Traffic Management Approach Research Articles

Effectiveness of Ring Roads in Reducing Traffic Congestion in Cities for Long Run: Big Almaty Ring Road Case Study

It is common to increase road capacity by constructing ring roads to reduce traffic congestion in city areas, although this is often found to be ineffective in the long run. Accordingly, this study investigates various traffic congestion management approaches and their effectiveness in major cities, and explores an identical transport problem in Almaty, Kazakhstan: The Big Almaty Ring Road (BAKAD). Several case examples from the existing literature are examined in which various approaches were taken for managing traffic congestion problems, and these approaches are classified into three concepts. The first concept comprises heavy engineering measures such as ring road development, new road construction, expansion of existing roads, etc. Such measures can initially reduce traffic congestion, but often become ineffective with time due to the generation of induced traffic. Many cities have taken Push and Pull measures that ensure more efficient use of existing capacity and have initiated environmentally friendly alternative transportation modes such as decreased car usage; promotion of public transport, biking and walking; minimization of the necessity of people’s movement by changing urban land use patterns; and so on. These approaches have been found to be effective in providing sustainable transportation solutions and are classified as concept 2. Nevertheless, Push and Pull measures might not be enough for managing traffic congestion, and it might be necessary to increase the road capacity through heavy engineering measures, especially if the city experiences heavy transit traffic. This combined approach is categorized as concept 3. Consequently, the BAKAD project is examined under the umbrella of three concepts, and recommendations are provided based on the findings from the experience of different cities and interviews with experts from Almaty city. Both the results and recommendations developed are relevant for this specific case only, and are not necessarily transferable.

A dynamic integer programming approach for free flight air traffic management (ATM) scenario with 4D-trajectories and energy efficiency aspects

The growth in demand for air transport has generated new challenges for capacity and safety. In response, manufacturers develop new types of aircraft while airlines open new routes and adapt their fleet. This excessive demand for air transport also leads to the need for further investments in airport expansion and ATM modernization. The current work was focused on the ATM problem with respect to new procedures, such as free flight, for addressing the air capacity issues in an environmental approach. The study was triggered by and aligned with the following performance objectives set by EUROCONTROL and the European Commission: (1) to improve ATM safety whilst accommodating air traffic growth; (2) to increase the ATM network efficiency; (3) to strengthen ATM’s contribution to aviation security and to environmental objectives; (4) to match capacity and air transport growth. The proposed mathematical model covers the aforementioned objectives by focusing on energy losses and costs of flights under the scenario of a controlled free flight and a unified airspace. The factors enhanced in the model were chosen based on their impact on the ATM energy efficiency, such as the airborne delays and flight duration, the delays due to ground holding, the flight cancellation, the flight speed deviations and the flight level alterations. Therefore, the presented mathematical model minimizes the energy costs due to the above terms under certain assumptions and constraints. Finally, simulation case studies, used as proof tests, have been conducted under different ATM scenarios to examine the complexity and the efficiency of the developed model.

Traffic management with elephant flow detection in software defined networks (SDN)

Multipath routing is to distribute the incoming traffic load among available paths between source and destination hosts. Instead of using the single best path, multipath scheme can avoid the congested path. Equal Cost Multi-Path (ECMP) performs the static traffic splitting based on some tuples of the packet headers. The limitation of ECMP does not consider the network parameters such as bandwidth and delay. Unlike the traditional networks, Software-Defined Network (SDN) has many advantages to support dynamic multipath forwarding due to its special characteristics, such as separation of control and data planes, global centralized control, and programmability of network behavior. In this paper, we propose a new architecture design for dynamic multipath-based traffic management approach in the SDN, which comprises of five components: detecting long (elephant) flow, computing shortest paths, estimating end-to-end delay and bandwidth utilization, calculating least cost path and rerouting traffic flow from the ongoing path to the best path. The simulation environment is created through the usage of Mininet emulator and ONOS controller. The evaluation outcomes show that the proposed traffic management method outperforms the ECMP and reactive forwarding method for both TCP and UDP traffic.

Cost-Aware Traffic Management Under Demand Uncertainty from a Colocation Data Center User’s Perspective

Burstable billing is widely adopted by colocation data center providers to charge their users for data transferring. This paper proposes a cost-aware traffic management approach for a colocation data center user under burstable billing where it is charged based on the 95th percentile bandwidth usage. To do this, we first develop a tractable mathematical expression to calculate the 95th percentile usage of a user. Then, we develop an optimization problem to maximize the user’s surplus based on both deterministic and stochastic predictions of the user’s demand. We show that the resulted optimization problem, while non-convex by nature, can be efficiently solved or approximated using a convex program. We also show that the proposed approach can also be applied in a more general scenario where the user gets services from multiple service providers. Using real-world workload traces, we show that the proposed approach can reduce a colocation data center user’s IP transit cost by 26 percent and increase its total surplus by 23 percent, compared to the current practice of allocating bandwidth on-demand.

Using advanced adaptive cruise control systems to reduce congestion at sags: An evaluation based on microscopic traffic simulation

Sags are roadway sections along which the gradient increases gradually in the direction of traffic. Sags are generally bottlenecks in freeway networks. Previous research suggests that traffic management measures using advanced Adaptive Cruise Control (ACC) systems could reduce congestion on freeways, but little is known about their potential effectiveness at sags. This article evaluates the effectiveness of a basic ACC system (B-ACC) and two advanced ACC systems – Traffic State-Adaptive ACC (TSA-ACC) and Cooperative ACC (C-ACC) – in mitigating congestion at sags. TSA-ACC adapts the ACC parameters to the macroscopic traffic state estimated by the vehicle itself. C-ACC uses information of other vehicles in the surroundings to adjust its accelerations. Results are obtained using microscopic traffic simulations with different penetration rates. They show that, under high-demand conditions, congestion decreases with increasing percentage of vehicles equipped with B-ACC. With high penetration rates (75% and above), traffic no longer becomes congested at the sag. Moreover, the results show that TSA-ACC and C-ACC reduce congestion more than B-ACC, mainly because they increase the queue discharge capacity of the sag. The two advanced ACC systems prevent the formation of congestion at the sag at lower penetration rates than B-ACC. TSA-ACC is the most effective system. C-ACC is only more effective than B-ACC in scenarios with 20% penetration rate or higher; below that, connectivity between equipped vehicles is too low. Our findings show the potential of using advanced ACC systems to mitigate congestion at sags and indicate some challenges of this traffic management approach.

A soft traffic management approach for achieving environmentally sustainable and economically viable outcomes: An Australian case study

Transport infrastructural strain is a pressuring issue for the urbanised world due to the increasing demand for public transport. Transport operators, planners and policymakers are constantly searching for low-cost solutions to such transport issues. Therefore, it is critical to developing an environmentally sustainable and economically viable efficient traffic network to relieve traffic pressure (i.e., traffic congestion, transport infrastructure investment needs). The objective of this research is to propose a “Soft Traffic Management (STM)” concept to proactively analyse the traffic impact of transport planning strategy before implementation. This study investigates the effectiveness of a STM for easing the traffic pressure by carrying out a pilot research project on the proposed South East Busway extension in Logan City, Australia, by employing a stepwise process consisting of a multi-stage analysis and stakeholder-based modelling approach. The results indicate that the extended busway can significantly relieve traffic congestion. In addition, the proposed strategy has significantly positive impacts on the environment since it aims to reduce air pollution and fuel consumption as well as to improve the safety and efficiency of the whole transport system. This study confirms the effects of STM on improving the use of existing infrastructure more efficiently and deferring future transport infrastructure investments.

Intelligent Transportation and Control Systems Using Data Mining and Machine Learning Techniques: A Comprehensive Study

Traffic congestion is becoming the issues of the entire globe. This study aims to explore and review the data mining and machine learning technologies adopted in research and industry to attempt to overcome the direct and indirect traffic issues on humanity and societies. The study's methodology is to comprehensively review around 165 studies, criticize, and categorize all these studies into a chronological and understandable category. The study is focusing on the traffic management approaches that were depended on data mining and machine learning technologies to detect and predict the traffic only. This study has found that there is no standard traffic management approach that the community of traffic management has agreed on. This study is important to the traffic research communities, traffic software companies, and traffic government officials. It has a direct impact on drawing a clear path for new traffic management propositions. This study is one of the largest studies with respect to the size of its reviewed articles that were focused on data mining and machine learning. Additionally, this study will draw general attention to a new traffic management proposition approach.

A Causal Model for Safety Assessment Purposes in Opening the Low-Altitude Urban Airspace of Chinese Pilot Cities

China has been gradually relaxing its ban on the use of low-altitude airspace across the country. To guarantee the high reliability of air traffic management (ATM), conflict detection and conflict resolution (CDR) approaches are indispensable to maintain safe separation between neighbouring small fixed-wing aircraft. In this study, we analyse a temporal and spatial integrated strategy for safety assessment purposes in opening the low-altitude urban airspace of Chinese pilot cities. First, we present a detailed mathematical description of the proposed algorithms based on a spatial grid partitioning system (SGPS). For our system, a conflict detection (CD) algorithm is designed to determine if two trajectories pass through the same grid space within overlapping time windows. A conflict resolution (CR) algorithm integrates a proposed time scheduling-based technique (TST) and vertical change-based technique (VCT), which operate under predetermined basic principles. Then, based on our novel CDR algorithms, a causal model is constructed in graphical modelling and analysis software (GMAS) to generate a state space that can provide a global perspective on scenario dynamics and better understanding of induced conflict occurrences. Finally, simulation results demonstrate that the proposed approach is practical and efficient.

Intelligent traffic light scheduling technique using calendar-based history information

One of the most serious problems that are due to the vast increase in the number of vehicles, is the traffic congestion. The severe traffic congestion in modern large cities puts huge pressure on the decision makers to invest in planning and deploying solutions to this problem. Using a proper traffic management methodology could alleviate the traffic congestion problem and contribute to the reduction in traffic jams. Several algorithms for traffic management were presented in the literature. In this paper, we propose a new novel history-based traffic management algorithm that relies on the previous all-year traffic information to predict the traffic flow on congested streets of a crowded city. Thus, we refer to this proposed technique as a calendar-based traffic congestion management system. The main idea is to use the recorded traffic history information to compute the green/red times for each direction on a congested intersection with a traffic light controller. A robust heuristic is proposed to use the history information in predicting the future traffic load on each street leading to an intersection controlled by a traffic light. The proposed algorithm is implemented and tested using MATLAB and the well-known traffic simulator, SUMO. Simulation results show that the proposed algorithm optimizes the traffic flow up to 18% more than a standard traffic system. We also apply the proposed algorithm using a multi-intersection model composed of a mesh of 4x4 intersections, and compare the results with other traffic time management approaches, such as the History-Based Self-Organizing Traffic Lights, HB-SOTL. The proposed algorithm outperforms HB-SOTL by about 18%.

Integration of real-time traffic management and train control for rail networks - Part 1: Optimization problems and solution approaches

We study the integration of real-time traffic management and train control by using mixed-integer nonlinear programming (MINLP) and mixed-integer linear programming (MILP) approaches. Three innovative integrated optimization approaches for real-time traffic management that inherently include train control are developed to deliver both a train dispatching solution (including train routes, orders, departure and arrival times at passing stations) and a train control solution (i.e., train speed trajectories). Train speed is considered variable, and the blocking time of a train on a block section dynamically depends on its real speed. To formulate the integrated problem, we first propose an MINLP problem (PNLP), which is solved by a two-level approach. This MINLP problem is then reformulated by approximating the nonlinear terms with piecewise affine functions, resulting in an MILP problem (PPWA). Moreover, we consider a preprocessing method to generate the possible speed profile options for each train on each block section, one of which is further selected by a proposed MILP problem (PTSPO) with respect to safety, capacity, and speed consistency constraints. This problem is solved by means of a custom-designed two-step approach, in order to speed up the solving procedure. Numerical experiments are conducted using data from the Dutch railway network to comparatively evaluate the effectiveness and efficiency of the three proposed approaches with heterogeneous traffic. According to the experimental results, the MILP approach (PTSPO) yields the best overall performance within the required computation time. The experimental results demonstrate the benefits of the integration, i.e., train delays can be reduced by managing train speed.

A Mean Field Game approach for multi-lane traffic management

Abstract In this work, we discuss a Mean Field Game approach to traffic management on multi-lane roads. The control is related to the optimal choice to change lane to reach a desired configuration of the system. Such approach is particularly indicated to model self-driven vehicles with complete information on the state of the system. The mathematical interest of the problem is that the system of partial differential equations obtained is not in the classic form, but it consists of some continuity equations (one for each lane) and a variational inequality, coming from the Hamilton-Jacobi theory of the hybrid control. We propose a consistent semi-Lagrangian scheme for the approximation of the system and we discuss how to improve its efficiency with the use of a policy iteration technique. We finally present a numerical test which shows the potential of our approach.

Distributed optimization for real-time railway traffic management

We introduce a distributed optimization method for improving the computational efficiency of real-time traffic management approaches for large-scale railway networks. We first decompose the whole network into a pre-defined number of regions by using an integer linear optimization approach. For each resulting region, a mixed-integer linear programming approach is used to address the traffic management problem, with micro details of the network and incorporated with the train control problem. For handling the interactions among regions, an alternating direction method of multipliers (ADMM) algorithm based solution approach is developed to solve the subproblem of each region through coordination with the other regions in an iterative manner. A priority rule based solution approach is proposed to generate feasible suboptimal solutions, in case of lack of convergence. Numerical experiments are conducted based on the Dutch railway network to show the performance of the proposed solution approaches, in terms of effectiveness and efficiency. We also show the trade-off between solution quality and computational efficiency.

Real-Time Traffic Flow Management Based on Inter-Object Communication: a Case Study at Intersection

Intersections become very congested when traffic volumes are high, creating inefficiency that results in user delay and frustration. There have been many approaches which focus on optimization signal of Traffic Light System and Vehicle Trajectory Analysis to improve traffic flow at intersection. However, to implement those approaches into reality become a challenges since real-time problem. In this study, inspired by recent advanced vehicle technologies, we propose an approach for traffic flow management at intersection. In particular, with the exploding at an enormous rate of Internet of Things (IoT), the connected object has been the most visible and familiar application. By this way, based on connected object, we design a model which communicating among objects to improve traffic flow at intersection with real time problem. Moreover, traffic congestion is also taken into consideration in case of high traffic volume. The simulation shows the potential results comparing with the existing traffic management system.

Multi-objective optimization approach for air traffic flow management

Air traffic has long been a generally high growth sector and all forecasts indicate that this trend will continue at a similar pace for the next twenty years. The regular traffic demand growth has led to congestionat airports and in space.In this paper, we will create in first a probabilistic model which describes the uncertainty of the aircraft’s trajectory,and its presence in a sector during a time interval. We define as result, amulti-objective optimization problem whose objective functions are the expected cost of delay and the expected cost of congestion.Then we use the Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve an instance included 21 flights and 1 sector, and is able to provide a good approximation of the Pareto front.The decision-making stage was then performed with the aid of data clustering techniques to reduce the sizeof the Pareto-optimal set and obtain a smaller representation of the multi-objective design space, there by making it easier for the decision-maker to find satisfactory and meaningful trade-offs, and to select a preferred final design solution.

50th Anniversary Invited Article—Autonomous Vehicles and Connected Vehicle Systems: Flow and Operations Considerations

The impacts of autonomous vehicles, coupled with greater inter-vehicle and system connectivity, may be far-reaching on several levels. They entail changes to (1) the demand and behavior side, (2) the supply of mobility services, and (3) network and facility operational performance. We focus here on their impact on traffic flow and operations, especially in mixed traffic situations in which autonomous vehicles share the road with regular, human-driven vehicles, along with connected vehicles that may also have some automated functions. These mixed traffic situations correspond to likely deployment scenarios of the technologies, especially in the long transition towards 100% deployment. We explain using elementary traffic science concepts how autonomous vehicles and connected vehicles are expected to increase the throughput of highway facilities, as well as improve the stability of the traffic stream. A microsimulation framework featuring varying behavioral mechanisms for the three classes of vehicles is introduced. The framework is used to examine the throughput and stability questions through a series of experiments under varying market penetration rates of autonomous and/or connected vehicles; at low market shares, the impacts are relatively minor on either throughput or stability. However, as market shares increase, autonomous vehicles exert a greater influence on both dimensions compared to the same shares of connected vehicles. Applications of the framework to examine the effectiveness of selected traffic management approaches are discussed, including dedicated lanes for autonomous vehicles (good only if its use is optional and when the market share of autonomous vehicles is greater than the percentage of nominal capacity represented by that lane), and speed harmonization.

AMUSE: Empowering Users for Cost-Aware Offloading with Throughput-Delay Tradeoffs

To cope with recent exponential increases in demand for mobile data, wireless Internet service providers (ISPs) are increasingly changing their pricing plans and deploying WiFi hotspots to offload their mobile traffic. However, these ISP-centric approaches for traffic management do not always match the interests of mobile users. Users face a complex, multi-dimensional tradeoff between cost, throughput, and delay in making their offloading decisions: while they may save money and receive a higher throughput by waiting for WiFi access, they may not wait for WiFi if they are sensitive to delay. To navigate this tradeoff, we develop AMUSE (Adaptive bandwidth Management through USer-Empowerment), a functional prototype of a practical, cost-aware WiFi offloading system that takes into account a user's throughput-delay tradeoffs and cellular budget constraint. Based on predicted future usage and WiFi availability, AMUSE decides which applications to offload to what times of the day. Since nearly all traffic flows from mobile devices are TCP flows, we introduce a new receiver-side bandwidth allocation mechanism to practically enforce the assigned rate of each TCP application. Thus, AMUSE users can optimize their bandwidth rates according to their own cost-throughput-delay tradeoff without relying on support from different apps' content servers. Through a measurement study of 20 smartphone users' traffic usage traces, we observe that though users already offload a large amount of some application types, our framework can offload a significant additional portion of users' cellular traffic. We implement AMUSE and evaluate its effectiveness with 3G and WiFi usage data obtained from a trial with 37 mobile users. Our results show that AMUSE improves user utility, when compared with AMUSE, other offloading algorithms yield 14\% and 27\% lower user utilities for light and heavy users, respectively.

New air traffic management approach based on expert system and using real-time scheduling algorithms

The air traffic management operations are considered as complex problems. In practice, they can formulate as a constrained optimisation problem that needs to be solved in a real-time environment, also they are non-regular problems, so we can use the expert system to schedule the air traffic management operations. Expert systems using extracted cognitive data, inspiring from the human expertise and its best practices. They are based on the machine performance and its ability to carry out a very large number of complex iterations. Almost all air traffic management operations are classified as scheduling operations. The goal of this paper is to present a new intelligent system with an evolving knowledge base and a creative inference engine. This system allows choosing the optimal way based on real time algorithms to schedule all air traffic management operations such as aircrafts' landing scheduling.

New air traffic management approach based on expert system and using real-time scheduling algorithms

New air traffic management approach based on expert system and using real-time scheduling algorithms

Performance evaluation of traffic management mechanisms for Peer-to-Peer networks

Most network operators, excluding tier 1, pay for the inter-domain transmissions, i.e., the data which is sent to and from their managed networks. As Peer-to-Peer applications generate a substantial amount of traffic in telecommunication networks today, the operators feel the need to manage it effectively. The problem is that the created overlay network is independent from the physical network, and is therefore, difficult to manage. We show that it is possible to efficiently influence Peer-to-Peer overlay networks in a way which provides benefits for both the operator and the peers. As a result, operators observe significant reduction of the inter-domain traffic, whereas, peers achieve better performance. We present and assess five approaches for economic traffic management. The most promising, i.e., QoS-awareness and Highly Active Peer are described in details and evaluated through simulations. We show that by providing proper incentives to end-users, traffic inside the overlay can be efficiently managed.

Estimating online vacancies in real-time road traffic monitoring with traffic sensor data stream

Real-time road traffic monitoring is widely considered to be a promising traffic management approach in urban environments. In the smart cities scenario, traffic trajectory sensor data streams are constantly produced in real time from probe vehicles, which include taxis and buses. By exploiting the mass sensor data streams, we can effectively predict and prevent traffic jams in a timely manner. However, there are two urgent challenges to processing the massive amounts of continuously generated trajectory sensor data: (1) the inhomogeneous sparseness in both spatial and temporal dimensions that is introduced by probe vehicles moving at their own will, and (2) processing stream data in real time manner with low latency. In this study, we aim to ameliorate the aforementioned two issues. We propose an online approach to addresses the major defect of inhomogeneous sparseness, which focuses on employing only real-time data rather than mining historical data. Furthermore, we set up a real-time system to process trajectory data with low latency. Our tests are performed using field test data sets derived from taxis in an urban environment; the results show that our proposed method lends validity and efficiency advantages for tackling the sparseness, and our real-time system is viable for low latency applications such as trafficmonitoring.