Congestion Of Corridors Research Articles

ASTRO: Reducing COVID-19 Exposure through Contact Prediction and Avoidance

The fight against the COVID-19 pandemic has highlighted the importance and benefits of recommending paths that reduce the exposure to and the spread of the SARS-CoV-2 coronavirus by avoiding crowded indoor or outdoor areas. Existing path discovery techniques are inadequate for coping with such dynamic and heterogeneous (indoor and outdoor) environments—they typically find an optimal path assuming a homogeneous and/or static graph, and hence they cannot be used to support contact avoidance. In this article, we pose the need for Mobile Contact Avoidance Navigation and propose ASTRO ( A ccessible S patio- T emporal R oute O ptimization), a novel graph-based path discovering algorithm that can reduce the risk of COVID-19 exposure by taking into consideration the congestion in indoor spaces. ASTRO operates in an A * manner to find the most promising path for safe movement within and across multiple buildings without constructing the full graph. For its path finding, ASTRO requires predicting congestion in corridors and hallways. Consequently, we propose a new grid-based partitioning scheme combined with a hash-based two-level structure to store congestion models, called CM-Structure , which enables on-the-fly forecasting of congestion in corridors and hallways. We demonstrate the effectiveness of ASTRO and the accuracy of CM-Structure ’s congestion models empirically with realistic datasets, showing up to one order of magnitude reduction in COVID-19 exposure.

China’s corridors-in-the-sky design and space-time congestion identification and the influence of air routes’ traffic flow

With rapid development of air transportation, the airspace structure of the future will need to be flexible and dynamic to accommodate the increase in traffic demand. The corridors-in-the-sky has become a new technology to support the full exploitation and utilization of airspace resources. This paper proposes a method of designing corridor, identifying congestion state, and analyzing the influence of air routes’ traffic flow. From this, we have reached a number of conclusions. (1) The congestion periods present the multi-peak “wavy” scattered distributions and the peaks back-end agglomeration characteristics in the whole day. (2) The congestion segments present the structural characteristics of unbalanced coverage and concentrated distribution to the crossing points. The corridors with high congestion level present as an italic “N-shaped” frame, which presents incomplete penetration of short segments. (3) For the temporal and spatial interaction, there are two types of congestion segments, and there are some common congestion periods in different congestion segments of multiple corridors. The high-density air route plays a relatively decisive role in corridor congestion, and the influence of two directions is unbalanced. This research can provide a basis for the dynamic evaluation of China’s airspace resources and corridors construction in the future.

Study of the classroom arrangement problem based on ant colony algorithm

In this paper, the ant colony algorithm is presented to optimize the university classroom arrangement and solve the corridor congestion problem. Firstly, the classroom capacity, the course time periods and the distance between classrooms are considered, and then the ant colony algorithm is applied to the corridor congestion problem by minimizing the distance between the students’ classroom of the courses. The experimental results show that the ant colony algorithm is able to construct feasible solutions and solve the classroom arrangement problem.

Automated Fire Evacuation System with Congestion Control

Most of the traditional fire evacuation systems present today are designed on the assumption that the people present in the building have prior knowledge about the nearest safety exits, but this assumption is not true in many cases and has also resulted in the loss of life in several instances. The Automated Fire Evacuation System is an embedded system which will effectively guide the victims of fire accidents to nearest safety exits. A network of temperature and smoke detector sensors placed strategically collects and delivers accurate information about the surroundings to the system. The Dijkstras algorithm is used to calculate the safest and shortest path to the exits. The evacuation path generated is conveyed to the victims through the use of LED strips which glows in a unidirectional pattern. The system also has an additional congestion control feature which uses image processing to detect congestion of corridors, if any congestion is detected the algorithm dynamically reroutes the people to other available evacuation paths.

Development model simulation of airport transport corridor based on system dynamics algorithm

The reasonable planning of airport transport plays important role in the development of air transportation industry and the construction of urban traffic system. From the point of view of economic growth model, the system dynamics method and system simulation technology were adopted to study and analyze the change law of airport transport corridor system, under three economic growth modes, namely, sustained and rapid economic growth, economic growth slowdown and economic cycle fluctuation. Furthermore, the change low of indictors, such as, airport transport corridor capacity, airport transport corridor congestion, airport transport corridor passenger flow, following with the three modes of economic growth, were analyzed. The purpose of this study is to provide some theoretical support for the planning of airport transport corridors.

Mobility-Aware and Congestion-Relieved Dedicated Path Planning for Group-Based Emergency Guiding Based on Internet of Things Technologies

This paper proposes a group-based framework with dedicated path planning for emergency guiding based on Internet of Things (IoT) technologies. The proposed framework can model the spatiotemporal mobility of indoor people to determine and relieve the congestion of corridors and exits. A dedicated path can be determined to provide the shortest evacuation time for each group of nearby people. The corridor and exit capacities, corridor lengths, clustering motion of a group, concurrent moving of different groups, and up-to-date distribution of group people are considered together to accurately estimate the evacuation time for each group. Based on the estimated evacuation time, evacuation load can be evenly distributed among corridors and exits to alleviate the congestion of all corridors and exits for minimizing total evacuation time. The performance of the proposed framework is evaluated by conducting mathematical analysis and computer simulations, which outperforms existing schemes and can achieve the shortest evacuation time for group-based emergency guiding. In addition, an Android-based prototype with indoor IoT localization technologies is implemented to verify the feasibility of our framework.

Distributed Emergency Guiding with Evacuation Time Optimization Based on Wireless Sensor Networks

This paper proposes a load-balancing framework for distributed emergency guiding based on wireless sensor networks. A load-balancing guiding scheme is designed and an analytical model is derived to reduce the total evacuation time of people indoors. The guiding scheme can provide the fastest path for people to reach an exit according to the evacuation time estimated using the analytical model. Based on thorough research, this is the first distributed solution in which corridor capacity and length, exit capacity, and the concurrent movement and distribution of people are considered in estimating the evacuation time and planning escape paths. Using the proposed framework, congestion in corridors and at exits can be eased to substantially reduce the total evacuation time. Analytical and simulation results show that this approach outperforms existing schemes and can prevent people from following local-optimal guiding directions that increase the evacuation time. A prototype called the Load-balancing Emergency Guiding System (LEGS) is implemented; this system can be used to compare the evacuation times and guiding directions provided by existing schemes and the proposed scheme for various distributions of people.

FUNCTION-SPACE ASSIGNMENT AND MOVEMENT SIMULATION MODEL FOR BUILDING RENOVATION

Building renovation is an effective way to revive the use of a building, the use efficiency of which is primarily determined by its layout. However, in architectural practice, architects and building owners renovate buildings based on their personal subjective perceptions of how occupants use the building instead of systematically analyzing their use behaviors. This study proposes a model, called the Function-space Assignment and MOvement Simulation (FAMOS) model, which integrates radio frequency identification (RFID), fast messy genetic algorithms (fmGA), and movement simulation techniques to solve the function-space assignment problem. The RFID equipment is specifically used to track the occupants’ movement data in a building, the fmGA is employed to identify the optimal result of function assignment, and the movement simulation technique is adopted to verify the result and support the decision-making of function-space assignment. This study presents a real case study to demonstrate the use of FAMOS and compare its assignments with those generated by a renovation architect. The objective function showed that FAMOS’s version had a 14.80% higher objective value than the architect’s version. The experiment also showed that FAMOS helped the architect find the best assignment or improve their assignment based on desired objectives such as preferred space size, minimized movement distance, or removal of corridor congestion.

Algorithms for Studying the Impact of Travel Time Reliability along Multisegment Trucking Freight Corridors

Performance measures allow planners and engineers to monitor and evaluate transportation facilities or projects and to justify the allocation of funds among alternative transportation improvement options. To capture the impact of corridor congestion on freight vehicles, new tools and methodologies are developed to analyze data from commercial vehicles and produce performance measures such as travel time, speed, and travel time reliability. Because long freight corridors comprise segments with varying reliability characteristics, the objective was to develop a programming logic that would use available truck Global Positioning System data to (a) identify natural segments or regions in a corridor between urban centers, Interstate junctions, or rural areas and (b) estimate corridorwide impact of travel time unreliability. The case study presented investigates the I-5 corridor in Oregon. The research applies statistical techniques to compute vehicle travel time and reliability for freight movements within each segment. The proposed methodology has been used successfully to identify distinct segments and characteristics of travel time reliability in freight corridors. Travel time information was used to compute cost effects of delays within rural and urban areas along the I-5 corridor. The research presents an advance in the processing and aggregation of Global Positioning System truck data to produce succinct yet informative performance measures and segments.