Route Planning Research Articles

Offline planning and online operation of zonal-based flexible transit service under demand uncertainties and dynamic cancellations

This paper introduces a comprehensive framework for planning and operating a zonal-based flexible transit (FT) service, a public transit mode designed to accommodate uncertain demand patterns. The framework addresses both offline planning based on stochastic demand distributions and cancellations, as well as online routing considering real-time orders and cancellation behaviour. Offline interzonal route planning is formulated as a two-stage recourse problem, while the intrazonal routing problem is modelled using a Markov decision process (MDP) that incorporates online information. To solve the problem, a service reliability-based decomposition method is employed to divide the problem into three mixed-integer subproblems. The first subproblem focuses on designing interzonal routes up to a specific demand level, taking into account a designated cancellation probability as determined by reliability measures. An insertion heuristic is developed for this subproblem to improve the solution efficiency. The second subproblem allocates passengers from certain categories to vehicles based on the passenger volume designated by reliability measures. Lastly, the third subproblem refines the vehicle intrazonal route according to the passenger assignment from the previous subproblem. The reliability measures are optimised iteratively until no further improvements are observed in consecutive iterations. The proposed FT service’s performance is evaluated using numerical simulations based on real New York City (NYC) taxi demand data, illustrating the effectiveness of the integrated planning and operational approach in accommodating uncertainties in on-demand transit systems.

FedGen: Federated learning-based green edge computing for optimal route selection using genetic algorithm in Internet of Vehicular Things

Time-efficient route planning is a significant research area of Internet of Vehicular Things. Optimal route selection is important to reach the destination in minimal time. Further, energy efficiency is vital for route planning in a sustainable environment. To address these issues, this paper proposes a federated learning and genetic algorithm-based green edge computing framework for optimal route planning in Internet of Vehicular Things. The vehicles are connected to the road side unit. The road side unit processes the image and video of the road, and predicts the number of vehicles on the road. For video processing Region-based Convolutional Neural Network is used. The road side units send the result and the local model parameters to the regional server. The regional server determines the optimal route using modified genetic algorithm, and sends it to the vehicles and the cloud. Also, the regional server updates its model and sends the updated model parameters to the road side units. The road side units update their local models accordingly. The regional server also sends the model parameters to the cloud, and the cloud updates the global model. The cloud sends the updated model parameters to the regional servers. The regional servers update their models accordingly. The results present that above 90% accuracy is achieved by the proposed model. The results also present that using modified GA the proposed approach reduces time and power consumption to find the optimal route by ∼62% and ∼66% than the cloud-only model.

Developed Status and Forecast of Intelligent Ship Application

The concept of smart ships is not limited to ship autonomous driving, it covers a broader scope, including the full life cycle of ship design, construction, operation and maintenance. By integrating advanced sensors, navigation systems and data analysis platforms, smart ships are able to monitor ship status and the Marine environment in real time, optimize route planning and improve fuel efficiency, thereby reducing energy consumption and greenhouse gas emissions. In addition, smart ships can revolutionize the shipping industry by improving maritime safety and reducing human error through remote control and autonomous decision-making. In recent years, smart ship technology has gradually moved from the theoretical research stage to practical application, and has attracted wide attention from the global shipping industry. A number of international cooperation and research projects have been launched to explore the practical application potential and promotion paths of smart ships. However, the development of smart ships also faces many challenges, including technical difficulties, legal and regulatory restrictions, and public acceptance issues. This paper will deeply discuss the current development status of smart ships, analyze the main challenges it faces, and forecast the future development trend, in order to provide reference and inspiration for the innovative development of the shipping industry.

Periodic Transformer Encoder for Multi-Horizon Travel Time Prediction

In the domain of Intelligent Transportation Systems (ITS), ensuring reliable travel time predictions is crucial for enhancing the efficiency of transportation management systems and supporting long-term planning. Recent advancements in deep learning have demonstrated the ability to effectively leverage large datasets for accurate travel time predictions. These innovations are particularly vital as they address both short-term and long-term travel demands, which are essential for effective traffic management and scheduled routing planning. Despite advances in deep learning applications for traffic analysis, the dynamic nature of traffic patterns frequently challenges the forecasting capabilities of existing models, especially when forecasting both immediate and future traffic conditions across various time horizons. Additionally, the area of long-term travel time forecasting still remains not fully explored in current research due to these complexities. In response to these challenges, this study introduces the Periodic Transformer Encoder (PTE). PTE is a Transformer-based model designed to enhance traffic time predictions by effectively capturing temporal dependencies across various horizons. Utilizing attention mechanisms, PTE learns from long-range periodic traffic data for handling both short-term and long-term fluctuations. Furthermore, PTE employs a streamlined encoder-only architecture that eliminates the need for a traditional decoder, thus significantly simplifying the model’s structure and reducing its computational demands. This architecture enhances both the training efficiency and the performance of direct travel time predictions. With these enhancements, PTE effectively tackles the challenges presented by dynamic traffic patterns, significantly improving prediction performance across multiple time horizons. Comprehensive evaluations on an extensive real-world traffic dataset demonstrate PTE’s superior performance in predicting travel times over multiple horizons compared to existing methods. PTE is notably effective in adapting to high-variability road segments and peak traffic hours. These results prove PTE’s effectiveness and robustness across diverse traffic environments, indicating its significant contribution to advancing traffic prediction capabilities within ITS.

A production and transport scheduling strategy of energy and resources of pelagic clustering islands based on generalized movable energy storage

At present, most pelagic islands rely on the mainland for resource replenishment. And due to the limited island area, it is not sufficient to simultaneously place enough basic load facilities and new energy generation and consumption facilities on a single island. Considering pelagic islands are distributed in groups, vigorously developing renewable energy around islands can be considered, and the islands can be divided into resource islands and load center island. Resource islands can use renewable energy to produce and transport electric power, fresh water and hydrogen which are needed for load center island. The whole islands group forms a cleaner and green integrated energy system. However, this kind of system will face the coexistence challenge of intra-island independent optimization and inter-island collaborative optimization, continuity of energy production and discreteness of transportation, production scheduling and route planning, which is less studied. To ensure reasonable operation of the energy system, according to multi-agent theory, the inter-island energy and resource production and transportation scheduling model based on generalized movable energy storage can be divided into two parts: load center island agent scheduling model and resource islands agent scheduling model. And scheduling objectives and constraints of each agent in two scenarios are determined. The simulation results show that the new model can comprehensively take into account the production efficiency of equipment, production effectiveness and transportation cost and realize the rational production scheduling and transportation of energy and resources under the premise of meeting the demand of load center island.

Планирование маршрутов судов на основе многомерной кластеризации данных о движении

Статья посвящена проблеме обеспечения безопасности движения судов на морских акваториях. В условиях насыщенного трафика навигационная безопасность может быть обеспечена только при соблюдении судами определённой схемы движения. В работе ставится задача планирования маршрута судна на акватории с интенсивным движением таким образом, чтобы он соответствовал практике судоходства, сложившейся в конкретном районе. В том случае, если правила и ограничения движения существуют неформально, перспективным путём планирования маршрута является обращение к ретроспективным данным о движении с использованием идей машинного обучения. Предлагаемый в работе метод планирования маршрутов основан на кластеризации данных о движении судов. Выделенные кластеры представляют собой области в трёх- или четырёхмерном фазовом пространстве с близкими значениями скоростей и курсов судов. На основе полученной таким образом информации формируется граф возможных маршрутов судна, вершины которого соответствуют границам выделенных кластеров и областям их пересечения, а ребра – выделенным характерным курсам судов. Каждая точка в многомерном фазовом пространстве характеризуется координатами, курсом и скоростью судна, вводится метрика расстояния между точками, для кластеризации данных о движении используется плотностный алгоритм пространственной кластеризации (DBSCAN). Обсуждается вычислительная сложность рассматриваемой задачи в условиях реальных акваторий, делается вывод о возможности конструктивного решения задачи стандартными вычислительными средствами. Приводится пример решения задачи на реальных данных о движении судов в Сангарском проливе, который свидетельствует о пригодности использования разработанных алгорит-мов в практике судовождения. The paper is devoted to the problem of ensuring the safety of vessel traffic in marine areas. In conditions of heavy traf-fic, navigational safety can be ensured only if ships adhere to a certain traffic pattern. The work sets the task of planning a vessel's route in water areas with intense traffic in such a way that it corresponds to the shipping practice that has de-veloped in a particular area. In the event that traffic rules and restrictions exist informally, a promising way to plan a route is to use historical traffic data using machine learning ideas. The route planning method proposed in this work is based on clustering data on vessel movement. The selected clusters represent areas in three- or four-dimensional phase space with similar speeds and courses of ships. Based on the information obtained in this way, a graph of possi-ble vessel routes is formed, the vertices of which correspond to the boundaries of the selected clusters and the areas of their intersection, and the edges correspond to the selected characteristic courses of the vessels. Each point in the mul-tidimensional phase space is characterized by the coordinates, course and speed of the vessel, a distance metric be-tween points is introduced, and a density spatial clustering algorithm (DBSCAN) is used to cluster motion data. The computational complexity of the problem under consideration in the conditions of real water areas is discussed, and a conclusion is drawn about the possibility of a constructive solution to the problem using standard computing tools. An example of solving the problem using real data on the movement of ships in the Tsugaru Strait is given, which indicates the suitability of using the developed algorithms in navigation practice.

Vehicle Route Planning for Relief Item Distribution under Flood Uncertainty

Flooding, a pervasive and severe natural disaster, significantly damages environments and infrastructure and endangers human lives. In affected regions, disruptions to transportation networks often lead to critical shortages of essential supplies, such as food and water. The swift and adaptable delivery of relief goods via vehicle is vital to sustain life and facilitate community recovery. This paper introduces a novel model, the Vehicle Routing Problem for Relief Item Distribution under Flood Uncertainty (VRP-RIDFU), which focuses on optimizing the speed of route generation and minimizing waiting times for aid delivery in flood conditions. The Genetic Algorithm (GA) is employed because it effectively handles the uncertainties typical of NP-Hard problems. This model features a dual-population strategy: random and enhanced populations, with the latter specifically designed to manage uncertainties through anticipated route performance evaluations, incorporating factors like waiting times and flood risks. The Population Sizing Module (PSM) is implemented to dynamically adjust the population size based on the dispersion of affected nodes, using standard deviation assessments. Introducing the Complete Subtour Order Crossover (CSOX) method improves solution quality and accelerates convergence. The model’s efficacy is validated through simulated flood scenarios that emulate various degrees of uncertainty in road conditions, affirming its practicality for real-life rescue operations. Focusing on prioritizing waiting times over travel times in routing decisions has proven effective. The model has been tested using standard CVRP problems with 20 distinct sets, each with varying node numbers and patterns, demonstrating superior performance and efficiency in generating vehicle routing plans compared to the shortest routes, which serve as the benchmark for optimal solutions. The results highlight the model’s capability to deliver high-quality solutions more rapidly across all tested scenarios.

Differentiating Everyday Map Tasks: Unique Attention-Related Eye Movements and Electrophysiological Signatures of Map Use

Cartographic maps are ubiquitous in spatial activities such as localization, navigation, and travel exploration. Understanding how the map users interact with maps remains a challenge, however. Research to date has focused on the overt aspects of map users’ cognitive processes, using conventional empirical methods and eye tracking, but the covert aspects (e.g., brain activity) have been largely neglected. In this study, participants used Google Maps for four everyday tasks: global search, distance comparison, route following, and route planning. We recorded and analyzed users’ attention-related visual (eye movement measures by eye tracking) and electrophysiological (fixation-evoked P3-ERP and task-induced alpha/theta ERD/ERS by scalp electroencephalograms) responses. Results demonstrated efficient visual processing and expanded spatial exploration in global search and distance comparison tasks. Interestingly, more attentional resources were devoted to recognizing whether information matches or not in global search tasks, as shown by the larger P3 component for the map target. In contrast, route following and route planning tasks required intensified attention for information decoding and complicated cognitive processes, focusing predominantly on pertinent narrow map regions. Our findings highlight that the visual and electrophysiological signatures effectively capture the heterogeneity of user attention during different map tasks.

A Meta-Analysis to Evaluate the Reliability of Depth-to-Water Maps in Predicting Areas Particularly Sensitive to Machinery-Induced Soil Disturbance

The careful planning of the extraction routes is one of the most important best management practices to limit soil disturbance related to ground-based forest operations. Over the recent years, this task has been commonly addressed in the framework of boreal forestry, by developing soil trafficability maps based on the depth-to-water (DTW) topographic index. The basic concept of trafficability maps developed with the DTW index is that soils at low DTW index, namely <1, could be more prone to soil compaction and rutting as they tend to have higher moisture content. However, previous studies that tried to assess the reliability of these maps reported contrasting results. Therefore, the present meta-analysis was developed to evaluate if soils at low DTW index (≤1) are actually more sensitive to soil compaction and rutting than soils at higher DTW index (>1). A database was created containing all the studies that assessed soil compaction and rutting in soils at low DTW index (experimental treatment) and high DTW index (control treatment), and a multivariate meta-analysis was used to check the presence of statistically significant effect size. Then the influence on the effect size of variables like soil texture, number of machine passage and weight of the machine, was checked by applying sub-group meta-analysis and meta-regression. Finally, a sensitivity analysis was performed by removing possible outliers from the database and repeating the analyses. No statistical differences were found in soil compaction and rutting severity in areas at low DTW index in comparison to the control areas (DTW index ≥1). The results showed that soil texture, number of machine passage and weight of the machine did not have a significant influence on the effect size. The sensitivity analysis developed after removing outliers from the database fully confirmed the obtained results. Thus our meta-analysis showed that the DTW index in its current form is not a fully reliable predictor of soil areas that could be particularly sensitive to machinery-induced disturbance. It is therefore recommended to use the DTW index to create trafficability maps, always taking into account that the results of the algorithms should be validated in the field before starting harvesting operations.

Improving Effectiveness of the Safety Service Patrol Programs: A Discrete Event-Based Simulation Approach

Safety service patrols (SSPs) play an important role in incident management on highways. It is critical to respond to incidents in a timely manner as this can significantly reduce nonrecurrent congestion and improve safety. Therefore, it is essential to allocate available SSP vehicles to highway segments such that their effectiveness is maximized. This study aimed to develop a simulation-based framework to assist with SSP service optimization. More specifically, a discrete event-based simulation tool (i.e., SSP-OPT) with customizable parameters was developed to help plan the optimum patrol routes based on available SSP resources and predicted incidents. The developed tool was tested with roadway traffic and incident data from the Virginia highway network. After model calibration, the simulation results showed that the developed SSP-OPT tool could replicate the patrol routes with similar performance to the field observations, validating the tool. Further, adopting the tool for corridor-level optimization could help to identify the best patrol plan to minimize SSP response time and maximize SSP response rates for a given number of SSP vehicles. The SSP-OPT tool requires minimal user input (e.g., segment lengths, annual average daily traffic) and has the flexibility to be easily applied to any highway corridor once calibrated. The tool generates various performance metrics to enable more informed decision making in SSP route planning.

A New Transportation Route Planning Method for Wind-Based Hydrogen Supply Chains

A New Transportation Route Planning Method for Wind-Based Hydrogen Supply Chains

Usage of clustering algorithms for automating route planning in transportation routing tasks

This scientific paper explores the comprehensive evaluation of clustering results applied to the geographical settlements of Ukraine. Diverse clustering methods, including K-means, DBSCAN, Agglomerative, Spectral, and Birch, were employed to analyze the spatial distribution of settlements. The assessment of each clustering method involved the application of relevant quality criteria, contributing to a thorough understanding of their performance in the context of Ukrainian settlements. The findings from this study offer valuable insights into the strengths and limitations of each clustering approach, facilitating informed decision-making in the selection of an appropriate method based on specific geographical characteristics. Additionally, the paper provides practical recommendations for optimizing the input data utilized in the clustering process, enhancing the overall efficacy of settlement analysis methodologies. This research contributes to the advancement of clustering techniques tailored to geographical datasets, with potential implications for urban planning, regional development, and geographic information systems.

Future Trends: Predicting the Evolution of Rail and Road Transportation in Logistics

The logistics industry represents the vital circulatory system of the global economy, responsible for the efficient movement of goods across sprawling supply chain networks. Historically, rail and road transportation modes have been the workhorses of logistics operations, facilitated by their extensive infrastructure and versatility in cargo handling. However, the convergence of rapid technological advancements, evolving consumer expectations, and mounting environmental pressures is catalyzing a transformative shift in the future evolution of these pivotal transportation sectors. This comprehensive study delves into the emerging trends that are poised to reshape the rail and road transportation landscape within the logistics realm. By synthesizing insights from industry leaders, academic research, and cutting-edge technological developments, the study aims to provide a holistic perspective on the forthcoming changes and their far-reaching implications. The study’s main objective is to integrate semi-autonomous and autonomous cars into fleets of transportation vehicles. The advent of self-driving trucks and trains promises to revolutionize logistics operations by enhancing efficiency, reducing human error, and optimizing route planning. Concurrently, this transition raises critical concerns related to workforce displacement, regulatory frameworks, and cybersecurity vulnerabilities that necessitate proactive mitigation strategies. Moreover, the logistics industry faces mounting pressure to embrace sustainable practices and curtail its environmental footprint. Consequently, the rail sector is anticipated to experience a renaissance fueled by its inherent energy efficiency and lower carbon emissions compared to road transportation. Innovations in electric and hydrogen-powered locomotives, coupled with advancements in rail infrastructure, could position rail as a greener and more cost-effective solution for long-haul freight movement. Concurrently, the road transportation sector is poised for a transformation driven by the proliferation of electric and alternative fuel vehicles. This adoption is projected to accelerate as battery technology improves, charging infrastructure expands, and governments implement stricter emissions regulations. Furthermore, the integration of advanced telematics systems and realtime data analytics could optimize route planning, load management, and vehicle maintenance, further enhancing the operational efficiency of road-based logistics.

Transaction Analysis for Operational Optimization in Transjakarta Public Transportation System

This study presents a comprehensive exploratory data analysis (EDA) of transaction data within the Transjakarta public transportation system, focusing on routes, passenger flow, and congestion patterns. Leveraging tap-in and tap-out transaction records collected through payment cards, the research aims to provide actionable insights for optimizing operational efficiency and enhancing service quality. By analyzing transaction data, the study identifies busy routes, peak hours, and passenger distribution across different modes of transportation, including big bus (BRT), medium and big bus (non-BRT), and mini-bus (Mikrotrans). Moreover, the research investigates correlations between transaction volume and traffic congestion levels, enabling the identification of areas prone to delays and bottlenecks. The findings of this analysis offer valuable information for route planning, scheduling adjustments, and infrastructure improvements within the Transjakarta system. By optimizing resources based on data-driven insights, the study seeks to contribute to the enhancement of public transportation services, promoting accessibility, reliability, and sustainability in urban mobility.

Reducing carbon footprint of ships in voyage planning: A case study of Atlantic passage

Determining the most efficient ship routes is crucial for ensuring both safety and fuel savings during voyage planning. Great circle Navigation (GC) and weather routing (WR) are the two most common methods used by navigators for ocean crossings. GC Navigation is renowned for its potential to save time and fuel by following shorter routes over long distances; however, it may expose vessels to adverse weather conditions and sea states due to navigating at higher latitudes. On the other hand, weather routing integrates pilot charts and meteorological data to identify safer routes, albeit potentially longer, minimizing risks associated with rough weather. This study focuses on route planning for a handy-sized tanker contracting a westbound Atlantic ocean voyage within a specific timeframe. The main objective of the study is to evaluate the efficacy of these two methods. The GC route components were computed using spherical trigonometry equations, while weather routing planning relied on pilot charts and meteorological data. Environmental conditions were simulated and tested in the Transas Full Mission Simulator (NTPRO 4000). The results reveal that the WR method provides 21.3% higher energy efficiency than GC. The insights derived from this research contribute significantly to enhancing the operational efficiency and safety standards of commercial vessels.

Tourists’ behavioural intentions to use ChatGPT for tour route planning: an extended TAM model including rational and emotional factors

ABSTRACT This study incorporates perceived coolness and perceived enjoyment as emotional factors into Technology Acceptance Model (TAM), which traditionally focuses on rational factors, to comprehensively understand how tourists develop usage intentions of ChatGPT for tour route planning. Structural equation modeling was utilized to analyse 491 online questionnaires. Findings support that perceived information quality is an antecedent factor of perceived ease of use and perceived usefulness. Consequently, perceived ease of use and perceived usefulness significantly influence tourists’ attitudes towards using ChatGPT for tour route planning, while perceived usefulness directly influences tourists’ behavioural intentions regarding ChatGPT usage. Moreover, perceived coolness directly affects tourists’ attitudes towards ChatGPT usage and indirectly affects their behavioural intentions through perceived enjoyment and attitudes. The primary contribution of this research lies in the integration of perceived coolness and perceived enjoyment into the traditional TAM, thus establishing a new rational-emotional extended TAM framework. This advancement provides a more comprehensive understanding of tourists’ adoption of generative AI tools such as ChatGPT for tour route planning.

Disaster Risk Reduction Literacy Through Participatory Mapping of Tsunami Disaster in Bobanehena

Halmahera Island in North Maluku consists of active faults that cause frequent tectonic earthquakes. In addition to earthquakes, tsunamis can also occur. Bobanehena village is one of the villages located on the coast of Jailolo sub-district and could be at great risk of a tsunami if an earthquake strikes Halmahera Island. The problems addressed are first, the limited capacity of individuals, households and communities in facing and dealing with tsunami disasters. Second, there is no tsunami evacuation route in Bobanehena Village based on participatory mapping. The method in this service is the Community Based Disaster Risk Reduction (CBDRR) approach which involves the community to be actively involved in disaster management. Participatory mapping of evacuation routes was carried out by Forum Group Discussion (FGD) and assessment of disaster capacity was carried out by submitting pre-test and post-test questionnaires conducted during the activity. The results of the service were a participatory map of the tsunami evacuation route plan and increased community capacity regarding tsunami disasters in Bobanehena Village.

Research on Intelligent Ship Route Planning Based on the Adaptive Step Size Informed-RRT* Algorithm

Research on Intelligent Ship Route Planning Based on the Adaptive Step Size Informed-RRT* Algorithm

The heterogeneous impact of China–Europe railway express on the efficiency of logistics industry in node cities

PurposeThe spatiotemporal compression effect of China–Europe Railway Express (CR-Express) can reduce the flow costs of resources between China’s node cities. Additionally, it can break through the limitations of low-added-value marine products, significantly impacting the logistics industry efficiency. However, there are few literature verifying and analyzing its heterogeneity. This study explores the impact of CR-Express on the efficiency of logistics industry in node cities and analyzes the heterogeneity.Design/methodology/approachFirst, this study uses panel data to measure the efficiency of node city logistics industry. Secondly, this study discusses the impact of the opening of CR-Express on the efficiency of logistics industry in node cities based on the multi-period differential model. Finally, according to the node city difference, the sample city experimental group is grouped for heterogeneity analysis.FindingsThe results show that CR-Express can promote the urban logistics industry efficiency, with an average effect of 4.55%. According to the urban characteristics classification, the heterogeneity analysis shows that the efficiency improvement effect of logistics industry in inland cities is more obvious. The improvement effect of node cities and central cities in central and western China is stronger, especially in the sample of megacities and type I big cities. Compared with non-value chain industrial products, the CR-Express has significant promotion effects on the logistics efficiency of the cities where main goods are value chain products.Originality/valueUnder the background of double cycle development, this paper can provide a scientific basis for the investment benefit evaluation of CR-Express construction and the follow-up route planning.

Artificial intelligence and human autonomy: the case of driving automation

AbstractThe present paper aims at contributing to the ethical debate on the impacts of artificial intelligence (AI) systems on human autonomy. More specifically, it intends to offer a clearer understanding of the design challenges to the effort of aligning driving automation technologies to this ethical value. After introducing the discussion on the ambiguous impacts that AI systems exert on human autonomy, the analysis zooms in on how the problem has been discussed in the literature on connected and automated vehicles (CAVs). On this basis, it is claimed that the issue has been mainly tackled on a fairly general level, and mostly with reference to the controversial issue of crash-optimization algorithms, so that only limited design insights have been drawn. However, integrating ethical analysis and design practices is critical to pursue the implementation of such an important ethical value into CAV technologies. To this aim, it is argued, a more applied approach targeted at examining the impacts on human autonomy of current CAV functions should also be explored. As an example of the intricacy of this task, the case of automated route planning is discussed in some detail.