Variable Traffic Conditions Research Articles

Drive on a Greener Way: A Case Study on Navigating Cross-Regional Traffic Networks in South China

Green navigation presents a challenge to sustainable mobility. Carbon emissions are a key indicator for evaluating the sustainability of a route. Some methods of green navigation consider the fastest and shortest route to be the least carbon-emitting option, solely from the driver’s perspective. To address this issue, various studies have incorporated road factors into the sustainability evaluation model and designed static route planning algorithms to minimize carbon emissions. However, there has been no comprehensive analysis of carbon emissions from both the road and the driver perspectives, and the influence of a sustainability evaluation using dynamic traffic states has not been considered. In this paper, we propose a carbon emission evaluation model based on the life cycle assessment (LCA) and a dynamic route planning algorithm that focuses on near-real-time traffic states. First, we develop an evaluation model for carbon emissions from both the road and the driver perspectives using a carbon footprint measurement method. Next, we propose a route planning method with both a static calculation and near-real-time adjustment to minimize carbon emissions. Finally, we select three cases from South China with different characteristics to verify the effectiveness of our model and algorithm. This evaluation model calculates the carbon emissions caused by all parties involved, providing a comprehensive assessment of the total carbon emissions generated by traffic. This approach addresses the problem of traditional route planning, which often fails to account for the influences of variable traffic conditions on the greenest route. We verify the effectiveness of the greenest navigation algorithm and near-real-time green navigation and apply these two aspects to cases where the greenest route is compared with other common navigation results from different dimensions. We compare carbon emissions from vehicles and roads from electric vehicles and gasoline-powered vehicles. If an electric vehicle is equipped with a wind power generator, the proportion of the vehicle’s carbon emissions will be very small. For other vehicle types, the carbon emissions will be more than 1000 times the road emissions.

Adaptive Repetition Strategies in IEEE 802.11bd V2X Networks

Vehicle-to-everything (V2X) communications have very strict throughput and latency requirements, even in scenarios with high mobility. IEEE 802.11bd is being developed as a WiFi amendment to improve V2X performance, allowing up to three repetitions per packet, along with other features. Message repetitions increase time diversity and enable maximum ratio combining at the receiver, thus improving the probability of correct decoding. This paper investigates the IEEE 802.11bd packet repetition feature. First, we analyze how the increased channel load due to repetitions may in some cases result in a higher collision rate leading to lower network performance. Then, we propose two strategies for exploiting the IEEE 802.11bd repetition feature. The proposed strategies use the channel busy ratio to adapt the number of transmissions to the channel load and are validated through network-level simulations, which account for both the acquisition and decoding processes. Results show that the proposed strategies improve network performance under variable traffic conditions and enable fair access to the channel.

Traffic Lights Detection and Recognition with New Benchmark Datasets Using Deep Learning and TensorFlow Object Detection API

Today, traffic lights are widely used in places with high vehicle traffic. Especially in autonomous vehicles, fast and high accuracy detection and recognition of traffic lights are critical. Machine learning methods are generally used to do this. Deep learning models give more successful results than machine learning methods in detecting the exact location of traffic lights in different climatic conditions. In this study, Faster R-CNN Inception v2 deep learning model was trained and tested on two different datasets that we prepared and published publicly under variable traffic and climatic conditions in Turkey. Successful results were obtained with fewer data by using the Transfer Learning method with the help of TensorFlow Object Detection API in the training of the model. It has been shown that the datasets we have prepared can be developed considering the conditions in other countries and successful results will be obtained.

Reliability of Accessibility: An Interpreted Approach to Understanding Time-Varying Transit Accessibility

Variable traffic conditions cause travel time uncertainty and further lead to time-varying accessibility. Most current studies do not fully consider fluctuations in accessibility and use complicated methods that likely overestimate the level of accessibility and hinder the application of accessibility measures. To address this issue, we utilized large-scale open-source data and proposed an interpretation of the reliability of accessibility concept that incorporates reliability, travel time uncertainty, and a cumulative opportunity measure. We examined the reliability of accessibility to Shenzhen, a major city in China, focusing on transit accessibility and job opportunities. The results demonstrate that the reliability of accessibility displays a bimodal distribution along urban railway lines and can be used to calculate the impact of urban railways in terms of punctuality. The new approach illustrates time-varying characteristics in the form of probabilities and provides further guidance on accessibility for governments.

Aggressive-dynamics metrics for drive-cycle characterization

In the pursuit of policies and regulations on real-world driving emissions, it becomes necessary to quantifiably define what constitutes “real-world driving.” Measurements of ambient temperature, pressure, and humidity can be used to quantify the weather. Routes can be quantified by their fixed characteristics such as length, road grades, and speed limits. Variable traffic conditions can be quantified by the number of traffic encounters, the number of traffic stops, the fraction of traffic limited driving, et cetera. A real-world drive cycle arises from the interactions between the driver, weather, route, and traffic. People sometimes colloquially described drive cycles in terms of aggressiveness. This subjective characteristic of aggressiveness is here quantified using dynamics metrics, and it is discussed how such metrics could be used by researchers and regulators. A new dynamics metric, the average judicative element, is presented in this investigation. This new metric is compared to previously developed metrics, and its advantages and disadvantages are considered. It is argued how the average judicative element is a superior aggressive-dynamics metric for characterizing drive cycles in scientific studies and governmental regulations.

A novel adaptive lane change method in transient dynamic traffic conditions for highly automated vehicles

Trajectory planning with consideration of surrounding vehicles and decision-making in the middle of complicated manoeuvres are some of the most important challenges regarding the implementation of automated driving. Since transient dynamic traffic conditions was limited to the start of the manoeuvre in previous research, no solution was provided for the surrounding vehicles’ transient changes during the manoeuvre. The algorithm presented in this paper is able to adapt to unstable variable traffic conditions and it is robust to changes in the surrounding vehicles’ conditions, even during the lane change manoeuvre. The Adaptive Lane Change algorithm provides all possible safe trajectories for any moment of manoeuvre via applying DE optimization method on a fifth-order polynomial equation. In this way, it is able to make a new decision and plan safe trajectories according to the new conditions of surrounding vehicles during the manoeuvre. Also, it guarantees collision avoidance at all-time via simultaneous longitudinal and lateral vehicle control. Improving the trajectory during a lane change manoeuvre regarding the surrounding vehicles’ conditions is considered as one of the main contributions of the presented algorithm. A second main contribution is the collision avoidance considering the vehicle’s dynamic via returning to the initial lane when there is no safe trajectory in the target lane, even during the lane change manoeuvre. The decision-making unit is evaluated by real driving tests. Then, the whole structure is simulated with MATLAB in complex transient dynamic traffic conditions via various scenarios and its performance is tested with IPG CarMaker in the presence of simulated surrounding vehicles.

Adaptive resource allocation with traffic peak duration prediction and admission control for cognitive Wi-Fi networks

Cognitive radio network (CRN) architecture can be efficiently utilized to support different QoS requirements under variable traffic and channel conditions. Generally, deterministic radio resource allocation algorithms could significantly increase the channel utilization as well as the network QoS. In this paper, we propose an advanced cognitive network resource allocation algorithm for IEEE 802.11 cognitive Wi-Fi networks. By making use of the status of the transmission channels and the traffic conditions, the proposed algorithm effectively allocates secondary radio resources to improve the overall radio resource utilization and the QoS of the CSMA/CA-based networks. To improve the accuracy and efficiency of the proposed algorithm, a Markov chain model based technique that estimates the achievable network throughput is employed. Furthermore, an autoregressive moving average (ARMA) based model is used to predict the traffic peaks when allocating the channels. OMNeT++ based simulation models are then developed to analyze the performance of the proposed algorithm. It is shown that our predictive resource allocation technique offers higher throughput and QoS compared to existing resource allocation techniques.

Adaptable Vehicle Detection and Speed Estimation for Changeable Urban Traffic With Anisotropic Magnetoresistive Sensors

This paper presents an adaptable roadside vehicle detection and speed estimation system for various traffic conditions on urban roads based on tri-axial anisotropic magnetoresistive sensors and wireless sensor network. The system consists of one master node and two sensor nodes, which are placed along the roadsides and can measure the earth's local magnetic field disturbance caused by passing vehicles. A dynamic threshold detection algorithm is proposed for vehicle detection, especially considering the actual variable traffic condition. The vehicle speed is estimated on the basis of the maximum values and the cross correlation of effective parts extracted from two sensor signals. We have tested the vehicle information at several roads under different traffic conditions. Validation study has revealed a high detection accuracy of 97.92% when using a dynamic threshold compared with 92.3% when using a fixed threshold. And, the average accuracy of speed estimation can reach up to 97.11% on the roads. The proposed algorithm has a significant increase in accuracy, reliability, and practicability compared with the fixed threshold algorithm.

Energy harvested roadside IEEE 802.15.4 wireless sensor networks for IoT applications

With the increasing deployment of the Internet of Things (IoT) applications, the demand for energy efficiency (EE) and quality-of-service (QoS) guarantee is on the rise. This paper considers the typically contradicting requirements for EE and QoS provisioning in an integrated manner for an IEEE 802.15.4-based wireless sensor network (WSN). Specifically, we consider variable traffic conditions of IoT applications in the presence of an energy harvesting technique based on the movement of vehicles. To this end, we introduce an adaptive energy-efficient algorithm, referred to as ABSD, that adapts the medium access control (MAC) parameters of IEEE 802.15.4 sensor nodes in response to the queue occupancy level of sensor nodes and the offered traffic load levels. By adapting the transmission parameters, the ABSD algorithm minimizes the network contention level which could in turn improve the EE as well as the network throughput. The ABSD algorithm is further enhanced by integrating with an energy harvesting technique and a new MAC parameter known as the ‘energy backoff’. The enhanced algorithm referred to as EH-ABSD offers priority to different classes of traffic by improving the battery lifetime and QoS values under variable traffic load conditions. Numerical results confirm that our proposed algorithms achieve high EE and QoS values while extending the lifetime of sensor nodes for outdoor applications.

Analysis and Modelling of the Relationship between Stopped and Control Delays by Differential Evolution Algorithm

Two delay definitions namely stopped delay and control delay are commonly used to determine the level of service of a signalized intersection. In this research, we first developed a number of new models to describe the relationship between stopped and control delays using Differential Evolution (DE) algorithm. In these models, we considered four cycle lengths of 60, 90, 120 and 150 seconds with the green ratio ranging from 0.35 to 0.60. Taking the saturation flow as 1800 vph, five different traffic volumes were considered ranging from 600 to 1400 vph with 200 vph increments. Three analysis periods were selected as 15, 30 and 60 minutes. The data needed for model development and verification were obtained from a four leg intersection by using CORSIM simulation. In the second part of the study, the effect and sensitivity of cycle length, traffic volumes and green ratio parameters on the conversion ratio (Ds/Dc) to relate stopped and control delays were evaluated based on the selected parameter ranges.The results revealed that the conversion ratio cannot be accepted constant usually taken as 0.76 for practical purposes since it is dependent on changing operation and traffic conditions. Therefore, it needs to be determined with the use of an appropriate model considering variable traffic conditions as functions of volume, cycle length and green ratio.

Freeway Multisensor Data Fusion Approach Integrating Data from Cellphone Probes and Fixed Sensors

Freeway traffic state information from multiple sources provides sufficient support to the traffic surveillance but also brings challenges. This paper made an investigation into the fusion of a new data combination from cellular handoff probe system and microwave sensors. And a fusion method based on the neural network technique was proposed. To identify the factors influencing the accuracy of fusion results, we analyzed the sensitivity of those factors by changing the inputs of neural-network-based fusion model. The results showed that handoff link length and sample size were identified as the most influential parameters to the precision of fusion. Then, the effectiveness and capability of proposed fusion method under various traffic conditions were evaluated. And a comparative analysis between the proposed method and other fusion approaches was conducted. The results of simulation test and evaluation showed that the fusion method could complement the drawback of each collection method, improve the overall estimation accuracy, adapt to the variable traffic condition (free flow or incident state), suit the fusion of data from cellphone probes and fixed sensors, and outperform other fusion methods.

Probabilistic deployment of dissemination points in urban areas to support vehicular communication

This work presents a probabilistic constructive heuristic to support the design of roadside infrastructure for information dissemination in vehicular networks. We formulate this as a Probabilistic Maximum Coverage Problem (PMCP) and we intend to maximize the number of vehicles that get in contact with the infrastructure. We compare our approach with non-probabilistic MCP in simulated urban areas following a Manhattan-style topology with variable traffic conditions. The main contributions of this work are (i) the formal definition of the Probabilistic Maximum Coverage Problem, (ii) the application of PMCP to solve one instance of the problem of facilities allocation and (iii) the application of a probabilistic approach to model the volume of vehicles along the urban area, where the position of each vehicle is no longer considered deterministic, but it is treated as a probability function distributed over all the intersections. The vehicles no longer have a position. Instead, vehicles have a probability of being in a given position at a given instant of time. The results reveal that PMCP requires less dissemination points (DPs) to achieve similar coverage ratio than non-probabilistic MCP, while preserving the same samples deviation. Keywords: vehicular networks, maximum coverage problem, information dissemination.

Towards Knowledge-Oriented Smart Vehicle Adaptive Traffic Service

The most of very large traffic system by growing the variety of services, the relationships between the vehicle network and the infrastructure are more complex. Moreover, intelligent transportation systems are getting more and more to develop a better combination of travel safety and efficiency since long time ago. Vehicle is being evolved and traffic environment is especially also organized well-defined schedules priorities, which is real time based wireless network traffic condition, variable traffic condition, and traffic pattern from the vehicle navigation system. Accordingly, we propose to Knowledge-oriented Smart Vehicle Adaptive Traffic Service using genetic algorithm in this paper.

Neural Network based Dynamic Multicast Routing

The Hopfield neural network is suggested for dynamic multicast routing in communication network of arbitrary topology and under variable traffic conditions. A new algorithm takes into account not only the most important parameters describing the actual network state (the network topology, link and router bandwidths, estimated link delays, and the traffic density), but also the history of link/router occupancy. The goal of the paper is to find the Pareto optimal path for multicast routing case and to avoid possible packets loss, due to heavy traffic. The effectiveness of the new routing algorithm has been verified under various network topologies and traffic conditions.DOI: http://dx.doi.org/10.5755/j01.eee.19.3.3703

Load-adaptive frequency reuse scheme for inter-cell interference coordination in relay networks

Cellular relay networks adopting orthogonal frequency division multiple (OFDM) technology has been widely accepted for next generation wireless communication due to its advantage in enlarging coverage scale as well as improving data rate. In order to improve the performance of user equipments (UEs) near the cell edge, especially to avoid the interference from inter-cell and intra cell, an enhanced soft frequency reuse scheme is adopted in this paper to assure inter-cell interference coordination (ICIC). Compared with traditional frequency allocation work, the proposed scheme is interference-aware and load-adaptive, which dynamically assigns available frequency among UEs under certain schedule method in variable traffic load condition and mitigates interference using information provided by interference indicator. It can improve signal-to-interference plus noise ratio (SINR) of the UE in each sub channel thus enable the system achieve better throughput and blocking probability performance. Simulation results prove that the proposed scheme may achieve desirable performance on throughput, blocking probability and spectral utilization in the sector under different traffic load compared with other schemes.

Quality of traffic performance assessment for priority type intersection with non-stationary traffic flow demands

Non-stationary traffic processes on controlled by traffic signs priority type intersections generate variable traffic conditions. Random traffic data can cause inaccurate or even incorrect assessment of an intersection performance. The paper presents the results of traffic performance analyses on a priority type intersection based on several realizations of real demand processes (time series of traffic flows). It has been demonstrated that key to intersection efficiency assessment is characteristics is traffic variability and its formal description and method of analysis. While commonly used in practice, the simplified analysis of intersection capacity and traffic performance in a peak quarter of an hour does not take account of the actual hourly traffic variability. If suitable capacity reserves ( X ⩽ 0.70) are made at the design stage, the negative effects of demand variability and the uncertainty of predicted traffic volumes may well be reduced.

Burst-mode EDFA based on a mid-position gain flattening filter with an overpumping configuration for variable traffic conditions in a WDM environment

The WDM flatness easily collapses due to substantial changes in input power, an inherent problem in optical packet communication because of dynamic changes in traffic density. We propose a burst-mode EDFA with a novel configuration based on the concept of a mid-position gain flattening filter (GFF) with overpumping. This configuration can effectively suppress gain transience while maintaining WDM flatness.

Time dependent vehicle routing problem with a multi ant colony system

The Time Dependent Vehicle Routing Problem (TDVRP) consists in optimally routing a fleet of vehicles of fixed capacity when travel times are time dependent, in the sense that the time employed to traverse each given arc, depends on the time of the day the travel starts from its originating node. The optimization method consists in finding solutions that minimize two hierarchical objectives: the number of tours and the total travel time. Optimization of total travel time is a continuous optimization problem that in our approach is solved by discretizing the time space in a suitable number of subspaces. New time dependent local search procedures are also introduced, as well as conditions that guarantee that feasible moves are sought for in constant time. This variant of the classic Vehicle Routing Problem is motivated by the fact that in urban contexts variable traffic conditions play an essential role and can not be ignored in order to perform a realistic optimization. In this paper it is shown that when dealing with time constraints, like hard delivery time windows for customers, the known solutions for the classic case become unfeasible and the degree of unfeasibility increases with the variability of traffic conditions, while if no hard time constraints are present, the classic solutions become suboptimal. Finally an application of the model to a real case is presented. The model is integrated with a robust shortest path algorithm to compute time dependent paths between each customer pairs of the time dependent model.

National Data Warehouse

Every day, traffic managers and road users use different sources of information on the current state of the road network in their decision process. The efficiency of these decisions strongly depends on how accurate, reliable, and timely the available information is. Moreover, the data collected are typically scattered in space and time; large areas are usually unmonitored, and data quality is undependable. Within this view, the distribution of a unique data set that contains sufficient levels of quality over the whole network may improve the way information is provided to the user and improve the effectiveness of management strategies. The need for guaranteed standard levels of data quality for road authorities and service providers motivated the establishment of the National Data Warehouse project to provide traffic information as well as information on the status of the road network system as a whole. This information is extended to a basic network level, which allows road authorities or service providers to combine this information with their own data set and obtain a broader view of the problems that occur on the network they manage or monitor. The requirements that such a data bank should satisfy—namely, the accuracy and reliability of information (which depend on the spatial location and aggregation time)—were investigated. The impact of these elements has been quantified through theoretical and numerical analysis, showing that both elements strongly affect good estimation and prediction of travel times and network states, especially under variable traffic conditions.

Profit-oriented resource allocation using online scheduling in flexible heterogeneous networks

In this paper, we discuss a generalized measurement-based adaptive scheduling framework for dynamic resource allocation in flexible heterogeneous networks, in order to ensure efficient service level performance under inherently variable traffic conditions. We formulate our generalized optimization model based on the notion of a profit center with an arbitrary number of service classes, nonlinear revenue and cost functions and general performance constraints. Subsequently, and under the assumption of a linear pricing model and average queue delay requirements, we develop a fast, low complexity algorithm for online dynamic resource allocation, and examine its properties. Finally, the proposed scheme is validated through an extensive simulation study.