Real-time Signal Timing Research Articles

Intelligent Traffic Management System with Adaptive Signal Control and Emergency Vehicle Prioritization

Abstract: Efficient traffic management is essential for ensuring smooth flow and minimizing congestion on roadways. In this project, we propose an Arduino-based solution that utilizes IR sensors for real-time traffic monitoring and signal timing adjustment, coupled with a Bluetooth module to prioritize emergency vehicles during critical situations. The IR sensors are deployed at intersections to monitor vehicle presence, enabling real-time traffic monitoring. The Arduino Uno microcontroller processes the sensor data and dynamically adjusts signal timings to ensure smooth traffic flow. Additionally, a Bluetooth module facilitates communication between the traffic management unit and ambulance vehicles, triggering an alert signal to clear the way for emergencies. The system's adaptability allows for easy integration into existing infrastructure. Results indicate improved traffic efficiency, reduced congestion, and enhanced emergency vehicle response times

Using CNN-LSTM to predict signal phasing and timing aided by High-Resolution detector data

This paper proposes a real-time signal timing prediction based on deep learning algorithms that takes various traffic flow parameters as input and predicts signal timing parameters. Signal retiming methods have been traditionally used to improve traffic flow at intersections by reducing delay and improving the level of service at signalized intersections. This can often be a lengthy process that includes several iterations of various optimization methods. In this paper, we have used detector data to calculate the traffic flow metrics at several intersections. This processed data is then used to predict the signal timing and phasing for the next six cycles with reasonable accuracy. Seventeen intersections from two distinct corridors have been used in this study. One of the corridors runs adaptive signal control and the other corridor runs actuated signal control. The proposed CNN-LSTM model shows that cycle length can be accurately predicted with an MAE of 7 to 16 s and phase duration can be predicted with an MAE of 3 to 8 s. The trained model was also successfully validated at five different unknown intersections.

Use of Real-Time Traffic and Signal Timing Data in Modeling Occupant Injury Severity at Signalized Intersections

This study explored the use of real-time traffic events and signal timing data to determine the factors influencing the injury severity of vehicle occupants at intersections. The analysis was based on 3 years (2017–2019) of crash and high-resolution traffic data. The best fit regression was first identified by comparing the conventional regression model and logistic models with random effect. The logistic model with a heavy-tailed distribution random effect best fitted the data set, and it was used in the variable assessment. The model results revealed that about 13.6% of the unobserved heterogeneity comes from site-specific variations, which underlines the need to use the logistic model with a random effect. Among the real-time traffic events and signal-based variables, approach delay and platoon ratio significantly influenced the injury severity of vehicle occupants at 90% Bayesian credible interval. Additionally, the manner of a collision, occupant seat position, number of vehicles involved in a crash, gender, age, lighting condition, and day of the week significantly affected the vehicle occupant injury. The study findings are anticipated to provide valuable insights to transportation agencies for developing countermeasures to mitigate the crash severity risk proactively.

Signal Parameters Estimation and Optimization Using Mobile Navigation Data

In the management and evaluation of traffic network, signal parameters are important for monitoring and evaluating the operation state and the traffic capacity of intersection. However, a wide range of real-time signal timing schemes lacks a clear and effective method. In this paper, we propose the signal parameter calculation method based on mobile navigation data. Then, the possibility of crossing intersection passing time of the stop line is studied. The time differences between passing times of different cycles are distributed periodically that several peaks appear cycle by cycle. The relationship between sampling rate and relative error is discussed. Combined with the distribution peak normality test, the appropriate distribution peak is selected through the actual case. The cycle lengths and effective red time parameters are calculated and compared with the known signal parameters. The result demonstrates the proposed method has high accuracy and provides data support for the research of the traffic management.

An efficient heterogeneous platoon dispersion model for real-time traffic signal control

In China, urban arterial traffic presents a heterogeneous flow feature because of a high proportion of buses. Existing research on heterogeneous platoon dispersion models is very complex, which are not suitable for real-time signal timing optimization. Considering the classic Robertson’s model has a simple form and high computation efficiency, an efficient heterogeneous platoon dispersion model is proposed based on Robertson’s model. Firstly, the classic Robertson’s model is used to capture different homogeneous platoon dispersion features. Then, these classic Robertson’s models for homogeneous traffic flow are superposed to capture the characteristic of heterogeneous traffic flow. Finally, based on field observations, the performance of the proposed model, the classic Robertson’s model, and the mixed Gaussian model are compared. The results show that the proposed model has a better prediction accuracy and higher computational efficiency, compared with the classic Robertson’s model and the mixed Gaussian model. It is noteworthy that the proposed model not only better describes the dispersion feature of heterogeneous traffic flow but also meets the requirement of real-time signal control.

Intersection Operation with Non-Traditional Dynamic Lane Scheme through Vehicle-to-Signal Connection

This paper proposes an information-based dynamic lane (IDYL) scheme for signalized intersections with exclusive left-turn phases. Similar to the tandem design, the proposed scheme aims to increase the capacity of an isolated intersection by sorting incoming vehicles based on their turning movements. Its novelty is to guide vehicles of different movements into pre-designated dynamic lanes without stopping them via pre-signal. The assumption is that vehicles themselves or their drivers have access to, and can act on, real-time signal timing information through vehicle-to-signal connection to select the correct lane to enter as they approach the intersection. A mixed integer program is proposed to optimize jointly the lane configuration, timing plan, and dynamic lane utilization for an intersection. Results from numerical and simulation experiments show that IDYL can increase the reserve capacity by more than 25% when implemented on all legs of a standard four-leg intersection, and reduce the delay by around 15% when implemented on two opposing legs. The results from this study could help traffic engineers to operate signalized intersections with dynamic lanes when vehicle-to-signal connection becomes widely available in the near future.

A real-time signal control strategy at an isolated pedestrian crossing based on radar data

Fixed-time traffic signal control strategy in an isolated pedestrian crossing tends to reduce traffic capacity and expose vulnerable road users to more danger. To mitigate the negative impact of previous control strategy, this study proposed an optimal real-time signal timing strategy to protect pedestrian crossing and at the same time minimize the system-wide traffic delay. With the application of a wide-area radar data, the features of vehicles, pedestrians, and the passing time of non-motor vehicles and pedestrian were captured considering conflicts and traffic delay. The support vector machine for regression was utilized to hypothesize traffic delay by training. The discrete values of hypothetical passing time will be tested. The minimum value of delay can be recognized and the corresponding hypothetical passing time will be recommended as the green time for crossing. The performance of the proposed ORSTS outperformed the fixed-time traffic signal control strategy in reducing traffic delay by 22.3%.

High-Resolution Traffic Flow Prediction Model Based on Deep Learning

The traditional platoon dispersion model is based on the hypothesis of probability distribution, and the time resolution of the existing traffic flow prediction model is too big to be applied to the adaptive signal timing optimization. Based on the view of the platoon dispersion model, the relationship between vehicle arrival at downstream intersection and vehicle departure from the upstream intersection was analyzed. Then, the high-resolution traffic flow prediction model based on deep learning was proposed. The departure flow rate at the upstream was taking as the input and the arrival flow rate at downstream intersection was taking as the output in this model. Finally, the parameters of the proposed model were trained by the field survey data, and this model was implemented to predict the arrival flow rate of the downstream intersection. The result shows that the proposed model can better reflect the fluctuant characteristics of traffic flow and reduced the sum of the squared errors (SSE), MSE, and MAE by 13.17%, 13.21%, and 14.24%, compared with Robertson’s model. Thus, the proposed model can be applied for real-time adaptive signal timing optimization.

Utilizing bluetooth and adaptive signal control data for real-time safety analysis on urban arterials

Real-time safety analysis has been widely adopted to reveal the relationship between real-time traffic characteristics and crash occurrence, and these results could be applied to improve active traffic management systems and enhance safety performance. Most of the previous studies have been applied to freeways and seldom to arterials. This study attempts to examine the relationships between crash occurrence and real-time traffic and signal timing characteristics based on four urban arterials in Central Florida. Bayesian conditional logistic models (BCL) were developed by incorporating the Bluetooth, adaptive signal control, and weather data, which were extracted for a period of 20 min (four 5-minute intervals) before the time of crash occurrence. Model comparison results indicated that the model based on 5–10 min interval dataset performed the best. It revealed that the average speed, upstream left-turn volume, downstream green ratio, and rainy indicator were found to have significant effects on crash occurrence. Furthermore, Bayesian random parameters conditional logistic model (BRPCL) outperformed Bayesian random parameters logistic (BRPL) and Bayesian conditional logistic models (BCL) in terms of the area under the receiver operating characteristics curve (AUC) and Deviance Information Criterion (DIC) values. These results are important in real-time safety applications in the context of Integrated Active Traffic Management (IATM).

Approach-level real-time crash risk analysis for signalized intersections

Intersections are among the most dangerous roadway facilities due to the complex traffic conflicting movements and frequent stop-and-go traffic. However, previous intersection safety analyses were conducted based on static and highly aggregated data (e.g., annual average daily traffic (AADT), annual crash frequency). These aggregated data may result in unreliable findings simply because they are averages and cannot represent the real conditions at the time of crash occurrence. This study attempts to investigate the relationship between crash occurrence at signalized intersections and real-time traffic, signal timing, and weather characteristics based on 23 signalized intersections in Central Florida. The intersection and intersection-related crashes were collected and then divided into two types, i.e., within intersection crashes and intersection entrance crashes. Bayesian conditional logistic models were developed for these two kinds of crashes, respectively. For the within intersection models, the model results showed that the through volume from “A” approach (the traveling approach of at-fault vehicle), the left turn volume from “B” approach (near-side crossing approach), and the overall average flow ratio (OAFR) from “D” approach (far-side crossing approach), were found to have significant positive effects on the odds of crash occurrence. Moreover, the increased adaptability for the left turn signal timing of “B” approach and more priority for “A” approach could significantly decrease the odds of crash occurrence. For the intersection entrance models, average speed was found to have significant negative effect on the odds of crash occurrence. The longer average green time and longer average waiting time for the left turn phase, higher green ratio for the through phase, and higher adaptability for the through phase can significantly improve the safety performance of intersection entrance area. In addition, the average queue length on the through lanes was found to have positive effect on the odds of crash occurrence. These results are important in real-time safety applications at signalized intersections in the context of proactive traffic management.

Real‐time traffic signal control for intersections based on dynamic O–D estimation and multi‐objective optimisation: combined model and algorithm

Detectors are challenged in providing stable and accurate information of the dynamic origin-destination (O-D) flows for real-time adaptive traffic signal timing operation. However, the dynamic O-D estimation technique is capable of providing a short-term turn-flow data for the signal-timing adjustment. Meanwhile, the real-time signal timing variations will affect the dynamic O-D flows in an actual network. Therefore, the dynamic O-D estimation and the real-time signal control closely interact with each. A combined model is proposed to dynamically calculate the signal adjustment answering the variation of the real-time O-D flows by minimising the accumulative queues in entering approaches. A case study was conducted to validate the proposed model and algorithms, and the result showed the traffic efficiency of the case study intersection was improved.

A trajectory smoothing method at signalized intersection based on individualized variable speed limits with location optimization

Traffic signals on urban highways force vehicles to stop frequently and thus causes excessive travel delay, extra fuel consumption and emissions, and increased safety hazards. To address these issues, this paper proposes a trajectory smoothing method based on Individual Variable Speed Limits with Location Optimization (IVSL-LC) in coordination with pre-fixed traffic signals. This method dynamically imposes speed limits on some identified Target Controlled Vehicles (TCVs) with Vehicle to Infrastructures (V2I) communication ability at two IVSL points along an approaching lane. According to real-time traffic demand and signal timing information, the trajectories of each approaching vehicle are made to run smoothly without any full stop. Essentially, only TCVs’ trajectories need to be controlled and the other vehicles just follow TCVs with Gipps’ car-following model. The Dividing RECTangles (DIRECT) algorithm is used to optimize the locations of the IVSLs. Numerical simulation is conducted to compare the benchmark case without vehicle control, the individual advisory speed limits (IASL) and the proposed IVSL-LC. The result shows that compared with the benchmark, the IVSL-LC method can greatly increase traffic efficiency and reduce fuel consumption. Compared with IASL, IVSL-LC has better performance across all traffic demand levels, and the improvements are the most under high traffic demand. Finally, the results of compliance analysis show that the effect of IVSL-LC improves as the compliance rate increases.

Dynamic Eco-Driving Speed Guidance at Signalized Intersections: Multivehicle Driving Simulator Based Experimental Study

Variations in vehicle fuel consumption and gas emissions are usually associated with changes in cruise speed and the aggressiveness of drivers’ acceleration/deceleration, especially at traffic signals. In an attempt to enhance vehicle fuel efficiency on arterials, this study developed a dynamic eco-driving speed guidance strategy (DESGS) using real-time signal timing and vehicle positioning information in a connected vehicle (CV) environment. DESGS mainly aims to optimize the fuel/emission speed profiles for vehicles approaching signalized intersections. An optimization-based rolling horizon and a dynamic programming approach were proposed to track the optimal guided velocity for individual vehicles along the travel segment. In addition, a vehicle specific power (VSP) based approach was integrated into DESGS to estimate the fuel consumption and CO2 emissions. To evaluate the effectiveness of the overall strategy, 15 experienced drivers were recruited to participate in interactive speed guidance experiments using multivehicle driving simulators. It was found that compared to vehicles without speed guidance, those with DESGS had a significantly reduced number of stops and approximately 25% less fuel consumption and CO2 emissions.

Connected vehicle-based red-light running prediction for adaptive signalized intersections

ABSTRACTThis article proposes a connected vehicle (CV)-based dynamic all-red extension (DARE) framework for adaptive signalized intersections to avoid potential crashes caused by red-light running (RLR) behavior. The conceptual framework consists of three components: CVs, road-ide equipment (RSE), and traffic control devices. The RLR prediction of signalized intersections is a crucial component of DARE that avoids potential collisions caused by RLR behavior. This article formulates the RLR prediction as a binary classification problem based on continuous trajectories measured by radar sensors. In the CV environment, vehicle trajectories and real-time signal timing could be obtained via vehicle-to-infrastructure and vehicle-to-vehicle communications. Using continuous trajectories, individual speed, acceleration, and distance to the stop line at the red-light onset time are selected as classification attributes. Nonweighted and weighted least square support vector machines (LS-SVM) are adopted to solve the RLR prediction problem. Parameter tuning is conducted by the cross-validation in the discrete parameter space and the Bayesian inference in the continuous parameter space, respectively. As a comparison, the existing DARE approach at adaptive signalized intersections based on inductive loop detectors is discussed. In field experiments, vehicle trajectories collected by radar sensors were used to simulate the CV environment. Results showed merits of the proposed continuous trajectories-based DARE approach in terms of prediction rate, missing rate and false alarm rate, and thus a better RLR prediction improved the intersection safety.

Signal Timing Detection Based on Spatial–Temporal Map Generated from CCTV Surveillance Video

Signal timing data are a critical component in the estimation of arterial traffic states. Estimating arterial travel time, intersection delay, and queue length from either detector or probe data relies on accurate input or detection of signal timing. Moreover, the emerging eco-intersection approaching and departure applications also require reliable signal timing input. With the accelerated deployment of adaptive and smart signal systems, signal timing data have become more and more readily available at arterial management centers. However, of the thousands of intersections around the country, many still do not report real-time signal timing data because of various maintenance, hardware, software, communication, cost, and other problems. Some existing studies have proposed the use of probe trajectories to infer signal timing on the basis of shock wave theory; however, because of the limited sample sizes, coverage and reliability are also limited. A computer vision–based algorithm to detect signal timing from the regular low-resolution CCTV cameras at many major arterial intersections around the United States is proposed in this paper. The algorithm detects vehicle trajectories by evaluating the movement of pixel colors at a predefined scan line on CCTV video footage. By detecting static objects on the scan line and their stalling duration, the proposed algorithm can efficiently detect the starting and ending times of red lights. The algorithm is calibrated and evaluated by using arterial traffic video collected from the intersection of Henderson Road at U.S. Hwy 1 in North Brunswick, New Jersey. The results show the promising performance of the algorithm in becoming a cost-effective signal detection solution.

Research of Traffic Signal Control Strategy Based on the Fuzzy Control

To ease the traffic pressure on urban traffic signal control strategy research started. Dynamic change prediction analysis of traffic flow through the flow of information as a basis for fuzzy reasoning, automatically adjust the signal cycle, green ratio and phase control parameters, real-time signal timing to generate optimal solutions for optimal control effect. The results show that this method can effectively alleviate traffic congestion, meet the design expectations.

Capability-enhanced AIMSUN with Real-time Signal Timing Control

In this paper, we enhanced the capability of Aimsun simulation including signal timing optimization of traffic control with real-time information on the network dynamics. The problem is formulated so as to find the duration of maximum green time for each stage in response to recurrent traffic flow fluctuation at an intersection. The approach used a simple version of the Webster method for determining the cycle length and green time split. The resulting algorithm was coded in Java and used TraSMAPI to dynamically link it to Aimsun's API, which allows the user to change the cycle length and green time duration of each traffic light's stage. TraSMAPI is a Traffic Simulation Manager API, designed to provide real-time interaction with traffic simulators. So far, this tool has been only tested with Sumo and Itsumo microscopic traffic simulators.The paper presents an example of the communication protocol using the API module linked to TraSMAPI, and contributes to the implementation of a novel real-time traffic control in Aimsun.

Real-time Arterial Performance Measurement Using BRT Probe Data and Signal Timing Data

AbstractReal-time arterial travel time estimation has been a very challengeable task due to the complexity of urban street traffic behaviors and lack of high resolution and high quality data. However, travel time information is considered as one of the most important tools to manage arterial flow. This study developed a series of data processing methods to estimate arterial travel time and LOS based on real-time BRT bus data and signal timing data. BRT buses have a few advantages over local buses to serve as probe vehicles to measure arterial performance. A few data processing procedures were introduced in this paper to estimate the arterial travel time by converting bus trajectory to general traffic travel time. The arterial travel time is estimated by adding the bus queuing delay, average traffic signal waiting time, and free-flow travel time. To verify the effectiveness of the developed method, a field test was conducted. The Valley Transportation Authority (VTA) BRT buses together with Intelli Drive corridor were selected as the data sources. The testing results demonstrate that the proposed method estimated arterial travel time and level of service well based on BRT probe data and traffic signal data. The root mean square error (RMSE) and root mean square percentage error (RMSPE) for the estimation of travel time are 49 seconds and 9%, respectively. The accuracy for the estimation of LOS is 73%.

Dynamic Lane Grouping at Isolated Intersections: Problem Formulation and Performance Analysis

This paper presents some fundamentals of the dynamic lane grouping (DLG) concept, the aim of which is to improve lane utilization under variations in traffic demand. The problem was formulated as a mathematical programming model to determine the optimal lane allocation of an isolated intersection in relation to minimizing the maximum lane flow ratio (defined as the assigned flow divided by the saturation rate). Shared lanes and user equilibrium status for lane use were also considered in the model. Numerical analyses were conducted for four cases with different numbers of lanes, saturation levels, and methods for setting signal timing (adaptive and fixed timing). The results indicated that DLG might provide significant performance improvement in reducing the maximum-flow ratio under the spatial variation in demand. The performance of DLG was also compared with real-time adaptive signal timing by means of the average delay; improvements to the average delay were also observed when the spatial variation was large. The maximum-flow ratio and the average delay of DLG remained almost unchanged for the full spectrum of spatial demand variation when total demand was constant. The results also implied that the more significantly the demand varied, the more benefits the dynamic lane grouping method could potentially provide.

Real-Time Traffic Signal Timing for Urban Road Multi-Intersection

this paper develops a real-time traffic signal timing model which is to be integrated into a single intersection for urban road, thereby solving the problem of traffic congestion. We analyze the current situation of the traffic flow with release matrix firstly, and then put forward the basic models to minimize total delay time of vehicles at the intersection. The optimal real-time signal timing model (non-fixed cycle and non-fixed split) is built with the Webster split optimal model. At last, the simulated results, which are compared with conventional model, manifest the promising properties of proposed model.