the festival, government sector should make full use of television, radio, newspapers, internet, and other major media to strengthen traffic control measures and real-time traffic information release, suggesting that the masses consciously abide by traffic laws and regulations and reasonably choose traveling routes. At the same time, drivers must carefully check the vehicle before the trip and ensure good condition. During the peak period of vehicle centralized travel of holidays, travelers should be called upon to abide by traffic regulations and travel civilly. Traffic departments should strengthen management and prohibit drivers from drunk driving, fatigue driving, and illegal overspeed.

5. External Factor Impact Analysis

5.1. Impact and Assessment on Casualties

By collecting the data of 2703 expressway traffic accidents from 2001 to present, the temporal and spatial distributions of TTAs, the distribution of accident types, and the distribution of accident vehicle types are studied. Considering the different casualties caused by accidents, according to the Road Traffic Safety Law, the casualties can be divided into four levels: slight, general, major, and extraordinarily large. Accordingly, the objective impact factors on casualties are calculated. Since there are no casualties in some traffic accidents, the calculation is divided into five accident grades and assigned as follows: no casualties (1 point), 1-2 injuries (2 points), 3–10 injuries (3 points), 1-2 deaths (4 points), and more than 3 deaths (5 points). The proportions of accidents and casualties caused by external factors at various levels are multiplied by the corresponding grades scores, presented in the following equation:where Ki is the influence weight, Pi is the proportion of casualties caused by external factors, and Ni is the accident grade.

By calculating the data of 2703 TTAs, the temporal and spatial distributions of traffic accidents, the distribution of accident types, and the influence weight of accident vehicle types on casualties are obtained as detailed in Table 3. Moreover, the impact of external factors such as accident type, tunnel length, and time and place of tunnel accident on casualties is pointed out. In tunnel traffic accidents, the most important external factor affecting casualties is Zone 1 with the influence weight of 1.98. That is, the casualties caused by accidents are more serious when vehicles are driven in Zone 1. Secondly, the influence weight of super-long tunnel is 1.38; that is, accidents in super-long tunnels are more likely to cause serious casualties. Additionally, the impact weight of large truck is 1.25; that is, accidents caused by large trucks can also cause serious casualties.

Accident cause (Pi)OverspeedingIcy pavementFront vehicles stopVehicle faultTunnel collapsePavement debrisOthers

Accident type (Pi)Rear end accidentCollision tunnel wallRolloverFireOthers
Vehicle type (Pi)MinibusMinivanMotorbusLarge truck
Tunnel length (Pi)Short tunnelMedium tunnelLong tunnelSuper-long tunnel
Tunnel section (Pi)Section 1Section 2Section 3
Accident month (Pi)JanuaryFebruaryMarchAprilMayJuneJuly
Accident month (Pi)AugustSeptemberOctoberNovemberDecember
Accident week (Pi)MondayTuesdayWednesdayThursdayFridaySaturdaySunday
Accident time (Pi)0:00∼2:003:00∼4:005:00∼6:007:00∼8:009:00∼10:0011:00∼12:0013:00∼14:00
Accident time (Pi)15:00∼16:0017:00∼18:0019:00∼20:0021:00∼22:0023:00∼24:00

5.2. Correlation Analysis of External Factors

Through research and statistics, the influence degree of different time, place, vehicle type, accident type, accident reason on traffic accident, and the influence and evaluation of external factors on casualty situation can be obtained. On this basis, the potential relationships between accident types and other factors causing traffic accidents are explored, so as to facilitate the safety education and management of travelers. The research results show that there is a great relationship between the accident type and tunnel length, tunnel section, vehicle type, and accident cause. Therefore, it is necessary to calculate the confidence level of 2703 cases of expressway traffic accidents, as shown in equation (2). To facilitate expression, the above factors are represented by specific letters, as follows:Accident type: rear end accident (A1), tunnel wall collision (A2), rollover (A3), fire (A4), and others (A5)Tunnel length: short tunnel (B1), medium tunnel (B2), long tunnel (B3), and super-long tunnel (B4)Tunnel zone: Zone 1 (C1), Zone 2 (C2), and Zone 3 (C3)Vehicle type: minibus (D1), minivan (D2), motorbus (D3), and large truck (D4)Accident cause: overspeeding (E1), icy pavement (E2), front vehicles stop (E3), vehicle fault (E4), tunnel collapse (E5), pavement debris (E6), and others (E7)

According to the correlation between accident types and other external factors, the attention should be paid when the confidence level exceeds 60%. As shown in Table 4, for the condition of the collision tunnel wall in super-long tunnel, the confidence level of the association rule A2=>B4 is as high as 69%; for the condition of tunnel fire in Zone 3, the confidence level of the association rule A4=>C3 is as high as 77%; and for the condition of rear end accident caused by overspeeding, the confidence level of the association rule A1=>E1 is as high as 72%.

B1 (%)B2 (%)B3 (%)B4 (%)C1 (%)C2 (%)C3 (%)D1 (%)D2 (%)D3 (%)D4 (%)E1 (%)E2 (%)E3 (%)E4 (%)E5 (%)E6 (%)E7 (%)


6. Conclusions

(1)Rear end accidents are the main form of TTAs, accounting for 60% of all accidents. Minibuses and large trucks are the most common vehicle types involved in traffic accidents. Additionally, the main causes of accidents are illegal speeding and icy pavement, which account for 60.1% of the total TTAs.(2)Human factors are some of the most important factors in TTAs. These human factors can be summarized as follows: fatigue driving, failure to keep a sufficient safety distance between vehicles, speeding, vehicle overloading, improper emergency measures, illegal lane change or lane occupation, and low speed. At the same time, the effect of vehicle type, tunnel factors, weather, and other environmental factors affecting traffic accidents also cannot be ignored.(3)Zone 1 is a highly accident-prone area. Traffic accidents in Zone 1 account for 58% of the total number of accidents. In addition, the probability of a traffic accident occurring will increase as the tunnel length increases. The traffic accidents are mainly concentrated in the fourth and sixth days of the Chinese Spring Festival holidays.(4)TTAs of expressway are distributed into three peak periods, 9:00-10:00, 11:00-12:00, and 13:00–15:00. Accidents during these three periods account for 48% of the total traffic number of traffic accidents. Additionally, TTA rates on the weekend account for 43.24% of the total traffic accidents, while the number of traffic accidents during weekdays shows little fluctuation. Also, more accidents occur during the months of January, February, April, May, and July. The number of traffic accidents occurred from January to March shows a law of sharp decline; from April to July, the occurrence of traffic accidents showed an upward trend; and monthly number of accidents from August–December is relatively steady.(5)Based on the impact and evaluation of external factors on casualties, the most disadvantageous combination of road tunnel traffic accidents is super-long tunnel + Zone 1 + large truck + overspeeding + rear end accident. After the statistics of external factors and unfavorable combination, the potential relationship between accident types and other factors causing traffic accidents can be explored, and the probability of unfavorable combination can also be calculated according to the confidence level. For the disadvantageous combination with high confidence level, effective measures should be taken to prevent and control it.

Data Availability

In this article, 2703 cases from 2001 up to now were collected by the transportation department (AQS2010).

Conflicts of Interest

The authors declare that they have no conflicts of interest.


This work was financially supported by the National Natural Science Foundation of China (no. 51278396).


  1. S. P. Jiang, “Data statistics of highway tunnels in China,” Tunnel Construction, vol. 37, no. 6, pp. 643-644, 2017. View at: Google Scholar
  2. F. H. Amundsen and G. Ranes, “Studies on traffic accidents in Norwegian road tunnels,” Tunnelling and Underground Space Technology, vol. 15, no. 1, pp. 3–11, 2000. View at: Publisher Site | Google Scholar
  3. M. Kinateder, P. Pauli, M. Müller et al., “Human behaviour in severe tunnel accidents: effects of information and behavioural training,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 17, pp. 20–32, 2013. View at: Publisher Site | Google Scholar
  4. J. Lai, J. Qiu, H. Fan, J. Chen, and Y. Xie, “Freeze-proof method and test verification of a cold region tunnel employing electric heat tracing,” Tunnelling and Underground Space Technology, vol. 60, pp. 56–65, 2016. View at: Publisher Site | Google Scholar
  5. A. Haack, “Current safety issues in traffic tunnels,” Tunnelling and Underground Space Technology, vol. 17, no. 2, pp. 117–127, 2002. View at: Publisher Site | Google Scholar
  6. J. Lai, H. Fan, J. Chen, J. Qiu, and K. Wang, “Blasting vibration monitoring of undercrossing railway tunnel using wireless sensor network,” International Journal of Distributed Sensor Networks, vol. 11, no. 6, Article ID 703980, 2015. View at: Publisher Site | Google Scholar
  7. Traffic Management Bureau of the Ministry of Public Security, “Road traffic accident statistic annual report of the People’s Republic of China,” Traffic Management Bureau of the Ministry of Public Security, Beijing, China, 2016, Research Report. View at: Google Scholar
  8. Accident Query System, State Administration of Work Safety, Accident Query System, 2017,
  9. H. J. Eirik, M. Karl, V. Erik, F. H. Amundsen, and R. Guro, “Experiences with subsea road tunnels in Norway-construction, operation, costs and maintenance,” Chinese Journal of Rock Mechanics and Engineering, vol. 26, pp. 2226–2235, 2007. View at: Google Scholar
  10. M. V. Corazza, P. Di Mascio, and L. Moretti, “Managing sidewalk pavement maintenance: a case study to increase pedestrian safety,” Journal of Traffic and Transportation Engineering (English Edition), vol. 3, no. 3, pp. 203–214, 2016. View at: Publisher Site | Google Scholar
  11. J. Lai, J. Qiu, J. Chen, Y. Wang, and H. Fan, “Application of wireless intelligent control system for HPS lamps and LEDs combined illumination in road tunnel,” Computational Intelligence and Neuroscience, vol. 2014, Article ID 429657, 7 pages, 2014. View at: Publisher Site | Google Scholar
  12. J. S. Yeung and Y. D. Wong, “Road traffic accidents in Singapore expressway tunnels,” Tunnelling and Underground Space Technology, vol. 38, pp. 534–541, 2013. View at: Publisher Site | Google Scholar
  13. J. J. Lu, Y. Xing, C. Wang, and X. Cai, “Risk factors affecting the severity of traffic accidents at shanghai river-crossing tunnel,” Traffic Injury Prevention, vol. 17, no. 2, pp. 176–180, 2016. View at: Publisher Site | Google Scholar
  14. Z.-L. Ma, C.-F. Shao, and S.-R. Zhang, “Characteristics of traffic accidents in Chinese freeway tunnels,” Tunnelling and Underground Space Technology, vol. 24, no. 3, pp. 350–355, 2009. View at: Publisher Site | Google Scholar
  15. S. R. Zhang, Z. L. Ma, and Q. Shi, “Distribution characteristics and counter measures of traffic accidents in expressway tunnel group,” Journal of Chang’an University (Natural Science Edition), vol. 27, no. 117, pp. 63–66, 2007. View at: Google Scholar
  16. Y. C. Zhang, C. He, D. X. Wu, and Y. Zeng, “Characteristics and countermeasures of traffic accidents in expressway tunnel,” Journal of Southwest Jiaotong University, vol. 44, pp. 776–781, 2009. View at: Google Scholar
  17. W. Zhan, “Research on traffic safety protective technology of long and large tunnel group area in Mountain Highway,” Zhejiang University, Hangzhou, China, 2013, Ph.D. thesis. View at: Google Scholar
  18. F. Zhao, Y. X. Xia, and T. Xie, “Analysis and study of operational accidents in road tunnel,” Highway, vol. 6, pp. 280–286, 2014. View at: Google Scholar
  19. Z. M. Cao, C. Guo, Z. W. Wang et al., “Characteristics and countermeasures of traffic accident in the south part of Beijing-Zhuhai expressway tunnels,” Guang Dong Gong Lu Jiao Tong, vol. 4, pp. 55–58, 2009. View at: Google Scholar
  20. H. L. Ni, Y. H. Dai, and Q. X. Zhao, “Research on distribution characteristics of traffic accidents in expressway tunnel,” Highway, vol. 4, pp. 126–129, 2010. View at: Google Scholar
  21. B. X. Wang, “The traffic accident characteristics analysis of highway tunnel,” Traffic and Transportation, vol. 7, pp. 164–167, 2012. View at: Google Scholar
  22. S. F. Wang, Z. Lin, J. Z. Chen et al., “Probe into traffic safety problems in tunnels of expressways in mountainous area,” Technology of Highway and Transport, vol. 6, pp. 137–143, 2009. View at: Google Scholar
  23. J. Lai, X. Wang, J. Qiu, J. Chen, Z. Hu, and H. Wang, “Extreme deformation characteristics and countermeasures for a tunnel in difficult grounds in Southern Shaanxi, China,” Environmental Earth Sciences, vol. 77, no. 19, pp. 1–14, 2018. View at: Publisher Site | Google Scholar
  24. W. H. Zhou, H. Y. Qin, J. L Qiu et al., “Building information modelling review with potential applications in tunnel engineering of China,” Royal Society Open Science, vol. 4, no. 8, Article ID 170174, 2017. View at: Publisher Site | Google Scholar
  25. H. Q. Shi, “Research on index system of the traffic accident in the expressway tunnel group,” Southwest Jiaotong University, Chengdu, China, 2009, Ph.D. thesis. View at: Google Scholar
  26. Ministry of Transport of the People’s Republic of China, JTG/T D70-2010 Guidelines for Design of Highway Tunnel, China Communications Press, Beijing, China, 2010.
  27. Y. Wu, J. Lu, H. Chen, and H. Yang, “Development of an optimization traffic signal cycle length model for signalized intersections in China,” Mathematical Problems in Engineering, vol. 2015, Article ID 954295, 9 pages, 2015. View at: Publisher Site | Google Scholar
  28. J. Qiu, Y. Qin, Z. Feng, L. Wang, and K. Wang, “Safety risks and protection measures for the city wall during the construction and operation of xi’an metro,” Journal of Performance of Constructed Facilities, vol. 33, no. 5, p. 12, 2019. View at: Google Scholar
  29. S. Das, X. Sun, F. Wang, and C. Leboeuf, “Estimating likelihood of future crashes for crash-prone drivers,” Journal of Traffic and Transportation Engineering (English Edition), vol. 2, no. 3, pp. 145–157, 2015. View at: Publisher Site | Google Scholar
  30. J. Lai, F. Niu, K. Wang et al., “Dynamic effect of metro-induced vibration on the rammed earth base of the Bell Tower,” Springer Plus, vol. 5, no. 1, p. 935, 2016. View at: Publisher Site | Google Scholar
  31. G. Ali, A. Rasool, and T. Zong, “Estimating intersection turning volumes from actuated traffic signal information,” Journal of Traffic and Transportation Engineering (English Edition), vol. 3, no. 6, pp. 507–519, 2016. View at: Publisher Site | Google Scholar
  32. A. Bracale, P. Caramia, P. Varilone, and P. Verde, “Probabilistic estimation of the energy consumption and performance of the lighting systems of road tunnels for investment decision making,” Energies, vol. 12, no. 8, p. 1488, 2019. View at: Publisher Site | Google Scholar
  33. A. Mehri, J. Sajedifar, M. Abbasi et al., “Safety evaluation of lighting at very long tunnels on the basis of visual adaptation,” Safety Science, vol. 116, pp. 196–207, 2019. View at: Publisher Site | Google Scholar
  34. X. Nie, S. Feng, S. Zhang, and Q. Gan, “Simulation study on the dynamic ventilation control of single head roadway in highaltitude mine based on thermal comfort,” Advances in Civil Engineering, vol. 2019, Article ID 2973504, 12 pages, 2019. View at: Publisher Site | Google Scholar
  35. Z. Luo, Y. Liu, and C. Guo, “Operational characteristics of mixed traffic flow under bi-directional environment using cellular automaton,” Journal of Traffic and Transportation Engineering (English Edition), vol. 1, no. 6, pp. 383–392, 2014. View at: Publisher Site | Google Scholar
  36. J. X. Lai, S. Y. He, J. L Qiu et al., “Characteristics of earthquake disasters and aseismic measures of tunnels in Wenchuan earthquake,” Environmental Earth Sciences, vol. 76, no. 2, 94 pages, 2017. View at: Publisher Site | Google Scholar
  37. Y. Li, G. Su, X. Zhang, S. Zhang, and H. Yuan, “Analysis of school bus accidents in China,” Natural Hazards, vol. 79, no. 2, pp. 723–734, 2015. View at: Publisher Site | Google Scholar
  38. X. L. Luo, X. Meng, W. J. Gan, and Y. H. Chen, “Traffic data imputation algorithm based on improved low rank matrix decomposition,” Journal of Sensors, vol. 2019, Article ID 7092713, 11 pages, 2019. View at: Publisher Site | Google Scholar
  39. J. Lai, J. Qiu, J. Chen, K. Fan, and K. Wang, “New technology and experimental study on snow-melting heated pavement system in tunnel portal,” Advances in Materials Science and Engineering, vol. 2015, Article ID 706536, 11 pages, 2015. View at: Publisher Site | Google Scholar
  40. L. Duan, Y. Zhang, and J. Lai, “Influence of ground temperature on shotcrete-to-rock adhesion in tunnels,” Advances in Materials Science and Engineering, vol. 2019, Article ID 8709087, 18 pages, 2019. View at: Publisher Site | Google Scholar
  41. J. X. Lai, K. Y. Wang, J. L. Qiu, F. Y. Niu, J. B. Wang, and J. X. Chen, “Vibration response characteristics of the cross tunnel structure,” Shock and Vibration, vol. 2016, Article ID 9524206, 16 pages, 2016. View at: Publisher Site | Google Scholar
  42. J. Qiu, T. Yang, X. Wang, L. Wang, and G. Zhang, “Review of the flame retardancy on highway tunnel asphalt pavement,” Construction and Building Materials, vol. 195, pp. 468–482, 2019. View at: Publisher Site | Google Scholar

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