Traffic Signal Changes Research Articles

Graph Model for Traffic Control in Surakarta

Surakarta City is the most populous city in Central Java that has 64 traffic signals to regulate traffic. Drivers' travel time in Surakarta City is heavily influenced by the amount of time they spend waiting for their turn to move at each traffic signals they pass. A time setting for traffic signal changes needs to be designed to optimize each driver's travel time on several routes in Surakarta City. Once the traffic has been researched and modeled, a time setting can be created. This study shows a graph model that represent roadways in Surakarta. The Dijkstra algorithm was used in this study to optimize the routes represented in the graph by finding all the shortest routes between two traffic signals points.

ADAPTIVE TRAFFIC CONTROL AT THE INTERSECTION

The solution to the problem is a typical technology of a smart cross-logic system. Due to the rapid growth in the number of cars in the city, various problems such as congestion and environmental pollution are increasingly emerging.
 Adaptive intersection management system, often implemented to solve the problem of congestion in the city. Constantly used control systems are usually considered illogical, that is, the traffic light signal changes statically. The purpose of this work is to present an adaptive system of logical control of the intersection, i.e. changing traffic light signals in real time depending on the type of vehicles.
 This system is an example of an adaptive automotive communication model that implements data representation between vehicles and traffic lights. When crossing with cars at an intersection, it sends an instant message to the traffic light, after which the traffic light corrects the information and adjusts the signal time in real time. Each traffic light in both directions has its own management strategy, without requiring orthogonality. This traffic light control system allows you to maximize the number of cars crossing the intersection and, as a result, reduce congestion and pollution. The algorithm of traffic light control based on the type of car and its modeling is proposed. The safety and reliability of this control system are also discussed.

Active Traffic Signal Decisions Using Vector‐Matrix Multiplication

A novel methodology in the manner of vector‐matrix multiplication (VMM) architecture is suggested for intelligently determining traffic signal changes to enhance the flow of urban traffic. Unlike the conventional prediction‐based traffic model, a real‐time decision model considering the traffic density at each transport section is established, which simplifies the traffic signal decision process as a convolutional transformation. Compared with a periodically repetitive signal changing system, the suggested VMM system actively optimizes the signal configuration in an irregular shape according to the traffic density distribution, resulting in reduction in the time cost with highly improved decision efficiency. With this system based on particle dynamics, the travel time is reduced by ≈10% at the same pass ratio for different road structures (one‐way, bidirectional, and intersectional transport). The pass ratio and resulting flow dynamics can be controllable using the different transformation matrix selections according to the traffic conditions. In addition, the analog conductance of the memristor device to the transformation matrix elements is applied, maintaining its reduction rate with a deviation tolerance of the VMM process up to ≈50%. It is believed that VMM‐based signal decision platform can lead to great progress for fast and efficient transport in complex urban traffic networks.

Determining Yellow Change and Clearance Intervals for Left-Turning Phases: Evaluation of the Current Guidelines with Connected Vehicle Data

In March 2020, the Institute of Transportation Engineers (ITE) published new guidelines for determining traffic signal change and clearance intervals that included using an extended kinematic equation for left turns. Whereas previous guidelines assumed constant speed for all vehicles approaching an intersection, this new equation accounted for left-turning vehicles needing more yellow time because they decelerate (assuming a maximum safe and comfortable rate) before making turning maneuvers. This paper evaluates these guidelines using real-world vehicle trajectories from the Ann Arbor Connected Vehicle Test Environment (AACVTE). These trajectories confirm that free-flowing left-turning vehicles decelerate, but deceleration usually starts at a moderate rate before reaching the critical distance and continues to the middle of the intersection. Vehicles then accelerate to a departure speed at the clearance point. Since the goal of the yellow change interval is to eliminate the dilemma zone such that a free-flowing vehicle can safely traverse the critical distance, these observations imply that the extended kinematic equation will overestimate the required duration for the following two reasons: (1) the critical distance is shortened by the speed reduction before the braking point; (2) the average traversing speed is higher as vehicles usually decelerate at a moderate rate instead of the maximum rate. The equation will underestimate the clearance interval as the average traversing speed through the intersection is slower than the intersection entry speed. We propose a new left-turn kinematic equation for determining yellow change and clearance intervals, and the results are validated from the observed vehicle trajectories.

Impact of COVID-19 on Traffic Signal Systems: Survey of Agency Interventions and Observed Changes in Pedestrian Activity.

The COVID-19 pandemic, the most significant public health crisis since the 1918-1919 influenza epidemic, is the first such event to occur since the development of modern transportation systems in the twentieth century. Many states across the U.S. imposed lockdowns in early spring 2020, which reduced demand for trips of various types and affected transportation systems. In urban areas, the shift resulted in a reduction in traffic volumes and an increase in bicycling and walking in certain land use contexts. This paper seeks to understand the changes occurring at signalized intersections as a result of the lockdown and the ongoing pandemic, as well as the actions taken in response to these impacts. The results of a survey of agency reactions to COVID-19 with respect to traffic signal operations and changes in pedestrian activity during the spring 2020 lockdown using two case study examples in Utah are presented. First, the effects of placing intersections on pedestrian recall (with signage) to stop pedestrians from pushing the pedestrian button are examined. Next, the changes in pedestrian activity at Utah signalized intersections between the first 6 months of both 2019 and 2020 are analyzed and the impact of land use characteristics is explored. Survey results reveal the importance of using technologies such as adaptive systems and automated traffic signal performance measures to drive decisions. While pedestrian pushbutton actuations decreased in response to the implementation of pedestrian recalls, many pedestrians continued to use the pushbutton. Pedestrian activity changes were also largely driven by surrounding land uses.

A multiagent systems with Petri Net approach for simulation of urban traffic networks

This paper presents a novel model framework for complex urban traffic systems based on the interconnection of a dynamical multi-agent system in a macroscopic level. The agents describe all the types of street segments, intersections, sources and sinks of cars, modelling the behavior of the flow of vehicles through them as simple differential equations. These agents include the phenomena of changes in the flow rate due to congestions, traffic signals and the density of the vehicles. Traffic signal changes are obtained by the evolution of Petri Nets, in order to represent a more real behavior. Therefore, a complex network can be constructed by the interconnection of the agents, in continuous time, and the Petri Nets, in a discrete-event behavior, becoming a hybrid and scalable system. In order to analyze the performance of the approach, a real set of streets and intersections in Montevideo City is studied. Also, the approach is compared with a simulation realized in the software TSIS-CORSIM, which contains real data of density of vehicles. The multi-agent system achieves comparable results, taking into account the differences in the level of details respect to TSIS-CORSIM. Thus, the results can represent the most important issues of vehicular traffic with less computational resources.

Predicting Coordinated Actuated Traffic Signal Change Times using Long Short-Term Memory Neural Networks

Vehicle acceleration and deceleration maneuvers at traffic signals result in significant fuel and energy consumption levels. Green light optimal speed advisory systems require reliable estimates of signal switching times to improve vehicle energy/fuel efficiency. Obtaining these estimates is difficult for actuated signals where the length of each green indication changes to accommodate varying traffic conditions and pedestrian requests. This study details a four-step long short-term memory (LSTM) deep learning based methodology that can be used to provide reasonable switching time estimates from green to red and vice versa while being robust to missing data. The four steps are data gathering, data preparation, machine learning model tuning, and model testing and evaluation. The input to the models includes controller logic, signal timing parameters, time of day, traffic state from detectors, vehicle actuation data, and pedestrian actuation data. The methodology is applied and evaluated on data from an intersection in Northern Virginia. A comparative analysis is conducted between different loss functions including the mean squared error, mean absolute error, and mean relative error used in LSTM and a new loss function that is proposed in this paper. The results show that while the proposed loss function outperforms conventional loss functions in overall absolute error values, the choice of the loss function is dependent on the prediction horizon. Specifically, the proposed loss function is slightly outperformed by the mean relative error for very short prediction horizons (less than 20 s) and the mean squared error for very long prediction horizons (greater than 120 s).

Using Stochastic Variation of Cyclic Green Distributions to Populate SAE J2735 Message Confidence Values along a Signalized Corridor

The communication between connected vehicles and traffic signal controllers is defined in SAE Surface Vehicle Standard J2735. SAE J2735 defines traffic signal status messages and a series of 16 confidence levels for traffic signal transitions. This paper discusses a statistical method for tabulating traffic signal data by phase and time of day and populating the SAE J2735 messages. Graphical representations of the red–green and green–yellow transitions are presented from six intersections along a 4-mile corridor for five different time-of-day timing plans. The case study provided illustrates the importance of characterizing the stochastic variation of traffic signals to identify locations, phases, and time-of-day periods when traffic indications operate with high predictability. Specific cases, such as low vehicle demand and occasional actuation of pedestrian phases are highlighted as situations that may reduce the predictability of traffic signal change intervals. The results from this study also open up discussion among transportation professionals on the importance of consistent tabulation of confidence values for both beginning and end of green signal states. We believe this paper will initiate dialog on how to consistently tabulate important data elements transmitted in SAE J2735 and perhaps refine those definitions. The paper concludes by highlighting the importance of traffic engineers and connected vehicle developers to work together to develop shared visions on traffic signal change characteristics so that the in-vehicle use cases and human–machine interface meet user expectations.

A Modified Cellular Automaton Model for Accounting for Traffic Behaviors during Signal Change Intervals

Previous cellular automata (CA) models have been developed for simulating driver behaviors in response to traffic signal control. However, driver behaviors during traffic signal change intervals, including cross/stop decision and speed adjustment, have not yet been studied. Based on this, this paper aims to propose a change interval CA model for replicating driver’s perception and response to amber light based on stopping probability and speed adjusting functions. The proposed model has been validated by exemplified and field cases. To investigate the applicability of the proposed model, macroscopic and microscopic analyses are conducted. Although the macroscopic fundamental diagram analysis reveals only a small decrease in maximum traffic flow rates with considering driver behaviors in change intervals, in the microscopic analysis, the proposed model can present reasonable vehicular trajectories and deceleration rates during slowdown process.

Deployment and Field Evaluation of In-Vehicle Traffic Signal Advisory System (ITSAS)

This research evaluates the impact of In-vehicle Signal Advisory System (ITSAS) on signalized arterial. ITSAS provides individual drivers equipped with a mobile communication device with advisory speed information enabling to minimize the time delay and fuel consumption when crossing intersection. Given the instantaneous vehicular driving information, such as position, speed, and acceleration rate, ITSAS produces advisory speed information by taking into consideration the traffic signal changes at a downstream intersection. The advisory speed information includes not only an optimal speed range updated every 300-ft for individual drivers but also a descriptive message to warn drivers stop to ensure safety at the downstream intersection. Unlike other similar Connected Vehicles applications for intersection management, ITSAS does not require Roadside Equipment (RSE) to disseminate the advisory speed information as it is designed to exploit commercial cellular network service (i.e., 3G and 4G-LTE). Thus, ITSAS can be easily plugged into existing traffic control management system to rapidly conduct its implementation without significant additional cost. This research presents the field evaluations of ITSAS on a signalized corridor in New Jersey, which discovered significant travel time savings for the equipped vehicle.

차량 IT 기반 의사결정 지원을 위한 교차로 신호 사전예보 시스템에 관한 연구

This paper proposed a Prior Forecast System of Crossroad Traffic Information that allows vulnerable road users to perceive traffic signal changes beforehand and respond in concentrated traffic areas such as crossroads. Also, it mapped out information linking procedures of the proposed system and it designed a PHY based wireless communication simulator to verify the operational feasibility of wireless communications. Conclusively, by conducting performance analysis according to crossroad channel environments using the simulator designed in this paper, it verified that a Prior Forecast System of Crossroads Traffic Information service for vehicle IT based decision-making support for vulnerable road users is a possibility.

Designing Traffic Signal Yellow and Change Intervals considering Truck Impacts

This research quantifies the effect of heavy vehicles on the design of yellow and all-red intervals. With empirical data from previous studies and truck performance characteristics, models are developed to simulate driver perception–reaction times and truck deceleration levels at signalized intersections. These models consider roadway surface and weather conditions (dry, fog, or rain); driver attributes (age and gender); roadway grade; approach speed; and time and distance to the intersection at the onset of the yellow indication. An agent-based approach is used to develop driver- and vehicle-specific yellow times. The yellow timings for light-duty and heavy-duty vehicles are then combined and consider different percentages of heavy vehicles in the traffic stream. Lookup tables are developed for grades, precipitation types and levels, and percentages of trucks to provide practical guidelines for the design of yellow and all-red signal timings. In general, the research demonstrates that adding trucks to the traffic stream increases yellow times by 2% to 20%, depending on approach speed, grade, and weather conditions, with the most significant increases occurring on dry roadways with high positive gradients at lower speeds. In absolute terms, the yellow time is increased from approximately 0.1 to 0.6 s when trucks constitute 5% of the traffic stream to 0.6 to 1.4 s when trucks are 30% of the traffic stream. The study also demonstrates a need to increase the all-red time by up to 0.5 s when trucks comprise 30% of the traffic stream.

Safety impacts of signal-warning flashers and speed control at high-speed signalized intersections

For many years, to reduce the crash frequency and severity at high-speed signalized intersections, warning flashers have been used to alert drivers of potential traffic-signal changes. Recently, more aggressive countermeasures at such intersections include a speed-limit reduction in addition to warning flashers. While such speed-control strategies have the potential to further improve the crash-mitigation effectiveness of warning flashers, a rigorous statistical analysis of crash data from such intersections has not been undertaken to date. This paper uses 10-year crash data from 28 intersections in Nebraska (all with intersection approaches having signal-warning flashers; some with no speed-limit reduction, and the others with either 5mi/h or 10mi/h reduction in speed limit) to estimate a random parameters negative binomial model of crash frequency and a nested logit model of crash-injury severity. The estimation findings show that, while a wide variety of factors significantly influence the frequency and severity of crashes, the effect of the 5mi/h speed-limit reduction is ambiguous—decreasing the frequency of crashes on some intersection approaches and increasing it on others, and decreasing some crash-injury severities and increasing others. In contrast, the 10mi/h reduction in speed limit unambiguously decreased both the frequency and injury-severity of crashes. It is speculated that, in the presence of potentially heterogeneous driver responses to decreased speed limits, the smaller distances covered during reaction time at lower speeds (allowing a higher likelihood of crash avoidance) and the reduced energy of crashes associated with lower speed limits are not necessarily sufficient to unambiguously decrease the frequency and severity of crashes when the speed-limit reduction is just 5mi/h. However, they are sufficient to unambiguously decrease the frequency and severity of crashes when the speed-limit reduction is 10mi/h. Based on this research, speed-limit reductions in conjunction with signal-warning flashers appear to be an effective safety countermeasure, but only clearly so if the speed-limit reduction is at least 10mi/h.

Designing Yellow Intervals for Rainy and Wet Roadway Conditions

The research presented in this paper quantifies and models the impact of wet pavement surface and rainy weather conditions on driver perception-reaction times (PRTs), deceleration levels, and traffic signal change interval durations. A total of 648 stop-run records were collected as part of the research effort for a 72 km/h (45 mi/h) approach speed where participant drivers encountered a yellow indication initiation at different distances from the intersection. The participant drivers were randomly selected in different age groups (under 40 years old, 40 to 59 years old, and 60 years of age or older) and genders (female and male). Using the gathered data, statistical models for driver PRT and deceleration levels were developed, considering roadway surface and environmental parameters, driver attributes (age and gender), roadway grade, approaching speed, and time and distance to the intersection at the onset of yellow. Inclement weather yellow timings were then developed and summarized in lookup tables as a function of different factors (driver age/gender, roadway grade, speed limit, precipitation level, and roadway surface condition) to provide practical guidelines for the design of yellow signal timings in wet and rainy weather conditions. The results indicate that wet roadway surface conditions require a 5 percent increase in the change interval and that rainy conditions require a 10 percent or more increase in the duration of the change interval. These recommended change durations can also be integrated within the Vehicle Infrastructure Integration (VII) initiative to provide customizable driver warnings prior to a transition to a red indication.

Evaluation of Two Treatments for Reducing Crashes Related to Traffic Signal Change Intervals

Two related evaluation studies that were conducted under NCHRP Project 17–35 are presented. The research objective was to use the most appropriate analytical methods and the best available data to build on previous research in developing crash modification factors (CMFs) for two treatments for reducing crashes related to traffic signal change intervals: modifying the change interval and installing dynamic signal warning flashers. Three evaluation methods were used as appropriate—the empirical Bayes before–after method, the comparison group before–after method, and cross-sectional multiple regression models. A secondary objective of using cross-sectional models for some evaluations was to examine the comparability of before–after and cross-sectional studies, a subject of topical interest in CMF development. There was a general safety benefit to installing dynamic signal warning flashers, with indications that crash reductions could be obtained overall and for several crash types, including injury, angle, and heavy-vehicle crashes. For the change-interval modification, the before–after study results showed significant reductions (at the 5% level) in total, injury, and rear-end crashes under various scenarios. For both treatments, the results from the cross-sectional analysis were relatively consistent with those from the before–after analysis.

Urban aerosol particulates on Hong Kong roadsides: size distribution and concentration levels with time

A detailed study on particulate matter from vehicle emission was carried out at roadsides in Hong Kong downtown area. The study aims to explore the variations of particulate matter at roadsides in morning and afternoon. Data of concentrations of different size groups of particulate matter were collected and analyzed. It was found that the particulate levels generally vary periodically with traffic signal changes. During the green traffic light period, concentrations of the particulate matter increase to peak point and then diffuse to a relatively stable level in the red traffic light period. Such stable level is regarded as background level, to which pedestrians are exposed when they walk-by and cross the zebra zones. To analyze and further explore the collected data, a statistical distribution model, i.e., goodness-of-fit test, was employed. It was noticed that the lognormal distribution best fits the particulate matter data in both morning and afternoon. In addition, the non-parametric test was also used to assess the differences between morning and afternoon data. The results show that both data sets statistically differ from each other at 5% significance level. It can be deduced that the change of traffic volume, humidity and wind speed between morning and afternoon may cause this difference.

Theory and applications of a new methodology for the random sequential probability ratio test

Wald’s [A. Wald, Sequential Analysis, Wiley, New York, 1947] sequential probability ratio test (SPRT) and group sequential probability ratio test (GSPRT) remain relevant in addressing a wide range of practical problems. The area of clinical trials owes a great debt to the theory and methodologies of SPRT and GSPRT. In recent years, there has been a surge of practical applications of these methodologies in many areas including low frequency sonar detection, tracking of signals, early detection of abrupt changes in signals, computer simulations, agricultural sciences, pest management, educational testing, economics, and finance. But, obviously, there are circumstances where sampling one observation at a time may not be practical. In contexts of monitoring “inventory” or “queues”, observations may appear sequentially but in groups where the group sizes may be ideally treated as random variables themselves. For example, one may sequentially record the number of stopped cars ( M i ) and the number of cars ( ∑ j = 1 M i X i j ) without “working brake lights” when a traffic signal changes from green to red, i = 1 , 2 , … . This can be easily accomplished since every “working brake light” must glow bright red when a driver applies brakes. In this example, one notes that (i) it may be reasonable to model M i ’s as random, but (ii) it would appear impossible to record data sequentially on the status of brake lights (that is, X i j = 0 or 1) individually for car #1, and then for car #2, and so on when a group of cars rush to stop at an intersection! In order to address these kinds of situations, we start with an analog of the concept of a best “fixed-sample-size” test based on data { M i , X i 1 , … , X i M i , i = 1 , … , k } . Then, a random sequential probability ratio test (RSPRT) is developed for deciding between a simple null hypothesis and a simple alternative hypothesis with preassigned Type I and II errors α , β . The RSPRT and the best “fixed-sample-size” test with k ≡ k min associated with the same errors α , β are compared. Illustrations of RSPRT include a one-parameter exponential family followed by substantive computer simulations that lead to a broad set of guidelines. Two real applications and data analysis are highlighted.

Driver Perception Response Time During the Signal Change Interval

The importance of perception response time (PRT) values for traffic signal change interval design, and the need to monitor the design PRT value, are challenges facing transportation professionals. However, current methods used to validate the design PRT value from on-site observational studies have failed to yield convincing proof that the 1 second design value is adequate. A modification to on-site data capture and extraction, using a transitional zone (TZ), is used to overcome this deficiency. The TZ allows the systematic identification and exclusion of those drivers who have time to delay their responses. Their inclusion in past studies inflated PRT values. Their exclusion in this study provides a more accurate value.

Changes in crash risk following re-timing of traffic signal change intervals

More than 1 million motor vehicle crashes occur annually at signalized intersections in the USA. The principal method used to prevent crashes associated with routine changes in signal indications is employment of a traffic signal change interval — a brief yellow and all-red period that follows the green indication. No universal practice exists for selecting the duration of change intervals, and little is known about the influence of the duration of the change interval on crash risk. The purpose of this study was to estimate potential crash effects of modifying the duration of traffic signal change intervals to conform with values associated with a proposed recommended practice published by the Institute of Transportation Engineers. A sample of 122 intersections was identified and randomly assigned to experimental and control groups. Of 51 eligible experimental sites, 40 (78%) needed signal timing changes. For the 3-year period following implementation of signal timing changes, there was an 8% reduction in reportable crashes at experimental sites relative to those occurring at control sites ( P=0.08). For injury crashes, a 12% reduction at experimental sites relative to those occurring at control sites was found ( P=0.03). Pedestrian and bicycle crashes at experimental sites decreased 37% ( P=0.03) relative to controls. Given these results and the relatively low cost of re-timing traffic signals, modifying the duration of traffic signal change intervals to conform with values associated with the Institute of Transportation Engineers’ proposed recommended practice should be strongly considered by transportation agencies to reduce the frequency of urban motor vehicle crashes.

Evaluation of Innovative Bike-Box Application in Eugene, Oregon

An innovative “bike box”—a right-angle extension to a bike lane (BL) at the head of the intersection—was installed with accompanying traffic signs but no extra traffic signals at a busy downtown intersection featuring two one-way streets in Eugene, Oregon, in summer 1998. The box allows bicyclists traveling to the intersection in a left side BL to get to the head of the traffic queue on a red traffic signal indication and then proceed ahead of motor vehicle traffic toward a right side BL when the traffic signal changes to green. Cyclists traveling through the intersection were videotaped before and after placement of the box. The videotapes were coded to evaluate operational behaviors and conflicts with motorists, other bicyclists, and pedestrians. Twenty-two percent of the bicyclists who approached in the left side BL and then crossed to the BL on the right side of the street (the bicyclists for whom the box was most intended) used the box. Many more bicyclists in this target group could have used the box (i.e., they had a red signal indication and enough time to move into the box). A problem with motor vehicle encroachments into the box likely diminished the frequency of use. The rate of conflicts between bicycles and motor vehicles changed little in the before and after periods. No conflicts took place while the bike box was being used as intended.