Traffic Signal Phase Research Articles

Prediction of Driver Action at Signalized Intersections by Using a Nested Logit Model

The primary objective of this research was to identify factors that influenced whether a vehicle would stop or proceed through, either legally or illegally, during the yellow indication when the vehicle was approaching a signalized intersection. For this objective to be accomplished, naturalistic driver behavioral data were collected at 72 signalized intersection approaches selected from four regions of the United States. Data were obtained for 6,208 vehicles that were approaching a study intersection during the yellow interval. With these data, a nested logit model was developed to investigate the influence of various factors on the likelihood that a driver approaching a signalized intersection during the yellow interval would stop, would proceed through legally, or would commit red light running (RLR). The nested logit model represented an improvement over prior binary logistic regression models, because it allowed the simultaneous estimation of all three potential driver actions. With the use of this model, RLR was determined to be more likely to occur under the following conditions: (a) the duration of the yellow interval was equal to or less than 4.5 s, (b) the subject vehicle was part of a platoon, (c) the approach speed limit was less than or equal to 40 mph, (d) the subject vehicle was farther from the intersection at the onset of the yellow indication, or (e) the subject vehicle approached at a lower rate of speed. The results may be used to improve the accuracy of algorithms for real-time prediction of RLR, including those used in conjunction with traffic signal phase extension systems.

Operational Effects of Continuous Flow Intersection Geometrics

The continuous flow intersection (CFI), also known as a displaced left-turn (DLT) intersection, increases capacity and reduces delays by eliminating the left-turn traffic signal phase. The geometric layout of the intersection is changed so that left turns cross opposing traffic lanes at a location upstream of the main intersection. Although the installation of CFIs has become more prevalent in the United States, insights into the geometrics of CFIs gained through their application have not yet been dispersed to industry or academia. This paper considers the impacts that geometric design has on the operations of CFIs. The paper presents a deterministic model that considers the effects of the DLT length and other CFI geometrics on traffic operations. Although academic in nature, this model evolved through practical application. The model takes functional and operational insights gained through the analysis, design, and observation of multiple operational CFIs in Utah and articulates them in mathematical terms. The quantitative relationships presented provide transportation professionals and officials more accurate and precise guidance to facilitate better decisions in considering, evaluating, and designing a CFI.

Control Strategies for Transit Priority Based on Queue Modeling and Surrogate Testing

To explore the potential benefits of bus priority based on signal phasing strategy, this article proposes a simplified statistical sampling method to simulate vehicle delays at intersections instead of using conventional microscopic traffic simulations. This method greatly reduces the calculation cost and time while maintaining the estimation accuracy of the delay. This research provides a surrogate testing system and tests intuitive traffic signal phasing strategies that shorten average waiting time of queuing vehicles by letting certain buses pass the intersection with a higher priority. Interestingly, the results reveal that the length of the green time should be adaptive with the proportion of buses in the traffic flow. Moreover, a higher priority is suggested for the bus at certain positions in a queue so that it can pass the intersection in one green period with its preceding vehicles. The benefits of this approach include (1) reduced delay of both buses and other vehicles when the proportion of buses is small; (2) keeping the influence on network-wide traffic at a low level even when the proportion of buses is large, because the green time is only extended for a short while; and (3) ease to implement such control strategies by modifying current traffic control systems.

Assessment of the Suitability of Microsimulation as a Tool for the Evaluation of Macroscopically Optimized Traffic Signal Timings

In practice, traffic signal timings are derived using macroscopic tools that are essentially deterministic. Traffic flows, signal phasing, and street geometry are processed to deliver optimized signal timings. Objective functions strive for efficiency through minimizing measures such as delay and journey time. We now have traffic microsimulation tools that model traffic by imitating its stochastic nature. This paper looks at microsimulation as a means of testing optimized signal timings. We assess the suitability of evaluating signal timings optimized macroscopically through microsimulation. We analyze a range of traffic demand and traffic control scenarios. A real-world arterial with 12 signalized intersections serves as a test bed for the experiments. The results show that when macroscopically optimized signal timings are subject to extensive evaluation through microsimulation, their efficiency is shown to be inconsistent. The paper concludes that the traffic microsimulation tools cannot always be relied upon to evaluate macroscopically optimized traffic signal timings because these timings sometimes perform worse, in microsimulation, than the nonoptimized signal timings.

Traffic signal phasing at intersections to improve safety for alcohol-affected pedestrians

Alcohol-affected pedestrians are among the highest-risk groups involved in pedestrian casualty crashes. This paper investigates the opportunities to use a modified form of traffic signal operation during high-risk periods and at high-risk locations to reduce alcohol-affected pedestrian crashes and the severity of injuries that might otherwise occur. The ‘Dwell-on-Red’ treatment involves displaying a red traffic signal to all vehicle directions during periods when no vehicular traffic is detected, so that drivers approach high-risk intersections at a lower speed than if a green signal were displayed. Vehicle speed data were collected before and after treatment activation at both a control and treatment site. Speed data were collected both 30 m prior to and at the intersection stop line. The treatment was associated with a reduction in mean vehicle speeds of 3.9 kph (9%) and 11.0 kph (28%) at 30 m and stop line collection points, respectively, and substantial reductions in the proportion of vehicles travelling at threatening speeds with regard to the severity of pedestrian injury. Other important road safety concerns may also benefit from this form of traffic signal modification, and it is recommended that other areas of application be explored, including the other severe trauma categories typically concentrated around signalised intersections.

Traffic Signal Phase Truncation in Event of Traffic Flow Restriction

This paper proposes an improvement to traffic signal controller logic. The concept consists of truncating a phase on which there is demand but with zero or minimal flow owing to a restriction of traffic. Flow restrictions occur for a variety of reasons including queue spillback, stalled vehicles, and railroad blockages. The logic presented in this paper recognizes such a flow restriction and, consequently, terminates the phase in favor of a conflicting call for service. The theory of the concept is addressed, and methods of implementation are explored. Although this research focuses on phase truncation, terminating a phase may not always be the most strategic decision; other priorities can override tactical detection of minimal flow. Therefore, this research considers the feasibility and potential benefit of including phase truncation logic as one component of a more intelligent and strategic controller algorithm. A limited experimental analysis was conducted to qualify the benefit of the phase truncation...

Traffic Signal Phase Truncation in Event of Traffic Flow Restriction

This paper proposes an improvement to traffic signal controller logic. The concept consists of truncating a phase on which there is demand but with zero or minimal flow owing to a restriction of traffic. Flow restrictions occur for a variety of reasons including queue spillback, stalled vehicles, and railroad blockages. The logic presented in this paper recognizes such a flow restriction and, consequently, terminates the phase in favor of a conflicting call for service. The theory of the concept is addressed, and methods of implementation are explored. Although this research focuses on phase truncation, terminating a phase may not always be the most strategic decision; other priorities can override tactical detection of minimal flow. Therefore, this research considers the feasibility and potential benefit of including phase truncation logic as one component of a more intelligent and strategic controller algorithm. A limited experimental analysis was conducted to qualify the benefit of the phase truncation concept. This analysis was conducted with VISSIM simulation software in conjunction with a hardware-in-the-loop configuration. On the basis of the results of this analysis, which was limited in scope, it was concluded that the phase truncation concept could result in reduced vehicle delay under at least one traffic scenario.

Online Turning Proportion Estimation in Real-Time Traffic-Adaptive Signal Control

An online method for traffic-adaptive turning proportion estimation for intersections based on a least-squares minimization approach is presented. It assumes that entering-vehicle lane counts and exiting-vehicle counts during every traffic signal phase and in each direction of an intersection are available. Estimations based on field data in the last three cycles (approximately 5 min) are compared with actual values. This method of turning proportion estimation was also implemented within RHODES, a real-time traffic-adaptive signal control system developed in the Advanced Transportation and Logistics: Algorithm and Systems Research Center at the University of Arizona. The resulting system was evaluated in a laboratory using CORSIM, a microscopic traffic simulation computer package. Performance of RHODES improves when the online traffic-adaptive turning proportions are used instead of constant turning proportions.

Development and evaluation of SBLINE, a suite of models for the prediction of pollution concentrations from vehicles in urban areas

The assessment of air quality impacts from roadways is a major concern to urban planners, developers, health officials and engineers. This paper describes the development of a suite of models, calied SBLINE, for prediction of pollution concentrations from vehicles in urban road networks. The first component of the suite is ROADFAC, an emission model for calculating emission rates for a road link with known vehicle fleet structure and operational details. ROADFAC can also calculate modal emission rates, caused by deceleration, idle, acceleration and cruise operational modes, by determining queue length and vehicle delay from traffic volume and signal phasing information. The other components of SBLINE are two dispersion models, called NOTLINE and CPB, for prediction of pollution concentrations contributed by different roadlinks in the network. These models use site geometry, meteorology, and traffic emissions calculated by ROADFAC to predict pollutant concentrations. The contribution from a given link is calculated by using NOTLINE if that link is situated in simple topography, or CPB is run if the link is inside a canyon or a cut-section. Finally, cumulative concentrations at any receptor location are calculated by adding the contributions from all roadlinks. SBLINE can be applied to any urban network of roads, with roadlinks located in either simple topography or street canyons. The program has been evaluated in one region of Leicester in the UK. The region represents a typical urban network of roads with some roads located in plain topography and some inside medium size canyons. Observed values of pollutant concentrations are compared with predictions made from detailed measurements of the vehicle population parameters, meteorology, and local street and building topography. Well-established statistical techniques have been used to show the potential of SBLINE for application to other road networks.

Principles of highway engineering and traffic analysis

Chapter 1: Introduction to Highway Engineering and Traffic Analysis. 1.1 Introduction. 1.2 Highways and the Economy. 1.2.1 The Highway Economy. 1.2.2 Supply Chains. 1.2.3 Economic Development. 1.3 Highways, Energy and the Environment. 1.4 Highways and the Transportation System. 1.5 Highway Transportation and the Human Element. 1.5.1 Passenger Transportation Modes and Traffic Congestion.1.5.2 Highway Safety.1.5.3 Demographic Trends.1.6 Highways and Evolving Technologies.1.6.1 Infrastructure Technologies.1.6.2 Vehicle Technologies. 1.6.3 Traffic Control Technologies.1.7 Scope of Study. Chapter 2: Road Vehicle Performance. 2.1 Introduction. 2.2 Tractive Effort and Resistance. 2.3 Aerodynamic Resistance. 2.4 Rolling Resistance. 2.5 Grade Resistance. 2.6 Available Tractive Effort. 2.6.1 Maximum Tractive Effort. 2.6.2 Engine-Generated Tractive Effort. 2.7 Vehicle Acceleration. 2.8 Fuel Efficiency. 2.9 Principles of Braking. 2.9.1 Braking Forces. 2.9.2 Braking Force Ratio and Efficiency. 2.9.3 Antilock Braking Systems. 2.9.4 Theoretical Stopping Distance. 2.9.5 Practical Stopping Distance. 2.9.6 Distance Traveled During Driver Perception/Reaction. Chapter 3: Geometric Design of Highways. 3.1 Introduction. 3.2 Principles of Highway Alignment. 3.3 Vertical Alignment. 3.3.1 Vertical Curve Fundamentals. 3.3.2 Stopping Sight Distance. 3.3.3 Stopping Sight Distance and Crest Vertical Curve Design. 3.3.4 Stopping Sight Distance and Sag Vertical Curve Design. 3.3.5 Passing Sight Distance and Crest Vertical Curve Design. 3.3.6 Underpass Sight Distance and Sag Vertical Curve Design. 3.4 Horizontal Alignment. 3.4.1 Vehicle Cornering. 3.4.2 Horizontal Curve Fundamentals. 3.4.3 Stopping Sight Distance and Horizontal Curve Design. 3.5 Combined Vertical and Horizontal Alignment. Chapter 4: Pavement Design. 4.1 Introduction. 4.2 Pavement Types. 4.2.1 Flexible Pavements. 4.2.2 Rigid Pavements 95. 4.3 Pavement System Design: Principles for Flexible Pavements. 4.3.1 Calculation of Flexible Pavement Stresses and Deflections. 4.4 The AASHTO Flexible-Pavement Design Procedure. 4.4.1 Serviceability Concept. 4.4.2 Flexible-Pavement Design Equation. 4.4.3 Structural Number. 4.5 Pavement System Design: Principles for Rigid Pavements. 4.5.1 Calculation of Rigid-Pavement Stresses and Deflections. 4.6 The AASHTO Rigid-Pavement Design Procedure. 4.7 Measuring Pavement Quality and Performance. 4.7.1 International Roughness Index. 4.7.2 Friction Measurements. 4.7.3 Rut Depth. Chapter 5: Fundamentals of Traffic Flow and Queuing Theory. 5.1 Introduction. 5.2 Traffic Stream Parameters. 5.2.1 Traffic Flow, Speed, and Density. 5.3 Basic Traffic Stream Models. 5.3.1 Speed-Density Model. 5.3.2 Flow-Density Model. 5.3.3 Speed-Flow Model. 5.4 Models Of Traffic Flow. 5.4.1 Poisson Model. 5.4.2 Limitations of the Poisson Model. 5.5 Queuing Theory and Traffic Flow Analysis. 5.5.1 Dimensions of Queuing Models. 5.5.2 D/D/1 Queuing. 5.5.3 M/D/1 Queuing. 5.5.4 M/M/1 Queuing. 5.5.5 M/M/N Queuing. 5.6 Traffic Analysis at Highway Bottlenecks. Chapter 6: Highway Capacity and Level-of-Service Analysis. 6.1 Introduction. 6.2 Level-of-Service Concept. 6.3 Level-of-Service Determination. 6.3.1 Base Conditions and Capacity. 6.3.2 Determine Free-Flow Speed. 6.3.3 Determine Analysis Flow Rate. 6.3.4 Calculate Service Measure(s) and Determine LOS. 6.4 Basic Freeway Segments. 6.4.1 Base Conditions and Capacity. 6.4.2 Service Measure. 6.4.3 Determining Free-Flow Speed. 6.4.4 Determining Analysis Flow Rate. 6.4.5 Calculating Density and Determining LOS. 6.5 Multilane Highways. 6.5.1 Base Conditions and Capacity. 6.5.2 Service Measure. 6.5.3 Determining Free-Flow Speed. 6.5.4 Determining Analysis Flow Rate. 6.5.5 Calculating Density and Determining LOS. 6.6 Two-Lane Highways. 6.6.1 Base Conditions and Capacity. 6.6.2 Service Measures. 6.6.3 Determining Free-Flow Speed. 6.6.4 Determining Analysis Flow Rate. 6.6.5 Calculate Service Measures. 6.6.6 Determine LOS. 6.7 Design Traffic Volumes. Chapter 7: Traffic Control and Analysis at Signalized Intersections. 7.1 Introduction. 7.2 Intersection and Signal Control Characteristics. 7.2.1 Actuated Control. 7.2.2 Vehicle Detection. 7.2.3 Typical Phase Operation. 7.2.4 Signal Controller Operation. 7.3 Analysis of Traffic at Signalized Intersections. 7.3.1 Concepts and Definitions. 7.3.2 Signalized Intersection Analysis with D/D/1 Queuing. 7.3.3 Signalized Intersection Analysis for Level of Service. 7.4 Optimal Traffic Signal Timing. 7.5 Development of a Traffic Signal Phasing and Timing Plan. 7.5.1 Select Signal Phasing. 7.5.2 Establish Analysis Lane Groups. 7.5.3 Calculate Analysis Flow Rates and Adjusted Saturation Flow Rates. 7.5.4 Determine Critical Lane Groups and Total Cycle Lost Time. 7.5.5 Calculate Cycle Length. 7.5.6 Allocate Green Time. 7.5.7 Calculate Change and Clearance Intervals. 7.5.8 Check Pedestrian Crossing Time. 7.6 Level-of-Service Determination. 7.7 Signal Coordination. 7.7.1 Fundamental Relationships. 7.7.2 Effective Green to Cycle Length Ratio (g/C). 7.7.3 Platoon Dispersion. 7.7.4 State of the Practice. 7.7.5 Progression Quality and Level of Service Analysis. 7.8 The Progression Adjustment Factor and Arrival Type. Chapter 8: Travel Demand and Traffic Forecasting. 8.1 Introduction. 8.2 Traveler Decisions. 8.3 Scope of the Travel Demand and Traffic Forecasting Problem. 8.4 Trip Generation. 8.4.1 Typical Trip Generation Models. 8.4.2 Trip Generation with Count Data Models. 8.5 Mode and Destination Choice. 8.5.1 Methodological Approach. 8.5.2 Logit Model Applications. 8.6 Highway Route Choice. 8.6.1 Highway Performance Functions. 8.6.2 User Equilibrium. 8.6.3 Mathematical Programming Approach to User Equilibrium. 8.6.4 System Optimization. 8.7 Traffic Forecasting in Practice. 8.8 The Traditional Four-Step Process. 8.9 The Current State of Travel Demand and Traffic Forecasting. Appendix 8A: Least Squares Estimation. Appendix 8B: Maximum-Likelihood Estimation. Appendix A: Metric Example Problems. Appendix B: Metric End-of-Chapter Problems. Index.

Intersection Carbon Monoxide Modeling

In this note the author discusses the need for better air quality mobile source models near roadways and intersections. To develop the improved models, a better understanding of emissions and their relation to ambient concentrations is necessary. The database for the modal model indicates that vehicles do have different emission levels for different engine operating modes. If the modal approach is used information is needed on traffic signal phasing, queue lengths, delay times, acceleration rates, deceleration rates, capacity, etc. Dispersion estimates using current air quality models may be inaccurate because the models do not take into account intersecting traffic streams, multiple buildings of varying setbacks, height, and spacing.