1. Introduction
As a critical component of motorway systems, exit ramps serve as a transitional facility for traffic flow conversion between motorways and lower-grade roads, playing a key role in the overall quality of traffic operations of motorway networks [
1,
2]. Within an exit ramp area, vehicles frequently engage in lane-changing and other behaviors, leading to interactions that increase traffic conflicts and disruptions [
3,
4]. These disturbances often result in traffic congestion and accidents, making exit ramps vital nodes that influence the operational efficiency of a motorway network [
5]. In recent years, the advancement of artificial intelligence (AI) technologies, particularly in the realm of automated driving, has emerged as a promising solution to address these challenges [
6,
7]. Automated driving technology, which integrates AI, visual computing, and monitoring systems, enables the safe and automated control of motor vehicles and has significantly impacted the future of traffic management [
8,
9]. At present, vehicles equipped with varying levels of automation are gradually penetrating traditional road traffic environments, initiating a transition from a fully manual-driving system to one increasingly composed of automated vehicles [
10,
11]. This transition will unfold over an extended period, giving rise to a heterogeneous traffic flow composed of both manual and automated vehicles [
12,
13].
In response to the emergence of heterogeneous traffic flows composed of both manual-driving and automated vehicles, researchers worldwide have conducted studies in this area. Due to the difficulty of large-scale real-vehicle testing, most research on heterogeneous traffic flow relies on network simulation software and traffic simulation platforms [
14,
15]. Network simulation studies often use tools such as MATLAB [
16] and cellular automata [
17] to model traffic dynamics, yielding valuable insights. Arnah Bose employed cellular automata to simulate heterogeneous traffic flow and analyze the operational states of traffic at varying vehicle ratios [
18]. Jiang et al. developed operational rules for automated vehicles within a cellular automata framework, demonstrating that automated vehicles could alleviate traffic congestion [
19]. Yuan et al. explored the characteristics of heterogeneous traffic flows in mixed environments, incorporating both automated and manual-driving vehicles through simulation modeling [
20]. Liu et al. also used cellular automata to examine the impact of automated vehicle penetration on traffic flow characteristics, such as accessibility and speed [
21]. Additionally, studies using traffic simulation software have contributed significantly to this field. Erfan Aria et al. carried out simulations of automated vehicles through secondary developments of the VISSIM software, revealing that automated vehicles have a particularly positive impact in high-density traffic scenarios [
22]. Recent studies by Gularte et al. [
23] have highlighted the potential of V2X communication technologies in enhancing traffic efficiency and safety. Likewise, emerging research on machine learning applications in traffic simulation environments underscores their promise in optimizing traffic signal control and predictive modeling [
24]. Furthermore, cooperative driving algorithms have attracted significant attention for their capability to improve traffic flow dynamics and mitigate congestion, demonstrating their potential in the advancement of intelligent transportation systems [
25]. Eunbi Jeong conducted similar VISSIM-based simulations to assess two types of longitudinal control strategies for automated vehicles, highlighting their potential to enhance traffic safety through effective traffic management [
26].
The microscopic traffic simulation software Simulation of Urban Mobility (SUMO) has been extensively utilized in studies of heterogeneous traffic flows [
27,
28]. Compared to traditional urban traffic simulation environments, SUMO, as a fully open-source platform, offers enhanced flexibility for researchers, allowing for more detailed simulations [
29,
30]. Moreover, it supports online adjustments through its remote control interface (TraCI), enabling vehicle-to-vehicle information interactions [
31]. Song et al. employed SUMO and the Scene Suite simulation platforms to model traffic flows under various vehicle-to-vehicle (V2V) communications and ADAS-ACC (Adaptive Cruise Control) penetration rates, both individually and in combination [
32]. Their findings indicate that different combinations of V2V and ADAS-ACC penetration rates could significantly improve road safety. Smith et al. used SUMO to modify vehicle-following models, simulating automated driving and demonstrating that the penetration of automated vehicles substantially increased road capacity and enhanced road safety [
33]. Berrazouane et al. also utilized SUMO to develop a simulation model based on real-world motorway data, analyzing the impact of varying proportions of automated vehicles on traffic flow performance [
34].
However, current research on the adaptability of motorway exit ramps under heterogeneous traffic flow conditions faces three primary challenges: (1) In terms of simulation methods for automated vehicles, existing studies predominantly rely on cellular automata, VISSIM, or modifications to car-following and lane-changing rules to simulate automated vehicles. There is a need for further research on how to utilize the fully open-source microscopic traffic simulation software SUMO to select appropriate car-following and lane-changing models, calibrate parameters for both manual and automated vehicles, and more accurately simulate the operational behaviors of automated vehicles. (2) While progress has been made in studying the impact of automated vehicles on the operation of heterogeneous traffic flows, the rapid advancement of automated driving technology necessitates a shift towards examining mixed heterogeneous traffic flows. This requires selecting key performance indicators to characterize the operational state of heterogeneous traffic flows, constructing a comprehensive evaluation model, and conducting an integrated assessment of these traffic dynamics. (3) Most current research on highway exit ramps focuses on traditional, manual-driving vehicle traffic flows. The adaptability of exit ramps to future mixed heterogeneous traffic flows, which will include a significant proportion of automated vehicles, remains underexplored and needs further investigation.
Therefore, as automated vehicles continue to penetrate traffic flows, the operation of current highway exit ramps will be impacted to some extent. With the emergence of heterogeneous traffic flows consisting of both automated and manual-driving vehicles, key questions remain: How will the operational quality of highway mainlines and ramp connections change? Which rules govern the interaction of heterogeneous traffic flows at exit ramps? And how does the adaptability of these exit ramps compare with and without automated vehicles? To address this, this paper focuses on the adaptability of highway exit ramps in the context of heterogeneous traffic flows. Based on an analysis of the traffic characteristics of exit-ramp road traffic, the open-source simulation software SUMO was used to model heterogeneous traffic flows. Key evaluation parameters were selected, and a comprehensive evaluation model was constructed to analyze the adaptability of a highway exit ramp under heterogeneous traffic conditions. The findings provide a solid foundation for future research on the adaptability of highway exit ramps in such environments, offering valuable insights and theoretical references for the design and operational management of highway exit ramps under heterogeneous traffic conditions. The proposed technological framework is illustrated in
Figure 1. The remainder of this paper is structured as follows:
Section 2 describes the process of data acquisition;
Section 3 outlines the methodology of this study;
Section 4 presents the simulation test design and an analysis of the results; and
Section 5 concludes this study, summarizing key findings.
2. Data Acquisition
In this study, aerial photography and field investigations were used to investigate a high-speed exit ramp. Aerial video data, totaling approximately 10 to 15 min, were collected at eight distinct time intervals: 8:00–8:30, 8:30–9:00, 9:00–9:30, 9:30–10:00, 16:00–16:30, 16:30–17:00, 17:00–17:30, and 17:30–18:00. These intervals were chosen to capture variations in traffic patterns, with data converted into equivalent traffic volumes at 5 min intervals. The ramp under investigation is a two-lane direct exit ramp with auxiliary lanes. The main road consists of three lanes, designed for a speed of 120 km/h, while the exit ramp has two lanes. The ramp’s transition section is 100 m, the auxiliary lane is 350 m, and the speed-change lane measures 180 m. Observation sections were selected based on areas where vehicles were either about to change lanes or where lane changes were most frequent. These include the upstream section of the exit ramp (
Section 1), the exit ramp separation point (
Section 2), the middle section of the auxiliary lane (
Section 3), the exit ramp diversion point (
Section 4), and the downstream section of the exit ramp (
Section 5). An overhead view of the surveyed exit ramp is shown in
Figure 2. The specific sections and lanes observed are illustrated in
Figure 3. The frequency distribution of vehicle speeds in each section is shown in
Figure 4, and the average headway for each lane is presented in
Figure 5, and the datasets are utilized as inputs for the methods detailed in
Section 3.
In the highway exit ramp area, vehicle lane-changing behavior leads to a redistribution of headway distances. When the traffic volume is low, vehicle following behavior exhibits greater variability, resulting in a more dispersed headway distribution. Conversely, as the traffic volume approaches full capacity, following behavior becomes more constrained, leading to a more concentrated headway distribution. As shown in the figure above, over 90% of vehicles in the exit ramp area have a headway distribution within 10 s. In
Section 3, it will be shown that there is minimal difference between lanes 1 and 2 of the main road, where 50% of vehicles have a headway within 0–5 s, and 95% fall within 0–10 s. Compared to these lanes, vehicles in lane 3 of the main road and the auxiliary lanes exhibit shorter headway distances, with 70% of vehicles having a headway within 0–5 s and 95% within 0–10 s. The variability in headway spacing between inner and outer lanes is influenced by two key factors. First, outer lanes are more affected by lane-changing behavior. Second, different lanes serve distinct functions within the mainline road, leading to variations in vehicle-following behavior across lanes.
Most of the exit ramps observed in this paper are mandatory lane changes, and the lane-change trajectories are mainly from the mainline road to the ramp. Data comprising 200 lane changes were selected for observation, as shown in
Figure 6.
Based on an analysis of the figure above, the histogram of lane-changing positions closely follows a normal distribution curve. The average lane-changing position is approximately 186 m from the beginning of the transition section of the exit ramp. Notably, 50% of vehicles complete their lane change before reaching half the length of the designated lane-changing area. The highest frequency of lane-changing activity occurs within the 100–150 m range from the start of the transition section.
5. Conclusions
In this paper, a simulation model for heterogeneous traffic flows was developed based on an analysis of the current road traffic characteristics of a motorway exit ramp. The adaptability of heterogeneous traffic flows was evaluated using a simulation framework constructed with the SUMO simulation software. An example analysis was conducted to assess the adaptability of the traffic flow at the exit ramp under various conditions. The main conclusions are as follows:
(1) Based on a qualitative analysis of vehicle operation rules, the Krauss following model was chosen as a longitudinal following model for manual-driving vehicles, while the ACC/CACC vehicle following model was selected for automated vehicles. The LC2013 lane-changing model was used for manual-driving vehicles, and the game-theoretic lane-changing model was employed for automated vehicles. The parameters of these traffic flow models were calibrated, and a heterogeneous traffic flow simulation model for the highway exit ramp was constructed using the SUMO simulation software.
(2) A set of macro- and micro-parameters was introduced to evaluate the traffic flow. The safety cost, equilibrium cost, control cost, efficiency cost, and lane-changing cost were identified as key evaluation indicators. A hierarchical analysis method was used to determine the weight coefficients of each cost, allowing for the construction of a comprehensive cost evaluation model for heterogeneous traffic flows.
(3) A simulation test for the highway exit ramp was developed, with the proportion of automated vehicle penetration and deceleration lane length as the key variables. The results indicate that under current conditions with no automated vehicles, a deceleration lane length of 215 m provides better traffic flow adaptability compared to the current length of 180 m. As the proportion of automated vehicles penetrates the traffic flow, a certain degree of maladaptation is observed when the penetration ratio exceeds 40%. Finally, when the deceleration lane length is adjusted to 200 m, the heterogeneous traffic flow exhibits the best adaptability at the exit ramp.
(4) In determining the lane-following and lane-changing models for automated vehicles, this study conducted a qualitative analysis based on the operating rules of automated vehicles and relevant research data. As automated driving technology continues to mature, future research can incorporate a quantitative analysis using real-world measured data. The heterogeneous traffic flow simulation scenario for the highway exit ramp was built using the SUMO simulation software, with this study focusing solely on passenger cars. However, in real-world exit ramps, large and medium-sized passenger and freight vehicles also significantly impact traffic operations. Future research should consider the adaptability of exit ramps under heterogeneous traffic flows involving different vehicle types.