🚗Transportation Systems Engineering Unit 6 – Traffic Control Systems & Signal Processing

Key Concepts in Traffic Control

• Traffic control systems manage the flow of vehicles, pedestrians, and other road users to ensure safe and efficient movement
• Objectives of traffic control include reducing congestion, minimizing delays, improving safety, and optimizing the use of available road capacity
• Traffic control devices consist of signs, signals, and markings that provide guidance, warnings, and regulations to road users
• Intersection control methods range from simple yield and stop signs to complex signalized intersections with multiple phases and timing plans
• Capacity analysis evaluates the maximum number of vehicles that can pass through a road segment or intersection under prevailing conditions
• Level of service (LOS) is a qualitative measure describing operational conditions within a traffic stream, generally in terms of factors such as speed, travel time, freedom to maneuver, traffic interruptions, and comfort and convenience
  • LOS is typically categorized from A (best) to F (worst) based on performance measures like density, delay, and volume-to-capacity ratio
• Traffic flow theory studies the interactions between vehicles, drivers, and infrastructure, using mathematical models to describe and predict traffic behavior
  • Key parameters in traffic flow theory include flow rate, density, speed, and headway

Traffic Signal Fundamentals

• Traffic signals are power-operated devices that alternately direct traffic to stop and proceed at intersections using red, yellow, and green indications
• Signal heads are the physical assemblies containing the light sources, lenses, and visors, mounted on poles or mast arms
  • Arrangement of signal heads should provide clear visibility and convey the intended meaning to approaching traffic
• Signal phasing refers to the sequence and timing of intervals during which specific movements are assigned the right-of-way
  • Typical phases include main street through, main street left turn, side street through, and side street left turn
• Cycle length is the total time required for a signal to complete one full sequence of indications, usually measured in seconds
• Interval duration is the length of time assigned to each signal indication (red, yellow, green) within a phase
• Clearance intervals (yellow and all-red) provide time for vehicles to safely clear the intersection before conflicting movements are released
• Pedestrian signals use Walk, Flashing Don't Walk, and Don't Walk indications to control pedestrian crossings in conjunction with vehicular phases

Signal Timing and Phasing

• Signal timing involves determining the appropriate cycle length, phase sequence, and interval durations to optimize traffic flow and minimize delays
• Webster's method is a widely used analytical approach for calculating optimal cycle lengths and green times based on critical lane volumes and saturation flow rates
• Actuated signal control adjusts the timing of phases in real-time based on traffic demand detected by sensors (loops, video, radar)
  • Actuated timing parameters include minimum and maximum green times, vehicle extension, and recall settings
• Coordination between adjacent signals allows for the progressive movement of traffic along a corridor, reducing stops and delays
  • Time-space diagrams are used to visualize and design coordinated signal timing plans, showing the relationship between signal offsets and vehicle trajectories
• Left-turn phasing options include permissive (yield to oncoming traffic), protected (exclusive arrow), and protected-permissive (both)
  • Left-turn phase selection depends on factors such as traffic volumes, sight distance, and crash history
• Pedestrian timing requirements, such as walk and flashing don't walk intervals, must be accommodated within the overall signal cycle
  • Pedestrian clearance time is based on the crossing distance and an assumed walking speed (typically 3.5 feet per second)

Detection and Actuation Systems

• Detection systems provide real-time information about traffic presence, volume, and speed to support actuated signal control and performance monitoring
• Inductive loop detectors are the most common type, consisting of wire loops embedded in the pavement that detect the presence of vehicles through changes in inductance
  • Loop configurations include presence (6'x6'), long presence (6'x40'), and passage (6'x6' spaced at intervals) for different detection purposes
• Video detection uses cameras and image processing algorithms to detect and classify vehicles, providing a non-intrusive alternative to loops
  • Advantages of video detection include ease of maintenance, lane-by-lane detection, and the ability to collect additional data (e.g., vehicle classification, turning movements)
• Radar detection employs microwave or millimeter-wave radar to detect vehicle presence and speed, offering a compact and all-weather solution
• Magnetometers are point detectors that sense changes in the earth's magnetic field caused by the presence of vehicles, providing a low-cost and non-intrusive option
• Actuation settings, such as passage time, minimum gap, and max out, control how the signal responds to detected vehicles and adjusts the timing of phases
  • Passage time is the maximum time allowed for a vehicle to travel from the detector to the stop line, while minimum gap is the minimum time between successive vehicle detections to extend the green

Coordinated Signal Systems

• Coordination of traffic signals along a corridor or network can significantly improve traffic flow, reduce delays, and increase capacity
• Time-based coordination relies on predetermined timing plans that are implemented according to a fixed schedule (e.g., time of day, day of week)
  • Timing plans are developed based on historical traffic data and are optimized for average conditions
• Traffic-responsive coordination adjusts the timing plans in real-time based on current traffic conditions measured by detectors
  • Pattern selection and transition algorithms are used to determine when to switch between timing plans and how to smoothly transition between them
• Centralized control systems allow for the remote monitoring, management, and optimization of signal timings across a network from a traffic management center
  • Advanced traffic management systems (ATMS) software provides tools for data collection, performance evaluation, and timing plan optimization
• Measures of effectiveness for coordinated systems include travel time, delay, stops, and fuel consumption along the coordinated routes
  • These measures can be estimated using analytical models (e.g., HCM) or measured directly using probe vehicles or Bluetooth/Wi-Fi re-identification techniques
• Challenges in coordinated signal systems include accommodating cross-street demand, managing queue spillback, and adapting to incidents or special events
  • Strategies such as split optimization, queue management, and preemption can help mitigate these issues

Advanced Traffic Management Strategies

• Adaptive signal control continuously adjusts signal timings based on real-time traffic conditions using advanced algorithms and optimization models
  • Examples of adaptive systems include SCOOT (Split Cycle Offset Optimization Technique), SCATS (Sydney Coordinated Adaptive Traffic System), and ACS-Lite (Adaptive Control Software Lite)
• Transit signal priority (TSP) provides preferential treatment to buses or light rail vehicles at signalized intersections to improve transit reliability and reduce delays
  • TSP strategies include green extension (extending a green phase to allow an approaching transit vehicle to pass), red truncation (shortening a red phase to expedite the return to green for an approaching transit vehicle), and phase insertion (adding a special phase to serve a transit vehicle)
• Freight signal priority (FSP) similarly prioritizes the movement of heavy trucks or commercial vehicles, particularly along designated freight corridors or near intermodal facilities
• Preemption is the interruption of normal signal operations to provide priority to emergency vehicles (e.g., fire trucks, ambulances) or to clear the tracks for approaching trains at railroad crossings
  • Preemption is typically triggered by onboard emitters, line-of-sight sensors, or direct communication between the vehicle and the traffic signal controller
• Ramp metering regulates the flow of traffic entering freeways using traffic signals at on-ramps to maintain optimal mainline throughput and prevent congestion
  • Ramp metering strategies can be fixed-time (based on historical data), traffic-responsive (based on real-time conditions), or coordinated (across multiple ramps)

Data Collection and Analysis

• Traffic data collection is essential for signal timing optimization, performance evaluation, and planning future improvements
• Turning movement counts (TMCs) record the number of vehicles making each possible movement (left, through, right) at an intersection, typically during peak periods
  • TMCs can be conducted manually by observers or automatically using video or radar detection
• Automatic traffic recorders (ATRs) continuously measure traffic volumes, speeds, and vehicle classifications at specific locations using permanent sensors (loops, radar, video)
  • ATR data is used to develop seasonal factors, growth rates, and traffic patterns for planning and analysis purposes
• Travel time and delay studies involve measuring the time it takes to traverse a route using floating car techniques, GPS probes, or Bluetooth/Wi-Fi re-identification
  • These studies help evaluate the effectiveness of signal timing plans and identify bottlenecks or problem areas
• Crash data analysis examines the frequency, severity, and contributing factors of crashes at signalized intersections to identify safety issues and potential countermeasures
  • Collision diagrams and crash modification factors (CMFs) are used to visualize crash patterns and estimate the safety benefits of proposed improvements
• Signal performance measures (SPMs) are quantitative metrics that assess the efficiency and effectiveness of signal operations using high-resolution controller data
  • Examples of SPMs include arrivals on red, split failures, and pedestrian delay, which can be used to diagnose problems and optimize timings
• Big data analytics leverages large-scale data from multiple sources (e.g., probe vehicles, connected vehicles, mobile devices) to gain insights into traffic patterns, travel behavior, and system performance
  • Machine learning and data mining techniques can be applied to detect anomalies, predict congestion, and support decision-making in traffic management

Emerging Technologies in Traffic Control

• Connected vehicles (CV) technology enables wireless communication between vehicles, infrastructure, and personal devices to exchange real-time information about traffic conditions, safety hazards, and signal status
  • CV applications for traffic control include intelligent traffic signal systems (I-SIG), which optimize timings based on predicted vehicle arrivals, and red light violation warning (RLVW), which alerts drivers to potential red-light running incidents
• Autonomous vehicles (AV) are equipped with sensors, cameras, and advanced control systems to operate without human intervention, potentially improving safety, efficiency, and accessibility
  • AVs may require specialized signal control strategies, such as platooning (grouping vehicles together to minimize headways) and intelligent intersection management (coordinating the movement of AVs through an intersection without traditional signals)
• Smart cities integrate various technologies, including IoT (Internet of Things) sensors, data analytics, and wireless networks, to enhance the performance and sustainability of urban systems, including transportation
  • Smart city applications for traffic control may include adaptive lighting (adjusting street lighting based on traffic volumes and weather conditions), dynamic lane management (changing lane configurations based on demand), and multimodal integration (coordinating signals with transit, pedestrian, and bicycle movements)
• Artificial intelligence (AI) and machine learning (ML) techniques can be applied to traffic control problems, such as optimizing signal timings, predicting traffic patterns, and detecting incidents
  • Examples include reinforcement learning for adaptive signal control, neural networks for short-term traffic forecasting, and computer vision for automated incident detection
• Cybersecurity is a critical concern in the deployment of emerging technologies, as the increasing connectivity and automation of traffic control systems create potential vulnerabilities to cyber-attacks
  • Strategies for enhancing cybersecurity include secure communication protocols, encryption, access control, and intrusion detection systems
• Equity and accessibility considerations are important in the implementation of emerging technologies to ensure that the benefits are distributed fairly across all users, particularly underserved or disadvantaged communities
  • This may involve engaging with community stakeholders, conducting equity impact assessments, and providing alternative options for those who may not have access to advanced technologies