Traffic Management Schemes Research Articles

Passing behaviour on two-lane suburban arterials: an observation under mixed traffic with a significant fraction of battery-run e-rickshaws

In recent times, battery-operated e-rickshaws have gained popularity as a mode of Intermediate Public Transport (also known as paratransit mode) in the majority of the developing countries. They are growing very fast and have started replacing existing motorised paratransit vehicles. Static and dynamic characteristics of such vehicles are, however, quite different than those of motorised ones, and as a result of it, they interfere with operations of traffic while sharing the same road space to a great extent. The focus of this research was, therefore, to understand their impact on traffic when it is mixed in character. Based on a field study on two suburban arterials, the paper has shown that the rising growth of e-rickshaws increases the frequency of vehicle platooning and eventually results in a significant speed drop in the traffic stream. Analysis of overtaking data obtained from about 50 test runs on each study section reveals that a section of drivers tend to undertake unsafe overtaking attempts and number of such offenders’ crops up when the proportion of such modes increases further. In view of the practical implications of increasing the physical size of the existing infrastructure, the present paper explicates the need for suggesting appropriate traffic management schemes under the circumstances of mixed traffic dynamics.

A multi-service differentiation traffic management strategy in SDN cloud data center

The cloud computing technology makes services more diverse, and users have different needs for multi-service experience. The operator cloud data centers support more services and users, resulting in a rapid growth in traffic. Consequently, the traditional network architectures and technologies can no longer meet these traffic management requirements. To address this problem, this paper proposes a multi-service differentiated (MSD) traffic management strategy for the operator SDN cloud data center. The MSD traffic management rules and model are designed according to the operational requirements in cloud data centers. Fibonacci tree optimization algorithm (FTO) is improved for matching the MSD model to optimize the differentiated traffic management solutions. An operator SDN cloud data center test platform is established to verify the effectiveness of the strategy. The results show that the MSD-FTO strategy can obtain multiple differentiated traffic management schemes through one single optimization and it outperforms ECMP. In addition, it can properly manage the multi-service traffic and greatly improve the network performance and service experience in cloud data centers.

Incorporating security in opportunistic routing and traffic management in opportunistic sensor network

Nowadays, wireless sensors or opportunistic sensor networks (OSN) and its technologies are mainly used for bridging the spaces between the physical world and the virtual electronics. Due to several potentials like limited storage, unreliable communication, higher latency in communication and unattended operations of networks and sensor node powers, OSN faces many problems on routing and traffic management. Security on OSN involves routing and data aggregation deployed in OSN involves collaborations among nodes on the network due to ad-hoc nature. To solve these problems, our proposed method involves the novel traffic management scheme and opportunistic secure routing on sensor networks. This method involves mainly three algorithms such as range-based clustering (RBC) for clustering the sensor nodes, minimum waiting time routing (MWTR) algorithm for routing the data packets and in order to provide secure communication while coalition attack and replica attacks present on the network, we propose light weight key generation mechanism (LWKG). Thus, our proposed system provides less computational complexity on the networks and we provide effective results on experimental evaluation based on average packet reception ratio, detection ratio of replicas, connectivity and coalition attack resistance.

A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places

Rising interest in the field of Intelligent Transportation Systems combined with the increased availability of collected data allows the study of different methods for prevention of traffic congestion in cities. A common need in all of these methods is the use of traffic predictions for supporting planning and operation of the traffic lights and traffic management schemes. This paper focuses on comparing the forecasting effectiveness of three machine learning models, namely Random Forests, Support Vector Regression, and Multilayer Perceptron—in addition to Multiple Linear Regression—using probe data collected from the road network of Thessaloniki, Greece. The comparison was conducted with multiple tests clustered in three types of scenarios. The first scenario tests the algorithms on specific randomly selected dates on different randomly selected roads. The second scenario tests the algorithms on randomly selected roads over eight consecutive 15 min intervals; the third scenario tests the algorithms on random roads for the duration of a whole day. The experimental results show that while the Support Vector Regression model performs best at stable conditions with minor variations, the Multilayer Perceptron model adapts better to circumstances with greater variations, in addition to having the most near-zero errors.

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.

Development of a Microsimulation Model for Motorway Roadworks With Narrow Lanes

This paper presents a newly developed microsimulation model for motorway roadwork sections to evaluate the efficiency of different temporary traffic management schemes (TTMSs) such as the use of narrow lanes, offside and inside lane closures. The effect on traffic performance of various parameters (e.g. flow rates, percentage of heavy goods vehicles (HGVs), roadwork zone lengths and speed limits) has been tested. The model was built using the FORTRAN programing language. It was developed based on car-following, discretionary lane changing, mandatory lane changing, gap acceptance and narrow lanes rules. Data from four sources (including data taken from different UK motorway sites) were collected and analyzed. The data were used in the verification, calibration and validation processes of the model. Observations from motorway roadwork sites with the narrow lanes scheme show certain prominent drivers’ behaviors, namely avoiding passing HGVs traveling on adjacent lanes and lane repositioning before passing a HGV. Such behaviors were included in the modeling process which suggested that the presence of HGVs had a noticeable impact on reducing section capacity.

Weighted Multi-attribute Framework to Identify Freeway Incident Hot Spots in a Spatiotemporal Context

The present work used the analytical hierarchy process supported by a machine learning technique (namely random forest variable importance) in a geospatial environment to identify traffic-incident hot spots where seven incident attributes (incident severity level, vehicle type, number of vehicles involved, number of weather condition factors, number of lanes blocked, time period, and incident duration) and incident frequency were considered. Using a frame of raster representation, each factor was ranked and weighted based on the literature, expert surveys, and random forest technique to perform hot spot analysis and generate a critical road index map. Statistical analysis of spatial clustering and hot spot spatial densities was carried out based on Moran’s I method of spatial autocorrelation, Getis–Ord Gi* statistics, and point kernel density. Based on weighted attributes of more than 130,000 incidents from Harris County, Texas, for the period between 2004 and 2013, the method successfully identified critical road segments and highlighted factors that contribute to criticality. The results show that high-severity segments are located in the downtown area during the period of 2004 to 2010, and shifted northwest of the downtown area during the period of 2010 to 2013. The random forest variable importance revealed that the number of lanes blocked along with time periods was the most critical factor affecting the severity of traffic incidents. Though the application of the method was demonstrated using incident database from Harris County, it can be generalized to other cities. Traffic agencies can use the suggested approach to garner and visualize reliable information that ensures better traffic management schemes.

Microscopic Estimation of Road Impedance by Decomposing Traffic Delay into Individual Road Segments: An Analytical Approach

Urban road dynamic impedance calculation is the important basis of dynamic traffic assignment and real-time traffic management and control scheme. The current impedance calculation is mostly based on BPR function, and the BPR function is derived from macroscopic statistical laws, in which the microscopic characteristics of traffic flow are insufficiently described. In order to more accurately express the change laws of traffic impedance at the microscopic level, a stochastic dynamic traffic assignment algorithm based on road impedance function is designed to analyze the time impedance of congested roads under random dynamic traffic assignment under different conditions of road network saturation. Compared with the current model, the comprehensiveness and portability are greatly improved. The results show that the impedance calculation error of this method is less than 10% when the load degree of the road is lower than 0.85, which proves that the method has good precision under unsaturated flow conditions.

A new design and evaluation approach for managed lanes from a sustainability perspective

This article presents a comprehensive framework that optimizes traffic management measures to reduce emissions and fuel consumption and evaluates their operating and secondary environmental impacts. A new managed lane strategy is presented that minimizes the total passenger travel time on the freeway. This managed lane focuses on simultaneously optimizing toll schemes on the high occupancy toll (HOT) lane and the operation of the general purpose (GP) lanes. With reduced congestion and thus reduced number of acceleration and deceleration events associated with stop-and-go traffic, fuel efficiency increases and emissions are reduced. PARAMICS microscopic traffic simulator, which considers the behavior of individual vehicles (e.g., acceleration, deceleration, and lane changing behavior) is used to collect traffic performance and emission data for estimating mobility and emissions measures (i.e., operating phase). While previous studies only focused on operating phase impact, this study uses a Leontief input-output (I-O) model to establish the financial flow between industries to capture the large-scale environmental impacts of HOT lane deployment (i.e., secondary impact). The core of the new evaluation approach lies in its capability to provide a more thorough assessment of the environmental impacts of traffic management schemes by quantifying the impacts associated with the interplay between the activities of various sectors and the transportation industry. The I-O model is utilized to assess the indirect impacts of induced demand generated from network improvements and evaluate the environmental impacts of HOT lane deployment in regional economies. The developed approach is applied to The City of Calgary. The results of the study show that the traditional approaches that only evaluate the operating phase impacts of transportation strategies considerably overestimate the reduction of greenhouse gas (GHG) emissions.

METHODOLOGIES’ APPLICATION FOR THE DEVELOPMENT OF TRANSPORT STRATEGY (URBAN PASSENGER TRANSPORT)

Introduction. The paper substantiates the relevance of the development of regional transport strategies, which are basic for the implementation of organizational, technical and economic solutions aimed at the reorganization of modern urban and regional transport systems. Moreover, it is indicated that the development of strategies should be accompanied by a comprehensive and coordinated survey and modeling of transport systems, including within the framework of the regional programs of integrated development of transport infrastructure, projects for the creation of integrated traffic management schemes, as well as integrated schemes of transport services to the population. The aim of the research is to formulate the tasks consisting the proposals at the coordination of all the above and other programs that ensure long-term planning, continuity of decisions and budget savings directed to the development of transport in the regions.Materials and methods. Methods of system analysis, theory of transport systems, as well as methods that are widely used in the community of transport engineers, including various options for calculation and experimental methods of transport planning and modeling of transport systems for the city or agglomeration, are used to solve the research problems.Results. Based on the information synthesis of the stages of regional transport strategies development, a sequence of actions is proposed, leading to the required methodological and organizational approaches, with which it is possible to perform the solution of the strategic task. The authors formulate a list of major programs that need to be addressed at the level of regional ministries (departments) or departments of transportation municipalities with the goal of implementing transport strategies and aligning them with projects noted above. Discussion and conclusions. The main result of this work is the conclusion about the feasibility of rational actions of regional and municipal transport governing bodies, which could be planned within the framework of regional (municipal) transport strategies. The basis is the application of the proposed methodological and organizational recommendations.

A Decision Support System for Proactive-Robust Traffic Network Management

Real-time traffic network management systems are envisioned to provide network operators with decision support capabilities to alleviate recurrent and non-recurrent congestion. These capabilities involve predicting the network congestion dynamics and facilitating the development of proactive traffic management schemes that integrate traffic control and demand management strategies. However, traffic networks are subject to numerous sources of stochasticity that make it difficult to accurately predict their operational conditions and generate effective traffic management schemes to cope with these conditions. This paper presents a decision support system for proactive-robust traffic network management, which accounts for uncertainty in the network operational conditions. The objective is to develop robust traffic management schemes such that the network overall performance remains close to optimality under all possible future operational conditions. The modeling framework of the system is presented, which adopts a rolling horizon framework that integrates a meta-heuristic search algorithm and a dynamic traffic assignment simulation-based methodology. The system performance is examined through application to the traffic network of the US-75 corridor in Dallas, TX, USA.

Mixed-Integer Linear Programming for Optimal Scheduling of Autonomous Vehicle Intersection Crossing

We propose an urban traffic management scheme for an all connected vehicle environment. If all the vehicles are autonomous, for example, in smart city projects or future's dense city centers, then such an environment does not need a physical traffic signal. Instead, an intersection control server processes data streams from approaching vehicles, periodically solves an optimization problem, and assigns to each vehicle an optimal arrival time that ensures safety while significantly reducing number of stops and intersection delays. The scheduling problem is formulated as a mixed-integer linear program (MILP), and is solved by IBM CPLEX optimization package. The optimization outputs (scheduled access/arrival times) are sent to all approaching vehicles. The autonomous vehicles adjust their speed accordingly by a proposed trajectory planning algorithm with the aim of accessing the intersection at their scheduled times. A customized traffic microsimulation environment is developed to determine the potentials of the proposed solution in comparison to two baseline scenarios. In addition, the proposed MILP-based intersection control scheme is modified and simulated for a mixed traffic consisting of autonomous and human-controlled vehicles, all connected through a wireless communication to the intersection controller of a signalized intersection.

Efficient Spectrum Sensing for Aeronautical LDACS Using Low-Power Correlators

Air traffic has seen tremendous growth over the past decade pushing the need for enhanced air traffic management schemes. The $L$ -band digital aeronautical communication system (LDACS) is gaining traction as a scheme of choice, and aims to exploit the capabilities of modern digital communication techniques and computing architectures. Cognitive radio-based approaches have also been proposed for LDACS to improve spectrum efficiency and communication capacity; however, these require intelligent compute capability in aircrafts that enforce limited space and power budgets. This paper proposes the use of multiplierless correlation to enable spectrum sensing in LDACS air-to-ground links, and its integration into the on-board LDACS system. The proposed architecture offers improved performance over traditional energy detection (ED) even at low signal-to-noise ratio (SNR) with lower energy consumption than a multiplier-based correlator, while also assisting in receiver synchronization. We evaluate the proposed architecture on a Xilinx Zynq field-programmable gate array and show that our approach results in 28.3% reduction in energy consumption over the multiplier-based approach. Our results also show that the proposed architecture offers 100% accuracy in detection even at −12-dB SNR without requiring additional circuitry for noise estimation, which are an integral part of ED-based approaches.

End‐to‐End Deep Learning Methodology for Real‐Time Traffic Network Management

AbstractThis article presents a novel real‐time traffic network management system using an end‐to‐end deep learning (E2EDL) methodology. A computational learning model is trained, which allows the system to identify the time‐varying traffic congestion pattern in the network, and recommend integrated traffic management schemes to reduce this congestion. The proposed model structure captures the temporal and spatial congestion pattern correlations exhibited in the network, and associates these patterns with efficient traffic management schemes. The E2EDL traffic management system is trained using a laboratory‐generated data set consisting of pairings of prevailing traffic network conditions and efficient traffic management schemes designed to cope with these conditions. The system is applied for the US‐75 corridor in Dallas, Texas. Several experiments are conducted to examine the system performance under different traffic operational conditions. The results show that the E2EDL system achieves travel time savings comparable to those recorded for an optimization‐based traffic management system.

Temporal Centrality-Balanced Traffic Management for Space Satellite Networks

Structural centrality metrics of the networks are important features to identify central nodes or links, to assess network vulnerability against removal of nodes or links, and to manage or control the network traffic to reduce the network congestion. Classic graph-based centrality metrics are not effective in dynamic space satellite networks due to the changing connectivity and they cannot effectively capture important temporal properties. In this paper, we employ a new technique called time-varying graphs (TVGs) to express temporal concepts and definitions of space satellite networks and propose several temporal centrality metrics in order to account for the temporal features of the networks. To calculate these temporal centrality metrics, we adopt a modified matrix multiplication algorithm to find the shortest journeys between nodes in the TVG. A temporal centrality-balanced traffic management scheme is further developed to enhance the network performance, which is integrated into a temporal centrality-balanced routing algorithm. To show the performance of the proposed centrality metrics and centrality balanced routing algorithm, we conduct some experiments performed on a scenario of the LEO satellite network. The simulation results show that the proposed centrality metrics are more powerful to analyze the evolution of time-varying centrality and vulnerability of space satellite networks than topological adaptation of vanilla centrality methods. Besides, the proposed centrality-balanced routing algorithm reaches the superior quality values of the network performance (in terms of several factors) in comparison with those attained by the traditional shortest journeys routing algorithm and proves to be more powerful to support the high traffic load.

Novel Active Queue Management Scheme for Routers in Wireless Networks

Congestion stability and prevention is the main aim of any decent network traffic management scheme. The protocol that drives congestion control in IP networks, called The Transmission Control Protocol (TCP), is plagued by bandwidth underutilization when implemented on wireless networks. The management of network trunk queue size is the goal of this paper, along with its antecedent delay issues with Active Queue Management (AQM) algorithms that overcomes the bandwidth underutilization issue faced by TCP flows in wireless networks. Two adaptive TCP models, specifically built for wireless networks, were created. This made it possible to implement feedback control techniques in the chosen AQM design for queue management. The two AQM policies were created with the help of Model Predictive Control (MPC) and Self-Tuning Control (STC). A very thorough comparison was made between the developed AQM schemes and two Early Detection Controllers, namely, Random Early Detection (RED) and Fixed-Parameter Proportional Integral (PI). Under varying wireless network conditions, the results obtained from simulating the proposed queue management scheme in MATLAB® confirmed that the fixed PI and RED controllers were, by comparison, inferior in performance. Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved.

High-occupancy Vehicle Lanes and Tradable Credits Scheme for Traffic Congestion Management: A Bilevel Programming Approach

High-occupancy vehicle (HOV) lanes, which are designed so as to encourage more people to use high-capacity travel modes and thus move more people in a single roadway lane, have been implemented as a lane management measure to deal with the growing traffic congestion in practice. However, the implementation has shown that some HOV lanes are not able to achieve the expected effects without proper HOV lane settings. In this study, the tradable credits scheme (TCS) is introduced to improve the HOV lane management and an optimal capacity of HOV lanes in a multilane highway is investigated to match TCSs. To approach the investigation, a bilevel programming model is proposed. The upper-level represents the decision of the highway authority and the lower-level follows the commuters’ user equilibrium with deterministic demand. The potential influence of TCSs is further investigated within the proposed framework. A modified genetic algorithm is proposed to solve the bilevel programming model. Numerical examples demonstrate that combining TCSs with the HOV lane management can obviously mitigate traffic congestion.

IoT Based Traffic Management Tool with Hadoop Based Management Scheme for Efficient Traffic Management

The paper presents a traffic management tool based on the concepts of IoT, GPS and Hadoop which is used to predetermine the traffic prediction of the area and based on this suggests alternative routes for the upcoming vehicle at the junction of traffic congestion. The mapping of information from the congestion area to the main server is achieved using sensor-based technology, while the data is analysed using Hadoop technology and alternative route can be determined using GPS technology. Thus, using all these technology at once, we can prevent the traffic jams at the area and also predict the traffic structure of the area. Using GPS technology, we can also suggest alternative routes to the upcoming vehicle to that particular area.

IoT-based traffic management tool with Hadoop-based management scheme for efficient traffic management

The paper presents a traffic management tool based on the concepts of IoT, GPS and Hadoop which is used to predetermine the traffic prediction of the area and based on this suggests alternative routes for the upcoming vehicle at the junction of traffic congestion. The mapping of information from the congestion area to the main server is achieved using sensor-based technology, while the data is analysed using Hadoop technology and alternative route can be determined using GPS technology. Thus, using all these technology at once, we can prevent the traffic jams at the area and also predict the traffic structure of the area. Using GPS technology, we can also suggest alternative routes to the upcoming vehicle to that particular area.

Traffic Management for Heterogeneous Networks With Opportunistic Unlicensed Spectrum Sharing

This paper studies how to maximize the per-user-based throughput in an $M$ -tier heterogeneous wireless network (HetNet) by optimally managing traffic flows among the access points (APs) in the HetNet. The APs in the first $M-1$ tiers can use the licensed spectrum at the same time whereas they share the unlicensed spectrum with the APs in the $M$ th tier by the proposed opportunistic CSMA/CA protocol. We characterize the statistical property of the cell load and channel access probability of each AP using a general AP association scheme. For an AP in each tier, the tight bounds on its mean spectrum efficiencies in the licensed and unlicensed spectra are derived in a low-complexity form for general random channel gain and AP association weight models and they can give some insights on how channel gains, AP association weights and void AP probabilities affect the mean spectrum efficiencies. We define the per-user link throughput and per-user network throughput based on the derived the mean spectrum efficiencies and maximize them by proposing the decentralized and centralized traffic management schemes for the APs in the first $M-1$ tiers under the constraint that the per-user link throughput of the tier- $M$ APs must be above some minimum required value. Finally, a numerical example of coexisting LTE and Wi-Fi networks is provided to validate our derived results and findings.