Traffic Configuration Research Articles

A virtual reality method for assessing human responses to soundscapes

Virtual realities and simulations are becoming increasingly popular due to constant technological advancement offering more and more realistic scenarios. However, cases of VR being used to assess soundscape-related aspects of perception still appear to be rare. Here, a method for conducting auditory perception experiments in a virtual urban environment is presented and discussed. Participants are exposed to virtual scenarios simulating an urban green area with nearby traffic noise sources, whose visualization draws on physically-based visual renderings. The acoustic scenario is auralized in real-time using geometrical acoustic approaches including simulated and recorded sounds. The virtual environment is presented via head-mounted display and headphones. In this setting, evaluation and behavioural experiments are conducted, with the environment’s acoustic and visual characteristics being experimentally manipulable or controllable. The presented method allows for perception experiments with a high degree of ecological validity while maintaining a controlled laboratory setting. As a practical application, the method is used to determine the impact of road traffic configurations on the restoration quality in an urban green area. Proceeding from the applied method, general opportunities and limitations of experiments performed in virtual urban environments to study noise effects in urban soundscapes are discussed.

A Framework of Real-Time Intelligent Transportation System Based on Hybrid Fog-Cloud Computing

Recent years have witnessed a mounting increase in the loss of lives and properties resulted from low-efficient and unsafe traffic management. However, traditional Intelligent Transportation System (ITS) cannot meet the strict requirements of transportation management on the efficiency, especially in terms of the latency. The emergence of fog computing provides a feasible solution to the aforementioned contradiction. In this paper, a framework of Real-time Intelligent Transportation System based on Hybrid Fog-cloud Computing System (RITS-HF) is proposed. All the components of RITS-HF, including the user layer, the fog layer, and the cloud layer in RITS-HF are presented clearly. Subsequently, main functions of RITS-HF are described, which are expected to greatly improve traffic efficiency and strengthen safety. In the meantime, the detailed processes and functions of each layer under different traffic configurations are presented. Finally, the challenges faced by RITS-HF are pointed out and analyzed in detail. RITS-HF is expected to play a vital role in improving the driving safety, increasing traffic efficiency, along with reducing energy consumption to some extent.

A composite approach to modelling aircraft noise contours for improved annoyance prediction

Aircraft noise contours are an essential tool for land-use planning around aerodromes. They are intended as predictors of the effects of aircraft noise on communities, mainly in terms of aircraft noise annoyance. In practicality, these contours often fail to encompass a large percentage of the severely annoyed population at the prescribed noise thresholds for the onset of significant annoyance. This paper proposes a novel approach to modelling noise exposure contours that can improve their predictive power. Using Toronto Pearson Airport as a case study, data from a 2020 noise annoyance questionnaire and calculated noise levels from 25 noise monitoring terminals were used to examine the relationships between severe annoyance and noise exposure for six different airport operational scenarios, representative of both average and alternate traffic configurations. The findings indicate that a composite scenario that models noise exposure for both average and alternate traffic configurations is more predictive of severe annoyance than an average type scenario alone.

Image Dataset for Neural Network Performance Estimation with Application to Maritime Ports

Automated surveillance systems based on machine learning and computer vision constantly evolve to improve shipping and assist port authorities. The data obtained can be used for port and port property surveillance, traffic density analysis, maritime safety, pollution assessment, etc. However, due to the lack of datasets for video surveillance and ship classification in real maritime zones, there is a need for a reference dataset to compare the obtained results. This paper presents a new dataset for estimating detection and classification performance which provides versatile ship annotations and classifications for passenger ports with a large number of small- to medium-sized ships that were not monitored by the automatic identification system (AIS) and/or the vessel traffic system (VTS). The dataset is considered general for the Mediterranean region since many ports have a similar maritime traffic configuration as the Port of Split, Croatia. The dataset consists of 19,337 high-resolution images with 27,849 manually labeled ship instances classified into 12 categories. The vast majority of the images contain the port and starboard sides of the ships. In addition, the images were acquired in a real maritime zone at different times of the year, day, weather conditions, and sea state conditions.

Impact of Platoon Management on Heterogeneous Traffic under V2X Communication Limitation

The advent of vehicle-to-everything (V2X) communication technology, as well as driving automation that is happening at a rapid pace, enables the potential for transforming the transportation system. The near future will, therefore, bring the coexistence of human-driven vehicles (HVs), connected vehicles (CVs), and connected and automated vehicles (CAVs). Correspondingly, platoon management based on V2X communication is expected to improve traffic capacity and fuel efficiency. However, there is a lack of analysis of the impact of platoon management on heterogeneous traffic combined with CVs. Moreover, V2X communication limitation is usually dismissed in existing studies. To address these limitations, this paper explores the impact of platoon management on the capacity of heterogeneous-traffic environments under different CV and CAV market penetration rates (MPRs) with the fundamental diagram considering V2X communication limitations, and further studies the changes in capacity and fuel efficiency of traffic combined with different platoon sizes. Firstly, in light of platoon management, we propose the heterogeneous traffic configurations through distribution characteristics of nine types with simulated models. Secondly, the fundamental diagram considering platoon management with V2X communication limitation is proposed under different CV and CAV MPRs. Thirdly, numerical simulations are conducted to demonstrate the impact of platoon size on the changes in capacity and fuel efficiency of traffic. Conclusions are drawn from the results of the simulations: with the cooperation of CVs and CAVs, the traffic capacity and the fuel efficiency will be promoted, particularly under high CV MPR or high CAV MPR environments.

Creating an asset management plan for traffic signal structures through interactive explorations of wind induced fatigue damage

This paper presents an asset management framework for remote monitoring of traffic signal structures for fatigue damage under wind loads, using historical wind measurements from nearby weather stations. Estimate wind profiles developed from the wind data are used to calculate fatigue damage at welded connections for a wide variety of common traffic pole configurations. The analysis is embedded within a novel remote monitoring application that performs the fatigue life analysis in real time for a user defined location and traffic pole configuration. Such a framework is the first of its kind that allows operators to make real time asset management decisions though interactive explorations of the relationship between traffic pole configuration and fatigue life due to local wind conditions. Results are demonstrated using several traffic poles located around Logan, Utah, which has complex topography that creates drastic changes in wind conditions over short geographical distances. The results illustrate the ability of the remote monitoring application to detect small-scale differences in wind conditions that result in significant differences in the estimated fatigue life of traffic structures within communities. Such results highlight the need for, and feasibility of developing site-specific asset management frameworks at a much finer spatial resolution than previous attempts.

Exploring General Aviation Pilot Perspectives on Proximate Urban Air Mobility Operations

Urban air mobility is a vision for new aviation operations that leverage advances in flight automation, electrical propulsion, and vertical flight technologies to address passenger and cargo needs for faster on-demand, short-range air transport. Enabling urban air mobility operations in the National Airspace System will require significant coordination across the aviation and transportation communities, including the general aviation community. The urban air mobility operating vision will introduce new and complex interactions to these pilots, driving a need for coordinated operating procedures that align with the needs of each community. This paper summarizes an exploratory human-in-the-loop experiment performed at The MITRE Corporation to explore general aviation pilot perspectives on proximate urban air mobility operations. Pilot participants used a certified flight deck simulator to fly a route in an environment with various urban air mobility traffic configurations. Objective and subjective measures were collected to understand general aviation pilot perspectives. Results indicate pilot preference for a limited number of corridors within a segment of airspace even at higher urban air mobility traffic rates. Acceptability of other urban air mobility operating concepts explored in this research study decreased with an increase in urban air mobility traffic rates.

Differentiated Toll Optimization on Goods Vehicles Based on Prediction of Demand for Large-Scale Network

The area rate of the toll road network is designed to influence the regional road network traffic flow configuration and an important factor of goods vehicle path selection, transportation, and other related departments in formulating the related standard. The toll rate is usually combined with the local passenger and cargo loading conditions. Adjusting and optimizing the charge policy, optimization of processes and methods is still facing many difficulties. In this paper, the model parameters and toll status of various goods vehicles are considered, and an optimization algorithm of toll rates for different vehicles in a large-scale road network is proposed. By constructing a traffic demand prediction model based on multiple truck classification, this paper analyzes the distribution status of network trucks and the final charging under the influence of different rates. At the bottom of the algorithm is an analytical model composed of nonlinear equations whose dimensions scale linearly with the scale of the road network. The scale of the road network is independent of the path set and link attribute. The model is verified and the parameters are calibrated by a small network. Finally, the Road network is selected for empirical analysis in Xinjiang Autonomous Region. A case study shows that the analytical structure information provided by the analytic network model enables the algorithm to quickly identify high quality solutions, and the algorithm has better simulation accuracy and premium rate design advantages.

Joint Optimization of Fractional Frequency Reuse and Cell Clustering for Dynamic TDD Small Cell Networks

In dense small cell networks, dynamic time-division duplex (D-TDD) technology has emerged as a promising solution to accommodate the fast variants of volatile traffic conditions because it allows each cell to dynamically configure the uplink and downlink transmission directions. However, the flexibility of traffic configuration introduces additional inter-cell interference, which largely deteriorates network throughput. This paper proposes an interference coordination technology for D-TDD small cell networks by integrating fractional frequency reuse (FFR) with cell clustering. To evaluate the system performance, we develop a theoretical framework to analytically characterize the mean packet throughput (MPT) performance by considering the impact of spatio-temporal traffic. The analytical model can be extended to further study the FFR-based D-TDD, clustered D-TDD, and traditional D-TDD networks. We verify the accuracy of our analysis through simulations and whereby explore the effect of different network parameters. Numerical results demonstrate that the proposed scheme outperforms clustered D-TDD and traditional D-TDD for both the downlink and uplink spatially averaged MPT, and can significantly improve the performance in uplink while slightly decreasing that in downlink compared with FFR-based D-TDD. Furthermore, by jointly optimizing network parameters, the spatially averaged MPT can be maximized while enduring MPT per user.

Throughput Analysis of Small Cell Networks Under D-TDD and FFR

Dynamic time-division duplex (D-TDD) has emerged as an effective solution to accommodate the unaligned downlink and uplink traffic in small cell networks. However, the flexibility of traffic configuration also introduces additional inter-cell interference. In this letter, we study the effectiveness of applying fractional frequency reuse (FFR) as an interference coordination technique for D-TDD small cell networks. We derive the analytical expressions of downlink and uplink mean packet throughput (MPT), then study a network parameter optimization problem to maximize MPT while guaranteeing each user's throughput. Numerical results corroborate the benefits of the proposed FFR-based D-TDD in terms of improving throughput.

Status of signalized intersection safety-A case study of Kolkata

Urban signalized intersections in Indian cities experience a high share of fatalities and serious injuries. However, assessment of safety using rigorous statistical technique is still in an early stage in India, primarily due to challenges in obtaining crash data, road geometric and traffic data. This study intended to focus on safety issues associated with the planning, design, and operation of signalized intersections, and identify the risk factors affecting the frequency and severity of crashes. Assuming that user behavior is influenced by the design and traffic operational characteristics of a location, this study specifically aims to investigate the effects of geometric, infrastructural, traffic control and land use parameters on the number of crashes and their severity across 52 signalized intersections of Kolkata. These data were associated with crash records from 2011 through 2014, to extract important insights, using statistical tests such as correlation analysis and independent samples t-test, as well as multivariate regression analysis for crash prediction purposes. Based on the assessments carried out in this study, there is evidence that factors such as total approach traffic volume, blocked carriageways, traffic configuration, presence of protected right turning phase, type of minor road, tram stops, etc. significantly affect the frequency of crashes while presence of all-red time, protected right turning phase, blocked carriageway, non-motorized traffic, and visibility of road markings affect the severity of crashes at signalized intersections of Kolkata. In the absence of other road traffic safety studies involving rigorous statistical analysis, these findings can significantly contribute to policy-making decisions that are aimed at improving the safety of signalized intersections in the city of Kolkata.

Enhancing pedestrian mobility in Smart Cities using Big Data

Smart City is an emerging concept in global urban development. A Smart City applies ICT technologies to provide greater efficiencies for its urban areas and civilian population. One of the key requirements for a Smart City is to exploit data from its ICT infrastructure (such as Internet of Things connected sensors) to improve city services and features such as accessibility and sustainability. To address this requirement, the City of Melbourne (COM) Smart City office maintains several hundred data sets relating to urban activity and development. These datasets address parking, mobility, land use, 3D data, statistics, environment, and major city developments such as rail projects. One promising dataset relates to pedestrian traffic. Data are obtained from sensors and updated on the COM website (City of Melbourne Open Data Platform: https://data.melbourne.vic.gov.au/.) at regular intervals. These data include the number of pedestrians passing 53 specific locations in the central business district and also their times and directions of travel. In a 24 h period, over 650,000 pedestrians were counted passing all locations. Peak rates of several thousand pedestrians per minute are regularly recorded during city rush hours at hotspots making the data amenable to Big Data analysis techniques. Results are obtained in graphical format as heatmaps and charts of city pedestrian traffic using both Microsoft Excel® for static analysis and PowerBI® for more advanced interactive visualisation and analysis. These findings can identify pedestrian hotspots and inform future locations of traffic lights and street configurations to make the city more pedestrian friendly. Further, the experience gained can be used to examine other data sets such as bicycle traffic that can be analysed to inform city infrastructure projects. Future work is suggested that could link these pedestrian flow data with social media data from smartphones and potentially wearable devices such as fitness monitors to correlate pedestrian satisfaction with traffic flow. The ‘happiness’ effect of pedestrians passing through green areas such as city parks can also be quantified. This research was undertaken with the assistance of Swinburne University under its Capstone Project scheme.

Problems on One Way Road Networks

A One-Way Road Network is an ordered pair $OWRN = (W_x,W_y)$ comprising of a set $W_x$ of $m$ directed horizontal roads along with another set $W_y$ of $n$ directed vertical roads. An $OWRN$ can also be viewed as a directed grid graph $GG=(V, E)$, where $V$ corresponds to intersections between every pair of horizontal and vertical roads, and there is a directed edge between every pair of consecutive vertices in $V$ in the same direction corresponding to that road. A vehicle $c$ is defined as a 3-tuple $(t,s,P)$, where $c$ starts moving at time $t$ and moves with a constant speed $s$ from its start vertex to destination vertex along pre-specified directed path $P$, unless a collision occurs. A collision between a pair of vehicles $c_i$ and $c_j$ $(i \ne j)$ occurs if they reach a vertex $v \in V$ (a junction in $OWRN$) orthogonally at the same time. A traffic configuration on an $OWRN$ is a 2-tuple $TC=( GG, C)$, where $C$ is a set of vehicles, each travelling on a pre-specified path on $GG$. A collision-free $TC$ is a traffic configuration without any collision. We prove that finding a maximum cardinality subset $C_{max}\subseteq C$, such that $TC = (GG, C_{max})$ is collision-free, is NP-hard. We also show that $GG$ can be preprocessed into a data-structure in $\mathcal{O}(n+m)$ time and space, such that the length of the shortest path between any pair of vertices in $GG$ can be computed in $\mathcal{O}(1)$ time and the shortest path can be computed in $\mathcal{O}(p)$ time, where $p$ is the number of vertices in the path.

Road Capacity and Throughput for Safe Driving Autonomous Vehicles

Safe driving is a relatively new concept that focuses on solving the responsibility attribution problem for autonomous vehicles (AVs), claiming that once the AVs follow a series of pre-defined safe driving policies, it is free from liability even when accidents happen. In this work, we propose safe driving policies for traffic configurations, including straight road, intersection, and Manhattan-like city. Base on the defined safe driving policies, we offer the concepts of safe driving capacity (SDC) and safe driving throughput (SDT) to measure the safe efficiency of the traffic configurations. The former measures the maximum AVs a traffic configuration could accommodate, and the latter measures the maximum throughputs of safe AVs of a traffic configuration. The values obtained are the fundamental limits of the traffic efficiencies for liability-free AVs under the defined conditions. The theoretical performance bounds give people insights on the potential limitations of the safe traffic efficiencies. Finally, this work provides analytical results of SDC and SDT on all the traffic configurations mentioned with explanations, implications, and trade-offs on the issues that may have effects on them.

Quantitative Evaluation on Parking Configuration Impact Factor for Promotion of Private Traffic in Zhangzhou Ancient City Historic District

As an important part of cultural heritage protection system, the protection of historic district seems to contradict with the inevitable motorised development of modern cities. However, in order to adapt to modern use, optimise and improve the traffic system and parking configuration of historic district based on goal of preservation and development may after all be accepted as a kind of alternative approach. There are many evidences shows that an appropriate optimisation on traffic system and parking configuration can enhance the connectivity and pedestrian-friendly environment, improve district dynamism, make tourists more satisfied, increase commercial activities and rental return. In addition, through the case study and analysis of Zhangzhou ancient city historic district, the paper finds that the positive elements of parking configuration factors are also reflected in a promotion of car owners' commercial activities and diverse consume content. The result plays a positive role in further understanding the optimisation of parking configuration enhance dynamism of historic district.

A probabilistic approach to measure aircraft conflict severity considering wind forecast uncertainty

An approach to statistically quantify the severity of aircraft conflicts subject to wind forecast uncertainty is presented. The conflicts are characterized by two indicators: conflict intensity and conflict probability, and the wind uncertainty is derived from Ensemble Weather Forecasts. The wind components are modeled as random variables, and are assumed to be distributed as four-parameter beta distributions. The conflict intensity is measured by the minimum distance between the aircraft, and the probability of conflict is obtained in terms of the probability density function of the minimum distance, which is obtained from the probability density functions of the wind components using the Probabilistic Transformation Method. The case of two en-route aircraft flying at constant altitude and subject to the same random wind is considered. Results are presented for different traffic configurations and a given wind forecast; in particular, a low-intensity and a high-intensity conflict scenarios are analyzed in detail. The results show that the conflict severity can be statistically quantified for any given scenario. The results are validated using the Monte Carlo method and a linear approximation. The applicability of the methodology presented is discussed. The context for this work is the development of decision support tools for conflict detection that integrate weather uncertainty information.

VANET routing protocol with traffic aware approach

Vehicular ad hoc network (VANET) is a type of mobile ad hoc network (MANET) that forms vehicles as nodes. Routing is the basic fundamental requirement of VANET applications. Therefore, it is necessary to devise a routing protocol that fits well for rapid topology changes and disconnected network conditions. To address these specific needs of VANET, we present a novel greedy routing protocol for vehicular networks called VRPTA that suite well for both city environment and the high way environment. With the help of localisation system named global positioning system (GPS), the proposed protocol is designed to efficiently relay the data in the network by considering different scenarios like road traffic variation and various environment characteristics. The protocol communicates in between vehicles as well as vehicle to infrastructure whichever is applicable, thereby ensuring reliable transmission. In addition, we also consider the information about vehicles speed, direction and density of a city traffic configuration consisting of double direction roads, multi lanes and highway scenario. The work is implemented using NS2 simulator.

Improvement of the Energy Efficiency of Subway Traction Systems Through the Use of Genetic Algorithm in Traffic Control

This paper proposes a subway energy regeneration model, based on control stops and train departures throughout his trip, with the use of energy from the regenerative braking in the drive system. The goal is to optimize the power consumption and improve efficiency, in view of sustainable management. Applying genetic algorithm to get the better of the trains’ traffic configuration, the research develops and tests the Traction Control Algorithm for Subway Energy Regeneration (ACTREM), using the Scilab program. To analyze the performance of ACTREM control algorithm in enhancing energy efficiency, there were fifteen simulations of applying ACTREM on line 4—Yellow subway in Sao Paulo. These simulations showed the ACTREM efficiency to generate automatically diagram schedules optimized for energy savings in metro systems, considering the system’s operational constraints such as maximum each train capacity, total wait time, total travel time and interval between trains. The results show that the proposed algorithm can save 9.5% of the energy and does not cause significant impacts on the transportation system capacity passengers and also suggest possible continuity studies.

Comparative Analysis of Stability to Induced Deadlocks for Computing Grids with Various Node Architectures

The classification and application of switching methods and their advantages and disadvantages are considered. A computing grid model was constructed in the form of a colored Petri net with a node, which implements cut-through packet switching. The model consists of packet switching nodes, traffic generators, and guns that form malicious traffic disguised as usual user traffic. The characteristics of the grid model are investigated under a workload with different intensities. The influence of malicious traffic such as “traffic duel” to the quality of service parameters of the grid is estimated. A comparative analysis of computing grid stability with nodes, which implement store-and-forward (SAF) and cut-through switching technologies, was conducted. It is shown that grid performance is approximately the same under workload, and under peak load the grid with a node implementing SAF packet transmission is more stable. The grid with nodes implementing SAF technology comes to a complete deadlock through an additional load, which is less than 10%. It is shown after detailed study that the traffic duel configuration does not affect the grid with cut-through nodes when increasing the workload up to peak load, at which the grid comes to a complete deadlock. The periodicity of execution of the guns, which generate malicious traffic, is determined by a random function with the Poisson distribution. The CPN Tools modeling system is used for constructing models and measuring characteristics. The grid performance and average packet delivery time are estimated under different variants of the grid load.

Traffic‐aware resource allocation scheme for mMTC in dynamic TDD systems

The asymmetry traffic between downlink (DL) and uplink (UL) in massive machine-type communication (mMTC) systems is so prominent that it makes the traditionally fixed frame protocols insufficient to handle. Meanwhile, the dynamic time-division duplexing (D-TDD) is a promising and attractive technology since its number of time slots for the DL and UL can be asymmetric and adjusted dynamically. In the cellular and mMTC co-existing network, to balance the discrepancy of the UL/DL ratio and alleviate the interference as well, in this study, the authors design a D-TDD-based transmission frame structure to first fulfil the basic transmission requirements of the human-type communication (HTC) users with a low power almost blank subframe. Herein, the stochastic geometry methods are adopted to calculate spectral efficiencies of the HTC user equipment. Then, focusing on the worst queue state in mMTC, they devise the slot allocation problem with the min–max objective of the UL/DL queues and utilise the sub-gradient descent (SGD) method for a solution. Simulation results show that the proposed traffic aware sub-frame configuration is more appropriate for the dynamical asymmetry environment. Meanwhile, the adopted dynamic step size SGD algorithm can achieve a trade-off between the worst-case queue and the network throughput.