Congestion Factor Research Articles

Communication dynamics of congestion warning information considering the attitudes of travelers

Traffic congestion is a serious problem faced by many cities worldwide today. Congestion warning information is one of the important influencing factors of urban road congestion; To this end, based on the dynamics of infectious diseases, a congestion warning information dissemination model considering the attitudes of travelers and the network structure was constructed. The existence and stability of the equilibrium points of non congestion warning information and congestion warning information in the model were analyzed, and the optimal control strategy of the model was proposed. Numerical simulation was conducted to verify the results of theoretical analysis, simulate and analyze the impact of changes in various parameters in the model on the dissemination of congestion warning information, and perform sensitivity analysis on several parameters. The results indicate that travelers are more inclined towards “fast” modes of transportation and have a stronger willingness to share congestion warning information. The dissemination range of warning information is wider, which can play a positive role in reducing traffic congestion pressure.

Simulation-Based Optimization of Truck Appointment Systems in Container Terminals: A Dual Transactions Approach with Improved Congestion Factor Representation

Simulation-Based Optimization of Truck Appointment Systems in Container Terminals: A Dual Transactions Approach with Improved Congestion Factor Representation

Solving Dynamic Full-Truckload Vehicle Routing Problem Using an Agent-Based Approach

In today’s complex and dynamic transportation networks, increasing energy costs and adverse environmental impacts necessitate the efficient transport of goods or raw materials across a network to minimize all related costs through vehicle assignment and routing decisions. Vehicle routing problems under dynamic and stochastic conditions are known to be very challenging in both mathematical modeling and computational complexity. In this study, a special variant of the full-truckload vehicle assignment and routing problem was investigated. First, a detailed analysis of the processes in a liquid transportation logistics firm with a large fleet of tanker trucks was conducted. Then, a new original problem with distinctive features compared with similar studies in the literature was formulated, including pickup/delivery time windows, nodes with different functions (pickup/delivery, washing facilities, and parking), a heterogeneous truck fleet, multiple trips per truck, multiple trailer types, multiple freight types, and setup times between changing freight types. This dynamic optimization problem was solved using an intelligent multi-agent model with agent designs that run on vehicle assignment and routing algorithms. To assess the performance of the proposed approach under varying environmental conditions (e.g., congestion factors and the ratio of orders with multiple trips) and different algorithmic parameter levels (e.g., the latest response time to orders and activating the interchange of trip assignments between vehicles), a detailed scenario analysis was conducted based on a set of designed simulation experiments. The simulation results indicate that the proposed dynamic approach is capable of providing good and efficient solutions in response to dynamic conditions. Furthermore, using longer latest response times and activating the interchange mechanism have significant positive impacts on the relevant costs, profitability, ratios of loaded trips over the total distance traveled, and the acceptance ratios of customer orders.

Efficient topology reconfiguration for NoC-based multiprocessors: A greedy-memetic algorithm

In multi-core processor systems, the Network-on-Chip (NoC) serves as a vital communication infrastructure. To ensure chip reliability during potential failures, this paper proposes a two-level topology reconfiguration algorithm with core-level redundancy technology. Initially, a heuristic topology reconfiguration method utilizing a greedy strategy is proposed to perform local replacement of faulty processing elements (PEs) and generate an initial logical topology with shorter interconnection paths between PEs. Then, an intelligent optimization method based on memetic algorithm is introduced to optimize the generated initial topology for better communication performance. The experimental results demonstrate that compared to the current state-of-the-art algorithm, the proposed algorithm achieves an average improvement of 13.92% and 30.83% on various size topologies in terms of distance factor (DF) and congestion factor (CF), which represent communication delay and traffic balance respectively. The proposed algorithm significantly enhances the communication performance of the target topology, mitigating communication latency and potential congestion problems.

A Spatiotemporal Hierarchical Analysis Method for Urban Traffic Congestion Optimization Based on Calculation of Road Carrying Capacity in Spatial Grids

Traffic congestion is a globally widespread problem that causes significant economic losses, delays, and environmental impacts. Monitoring traffic conditions and analyzing congestion factors are the first, challenging steps in optimizing traffic congestion, one of the main causes of which is regional spatiotemporal imbalance. In this article, we propose an improved spatiotemporal hierarchical analysis method whose steps include calculating road carrying capacity based on geospatial data, extracting vehicle information from remote sensing images to reflect instantaneous traffic demand, and analyzing the spatiotemporal matching degree between roads and vehicles in theory and in practice. First, we defined and calculated the ratio of carrying capacity in a regional road network using a nine-cell-grid model composed of nested grids of different sizes. By the conservation law of flow, we determined unbalanced areas in the road network configuration using the ratio of the carrying capacity of the central cell to that of the nine grid cells. Then, we designed a spatiotemporal analysis method for traffic congestion using real-time traffic data as the dependent variables and five selected spatial indicators relative to the spatial grids as the independent variables. The proposed spatiotemporal analysis method was applied to Chengdu, a typical provincial capital city in China. The relationships among regional traffic, impact factors, and spatial heterogeneity were analyzed. The proposed method effectively integrates GIS, remote sensing, and deep learning technologies. It was further demonstrated that our method is reliable and effective and enhances the coordination of congested areas by virtue of a fast calculation speed and an efficient local balance adjustment.

Effect of network topologies and attacking strategies on cascading failure model with power-law load redistribution

Various traffic networks play an important role in daily life and have different topological characteristics such as small-world and scale-free. The factors of traffic congestion, natural disasters and traffic accidents may induce cascading failure in which the load redistribution usually has the characteristic of power-law (that is to say, when a station is broken, the great majority of passengers would evacuate to the nearby stations, but the very few would evacuate to further). Based on them, this paper studies the load-capacity cascading failure model with power-law load redistribution using three kinds of attacking strategies (removing a single node according to the load, removing multiple nodes according to the load and removing multiple nodes randomly) for different network structures (random networks, WS small-world networks and BA scale-free networks). We confirm the robustness of our results by further performing simulations on real-world traffic networks. A quantitative description of network topologies and attacking strategies vs. the cascading failure model with power-law load redistribution is obtained by computer simulation. The results demonstrate that in contrast to removing the node according to the load, network robustness increases with the increase of the load control parameter α when randomly removing the node; when θ is small, the load tolerance parameter β is sensitive to the network robustness in random networks and WS small-world networks when removing a single node according to the load.

Analysis of Traffic Volume and Travel-Time Relationship Using Continuous One-Hour Values on Urban Expressway

To make more efficient use of the expanded freeway and urban expressway networks, various measures such as bottleneck management and wide-area congestion pricing based on traffic data obtained from traffic detectors, including traffic volume and travel time, have been considered. Generally, the congestion status of the data varies from day to day. This study proposes a method for analyzing a graph of traffic volume and travel time to visually and intuitively grasp the change in the daily traffic situation using continuous one-hour values. These values are continuously generated hourly values obtained by shifting data every minute. Twenty-four hours 1 minute data for 128 days on 32 segments with detectors in the Nagoya Expressway Network in Japan were used to draw a continuous one-hour value graph. A number of graphs showed loops of continuous one-hour values with congestion and a smooth variation characteristic of values over time. These graphs provide an accurate estimate of the daily maximum one-hour traffic volumes and facilitate a sequential understanding of the congestion pattern changes on successive route segments. Hourly travel-time prediction models were constructed to macroscopically examine congestion measures over a range of several hours. These models were fabricated with high accuracy using multiple regression analysis based on the characteristics of continuous one-hour values. Exploratory predictive analysis of hourly travel-time models has allowed us to study and discuss various congestion factors in road structures and traffic flows, and it has been found to be easy to grasp the phenomenon and ensure accuracy and operability.

SIMULATION OF TRAFFIC MANAGEMENT AND ENGINEERING TO IMPROVE THE PERFORMANCE OF THE FIVE POINT INTERSECTION ON JALAN KEPUTIH SURABAYA

The five-point intersection on Keputih Street is one of the unsignaled intersections in the city of Surabaya with traffic problems. At certain times, especially rush hour, traffic problems will occur. At 07.00 WIB it was seen that several drivers were trying to cut through the traffic jam by squeezing in between other vehicles, this was done by road users to avoid long queues of vehicles. Queues can be approximately 1.2km long and the surrounding existing conditions are commercial or shopping areas, housing with several sides of the road used as a means for vehicles to stop or park as an additional congestion factor. Thus, there is a need for management planning and traffic engineering to improve the quality of intersections in serving road users. The method used for data analysis is based on existing data obtained directly from the field and processed according to PKJI 2023. The results obtained were that the degree of saturation at the intersection was much different before and after the traffic engineering planning was carried out. Projection results with the installation of traffic light signs and geometric widening of Keputih Tegal, the degree of saturation value obtained was 0.78 where previously DJ = 0.98 0.85. Keywords: Management and Traffic Engineering; PKJI 2023; Delay; Five-point Unsignalized Intersection in Keputih Surabaya.

STUDY OF SOCIETY BEHAVIOUR TO EARLY WARNING IN THE RAILWAY LEVEL CROSSING WITHOUT BARRIER IN GAYUNG KEBONSARI, SURABAYA

Railway level crossing safety is one of the most critical issues for railways. Collisions between trains and motorized vehicles contribute most to LX accidents. Population growth and the development of economic in Indonesia affect the increase in vehicle volume, especially in the city of Surabaya. Furthermore, the increasing of vehicles volume, causing congestion at some points in Surabaya including Gayung Kebonsari railway level crossing. One of the congestion factors at the railway level crossings is the duration of closing time as the train passes through the crossing. The uncertain duration of gate closing time cause road users to be undisciplined by break through the crossing gates while the train pass through the crossings. Considering of those problems, respondents’ opinion is being evaluated to see the society behavior to early warning on reducing the number of traffic violation. A comprehensive discussion of the existing problems, lessons learned and the possible future implications that can be applied in Indonesia are presented.

Research on logistics Route Optimization based on AFSA

Based on the characteristics of logistics goods, in the process of logistics to choose a reasonable or optimal path, will save more logistics costs. In the process of using artificial fish swarm algorithm to find the optimal path planning, through the artificial fish swarm algorithm to improve the visual range, congestion factor and other factors, and record and mark the feasible solution, the target point of the fish swarm trajectory, form the feasible solution of the path planning and select the best solution, to achieve the optimal logistics path, reduce the cost of goods logistics.

Research on Cold Chain Routing Optimization of Multi-distribution Center Considering Traffic Performance Index

In response to the practical situation where traffic congestion factors reduce the distribution efficiency and increase the cost of cold chain logistics in the actual transportation process, a mathematical model for optimizing the cold chain path of multiple distribution centers is established, with the goal of minimizing the comprehensive cost and incorporating a traffic congestion index. By analyzing routine factors such as customer requested time windows, loading and unloading time, and road congestion, an improved particle swarm algorithm that introduces exchange offspring is proposed. The proposed method is tested through experimental simulations and the results are compared with traditional methods. The results show that considering the traffic congestion index saves 7.12% of the comprehensive cost compared to not considering the traffic congestion index, and completes the delivery service with a shorter path length.

An Analysis of Congestion Factors at Signalized Intersections using Interpretable Machine Learning

An Analysis of Congestion Factors at Signalized Intersections using Interpretable Machine Learning

Robustness optimization of aviation-high-speed rail coupling network

Recently, interactions between the real networks have received extensive attention from scholars in different fields. Considering the edge coupling relationship between aviation networks (AN) and high-speed rail networks (HRN), we established the aviation-high-speed-rail coupling network (AHSRCN) model of China according to the real database of flights and trains. We defined the initial load of each edge based on the importance index of edges obtained by importance evaluation method, and then established a nonlinear load capacity model. Furthermore, according to the actual situation, we applied two dynamic load distribution strategies for each network with different load capacity exponential parameters and load capacity coefficient parameters, and use network congestion factor defined to reflect the robustness of the networks to study optimization of robustness of AHSRCN. The results show that as the proportion of remained edges increases, the aviation network and the high-speed rail network’s congestion reach the peak when the proportion of remained edges is 6% and 2%, respectively. The increase of load capacity parameters can effectively reduce the congestion factor of networks in nonlinearity way. For the case that the load capacity exponential parameter is less than 0, the Load Balancing Distribution (LBD) strategy can reduce the network congestion to the maximum extent. However, for the case that the load capacity exponential parameter is greater than 0, the Load Extreme Distribution (LED) strategy can suppress the network congestion more effectively, while the change of load capacity coefficient parameter will not cause this difference.

The Congestion Factors of Container Truck Travel from Tanjung Emas Port to the Hinterland Region

Semarang Container Terminal or Terminal Petikemas Semarang (TPKS) at Tanjung Emas Port is one of the container terminals in Indonesia with growing container traffic, recorded in 2020 reached 712.062 TEUs. The interaction of the components of the transportation system between ports, road facilities and container trucks needs to be synergized, for the smooth flow of container traffic and avoiding travel obstacles or road congestion. Container movement congestion is the result of an imbalance between traffic volume and road capacity, especially container trucks, resulting in the level of road service which ultimately results in a decrease in vehicle speed. This research aims to find the clarity about the main factors forming congestion based on the dynamics of environmental change, using an analysis of the variables causing congestion based on the results of a survey in 2013 with 30 samplings of container trucks and 60 samplings of container trucks in 2019 on the road from Tanjung Emas Port, Semarang to the hinterland area around Ungaran, Semarang Regency. The analysis was conducted by using descriptive regression method and model testing with Minitab 18 software. It can be explained that the main factors causing congestion that have an impact on decreasing speed affecting the travel time of container trucks are road geometric such as road gradients, vertical alignments, intersection and activity on shoulder.

Pre-Predictive Congestion-Based Data Allocation for Sixth Generation Cooperative Intelligent Transportation Systems

Cooperative intelligent and autonomous transportation systems rely on intelligent sensing, computing, and actuating technologies for unmanned freight and public movements. The information gained from neighbors and communication infrastructures provides efficient actuation for safe and sustained transportation. This article resolves traffic data management congestion using sixth-generation (6G) communication and computing techniques. Terahertz and machine-type communications are exploited for swift information exchange, bypassing the congestion effects. Congestion occurs when demand for road space exceeds supply. This proposal incorporates prediction-based learning to compute the feasibility of handling traffic information and cooperative intelligent transportation. This model is named Congestion-aware Pre-predictive Data Allocation (CPPDA). The traffic flows causing congestion in the data exchange process are predicted for re-allocation and independent channel utilization. In this learning, the pre-predicted instances are updated with the actual identified utilization-to-congestion rate. Therefore, the congestion-causing channels for sensing are identified with ease, reducing the outage. The outage is examined for a basic inter-vehicle data link. Through the optimal allocation of channels for actuation, cloud-aided resources are utilized to a maximum level, leveraging infrastructure support. In addition to an outage of 10.83%, the response time of 14.75%, congestion factor of 8.2%, computational overhead of 6.4%, and information gain factors of 6.86% are analyzed through a comparative study.

Rate-Aware Fuzzy Clustering and Stable Sensor Association for Load Balancing in WSNs

In wireless sensor networks (WSNs), topology control is of great significance for energy efficiency. While various clustering algorithms have been proposed to handle the topology control of WSNs, the joint optimization of fairness, Quality of Service (QoS), load balancing, and congestion management has received much less attention. Additionally, the constrained resources and processing capacity of sensor nodes complicate the topology control problem. This article proposes a three-layer framework based on joint rate-aware fuzzy clustering and stable sensor association that considers various factors of sensor energy efficiency. First, a rate-aware fuzzy clustering method is proposed for initial clustering, and energy-aware cluster head (CH) selection is applied considering the total energy consumption and the residual energy of the potential CHs to improve the energy efficiency of WSNs. After that, the payoff functions of the CHs and the cluster members are constructed for sensor association with resource capacity limitations and QoS constraints. The optimal sensor association is obtained to maximize the payoffs. Furthermore, a low-complexity suboptimal sensor association approach is proposed to reduce the complexity with a tolerable performance gap. Finally, a congestion factor is introduced to balance the load of CHs, and the Gale–Shapley (GS) algorithm is used to avoid unstable sensor association caused by the processing capacity constraints. The simulation results show that the proposed algorithms can effectively improve the resource utilization and the energy efficiency of the WSNs.

Stochastic user equilibrium path planning for crowd evacuation at subway station based on social force model

Evacuation path of pedestrians at subway station can directly affect the evacuation efficiency, and then affect the service level of the station. A Fisk stochastic user equilibrium model considering the congestion factor is established to assist evacuation path planning, which could avoid the bottleneck area and dangerous situation in advance. By comparing the field statistical data with simulation data of the number of pedestrians at the entrance gate of subway station, it is verified that the social force model and the minimum cost model can basically reproduce the movement law and path selection behavior of pedestrians at the subway station. Simulation experiment is carried out to compare the evacuation efficiency at the subway station under the stochastic user equilibrium model and the minimum cost model. The results indicate that the number of pedestrians at each gate is relatively balanced which can avoid overcrowding under the stochastic user equilibrium model. The total evacuation time can be reduced by 15%, and the individual evacuation time can be saved about 0.57 min. By comparing the 3D overall pedestrian trajectory and the 2D local pedestrian trajectory, it is found that the stochastic user equilibrium model is of great significance for evacuation path optimization at the subway station. The quantitative relationship between the number of pedestrians and evacuation time at the subway station is given through regression analysis under stochastic user equilibrium model and minimum cost model. The suspension of escalators can obviously result in congestion at the gate and increase the total evacuation time. When pedestrian density at the station is low, closing a certain exit can significantly improve the pedestrian traffic efficiency at the gate and greatly reduce the total evacuation time. The stochastic user equilibrium evacuation path planning model constructed in this paper provides a guidance strategy for pedestrian evacuation, which could improve the efficiency and safety of subway evacuation system.

Delineating the risk factors of venous congestion: An analysis of 455 deep inferior epigastric perforator flaps with radiographic correlation

Venous congestion occurs in 2-15% of DIEP flaps for breast reconstruction. We previously showed that thicker suprascarpal fat pads are associated with increased SIEV caliber and may, by extension, indicate a dominant superficial venous system. In this study, we aim to provide clinical correlation and to determine the risk factors of venous congestion in order to identify high-risk patients who may benefit from prophylactic SIEV dissection. An IRB-approved retrospective study was performed in patients who underwent DIEP flap reconstruction from August 2011 to August 2020. Radiographic measurements of suprascarpal fat pad thickness and SIEV diameter were collected per hemi-abdomen from preoperative imaging. The statistical analysis explored whether certain variables were associated with venous congestion. A total of 258 patients underwent 455 DIEP flaps. Suprascarpal fat pad thickness was positively correlated with SIEV diameter (r=0.51, p<0.001), each with a mean caliber of 19.8mm and 2.5mm, respectively. Seven flaps (1.5%) developed venous congestion, with five requiring SIEV salvage and secondary venous anastomosis. Congested flaps had significantly thinner suprascarpal fat pads (12.3vs. 20.0mm, p=0.043). All six congested flaps with imaging had suprascarpal thickness less than 18mm, compared to 182 out of 335 non-congested flaps with imaging (p=0.035). The risk of venous congestion following DIEP flap reconstruction is significantly increased with thinner suprascarpal fat pads, suggesting that the mechanism of venous congestion may not be limited to superficial venous dominance. We recommend prophylactic SIEV dissection in all patients with suprascarpal fat pad thickness less than 18mm.

An Optimal Path-Finding Algorithm in Smart Cities by Considering Traffic Congestion and Air Pollution

Finding the shortest and cleanest path in the cities is vital, especially in metropolises. Although several algorithms and some software have been introduced to manage the traffic or suggest a path with minimum traffic congestion, none considers air quality a deciding factor. This paper introduces a novel algorithm to find the shortest path based on traffic congestion and air quality. In the proposed algorithm, the city map is fetched from the Google Map app and is converted into a weighted graph. Traffic data is collected from GPS devices, which will be available through the local cloud services. The C-means clustering method is used to cluster traffic congestion. Also, the air quality information is collected from air pollution monitoring stations. The graph weights are calculated based on both air quality and traffic congestion factors, simultaneously. Finding the shortest path problem is then defined as an optimization problem, and the linear programming method is used to solve it. Finally, the proposed algorithm’s performance is evaluated by finding the shortest path in Tehran, Iran in different scenarios.

A Novel Tradeoff Analysis between Traffic Congestion and Packing Density of Interconnection Networks for Massively Parallel Computers

From disaster prevention to mitigation, drug analysis to drug design, agriculture to food security, IoT to AI, and big data analysis to knowledge or sentiment mining, a high computation power is a prime necessity at present. As such, massively parallel computer (MPC) systems comprising a large number of nodes are gaining popularity. To interconnect these huge numbers of nodes efficiently, hierarchical interconnection networks are an attractive and feasible option. A Tori-connected flattened butterfly network (TFBN) has been proposed by the authors in a prior work for future generation MPC systems. In the previous study, the static network performance and static cost-effectiveness were evaluated. In this research, a novel trade-off factor named message traffic congestion vs. packing density trade-off factor has been proposed, which characterizes the message congestion in the network and its packing density. The factor is used to statically assess the suitability of the implementation of an interconnection network. The message traffic density, packing density, and new factor have been evaluated for the proposed network and similar competitive networks such as TTN, TESH, 2D-Mesh, 3D-Mesh, 2D-Torus, and 3D-Torus. It has been found that the performance of the TFBN is superior to the other networks.