Route Adjustment Research Articles

A two-context-aware approach for navigation: A case study for vehicular route recommendation

In contemporary urban environments, route recommendation systems have become an indispensable tool in moving the population from large centers, serving as valuable resources for circumventing traffic congestion. Enhancing vehicular traffic flow through strategic route adjustments is a pivotal element in improving traffic mobility. However, depending exclusively on traffic-related data for route recommendations fails to meet the essential criteria for ensuring effective management and safety for drivers and passengers during travel. Thus, context awareness and traffic data are crucial for enhancing efficiency and safety in traffic management. Our study proposes a two-context-aware approach to recommend safe routes for urban traffic management, considering road safety and travel time. Experiments were carried out using the widely recognized tool — HERE Navigation. Comparatively, our approach signifies a progressive stride in balancing mobility and security when contrasted with a single focus on travel time.

A near real-time carbon accounting framework for the decarbonization of maritime transport

The recently proposed marine greenhouse gas (GHG) emission pricing mechanism has pressured shipping companies and regulators to adopt effective methods for the real-time monitoring of carbon emissions. This study proposes a Near Real-Time (NRT) carbon accounting framework that leverages machine learning models to enable carbon emission tracking at a 15-minute time interval. The framework incorporates critical factors, such as ship navigation characteristics, weather, and sea conditions to achieve accurate carbon accounting. We validate the framework’s efficacy through a case study of four mega-container ships of varying sizes and navigation scenarios. Our results show a maximum cumulative error of 5.83% for all ship navigation scenarios, even without critical data, and during the most extended voyages of their respective services. The proposed framework provides a new perspective on the decarbonization application of ship energy efficiency prediction research. By integrating it with a cloud-computing platform, shipping companies can enhance their voyage planning and route adjustment to optimize operational efficiency and reduce carbon footprints. Using this framework, international maritime transport regulators can develop an early warning system for carbon emissions to coordinate and improve environmental sustainability practices in the shipping industry.

Bus Route Sketching: A Multimetric Analysis from the User’s and Operator’s Perspectives

The purpose of this study is to develop an optimal bus route search algorithm that considers both the user’s and supplier’s perspectives. The process of providing bus route service involves route network design, route allocation, and operation and management in sequence. Among these, establishing the optimal rationality for route network design in practical applications is challenging, and route modifications often occur during the operation process. To minimize these practical difficulties, this study proposes the Bus Route Sketch (BRS) methodology. This methodology, designed for network-level optimization, distinguishes itself from existing bus route setting methodologies by minimizing travel costs while taking user needs into account. This study yielded positive results, with the evaluation score improving from 8.83 to 9.50 from the supplier’s perspective and from 7.13 to 9.89 from the user’s perspective. This BRS methodology, developed to suit both route planning and operation processes, is expected to be utilized in the practical evaluation, adjustment, and design of bus routes.

Alternative service network design for bus systems responding to time-varying road disruptions

In practice, road disruptions occur frequently, interrupting multiple bus routes at the same time and causing widespread passenger delays. Typically, these disrupted roads are repaired sequentially and then gradually put into service. In response to such time-varying road disruptions, this paper aims to assist bus operators in developing effective alternative service networks for passengers. The proposed approach involves the joint optimization of service-based route adjustments, bus timetables, and passenger assignment to minimize the total passenger cost and weighted bus operation time. Specifically, a novel service-based adjustment strategy is introduced to flexibly adapt each bus service to time-varying road disruptions. An integer programming model is built for the studied problem based on the set of passengers’ time-space itineraries. To efficiently generate these time-space itineraries and solve models for large-scale problems, this paper develops a hierarchical solution framework. The framework consists of three key parts: (1) a column generation procedure to iteratively explore passengers’ spatial paths; (2) a customized extension algorithm to extend these spatial paths to time-space itineraries; and (3) a tailored adaptive large neighbourhood search heuristic to solve the final itinerary-based model. After that, the overall methodology is tested with both an illustrative example and a real-world example in Beijing. Experimental results show that our methodology produces a high-performance solution with only 7.3% of unserved passengers. Besides, compared to the two benchmark adjustment strategies, our service-based adjustment strategy reduces the average itinerary cost for all passengers by 27.0% and 43.3%, respectively.

Spatial Structure and Vulnerability of Container Shipping Networks: A Case Study in the Beibu Gulf Sea Area

Ports play an important role in maintaining the effectiveness of maritime logistics. When ports encounter congestion, strikes, or natural disasters, the maritime container transportation network might be significantly affected. The Beibu Gulf sea area is a key channel to supporting China’s participation in international economic cooperation in the western region. It is highly susceptible to the influence of the political and economic instability. This study introduces a dual-component framework to analyze the inherent structure and potential vulnerabilities of the container transportation network in the Beibu Gulf Sea areas. The findings show that the core layer of the network exhibited circular solidification characteristics. The entire network heavily relies on some core ports, such as Haiphong Port, Ho Chi Minh Port, and Qinzhou Port, and it highlights the potential increases in vulnerability. The finding shows that deliberate attacks have a greater impact than random attacks on the normal operations of maritime networks. If ports with high intermediary centrality are attacked, the connectivity and transportation efficiency of the Beibu Gulf maritime network will be significantly affected. However, under such circumstances, redistributing cargo transportation through route adjustments can deal with the transmission of cascading failures and maintain the network’s resilience. Based on the existing knowledge and the data collected in a case study, this research stands out as the first to provide a critical examination of the spatial structure and vulnerability of container shipping networks in the Beibu Gulf sea.

Leveraging Copernicus for Ice Block Detection: A Step Towards Safer Arctic Navigation

Background The Arctic is becoming more accessible due to ice melting, posing challenges for navigation due to floating ice blocks. Detecting these ice blocks is crucial for ensuring the safety of vessels navigating these waters. Methods This study introduces a novel method for detecting ice blocks in the Arctic using Artificial Intelligence (AI) and Synthetic Aperture Radar (SAR) satellite images from the Sentinel-1 mission of the Copernicus program. Our approach combines state-of-the-art image segmentation and deep learning techniques. We utilize a Convolutional Neural Network (CNN) classifier to accurately locate ice blocks within the images and retrieve their geographic coordinates. The method's performance is validated using precision, recall, and F1-score metrics. Results Our CNN classifier demonstrates robust performance in detecting ice blocks, validated through precision, recall, and F1-score metrics. The practical application of our technology was showcased in the AI-ARC project’s Arctic demo, receiving positive feedback from coast guards across various European countries. The system provides near-real-time alerts about detected ice blocks, allowing for timely route adjustments and reducing collision risks. Conclusions The developed system significantly contributes to Arctic navigation safety by providing accurate and timely detection of ice blocks. This work underscores the transformative potential of AI in environmental monitoring and maritime safety. Future refinements will be based on user feedback and advancements in AI technology, enhancing the system's effectiveness and reliability.

Strategies of Coverage Hole Repair in Wireless Sensor Networks

With the widespread application of Wireless Sensor Networks (WSNs) in daily life, enhancing the practicality and effectiveness of WSNs has gradually become an urgent demand. The issue of repairing coverage holes in WSNs has emerged as a hot research topic. Therefore, this paper aims to categorize and organize various repair strategies to provide research insights for more scientific researchers. This paper systematically categorizes and summarizes repair strategies for coverage holes in WSNs from four aspects: Adjustment of Mobile Nodes, Network Topology Optimization, Dynamic Routing Adjustment, and Adjustment of Coverage Range. It discusses the advantages and disadvantages of these strategies and the problems they address.

Multilevel routing for data transmission in internet of things

Internet of things (IoT) is the network in which sensor nodes sense information and transmit sensed information to sink. The small size and far deployment of SN results in causing problems related to higher energy utilization. Many techniques have been proposed in the last years to improve lifetime of the network. The already developed methods are the clustering techniques which are path optimization algorithms. The virtual grid-based dynamic routes adjustment (VGDRA) is the protocol which is already been proposed to increase network’s duration. The VGDRA protocol improve life span but doesn't solve the issue of energy hole which affect network performance. This work aims to improvise the VGDRA algorithm to solve the power hole problem. The utilization of cache motes is done in the network and the sink will move energy to cache nodes for the data collection. MATLAB is executed to simulate the suggested model, and amount of dead motes, active ones and amount of packets, whose transmission is done to base station (BS).

EVALUASI KEBIJAKAN PENGADAAN TANAH DI KABUPATEN BANDUNG

This research aims to evaluate the success of land procurement policies in the Jakarta-Bandung High-Speed Railway Development Project carried out by the Land Office of Bandung Regency. The method employed is a qualitative approach using a descriptive method that delineates specific circumstances from the emerging facts during the research process without altering the observed phenomena. The evaluation encompasses several indicators, including Inputs, processes, Outputs, and outcomes. Several issues identified include suboptimal planning, constraints in land relocation, land acquisition methods following a Business to Business (B to B) pattern, community dissatisfaction, delayed land payments, land measurement errors, insufficient comprehensive socialization, and inadequate coordination in addressing on-site issues. The research findings indicate that the implementation of Land Procurement Policies for the Jakarta-Bandung High-Speed Railway Development Project by the Land Office of Bandung Regency has yielded moderately satisfying results across most indicators. However, shortcomings exist in slow and unclear information dissemination to the public, low intensity in policy socialization influenced by third parties directly engaging the community, coordination and authority overlap issues between policy implementers and project executors, challenges in facility relocation, emerging land disputes, and additional land route adjustments beyond the initial planning requiring further attention. Hence, it is recommended to enhance information distribution and broader, more intensive policy socialization, improve coordination and collaboration with relevant parties, implement structured dispute resolution, and enhance precision in reviewing planning documents. Keywords: Policy Evaluation; Land Procurement; High-Speed Railway.

Forecasting technique of transport need for population of cities with high tourist potential during peak seasons

Introduction. One of the key problems in ensuring the quality of recreation for the population is the provision of transport services that meet the transport needs of vacationers. The study of the transport needs of the population of resort cities during the holiday season is caused by the need to assess changing passenger flows as a result of a multiple increase due to vacationers in accordance with their place of attraction and possible adjustments to the routes of regular passenger transportation by road. This determines the relevance of the topic of this article. The purpose of the article is to develop a methodology for determining the needs for transport services for the population and vacationers in cities with high resort potential during the busiest periods, which makes it possible to design transportation processes along regular routes.Materials and methods. General scientific methods of analysis and synthesis, the provisions of probability theory and mathematical statistics, mathematical modeling, as well as the provisions of the technology of transport processes were used as the main research methods. The work carried out the zoning of the city into transport areas with a center of gravity at stopping points of urban passenger transport. Calculations of potential transport needs were carried out on the basis of a survey of vacationers and field observations, which were processed using standard Microsoft Excel and Statistika software.Results. The main result of the work is a methodology for determining the potential transport needs of the population and vacationers in cities with high tourism potential for the development and adjustment of routes for regular passenger transportation by road during the holiday season, which represents the scientific novelty of the research. The technique contains a mathematical model and algorithm.Discussion and conclusion. The use of this methodology will enable the organizer of transportation in resort cities to optimize the organization of regular passenger transportation by road during peak seasons.

Route selection of periodic multimodal transport for logistics company: An optimisation approach

For transporting cargo from an origin to a destination, there are several routes with various transport modes, but in practice, only one route should be selected. In this regard, one of the main activities of Logistics Companies (LCs) is to select an optimal route for transporting freight. This complexity intensifies in periodic transport across multiple origins and destinations. This paper aims to develop a dual-phase mathematical model for LCs, considering cost, time, reliability, and air and noise pollution. Indeed, in periodic transport, there is a possibility of merging the cargo with the next shipment, which can lead to lower total costs. Multimodality is also another important consideration for LCs that can reduce cost and environmental impacts. The model leverages Data Envelopment Analysis in the tactical planning phase to filter efficient routes and a multi-objective model in the operational phase for optimal route selection. The proposed approach is validated by the real case of Thailand and Vietnam, which presents practical and managing insights for LCs and shippers. The study underscores the significance of route adjustments in periodic transport and provides valuable insights for LCs and shippers, emphasising the benefits of multimodal solutions in optimising freight logistics.

Data Aggregation through Hybrid Optimal Probability in Wireless Sensor Networks


 INTRODUCTION: In the realm of Wireless Sensor Networks (WSN), effective data dissemination is vital for applications like traffic alerts, necessitating innovative solutions to tackle challenges such as broadcast storms.
 OBJECTIVES: This paper proposes a pioneering framework that leverages probabilistic data aggregation to optimize communication efficiency and minimize redundancy.
 METHODS: The proposed adaptable system extracts valuable insights from the knowledge base, enabling dynamic route adjustments based on application-specific criteria. Through simulations addressing bandwidth limitations and local broadcast issues, we establish a robust WSN-based traffic information system.
 RESULTS: By employing primal-dual decomposition, the proposed approach identifies optimal packet aggregation probabilities and durations, resulting in reduced energy consumption while meeting latency requirements.
 CONCLUSION: The efficacy of proposed method is demonstrated across various traffic and topology scenarios, affirming that probabilistic data aggregation effectively mitigates the local broadcast problem, ultimately leading to decreased bandwidth demands.

Real Time Route Adjustment of a UAV Based on Dust Measurement with an Onboard Sensor

This research is driven by a pressing requirement to improve the effectiveness and safety of unmanned aerial vehicle (UAV) operations, specifically in atmospheric particle sensing. Airborne particulates present a substantial health hazard, leading to an increasing demand for inventive solutions capable of autonomously guiding UAVs along optimal routes, steering clear of regions with heightened dust concentrations. This research aims to address this challenge by developing a sophisticated approach that integrates atmospheric particle sensors with UAV flight control systems. The approach outlined in this study relies on atmospheric sensors mounted on a UAV to measure dust levels in a specified region. The UAV, following a predetermined route over the area, detects dust pollution. It dynamically adjusts its route based on the observed dust levels, avoiding areas with high concentrations of dust. This adaptive route determination aims to identify safe paths, avoiding regions with elevated dust levels that may pose risks to human health. The flight strategy and area-scanning methodology tailored for this objective are established, enabling the UAV to execute the assigned task. The ultimate goal is to create a system that not only minimizes energy consumption but also prioritizes human health by autonomously redirecting the UAV away from potentially harmful dust levels. The real-time monitoring and telemetry data feedback mechanisms further contribute to the advancement of UAV technology for environmental sensing and risk mitigation. Additionally, the integration of the laser-based dust sensor, i.e., Gp2y10, with the ArduPilot autopilot flight computer ensures seamless coordination, with the UAV adjusting its flight path based on the sensor's output.

Train rescheduling and platforming in large high-speed railway stations

To deal with train delays in large high-speed railway stations, a multi-objective mixed-integer nonlinear programming (MO-MINLP) optimization model was proposed. The model used the arrival time, departure time, track occupation, and route selection as the decision variables and fully considered the station infrastructure layout, train operational requirements, and time standards as limiting factors. The optimization objective was to minimize train delays and reduce track and route adjustments. To realize the large-scale and rapid solution of the MO-MINLP model, this study proposed a rolling horizon optimization algorithm that used “half an hour” as a time interval and solved the rescheduling and platforming problem of each time interval step-by-step. In numerical experiments, 227 train movements under delay circumstances in Hangzhoudong station were optimized by using the proposed model and solution algorithm. The results show that the proposed MO-MINLP model could resolve route conflicts, compress unnecessary dwell times, and reduce train delays; the solution algorithm could efficiently increase the computational speed. The maximum solution time for optimizing the 227 train movements is 15 min 24 s.

Hierarchical energy-saving routing algorithm using fuzzy logic in wireless sensor networks

Currently, sensor energy assembly in wireless sensor networks is limited, and clustering methods are not effective to improve sensor energy consumption rate. Thus, a hierarchical energy-saving routing algorithm based on fuzzy logic was constructed by considering three aspects: residual energy value, centrality, and distance value between nodes and base stations. The remaining sensor nodes selected by fuzzy logic algorithm have a longer time to live and greater residual energy than those selected by low-power adaptive clustering hierarchical protocol algorithm, fuzzy unequal clustering algorithm, and fuzzy logic cluster head election algorithm. For network life cycle, the number of rounds in which the first dead node appears, in descending order, is studied: energy-saving routing algorithm (400 rounds) > new geographic cellular structure algorithm (300 rounds) > virtual grid based dynamic routes adjustment algorithm (100 rounds). Under the same experimental round, energy-saving routing algorithm’s remaining energy curve always reaches its maximum. The energy-saving routing algorithm by fuzzy logic constructed by this research institute can significantly improve network energy utilization, which has certain reference value.

Deadline-Constrained Multi-Agent Collaborative Transmission for Delay-Sensitive Applications

Delay-sensitive applications impose the strict deadline-constrained latency requirement for end-to-end transmission. However, due to the high dynamics of link status and the limited network resources, it is still challenging to ensure deterministic end-to-end latency for deadline-constrained flows. To this end, we propose a deadline-constrained multi-agent collaborative transmission mechanism (DMCT). Specially, DMCT is featured by combining dynamic routing and packet active dropping control to avoid additional end-to-end latency. Firstly, we design a specific packet structure to detect network status and aware the deadline of each flow. Secondly, to enhance the precision of routing decision, we further formulate deadline-constrained flow scheduling as a joint minimization problem that considers the deadline of flows, queuing delay, and network status. Then, we propose a multi-agent collaborative routing algorithm to find the solution to the joint minimization problem. Finally, we propose a local-agent deadline-constrained active queue management algorithm for packet active dropping control and routing adjustment. Simulation results show that the proposed DMCT outperforms the state-of-the-art solutions, e.g., graph convolutional network-based deep reinforcement learning (GCNL) and delay-based shortest path first (D-SPF), in terms of one-way round-trip time, drop rate, and queue length.

Non-Aggressive Adaptive Routing in Traffic

Routing a person through a traffic road network presents a tension between selecting a fixed route that is easy to navigate and selecting an aggressively adaptive route that minimizes travel time. In this paper, we propose a novel routing framework that strikes a balance between adaptability and simplicity. Specifically, we propose to create non-aggressive adaptive routes that seek the best of both these extremes in the navigation world. These selected routes still adapt to changing traffic conditions, but we limit the number of adjustments made en route. This framework improves the driver experience by providing a continuum of options between saving travel time and reducing navigation stress. We design strategies to model single and multiple route adjustments, and investigate numerous techniques to solve these models for better route selection. To alleviate the intractability of handling real-life traffic data, we devise efficient algorithms with easily computable lower and upper bounds. We finally perform computational experiments on our algorithms to demonstrate the benefits of limited adaptability in terms of reducing the travel time.

Research On Emergency Parcel Transfer and Structure Optimization of E-Commerce Logistics Network Based on Multi-Objective Planning

This paper addresses the problem of urgent forwarding and structural optimization of parcels in e-commerce logistics networks. Firstly, based on the historical data of past freight volumes of different routes, this paper predicts the freight volume forecast data of each route by establishing an ARIMA time series forecast model; secondly, a multi-objective optimization model based on genetic algorithm is established to derive the optimal freight volume allocation scheme after the disappearance of route DC5; again, a multi-objective optimization model based on bat algorithm is established to derive the DC9 route Finally, a multi-objective optimization model based on the bat algorithm was developed to derive the route adjustment and cargo allocation scheme after the disappearance of route DC9. This paper concludes that after the cancellation of the DC5 route, the number of routes that are not in normal operation is 23 and the number of logistics paths that are allocated to new cargo is 56 in order to achieve the most balanced route load possible; after the closure of the DC9 route, 21 new roads are opened and 15 roads are closed, which are in a normal flow state and the transhipment is balanced. This paper has more far-reaching applications for multi-objective large-scale NP problems by establishing a multi-objective optimization model based on a heuristic algorithm, which can reduce the solution time of the calculation.

43‐1: Invited Paper: The Application and Future Development Trend of Oxide Technology in the Meta‐Universe

With the rise of concept “meta‐universe”, VR/AR products get more attention currently. This paper introduces the applied oxide technology in ultra‐high resolution VR products and its future development trend. It was investigated how oxide technology become the choice of ultra‐high resolution due to its transparent property. It was also highly researched how to ensure the device characteristics with short channel through adjustment of process route. In the end, the future trend, such as higher mobility and removal of light shield layer, is simply introduces.

Composition and recycling of smartphones: A mini-review on gaps and opportunities.

After more than a decade since smartphones became consolidated in the market, many recycling solutions have been proposed to deal with them. To continue developing useful solutions and enable adjustment of routes, this mini-review aims to analyse the current research scenario, presenting relevant gaps, trends and opportunities. From a structured searching and screening procedure, a vast source of data was arranged and is available to extract useful information (43 studies on composition and 93 studies on recycling). The study provides discussions about the history of smartphone development, constituent materials and recycling methods for different components, comparisons between feature phones and smartphones and others. Among some conclusions, the authors highlight the lack of studies on pre-extractive methods, green chemistry, recovery of critical and precious metals, determination of priority materials for recovery and solutions for entire devices. In the end, a list containing six research gaps for composition studies and seven research gaps for recycling studies is provided and may be seen as opportunities for future research.