Traffic Management Solutions Research Articles

A services operations performance measurement framework for multimodal logistics gateways in emerging megaregions

ABSTRACT Gateways in emerging megaregions have the existing transport infrastructure tο support modal shift and deliver sustainable development. To address these issues, there is a need for more efficient traffic management solutions and an accompanying shift away from road transportation towards more environmentally friendly modes both for freight and for passengers. More importantly, an integrated methodological approach needs to be adopted by decision-makers when they assess existing and planned transport infrastructure for its ongoing sustainable development. This research provides a novel approach to the performance evaluation of modal shift and transport infrastructure service quality. The application and combination of Importance-Performance Analysis (IPA) and Analytical Hierarchical Process (AHP) in an intermodal regional case study provides a straightforward methodology for the assessment of sustainable development. In addition, added value is provided to the literature for long term scenarios’ planning for sustainable transport infrastructure.

Optimising the Flow

A round-up of new traffic and data management solutions

Evaluation of Reinforcement Learning Traffic Signalling Strategies for Alternative Objectives: Implementation in the Network of Nicosia, Cyprus

Abstract Smart Cities promise to their residents, quick journeys in a clean and sustainable environment. Despite, the benefits accrued by the introduction of traffic management solutions (e.g. improved travel times, maximisation of throughput, etc.), these solutions usually fall short on assessing the environmental impact around the implementation areas. However, environmental performance corresponds to a primary goal of contemporary mobility planning and therefore, solutions guaranteeing environmental sustainability are significant. This study presents an advanced Artificial Intelligence-based (AI) signal control framework, able to incorporate environmental considerations into the core of signal optimisation processes. More specifically, a highly flexible Reinforcement Learning (RL) algorithm has been developed towards the identification of efficient but-more importantly-environmentally friendly signal control strategies. The methodology is deployed on a large-scale micro-simulation environment able to realistically represent urban traffic conditions. Alternative signal control strategies are designed, applied, and evaluated against their achieved traffic efficiency and environmental footprint. Based on the results obtained from the application of the methodology on a core part of the road urban network of Nicosia, Cyprus the best strategy achieved a 4.8% increase of the network throughput, 17.7% decrease of the average queue length and a remarkable 34.2% decrease of delay while considerably reduced the CO emissions by 8.1%. The encouraging results showcase ability of RL-based traffic signal controlling to ensure improved air-quality conditions for the residents of dense urban areas.

LoRa based architecture for smart town traffic management system

While traffic congestion has been pointed out as everyday driving stress, few attempts are specialized in traffic management by using current IoT technology. In order to help alleviate traffic stress from drivers, this article proposes a cross-layer LoRa architecture and a machine-learning algorithm for smart town’s traffic management systems. LoRa is selected since it has strengths in range and power when compared to other wireless communication technologies. We introduce the cross-layer LoRa architecture, which is devised to facilitate its cognitive analysis. By dynamically allocating network and information resources, it complements the limitations of the standard LoRa protocol. We also have designed the logistic regression algorithm-which runs above its cognitive engine. The proposed algorithm outputs traffic coefficients based on density and travel time. This algorithm has achieved 97% of accuracy in the simulation. With further research, we believe the proposed system could be an excellent solution for smart traffic management.

Procedures for the Integration of Drones into the Airspace Based on U-Space Services

A safe integration of drones into the airspace is fundamental to unblock the potential of drone applications. U-space is the drone traffic management solution for Europe, intended to handle a large number of drones in the airspace, especially at very low level (VLL). This paper presents the procedures we have designed and tested in real flights in the SAFEDRONE European project to pave the way for a safe integration of drones into the airspace using U-space services. We include three important aspects: Design of procedures related to no-fly zones, ensure separation with manned aircraft, and autonomous non-cooperative detect-and-avoid (DAA) technologies. A specific U-space architecture has been designed and implemented for flight campaigns with up to eight drones with different configurations and a manned aircraft. From this experience, specific recommendations about procedures to exit and avoiding no-fly zones are presented. Additionally, it has been concluded that the use of surveillance information of manned aircraft will allow a more efficient use of the airspace while maintaining a proper safety level, avoiding the creation of large geofence areas.

ADS-B Like UTM Surveillance Using APRS Infrastructure

Automatic packet reporting system (APRS) is selected as a candidate for automatic dependent surveillance-broadcast (ADS-B) like solution for unmanned aircraft system traffic management (UTM). The APRS on-board unit (OBU) at 0.5 W radio power and a proper ground transceiver station (GTS) deployment together makes up the infrastructure for unmanned aerial vehicle (UAV) surveillance. The airborne APRS OBU, designed and built using an available LightAPRS module, and the GTS to relay received data into the UTM Cloud is developed in this study. By system integration, the APRS OBU reports position data and flight data periodically to UTM Cloud. This paper presents the development of the ADS-B like operation for UTM using APRS. To avoid communication jamming by HAMs, the adopted APRS shifts its carrying frequency from 144.64 MHz to 144.61 MHz. In addition, the data period is tuned to around 10 s to test its functional capability. The APRS OBU downlinks 90 bytes of surveillance data onto the UTM cloud using the Internet, including position and flight data from Pixhawk flight controller (FC). A series of system performance verifications are conducted to examine APRS ADS-B like reliability and continuity with coverage limit. Through 19 flight tests, the GTS collected 1330 packets of data out of 1331 transmitted from the APRS OBU. Each data packet has the complete 90 bytes for surveillance with position and six degree-of-freedom (DoF) flight data on the UTM cloud. The APRS GTS deployment, with a low rate of missing data, covers a 40 km radius at the specific frequency of 144.61 MHz. The test results verify the functional capability of APRS to support an ADS-B like for UTM in Taiwan.

U-Space Concept of Operations: A Key Enabler for Opening Airspace to Emerging Low-Altitude Operations

Opening the sky to new classes of airspace user is a political and economic imperative for the European Union. Drone industries have a significant potential for economical growth according to the latest estimations. To enable this growth safely and efficiently, the CORUS project has developed a concept of operations for drones flying in Europe in very low-level airspace, which they have to share that space with manned aviation, and quite soon with urban air mobility aircraft as well. U-space services and the development of smart, automated, interoperable, and sustainable traffic management solutions are presented as the key enabler for achieving this high level of integration. In this paper, we present the U-space concept of operations (ConOps), produced around three new types of airspace volume, called X, Y, and Z, and the relevant U-space services that will need to be supplied in each of these. The paper also describes the reference high-level U-space architecture using the European air traffic management architecture methodology. Finally, the paper proposes the basis for the aircraft separation standards applicable by each volume, to be used by the conflict detection and resolution services of U-space.

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.

Research trends and solutions for secure traffic management of SDN

Software Defined Network (SDN) decouples the responsibilities of route management and datatransmission of network devices present in network infrastructure. It integrates the control responsibility at thecentralized software component which is known as controller. This centralized aggregation of responsibilities mayresult the single point of failure in the case malicious attack at the controller side. These attacks may also affect thetraffic flow and network devices. The security issues due to such malicious attacks in SDN are dominating challengesin the implementation and utilization of opportunities provided by this new paradigm. In this paper we haveinvestigated the several research papers related to proposal of new research trends for security and suggestionswhich fulfil the security requirements like confidentiality, integrity, availability, authenticity, authorization,nonrepudiation, consistency, fast responsiveness and adaptation. We have also investigated the new future researchfor creating the attack free environment for implementing the SDN.

Calibrating Traffic Microscopic Simulation Model Parameters Using an Evolutionary Approach

Traffic management solutions are often evaluated by microscopic simulation models. From this strategy it is possible to test and analyze the impacts of strategies without demanding new constructions or costly investments. In order to reproduce, as close as possible, the real condition of the traffic network to be analyzed in the simulation model, it is necessary to calibrate the model. In the calibration process, vehicle performance and driver behavior parameters need to be adjusted so that the model outputs are similar to observed data. Therefore, this paper presents a genetic algorithm-based microscopic simulation model to calibrate the parameters of AIMSUN simulator to a network of intersections in Belo Horizonte city, Brazil. Genetic algorithm (GA) is used to find a good set of theses control parameters that minimize the mean absolute normalized error (MANE) between the AIMSUN delay time estimation model used by GA and the field observed delay time data. Results obtained showed that calibration process is essential in the use of microscopic simulation models to define and predict traffic managements strategies. The application of the best solution found by the GA in the traffic model provided a MANE value lower than when using the default values of the parameters.

Smart technology implementation for road traffic management

The escalation of road traffic appears to be a tremendous problem. Various metropolises are influenced by traffic flow congestion and the growth of emissions from petrol usage. In big agglomerations, the expanding quantity of private cars and public transport has caused traffic problems. They have a harmful effect on economy, ecosystem, and on the quality of life in general. It is vital to obtain smart solutions for road traffic management. In this paper, authors propose a way to solve this problem by using smart traffic regulation, which is a part of the bigger smart logistics concept. Agent-based traffic simulation has been chosen to perform this research. This type of modeling is related to the object-oriented way of coding. For modeling and experimental simulation of the intersection in St. Petersburg, AnyLogicmodeling software was used. The results show that proposed algorithm allowed to reduce the average waiting time by 37%. Moreover, the average waiting car number at the intersection has been dropped by 2.5 times after applying the new solution. Thus, projected way of reducing road overload on the selected intersection in St. Petersburg displayed excellent outcomes. However, implementation of the algorithm on other infrastructure objects requires further investigation and analysis.

Application of Intelligent Transportation Systems in Logistics

In the second half of the 20th century, occasional intelligent traffic management solutions were used to control road traffic in more developed areas of large cities. Today, the requirements for ITS have extended, such as intelligent and integrated operation, multi-level information, continuous information, traffic management intervention, environmental impact assessment, decision support statistics, and so on. In the last 10 years, the use of intelligent transport systems has become widespread in Hungary, although the data provided by ITS is not used to the appropriate extent in the developments. The main goal of this article to present these oppurtinities of ITS starting with a systematic review of the literature.

Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management

The technological landscape of intelligent transport systems (ITS) has been radically transformed by the emergence of the big data streams generated by the Internet of Things (IoT), smart sensors, surveillance feeds, social media, as well as growing infrastructure needs. It is timely and pertinent that ITS harness the potential of an artificial intelligence (AI) to develop the big data-driven smart traffic management solutions for effective decision-making. The existing AI techniques that function in isolation exhibit clear limitations in developing a comprehensive platform due to the dynamicity of big data streams, high-frequency unlabeled data generation from the heterogeneous data sources, and volatility of traffic conditions. In this paper, we propose an expansive smart traffic management platform (STMP) based on the unsupervised online incremental machine learning, deep learning, and deep reinforcement learning to address these limitations. The STMP integrates the heterogeneous big data streams, such as the IoT, smart sensors, and social media, to detect concept drifts, distinguish between the recurrent and non-recurrent traffic events, and impact propagation, traffic flow forecasting, commuter sentiment analysis, and optimized traffic control decisions. The platform is successfully demonstrated on 190 million records of smart sensor network traffic data generated by 545,851 commuters and corresponding social media data on the arterial road network of Victoria, Australia.

An Optimized Load Balance Solution for Multi-homed Host in Heterogeneous Wireless Networks.

The imminent wireless technologies are demanding fusion of several networks with diverse technologies. A convenient user device for such an environment is the multihomed host, which is capable of making use of simultaneous multiple connections of heterogeneous networks and smartly distributing/receiving data among them. The aim of this work is to develop a mechanism for assessing the multihoming concept and to propose a system model for increasing its applicability. The proposed model provided a novel user-centric scheme for multihoming for multi-radio access technologies (multi-RAT). It is considered an evolution of traffic offloading for gaining more capacity, higher data rates, and real-time services. This work assumes data classifications, evaluates and ranks the available connections, and utilizes the application data in an unequal load balance scheme. This is achieved by analyzing the performance of three of the most widely used alternative-choice for multiple attributes decision making (MADM) methods. The analytical hierarchy process (AHP), along with a utility equation, is applied to the system model for unequal load balance solution. The proposed model is targeting an energy efficient mechanism which satisfies application needs. Moreover, it reveals an efficient transmission mechanism for a better quality of service, traffic management, and availability solutions. The simulation results show that the proposed system surpassed its former counterparts.

Traffic safety on cycle track crossings – traffic conflict technique

In Poland, bicycle traffic is on the rise. Over the last 10 years the share of bicycle trips in Warsaw has increased from 0.9% to 3.1%. This development is accompanied by an increase in incidents in which cyclists are involved. Between 2010 and 2014 there were approximately twice as many accidents and almost three times more collisions, 25% of which took place at cycle track crossings. The aim of the study conducted within a larger research project was to assess safety of bicycle users at various types of crossings. Accidents involving cyclists are very scattered, thus epidemiological analyses may not give satisfactory results. In the reported study the traffic conflict technique was used. Surveys were carried out at three cycle track crossings, differing with respect to geometrical layout and traffic control method. Video recording technique was applied – approximately 70 hours of material were analysed at each survey point. Dangerous situations were identified (approximately 700) and categorised into six types of incidents. Subsequently, the relationship between the number of conflicts and traffic volumes was analysed. This allowed to identify traffic management solutions that are safer and to list design elements that should be avoided.

Similarity Search of Spatiotemporal Scenario Data for Strategic Air Traffic Management

Designing strategic air traffic management (ATM) solutions at a large spatiotemporal scale in real time is challenging, considering the range of uncertainties at the strategic time frame. Big data techniques have drawn increasing attentions to develop optimal ATM solutions to address these challenges. ATM data, such as convective weather spread and congestion propagation, represent a new data type called spatiotemporal scenario data, which has not been systematically studied. This new data type differs from the traditional spatiotemporal pointwise data in its unique spatiotemporally correlated spread patterns. As a step toward closing the loop of big data and real-time decision making for ATM, this paper introduces an effective similarity search algorithm for this new data type. This similarity search algorithm uses a multiresolution distance measure, which captures the difference between spatiotemporal scenarios. Unique properties of this distance measure are exploited to significantly reduce the computational cost associated with accessing and processing scenarios in a large database. Using real weather forecast datasets as the case study, this study investigates feasibility of the proposed similarity search algorithm. Systematic parameter impact analysis is conducted through simulation studies, which provide guidelines for parameter selection. Comparative simulation studies validate the effectiveness and efficiency of the proposed similarity search algorithm for spatiotemporal scenario data.

Smart Traffic Lights over Vehicular Named Data Networking

The existing traffic light system fails to deal with the increase in vehicular traffic requirements due to fixed time programming. Traffic flow suffers from vehicle delay and congestion. A new networking technology called vehicular ad hoc networking (VANET) offers a novel solution for vehicular traffic management. Nowadays, vehicles communicate with each other (V2V), infrastructure (V2I), or roadside units (V2R) using IP-based networks. Nevertheless, IP-based networks demonstrate low performance with moving nodes as they depend on communication with static nodes. Currently, the research community is studying a new networking architecture based on content name called named data networking (NDN) to implement it in VANET. NDN is suitable for VANET as it sends/receives information based on content name, not content address. In this paper, we present one of VANET’s network applications over NDN, a smart traffic light system. Our system solves the traffic congestion issue as well as reducing the waiting time of vehicles in road intersections. This system replaces the current conventional system with virtual traffic lights (VTLs). Instead of installing traffic lights at every intersection, we utilize a road side unit (RSU) to act as the intersection controller. Instead of a light signal, the RSU collects the orders of vehicles that have arrived or will arrive at the intersection. After processing the orders according to the priority policy, the RSU sends an instant message for every vehicle to pass the intersection or wait for a while. The proposed system mimics a human policeman intersection controlling. This approach is suitable for autonomous vehicles as they only receive signals from the RSU instead of processing many images. We provide a map of future work directions for enhancing this solution to take into account pedestrian and parking issues.

Content accessibility preference approach for improving service optimality in internet of vehicles

The Internet of Vehicles (IoV) is an application of Internet of Things that provides a solution for traffic and safety management in technology-dependent cities. The most competent resource for IoV functionalities is the distributed Information System (IS). Services outsourced from the IS are not sustained over a long period of time, owing to unpredictable network dynamics and localization errors. Considering that the IS is the backbone for the IoV, this manuscript proposes a low-delay information accessing technique for improving the service optimality of vehicle-assisted smart applications. The vehicles for gaining access to information by selecting optimal gateways exploit the benefits of epidemic spread routing (ESR). The greedy behavior of ESR is confined by designing a content accessibility preference (CAP) model that facilitates precise vehicle selection. This selection relies on the information relevance, granted by the neighboring vehicles to deliver improved service optimality. The reliability of the vehicle with regard to the relevance and information retrieval time is verified by the cooperative on-demand relationship between the service requesting on-board unit and the roadside unit. Both gateway selection and neighbor vehicle selection are assimilated for providing better service via distributed information access and relevant information retrieval. The performance of the proposed CAP model is evaluated using the following metrics: throughput, access delay, vehicle service ratio, and optimality ratio. The results show the proficiency of the proposed CAP method.

Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management

Dominated by delay-sensitive and massive data applications, radio resource management in 5G access networks is expected to satisfy very stringent delay and packet loss requirements. In this context, the packet scheduler plays a central role by allocating user data packets in the frequency domain at each predefined time interval. Standard scheduling rules are known limited in satisfying higher quality of service (QoS) demands when facing unpredictable network conditions and dynamic traffic circumstances. This paper proposes an innovative scheduling framework able to select different scheduling rules according to instantaneous scheduler states in order to minimize the packet delays and packet drop rates for strict QoS requirements applications. To deal with real-time scheduling, the reinforcement learning (RL) principles are used to map the scheduling rules to each state and to learn when to apply each. Additionally, neural networks are used as function approximation to cope with the RL complexity and very large representations of the scheduler state space. Simulation results demonstrate that the proposed framework outperforms the conventional scheduling strategies in terms of delay and packet drop rate requirements.

A novel digital twin-centric approach for driver intention prediction and traffic congestion avoidance

Road traffic has been exponentially growing with surging people and vehicle population. Road connectivity infrastructure has not been growing correspondingly and hence the research endeavors for optimal resource allocation and utilization of connectivity resources has gained a lot these days. Therefore, insights-driven real-time traffic management is turning out to be an important component in establishing and sustaining smarter cities across the globe. IT solution and service organizations have come forth with a number of automated traffic management solutions and the primary problem with them is they are unfortunately reactive and hence an inefficient solution for the increasingly connected and dynamic city environments. Therefore, unveiling real-time, adaptive, precision-centric and predictive traffic monitoring, measurement, management and enhancement solutions are being insisted as an indispensable requirement toward sustainable cities. We have come out with a novel approach leveraging a few potential and promising technologies and tools such as a reliable and reusable virtual model for vehicles, a machine learning model, the IoT fog or edge data analytics, a data lake for traffic and vehicle data on public cloud environments, and 5G communication. The paper details all these in a cogent fashion and how these technological advancements come handy in avoiding the frequent traffic congestions and snarls due to various reasons.