
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)13121-0
10.1016/j.heliyon.2024.e37090
e37090
Research Article
Effect of interchange spacing on drivers' visual characteristics in interchange merging areas
Liu Yanling 1307655650@qq.com
a
He Han 390567950@qq.com
b
Wang Siqi 354950092@qq.com
c
Li Xiangwei 7299960728@qq.com
b
Su Shenwan 2645399974@qq.com
b
Xu Jin jin_hsu@cqjtu.edu.cn
a⁎
a College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing, 400074, China
b Shenzhen General Integrated Transportation and Municipal Engineering Design & Research Institute Co., Ltd, Shenzhen, 518003, China
c Changsha BYD Automobile Co., Ltd., Changsha, 410019, China
⁎ Corresponding author. jin_hsu@cqjtu.edu.cn
28 8 2024
15 9 2024
28 8 2024
10 17 e3709011 5 2024
26 8 2024
27 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
To explore the effect of interchange spacing on drivers' visual characteristics in the merging areas of interchange, a high-density group of five interchanges on the expressway of Chongqing, China, was selected as the test site. An naturalistic driving test was conducted with 47 participants, and the Tobii Glasses II portable eye tracker was used to collect gaze data during driving. The drivers' fixation field was divided into six areas by applying a K-means dynamic clustering algorithm combined with the actual scenario. Markov chains were used to calculate the drivers' gaze transition probability matrices under different driving conditions, and the analysis of gaze transition behaviors was directed at common spacing interchanges, small spacing interchanges, and composite interchanges. Under the ramp-mainline condition, drivers’ fixations were primarily concentrated on the near ahead and the left side areas, with higher rates of repeated fixations on the left rearview mirror and left-side line areas. The average value of fixation duration, saccade distance, and saccade speed of small spacing interchange is higher than common spacing interchange. Additionally, under the mainline condition, the probability of one-step transition and repeated fixation rates significantly increased for the right-side lane areas, and the average values of fixation index and saccade index of small spacing interchange are lower than those of common spacing interchange. The results show that the highest probabilities of repeated fixation by drivers occurred in the near ahead and far ahead areas in the interchange merging areas. Insufficient spacing resulted in more frequent occurrences of zero values in one-step transition probability matrices. The research conclusions provide theoretical support for the optimal design and safe operation of the merging area of high-density interchange group of urban expressway.

Keywords

Traffic engineering
Interchange
Interchange spacing
Merging areas
Eye movement
Visual characteristic
==== Body
pmc1 Introduction

With the continuous construction of freeways and expressways, the scale of the road network keeps expanding, leading to an increase in the density of interchanges and a continuous decrease in interchange spacing. Consequently, high-density groups of interchanges with small spacing have formed. The merging areas of interchanges, pivotal points for the connection of ramps and mainlines, present complex driving conditions and considerable driving difficulty. When the traffic density is high and the spacing between interchanges is small, vehicles weave frequently, requiring drivers to merge from ramps to mainlines within a short distance and time. This results in a high visual workload for drivers, thereby increasing driving risks and the potential for accidents. Therefore, exploring the visual characteristics of drivers in the merging areas of interchanges and the effect of interchange spacing on these characteristics is important for the optimization of interchange design, traffic management, and accident prevention.

For the driving behavior and traffic flow characteristics of interchange merging areas, most scholars analyze it from the level of vehicle operation estate. For instance, Chu et al. [1]collected video data of vehicles driving in the merging areas of the expressway and found that the traffic volume in the merging areas affects the driver's lane change choice behavior. Gu et al. [2]discovered that the closer the merging position chosen by drivers is to the site of the merging areas, the higher the risk of vehicle collision. Based on the vehicle trajectory data of the merging areas, Liu et al. [3] quantified a variety of dangerous driving behaviors in the merging areas. Chen and Ahn [4] found that the longer the driver's reaction time in the merging areas, the lower the traffic efficiency in the merging areas. Mohammad et al. [5] collected speed and traffic data from 23 merging points. The study found that merging speed depends on both ramp and speed-change lane geometrics. Synthia and Srinivas S [6]. collected and used data from five years of selected combined speed lanes in Charlotte, North Carolina, USA. They found if the upstream and downstream ramps are too close to the main interchange ramp, it will increase the risk of fatal accidents in the confluence variable speed lane. Eustace et al. [7] analyzed the 4-year collision data of the expressway and found that the accident rate of the left merging areas setting type was much higher than that of other setting methods. Xue et al. [8] analyzed the speed of ramp vehicles waiting to merge on the acceleration lane and the time required for speed adjustment and found that the traffic bottleneck is easy to form when the traffic flow of the outer lane of the main line is between 1300 pcu/h and 1500 pcu/h.

Meanwhile, some scholars have added the study of drivers' psychological characteristics while analyzing vehicle operations. Yang et al. [9] found that the mental workload of the interchange merging areas is higher than that of the interchange diversion areas, and the psychological workload of the driver of the small spacing interchange is higher than that of other interchange types. Fallahi et al. [10] studied the influence of traffic flow density on a driver's mental workload based on data such as the driver's physiological indicators and found that the driver's mental workload was greater when the traffic flow density was high. Xu et al. [11] carried out naturalistic driving tests in four mountain city interchanges and found that the sections that affect the driver's psychological workload are mainly interchange merging and diversion sites positions. Johan et al. [12] found that visual demands led to a decrease in speed and an increase in lane keeping variation. Chihara et al. [13]found eye rotation angle, head rotation angle, blink frequency and task difficulty by using driving simulator and N-back task.Faure et al. [14] studied drivers' blinking behavior in complex driving environments and concluded that blinking frequency can characterize visual workload. Choudhary and Velaga [15] found that when the driver is visually distracted, the number of lane offsets, lateral acceleration, and other parameters will fluctuate significantly. Tina et al. [16]carried out a real vehicle test of 23 drivers to study the effect of distraction on drivers' visual attention and found that visual distraction led to a decrease in fixation points in the window areas. Jo et al. [17] observed that increased vehicle speed causes a reduction in interpupillary distance, resulting in a "visual tunnel phenomenon," which adds to drivers' visual workload. Based on the driver 's eye movement characteristics, Arakawa et al. [18] used time series to analyze the driver 's visual characteristics and established a driver state detection model. Anh Son [19] established a driver distraction assessment model based on 2 * 2 factor design analysis.Based on the driving simulator, Itoh and Inagaki [20] studied the change in the driver's eye fixation duration when the driver was enforcing the cognitive task and found that the fixation duration can be used to characterize the mental state under high workload. Yan et al. [21] conducted a series of driving experiments at the entrance of freeway tunnels in plain and high-altitude areas and found that the driver's pupil size is mainly related to ambient illumination. The illumination changes greatly, and the rate of pupil diameter changes greatly. Di et al. [22] selected EEG as the evaluation index of the driver's mental workload and carried out experiments in urban primary and secondary streets. It was found that traffic volume and road type had a significant effect on the driver's mental workload. By collecting the driver's visual data on the ring interchange ramp and combining the driving behavior, Namgung and Sung [23] determined that the driver's visual search mode will change when he takes deceleration measures when driving into the ring road. Xu et al. [24] analyzed the driver's saccade data at the interchange exit and found that at the interchange exit ramp, insufficient spacing will lead to an increase in saccade demand.

In summary, scholars have extensively explored the field of visual characteristics. However, when it comes to interchange merging areas, most studies have focused on analyzing vehicle operating traits. There have been few studies on the effect of interchange spacing on drivers' visual characteristics in interchange merging areas. Therefore, this paper selects the merging areas of urban expressway interchanges as the test site and collects drivers' eye movement data through naturalistic driving tests. Basing these data, this paper examines the variation rules of ocular-motor indicators, such as fixation duration and saccade distance, along different driving sections of merging areas in urban expressway interchanges and analyzes the effect of interchange spacing on drivers' visual characteristics. The aim is to provide references for the optimized design of interchange merging areas, the improvement of traffic safety facilities, and safe operations management.

2 Experimental design

2.1 Scenario of naturalistic driving test

Taking the interchange spacing as the discriminant index, the interchange group with more than three interchanges on the 10 km continuous road is defined as the high-density interchange group.To obtain eye movement data from drivers in natural driving conditions, this paper conducted a f naturalistic driving test. The test sites were a high-density group of five interchanges on the expressway in Chongqing. Among these five interchanges, the Cuntan Interchange is located on the main road (with a speed limit of 80 km/h), while the Wugui, Wutong, Donghuan, and Renhe interchanges are located on the expressway (with a speed limit of 100 km/h), as shown in Fig. 1. The spacing between the Wutong Interchange and the Donghuan Interchange, as well as between the Wugui Interchange and the Wutong Interchange, is less than the minimum spacing requirements for interchanges specified in the “Urban Road Design Code” [25] and the “Highway Route Design Code” [26], which is 1 km. As a result, the road sections between these three interchanges are classified as small spacing interchange areas. Wugui Interchange is a composite interchange connected by auxiliary lanes. Other interchanges meet the requirements of minimum spacing, which are common spacing interchanges.Table 1 and Fig. 1 display the numbering and basic information of each merging area of the interchanges.Fig. 1 Experimental interchange. Panel (a) represents the experimental route and location, Panel (b) represents satellite image of the interchange merging areas.

Fig. 1

Table 1 Basic information of the test interchanges.

Table 1Interchange Name	Merging Areas Number	Vehicle Operating Conditions	Acceleration Lane Type	Interchange Spacing Type	
Wutong Interchange	WT1	Ramp to Mainline	Direct Type	Small spacing	
WT2	Mainline Driving	Parallel Type	Small spacing	
WT3	Ramp to Mainline	Parallel Type	Common spacing	
Wugui Interchange	WG1	Ramp to Mainline	Parallel Type	Composite Type	
WG2	Ramp to Mainline	Parallel Type	Common spacing	
WG3	Ramp to Mainline	Parallel Type	Small Clearance	
Donghuan Interchange	DH1	Ramp to Mainline	Parallel Type	Common spacing	
DH2	Ramp to Mainline	Direct Type	Small spacing	
Cuntan Interchange	CT1	Ramp to Mainline	Parallel Type	Common spacing	
CT2	Mainline Driving	Direct Type	Common spacing	
Renhe Interchange	RH1	Mainline Driving	Direct Type	Common spacing	
RH2	Ramp to Mainline	Direct Type	Common spacing	

The merging influence area of the interchange starts from 150 m upstream of the merging nosal site [27]. According to the different driving routes and directions, driving behaviors in the interchange merging areas are divided into two types: ramp to mainline and mainline driving, and are subdivided into four driving stages. For the ramp to mainline condition, the stages are divided into ramp sections and merging sections; for the mainline driving condition, into mainline sections Ⅰ and Ⅱ, as shown in Fig. 2. In the figure, the ramp section (marked as ZD) refers to the road section where the vehicle travels from the ramp to the merging nasal site, the merging section (marked as HL) refers to the road section where the vehicle travels from the merging nasal site to the end of the acceleration lane transition section, the mainline section Ⅰ (marked as ZX Ⅰ) refers to the road section where the driver drives from the mainline to the merging nasal site, and the mainline section Ⅱ (marked as ZX Ⅱ) refers to the road section where the driver continues to drive along the mainline after the merging nasal site.Fig. 2 Schematic Diagram of Driving Stage Division in Merging Areas for Two Driving Conditions. Panel (a) represents the driving stage division under the ramp to mainline conditions; While panel (b) represents driving stage division under mainline driving conditions.

Fig. 2

2.2 Apparatus and vehicle

The experiment used the Tobii Glasses 2 portable eye tracker to collect eye movement data from drivers in their natural driving states. This eye tracker uses a dual-image sensor and a combination of bright and dark pupil tracking technology to capture eye images, which are then processed through a dynamic LED eye image processing system and a 3D model to determine the position of the eyes and the fixation point. With a sampling frequency of 50 Hz and a video resolution of 1920 × 1080px, this device can effectively collect eye movement data during driving; the test vehicle was a Buick GL8 (see Fig. 3) .Fig. 3 Experimental vehicle and equipment. Panel (a) represents experimental vehicle, panel (b) represents wearing of eye tracker, panel (c) represents eye tracker.

Fig. 3

2.3 Participants and procedure

A total of 47 drivers were recruited for this experiment, with their main information shown in Table 2. All participants had driving experience ranging from 2 to 25 years, driving mileage between 20, 000 and 500,000 km, ages between 25 and 51 years, with an average age of 37 years, and good driving habits without any major traffic accidents. Among them, there are 17 drivers with a driving experience of less than 10 years, 20 drivers with a driving age of 10–15 years, 8 drivers with a driving age of 15–20 years or more, and 2 drivers with a driving age of more than 20 years.Table 2 Information on participants.

Table 2Number	Age/Years	Accumulated Mileage/10,000 km	Years of Driving	
Male	Female	Mean	Variance	Mean	Variance	Mean	Variance	
35	12	37.4	6.8	11.5	4.5	14.4	10.6	

The naturalistic driving test was conducted over 16 days. To avoid the influence of peak-hour traffic flow, the test duration was from 10:00 to 17:00. Before the start of the test, eye trackers were adjusted and calibrated on the participants to ensure that the drivers’ fixation points and pupils could be tracked by the eye tracker. Participants were asked to drive the test route for 2 to 3 cycles, following their driving habits. To ensure the accuracy of the data, only data with an effective eye movement recognition rate above 90 % was extracted for analysis. Outlier data was cleaned and filtered using MATLAB software to remove abnormal pupil diameter values before proceeding to the subsequent analysis.

3 Methodology

Drivers need to constantly search for road information to assist driving during driving. In visual search, the driver 's attention to different fixation areas is different, and the distribution of fixation points can reflect the driver 's interest in different fixation areas. At present, the division methods of fixation area mainly include visual field plane method, video playback method and dynamic clustering method. However, the accuracy of the visual field plane method is low, and the video playback method is suitable for small sample analysis. Therefore, the driver 's fixation points can be clustered, the distribution area of fixation points can be divided, and the driver 's visual preference can be determined, so as to study the driver 's visual change rule and fixation transition characteristics in different fixation areas.

3.1 Principles of K-means clustering

The basic principle of K-means clustering involves identifying cluster centers based on the distances and correlation coefficients between samples, grouping the closest samples, and classifying them iteratively until there is no change in the cluster centers. During driving, drivers observe scenarios besides the road. When aggregating the driver's fixation points, it will be affected by isolated irrelevant fixation point data. Therefore, combined with the characteristic features of the fixation data in this paper, the Euclidean distance is chosen to calculate the distance from the data points to the initial cluster centers, with the specific formula as follows:(1) d(xi,cj)=∑k=1d(xik−cjk)2=(xi1−cj1)2+⋯⋅⋅⋅+(xik−cjk)2+⋅⋅⋅+(xid−cjd)2

where: d(xi, cj) is the distance between the sample xi and the cluster center cj; xi is the i-th sample variable; cj is the cluster center of the j-th cluster; k is the dimensionality of the data.

The data mean for each cluster is recalculated as the new cluster center for each cluster, with the calculation formula as follows:(2) cjk=xj1k+xj2k+⋅⋅⋅+xjnjknj

where: cjk is the k-th dimension of the cluster center c of the j-th cluster; xjnj is the n-th sample in the j-th cluster; nj is the number of samples in the j-th cluster.

These steps are repeated until the criterion function converges. The squared error criterion function is commonly used to assess clustering performance, with the function formula as follows:(3) E=∑i=1k∑j=1nj(xij−cj)2

where: E is the sum of squared errors for all variables in the sample database.

3.2 Fixation areas division results

The fixation points within the driver's visual field were taken as clustering variables and cluster analyses were conducted on the fixation data of drivers in the interconnecting traffic flow sections of the experimental route. To address the issue of significant differences in the magnitude of fixation points across different areas and bias in clustering outcomes, this study employed the K-means dynamic clustering algorithm available in Origin software to cluster analyze the positions of different fixation points in various areas. Cluster sizes of k = 6, 7, and 8 were selected to distinguish the fixation areas. By comparing the three clustering outcomes, it was observed that when k = 6, the clustering result was closer to reality, with a clearer delineation of the fixation point ranges. Fig. 4 presents the clustering results of fixation points for all driverss at different k values. Based on these results, the visual field plane was divided into six sections, as illustrated in Fig. 5.Fig. 4 Clustering results of fixation points for Driver at different k values.

Fig. 4

Fig. 5 Schematic diagram of fixation areas division.

Fig. 5

4 Results

4.1 Analysis of fixation duration

In the process of driving, the length of the driver's fixation duration on a certain area reflects the difficulty of information extraction to a certain extent. The more complex the information is, the longer the fixation duration is. The driving behavior of vehicles in interchange merging areas was divided into two operational conditions: ramp to mainline and mainline driving, which are further segmented into four driving stages. The test interchanges were categorized into common spacing interchanges, small spacing interchanges, and composite interchanges, with WT3, WG2, DH1, CT1, CT2, RH1, and RH2 representing common spacing interchanges; WT1, WT2, WG3, DH2 as small spacing interchanges; and WG1 as a composite interchange. The eye-tracking data from 47 drivers in 5 interchanges were collected, and average fixation durations of drivers in common spacing interchanges, small spacing interchanges, and composite interchanges were organized and analyzed to determine the distribution characteristics of average fixation duration in different driving stages under different operating conditions and interchange spacing, as shown in Fig. 6. Analysis of variance shows that different types of interchanges (F = 10.214, p < 0.05) and different driving stages(F = 8.823, p < 0.05) have a significant impact on the average fixation duration of drivers.Fig. 6 Distribution of fixation duration across different driving conditions and interchange spacing. Panel (a) represents Ramp-to-mainline condition; While Panel (b) represents Mainline driving condition.

Fig. 6

From Fig. 6(a), the fixation duration for small spacing interchanges was more focused, suggesting that there is a correlation between the fixation duration of the interchange spacing and the complexity of the interchange. With shorter spacing at interchanges, drivers need to spend a longer time searching and judging markers and other vehicles in the merging areas under the same driving distance, which increases the visual workload. When the complexity of the interchange is high, the driver needs continuous fixation behavior to obtain road information, which increases the difficulty of driving.

From Fig. 6(b), when driving in the mainline segment I (upstream of the merging site), the average fixation duration for drivers in small spacing interchanges is greater than in common spacing interchanges. However, when driving in the mainline segment II (downstream of the merging site), the difference in average fixation duration for drivers at common and small spacing interchanges is relatively small.

4.2 Distribution characteristics of fixation duration in gaze window

The average percentage of fixation duration in a region represents the difficulty of extracting information about the driver in different regions of interest, and then evaluating the driver's attention assigned to each region. This indicator also reflects the driver's level of interest in the fixation targets within different regions. The proportion of fixation durations across various interest areas (as shown in Fig. 5, gaze areas division diagram) under different driving stages of ramp-to-mainline and mainline conditions is shown in Fig. 7.Fig. 7 Proportion of fixation duration in the interchange merging areas. Panel (a) represents Ramp-to-mainline condition ramp section; Panel (b) represents Ramp-to-mainline condition merge ramp section; Panel (c) represents Mainline driving condition mainline ramp section I; Panel (d) represents Mainline driving condition mainline ramp section II.

Fig. 7

As Fig. 7(a) and (b) elucidate, under ramp-to-mainline driving conditions, drivers' attention is principally concentrated on the far and near areas in front of the road, with less focus on the instrument panel and left-side mirror areas. When driving on the ramp section of the composite interchange, the proportion of the driver's fixation on the left road areas is 17 %, which is higher than that of the common and small spacing interchanges. When driving on the merge section, the situation is reversed, and compared to the ramp section the driver's fixation on the right road areas on the merging section increases, likely because of the presence of 2 lanes and 3 lanes on the ramp, resulting drivers to meet visual demands from both left-side traffic flow and incoming vehicles from the right rear side of the ramp.

Fig. 7(c) and (d) indicate that under mainline driving conditions, the proportion of the driver's fixation duration is similar to that of ramp-mainline driving. Nevertheless, on the mainline section I (upstream of the merging site), drivers' focus on the right lane areas significantly intensifies, primarily due to vehicles entering from the right side, which leads to a slight reduction in attention towards the front and left areas. On mainline section II (downstream of the merging site), the allocation of drivers' attention is similar to that on mainline section I, but the degree of attention to the instrument panel areas increases. This indicates that they primarily gather information from the areas ahead and on the right-side line while paying more attention to the traffic environment and vehicle speed on the right side.

4.3 Analysis of saccade distance and speed

The magnitude of the saccade distance can reflect the difficulty of the driver's access to information and processing information. The saccade speed is the ratio of the saccade distance to the saccade duration. When the driving workload increases, the saccade speed will decrease. This index can be used to reflect the speed of the driver's search for the target information. Analysis of variance shows that different types of interchanges (p < 0.01) and different driving stages(p < 0.01) have a significant impact on the saccade distance and speed of drivers. By sorting out the average horizontal saccade distance and average saccade speed of each driver in common spacing, small spacing, and composite interchanges, the box plot of horizontal saccade distance and average saccade speed of the driver in different driving stages is drawn, as shown in Fig. 8.Fig. 8 Distribution of horizontal saccade speed across different driving conditions and interchange spacing. Panel (a) represents Ramp-to-mainline condition saccade distance; Panel (b) represents Mainline driving condition saccade distance; Panel (c) represents Ramp-to-mainline condition saccade speed; Panel (d) represents Mainline driving condition saccade speed.

Fig. 8

According to Fig. 8, the driver's saccade distance and speed distribution characteristics in the interchange area under different spacing and different working conditions are analyzed, and the following findings are obtained.(1) When driving on the ramp to mainline condition, the average horizontal saccade distance and saccade speed of the driver at the small spacing interchange are larger than those at the common spacing interchange. This is due to the insufficient spacing of the interchange. The vehicle interweaves frequently, and the difficulty of identifying and processing information increases. The driver needs to increase the transition speed of the fixation point to obtain more information, increasing the visual search range.

(2) When driving on the mainline, the driver's saccade distance and speed in the common spacing interchange are greater than those in the small spacing interchange. This is because the information distribution area of the common spacing interchange is more discrete under the mainline driving condition. When the driver extracts the information, the fixation area expands the saccade distance increases, and the saccade speed increases.

4.4 Proportion of interest fixation points across regions

Interest fixation points reflect the targets that drivers frequently gaze at during the process of driving, which indicates the positions that they are most concerned about. As the driving tasks differ under various driving conditions in the interchange merging areas, it is evident that there will be differences in the interested fixation points on different road sections. Fig. 9 illustrates the distribution of fixation points in different driving sections. It can be shown that when the driver is driving on the ramp sections, the fixation points are concentrated on the left side areas, and the fixation points of the merging section are concentrated in the left road and the left rearview mirror areas. Meanwhile, on the mainline sections I and II, fixation points are mainly distributed in the right roadside areas.Fig. 9 Distribution of fixation points for different stages under mainline driving conditions. Panel (a) represents ramp section; Panel (b) represents merge section; Panel (c) represents mainline section I; Panel (d) represents mainline section II.

Fig. 9

The percentage of the number of fixation points in different regions is used to characterize the driver's attention distribution in each region. The number of fixation points in each region under different driving sections and different interchange spacing is counted, and the proportion of fixation points in different interest regions t is analyzed. The statistical results are shown in Fig. 10. The following findings can be obtained from Fig. 10.(1) For two driving conditions and the four driving sections in the interchange merging areas, the drivers' fixation points are primarily focused on the road areas ahead, including far ahead and near ahead areas, with the sum percentage of fixation points spanning a range of 74 %–91 %. This demonstrates that drivers have a high demand for gazing at the near and far ahead areas while passing through the interchange merging areas.

(2) In the ramp-to-mainline condition, when driving on the ramp section of small spacing and composite interchanges, the percentage of fixation points for the far and near ahead areas exceeds that of the common spacing interchanges, and the proportion of small spacing interchanges for the left side areas is higher than that of common spacing and composite interchanges. Compared to ramp sections, drivers show a significant decrease in the percentage of fixation points in the far ahead areas and a significant increase in the left rearview mirror and left roadside areas. It shows that the traffic behavior on the left side of the merging section is complex, and the driver mainly focuses on the traffic information of the front road and the left road.

(3) Under mainline driving conditions, due to the presence of merging vehicles on the right side of the road, the proportion of the driver's fixation point in the right lane areas increased significantly. The proportion of fixation points in the far ahead areas of the small spacing interchange is significantly higher than that of the common spacing interchange, indicating that in the complex interchange environment with reduced spacing, drivers pay more attention to the traffic information in the far ahead areas.

Fig. 10 Proportion of interest fixation points across different areas. Panel (a) represents ramp-to-mainline condition, and Panel (b) represents mainline driving condition.

Fig. 10

4.5 Analysis of gaze transition characteristics

During the driving process, the location of the next fixation point is only related to the current fixation point and is independent of the previous one, with the driver's fixation points being discrete in both time and space. Therefore, the Markov chain can be used to study the characteristics of visual transition in the driver's fixation behavior in the interchange merging areas.

Assuming a discrete state space S={1,2,⋯,m} for the random variable set X={Xn,n=0,1,2⋯}, and a discrete-time Markov chain with the current state being i and the next state being j, the conditional probability of the random variables satisfies:(4) Pij=P(Xn+1=j|Xn=i),i,j∈S

The random variable set X={Xn,n=0,1,2⋯} is thus defined as a Markov chain, and the transition probability from the current state i to the next state j is referred to as the transition probability Pij.

The fixation window areas determined in section 2.2 act as the state space for the Markov chain, and the fixation points within each area are viewed as the random variable set of the Markov chain. Fig. 11 illustrates examples of gaze transitions between different fixation areas.Fig. 11 Gaze transition within different areas of interest.

Fig. 11

4.5.1 Gaze transition characteristics under ramp to mainline conditions

Based on the six fixation areas partially divided in Section 2.2 as different states of the Markov chain, the statuses of drivers’ fixation points are marked in sequence. The number of fixation point transitions between each state is counted, and the one-step transition probabilities between different states are calculated. Thus, the one-step gaze transition probability matrices for drivers in the merging section and ramp section are obtained, as shown in Fig. 12. The following findings can be found in the figure.(1) Under the ramp to mainline driving condition, the one-step transition probabilities of drivers' fixation points mainly concentrate in the near ahead and far ahead areas, with the highest probabilities of repeated fixation being in these two areas. Additionally, when the fixation point is in other areas of interest, the probability of transition to the near-ahead and far-ahead areas is also high. This suggests that in the ramp to mainline conditions, drivers pay greater attention to traffic conditions in the near ahead and far ahead areas, with small spacing interchanges demanding more attention to these areas than small spacing interchanges.

(2) When driving on the ramp section, the transition probabilities for the instrument panel areas and the right-side road areas are low. For the small spacing interchange, there were 8 instances of a zero probability for gaze transition, which is significantly higher than that of the common spacing interchange and composite interchange. This suggests that the driver has a high driving workload in the small spacing interchange ramp section, and the spirit is highly concentrated, paying more attention to the information of the near ahead areas and the far ahead areas.

(3) When driving on the merging section, the repeat fixation rate for the left rearview mirror areas and the left-side road areas are in the following order: common spacing interchange > small spacing interchange > composite interchange. Furthermore, the common spacing interchange has a repeat fixation probability of 0.4 for the instrument panel areas, higher than the other two interchanges. This indicates that while driving on the common spacing interchange, the demand for changing the lane to the left near the acceleration lane and accelerating into the main line is more obvious, thus paying more attention to the left rearview mirror, the left-side road, and vehicle speed.

Fig. 12 One-step gaze transition probability distribution heat map for ramp-mainline condition. Panel (a) represents ramp section common spacing interchange; Panel (b) represents ramp section small spacing interchange; Panel (c) represents ramp section composite interchange; While panel (d) represents merge section common spacing interchange; Panel (e) represents merge section small spacing interchange; Panel (f) represents merge section composite interchange.

Fig. 12

4.5.2 Gaze transition characteristics under mainline driving condition

Similarly, the one-step gaze transition probability matrix of the mainline segment I and the mainline segment II under the mainline driving condition was obtained, as shown in Fig. 13, and the following findings are found.(1) Consistent with the ramp-to-mainline driving condition, when driving on the mainline, drivers exhibit higher gaze transition probabilities for near ahead and far ahead areas, indicating the driver focuses on the near ahead and the far ahead areas. In the mainline segment I, the repeat fixation probability and probabilities of transferring to other areas for the small spacing interchange are greater than those of the common spacing interchange.

(2) Compared to mainline segment I, during mainline segment II, drivers show a significant increase in both one-step gaze transition probability and repeat fixation rate for the right-side road areas. This indicates that when vehicles are approaching merging vehicles from the right-side road, drivers have a greater demand to observe the right-side lane and will allocate more attention to prepare for slowing down and changing lanes to the left to avoid the merging stream from the right-side ramp. Additionally, there were 12 occurrences of zero one-step transition probabilities for the small spacing interchange, showing a high level of mental concentration and a substantial driving workload.

Fig. 13 One-step gaze transition probability distribution heat map for the mainline driving condition. Panel (a) represents Mainline section Ⅰ common spacing interchange; Panel (b) represents Mainline section Ⅰ small spacing interchange; While panel (c) represents Mainline section Ⅱ common spacing interchange; Panel (d) represents Mainline section Ⅱ small spacing interchange.

Fig. 13

5 Discussion

The purpose of this paper is to study the effect of interchange spacing on the visual characteristics of drivers in the interchange merging areas, and the naturalistic driving test of 47 participants was carried out. T This study analyses the driver's visual preference characteristics by combining three scenarios: small spacing interchange, common spacing interchange, and composite interchange, and uses the driver's fixation duration, the proportion of fixation duration, saccade distance, saccade speed and fixation transition frequency of the divided window area to analyze the driver's visual preference characteristics. The study found that when driving on the ramp to the mainline, drivers tend to repeatedly fixate on the left rearview mirror and left-side area, which is because the driver needs to repeatedly gaze at the left rear area to obtain road information. When driving on the mainline, drivers need to slow down or change lanes to avoid merging into the traffic flow, which increases the fixation demand on the right lane.

Pilko et al. [28] investigated the correlation between the frequency of fatal and injury accidents and interchange spacing under varying traffic volumes. Le and Porter [29] developed a relationship model between interchange ramp spacing and traffic safety using geometric and traffic characteristics, as well as conflict data from 404 freeway sections in California and Washington. Their findings indicate that the number of conflicts increases as interchange spacing decreases. This section concludes that the visual characteristics of drivers differ in small spacing interchange, common spacing interchange, and composite interchange. When the interchange spacing is insufficient, the zero value of the one-step transition probability matrix appears more frequently, increasing the driver's visual workload. When the interchange spacing is smaller, the conflict rate in the merging areas increases [30]. Reducing the setting of non-essential road traffic signs between interchanges with smaller spacing can increase traffic safety [31].

The reduction of interchange spacing is one of the main factors leading to traffic accidents [32,33]. It is of positive significance to clarify the influence of the interchange spacing on the driver's visual characteristics for the improvement of traffic safety facilities in the interchange merging areas. One concern about the findings was that this paper only analyzes the fixation and saccade behaviors of drivers in common spacing interchanges, small spacing interchanges, and composite interchanges, examining the influences of different spacing and driving conditions on drivers' visual characteristics. However, under the mainline driving condition, it is unknown that the visual characteristics of drivers in different lanes are affected by the on-ramp traffic flow, and the effects of factors such as ramp radius and lane-changing situations on drivers' visual characteristics were not discussed, and will require further research.

6 Conclusion

(1) Through the dynamic clustering method and combined with the actual research scene, the driver's visual field plane is divided into 6 fixation areas. The Markov chain is used to calculate the one-step transition probability matrix under different driving conditions, it was found that drivers in the interchange merging areas had the highest repeat fixation probabilities in the near ahead and far ahead areas. When spacing is insufficient, the driving difficulty increases, with drivers reducing their search for non-critical road information and preferring to focus on the road ahead, leading to more frequent occurrences of zero probabilities in the one-step transition probability matrix.

(2) Compared to common spacing interchanges, the driver's fixation duration on the composite interchange and the small spacing interchange is longer, with fixation behaviors mainly concentrated on the near ahead and far ahead areas and allocating less attention to other areas than on common spacing interchanges. When driving from ramp to mainline, the average saccade speed and saccade distance of the driver in the small spacing interchange are greater than those of the common spacing interchange, while during mainline driving, the average saccade index of the driver in the small spacing interchange is lower than that of the common spacing interchange. When interchange spacing is insufficient or the environment is complex, the visual workload of the driver will increase.

(3) When driving from ramp to mainline, drivers need to repeatedly glance at the left rear areas to obtain road information, showing low transition probabilities for the instrument panel areas and right-side areas, and higher repeat fixation probabilities for the left rearview mirror areas and left-side areas, and the performance is more significant in the composite interchange. When driving on the mainline, drivers need to slow down or change lanes to avoid merging traffic from the right-side ramp, leading to the one-step transition probability and repeated fixation probability in the right lane areas significantly increased.

Data availability statement

The data related to the research has not been stored in a publicly available repository, and the data will be provided as required.

Ethics and consent statement

Review and approval by an ethics committee was not needed for this study because all personal information will be properly preserved and accessed only by me and members of the research team. And the personal identity of the participants will be anonymized, and all data will be analyzed and stored in the form of coding. All interview and survey forms will be stored on secure servers and access will be severely restricted.

All participants were informed that consent to participate in the study and publish their data would be assumed on completion and submission of the study survey. The reason why the written informed consent of the participants was not obtained was that the specific identity information of the participants was not involved in the article, and only the oral consent of each participant was obtained.

CRediT authorship contribution statement

Yanling Liu: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualization. Han He: Resources, Data curation. Siqi Wang: Writing – review & editing, Validation. Xiangwei Li: Formal analysis. Shenwan Su: Supervision, Investigation. Jin Xu: Writing – review & editing, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was supported by the 10.13039/501100001809 National Natural Science Foundation of China (Grant No. 52172340 ), Chongqing University Innovation Research Group Project (Grant No. CXQT21022 ).
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