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Traffic flow theory serves as the scientific foundation for understanding and managing how vehicles move through complex transportation networks. By examining the fundamental relationships between vehicle speed, density, and flow rate, traffic engineers and planners can design signal timings and network solutions that significantly improve traffic efficiency, reduce congestion, and enhance overall safety. This comprehensive guide explores the critical role of traffic flow theory in modern transportation engineering and its practical applications in creating effective traffic management systems.
Understanding the Fundamentals of Traffic Flow Theory
Traffic flow theory examines three key traffic stream variables: speed, flow, and density, which are fundamentally interconnected. These relationships form the basis for understanding how traffic behaves under various conditions and enable transportation professionals to predict congestion patterns and develop optimal traffic management strategies.
The Three Primary Traffic Variables
The primary elements of traffic flow are flow, density, and speed. Flow refers to the number of vehicles passing a fixed point per unit of time, typically measured in vehicles per hour. Density, also known as concentration, represents the number of vehicles occupying a given length of roadway at a specific moment, usually expressed as vehicles per mile or per kilometer. Speed indicates the distance traveled by a vehicle during a unit of time, commonly measured in miles per hour or kilometers per hour.
The fundamental relationship “q=kv” (flow equals density multiplied by speed) is illustrated by the fundamental diagram. This simple yet powerful equation forms the cornerstone of traffic flow analysis, demonstrating how these three variables interact to describe traffic conditions on any given roadway segment.
The Fundamental Diagram of Traffic Flow
The fundamental diagram of traffic flow gives a relation between road traffic flux (vehicles/hour) and the traffic density (vehicles/km). This diagram is essential for understanding traffic behavior and serves as a critical tool for traffic engineers in designing and evaluating transportation systems.
The speed-density relationship is linear with a negative slope; therefore, as the density increases the speed of the roadway decreases. The line crosses the speed axis at the free flow speed, and the line crosses the density axis at the jam density. This relationship helps engineers understand how traffic transitions from free-flowing conditions to congested states.
Free-flowing traffic is characterized by fewer than 12 vehicles per mile per lane, whereas higher densities can lead to unstable conditions and persistent stop-and-go traffic. Understanding these thresholds is crucial for designing effective traffic control strategies that maintain optimal flow conditions.
Macroscopic and Microscopic Approaches
Mathematical relationships describing traffic flow can be classified into two general categories—macroscopic and microscopic. The macroscopic approach considers flow-density relationships, whereas the microscopic approach considers the spacing between two consecutive vehicles and the speeds of individual vehicles.
Macroscopic properties like flow and density are the product of individual (microscopic) decisions. Yet those microscopic decision-makers are affected by the environment around them, i.e. the macroscopic properties of traffic. This bidirectional relationship between individual driver behavior and aggregate traffic patterns makes traffic flow modeling both challenging and fascinating.
The most widely used model is the Greenshields model, which posited that the relationships between speed and density is linear. While this model provides a simplified representation of traffic behavior, it has proven remarkably useful for practical applications in traffic engineering and continues to be widely employed in signal timing design.
Traffic Flow Dynamics and Behavior
Traffic behaves in a complex and nonlinear way, depending on the interactions of a large number of vehicles. Due to the individual reactions of human drivers, vehicles do not interact simply following the laws of mechanics, but rather display cluster formation and shock wave propagation, both forward and backward, depending on vehicle density.
At a critical traffic density and a corresponding critical velocity the state of flow will change from stable to unstable. If one of the vehicles brakes in unstable flow regime the flow will collapse. This phenomenon explains why small disturbances in heavy traffic can cascade into major congestion events, highlighting the importance of maintaining traffic flow below critical density thresholds.
Designing Effective Signal Timings Using Traffic Flow Theory
Signal timing design represents one of the most direct applications of traffic flow theory. By leveraging accurate traffic flow data and applying theoretical principles, engineers can optimize signal operations to minimize delays, reduce stops, and improve overall intersection performance.
The Three Critical Signal Timing Parameters
Coordination parameters are the cycle length, split and offset. These three parameters work together to determine how efficiently a signalized intersection or corridor operates. Understanding how to optimize each parameter based on traffic flow principles is essential for effective signal timing design.
Cycle Length: Cycle length is the total time (in seconds) to complete one full cycle. The selection of an appropriate cycle length depends on traffic volumes, the number of phases required, and coordination requirements with adjacent signals. The relationship between cycle length and capacity is important to understand. If the cycle length is increased and the number of phases remains the same, the percentage of green time per hour is increased (assuming the yellow plus all-red intervals remain constant) which increases capacity.
Green Splits: The percentage of cycle length allocated to each phase determines how much time each movement receives. Split the green time among different traffic phases proportional to the critical lane volumes of each phase. This proportional allocation ensures that phases with higher demand receive adequate time while preventing excessive delays for lower-volume movements.
Offsets: The offset of a signal is the delay of the center of the green period of that signal, compared to the center of the green period of the reference signal. Proper offset design enables vehicle platoons to progress through multiple intersections with minimal stops, significantly improving travel times and reducing fuel consumption along corridors.
Analyzing Peak Hours and Traffic Patterns
Effective signal timing depends fundamentally on accurate understanding of traffic demand patterns. For signal timing applications, peak hour volumes are required for each turning movement. Peak hour volumes are determined by first identifying the peak hour for the intersection, which is the 60-minute time period with the highest total intersection volume for four consecutive 15-minute periods.
Traffic patterns vary significantly throughout the day, requiring different signal timing plans for different time periods. Traffic signals can be programmed to have different signal timing plans, depending on the time of day. Some signal control systems adapt signal timings depending on measured traffic conditions. This time-of-day operation allows signals to respond to changing demand patterns, providing longer cycle lengths and different split allocations during peak periods while operating more efficiently during off-peak times.
Optimization Methods and Approaches
Optimal progression bandwidth and signal settings are interdependent, meaning that signal timing parameters cannot be optimized in isolation. Modern optimization approaches recognize this interdependence and employ sophisticated algorithms to find the best combination of cycle length, splits, and offsets simultaneously.
Design includes cycle length, offsets, and splits based on optimizing such objective functions as increasing progression opportunities; reducing delay, stops, and fuel consumption; reducing total operating cost; or a combination of these. Different optimization objectives may lead to different optimal timing plans, requiring engineers to balance competing goals based on corridor-specific priorities.
For lower-volume intersections, simpler methods may be appropriate. The Greenshields-Poisson Method is best suited to lower volume intersections. When the critical lane volume exceeds 400 vph, then the basic assumption of random arrivals (no vehicle interactions) is probably not valid. This highlights the importance of selecting optimization methods appropriate to the specific traffic conditions being addressed.
Capacity Analysis and Critical Lane Volumes
Critical lane volume analysis is a fundamental tool for calculating green times and evaluating signal phasing schemes because it identifies movements that are “critical” to the signal operations. Specifically, the results provide a baseline for determining signal timing parameters such as splits and cycle length.
The capacity of a lane at a signalized intersection is determined by factoring the saturation flow rate by the percentage of green time allocated to serve the lane. Understanding this relationship allows engineers to ensure that signal timings provide adequate capacity for all critical movements while avoiding excessive cycle lengths that would increase delay for all users.
Network Solutions Based on Traffic Flow Theory
Beyond individual intersections, traffic flow theory provides the foundation for designing comprehensive network solutions that optimize traffic operations across multiple facilities. These solutions range from coordinated signal systems to advanced adaptive control strategies that respond to real-time conditions.
Coordinated Signal Systems
Coordination is a tool to provide the ability to synchronize multiple intersections to enhance the operation of one or more directional movements in a system. Examples include arterial streets, downtown networks, and closely spaced intersections such as diamond interchanges.
An arterial series of signals operating in a semi-actuated or fixed mode, and importantly, with the same cycle length, can be coordinated. During signal coordination, most signal systems are typically designed to operate in a semi-actuated mode. This coordination allows vehicle platoons to move through multiple intersections with minimal stops, dramatically improving travel times and reducing emissions.
The effectiveness of signal coordination depends heavily on proper offset design. The cycle length is no longer independent of the distance between signals. Now the allowable cycle lengths depend on the spacings between signals and the normal speed of the traffic. This geometric constraint means that coordination design must carefully consider the physical spacing of intersections along with traffic demand patterns.
Adaptive Traffic Control Systems
Adaptive traffic control represents an advanced application of traffic flow theory, where signal timings adjust automatically in response to real-time traffic conditions. These systems continuously monitor traffic flow and modify signal parameters to maintain optimal operations as conditions change throughout the day.
Modern adaptive systems employ sophisticated algorithms that process data from detectors throughout the network, using traffic flow principles to predict the impacts of timing changes before implementing them. This predictive capability allows the system to proactively respond to developing congestion rather than simply reacting to existing problems.
The benefits of adaptive control are most pronounced in networks with highly variable traffic patterns, where fixed-time plans cannot adequately respond to changing conditions. By continuously optimizing signal timings based on current traffic flow, adaptive systems can reduce delays by 10-30% compared to well-designed fixed-time plans, with even greater benefits during incident conditions or special events.
Traffic Demand Management Strategies
Traffic flow theory also informs demand management strategies that seek to influence when, where, and how people travel. To prevent congestion and to keep traffic flow stable, the number of vehicles entering the control zone has to be smaller or equal to the number of vehicles leaving the zone in the same time. This principle underlies various demand management approaches including ramp metering, area access control, and dynamic pricing strategies.
Ramp metering uses traffic signals at freeway entrance ramps to regulate the rate at which vehicles enter the mainline, preventing demand from exceeding capacity and maintaining stable flow conditions. By applying traffic flow theory to determine optimal metering rates, these systems can significantly improve freeway operations and reduce the frequency and severity of congestion.
Real-Time Traffic Monitoring and Management
Effective network management requires comprehensive real-time monitoring of traffic conditions. Modern traffic management centers collect data from thousands of sensors, including loop detectors, cameras, and probe vehicles, to maintain continuous awareness of network performance.
This data serves multiple purposes: validating traffic flow models, detecting incidents and abnormal conditions, evaluating the effectiveness of signal timing plans, and providing information to travelers. By comparing observed conditions against theoretical expectations based on traffic flow models, operators can quickly identify problems and implement appropriate responses.
Advanced traffic management systems integrate real-time monitoring with automated response capabilities, using traffic flow theory to determine optimal control strategies for current conditions. These systems can automatically adjust signal timings, activate special timing plans for incidents or events, and provide dynamic route guidance to help distribute traffic more evenly across the network.
Optimized Route Guidance Systems
Route guidance systems leverage traffic flow theory to help individual drivers select optimal paths through the network. By predicting travel times on different routes based on current and anticipated traffic conditions, these systems can guide drivers toward less congested alternatives, helping to balance loads across the network.
The effectiveness of route guidance depends on accurate travel time prediction, which in turn relies on sound application of traffic flow principles. Systems must account for how traffic conditions evolve over time, recognizing that routes that appear optimal based on current conditions may become congested by the time the driver arrives if many other drivers make the same choice.
Modern navigation applications use sophisticated algorithms that incorporate historical patterns, current conditions, and predictive models based on traffic flow theory to provide reliable route recommendations. As these systems become more widely adopted, they increasingly function as network-wide traffic management tools, helping to distribute demand more efficiently across available capacity.
Advanced Applications of Traffic Flow Theory
Simulation and Modeling
Traffic flow theory is used in many aspects of design; for example, to determine adequate lane lengths for storing left-turn vehicles on separate left-turn lanes, the average delay at intersections and freeway ramp merging areas, and changes in the level of freeway performance due to the installation of improved vehicular control devices on ramps. Another important application of traffic flow theory is simulation, where mathematical algorithms are used to study the complex interrelationships that exist among various traffic elements.
Traffic simulation models range from simple analytical tools to complex microsimulation platforms that model individual vehicle movements. These models allow engineers to test proposed designs and operational strategies before implementation, evaluating their likely performance under various scenarios and identifying potential problems that might not be apparent from simple calculations.
Microsimulation models are particularly valuable for analyzing complex situations where vehicle interactions play a critical role, such as weaving sections, roundabouts, and closely-spaced intersections. By simulating individual driver decisions based on car-following and lane-changing models derived from traffic flow theory, these tools can predict system performance with remarkable accuracy when properly calibrated.
Macroscopic Fundamental Diagrams for Network Analysis
A macroscopic fundamental diagram (MFD) is type of traffic flow fundamental diagram that relates space-mean flow, density and speed of an entire network with n number of links. This extension of traditional fundamental diagram concepts to entire networks provides powerful insights for managing urban traffic systems.
The maximum capacity or “sweet spot” of the network is the region at the peak of the MFD function. Understanding this network-level capacity allows traffic managers to implement perimeter control strategies that prevent too many vehicles from entering congested areas, maintaining network-wide efficiency even during high-demand periods.
Time-Space Diagrams for Progression Analysis
Traffic engineers represent the location of a specific vehicle at a certain time with a time-space diagram. This two-dimensional diagram shows the trajectory of a vehicle through time as it moves from a specific origin to a specific destination. Multiple vehicles can be represented on a diagram and, thus, certain characteristics, such as flow at a certain site for a certain time, can be determined.
With the traffic conditions, time-space diagrams can be created to give travel time, delay, and queue lengths of a road segment. These diagrams are invaluable tools for designing and evaluating signal coordination plans, allowing engineers to visualize how vehicle platoons interact with signal timings and identify opportunities for improvement.
Time-space diagrams make the quality of progression immediately apparent, showing whether vehicles arrive at downstream signals during green or red phases. By adjusting offsets and observing the resulting changes in the diagram, engineers can iteratively refine coordination plans to maximize the bandwidth available for through movements while accommodating side-street traffic.
Practical Considerations in Applying Traffic Flow Theory
Data Collection and Quality
Data collection is an important element of the signal timing process. The accuracy and reliability of traffic flow analyses depend fundamentally on the quality of input data. Engineers must carefully plan data collection efforts to ensure that measurements accurately represent typical conditions and capture the full range of variability in traffic patterns.
Modern detection technologies provide unprecedented opportunities for continuous traffic monitoring, but also present challenges in managing and interpreting large volumes of data. Automated data quality checking procedures are essential to identify and correct errors such as detector malfunctions, communication failures, and anomalous readings that could compromise analysis results.
Balancing Multiple Objectives
Traffic signal timing is a very complex topic. For example, timing a ‘WALK’ signal for a wide pedestrian crossing and slower pedestrians could result in very long waits for vehicles, thereby increasing the likelihood of cars running the light, which could cause accidents. Therefore, optimizing the safety of intersections involves multiple factors, such as street width, lane width, the number of intersecting streets, the availability of electricity for a signal, the number of cars per unit of time, the evenness or unevenness of flow, the number and types of pedestrians, and many other factors.
Maximizing capacity is not always the primary objective. For example, minimizing crashes requires fewer conflicting movements, which leads to multiple phases and longer cycle lengths. It is necessary to exercise engineering judgment to achieve a feasible balance of objectives. This balancing act requires engineers to consider not only traffic flow efficiency but also safety, environmental impacts, pedestrian and bicycle accommodation, and community preferences.
Implementation and Field Evaluation
The final step in the process is to install and evaluate the new timing plans in the field. Even the most sophisticated analysis and optimization cannot account for all real-world factors that influence traffic operations. Field evaluation is essential to verify that implemented timing plans perform as expected and to identify any necessary adjustments.
Before-and-after studies provide valuable feedback on the effectiveness of signal timing changes, documenting improvements in delay, stops, travel time, and other performance measures. This empirical validation helps build confidence in traffic flow models and optimization methods while identifying situations where theoretical predictions may not align with observed results.
Continuous monitoring after implementation allows agencies to track performance over time and identify when conditions have changed sufficiently to warrant retiming. Traffic patterns evolve due to land use changes, population growth, and shifting travel behaviors, requiring periodic updates to signal timings to maintain optimal performance.
Emerging Trends and Future Directions
Connected and Automated Vehicles
The emergence of connected and automated vehicles promises to fundamentally transform traffic flow characteristics and create new opportunities for traffic management. Signal Phase and Timing (SPaT) data refers to messages that indicate which signals are green, yellow, or red and, in some cases, the time remaining to cross an intersection. Currently, SPaT transmits to vehicles using either DSRC (Dedicated Short Range Communications) or V2X (Vehicle to Everything).
As vehicles gain the ability to communicate with infrastructure and each other, traffic flow theory will need to evolve to account for new behavioral patterns. Automated vehicles may follow more precisely, maintain more consistent speeds, and respond more predictably to traffic signals, potentially allowing higher capacities and more stable flow conditions. However, mixed traffic with both human-driven and automated vehicles may exhibit complex interactions requiring new modeling approaches.
Artificial Intelligence and Machine Learning
Artificial intelligence and machine learning techniques are increasingly being applied to traffic flow prediction and signal timing optimization. These methods can identify complex patterns in traffic data that might not be apparent through traditional analysis, potentially improving the accuracy of demand forecasts and the effectiveness of control strategies.
Machine learning algorithms can also enable more sophisticated adaptive control systems that learn from experience, continuously refining their decision-making processes based on observed outcomes. As these systems accumulate more data and experience, they may be able to anticipate and respond to traffic conditions more effectively than systems based solely on predetermined rules and models.
Sustainability and Environmental Considerations
Optimizing signal timing improves sustainability metrics (e.g., fuel consumption). This study attempts to fill gaps by proposing a methodology that optimizes traffic-signal timing (i.e., cycle length, green, and offsets) by minimizing the FC Intersection Control Performance Index (FCIC-PI) as a surrogate measure for fuel consumption.
As concerns about climate change and air quality intensify, traffic flow theory is being extended to explicitly consider environmental impacts. Signal timing optimization increasingly incorporates objectives related to fuel consumption, emissions, and noise, recognizing that the most efficient traffic flow from a delay perspective may not always minimize environmental impacts.
Future developments in this area may include real-time optimization that responds to air quality conditions, special timing strategies for electric and hybrid vehicles, and integration with broader urban sustainability initiatives. Traffic flow models will need to incorporate vehicle fleet composition, road gradients, and other factors that influence emissions to support these environmental objectives.
Key Implementation Strategies for Traffic Professionals
Developing a Systematic Approach
There are eight distinct steps that define the signal timing development process. Not every step requires a costly effort to complete in every instance. Successful application of traffic flow theory requires a systematic methodology that ensures all relevant factors are considered while making efficient use of available resources.
This systematic approach should begin with clearly defined objectives, proceed through careful data collection and analysis, employ appropriate optimization methods, and conclude with thorough field evaluation. Documentation throughout the process creates an institutional knowledge base that supports future retiming efforts and helps new staff understand the rationale behind existing timing plans.
Building Technical Capacity
Effective application of traffic flow theory requires staff with appropriate technical skills and knowledge. Agencies should invest in training programs that help traffic engineers and technicians understand fundamental principles, analysis methods, and optimization tools. This technical capacity enables agencies to make informed decisions about signal timing and traffic management strategies.
Partnerships with universities and research institutions can provide access to cutting-edge developments in traffic flow theory and help agencies stay current with evolving best practices. Professional organizations and industry conferences offer additional opportunities for knowledge sharing and professional development.
Leveraging Technology and Tools
Modern software tools make sophisticated traffic flow analysis accessible to agencies of all sizes. Signal timing optimization software, traffic simulation models, and performance monitoring systems automate many analytical tasks while providing powerful visualization and reporting capabilities. Agencies should carefully evaluate available tools and select those that best match their needs and technical capabilities.
However, technology should complement rather than replace engineering judgment. Some model input parameters may be estimated. It is important to note that it is always better to measure or observe the parameter. Engineers must understand the assumptions and limitations of analytical tools and validate their results against field observations.
Common Challenges and Solutions
Dealing with Data Limitations
Many agencies face constraints in data availability, whether due to limited detector coverage, equipment malfunctions, or budget limitations. While comprehensive data is ideal, practical solutions can often be developed with limited information by focusing resources on critical locations and time periods, supplementing automated detection with manual counts where necessary, and using estimation techniques based on traffic flow principles to fill gaps.
Managing Conflicting Demands
Traffic signals must serve multiple user groups with potentially conflicting needs: through traffic seeking minimal delay, turning vehicles requiring adequate gaps, pedestrians needing safe crossing opportunities, and transit vehicles operating on schedules. Traffic flow theory provides a framework for understanding these conflicts and finding solutions that reasonably balance competing demands.
Special signal phasing strategies, such as protected-permissive left turns, pedestrian lead intervals, and transit signal priority, can help accommodate diverse needs. The key is to apply these strategies judiciously, recognizing that every special treatment has costs as well as benefits and that the optimal solution depends on site-specific conditions.
Addressing Geometric Constraints
Short block lengths make it harder to find an optimum progression plan. Uneven signal spacing makes the job of finding a good two-way progression plan more difficult. In some cases, there is no solution to the problem of finding a two-way progression. When geometric constraints limit what can be achieved through signal timing alone, engineers may need to consider complementary strategies such as turn restrictions, one-way street conversions, or physical improvements.
Essential Resources and Further Learning
For traffic professionals seeking to deepen their understanding of traffic flow theory and its applications, numerous resources are available. Standardizing signal timing procedures, standards, and best practices has been completed through the Signal Timing Manual, sponsored by the Federal Highway Administration. This comprehensive manual provides detailed guidance on all aspects of signal timing design and optimization.
Additional valuable resources include the Highway Capacity Manual, which provides methods for analyzing traffic operations on various facility types, and the Manual on Uniform Traffic Control Devices, which establishes standards for traffic signal design and operation. Professional organizations such as the Institute of Transportation Engineers offer training courses, webinars, and publications that help practitioners stay current with evolving practices.
Online resources and communities provide opportunities for knowledge sharing and problem-solving. Many transportation agencies maintain websites with technical guidance, case studies, and lessons learned from their signal timing programs. Academic journals publish research on traffic flow theory and its applications, offering insights into emerging methods and technologies.
For those interested in exploring traffic flow theory in greater depth, university courses in transportation engineering provide rigorous treatment of fundamental principles and advanced topics. Many universities also offer continuing education programs and certificate courses designed for working professionals.
Conclusion: The Continuing Importance of Traffic Flow Theory
Traffic flow theory remains as relevant today as when it was first developed, providing the scientific foundation for understanding and managing traffic operations. As transportation systems become more complex and demands on infrastructure continue to grow, the principles of traffic flow theory become even more critical for developing effective solutions.
The application of traffic flow theory to signal timing design and network management has proven remarkably successful in improving traffic operations, reducing delays, and enhancing safety. From simple isolated intersections to sophisticated adaptive control systems managing entire urban networks, traffic flow principles guide the design and operation of traffic control systems worldwide.
Looking forward, traffic flow theory will continue to evolve in response to new technologies, changing travel patterns, and emerging priorities. Connected and automated vehicles, artificial intelligence, and sustainability concerns will drive new developments in how we model and manage traffic. However, the fundamental relationships between speed, density, and flow will remain central to understanding traffic behavior and designing effective solutions.
For traffic engineers and transportation planners, mastering traffic flow theory and its applications is essential for professional success. The ability to analyze traffic conditions, design optimal signal timings, and develop effective network management strategies depends on a solid understanding of these fundamental principles. By continuing to refine and apply traffic flow theory, transportation professionals can create safer, more efficient, and more sustainable transportation systems that serve the needs of all users.
Whether designing signal timings for a single intersection or managing traffic across an entire metropolitan area, the principles of traffic flow theory provide the essential framework for understanding problems and developing solutions. As we face the transportation challenges of the 21st century, this theoretical foundation will remain indispensable for creating transportation systems that move people and goods efficiently while minimizing environmental impacts and enhancing quality of life.
Key Takeaways for Effective Traffic Management
- Understand the fundamentals: The relationships between speed, density, and flow form the foundation for all traffic analysis and signal timing design
- Collect quality data: Accurate traffic flow analysis depends on reliable measurements of traffic volumes, speeds, and patterns
- Optimize systematically: Cycle length, splits, and offsets must be optimized together, recognizing their interdependence
- Balance multiple objectives: Effective signal timing requires balancing efficiency, safety, environmental impacts, and accommodation of all users
- Implement adaptive strategies: Real-time traffic monitoring and adaptive control can significantly improve network performance
- Coordinate across networks: Signal coordination and network-wide management strategies leverage traffic flow theory to improve corridor and area-wide operations
- Validate in the field: Field evaluation is essential to verify that implemented timing plans perform as expected
- Embrace new technologies: Connected vehicles, artificial intelligence, and advanced detection systems offer new opportunities for applying traffic flow principles
- Invest in capacity building: Developing staff expertise in traffic flow theory and its applications is essential for long-term success
- Stay current: Traffic flow theory continues to evolve, requiring ongoing professional development and engagement with research and best practices
By applying these principles and leveraging the power of traffic flow theory, transportation agencies can design and operate traffic control systems that significantly improve mobility, safety, and sustainability. The science of traffic flow provides the tools and insights needed to address today’s transportation challenges while preparing for the opportunities and demands of tomorrow’s transportation systems. For more information on traffic engineering best practices, visit the Federal Highway Administration or explore resources from the Institute of Transportation Engineers.