Optimal Traffic Management Research Articles

Ismail’s Ratio Conquers New Horizons The Non-Stationary M/D/I Queue’s State Variable Closed Form Expression with Queueing Applications to Traffic Management Optimization

Ismail’s Ratio Conquers New Horizons The Non-Stationary M/D/I Queue’s State Variable Closed Form Expression with Queueing Applications to Traffic Management Optimization

Acquisition of Flight Data from Mini Unmanned Aerial Systems

Swift progress in unmanned aerial vehicle (UAV) system technology mandates effective real-time monitoring and accurate flight data capture for building an optimum target-based cutting-edge UAVs. In the current epoch of modern-UAV era, small size and mission-oriented UAV prototypes are increasingly pervasive. This study highlights the design of a data acquisition target-based mini-UAV and introduces a novel telemetry software solution to safe and reliable communication between mini-UAV and ground control station (GCS). The primary aim is to demystify the proposed wireless communication software solution on mini-UAV capability in obtaining and managing comprehensive flight data. While creating the appropriate target-based mini-UAV prototype architecture, the features that we aim to realize for the UAV components are addressed with a system approach and the component selection is presented in detail. The integration point of the selected components is conveyed in general terms. Thus, a guiding resource for future UAV studies will be presented. Then, the processes of obtaining the flight data of mini-UAVs will be addressed. At this point, the design logic of the required commands and background software will be conveyed. Obtaining the flight data of mini-UAVs will provide important scientific contributions to the literature in terms of aviation research and safety analysis. Obtaining the flight data of mini-UAVs will meet the data set needs for flight safety improvements and air traffic management optimization studies. A general analysis of the flight parameters obtained as a result of obtaining the flight data will be performed, the predictability of the parameters will be evaluated, and the analysis of the flight phases will be performed by presenting the relationships of the parameters according to the flight scenarios and the sections taken from the parameter set in the study. This study can be considered as a concrete example of situational monitoring of UAVs.

Examining the possibility of short-term prediction of traffic volume in smart city control systems with the use of regression models

This article deals with issues related to the optimization of traffic management in modern cities, the so-called Smart City. In particular, the article presents the process of evolution of the traffic flow prediction model at a selected crossroads in a selected city in Poland - the city of Rzeszów. Rzeszow is an example of a smart city equipped with an extensive system of real-time data collection and processing from multiple road points in the city. The research was aimed at a detailed analysis of the feasibility and degree of fit of different variants of the regression model: linear, polynomial, trigonometric, polynomial-trigonometric, and regression-based Random Forest algorithm. Several studies were carried out evaluating different generations of models, in particular, an analysis was carried out based on which the superiority of the trigonometric model was demonstrated. This model had the best fit and the lowest error rate, which could be a good conclusion for widespread use and implementation in Smart City supervisory systems.

Research on Optimization of Traffic Management in Smart Tourism Cities for Sustainable Development

Abstract In order to accelerate the efficiency of urban traffic operation in tourism and maximize the efficiency of urban traffic operation. This paper conducts systematic research on urban traffic conditions and proposes the construction of an intelligent urban traffic management system. First of all, the system structure is designed from the whole, and the system is divided into the information collection stage, the information processing stage, and the information release stage to clarify the functions that the system needs to realize. The design of the system includes queries for road conditions, bus transfer, walking navigation, and background management of each functional module. By building a good system development environment, system functions can be realized in code. Visitor satisfaction was evaluated using the fuzzy comprehensive evaluation method, and simulation experiments were carried out to evaluate the system’s optimization effect. The satisfaction of tourists in terms of convenience and comfort is improved compared with that before the use of the system. After the implementation of the intelligent traffic management system, the delay time of the intersection with the most complex road conditions in peak hour E is reduced by 19.8 s. The delay time in the peak hour is reduced by 4 s. It shows that the system improves the satisfaction of tourists in terms of convenience and comfort reduces the possibility of congestion for the tourists and the residents to travel, and improves the utilization rate of the road. The utilization rate of the road.

Traffic Management Optimization via Iot-Enhanced Cooperative Vehicle-Infrastructure Systems

Traffic Management Optimization via Iot-Enhanced Cooperative Vehicle-Infrastructure Systems

Optimization Models for Urban Traffic Management

The main control tool for traffic management in urban areas is traffic light settings. The goal is to decrease the queue lengths at intersections. Usually, the duration of the green light of the traffic light is used for control. The control approach is based on the so-called “store-and-forward” model. However, this model does not reflect the stochastic nature of traffic dynamics. This study presents a model with some probabilistic conditions approximating real traffic behavior. An additional contribution concerns the definition of a bi-level optimization model that simultaneously optimizes the green light and traffic light cycle duration of an urban network of four intersections. Three traffic management optimization problems are defined and solved. Their solutions are graphically illustrated and commented on. Bi-level optimization outperforms by giving lower values of queue lengths compared to classical and stochastic nonlinear optimization problems in the considered network.

Towards Optimal TMS Integration Scenario Based on HRM and SWOT Analysis

To meet the collective goal of providing an efficient, sustainable, and safe transportation system, motorway managers are oriented toward establishing an optimal traffic management system (TMS). However, there are no straightforward guidelines through which to deploy the optimal integration scenario for traffic control centers (TCCs). An important component of TMSs is traffic control decisions, implying that human resources represent a critical feature of a TMS. In this study, we introduce human resource management (HRM) into the process of selecting the most appropriate integration scenario, which is achieved by developing a model to determine the number of employees. We formulated the problem as the mixed integer nonlinear programming problem based on the analysis of traffic incidents and road infrastructure data, considering also the employee voice and some HRM specificities in incident situations. Combining these findings with a SWOT analysis of the possible integration scenarios in Slovenia, we propose the most suitable scenario, in which TMS is organized as a cloud service. Moreover, this study predicts TCCs’ space requirements and identifies threats of neglecting HRM in TMS design. This paper can serve as a guideline and a baseline of expertise for motorway managers to create an effective and sustainable TMS in their region.

Application of Mathematical Modeling Methods for Operational Optimization of Urban Traffic in an Accident on a Section of the Road Network

Simulating traffic flows is an urgent task when choosing the optimal movement scheme of vehicles along the road network. One of the problems affecting the convenience of moving on the road network is road accidents. The article considers the problem of simulating the movement of vehicles when bypassing the scene of a traffic accident. A mathematical model reflecting the movement of motor vehicles in a dense traffic flow has been developed in the case when traffic along one of the lanes is blocked as a result of a traffic accident or repair work. The time intervals between cars in each lane are assumed to be subordinate to Erlang's law. The scene of the incident is presented as a queuing system. Using the method of pseudo-states and Markov chains, the author calculated the movement characteristics of the vehicles near the scene of a traffic accident. Simulating the movement of traffic flows near the accident sites allows predicting the delays caused by vehicles, and adjusting the optimal traffic management schemes.

Compositional modeling of railway Virtual Coupling with Stochastic Activity Networks

The current travel demand in railways requires the adoption of novel approaches and technologies in order to increase network capacity. Virtual Coupling is considered one of the most innovative solutions to increase railway capacity by drastically reducing train headway. The aim of this paper is to provide an approach to investigate the potential of Virtual Coupling in railways by composing stochastic activity networks model templates. The paper starts describing the Virtual Coupling paradigm with a focus on standard European railway traffic controllers. Based on stochastic activity network model templates, we provide an approach to perform quantitative evaluation of capacity increase in reference Virtual Coupling scenarios. The approach can be used to estimate system capacity over a modelled track portion, accounting for the scheduled service as well as possible failures. Due to its modularity, the approach can be extended towards the inclusion of safety model components. The contribution of this paper is a preliminary result of the PERFORMINGRAIL (PERformance-based Formal modelling and Optimal tRaffic Management for movING-block RAILway signalling) project funded by the European Shift2Rail Joint Undertaking.

Modern Sediment Model of Traffic Flow

Abstract The work deals with the mathematical modeling of traffic phenomena. The submitted model is based on a prospective analogy of some described phenomena with particle sedimentation. Both the qualitative analysis of the model and the numerical experiment is carried out. Qualitative results of the research have been compared with the known data of supervision of the traffic on city highways. As a result, the main control parameters which can use for optimal traffic management are highlighted and justified.

Accelerated construction of urban intersections with Portland Cement Concrete Pavement (PCCP)

The frequent maintenance required on asphalt concrete (AC) pavement sections has made reconstruction with portland cement concrete pavement (PCCP) a feasible alternative. However, many constructability issues need to be addressed in order to realize the full potential of this alternative. Accelerated paving encompasses three classes of activities: methods to accelerate the rate of strength gain, methods to minimize the construction time, and traffic control strategies to minimize user delay. In this paper a case study will be presented in which an AC intersection was reconstructed with portland cement concrete pavement. The entire reconstruction of the intersection, including demolition of the AC pavement and its replacement with PCCP, took place over a period of three days, starting on Thursday evening and opening the intersection to the traffic on Sunday afternoon. This paper documents this effort in order to provide practitioners additional options for rapid reconstruction of urban intersections and includes documentation of the construction process, traffic management strategies, and an analysis of the costs. The results of this investigation can be used to educate pavement construction professionals and the academic community on the use of PCCP for accelerated reconstruction of major urban intersections with minimal user and traffic disruption, using innovative construction techniques and traffic management optimization principles. This investigation produced valuable information to demonstrate that concrete pavements can be constructed efficiently and quickly.

Decomposition and distributed optimization of real-time traffic management for large-scale railway networks

This paper introduces decomposition and distributed optimization approaches for the real-time railway traffic management problem considering microscopic infrastructure characteristics, aiming at an improved computational efficiency when tackling large-scale railway networks.Based on the nature of the railway traffic management problem, we consider three decomposition methods, namely a geography-based (GEO) decomposition, a train-based (TRA) decomposition, and a time-interval-based (TIN) decomposition, in order to partition the large railway traffic management optimization problem into several subproblems. In particular, an integer linear programming (ILP) model is developed to generate the optimal GEO solution, with the objectives of minimizing the number of interconnections among regions and of balancing the size of regions. The decomposition creates couplings among the subproblems, in terms of either capacity usage or transit time consistency; therefore the whole problem gets a non-separable structure. To handle the couplings, we introduce three distributed optimization approaches, namely an Alternating Direction Method of Multipliers (ADMM) algorithm, a priority-rule-based (PR) algorithm, and a Cooperative Distributed Robust Safe But Knowledgeable (CDRSBK) algorithm, which operate iteratively.We test all combinations of the three decomposition methods and the three distributed optimization algorithms on a large-scale railway network in the South-East of the Netherlands, in terms of feasibility, computational efficiency, and optimality. Overall the CDRSBK algorithm with the TRA decomposition performs best, where high-quality (optimal or near-optimal) solutions can be found within 10 s of computation time.

Minimum Time—Minimum Jerk Optimal Traffic Management for AGVs

A combined minimum time - minimum jerk traffic management system for the vehicle coordination in an automated warehouse is presented. The algorithm is organised in two steps: in the first, a simple minimum time optimisation problem is solved, in the second step, this time-optimal solution is refined into a smooth minimum jerk plan for the autonomous forklifts in order to avoid impulsive forces that may unbalance the vehicle. For the first step, we propose a novel approach based on Linear Programming, which guarantees convergence to the optimal solution starting from a feasible point, and a low computational overhead, which makes it suitable for real-time applications. The output of this step is a piecewise constant velocity profile for all the moving robots that ensures collision avoidance. The second step takes such speed profile and generates its smoothed version, which minimises the jerk while respecting the same levels of safety of the solution generated by the first step. We discuss the different solutions with simulation and experimental data.

Research on optimization of traffic management and control measures for expressway emergency based on VISSIM simulation

With the continuous enhancement of China’s economic strength, the scale and quantity of expressway construction are constantly improving, and the expressway network has basically covered the whole country. With the rapid increase of the number of vehicles, the traffic pressure is also increasing, which poses a more serious challenge to keep the expressway unblocked. First, the increasing traffic pressure is very likely to lead to the occurrence of various emergencies. Second, the completely closed expressway and non-central city promote the emergence of an independent environment, which makes the expressway once an emergency, will have a very negative impact on the effective use of the expressway. Third, every single accident on the expressway will lead to serious traffic jam. At the same time, if the emergency rescue on the expressway is not properly implemented, it will not only bring all kinds of negative obstacles to people’s lives, but also seriously affect people’s property safety and social order stability. Therefore, the construction and improvement of the expressway emergency and related emergency rescue system has gradually become the focus of people’s attention. Traffic management and control under incident conditions is an important link in the emergency rescue system, so this paper conducts research on traffic management and control measures under emergency conditions. Expressway design phase, normally takes into account the traffic management and control measures, such as the location of the median opening, the location of the emergency rescue centre, and the traffic organization mode. However, in-depth research should be carried out to ensure its rationality. This paper takes as an example of an expressway in our country, based on VISSIM simulation technology, quantitative analysis was carried out on the design phase of the traffic management and control measures, provide the basis for the optimization of traffic management and control measures. The evaluation method proposed in this paper is universal, and other expressways can also use this method to evaluate the effectiveness of traffic management and control measures in the design stage.

Optimal traffic management policies for mixed human and automated traffic flows

Abstract Although Autonomous Vehicles (AVs) will enhance mobility and safety, their impact on congestion is not clear yet. AVs may increase roadway capacity due to their connectivity features. The capacity enhancement highly depends on the AV proportion in traffic. This study models user equilibrium traffic assignment when the link capacity is a function of AV proportion of traffic. The mixed traffic flow of AVs and human-driven vehicles is considered as a multiclass traffic assignment problem. This problem is formulated as a non-linear complementarity problem which is solved to find optimal traffic management policies. We show that simple policies such as AV exclusive links can improve network performance in mixed traffic of AVs and human-driven vehicles. We also show that if these policies are implemented the network performance would be very close to system optimal condition even when users choose their routes selfishly following a user equilibrium. Results of numerical examples for a real size network show that management policies can decrease the gap between user equilibrium and system optimal to less than 1%.

Speed profile optimization of an electrified train in Cat Linh-Ha Dong metro line based on pontryagin's maximum principle

An urban railway is a complex technical system that consumes large amounts of energy, but this means of transportation still has been obtained more and more popularity in densely populated cities because of its features of high-capacity transportation capability, high speed, security, punctuality, lower emission, reduction of traffic congestion. The improved energy consumption and environment are two of the main objectives for future transportation. Electrified trains can meet these objectives by the recuperation and reuse of regenerative braking energy and by the energy - efficient operation. Two methods are to enhance energy efficiency: one is to improve technology (e.g., using energy storage system, reversible or active substations to recuperate regenerative braking energy, replacing traction electric motors by energy-efficient traction system as permanent magnet electrical motors; train's mass reduction by lightweight material mass...); the other is to improve operational procedures (e.g. energy efficient driving including: eco-driving; speed profile optimization; Driving Advice System (DAS); Automatic Train Operation (ATO); traffic management optimization...). Among a lot of above solutions for saving energy, which one is suitable for current conditions of metro lines in Vietnam. The paper proposes the optimization method based on Pontryagin's Maximum Principle (PMP) to find the optimal speed profile for electrified train of Cat Linh-Ha Dong metro line, Vietnam in an effort to minimize the train operation energy consumption.

A Method to Assess and Reduce Pollutant Emissions of Logistic Transportation under Adverse Weather

With the increase of vehicle activities and adverse weather under the background of modernization and global warming, more attention should be paid to vehicle emissions reduction in such circumstances for environmental protection and sustainable transportation. Different from some typical measures, e.g., relevant government policies, improvement of vehicle hardware technologies, and optimization of traffic management, this paper develop a new method based on emergency path optimization to evaluate and reduce pollutant emissions of logistic transportation under adverse weather. Firstly, we establish a calculation model of pollutant emissions (LT-PE model) to calculate the amount of vehicle pollutant emissions produced under adverse weather. Then, a co-evolving path optimization (CEPO) method-based ripple-spreading algorithm is proposed in order to reduce pollutant emissions. To validate the effectiveness of the proposed method, this paper selects fruit logistics transportation affected by typhoon in China’s Hainan Island as a case study. The results show that total vehicle pollutant emissions from the fruit transportation of 35 farms under the typhoon are 28.1% more than when there is no typhoon. The proposed method can reduce pollutant emissions by 21.2% compared with the traditional methods under typhoon.

A Vehicle-in-the-Loop (VIL) verification of an all-autonomous intersection control scheme

In this paper, an implementation of a vehicle-in-the-loop (VIL) verification environment is presented tailored to automated intersection traffic management applications. Although the fundamental goal of this implementation is to evaluate our proposed optimal traffic management scheme for autonomous vehicles, the test environment is configurable and reproducible to incorporate most of the real time intersection control methods for verification purposes. In our cyber-physical virtual testing environment, actual test vehicles and intersection control server at the physical layer interact with simulated vehicles in an implemented traffic microsimulation layer. The intersection control server continuously runs a Mixed Integer Linear Program (MILP) that was previously proposed by authors for vehicle arrival scheduling at all-autonomous intersections with no physical traffic lights. In addition to the novel intersection traffic management scheme at the cyber layer, this VIL environment also includes a fuel consumption tracker that is based on our proposed fuel rate estimation methods using the basic vehicle diagnostic data. Using this environment, the mobility efficacy and the fuel consumption reduction potential of the proposed intersection control scheme are compared with two baseline scenarios in VIL experiments.

A Bayesian Stochastic Kriging Optimization Model Dealing with Heteroscedastic Simulation Noise for Freeway Traffic Management

Since advanced traveler information and traffic management systems have become popular, it is vital to capture the joint impact of various strategies on transportation systems. Developing analytical models to incorporate traffic dynamics and travel behavior is challenging. Based on observation of the heteroscedasticity of the simulation noise in the stochastic simulator, this paper develops the Bayesian stochastic Kriging (BSK) model to adapt for the heteroscedastic noise and metamodel parameter uncertainty in a Bayesian framework, which enhances the existing surrogate-based optimization methods. This paper presents a metamodel for large scale simulation-based freeway traffic management optimization problems. Simulation-based optimization combines the advantages of simulation models and mathematical optimization methods. The parameter estimation of the BSK model is accomplished by the Bayesian inference. The proposed methodology enables the efficient use of large scale high-resolution traffic simulation models for simulation-based optimization, while accounting for travelers’ behavioral responses to information provision. We demonstrate the advantages of BSK compared to other existing metamodels using a numerical example of a synthetic network and a mathematical example. In a work zone scenario on a real-world freeway/arterial corridor of I-270 and MD-355 in the State of Maryland, the BSK model is applied to the freeway traffic management via optimizing the high-occupancy/toll rate and deploying dynamic message signs. Field traffic measurements by loop/microwave detections are used to calibrate travel demand and to supply the simulation parameters. The optimization results are promising in reducing the corridor-wide travel delay and enhancing the vehicle throughput. The online appendix is available at https://doi.org/10.1287/trsc.2018.0819 .

Traffic forecasting in Morocco using artificial neural networks

Due to industrialization and the growth of transportation systems, the number of vehicles continues to increase which causes a significant traffic jam problem especially in big cities. Consequently, the prediction of traffic flows is a key component to an optimal traffic management. As a solution to this issue, the present paper aims at applying the artificial intelligence of neural networks, which offers an interesting approach to modelling in complex, and nonlinear situations. Our resolution method is based on the design of a neural network to predict daily traffic flow. Then, the forecasted traffic flow is compared with a real dataset recorded on a road section and provided by a recognized infrastructure manager in Morocco. Indeed, neural networks have the ability to learn from the past and predict the future. In this study, various neural networks structures are exanimated and simulation results show that the best forecasts are obtained with the use of Multi-Layer Perceptron architecture that has a good generalization capacity with a total Mean Square Error of 0.00927 in the train set and 0.01321 in the test set.