Investigating Traffic Characteristics at Freeway Merging Areas in Heterogeneous Mixed-Flow Environments
Abstract
:1. Introduction
- (1)
- This study examines the traffic operational characteristics of mixed traffic flows, consisting of CAVs, cars, and trucks, through the implementation of microscopic simulations on the SUMO platform.
- (2)
- A comprehensive evaluation of mixed traffic flow is conducted, examining key metrics such as capacity, stability, safety, oscillation, fuel consumption, and emissions. This analysis considers the impacts of varying MPRs of CAVs, truck proportions, and the distribution of human driving styles.
2. Methodology
2.1. Car-Following Models for Vehicle Control
2.1.1. Intelligent Driver Model
2.1.2. Cooperative Adaptive Cruise Control
2.2. Calibration of Car-Following Model
2.3. Analysis of Traffic Flow Characteristics
2.3.1. Fundamental Diagram Model
2.3.2. Stability
2.3.3. Fuel Consumption and Emissions
3. Experimental Results
3.1. Data Preparation
3.1.1. HighD Dataset
3.1.2. Car-Following Event Extraction
- The leading and following vehicles must be in the same lane and travel in the same direction;
- The DHW between vehicles must be less than 150 m;
- The THW between vehicles must be less than 6 s;
- The duration of the car-following event must exceed 30 s.
3.2. Classification of Driving Styles
3.3. Calibration Results Analysis
4. Simulation Analysis
4.1. Simulation Settings
4.2. Fundamental Diagrams
4.3. Traffic Stability
4.3.1. Stability Analysis
4.3.2. Fluctuation in Speed and Acceleration
4.4. Traffic Safety
4.5. Traffic Oscillation
4.6. Fuel Consumption and Emissions
5. Conclusions
- (1)
- As the MPR of CAVs increases, the road capacity improves significantly. However, this enhancement is more sensitive to variations in the truck proportions within the traffic flow. For instance, when the MPR of CAVs increases from 40% to 80%, the road capacity improves by approximately 25% under the same truck proportion. Moreover, as the proportion of mild driving styles among HDVs increases, the effect on road capacity becomes more significant, particularly at higher MPRs of CAVs. This is primarily reflected in greater reductions in capacity when the truck proportion increases or when the proportion of mild drivers rises.
- (2)
- With an increase in the MPR of CAVs and the proportion of normal driving styles among human drivers, the stability improves markedly, resulting in smaller speed differences within the vehicle string once stability is restored. As the MPR of CAVs increases from 40% to 80%, the stability recovery time decreases by approximately 33%. When all human drivers exhibit mild driving styles, the recovery time increases by 25% compared to scenarios where all drivers adopt normal driving styles. When the truck proportion rises, the trend of longer transition times from disturbance to stability becomes less pronounced. Nevertheless, it remains evident that a higher truck proportion correlates with greater speed disparities within the vehicle string after stability is regained.
- (3)
- In traffic safety, when the proportion of normal driving styles among human drivers is high, an increase in the MPR of CAVs has a minimal impact on the safety of human drivers. However, when all human drivers adopt mild driving styles, an increase in the MPR of CAVs results in a significant decline in safety, as evidenced by higher CIF values. In this context, an increase in the truck proportion enhances safety, with this improvement being particularly noticeable in environments with a high MPR of CAVs. Hence, the introduction of CAVs should prioritize adaptation to the mild driving styles of human drivers to avoid safety degradation caused by higher MPRs of CAVs, ultimately leading to an overall improvement in traffic safety.
- (4)
- Finally, an increase in the truck proportion or the proportion of mild drivers leads to higher fuel consumption and emissions, with this effect being more pronounced in environments with low MPRs of CAVs. Traffic oscillation analysis reveals that these scenarios result in more severe congestion, contributing to poorer ecological and environmental outcomes. Additionally, fuel consumption and emissions are higher in the CC type than in the CT type as cars tend to exhibit more aggressive behaviors when following other cars.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Studies | Vehicle Type | Driving Style | Traffic Flow Characteristics | ||||||
---|---|---|---|---|---|---|---|---|---|
HDV | CAV | Fundamental Diagram | Stability | Safety | Consumption | Emission | |||
Car | Truck | ||||||||
Yang et al. [33] | ✓ | ✓ | ✓ | ||||||
Qin et al. [34] | ✓ | ✓ | ✓ | ✓ | |||||
Park et al. [35] | ✓ | ✓ | ✓ | ✓ | ✓ | ||||
Ye and Yamamoto [36] | ✓ | ✓ | ✓ | ||||||
Zhu et al. [28] | ✓ | ✓ | ✓ | ||||||
Zhang et al. [21] | ✓ | ✓ | ✓ | ✓ | |||||
Sala and Soriguera [37] | ✓ | ✓ | |||||||
Zhao et al. [16] | ✓ | ✓ | ✓ | ✓ | |||||
Wang et al. [38] | ✓ | ✓ | ✓ | ||||||
Li et al. [31] | ✓ | ✓ | ✓ | ✓ | |||||
Oikonomou et al. [39] | ✓ | ✓ | ✓ | ✓ | |||||
Yao et al. [40] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||
Sun et al. [32] | ✓ | ✓ | ✓ | ✓ | ✓ | ||||
Jiang et al. [41] | ✓ | ✓ | ✓ | ✓ | |||||
Liu et al. [42] | ✓ | ✓ | ✓ | ✓ | |||||
This paper | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Type | Method | Silhouette Coefficient | Cluster 0 | Cluster 1 |
---|---|---|---|---|
CC | Spectral | 0.3541 | 656 (77.00%) | 196 (23.00%) |
CT | Ward agglomerative | 0.4757 | 77 (83.70%) | 15 (16.30%) |
TC | Spectral | 0.4044 | 38 (45.24%) | 46 (54.76%) |
TT | K-means | 0.4451 | 45 (58.44%) | 32 (41.56%) |
Type | Statistic | Intercept | Cluster | F-Value (Cluster) | p-Value (Cluster) |
---|---|---|---|---|---|
CC | Wilks’ Lambda | 0.7304 | 0.3839 | 339.8376 | <0.001 |
Pillai’s Trace | 0.2696 | 0.6161 | 339.8376 | <0.001 | |
CT | Wilks’ Lambda | 0.7814 | 0.3683 | 37.3130 | <0.001 |
Pillai’s Trace | 0.2186 | 0.6317 | 37.3130 | <0.001 | |
TC | Wilks’ Lambda | 0.4766 | 0.3327 | 39.6101 | <0.001 |
Pillai’s Trace | 0.5234 | 0.6673 | 39.6101 | <0.001 | |
TT | Wilks’ Lambda | 0.4779 | 0.2756 | 47.3139 | <0.001 |
Pillai’s Trace | 0.5221 | 0.7244 | 47.3139 | <0.001 |
Type | Driving Style | ||||
---|---|---|---|---|---|
CC | Cluster 0 (Normal) | 1.13 | 4.07 | 3.83 | 1.33 |
Cluster 1 (Mild) | 0.97 | 4.11 | 5.36 | 2.07 | |
CT | Cluster 0 (Normal) | 1.33 | 4.01 | 3.51 | 1.31 |
Cluster 1 (Mild) | 1.09 | 3.92 | 5.05 | 2.26 | |
TC | Cluster 0 (Normal) | 0.91 | 3.95 | 4.56 | 1.51 |
Cluster 1 (Mild) | 0.76 | 3.96 | 5.42 | 2.28 | |
TT | Cluster 0 (Normal) | 1.22 | 4.08 | 3.65 | 1.59 |
Cluster 1 (Mild) | 1.05 | 3.78 | 5.35 | 2.57 |
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Wu, S.; Zou, Y.; Liu, D.; Chen, X.; Wang, Y.; Moeinaddini, A. Investigating Traffic Characteristics at Freeway Merging Areas in Heterogeneous Mixed-Flow Environments. Sustainability 2025, 17, 2282. https://doi.org/10.3390/su17052282
Wu S, Zou Y, Liu D, Chen X, Wang Y, Moeinaddini A. Investigating Traffic Characteristics at Freeway Merging Areas in Heterogeneous Mixed-Flow Environments. Sustainability. 2025; 17(5):2282. https://doi.org/10.3390/su17052282
Chicago/Turabian StyleWu, Shubo, Yajie Zou, Danyang Liu, Xinqiang Chen, Yinsong Wang, and Amin Moeinaddini. 2025. "Investigating Traffic Characteristics at Freeway Merging Areas in Heterogeneous Mixed-Flow Environments" Sustainability 17, no. 5: 2282. https://doi.org/10.3390/su17052282
APA StyleWu, S., Zou, Y., Liu, D., Chen, X., Wang, Y., & Moeinaddini, A. (2025). Investigating Traffic Characteristics at Freeway Merging Areas in Heterogeneous Mixed-Flow Environments. Sustainability, 17(5), 2282. https://doi.org/10.3390/su17052282