Signal Timing Plans Research Articles

Multi-objective optimal control formulations for bus service reliability with traffic signals

This paper presents a set of optimal control formulations for maximising bus service reliability through deriving optimal adjustments on signal timings. The traffic dynamics is captured by an underlying kinematic wave model in Hamilton–Jacobi formulation. With traffic data collected through loop detectors and bus positioning devices, the control actions are carried out through adjusting signal timing plans according to short-term estimations of traffic flows and bus arrivals. We derive the optimality conditions of multi-objective control formulations and present an open loop solution algorithm. The proposed control system is applied to a test arterial developed based upon a real-world scenario in Central London, UK. It is found that the model is capable of regulating bus service reliability through utilising traffic signals while managing delays induced to surrounding traffic. The study generates new insights on managing bus service reliability in busy urban networks.

Predicting arterial breakdown probability: A data mining approach

ABSTRACTPredicting the probability of traffic breakdown can be used as an important input for creating advanced traffic management strategies that are specifically implemented to reduce this probability. However, most, if not all, past research on the probability of breakdown has focused on freeways. This study focuses on the prediction of arterial breakdown probability based on archived traffic data for use in real-time transportation system operations. The breakdown of an arterial segment is defined in this study as a segment's operating condition under the level of service F according to the highway capacity manual threshold, although any other level of service could be used. Data from point detection and automatic vehicle identification matching technologies are aggregated in space and time to allow their use as inputs to the prediction model. A decision tree approach, combined with binary logistic regression, is used in this study to predict the breakdown probability based on these inputs. The model is validated using data not used in the development of the model. The research shows that the root mean square error and the mean absolute error of the prediction was 13.6 and 11%, respectively. The analysis also shows that the best set of parameters used in the prediction can be different for different links, due to the various causes of breakdown and characteristics of different links. Predicting the probability of breakdown in ahead of time will allow the agencies to change the signal-timing plan that can delay or eliminate the breakdown.

Assessment of the Robustness of Signal Timing Plans in an Arterial Corridor Through Seasonal Variation of Traffic Flows

Arterial traffic signal systems, predominantly in the United States, deploy multiple signal timing plans to account for daily variability of traffic demand. Those types of traffic flow deviations should be anticipated when timing plans are designed and, therefore, serviced satisfactorily. When traffic flow patterns are no longer predictable, a predetermined time-of-day (TOD) plan may no longer be the optimal one. This research aimed to examine signal timing optimality by applying a method similar to the selection of a traffic responsive plan to recognize automatically the best timing plan suited to current traffic conditions. The proposed method attempted to determine whether the optimality of signal timing settings could have been effectively estimated when systematic detector counts of the major approach were available. The study used 4 months of data from field microwave detectors coupled with data of turning-movement counts obtained over several days. The findings show that TOD signal timing plans mainly depended on adequate data collection that best describes a specific set of traffic conditions. Thus, the designed plan was as optimal as the related traffic information was reliable, whereas a problem arose in the case of limited-availability and low-quality data. New technologies are capable of collecting and storing massive amounts of data. Even if the granularity of collected data is low, the data can be used to improve traffic performance (i.e., reduce corridor delay). This realization could be of particular importance to traffic agencies that have installed, or plan to install, new field devices.

Design and Evaluation of a Mathematical Optimization Model for Traffic Signal Plan Transition Based on Social Cost Function

Signal plan transition is the process of changing from one timing plan to another. It begins when the first intersection starts adjusting signal timing plans and ends when the last intersection completes adjusting signal timing plans. The transition between signal timing plans is required because traffic patterns change during the day. Therefore, it is necessary to modify signal timing parameters offset, phase split, and cycle length for different expectations of traffic volume. This paper presents an alternative and new mathematical model to enhance the performance of traffic signals coordination at intersections during the transition phase. This model is oriented to describe the transition regarding coordination parameters in all intersections of an arterial road for minimizing the social cost during the transition phase expressed in function of costs due to delays, fuel consumption, and air emissions. An ant colony algorithm was designed, coded, and simulated to find the optimal transition parameters using available data. The model was evaluated based on its ability to minimize social costs during the transition period. Results showed that the proposed method performs better than traditional ones.

Analysis of Trends in Data on Transit Bus Dwell Times

Transit vehicles create special challenges for urban traffic signal control. Signal timing plans are typically designed for the flow of passenger vehicles, but transit vehicles—with frequent stops and uncertain dwell times—may have different flow patterns that fail to match those plans. Transit vehicles stopping on urban streets can also restrict or block other traffic on the road. This situation results in increased overall wait times and delays throughout the system for transit vehicles and other traffic. Transit signal priority (TSP) systems are often used to mitigate some of these issues, primarily by addressing delay to the transit vehicles. However, existing TSP strategies give unconditional priority to transit vehicles, exacerbating quality of service for other modes. In networks for which transit vehicles have significant effects on traffic congestion, particularly urban areas, the use of more-realistic models of transit behavior in adaptive traffic signal control could reduce delay for all modes. Estimating the arrival time of a transit vehicle at an intersection requires an accurate model of dwell times at transit stops. As a first step toward developing a model for predicting bus arrival times, this paper analyzes trends in automatic vehicle location data collected over 2 years and allows several inferences to be drawn about the statistical nature of dwell times, particularly for use in real-time control and TSP. On the basis of this trend analysis, the authors argue that an effective predictive dwell time distribution model must treat independent variables as random or stochastic regressors.

Safety-Based Left-Turn Phasing Decisions for Signalized Intersections

The efficient and safe movement of traffic at signalized intersections is the primary objective of any signal-phasing and signal-timing plan. Accommodation of left turns is more critical because of the higher need for balancing operations and safety. The objective of this study was to develop models to estimate the safety effects of the use of left-turn phasing schemes. The models were based on data from 200 intersections in urban areas in Kentucky. For each intersection, approaches with a left-turn lane were isolated and considered with their opposing through approach to examine the left-turn–related crashes. This combination of movements was considered to be one of the most dangerous in intersection safety. Hourly traffic volumes and crash data were used in the modeling approach, along with the geometry of the intersection. The models allowed for the determination of the most effective type of left-turn signalization that was based on the specific characteristics of an intersection approach. The accompanying nomographs provide an improvement over existing methods and warrants and allow for a systematic and quick evaluation of the left-turn phase to be selected. The models used the most common variables that were already known during the design phase, and they could be used to determine whether a permitted or protected-only phase would suit the intersection when safety performance was considered.

Outcome Assessment of Peer-to-Peer Adaptive Control Adjacent to a National Park

In the town of Moab, Utah, a combination of seasonal tourist traffic, heavy truck traffic, and high pedestrian volumes creates a unique traffic management challenge; Moab’s remote location adds additional challenges for real-time traffic monitoring and maintaining of signal timing plans. The Main Street corridor is a strong candidate for an adaptive traffic control system (ATCS). Peer-to-peer (P2P) communication and user-definable control logic were used to develop and implement a cost-effective ATCS called “P2P adaptive control” that used only the existing local controllers and detection. The adaptive control logic adjusts green time along the mainline in response to detector inputs while keeping the side streets at the minimum time needed for pedestrian service. System performance was evaluated by comparing performance measures generated from high-resolution signal controller data before and after implementation of P2P adaptive control. The P2P adaptive control increased the through bandwidth of the corridor and reduced the number of split failures (i.e., the number of phase occurrences with insufficient green). Future work will include adjusting the algorithm to improve service on side streets and expanding P2P adaptive control to additional signals expected to be constructed in the area.

Use of Big Data to Evaluate and Improve Performance of Traffic Signal Systems in Resource-Constrained Countries

Deployment of an adaptive area traffic control system is expensive; physical sensors require installation, calibration, and regular maintenance. Because of the high level of technical and financial resources required, area traffic control systems found in developing countries often are minimally functioning. In Cebu City, Philippines, for example, the Sydney Coordinated Adaptive Traffic System was installed before 2000, and fewer than 35% of detectors were still functioning as of January 2015. To address this challenge, a study was designed to determine whether taxi company GPS data are sufficient to evaluate and improve traffic signal timing plans in resource-constrained environments. If this work is successful, the number of physical sensors required to support those systems may be reduced and thereby substantially lower the costs of installation and maintenance. Taxi GPS data provided by a regional taxi-hailing app were used to design and implement methodologies for evaluating the performance of traffic signal timing plans and for deriving updated fixed-dynamic plans, which are fixed plans (with periods based on observable congestion patterns rather than only time of day) iterated regularly until optimization is reached. To date, three rounds of iterations have been conducted to ensure the stability of the proposed signal timings. Results of exploratory analysis indicate that the algorithm is capable of generating reasonable green time splits, but cycle length adjustment must be considered in the future.

Visualizations of Travel Time Performance Based on Vehicle Reidentification Data

This paper provides a visual reference of the breadth of arterial performance phenomena based on travel time measures obtained from reidentification technology that has proliferated in the past 5 years. These graphical performance measures are revealed through overlay charts and statistical distribution as revealed through cumulative frequency diagrams (CFDs). With overlays of vehicle travel times from multiple days, dominant traffic patterns over a 24-h period are reinforced and reveal the traffic behavior induced primarily by the operation of traffic control at signalized intersections. A cumulative distribution function in the statistical literature provides a method for comparing traffic patterns from various time frames or locations in a compact visual format that provides intuitive feedback on arterial performance. The CFD may be accumulated hourly, by peak periods, or by time periods specific to signal timing plans that are in effect. Combined, overlay charts and CFDs provide visual tools with which to assess the quality and consistency of traffic movement for various periods throughout the day efficiently, without sacrificing detail, which is a typical byproduct of numeric-based performance measures. These methods are particularly effective for comparing before-and-after median travel times, as well as changes in interquartile range, to assess travel time reliability.

Comparison of adaptive traffic control benefits for recurring and non‐recurring traffic conditions

When adaptive traffic control systems are deployed in the field, agencies install them for one of two reasons: (i) to cope with day-to-day traffic fluctuations and/or (ii) to address frequent but sudden cases with special traffic (incidents, inclement weather etc.). There is an ongoing debate among researchers in which of these two cases adaptive systems are more effective. Yet, no previous studies have examined this issue with enough rigour and attention. This study compares InSync adaptive traffic control system to three conventional time-of-day (TOD) signal timing plans in a microsimulation environment to address aforementioned research question. A comprehensive set of scenarios and performance measures has been prepared to evaluate, in microsimulation, operations of an adaptive system InSync on a 12-intersection corridor along SR-421 in Volusia County, FL. The Vissim model of SR-421 was carefully calibrated and validated to resemble the field conditions. The findings show that InSync outperformed the TOD signal timing plans on various spatial levels and its performance made bigger savings in the case of special non-recurring traffic conditions.

Pareto front–based multi-objective real-time traffic signal control model for intersections using particle swarm optimization algorithm

Real-time traffic control is very important for urban transportation systems. Due to conflicts among different optimization objectives, the existing multi-objective models often convert into single-objective problems through weighted sum method. To obtain real-time signal parameters and evaluation indices, this article puts forward a Pareto front–based multi-objective traffic signal control model using particle swarm optimization algorithm. The article first formulates a control model for intersections based on detected real-time link volumes, with minimum delay time, minimum number of stops, and maximum effective capacity as three objectives. Moreover, this article designs a step-by-step particle swarm optimization algorithm based on Pareto front for solution. Pareto dominance relation and density distance are employed for ranking, tournament selection is used to select and weed out particles, and Pareto front for the signal timing plan is then obtained, including time-varying cycle length and split. Finally, based on actual survey data, scenario analyses determine the optimal parameters of the particle swarm algorithm, comparisons with the current situation and existing models demonstrate the excellent performances, and the experiments incorporating outliers in the input data or total failure of detectors further prove the robustness. Generally, the proposed methodology is effective and robust enough for real-time traffic signal control.

Traffic signal timing via deep reinforcement learning

In this paper, we propose a set of algorithms to design signal timing plans via deep reinforcement learning. The core idea of this approach is to set up a deep neural network (DNN) to learn the Q-function of reinforcement learning from the sampled traffic state/control inputs and the corresponding traffic system performance output. Based on the obtained DNN, we can find the appropriate signal timing policies by implicitly modeling the control actions and the change of system states. We explain the possible benefits and implementation tricks of this new approach. The relationships between this new approach and some existing approaches are also carefully discussed.

Adaptive Traffic Controller Based On Pre-Timed System

Adaptive traffic controller systems based on image processing have been developed widely. Nevertheless, in a developing country, the systems often could not be easily applied because all types of vehicle use the same road. Therefore, to overcome the problem, the new concept of the systems is proposed. The systems were developed from a pre-timed traffic controller system that based on AVR microcontroller. By default, the systems use the signal-timing plans to control the vehicle flow. To accommodate the traffic variations, a new method of vehicle detection has been built. The method calculated an intensity histogram standard deviation of the image representing a detection area to determine traffic density of each intersection lane. The systems modified the green-time of each lane based on the traffic density. The method could detect all types of vehicles and work properly in a day and a night time.

Modeling Evaluation of Eco–Cooperative Adaptive Cruise Control in Vicinity of Signalized Intersections

Vehicle stops caused by traffic signals reduce vehicle fuel economy ratings along arterial roadways. Eco–cooperative adaptive cruise control (eco-CACC) systems are being developed in an attempt to improve vehicle fuel efficiency in the vicinity of signalized intersections. These eco-CACC systems utilize traffic signal phasing and timing data received through vehicle-to-infrastructure communication, together with vehicle queue predictions, to compute fuel-optimum vehicle trajectories that are continuously updated as the vehicle travels in the vicinity of signalized intersections. The algorithm computes a desired speed for the vehicle that is either displayed to the driver or directly integrated into the vehicle’s adaptive cruise control system. In this paper, the INTEGRATION microscopic traffic assignment and simulation software is used to evaluate the performance of a proposed eco-CACC algorithm to assess its networkwide energy and environmental impacts. A simulation sensitivity analysis demonstrates that as the market penetration rate of CACC-equipped vehicles increases, the energy and environmental benefits also increase, and that the overall savings in fuel consumption are as high as 19% when the market penetration rate is 100%. On multilane roads, the algorithm may produce networkwide increases in the fuel consumption level when the market penetration rate is less than 30%. The analysis also demonstrates that the length of control segments, the signal phasing and timing plan, and the traffic demand levels significantly affect the algorithm performance. The study further demonstrates that the algorithm may produce increases in fuel consumption levels when the network is oversaturated; thus, further work is needed to enhance the algorithm for these conditions.

Development of Vehicle Delay and Queue Length Models for Adaptive Traffic Control at Signalized Roundabout

Vehicle delay and queue length models are important indexes to optimize signal timing plan for a signalized roundabout. However, at present much attention is paid on unsignalized roundabouts. In this paper, we firstly analyzed the impacts of phasing schemes on vehicle movements and brought forward two typical phasing schemes. The loop detector layout plan was established to detect vehicle volumes of different streams in real time. Then under each phasing scheme, the models for average vehicle delay and queue length were developed respectively. Finally, case study was conducted to evaluate the models using field data collected from a real signalized roundabout. The results show that: all precision errors are smaller than 15% and average precision errors are smaller than 10%. The developed two types of models can satisfy the requirements of engineering.

Improving signalised intersections performance for better built environment

Traffic delays are still a significant part of the daily commutes in most urban areas despite significant investments in infrastructure. Policy, design and operational measures are usually taken to reduce emissions from mobile sources by reducing congestion, promoting modal diversity, or using advanced technologies. Although reduced emissions and improved fuel efficiency have resulted from proper traffic control, municipalities and public agencies often overlook the need for better traffic control through revised control schemes. Simple changes to traffic control schemes and/or incremental geometric designs improvements is all that is needed to improve traffic system performance without the need for expensive and highly advanced technologies. In this paper, the issue of using proper signal timing plans at signalised intersections and its effect on traffic operations and environment is highlighted. A real-life example is studied. Careful considerations to signal timings parameters resulted in lower traffic delay, reduced emissions, and better fuel consumption efficiency.

Optimal Cycle-Length Formulas for Intersections With or Without Transit Signal Priority

Webster’s formula for optimal green split and cycle length is a common way to optimize signal settings at signalized intersections. However, these equations are intended to be used for optimizing signal timing plans for fixed-time isolated intersections, balancing phase utilization and minimizing car delay for undersaturated conditions. These formulas are less appropriate for near-saturated and oversaturated conditions because of the asymptotical behavior of the cycle-length formula when the intersection flow ratio approaches one. These formulas were not conceived for operation under transit signal priority (TSP) strategies. The main objective of this paper is to develop formulas for the optimal cycle length that can be applied when mild TSP strategies, such as green extension or green extension with phase rotation, are implemented or when the intersection operates under near-saturated or oversaturated conditions. A formula for optimal cycle length is developed as a function of lost time and intersection flow ratio. The formula is applicable both with and without TSP. It appears that for green split, no new formulas are required because the Webster equisaturation approach is close to optimal. The cycle-length formula is validated for undersaturated, near-saturated, and oversaturated conditions with and without TSP for two-, three-, and four-phase signalized intersections. Compared with Webster’s formula, the proposed formula shows a significant improvement in reducing person delay. Compared with Webster’s formula, average person delay is 6%, 17%, and 22% less and average bus delay is 16%, 27%, and 35% less for two-, three- and four-phase signalized intersections, respectively.

Optimization of Signal Timing at Critical Intersections for Evacuation

Aimed to make the evacuated vehicles leave the evacuation zone as quickly as possible, a bi-level programming model is proposed to determine the signal timing at critical intersections (CI) by means of dynamic traffic simulation. The upper-level designs the optimal signal timing plan with the objective that minimize delays of critical intersection approaches inside of the evacuation zone based on known traffic distribution, while the lower-level simulates the dynamic propagation process of evacuated vehicles in the evacuation zone based on CTM and the principle of User Equilibrium. To demonstrate the effectiveness of the proposed method, we apply the model and algorithm based on the survey of parking lot, the urban street network around Nanjing Olympic Sports Center, to obtain the traffic volume of each road section, arrival distributions of evacuated vehicles at CIs, and optimal signal timing plan of each CI. It can be seen that the proposed method can effectively solve the integrated problems of signal timing optimization and dynamic traffic simulation.

Integrated optimization of location and signal timings for midblock pedestrian crosswalk

SummaryFinding the optimal location and signal timing plan is one of the most critical operational issues for a signalized midblock crosswalk on an arterial section, which is increasingly being installed in highly populated areas in developing countries such as China. This paper presents a multiobjective optimization model and an efficient solution algorithm for a one‐ or two‐stage midblock crosswalk on an arterial section. The proposed model aims to produce the optimal location and corresponding signal settings to balance the trade‐off between pedestrian delays and vehicular bandwidth when the signals of the crosswalk and adjacent intersections are coordinated. The proposed model has three distinguishing features: (i) the costs for both pedestrians and vehicles are considered in a unified framework; (ii) the location and signal settings of the midblock crosswalk are simultaneously optimized; and (iii) a multiobjective optimization approach is developed to study the effectiveness of the midblock crosswalk under conditions in which the priorities between pedestrian and vehicle flows differ. A nondominated sorting genetic algorithm II (NSGA II)‐based algorithm is developed to solve the model efficiently. The results of the case study showed that the proposed model would help traffic practitioners, researchers, and authorities properly locate and signalize a one‐ or two‐stage midblock pedestrian crosswalk on an arterial section. Copyright © 2015 John Wiley & Sons, Ltd.

Impact of transition between signal timing plans in social cost based in delay, fuel consumption and air emissions

Significant effects of traffic congestion include the cost associated with extra travel time, fuel consumption, and gas emissions. This paper develops a mathematical function to quantify the monetary impact of transition designs between signal timing plans on users and the environment. This function offers an approach to reduce problems such as excessive travel time, pollution emissions and fuel consumption. The proposed social cost function is evaluated for various transition plans to assess the impact of the number of steps required to adjust signal timing. The relationships between delay, fuel consumption and gas emissions and the number of steps needed to achieve the transition are also analysed.