Introduction
The construction of intelligent roadside facilities has become increasingly crucial in the context of smart highway development. The effectiveness of these facilities in traffic management directly impacts the overall efficiency and safety of transportation systems. However, existing methods for measuring the control effects of intelligent roadside facilities face several challenges, including high costs of field experiments, uncontrollable scenarios, and limitations in simulation software that primarily focus on single-vehicle simulation rather than traffic flow-level analysis.
To address these issues, this study proposes a control effect measurement method based on traffic flow simulation using VISSIM COM secondary development. The method integrates lane-changing control and dynamic speed limit strategies to evaluate the effectiveness of intelligent roadside facility deployment on highways. By constructing a comprehensive evaluation model, this approach provides a more accurate and adaptable solution for assessing traffic management performance in complex highway environments.
Background and Challenges
Current State of Intelligent Roadside Facilities
Intelligent transportation systems (ITS) have evolved significantly, with highways transitioning from basic smart levels to collaborative smart levels. Traditional highway systems focused on functional implementation, whereas modern smart highways emphasize data-driven management and services. The deployment of intelligent roadside facilities plays a pivotal role in enhancing traffic flow efficiency and safety. However, the effectiveness of these facilities depends on their optimal placement and control strategies.
Limitations of Existing Measurement Methods
Existing research on control effect measurement predominantly relies on field experiments, which are resource-intensive and limited by uncontrollable real-world conditions. For instance, studies using speed measurement systems or fixed monitoring points provide localized insights but fail to capture the broader traffic flow dynamics. Additionally, simulation-based approaches often lack integration with traffic flow-level control strategies, focusing instead on individual vehicle behavior.
To overcome these limitations, this study leverages VISSIM COM secondary development to simulate traffic flow scenarios, enabling a more comprehensive evaluation of control strategies.
Methodology
Scenario Construction
The study defines a control scenario for highways, where intelligent roadside facilities detect accidents and implement management strategies. The scenario includes:
- Accident Detection: Sensors record accident details such as time, location, and affected lanes.
- Traffic Data Collection: Non-accident vehicle speeds, positions, and lane information are captured in real-time.
- Control Strategy Implementation: Lane-changing and dynamic speed limit strategies are activated, with instructions relayed to drivers via upstream variable message signs (VMS).
Key assumptions include:
- The scenario is set in normal daytime highway conditions.
- Accidents do not completely block lanes, allowing lane-changing.
- Drivers fully comply with control instructions.
- The study focuses on the relationship between facility spacing and control effectiveness.
Control Effect Evaluation Model
The evaluation model quantifies the effectiveness of intelligent roadside facility deployment using two primary metrics:
- Lane-Changing Control: Measured by the distance between the lane-changing location and the accident point.
- Dynamic Speed Limit: Evaluated based on the variance between actual speeds and posted speed limits.
The model combines these metrics with a coverage factor representing the detection range of sensing devices. The final evaluation formula balances the importance of lane-changing control (60%) and speed limit effectiveness (40%), as determined by expert surveys.
Lane-Changing Control Strategy
The lane-changing control algorithm involves several steps:
- Accident Information Recording: Sensors log accident details and vehicle data.
- Lane-Changing Interval Calculation: Determines the safe distance for lane-changing based on facility spacing.
- Vehicle Count and Position Analysis: Estimates the number of vehicles needing to change lanes and their positions.
- Lane-Changing Execution: Vehicles are guided to change lanes at optimal locations to avoid congestion near the accident site.
The algorithm ensures that lane-changing occurs at safe distances from the accident, minimizing disruptions to traffic flow.
Dynamic Speed Limit Strategy
The dynamic speed limit algorithm adjusts speed limits based on real-time traffic conditions:
- Average Speed Calculation: Sensors measure the average speed of vehicles in designated intervals.
- Speed Limit Determination: The 85th percentile speed is used to set appropriate speed limits, ensuring compliance with safety standards.
- Speed Limit Updates: VMS displays updated speed limits at regular intervals (e.g., every 5 minutes).
This approach reduces speed variance, enhancing traffic flow stability and safety.
Simulation Experiments
Experimental Design
The study conducts two types of experiments:
- Adaptability Experiments: Investigate the impact of factors such as facility spacing, traffic volume, and update frequency on control effectiveness.
- Sensitivity Experiments: Examine the relationship between sensing device detection range and control performance.
Key parameters include:
- Facility spacing: 250m to 10,000m.
- Traffic volume: 500 to 2,500 vehicles per hour.
- Update intervals: 300s and 600s.
Results and Analysis
Adaptability Experiment Findings
- Facility Spacing: Smaller spacing between devices improves control effectiveness by increasing accident detection coverage and reducing response times.
- Traffic Volume: Higher traffic volumes (up to 2,500 vehicles/hour) yield better control outcomes, as drivers are more likely to adhere to speed limits and lane-changing instructions.
- Update Intervals: Shorter update intervals (300s) provide more responsive control but show diminishing returns at extreme traffic volumes.
Sensitivity Experiment Findings
- Lane-Changing Control: Larger detection ranges and smaller facility spacing result in lane-changing occurring farther from accidents, improving safety.
- Dynamic Speed Limits: Increased detection ranges reduce speed variance, enhancing compliance with posted limits.
Formula Fitting
The study derives empirical formulas to predict control effectiveness based on facility spacing and detection range:
- Lane-Changing Control: The distance from lane-changing to the accident point increases with detection range and decreases with facility spacing.
- Dynamic Speed Limit: Speed variance decreases with larger detection ranges and smaller facility spacing.
These formulas provide a practical tool for optimizing roadside facility deployment.
Comparative Analysis
The proposed method is compared with existing approaches from literature:
- Dynamic Speed Limit Effectiveness: The study’s method reduces speed variance by 39.51% and 25.65% compared to two benchmark methods, demonstrating superior performance.
- Lane-Changing Safety: Unlike existing methods, the proposed approach ensures that lane-changing distances decrease with larger facility spacing, aligning with traffic flow principles.
Conclusion
This study presents a comprehensive method for measuring the control effects of intelligent roadside facilities using traffic flow simulation. By integrating lane-changing and dynamic speed limit strategies, the approach provides a robust framework for evaluating facility deployment in highway environments. Key findings include:
- Smaller facility spacing and larger detection ranges enhance control effectiveness.
- The 85th percentile speed is an effective basis for dynamic speed limits.
- The proposed method outperforms existing approaches in reducing speed variance and ensuring safe lane-changing.
Future research could validate the method with real-world data and explore additional factors such as adverse weather conditions or mixed traffic (e.g., autonomous and human-driven vehicles).
DOI: 10.19734/j.issn.1001-3695.2024.07.0306
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