Performance Of Traffic System Research Articles

Impact of navigation apps on congestion and spread dynamics on a transportation network

In recent years, the widespread adoption of navigation apps by motorists has raised questions about their impact on local traffic patterns. Users increasingly rely on these apps to find better, real-time routes to minimize travel time. This study uses microscopic traffic simulations to examine the connection between navigation app use and traffic congestion. The research incorporates both static and dynamic routing to model user behavior. Dynamic routing represents motorists who actively adjust their routes based on app guidance during trips, while static routing models users who stick to known fastest paths. Key traffic metrics, including flow, density, speed, travel time, delay time, and queue lengths, are assessed to evaluate the outcomes. Additionally, we explore congestion propagation at various levels of navigation app adoption. To understand congestion dynamics, we apply a susceptible–infected–recovered (SIR) model, commonly used in disease spread studies. Our findings reveal that traffic system performance improves when 30–60% of users follow dynamic routing. The SIR model supports these findings, highlighting the most efficient congestion propagation-to-dissipation ratio when 40% of users adopt dynamic routing, as indicated by the lowest basic reproductive number. This research provides valuable insights into the intricate relationship between navigation apps and traffic congestion, with implications for transportation planning and management.

Current Approaches in Traffic Lane Detection: a minireview

The continuous development and importance of the field of road transport these days make it necessary to design, develop and implement technological solutions that reduce (eliminate as much as possible) the risk of road accidents. Such a technological solution is also represented by advanced driver assistance systems (ADAS), systems that assist drivers in various ways, such as collision avoidance, automatic parking, adaptive cruise control, attention and lane departure warnings. Over the next ten years, there will likely be a rise in the need for ADAS system deployment in automobile construction, driven by consumer and regulatory interest in safety applications that protect drivers and lower accident rates. At the moment, autonomous emergency braking and forward collision warning systems are mandated for all cars in the US and the EU. Additionally, advanced driver assistance systems (ADAS) may soon distinguish automobile brands and have a significant impact on consumer preference. The present work aims to provide a general picture related to the current research and development of ADAS systems that refer to the detection of the traffic lane and lane markings. The approaches are presented regarding: the current development directions of ADAS systems, current traffic lane detection techniques, traffic lane detection methods and the use of artificial intelligence techniques in this field. The general conclusion is that further research is needed in the field, research to increase the performance of traffic lane detective systems by using advanced algorithms and easy-to-implement methods that do not require large hardware resources.

An explicit approach for modeling the performance of transportation networks immediately after an earthquake

Surface transportation systems play a vital role in supporting a region’s functionalities. They are expected to remain operational before and even after a hazardous event (e.g. an earthquake). The importance is evident to estimate the post-disaster performance of traffic systems under a probability-based framework, considering the uncertainties arising from both the hazards and the transportation infrastructure fragilities. This paper proposes an explicit approach for evaluating the performance of transportation systems immediately after an earthquake event. The method estimates the spatial distribution of vehicles in the traffic network in a closed form and thus is relatively efficient compared with traditional methods (e.g. an agent-based method). The applicability of the proposed approach is demonstrated through an application to the post-earthquake performance assessment of the traffic network in Tangshan City, China, a city that suffered catastrophically from the 1976 Tangshan Earthquake. Analytical results show that the proposed method can well reflect both the temporal and the spatial variations of the traffic flow, and thus offers rational support for predicting the post-earthquake traffic scenarios and for optimizing strategies to improve the transportation capability under emergent conditions.

Quasi-Stochastic Departure Metering Model Considering Non-Deterministic Taxi Time, Standard Instrument Departure Track Time, and a New Delay–Conflict Relationship

This paper presents a quasi-stochastic mixed integer programming model to determine the optimal flight departure schedule and sequence at airports. The proposed formulation addresses two key gaps in the existing departure metering strategies: (i) the variability and effects of the standard instrument departure (SID) procedure and (ii) the prospect of en route conflicts because of departure delays. The variability and effects of the SID procedure are captured through a non-deterministic parameter defined as the SID track time. The probability of en route conflicts because of departure delay is estimated based on a new delay–conflict relationship. Data used for implementation and validation of the model was collected from operations on Runway 7 of Chennai International Airport, India. Overall results indicate that the proposed departure metering model can act as a pre-emptive departure delay strategy framework that takes care of the SID procedure and en route conflict possibility with no harm to the runway throughput. The implications of this work hint toward better management of the airport operations and the performance of the overall air traffic management system.

An Urban Traffic Dataset Composed of Visible Images and Their Semantic Segmentation Generated by the CARLA Simulator

A dataset of aerial urban traffic images and their semantic segmentation is presented to be used to train computer vision algorithms, among which those based on convolutional neural networks stand out. This article explains the process of creating the complete dataset, which includes the acquisition of the images, the labeling of vehicles, pedestrians, and pedestrian crossings as well as a description of the structure and content of the dataset (which amounts to 8694 images including visible images and those corresponding to the semantic segmentation). The images were generated using the CARLA simulator (but were like those that could be obtained with fixed aerial cameras or by using multi-copter drones) in the field of intelligent transportation management. The presented dataset is available and accessible to improve the performance of vision and road traffic management systems, especially for the detection of incorrect or dangerous maneuvers.

Architecture design of a vehicle–road-cloud collaborative automated driving system

Transportation systems serve as a crucial foundation for maintaining the normal operation of cities and satisfying the requirements of public life. With the development of next-generation information technologies, automated driving technologies have brought new opportunities to improve the performance of traffic systems and the intelligence level of cities. Currently, significant research efforts have been conducted to develop automated driving systems in three major industries, i.e., automobile, roadway, and telecommunication. However, the collaboration and integration of automated driving systems among automobile, roadway, and telecommunications are still lacking, especially for collaborative development of system architecture and objectives. To address the need, this study first proposes a system architecture of vehicle–road-cloud collaborative automated driving system (VRC-CADS). Three levels of collaborative development, i.e., collaborative sensing, collaborative decision-making, and collaborative control, are designed for the VRC-CADS. Based on that, the typical scenarios of automated driving for each level of the system are further defined and interpreted. Moreover, feasible and systematic suggestions for the collaborative development of the VRC-CADS are provided, considering the cross-cutting collaboration among government agencies, academia, and industry. The proposed system architecture of the VRC-CADS will facilitate the optimization of urban lifelines and the evolution of intelligent cities.

Ship Detection under Low-Visibility Weather Interference via an Ensemble Generative Adversarial Network

Maritime ship detection plays a crucial role in smart ships and intelligent transportation systems. However, adverse maritime weather conditions, such as rain streak and fog, can significantly impair the performance of visual systems for maritime traffic. These factors constrain the performance of traffic monitoring systems and ship-detection algorithms for autonomous ship navigation, affecting maritime safety. The paper proposes an approach to resolve the problem by visually removing rain streaks and fog from images, achieving an integrated framework for accurate ship detection. Firstly, the paper employs an attention generation network within an adversarial neural network to focus on the distorted regions of the degraded images. The paper also utilizes a contextual encoder to infer contextual information within the distorted regions, enhancing the credibility of image restoration. Secondly, a weighted bidirectional feature pyramid network (BiFPN) is introduced to achieve rapid multi-scale feature fusion, enhancing the accuracy of maritime ship detection. The proposed GYB framework was validated using the SeaShip dataset. The experimental results show that the proposed framework achieves an average accuracy of 96.3%, a recall of 95.35%, and a harmonic mean of 95.85% in detecting maritime traffic ships under rain-streak and foggy-weather conditions. Moreover, the framework outperforms state-of-the-art ship detection methods in such challenging weather scenarios.

Two-layer adaptive signal control framework for large-scale dynamically-congested networks: Combining efficient Max Pressure with Perimeter Control

Traffic-responsive signal control is a cost-effective, easy-to-implement, network management strategy, bearing high potential to improve performance in heavily congested networks with dynamic traffic characteristics. Max Pressure (MP) distributed control gained significant popularity due to its theoretically proven ability of throughput maximization under specific assumptions. However, its effectiveness is questionable in over-saturated conditions, while network-scale implementation is often practically limited due to high instrumentation cost, which increases proportionally to the number of controlled intersections. Perimeter control (PC) based on the concept of Macroscopic Fundamental Diagram (MFD) is a state-of-the-art aggregated control strategy that regulates exchange flows between homogeneously congested regions, with the objective of maximizing traffic system performance and prevent over-saturation. However, homogeneity assumption is hardly realistic under congested conditions, which can compromise PC effectiveness. In this paper, network-wide parallel application of PC and MP strategies embedded in a two-layer control framework is evaluated in a macroscopic simulation environment. With the aim of reducing implementation cost of network-wide MP without significant performance drop, we propose a critical node identification algorithm that is based on node traffic characteristics and assess partial MP deployment to the most critical nodes. An enhanced version of Store-and-forward dynamic traffic paradigm incorporating finite queues and spill-back consideration is used to test different configurations of the two-layer framework, as well as each layer individually, for a real large-scale network, in moderate and highly congested conditions. Results show that: (i) combination of MP and PC outperforms individual layer application in almost all cases for both demand scenarios tested; (ii) MP control in critical node sets formed by the proposed strategy leads to similar or even better performance compared to full-network implementation, thus allowing for significant cost reduction; iii) the proposed control schemes improve system performance even under stochastic demand fluctuations of up to 20% of mean.

Achieving realistic cyclist behavior in SUMO using the SimRa dataset

Increasing the modal share of bicycle traffic to reduce carbon emissions, reduce urban car traffic, and to improve the health of citizens, requires a shift away from car-centric city planning. For this, traffic planners often rely on simulation tools such as SUMO which allow them to study the effects of construction changes before implementing them. Similarly, studies of vulnerable road users, here cyclists, also use such models to assess the performance of communication-based road traffic safety systems. The cyclist model in SUMO, however, is very imprecise as SUMO cyclists behave either like slow cars or fast pedestrians, thus, casting doubt on simulation results for bicycle traffic. In this paper, we analyze acceleration, deceleration, velocity, and intersection left-turn behavior of cyclists in a large dataset of real world cycle tracks. We use the results to improve the existing cyclist model in SUMO and add three more detailed cyclist models and implement them in SUMO.

Biobjective robust Network-wide traffic signal optimization against Cyber-attacks

In the cyber age, modern traffic signal control systems are inevitably exposed to a wide range of cyber-attacks, which would negatively impact traffic system performance. To defend against direct cyber-attacks that alter signal settings directly and maliciously by hacking into the wireless connected traffic control center or signal controllers, this study applies a precautionary and inexpensive measure of designing the robust signal control plan. This is realized by modelling the biobjective traffic signal control simulation-based optimization (SO) problem under cyber-attacks in a bi-level framework, with the aim of minimizing the negative impact of potential cyber-attacks on traffic efficiency and safety performance. Depending on the type of attacker with complete or incomplete knowledge on traffic signal control system, two typical bi-level models exist. Then, we propose an improved biobjective robust simulation-based optimization (IBORSO) algorithm to solve the two models, during which the sequential interaction between the attacker and the defender is seen as the Stackelberg game, and the resulting equilibrium solutions are the desired robust signal control plans to withstand cyber-attacks. An urban road network in Changsha, China, is modelled as the experimental site and numerical results show the effectiveness of IBORSO in solving the two bi-level models. Meanwhile, the solved robust signal control plans can defend against two typical attacks satisfactorily and achieve better or comparable biobjective performance compared with the field-implemented one and optimized ones by biobjective efficient global optimization (BOEGO).

THUẬT TOÁN SAI PHÂN HIỆU CHỈNH LAX FRIEDRICHS CHO BÀI TOÁN DỰ BÁO MẬT ĐỘ GIAO THÔNG CÓ TRỄ

In recent years, traffic density forecasting has been playing an important role in developing and improving the performance of intelligent traffic systems. Traffic density forecasting to optimize traffic management, urban management of vehicular traffic have the ability to coordinate traffic, optimize traffic light signals and apply intelligent regulation based on forecasts, thereby improving the handling of the road segment and reducing travel time. Therefore, the construction of predictive algorithms along with integration into traffic management systems is essential to promote the sustainable development of intelligent transportation systems in the future. In this research, an algorithm has been developed to predict traffic density on the delayed Lighthill - Whitham - Richards (LWR) model, in which a regularization difference method has been proposed as the basis for the algorithm. This article mainly focus on building an algorithm and performing experimental calculations to verify the correctness of the algorithm on a mathematical model.

A Review of Different Fuzzy Models to Evaluate Vehicular Traffic System

These days, the number of vehicles is increasing due to population growth. Consequently, the traffic delay and congestion are growing fast and being a major problem in the world. Due to this traffic problem, our daily life is affected and we have to face many problems such as pollution, accidents, delay in services, wastage of time & money, etc. The improvement of traffic system performance by the traditional control system is difficult. So, this paper includes a review of different methods which are used to develop a Fuzzy-logic controller (FLC) to solve road traffic problems. In this paper, the comparison of various methods is given with the advantages and disadvantages of each method.

Impacts of bus holding strategies on the performance of mixed traffic system

The holding strategy performs well in resisting bus bunching in the dedicated bus route. Yet, its efficiency in a non-dedicated bus route has not been well studied, and its external effects on other road participants are unclear. We study this topic in a circular two-lane traffic system consisting of buses and cars. A cellular automaton model is used to depict the vehicles’ dynamic behaviors. By numerical simulations, we obtained the phase diagram of the bus system, and identified the four states concerning the stability and level of service. Then we analyzed the flow and velocity characteristics and distinguished three states of the traffic system. Our simulations prove the separation effect of car flows on the bus headway regulation. Combined with this effect, the holding strategy makes the bus running more homogeneous and the bus utilization more evenly in the mixed traffic flow condition. Interestingly, from a macro perspective, the holding strategy incurs no detrimental effects on the whole traffic system, though it might intensify local interference between buses and cars near bus stops. Under certain conditions, the average speed of buses and cars increases, eventually increasing the system’s capacity.

A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies

Freeway networks, despite being built to handle the transportation needs of large traffic volumes, have suffered in recent years from an increase in demand that is rarely resolvable through infrastructure improvements. Therefore, the implementation of particular control methods constitutes, in many instances, the only viable solution for enhancing the performance of freeway traffic systems. The topic is fraught with ambiguity, and there is no tool for understanding the entire system mathematically; hence, a fuzzy suggested algorithm seems not just appropriate but essential. In this study, a fuzzy cognitive map-based model and a fuzzy rule-based system are proposed as tools to analyze freeway traffic data with the objective of traffic flow modeling at a macroscopic level in order to address congestion-related issues as the primary goal of the traffic control strategies. In addition to presenting a framework of fuzzy system-based controllers in freeway traffic, the results of this study demonstrated that a fuzzy inference system and fuzzy cognitive maps are capable of congestion level prediction, traffic flow simulation, and scenario analysis, thereby enhancing the performance of the traffic control strategies involving the implementation of ramp management policies, controlling vehicle movement within the freeway by mainstream control, and routing control.

Monocular Visual Traffic Surveillance: A Review

To facilitate the monitoring and management of modern transportation systems, monocular visual traffic surveillance systems have been widely adopted for speed measurement, accident detection, and accident prediction. Thanks to the recent innovations in computer vision and deep learning research, the performance of visual traffic surveillance systems has been significantly improved. However, despite this success, there is a lack of survey papers that systematically review these new methods. Therefore, we conduct a systematic review of relevant studies to fill this gap and provide guidance to future studies. This paper is structured along the visual information processing pipeline that includes object detection, object tracking, and camera calibration. Moreover, we also include important applications of visual traffic surveillance systems, such as speed measurement, behavior learning, accident detection and prediction. Finally, future research directions of visual traffic surveillance systems are outlined.

Dataset: Traffic Images Captured from UAVs for Use in Training Machine Vision Algorithms for Traffic Management

A dataset of Spanish road traffic images taken from unmanned aerial vehicles (UAV) is presented with the purpose of being used to train artificial vision algorithms, among which those based on convolutional neural networks stand out. This article explains the process of creating the complete dataset, which involves the acquisition of the data and images, the labeling of the vehicles, anonymization, data validation by training a simple neural network model, and the description of the structure and contents of the dataset (which amounts to 15,070 images). The images were captured by drones (but would be similar to those that could be obtained by fixed cameras) in the field of intelligent vehicle management. The presented dataset is available and accessible to improve the performance of road traffic vision and management systems since there is a lack of resources in this specific domain.

Analysis and modeling for illuminance and signal-to-noise of smart traffic information system

The traffic lights using by light emitting diode (LED) are able to indicate and provide the information about the ambient. The signal transimission performance of traffic system is related to a variety of factors such as the signal-to-noise ratio (SNR), illuminance. Several factors are influence on illumiance and SNR, such as eatsink temperature and driver parameters. It has not been any substantial investigate on the operated effects of traffic light with sinusoidal waveform driver on illuminance and SNR. With the use of heatsink temperature and driver parameters of the LED source with sinusoidal waveform driver, prediction modeling for the illuminance and SNR of the mixed white LED sources with heatsink temperature, frequency, amplitude voltage, bias voltage and amplification factors are proposed in this paper to enhance the illuminance and SNR model of LED source inside smart traffic information system. The proposed model should carry out a series of calibration step for illuminance, heatsink temperature and driver parameters, which are easy for traffic designer and source system designers to follow in the traffic information system. The proposed model has been measured for the mixed white LED sources, with reasonably good agreements between theoretical and practical results.

Bayesian analysis of an M|M|1|∞ queueing model

In this paper, by considering an M|M|1|∞ queueing model, Bayes estimators of traffic intensity and measures of system performance are worked out under squared error loss function (SELF) based on observed data on the independent interarrival and service times. Further, minimum posterior risk associated with Bayes estimators of traffic intensity and system performance measures are obtained under SELF. Numerical illustration of the performance of the estimates is given through simulation study. It is shown that Bayes estimators perform better than the maximum likelihood estimators under the influence of prior information.

Impact of lane function layout on the performance of urban traffic system

In urban traffic systems, alleviating traffic congestion originated from left-turn/U-turn lanes at highly demanded intersections plays an important role in enhancing the traffic efficiency. For a typical intersection, the layout with double left-turn (DLT) lanes is a relatively common geometric feature. However, the effect of DLT scheme on the performance of the entire traffic system is not clear. This paper compares the performance of urban traffic system with different lane function schemes with extended cellular automaton (CA) model considering the car-following, lane-changing, intersection-traversing and route-choice behavior of vehicles. Using the flow-density relation, three macroscopic parameters (average trip completion rate, average traffic flow and average velocity) are examined to evaluate the system performance. With the wide application of DLT scheme, the vehicles can be distributed more evenly among the lanes. As a result, the traffic flow and the trip completion rate will be higher, especially at high density. Case studies of realistic Hefei city network and Sioux Falls networks are also carried out. The results show that the performance of traffic network will be better if more intersections adopt the DLT scheme.

A dense background representation method for traffic surveillance based on roadside LiDAR

The performance of traditional video-based traffic surveillance systems is susceptible to illumination variation and perspective distortion. This has been a significant motivation in recent years for research into Light Detection and Ranging (LiDAR)-based traffic surveillance systems, as LiDAR is insensitive to both factors. The first step in LiDAR data processing involves effective extraction of moving foreground objects from a referenced background. However, existing methods only detect a static background based on LiDAR point density or relative distance. In this research, we develop a novel dense background representation model (DBRM) for stationary roadside LiDAR sensors to detect both static and dynamic backgrounds, for freeway traffic surveillance purposes. Background objects tend to be stationary in space and time. DBRM utilizes this property to detect two types of background: both static and dynamic. While the static background is represented by fixed structures, the dynamic background – which may be characterized by quasi-static objects such as tree foliage – is modeled by mixtures of Gaussian probability distributions. Experiments were carried out in two different scenarios to compare the proposed model with two other state-of-the-art models. The results demonstrate the effectiveness, robustness, and detail-preserving advantages of the proposed model.