Traffic Signal Timing Plans Research Articles

Factors Influencing Effectiveness of Transit Signal Priority and Late-Bus Recovery at Signalized-Intersection Level

Transit priority systems have the potential to improve transit performance and address capacity constraints by giving priority to transit movements over other traffic. This research focused on the effectiveness of conditional transit priority or the manipulation of traffic signal timing plans to reduce delay of late transit buses. The integration of two transportation subsystems—traffic signals and public transit systems—was studied. These subsystems interact along a congested corridor where they share a common roadway infrastructure and transit signal priority (TSP) regulates the interaction between traffic signals, passenger traffic, and buses. Previous research has focused on the evaluation of bus TSP performance at the route level. In practice, it is important to understand not only TSP performance at the route level but also the impact of TSP at the level of the traffic signal intersection (e.g., to allow progression in major cross streets). TSP can significantly improve performance at specific intersections, even though at the route level TSP shows a more modest impact. This study proposed the integration of several data sets such as bus scheduling and location, passenger flows, and TSP requests to evaluate schedule adherence at the stop level and TSP performance at the level of the signalized intersection. A congested arterial corridor was analyzed and regression analysis was used to determine the key factors that affect bus travel time and schedule recovery for late buses. TSP was found to be most effective at lower-volume intersections where queuing was less problematic. Implications of the findings are analyzed and discussed.

Impacts of Signal System Timings on Rain Related Congestion

It is known that inclement weather can affect traffic volumes, vehicle speeds, speed variance, saturation flow rates, and sometimes discharge rates from traffic signals. These parameters in turn can have a significant impact on the efficiency of traffic signal timing plans. Timing plans developed for dry conditions with good visibility may not be as effective under wet conditions with poor visibility, even for the same traffic volumes. Previous studies have looked at the effects of poor weather on traffic signal efficiency but they have focused primarily on winter weather conditions involving snow and ice, which have much more dramatic impacts on traffic flow. In regions like the South, rain is the primary adverse weather event and even though its effects on traffic flow may be smaller than snow or ice, its impacts can still be seen. This study sought to assess the impact of rain on traffic flow and determine whether there can be a meaningful benefit to developing and implementing signal timing plans specifically for rain events. Using simulations for two study corridors in Birmingham, Alabama, it was found that moderate and heavy rain events do impact traffic flow, increasing stops and average delays up to 6% compared to dry conditions. Signal timing plans optimized for wet conditions were found to reduce these delays, though in absolute terms the benefits were modest. It was found, however, that the effectiveness of rain-specific timing plans depended to a large extent on prevailing traffic volumes. An evaluation of potential costs and benefits resulting from rain-optimized signal timing plans found that the net benefits, at least in the two study corridors, are questionable. The analysis found that projected costs over a five-year life cycle would significantly exceed potential benefits. Rain-specific timing plans may have useful applications in certain corridors but the potential benefits and costs should be carefully evaluated beforehand.

Outcome-Oriented Performance Measures for Management of Signalized Arterial Capacity

The design of traffic signal timing plans is typically based on a single day of manual turning movement counts. Once systems are constructed and settings are implemented, they receive maintenance on fixed schedules of a number of years. Public feedback (e.g., phone calls) is in many cases the primary channel by which an agency obtains feedback about the system. System assessment is often conducted by collecting a new small data sample and repeating the design process. One common and frustrating result is that the same settings are frequently recommended by the design software even when problems are known to exist. This paper illustrates how fundamental traffic engineering concepts can be integrated with traffic signal system detection and controller status information to provide outcome-based measures of arterial-system performance. These outcome-based measures of performance characterize the operation of a signal system and provide a data set that can be used to identify and prioritize opportunities for operational improvements in the system. A consistent set of performance measures used across a signal network would affect traffic signal operations at two distinct levels. At the regional or district level, it would provide a continuously updated list of problem areas with greater accuracy than telephone reports. At the agency jurisdiction level, it would provide quantitative data for prioritizing resources across multiple districts and by time of day.

Generating More Equitable Traffic Signal Timing Plans

Signal timing plans designed under a total delay minimization strategy can induce excessively high delays for low-volume movements and result in an uneven distribution of delay among motorists. A multiobjective optimization approach is considered for the minimization of both overall delay and delay imbalance. The multiobjective method is found to be highly successful. Compared with a pure delay minimization strategy, the multiobjective optimizer is able to produce signal timing plans with similar overall delay that have a considerably more equitable delay distribution. Multi-objective optimization provides the traffic engineer with a choice from an entire range of compromise solutions in the two competing objectives. Large reductions in the delay imbalance can be achieved at the cost of small sacrifices in overall delay. Multiphase signal control, shifting green splits in favor of low-volume movements, and decreasing bandwidth provide more-balanced delays at the cost of increasing overall delay.

Traffic Responsive Signal Timing Plan Generation Based on Neural Network

This article proposes a neural network based traffic signal controller, which eliminates most of the problems associated with the Traffic Responsive Plan Selection (TRPS) mode of the closed loop system. Instead of storing timing plans for different traffic scenarios, which requires clustering and threshold calculations, the proposed approach uses an Artificial Neural Network (ANN) model that produces optimal plans based on optimized weights obtained through its learning phase. Clustering in a closed loop system is root of the problems and therefore has been eliminated in the proposed approach. The Particle Swarm Optimization (PSO) technique has been used both in the learning rule of ANN as well as generating training cases for ANN in terms of optimized timing plans, based on Highway Capacity Manual (HCM) delay for all traffic demands found in historical data. The ANN generates optimal plans online to address real time traffic demands and thus is more responsive to varying traffic conditions.

Stochastic Optimization for Sustainable Traffic Signal Control

ABSTRACT The primary research focus of traffic signal control systems has been the development of traffic signal timing plans minimizing vehicular delay and stops. Very little is known if such a strategy would be optimal for a sustainable traffic signal control system minimizing emission and fuel consumption. This paper presents a development of a sustainable traffic signal control system and speed management framework consisting of a microscopic simulation model, a microscopic fuel consumption and emission model, and a genetic algorithm – based optimizer. Based on an implementation of the proposed framework on a case study network, it was found that the proposed framework was very effective in minimizing fuel consumption and emission with moderate trade-offs in delay and stops.

Signalized Intersection Analysis and Design: Implications of Day-to-Day Variability in Peak-Hour Volumes on Delay

Traffic signal timing plans are typically developed on the basis of turning movement traffic and pedestrian volume counts aggregated to 15-min intervals and obtained over a 4 or 8 h period on a single day. These data are used to identify the peak hour and to compute the peak-hour turning movement traffic volumes. They may also be used to compute the peak-hour factor. These values are then used as input to the analysis methods defined in the Highway capacity manual or within popular signal timing optimization software to estimate signal performance in terms of expected average vehicle delay. Delay estimation methods explicitly consider several sources of variability (e.g., an assumed distribution of individual vehicle headways in the arrival traffic stream). However, these methods do not consider the day-to-day variability that exists within key delay estimation parameters such as the PHF and peak-hour traffic volume. The lack of consideration of this variability may be because: (1) it is assumed that the ...

Methodology to Assess Traffic Signal Transition Strategies for Exit Preemption Control

Enabling vehicles to preempt the normal operation of traffic signals has the potential to improve the safety and efficiency of both the requesting vehicle and all other vehicles. Little is known about which strategy is the most effective to exit from preemption control and transition back to the normal traffic signal timing plan. This research evaluates the commonly available transition strategies: best-way, long, short, and hold strategies. The major contribution of this research is to enhance the methodology to evaluate the impacts of using these alternative transition strategies with the software-in-the-loop simulation tool. The research concludes that a statistically significant interaction exists between traffic volume levels and traffic signal transition strategies; this eliminates the ability to determine the isolated effects of either the transition strategies on average travel delay and average travel time or the effects of changes in traffic volume levels on average travel delay and average travel time. Conclusions, however, could be drawn on the performance of different transition strategies for specific traffic volume levels. The research also concludes that for the base traffic volume and a 40% increase in traffic volume, the most effective transition strategies are the best-way and long or hold alternatives. The best way was the most effective transition strategy for a 20% increase in traffic volume. The least effective strategy is the short transition strategy for both the base and 40% increase in traffic volume and the long and short for a 20% increase in traffic volume. Further research needs to be conducted to assess the performance of different transition strategies in returning to coordinated operation under higher levels of traffic volume with varying cycle lengths and different signal timing plans and when different roadway geometric configurations are present.

Demonstrative comparison of two traffic signal optimization programs: Synchro-6 and PASSER II

To effectively examine and properly response to the traffic congestion on the urban arterial, numerous traffic signal timing optimization programs have been introduced such as Synchro-6 and PASSER II-02. This study is aimed to provide the better insight into the basic difference between these two programs to traffic engineers or transportation practitioners by demonstratively comparing vehicle delay and bandwidth efficiency computed by two programs on the hypothetical arterial having five calibrated intersections spaced by 430-meter each other with varying traffic volume. This study concluded that difference in vehicle delay and bandwidth efficiency results from the characteristic of each program to optimize the traffic signal timing plan and to calculat ethe uniform delay, not from the used parameters (traffic volume). Further studies are requested to present more comprehensive characteristics of two programs to the traffic engineering group by comparing and evaluating them with the other factors that a ffect vehicle delay and bandwidth efficiency, such as intersection spacing, vehicle speeds, intersection capacity, and so on.

Prototype Classification Tool for Supporting Maintenance of Traffic Signal Timing Plans

Advanced traffic control systems, such as traffic signal systems, include large numbers of sensors intended to support the monitoring of traffic conditions. In addition, transportation agencies frequently archive data collected by these detectors, on the assumption that important information can be extracted from the archives with the proper tools. The development of a data mining tool intended to support the maintenance of traffic signal systems that operate in the time-of-day (TOD) mode by identifying when traffic conditions have changed significantly in a corridor is described. The data mining approach used is classification. A case study was conducted to demonstrate that accurate classification models can be developed by using archived data to map between a set of traffic conditions and the associated TOD interval or timing plan for which the conditions are best suited. The 92.4% classification rate achieved in the case study indicates that this data mining tool has the potential to effectively support TOD signal operations.

Traffic Signal Optimization Program for Oversaturated Conditions: Genetic Algorithm Approach

Traffic signal optimization programs have been used widely among transportation professionals. However, none of the existing computer programs can optimize all four traffic control parameters (i.e., cycle length, green split, offset, and phase sequence) simultaneously, even for undersaturated conditions. In this paper, a genetic algorithm-based signal optimization program that can handle oversaturated signalized intersections is presented. The program consists of a genetic algorithm (GA) optimizer and a mesoscopic traffic simulator. The GA optimizer is designed to search for a near-optimal traffic signal timing plan on the basis of a fitness value obtained from the mesoscopic simulator. The proposed program is compared with the newly released TRANSYT-7F version 8.1 on the basis of CORSIM simulation program. Three different demand volume levels—low, medium, and high demand—are tested. For the low-demand and high-demand volume cases, the GA-based program produced statistically better signal timing plans than did TRANSYT-7F in terms of queue time. In the case of medium-demand volume level, the signal timing plan obtained from the GA-based program produced statistically equivalent queue time compared with TRANSYT-7F. Both programs are judged to provide superior capability for oversaturated conditions due to their queue blockage model when compared with previously available signal timing optimization software.

The Los-Angeles Automated Traffic Surveillance and Control (ATSAC) System

The Los Angeles Automated Traffic Surveillance and Control (ATSAC) System is described, emphasizing those features that provide a powerful and flexible capability to manage traffic. Many traffic detectors are used to collect data used for critical intersection control, traffic responsive control in networks, real-time surveillance of traffic conditions using color-graphic monitors, real-time evaluation of system performance, and automated generation of traffic signal timing plans. Closed-circuit television is used to provide additional information regarding the cause of congestion. Manual control of intersections from the control center is used to correct nonrecurring congestion. The hierarchical system architecture consists of a very fast supervisor computer networked with the control console, color-graphic monitors, and area computers that communicate with up to 400 intersections and 1600 detectors each. An evaluation of system performance concluded that stops were reduced by 35%, intersection delay by 20%, travel time by 13%, fuel consumption by 12.5%, and air emissions by 10%. The benefit/cost was 9.8:1, and the system paid for itself in less than one year.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.