Queue Length Estimation Research Articles

Queue Length Estimation for Signalized Intersections under Partially Connected Vehicle Environment

Queue length is a crucial measurement of traffic signal control at urban intersections. Conventional queue length estimation methods mostly still rely on fixed detectors. The development of connected vehicles (CV) provides massive amounts of vehicle trajectory data, and the queue length estimation based on CV data has received considerable attention in recent years. However, most existing CV-based methods require the prior knowledge of the penetration rate of CV and vehicle arrivals, but the estimation of these prior distributions has not been well studied. To address this issue, this paper proposes a cycle-based queue length estimation method under partially connected vehicle (CV) environment, with the prior vehicle arrivals being unknown. The empirical Bayes method is adopted to estimate the arrival rate by leveraging the observed queued CV information such as the number and positions. The hyperparameter estimation problem of the prior distribution is solved by the maximum likelihood estimation (MLE) method. To validate the proposed queue length estimation method, a simulation environment with partially connected vehicles is established using VISSIM and Python for data generating. The results in terms of normalized mean absolute errors (NMAE) and normalized root mean square errors (NRMSE) show that the proposed method could produce accurate and reliable estimated queue length under various CV penetration rates.

GPS data Mining at Signalized Intersections for Congestion Charging.

Nowadays more private car trips have caused worse congestion due to the Covid-19 pandemic in many cities. Congestion charging is one of the taxes that is levied on vehicle owners to reduce urban traffic congestion. One of the most important reasons congestion charging is not accepted by the public is the high cost. Monitoring the state of traffic congestion in real time requires a lot of expensive installations. The purpose of this paper is to make congestion charging more accurate and acceptable using artificial intelligent algorithm. Massive real-time Global Positioning System (GPS) data provides new data for road congestion charging. The queuing length at intersections is an important measurement for the degree of traffic congestion, and it is also the basis for road congestion pricing. GPS positioning cannot provide sufficient position accuracy for lane identification of vehicles. In this study, a comprehensive model consisting of a real-time lane identification model and a real-time queue length estimation model is developed based on the traffic shockwave theory using GPS data. The comprehensive model can identify the lane where the queuing vehicle is located and estimate the real-time queue length of the lane. The proposed models were evaluated using field-collected data in Guangzhou, China. The testing results show that the proposed comprehensive model can identify lanes and estimate queue lengths with satisfactory accuracy. The model proposed in this paper provides real-time data for road dynamic pricing in a cost-effective way, which can promote the implementation of congestion charging in cities.

A Machine Learning Based Method for Real-Time Queue Length Estimation Using License Plate Recognition and GPS Trajectory Data

Real-time and accurate queue length information is crucial to developing effective queue management applications in modern traffic control systems to alleviate traffic congestion. A Random Forests (RF) based real-time queue length estimation method is proposed using the vehicle Global Position System (GPS) trajectory and License Plate Recognition (LPR) Data. The RF model is trained to predict the vehicle stop locations provided by the GPS data by features of traffic flow characteristics extracted from the LRP data. The predicted stop locations are further used to estimate the cyclic maximum queue length for each approach lane. The proposed method has been implemented on sixteen lanes of eight links from both major and minor arterials in Kunshan City, China. Key findings and conclusions include: 1) By feature selection, the travel time has the most significant impact on the prediction accuracy of the vehicle stop location, and the number of departed vehicles is the secondary informative feature. 2) The RF model achieves a satisfying accuracy for the stop location prediction and cyclic maximum queue length estimation, which has the best performance with a larger sample size in the training data. 3) Comparative analysis also shows the superiorities of the proposed model to have more accurate results by incorporating comprehensive features and a machine learning process.

On Estimates of the Mean Queue Length for Single-Channel Queuing Systems in Terms of Statistical Unconditional Second-Order Moments of the Modified Arrival Flow

On Estimates of the Mean Queue Length for Single-Channel Queuing Systems in Terms of Statistical Unconditional Second-Order Moments of the Modified Arrival Flow

A non-stationary queuing approach and genetic algorithm for the optimisation of truck appointment system in a container terminal in Casablanca City

Due to the increasing container traffic, many terminals face a considerable number of truck arrivals. This situation leads to congestion problems at the gates and generates serious air pollution while decreasing terminal efficiency. To consider this issue, many terminals use a truck appointment system. However, the latter should take, necessarily, into account the terminal's local conditions to ensure a satisfying performance. In the present work, one proposes an appointment model to control truck arrivals in one of the busiest terminals in Morocco. The model is based on an improvement of the approximation approach related to the queue length estimation. The treatment adopts a genetic algorithm with a novel testing scenarios highlighting more the solution performances by crossing several basic existing scenarios. Then, some numerical experiments are conducted based on literature works data to calibrate the model and ensure its accuracy. Finally, the best configuration was approved for the local terminal.

Lane-based queue length estimation at signalized intersections using single-section license plate recognition data

Due to full record of discharging vehicle headway, License Plate Recognition (LPR) is used as an ideal source in most existing queue length estimation methods through a double-section detection using shock-wave models or input-output models. However, the impacts of heavy vehicles and miss detection by LPR detectors are mostly ignored. Therefore, this paper proposes a lane-based queue length estimation method using single-section LPR detection, considering miss detection and heavy vehicles. The queue length estimation problem is transformed to a change-point identification problem for discharging headways time-series, using E-Divisive with Medians (EDM) method. The maximal queue length is identified as the change-point of the discharging headways with the maximal differences between queued and non-queued vehicles, considering the queuing homogeneity of a lane group and the miss detection rate of LPR. The proposed method is validated using simulation and empirical cases with promising performance and good robustness under various conditions.

Real-time decentralized traffic signal control for congested urban networks considering queue spillbacks

This paper proposes a decentralized network-level traffic signal control method addressing the effects of queue spillbacks. The method is traffic-responsive, does not require data communication between intersections’ controllers, uses lane-based queue measurements, and is acyclic. Each traffic controller operating at an intersection aims at maximizing the effective outflow rate locally and independently with the goal of maximizing global throughput of the entire network. At each intersection, the signal control method estimates and adopts the maximum possible phase time in which all active movements discharge at their full capacity. This is modeled using a shockwave based queue length estimation model while capturing the spillback at the downstream links. The method demands real-time data including, the queue lengths, the arrival flows, and the downstream queue lengths in all the lanes at the control decision times. The proposed method results in a feasible solution in all conditions in the entire network with any scale within a short amount of time. A stability concept for the traffic network is defined, and asymptotic stability of the controlled traffic network are verified. Moreover, a sufficient condition for the optimality of the proposed control algorithm for maximizing the instantaneous total throughput of the network intersections is demonstrated. Numerical results show that the proposed method outperforms benchmark methods in both isolated intersection and network configurations.

Efficient Video-based Vehicle Queue Length Estimation using Computer Vision and Deep Learning for an Urban Traffic Scenario

In the Intelligent Transportation System (ITS) realm, queue length estimation is one of an essential yet a challenging task. Queue lengths are important for determining traffic density in traffic lanes so that possible congestion in any lane can be minimized. Smart roadside sensors such as loop detectors, radars and pneumatic road tubes etc. are promising for such tasks though they have a very high installation and maintenance cost. Large scale deployment of surveillance cameras have shown a great potential in the collection of vehicular data in a flexible way and are also cost effective. Similarly, vision-based sensors can be used independently or if required can also augment the functionality of other roadside sensors to effectively process queue length at prescribed traffic lanes. In this research, a CNN-based approach for estimation of vehicle queue length in an urban traffic scenario using low-resolution traffic videos is proposed. The queue length is estimated based on count of total vehicles waiting on a signal. The proposed approach calculates queue length without the knowledge of any onsite camera calibration information. Average vehicle length is approximated to be 5 m. This caters for the vehicles at the far end of the traffic lane that appear smaller in the camera view. Identification of stopped vehicles is done using Deep SORT based object tracking. Due to robust and accurate CNN-based detection and tracking, the queue length estimated by using only the cameras has been very effective. This mostly eliminates the need for fusion with any roadside or in-vehicle sensors. A detailed comparative analysis of vehicle detection models including YOLOv3, YOLOv4, YOLOv5, SSD, ResNet101, and InceptionV3 was performed. Based on this analysis, YOLOv4 was selected as a baseline model for queue length estimation. Using the pre-trained 80-classes YOLOv4 model, an overall accuracy of 73% and 88% was achieved for vehicle count and vehicle count-based queue length estimation, respectively. After fine-tuning of model and narrowing the output classes to vehicle class only, an average accuracy of 83% and 93% was achieved, respectively. This shows the efficiency and robustness of the proposed approach.

Development of AI-Based Vehicle Detection and Tracking System for C-ITS Application

There are various means of monitoring traffic situations on roads. Due to the rise of artificial intelligence (AI) based image processing technology, there is a growing interest in developing traffic monitoring systems using camera vision data. This study provides a method for deriving traffic information using a camera installed at an intersection to improve the monitoring system for roads. The method uses a deep-learning-based approach (YOLOv4) for image processing for vehicle detection and vehicle type classification. Lane-by-lane vehicle trajectories are estimated by matching the detected vehicle locations with the high-definition map (HD map). Based on the estimated vehicle trajectories, the traffic volumes of each lane-by-lane traveling direction and queue lengths of each lane are estimated. The performance of the proposed method was tested with thousands of samples according to five different evaluation criteria: vehicle detection rate, vehicle type classification, trajectory prediction, traffic volume estimation, and queue length estimation. The results show a 99% vehicle detection performance with less than 20% errors in classifying vehicle types and estimating the lane-by-lane travel volume, which is reasonable. Hence, the method proposed in this study shows the feasibility of collecting detailed traffic information using a camera installed at an intersection. The approach of combining AI and HD map techniques is the main contribution of this study, which shows a high chance of improving current traffic monitoring systems.

Real-time Queue Length Estimation Applying Shockwave Theory at Urban Signalized Intersections

Signal control is a basic need for urban traffic control; however, it is a very rough intervention in the free flow of traffic, which often results in queues in front of signal heads. The general goal is to reduce the delays caused, and to plan efficient traffic management on the network. For this, the exact knowledge of queue lengths on links is one of crucial importance. This article presents a link-based methodology for real-time queue length estimation in urban signalized road networks. The model uses a Kalman Filter-based recursive method and estimates the length of the queue in every cycle. The input of the filter, i.e. the dynamics of queue length is described by the traffic shockwave theory and the store and forward model. The method requires one loop-detector per link placed at the appropriate position, for which the article also provides suggestions.

Cycle-based Queue Length Estimation Based on Connected Vehicle Trajectory Data

Cycle-based Queue Length Estimation Based on Connected Vehicle Trajectory Data

Queue length estimation from connected vehicles with range measurement sensors at traffic signals

This paper presents a method for queue length estimation from connected vehicles equipped with range measurement sensors such as light detection and ranging, stereo camera, and ultrasonic. Simple plug-and-play models are developed for queue length estimations that do not require ground truth queue lengths. The proposed method is simple to implement and can be adopted to cyclic queues like traffic signals with known phase lengths. The models are evaluated with microscopic traffic simulations and connected vehicle field test data. Compared to queue length estimations without range sensors, the numerical experiments performed using microsimulations show that the model with range sensors improves the errors by 25% in variance-to-mean ratio and 5% in coefficient of variation at less than 20% market penetration rates. Using the connected vehicle field test data, it is observed that the fusing range sensor can improve close to 10% without sensor estimators, 30% of the queue back, and 48% of the Highway Capacity Manual’s control delay-based estimations at about 25% market penetration rate.

Kalman Filtering Method for Real-Time Queue Length Estimation in a Connected Vehicle Environment

Queue length estimation is of great importance for measuring traffic signal performance and optimizing traffic signal timing plans. With the development of connected vehicle (CV) technology, using mobile CV data instead of fixed detector data to estimate queue length has become an important research topic. This study focuses on real-time queue length estimation for an isolated intersection with CV data. A Kalman filtering method is proposed to estimate the queue length in real time using traffic signal timing and real-time traffic flow parameters (i.e., saturated flow rate, traffic volume, and penetration rate), which are estimated using CV trajectories data. A simulation intersection was built and calibrated using field data to evaluate the performance of the proposed method and the benchmark method. Results show that when the CV penetration rate is at 30%, the average values of mean absolute errors, mean absolute percentage errors, and root mean square errors are just 1.6 vehicles, 20.9%, and 2.5 vehicles, respectively. The performance of the proposed model is also better than the benchmark method when the penetration rate of CVs is higher than 20%, which proves the validity of the proposed method. Furthermore, sensitivity analysis indicates that the proposed method requires a high penetration rate of at least 30%.

A hidden Markov model for the estimation of correlated queues in probe vehicle environments

Queue length estimation is critical for traffic signal control and performance measures. With the development of connected vehicle technologies and the popularization of ride-hailing services, probe vehicle data are now being collected on a large scale. Some studies have shown that queue lengths can be estimated using only probe vehicle data. The relevant literature usually assumes the queue lengths in different traffic signal cycles are independent and identically distributed or treats the queues independently. However, in the real world, the queue lengths in different cycles might be correlated. For instance, when there exists an overflow queue, the queue length in the following cycle is correlated with the queue length in the previous cycle. In fact, the correlation of different cycles can provide additional information and thus improve the queue length estimation accuracy. In this paper, we model such queueing processes in probe vehicle environments using a hidden Markov model (HMM), where the queue length in each cycle is a hidden state, and the observed pattern of probe vehicles is an observation. Based on the HMM, we propose two novel cycle-by-cycle queue length estimation methods. In the case where the parameters of the HMM are unknown, we also provide an algorithm that can estimate the parameters from historical probe vehicle data. Validation results show that the proposed cycle-by-cycle queue length estimation methods outperform the existing methods, and the parameter learning algorithm can estimate the parameters adequately.

Cycle-to-cycle queue length estimation from connected vehicles with filtering on primary parameters

Estimation models from connected vehicles often assume low level parameters such as arrival rates and market penetration rates as known or estimate them in real-time. At low market penetration rates, such parameter estimators produce large errors making estimated queue lengths inefficient for control or operations applications. In order to improve accuracy of low level parameter estimations, this study investigates the impact of connected vehicles information filtering on queue length estimation models. Filters are used as multilevel real-time estimators. Accuracy is tested against known arrival rate and market penetration rate scenarios using microsimulations. To understand the effectiveness for short-term or for dynamic processes, arrival rates, and market penetration rates are changed every 15 min. The results show that with Kalman and Particle filters, parameter estimators are able to find the true values within 15 min and meet and surpass the accuracy of known parameter scenarios especially for low market penetration rates. In addition, using last known estimated queue lengths when no connected vehicle is present performs better than inputting average estimated values. Moreover, the study shows that both filtering algorithms are suitable for real-time applications that require less than 0.1 second computational time.

Estimation of Queue Length at Signalized Intersection : A Review

In this growing world, traffic conjunction is considered as most growing problems in both urban as well as sub-urban areas. Traffic congestion is a major issue in day to day life. It has a tremendous impact on the nation’s economy, air pollution, water pollution, travel behavior, accidental risk, land-use and to its end users. Various studies have shown that there is a sudden increase in the traffic congestion. Traffic congestion leads to queuing. Queuing process takes place in both signalized as well as non-signalized intersections, freeways, bottlenecks, incident sites, toll plazas, parking and the areas near the freeways. Queuing also occurs in many non transportation fields. Hence it is very important to study the queue length pattern, its estimation, methods of calculating queue length and give a solution. Hence various methods for estimation of queue length is reviewed.

Maximum Likelihood Estimation of Probe Vehicle Penetration Rates and Queue Length Distributions From Probe Vehicle Data

Queue length estimation plays an important role in traffic signal control and performance measures of signalized intersections. Traditionally, queue lengths are estimated by applying the shockwave theory to loop detector data. In recent years, the tremendous amount of vehicle trajectory data collected from probe vehicles such as ride-hailing vehicles and connected vehicles provides an alternative approach to queue length estimation. To estimate queue lengths cycle by cycle, many existing methods require the knowledge of the probe vehicle penetration rate and queue length distribution. However, the estimation of the two parameters has not been well studied. This paper proposes a maximum likelihood estimation method that can estimate the parameters from historical probe vehicle data. The maximum likelihood estimation problem is solved by the expectation-maximization (EM) algorithm iteratively. Validation results show that the proposed method could estimate the parameters accurately and thus enable the existing methods to estimate queue lengths cycle by cycle.

Cycle-Based Queue Length Estimation for Signalized Intersections Using Sparse Vehicle Trajectory Data

Intersection queue length estimation using high-resolution probe vehicle trajectory data has received increasing attentions in recent years. Existing methods for cycle-based queue length estimation still face the challenge of low and/or unstable estimation accuracies under the condition of sparse vehicle trajectory data, i.e., there is no greater than one vehicle trajectory per cycle on average. To address this challenge, this study proposed a novel approach for cycle-based queue length estimation by fusing real-time and historical probe vehicle trajectory data, through a statistical parameter estimation method, i.e., maximum likelihood estimation (MLE). With known signal timing information, firstly, the historical probe trajectory data are used to acquire the arrival flow rate distribution over the entire study period. Then, a likelihood function of queue length is derived by fully exploiting real-time traffic flow information provided by the queued and non-queued probe vehicles. Finally, the MLE method is adopted to estimate the cycle-based queue lengths with the maximum probability. The proposed approach is verified using both simulation and empirical data. Results indicate that precise estimation for cycle-based queue lengths can be realized based on sparse vehicle trajectory data, while showing superiority to a representative existing method. The proposed method is basically an offline method, but it can also work in an online manner if provided a priori arrival distribution either acquired from historical probe vehicle trajectory data or a theoretical assumption.

Analysis of queue estimation process at signalized intersections under low connected vehicle penetration rates

This study investigates the factors affecting estimation accuracy of queue length at signalized intersections under low penetration of connected vehicles. A shockwave-based algorithm is proposed to estimate the maximum queue length and residual queue on a cycle-by-cycle basis. Simulation data collected from three consecutive signalized intersections were used to extract trajectories of connected vehicles under five different market penetration rates and two different traffic conditions (under-saturated and moderate). The results confirm that the queue length estimation process is probabilistic and affected by the stochastic changes in traffic conditions. This probabilistic nature is defined by a queue formation coverage index (QI) that proved to significantly affect the queue length estimation accuracy. Overall, the results show that the accuracy of the queue estimates is acceptable when a QI value of at least 50% is achieved. In such limited data environments, the QI showed potential to help as an assessment tool to evaluate the obtained queue estimates.

A queue length estimation and prediction model for long freeway off-ramps

Real-time queue length estimation and prediction provides useful information for proactively managing transportation networks. Queue spillback from off-ramps onto main lanes of freeways is one of the traffic issues caused by vehicular queues that can be efficiently managed using dynamic queue information. In this paper, a case-based reasoning algorithm combined with a Kalman filter is developed to provide real-time queue length estimations and predictions on freeway off-ramps. The estimations are based on occupancy readings from three loop detectors installed on a ramp. The proposed method is examined using a micro-simulation model of an off-ramp with a length of 650 meters and a traffic signal downstream of the ramp. The simulation results show an accuracy of ±3.15 vehicles in the queue in 60-second time intervals. In addition, a rigorous sensitivity analysis is conducted to examine the performance of the algorithm under various demand loading scenarios, time intervals, number of detectors used, and errors in prior estimations. The results show that the model performs well in terms of estimating and predicting the length of long queues on freeway off-ramps at various congestion levels. The outcomes of this study can be utilized to activate dynamic, responsive, and proactive queue management and traffic control measures.