A Travel Planning Method for Intelligent Networked Vehicles at Unsignalised Intersections based on Transformer Algorithm

This paper conducts an in-depth research on mobile robotic drilling trajectory planning for large-scale components. And it proposes a path planning method of step-close for mobile robotic station. According to the thought of “drilling area-robot station-process step”, the drilling order is defined. By adding conditions and simulation, we also present a simple method of optimizing robot posture in this paper. Using the actual position and the nominal position of two reference holes, a correcting algorithm of three-dimensional transformation is introduced to improve the drilling accuracy. Taking the aircraft wing-box drilling as main study object, this paper develops a trajectory planning and a drilling simulation system on the CATIA and DELMIA platform to validate the effectiveness of the methods introduced. Experiments show that the trajectory planning method and the three-dimensional transformation method for correcting drilling position in this paper not only improves the wing-box drilling efficiency and accuracy, but also reduces the production cost.

- The integration of artificial intelligence (AI) in travel planning shows a transformative change in how individuals and organizations approach drafting, decision-making, and personalized travel experiences in travel design. This paper introduces AI-driven travel planners that use machine learning, natural language processing and real-time data analytics to provide ultra-personal, efficient and adaptive travel solutions. By analyzing user preferences, budget constraints, historical behavior, and dynamic external factors such as weather, local events, and availability of transport companies, the system generates optimized travel routes with minimal user input. We recommend a hybrid model that combines recommended algorithms with reinforcement learning to improve adaptability over time. User test and simulated assessments of traditional planning methods show significant improvements related to satisfaction, planning time and route relevance. This study highlights that it can intelligently increase, as well as automate the trave l planning process. The rapid development of artificial intelligence has opened up new restrictions for personalized travel plans. This white paper presents the design and development of an AI travel planner. This prioritizes user centering with deep learning models trained on various travel days and user profiles. By integrating mood analysis from reviews, geospatial intelligence, and context-related prioritized learning, the system dynamically adapts travel routes to individual users. The platform provides an intuitive interface that adapts to user behavior and changes in external conditions in real time, minimizing cognitive load. Our results show a significant increase in user engagement and satisfaction compared to traditional tools for travel planning. This study highlights the importance of transparency, explanation and trust in AI control systems within the tourism sector. Key Words: Artificial Intelligence, Travel Planning, Recommendation System, Real - Time data analytics , Digital Travel Assistance,

Planning and decision-making technology at intersections is a comprehensive research problem in intelligent transportation systems due to the uncertainties caused by a variety of traffic participants. As wireless communication advances, vehicle infrastructure integrated algorithms designed for intersection planning and decision-making have received increasing attention. In this paper, the recent studies on the planning and decision-making technologies at intersections are primarily overviewed. The general planning and decision-making approaches are presented, which include graph-based approach, prediction base approach, optimization-based approach and machine learning based approach. Since connected autonomous vehicles (CAVs) is the future direction for the automated driving area, we summarized the evolving planning and decision-making methods based on vehicle infrastructure cooperative technologies. Both four-way signalized and unsignalized intersection(s) are investigated under purely automated driving traffic and mixed traffic. The study benefit from current strategies, protocols, and simulation tools to help researchers identify the presented approaches’ challenges and determine the research gaps, and several remaining possible research problems that need to be solved in the future.

Advances in technology especially mobile computing has encouraged various travel recommendation applications to flourish in the market. Many just generate the itinerary according to the events and places of interest chosen by the users. Some involve higher level of intelligence where itineraries are recommended based on community review score and historical itineraries. However, very few have factored in the business operator layer in decision modeling. Involvement of business operator is currently at the minimal level where most of them are just providing business description and feedback to the comments. In this paper, we proposed a novel Cloud-Mobi framework to integrate three information layers from the community, the business operator and the user in itinerary recommendation. Business operator is given a more influential role in decision modeling by sharing their news and promotion plans. However, community reviews still make remarkable impact to avoid misleading information. We also enhance the framework by hybrid the AHP with ACO route optimization algorithm from our previous research to suggest an optimum itinerary and travelling path. A fusion of two levels decision modeling is proposed. The first level calculates interest score for places and events of interest based on user preference, business description and community review with Analytical Hierarchy Process (AHP). Second level generates the optimum travelling path using the Ant Colony Optimization (ACO) method of our past research. The paper includes an example of step-by-step AHP implementation in level 1 decision modeling to calculate the interest score. The implementation has shown that the proposed Cloud-Mobi framework is promising for travel recommendation applications. Our future work will focus on developing the travel recommendation system prototype to implement the proposed framework.

The development of autonomous vehicles (AVs) is becoming increasingly important as the need for reliable and safe transportation grows. However, in order to achieve level 5 autonomy, it is crucial that such AVs can navigate through complex and unconventional scenarios. It has been observed that currently deployed AVs, like human drivers, struggle the most in cases of adverse weather conditions, unsignalized intersections, crosswalks, roundabouts, and near-accident scenarios. This review paper provides a comprehensive overview of the various navigation methodologies used in handling these situations. The paper discusses both traditional planning methods such as graph-based approaches and emerging solutions including machine-learning based approaches and other advanced decision-making and control techniques. The benefits and drawbacks of previous studies in this area are discussed in detail and it is identified that the biggest shortcomings and challenges are benchmarking, ensuring interpretability, incorporating safety as well as road user interactions, and unrealistic simplifications such as the availability of accurate and perfect perception information. Some suggestions to tackle these challenges are also presented.

Since travelers seek efficient transnational door-to-door journey planners, seamless mobility solutions and multimodal transport networks connecting distinct systems should be in transport planners and researchers’ focus. Thus, in current research, a method is elaborated to implement a seamless multimodal route planning solution by identifying potential exchange points between various networks, filtering the relevant exchange points, running a routing algorithm, and presenting a utility function for the ranking of the alternatives. Exchange points are discovered by an algorithm using the GPS coordinates of stops. If the coordinates are close, a connection is indicated. To identify the potential exchange points, solely the stops of different local journey planners are considered by the algorithm. Some specific exchange points are chosen for route calculation. The selection is necessary as the number of exchange points is high due to the involvement of international and multimodal networks. By using a heuristic optimization algorithm, a rough estimation of the routes is conducted. The proposed method is flexible; the parameters can be easily updated and enhanced. Therefore, the framework provides an up-to-date and pragmatic implementation in case of changes, too. Furthermore, the developed method is applicable to wide geographical areas and by any traveler information service provider.

Universal path planning for an indoor drone

Leisure travel has become a topic of great interest to Taiwanese residents in recent years. Most residents expect to be able to relax on a vacation during the holidays; however, the complicated procedure of travel itinerary planning is often discouraging and leads them to abandon the idea of traveling. In this paper, we design an automatic travel itinerary planning system for the domestic area (ATIPS) using an algorithm to automatically plan a domestic travel itinerary based on user intentions that allows users to minimize the process of trip planning. Simply by entering the travel time, the departure point, and the destination location, the system can automatically generate a travel itinerary. According to the results of the experiments, 70% of users were satisfied with the result of our system, and 82% of users were satisfied with the automatic user preference learning mechanism of ATIPS. Our algorithm also provides a framework for substituting modules or weights and offers a new method for travel planning.

Tourist route planning in large and diverse regions, such as China, presents significant challenges due to the vast number of destinations and varying preferences of travelers. This paper introduces the Tourist Route Optimization Algorithm (TROA), a novel approach combining entropy-based evaluation, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Grey Relational Analysis (GRA) to assess and rank 352 cities by their tourism attractiveness. TROA integrates advanced heuristic techniques, including Simulated Annealing (SA) and Genetic Algorithms (GA), to construct optimized travel itineraries that minimize costs and maximize time efficiency. The algorithm is validated through case studies, including a 144-hour constrained route starting in Guangzhou and a mountain-themed travel plan. Results demonstrate TROA’s capability to adapt to diverse tourist requirements and dynamic datasets, providing scalable and intelligent solutions for real-time multi-objective route optimization. This work advances computational methods in tourism planning and offers practical insights for enhancing tourist experiences and regional development strategies.

A line planning approach based on time-varying demand for high-speed rail under the combined operation of periodic and aperiodic services

The tourism sign is a medium between tourists and scenic spots, and a window to open tourist spots. The tourism sign system is mainly to enable tourists within a certain tourism area to obtain corresponding tourism information in a timely manner, including tourism positioning, scenic spot information, play routes and other content, to provide tourists with comprehensive play services. This paper takes Helong City, Jilin Province, China as an example to discuss the planning and setting methods of tourism sign system in minority cities to aim to provide theoretical and practical basis for the application of tourism sign. According to the tourism development framework of “The all-for-one tourism planning of Helong City”, it follows the linkage radiation ring of three groups in one belt, cultural relics and ecological farms. Utilize the tourism resources of Helong City to construct a complete tourism sign system based on points, lines and surfaces to achieve a comprehensive three-dimensional network-style boutique tourism route guidance. The perfect tourism sign system provides a series of services, which can effectively help tourists make travel plans and improve the quality of tourists’ play.

Technological advancements have brought significant changes to the travel sector, enhancing user experience and enabling more streamlined trip planning. Despite this progress, most current itinerary planning platforms fall short in offering personalized and holistic travel plans that align with the diverse expectations of travelers. To bridge this gap, the "AI-Based Travel Itinerary Planner" proposes a transformative solution that utilizes artificial intelligence (AI) and natural language processing (NLP) to create smart, optimized travel schedules. This system factors in vital elements like travel duration, destinations, and weather conditions to deliver more meaningful and efficient journeys. The primary aim of this project is to equip users with an intuitive platform that merges their interests, constraints, and preferences to generate fully customized itineraries. By incorporating advanced algorithms and leveraging real-time information, the system presents a modern alternative to conventional planning methods, which often lack personalization and adaptability. The integration of AI and NLP allows for dynamic itinerary creation that evolves with user input and environmental factors. Unlike traditional tools, this approach ensures a rich and user-centered planning experience. With the implementation of intelligent technologies and a data-centric framework, the "AI-Based Travel Itinerary Planner" seeks to redefine how travelers organize and enjoy their trips, offering a comprehensive, adaptive, and user-focused planning tool.