Traffic Management Functions Research Articles

Road Transport Efficiency and Organizational Performance: Does Traffic Infrastructure Management Matter?

The objective of this study is to assess the relationships between road transport efficiency and organizational performance. In addition, the investigation explored the intermediary function of traffic infrastructure management in the relationship between road transport efficiency and organizational performance. A study model was built, drawing on a resource-based approach and stakeholder theories, to investigate the interactions between the constructs. The data was collected from a sample of 30 logistics enterprises located in Ghana. The research model underwent analysis utilizing the partial least squares structural equation modeling technique. The results of the study indicate that there is a strong and statistically significant relationship between road transport efficiency and organizational performance. Once more, the analysis has demonstrated a favorable impact of traffic infrastructure management on the performance of organizations. Furthermore, the management of transportation infrastructure has shown a noteworthy and substantial role in mediating the impact on organizational performance.

TRAPROB: An Innovative Traffic Police Robot with Arduino Intelligence

In an era of ever-increasing urbanization and traffic congestion, the development of innovative solutions for traffic management is imperative. This research paper presents the design, fabrication, and implementation of a humanoid traffic police robot, lovingly named “TRAPROB”, which is equipped with a variety of functionalities to enhance traffic control and safety. TRAPROB's hardware foundation includes an Arduino Mega microcontroller, multiple servo motors for live gestures, and an ESP32 cam for real-time monitoring. This research involves both the physical construction of the robot's iron sheet body and the complex programming required to organize its movements and traffic management functions. Robot activities range from cordial interactions like waving and shaking hands to critical traffic management tasks. TRAPROB specializes in directing the flow of vehicles, acknowledging right of way, signaling, and accurately enforcing traffic rules. It can manage intersections efficiently, ensuring a smooth and safe traffic environment. Its unique ability to seamlessly communicate with oncoming vehicles, stop traffic from different directions and give synchronized commands promotes a holistic approach to traffic control. This research paper emphasizes the practicality and simplicity of building a humanoid traffic police robot from scratch. TRAPROB represents an innovative step forward in the field of autonomous traffic management, offering a customizable, cost-effective, and adaptable solution to the challenges posed by modern urban traffic. As traffic congestion continues to increase, TRAPROB's ability to reduce congestion and increase road safety is becoming increasingly important. This paper explores the design, construction, and functionality of TRAPROB, providing information about its real-world applicability and the prospects of this DIY traffic management solution.

ОЦІНЮВАННЯ ЕКОЛОГІЧНОГО РИЗИКУ ЗАБРУДНЕННЯ НАВКОЛИШНЬОГО СЕРЕДОВИЩА АВТОМОБІЛЬНИМ ТРАНСПОРТОМ

Assessment of the quality of traffic management and design solutions in the field of traffic management in cities, complex systems for organizing the movement of cities with a population of more than 100 thousand people residents, temporary schemes of organization for the period of overlapping of significant sections of the street network is combined with the need to take into account a large amount of data to resolve contradictions of uncertainty of an objective and subjective nature. Taking into account the variety of factos to assess the effectiveness of the organization and management of traffic is possible only for small sections of the street network using advanced information technologies to improve the quality of automated traffic management through the implementation of optimization principles. The effectiveness of traffic management can be assessed by many factors that depend on its management. As an objective function of traffic management, a number of authors suggest using the following values: the number of road accidents, socio-economic damages from road accidents, the amount of harmful substances, the total travel time along the route, the number of stops per trip, the skip rate, the average delay of the crew per cycle, the average downtime, connection speed, traffic intensity, etc. The proposed calculations of the amount of the total risk and the average length of a person's life, which use basic normative data, allow to give a quantitative assessment of the danger of atmospheric air pollution. In this regard, environmental risk can be applied as an objective function of traffic management. As can be seen from the above results, both the environmental risk, and the time the car travels from one point to another, and fuel consumption are determined depending on the delays of vehicles in front of regulated intersections. Therefore, it is advisable to recommend as a target function for optimizing traffic management the value of the total time of vehicle delays in front of intersections of the urban street network in one cycle of regulation.

Beacon Non-Transmission attack and its detection in intelligent transportation systems

Message dropping by intermediate nodes as well as RF jamming attack have been studied widely in ad hoc networks. In this paper, we thoroughly investigate a new type of attack in intelligent transportation systems (ITS) defined as Beacon Non-Transmission (BNT) attack in which attacker is not an intermediate vehicle, but rather a source vehicle. In BNT attack, a vehicle suppresses the transmissions of its own periodic beacon packets to get rid of the automated driving misbehavior detection protocols running in ITS, or to mount a Denial-of-Service (DoS) attack to cripple the traffic management functionality of ITS. Considering BNT attack as a critical security threat to ITS, we propose two novel and lightweight techniques to detect it. Our first technique bases its detection by assuming a certain distribution of the number of beacons lost from a vehicle while accounting for loss due to channel-error. However, it fails to classify shortish BNT attacks wherein amount of denial and channel-error loss are comparable. Our second technique, suitable for identifying both shortish and longish BNT attacks, considers beacon loss pattern of a vehicle as a time-series data and employs autocorrelation function (ACF) to determine the existence of an attack. In order to trade-off detection accuracy for equitable use of limited computational resources, we propose a random inspection model in which the detection algorithm is executed at random time instances and for randomly selected set of vehicles. We have performed extensive simulations to evaluate the performance of proposed detection algorithms under random inspection and a practical attacker model. The results obtained corroborate the lightweight nature of both techniques, and the efficacy of ACF based technique over simple threshold based technique in terms of higher detection accuracy as well as smaller reaction delay.

Field Assessment of Intersection Performance Enhanced by Traffic Signal Optimization and Vehicle Trajectory Planning

The development of Connected Automated Vehicle (CAV) technology introduces vehicle connectivity and automation into urban intersection management. It offers powerful means for accurate traffic state perception and effective traffic control actuation. This enabled the development of an intersection control algorithm that included an optimal traffic signal control algorithm and a trajectory planning function. The intersection controller aimed to maximize the intersection throughput while improving the vehicle energy efficiency via implementing adaptive signal phasing and time plans and sending reference trajectories to CAVs. Its effectiveness was tested on a closed test track equipped with a real-time traffic simulation platform, a traffic signal controller, and three physical test vehicles. The test results suggest that the intersection control algorithm was able to improve the average speed of the test vehicles by 9%, although the optimal signal controller or the trajectory planning algorithm alone could only provide marginal speed benefits. This demonstrated the advantage of combining multiple advanced traffic management functions for obtaining an elevated performance increase. For the energy efficiency analysis, both traditional gasoline-engine and hybrid vehicles were used. The hybrid test vehicles obtained fuel savings mostly via the optimal traffic signal controller (e.g., 17% fuel consumption reduction). On the other hand, the gasoline-engine test vehicle’s fuel savings could be largely attributed to the trajectory planning (e.g., 21% fuel consumption reduction). Overall, the proposed intersection controller demonstrated significant vehicle mobility and energy efficiency improvements for vehicles with different powertrains in a nearly realistic traffic condition.

Electric Vehicle Integration into Road Transportation, Intelligent Transportation, and Electric Power Systems: An Abu Dhabi Case Study

Recently, electric vehicles (EV) have gained much attention as a potential enabling technology to support CO2 emissions reduction targets. Relative to their internal combustion vehicle counterparts, EVs consume less energy per unit distance, and add the benefit of not emitting any carbon dioxide in operation and instead shift their emissions to the existing local fleet of power generation. However, the true success of EVs depends on their successful integration with the supporting infrastructure systems. Building upon the recently published methodology for the same purpose, this paper presents a “systems-of-systems” case study assessing the impacts of EVs on these three systems in the context of Abu Dhabi. For the physical transportation system, a microscopic discrete-time traffic operations simulator is used to predict the kinematic state of the EV fleet over the duration of one day. For the impact on the intelligent transportation system (ITS), the integration of EVs into Abu Dhabi is studied using a multi-domain matrix (MDM) of the Abu Dhabi Department of Transportation ITS. Finally, for the impact on the electric power system, the EV traffic flow patterns from the CMS are used to calculate the timing and magnitude of charging loads. The paper concludes with the need for an intelligent transportation-energy system (ITES) which would coordinate traffic and energy management functionality.

Does Free Route Implementation Influence Air Traffic Management System? Case Study in Poland.

The issue addressed in this publication concerns new Air Traffic Management (ATM) functionality, identified in the Commission Implementing Regulation (EU) No 716/2014, known as Flexible Airspace Management and Free Route (FRA). The authors pose a question—does free route implementation influence air transport safety? What can be done to maintain the current level of safety and still implement modern solutions? To achieve the aim of this paper a developed concept of Risk Priority Number (RPN) calculation, with determination of main RPN components rating scales, in order to carry out the FMEA (Failure Mode and Effects Analysis) risk analysis of FRA implementation was done. The results allow lining up of the identified potential incompatibilities according to their criticality to the system. In effect it can be said that each modification in a management system, related to safety, influence the safety itself. Nevertheless, this influence does not always lead to negative impact.

CoNFV

Network function virtualization (NFV) is a powerful networking approach that leverages computing resources to perform a time-varying set of network processing functions. Although microprocessors can be used for this purpose, their performance limitations and lack of specialization present implementation challenges. In this article, we describe a new heterogeneous hardware-software NFV platform called CoNFV that provides scalability and programmability while supporting significant hardware-level parallelism and reconfiguration. Our computing platform takes advantage of both field-programmable gate arrays (FPGAs) and microprocessors to implement numerous virtual network functions (VNF) that can be dynamically customized to specific network flow needs. The most distinctive feature of our system is the use of global network state to coordinate NFV operations. Traffic management and hardware reconfiguration functions are performed by a global coordinator that allows for the rapid sharing of network function states and continuous evaluation of network function needs. With the help of state sharing mechanism offered by the coordinator, customer-defined VNF instances can be easily migrated between heterogeneous middleboxes as the network environment changes. A resource allocation and scheduling algorithm dynamically assesses resource deployments as network flows and conditions are updated. We show that our deployment algorithm can successfully reallocate FPGA and microprocessor resources in a fraction of a second in response to changes in network flow capacity and network security threats including intrusion.

Lane-based short-term urban traffic parameters forecasting using multivariate artificial neural network and locally weighted regression models: A genetic approach

Short-term prediction of traffic conditions on urban arterials has recently become increasingly important because of its vital role in the basic traffic management functions and trip decision-making processes. Such information is useful for optimal infrastructure operation, routing, and trip scheduling. However, forecasting models offering a high accuracy at a fine temporal resolution (e.g., 1 or 5 min) and, especially, lane-based are still rare and need special attention. Given the dynamic and stochastic nature of traffic, this study proposes a genetically optimized artificial neural network (GA-ANN) and locally weighted regression (GA-LWR) multivariate models, for short-term traffic prediction using a combination of multiple traffic variables such as volume, occupancy, and speed, during peak and off-peak periods. The proposed 5-min GA-ANN and GA-LWR disaggregate multivariate models show lower average and 95th percentile (P95) errors, when compared to those reported in the literature. In particular, for peak and off-peak time prediction, the GA-ANN disaggregate multivariate models result in most of the average errors being from 2% to 5% and the 95th percentile errors being from 9% to 10%. On the other hand, for peak and off-peak time traffic prediction, the GA-LWR disaggregate multivariate models show that most of the average errors are lower than 5% and the 95th percentile errors are lower than 10%. Meanwhile, for peak and off-peak time prediction, both GA-ANN and GA-LWR disaggregates models show lower MSE of 0.11–1.84. Hence, such techniques are believed useful for developing a robust urban traffic forecasting system.

Urban link travel time estimation using traffic states‐based data fusion

Estimated travel time is a key input for many intelligent transport systems (ITS) applications and traffic management functions. There are numerous studies that show that fusing data from different sources such as global positioning system (GPS), Bluetooth, mobile phone network (MPN), and inductive loop detector (ILD) can result in more accurate travel time estimation. However, to date, there has been little research investigating the contribution of individual data sources to the quality of the final estimate or how this varies according to source-specific data quality under different traffic states. Here, three different data sources, namely bus-based GPS (bGPS) data, ILD data, and MPN data, of varying quality are combined using three different data fusion techniques of varying complexity. In order to quantify the accuracy of travel time estimation, travel time calculated using automatic number plate recognition (ANPR) data are used as the `ground truth'. The final results indicate that fusing multiple data together does not necessarily enhance the accuracy of travel time estimation. The results also show that even in dense urban areas, bGPS data, when combined with ILD data, can provide reasonable travel time estimates of general traffic stream under different traffic states.

An analysis and model for Automatic Dependent Surveillance Broadcast (ADS-B) continuity

The surveillance function of air traffic management is critical to aviation safety. It determines the identity of an aircraft, its state and intent to support air traffic controllers in ensuring that aircraft comply with the specified separation minima. The conventional surveillance system in use for many years is the radio detection and ranging (radar) system, with the more recent ones being wide area multi-lateration based on distance measurement equipment, and Automatic Dependent Surveillance System Broadcast (ADS-B) exploiting the navigation and communications functions of Air Traffic Management to disseminate aircraft state vector and other relevant data to air traffic controllers and other suitably equipped aircraft. The differences between these systems lie in their performance measured in terms of accuracy, integrity, continuity, availability, reliability, adaptability and scalability to the operational environment. The ADS-B system was proposed by the International Civil Aviation Organization, to address the limitations of the radar system, which include unavailability of coverage in remote, oceanic and lower altitude areas, limited services in extreme weather conditions, obsolescence of spare parts, inability to accommodate increasing air traffic and incapability to support future enhanced airborne and ground surveillance applications. However, to date research on the characterization of ADS-B system performance is lacking. This work analyzes ADS-B continuity using real-time data collected from ADS-B ground stations in the London Terminal Maneuvering Area. ADS-B continuity is measured as the rate (in seconds) at which periodic ADS-B messages are received at the ground stations. The results reveal that there is a deterministic pattern in the update rate, with 80% of the messages broadcast within the required 2 s. The factors that influence continuity are identified using statistical methods and incorporated in a mathematical model relating the factors to ADS-B continuity. This model is recommended to the relevant stakeholders including Air Navigation Service Providers and regulators to evaluate and monitor the system performance.

A new rail optimisation model by integration of traffic management and train automation

This paper reviews and classifies the traffic optimisation schemes of current mainline railway into two groups. One is to improve the efficiency of traffic management by providing resolutions for traffic conflicts, while the other is to improve trains’ driving behaviour by providing driver assistance or introducing train automation. Based on a comparison of these two groups, this paper proposes to combine the functions of traffic management and train automation into an integrated optimisation model.This model includes the following contributions. First, in the function of traffic management, this paper explores the flexibility in generating different train running trajectories to prevent potential traffic conflicts. The trajectory can improve traffic flow by avoiding unplanned train stops. This is regarded as a supplementary conflict resolution to train reordering or rerouting or retiming. Second, this paper defines a series of train control commands to determine different intensities of the train’s tractive force and braking force. These commands are seen as the key to train automation. Moreover, a decision-making procedure is introduced to select the most attractive train running trajectory or train control command according to different optimisation objectives. Lastly, this paper proves the importance of bidirectional communication between traffic management and train automation based on a case study.

Effects of NextGen Concepts of Operation for Separation Assurance and Interval Management on Air Traffic Controller Situation Awareness, Workload, and Performance

ABSTRACTObjective: Determine how combinations of NextGen-automation concepts for separation assurance and spacing affect air traffic controller (ATCo) situation awareness, workload, and performance.Background: In previous research, situation awareness was not measured with valid and reliable instruments. Previous work also evaluated separation assurance and spacing concepts individually, and did not examine weather.Method: Retired ATCos worked en route and transitional sectors. Four operating concepts for separation assurance and spacing were tested based on whether automation or ATCo was responsible for each function. Standard methods for assessing workload and situation awareness were used; performance measures included safety and efficiency.Results: Workload was lowest when both functions were automated; however, situation awareness depended on operating concept and sector. In the en route sector, the highest levels of situation awareness were found for ATCo-managed separation assurance and automation-managed spacing. In the transitional sector, the highest situation awareness occurred when ATCos performed both functions. The numbers of loss of separation were highest for ATCo-managed separation assurance; sector complexity depended on weather, but only for automation-managed separation assurance. Spacing efficiency was highest for ATCo-managed separation assurance, but more communications were required.Conclusion: In air traffic management, separation assurance and spacing functions interact with each other in determining ATCo workload, situation awareness, and performance, depending on sector characteristics and weather. Therefore, evaluations of NextGen-automation solutions must include multiple concepts of operation, and involve different sectors and environmental conditions.

Air traffic predictability framework – Development, performance evaluation and application

The performance of the Air Traffic Flow & Capacity Management (ATFCM) function relies fundamentally on the accuracy of air traffic predictability. Characterising this accuracy and assessing the potential benefits of increased accuracy is fundamental to enhance the performance of the Air Traffic Management (ATM) system and identifying areas that require improvement. This paper develops a framework to assess air traffic predictability. It validates the proposed framework with real operational data and applies it to the Maastricht Upper Area Control centre. The paper develops a methodology to assess the benefits of the deployment of enhanced predictability including capacity, resulting from improved operational concepts such as Airport-Collaborative Decision Making (A-CDM).

A Hidden Markov Model for short term prediction of traffic conditions on freeways

Accurate short-term prediction of traffic conditions on freeways and major arterials has recently become increasingly important because of its vital role in the basic traffic management functions and trip decision making processes. Given the dynamic and stochastic nature of freeway traffic, this study proposes a stochastic approach, Hidden Markov Model (HMM), for short-term freeway traffic prediction during peak periods. The data used in the study was gathered from real-time traffic monitoring devices over six years on a 60.8-km (38-mile) corridor of Interstate-4 in Orlando, Florida. The HMM defines traffic states in a two-dimensional space using first-order statistics (Mean) and second-order statistics (Contrast) of speed observations. The dynamic changes of freeway traffic conditions are addressed with state transition probabilities. For a sequence of traffic speed observations, HMMs estimate the most likely sequence of traffic states. The model performance was evaluated using prediction errors, which are measured by the relative length of the distance between the predicted state and the observed state in the two-dimensional space. Reasonable prediction errors lower than or around 10% were obtained from HMMs. Also, the model performance was not remarkably affected by location, travel direction, and peak period time. The HMMs were compared to two naïve predication methods. The results showed that HMMs perform better and are more robust than the naïve methods. Therefore, the study concludes that the HMM approach was successful in modeling short-term traffic condition prediction during peak periods and in accounting for the inherent stochastic nature of traffic conditions.

An On-Demand Queue Management Architecture for a Programmable Traffic Manager

A queue manager (QM) is a core traffic management (TM) function used to provide per-flow queuing in access and metro networks; however current designs have limited scalability. An on-demand QM (OD-QM) which is part of a new modular field-programmable gate-array (FPGA)-based TM is presented that dynamically maps active flows to the available physical resources; its scalability is derived from exploiting the observation that there are only a few hundred active flows in a high speed network. Simulations with real traffic show that it is a scalable, cost-effective approach that enhances per-flow queuing performance, thereby allowing per-flow QM without the need for extra external memory at speeds up to 10 Gbps. It utilizes 2.3%-16.3% of a Xilinx XC5VSX50t FPGA and works at 111 MHz.

An efficient mechanism for service OAM performance monitoring metric collection for MEF carrier ethernet

The Metro Ethernet Forum, which is setting global carrier Ethernet standards, has recently announced a Carrier Ethernet Generations Framework. The Framework defines three carrier Ethernet generations: carrier Ethernet 1.0, carrier Ethernet 2.0, and Future. The Future generation focuses on achieving the goals of simplified management and automation of carrier Ethernet services (e.g., service configuration, activation, and monitoring). In addition to making the service operational, this includes on-demand and proactive performance monitoring for assessing the quality of the service; traffic management; and compliance with the service level agreement. This article proposes a more efficient and scalable performance measurement metrics collection architecture, using Internet Protocol Detail Record Streaming Protocol for the new generation back office. The goal is to move multiple service operators away from SNMP, with its known limitations, to a more compact and powerful standards-based streaming protocol and XML-based data modeling language. The intended audience for this article includes MSOs deploying a DPoE Network with devices, including DPoE Systems, converged cable access platforms, and DPoE optical network units, that implement service OAM PM and traffic management functionality based on MEF specifications. The audience also includes application and equipment suppliers who are developing back office metrics collection systems and DPoE/MEF equipment.

Design and Implementation of Unified Network Security System support for Traffic Management

The importance of networking capability is gaining more weight for enterprise business and high-speed Internet access with guaranteed security management is essential to companies. This paper presents a unified network security management solution to support high-speed Internet access, active security management, traffic classification and control. The presented system provides firewall, VPN, intrusion detection, contents filtering, traffic management, QoS management, and history log functions in unified manner implemented in a single appliance device located at the edge of enterprise networks. This will enable cost effective unified network security solution to companies.

Metrics for Traffic Complexity Management in Self-Separation Operations

Distributed control concepts, where some air traffic management functions are performed by airspace users, have been proposed to help address the rising demand for air travel. For example, delegation of traffic separation responsibility to the flight crew can be enabled by new cockpit automation that supports the separation task for that aircraft. As more equipped aircraft provide their own separation service, it creates the potential to adapt airspace capacity to the increasing demand. However, this distribution of tasks and decision making raises the concern that independent user actions will increase the complexity of the traffic system, possibly leading to a disruption of scheduled operations as safety is maintained. To address this concern, the authors propose the introduction of decision-making metrics for preserving user trajectory flexibility. The hypothesis is that such metrics, when added to trajectory management automation, will make independent user actions naturally mitigate traffic complexity. In this paper, trajectory flexibility is formally and mathematically defined in terms of robustness and adaptability to disturbances. A metric estimation method is developed assuming discrete time and discrete maneuvering-degree-of-freedom changes, resulting in a map of flexibility over the trajectory solution space which can be used by a real-time trajectory planner. Initial insights into the relationship between trajectory planning using this metric map and traffic complexity are demonstrated through analyzing a simple scenario including required time of arrival and traffic constraints. The scenario demonstrates that preserving flexibility results in reducing relative heading between aircraft—one method for potentially reducing apparent traffic complexity.

An optimal downsampling procedure for microscopic simulation modeling of transportation networks: a proof-of-concept study

This paper investigates and demonstrates the concept of scalability of microscopic traffic simulation systems as a means of reducing the associated computational requirements and maximizing their potential support for real-time traffic control and management functions. The primary goal of this research is to examine the feasibility of transforming the original simulation environment into a downsampled simulation environment, where fewer representative entities are simulated. This must be achieved, however, while retaining maximum fidelity to microscopic simulation properties and preserving most of the macroscopic characteristics. The methodology is presented as an optimization problem whose objective is to minimize the errors resulting from the transformation process and to seek optimal values of the behavioral parameters in the downsampled environment. In this proof-of-concept stage, experimental analysis was conducted on a homogeneous freeway segment using one of the well-known, and arguably sufficiently calibrated, car-following models developed by General Motors Laboratories (GM3). The results were promising and showed that optimal relationships between the behavioral parameters in both environments can be established to minimize the information loss associated with the transformation process.Key words: microscopic simulation, scalability, computational efficiency, downsampling, car-following models.