Transportation Route Planning Research Articles

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

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

Intelligent Distribution Transportation Route Planning Considering Traffic Congestion

Intelligent Distribution Transportation Route Planning Considering Traffic Congestion

Dynamic assessment of the abatement effects of the low-carbon practices in the prefabricated building supply chain

PurposePrefabricated buildings (PBs) have proven to effectively mitigate carbon emissions in the construction industry. Existing studies have analyzed the environmental performance of PBs considering the shift in construction methods, ignoring the emissions abatement effects of the low-carbon practices adopted by participants in the prefabricated building supply chain (PBSC). Thus, it is challenging to exploit the environmental advantages of PBs. To further reveal the carbon reduction potential of PBs and assist participants in making low-carbon practice strategy decisions, this paper constructs a system dynamics (SD) model to explore the performance of PBSC in low-carbon practices.Design/methodology/approachThis study adopts the SD approach to integrate the complex dynamic relationship between variables and explicitly considers the environmental and economic impacts of PBSC to explore the carbon emission reduction effects of low-carbon practices by enterprises under environmental policies from the supply chain perspective.FindingsResults show that with the advance of prefabrication level, the carbon emissions from production and transportation processes increase, and the total carbon emissions of PBSC show an upward trend. Low-carbon practices of rational transportation route planning and carbon-reduction energy investment can effectively reduce carbon emissions with negative economic impacts on transportation enterprises. The application of sustainable materials in low-carbon practices is both economically and environmentally friendly. In addition, carbon tax does not always promote the implementation of low-carbon practices, and the improvement of enterprises' environmental awareness can further strengthen the effect of low-carbon practices.Originality/valueThis study dynamically assesses the carbon reduction effects of low-carbon practices in PBSC, informing the low-carbon decision-making of participants in building construction projects and guiding the government to formulate environmental policies.

Smart City Public Transportation Route Planning Based on Multi-objective Optimization: A Review

Smart City Public Transportation Route Planning Based on Multi-objective Optimization: A Review

Problem of Patient Transport Route Planning for Battery Electric Vehicles Considering a Flexible Charging Strategy

In view of the current problem of non-emergency patient transport route planning, the research is mainly carried out on fuel vehicles, and in the related research on the vehicle routing problem of electric vehicles, the relevant research results considering flexible charging strategies of different charging technologies are relatively weak. Therefore, this paper studies the non-emergency patient transport route planning problem based on pure electric vehicles, introduces a flexible charging strategy, constructs a mathematical model with the goal of maximizing the average patient satisfaction and minimizing the total cost of patient transport, and designs a hybrid heuristic algorithm to solve the problem. Then, different algorithms are used to solve the mathematical model considering patient satisfaction and the mathematical model not considering patient satisfaction. The feasibility and applicability of the mathematical model and the hybrid algorithm are verified by comparing and analyzing the solution results of different algorithms. Finally, based on the hybrid heuristic algorithm, the mathematical model of non-emergency patient transport route planning considering different charging strategies is solved. The applicability and reliability of the hybrid algorithm are further verified by comparing the solution results of different charging strategies, which shows that the flexible charging strategy can not only achieve a better balance between patient satisfaction and the total cost of patient transport, but also effectively improve the utilization efficiency of the remaining power of the vehicle.

A novel framework for road vectorization and classification from historical maps based on deep learning and symbol painting

Road networks in the past are imperative for understanding evolution of transportation infrastructure, urban sprawl, and route planning, etc. Various approaches have been developed for road extraction from historical maps, among which deep learning techniques stand out as the most effective ones. However, little attention has been paid to investigating road vectorization and classification from historical maps. Moreover, road classification via machine learning methods usually requires large amounts of dedicated training data. To address these issues, this paper proposes a novel and comprehensive framework for road vectorization and classification on the basis of road segmentation from historical maps. First, deep learning is used to get pixel-wise raster road segmentation results, which are further skeletonized using morphological operations. Then, considering that each road class is represented with a certain symbol, a painting function is defined for each class able to paint the corresponding symbol. These painting functions are then used to draw road segments along the skeletons. Since the start and end points in each painting function are used to vectorise the segment, this method achieves vectorization and classification at the same time. Our method is validated on four Siegfried map sheets in Switzerland, and evaluated via both visual and quantitative assessments. The results indicate that the method is capable of classifying roads accurately. In particular, two evaluation metrics completeness and correctness achieve 90.69% and 72.71% respectively for road class 2 which accounts for the highest portion in the map. Moreover, the results of this method avoid the saw-toothed issue of vectorised road lines. This research is beneficial for creating complete vector road network datasets with class information to support decision-making in urban planning and transportation.

Discovering urban mobility structure: a spatio-temporal representational learning approach

ABSTRACTThe urban mobility structure is a summary of individual movement patterns and the interaction between persons and the urban environment, which is extremely important for urban management and public transportation route planning. The majority of current research on urban mobility structure discovery utilizes the urban environment as a static network to detect the relationship between people groups and urban areas, ignoring the vital problem of how individuals affect urban mobility structure dynamically. In this paper, we propose a spatio-temporal representational learning method based on reinforcement learning for discovering urban mobility structures, in which the model can effectively consider the interaction knowledge graph of individuals with stations while accounting for the spatio-temporal heterogeneity of individual travel. The experimental results demonstrate the advantages of individual travel-based urban mobility structure discovery research in describing the interaction between individuals and urban areas, which can account for the intrinsic influence more thoroughly.

Municipal Transport Route Planning Based on Fair Mobility Budget

Abstract A series of initiatives have been adopted in the European Union to address greenhouse gas emissions and establish a society that is resilient to climate change. In response to these initiatives, the implementation of mobility budgets offers a more precise strategy for addressing carbon footprints associated with travel. Prioritizing localized carbon footprint control, mobility budgets are calculated and customized according to distinct regions, goals, and target demographics. When prioritizing the mobility budget as the central objective in municipal transport route planning, the focus should encompass principles of fairness and equity in travel. This entails considering factors such as accessibility, variety of mobility choices, inclusivity of transportation modes, and social justice. Therefore, this article aims to formulate an enhanced activity modelling methodology that would aid data-driven decision-making in municipal transport route planning, while upholding the principles of travel fairness and equity. Тhe results obtained from scrutinizing data related to public bus services and mobile networks are presented. The evaluation of Jelgava’s city transportation network to facilitate mobility budget reduction is undertaken, and this assessment is based on an analysis of data derived from a survey on public transport use coupled with an examination of the city’s mobility budget. The research presents the communication challenges that municipalities will face in planning and implementing changes that are needed to meet the greenhouse gas emission targets and outlines the possible use of visualization tools for modelling, explanation and communication of scenarios.

A GIS-Based Damage Evaluation Method for Explosives Road Transportation Accidents

The road transportation of explosives is highly concerning due to its substantial impact on social safety. For the safety management of explosive transportation, e.g., transport route planning and emergency rescue, explosion consequence evaluation is of paramount importance. The consequence evaluation of explosion accidents is affected by many factors, especially spatial features, such as the location of transport vehicles, the distribution of buildings, and the presence of individuals around the road, etc. However, there is still a lack of quantification methods for building damage evaluation, human casualty evaluation that considers real-time population density, and efficient interactive damage evaluation methods. In this paper, we formalize three typical scenarios of damage evaluation for explosive road transportation accidents, i.e., explosion point-based, road segment-based, and route-based damage evaluation. For each scenario, we propose a Height-aware Hierarchical Building Damage (HHBD) model and a Shelter-aware Human Casualty (SHC) model for building damage evaluation and human casualty evaluation, respectively. We also develop a GIS-based interactive visualization platform that integrates multi-source geospatial data and that enables efficient geospatial computation. In addition, a case study of liquefied natural gas (LNG) transportation in Wuhan is demonstrated in order to verify the effectiveness and efficiency of the proposed system. The research results can support the decision-making process of explosive transportation safety warnings and emergency rescue.

A Comprehensive Review of the Recent Advancement in Integrating Deep Learning with Geographic Information Systems

The integration of deep learning (DL) techniques with geographical information system (GIS) offers a promising avenue for gaining novel insights into environmental phenomena by using the capabilities of spatial, temporal, and spectral resolutions, as well as data integration. The integration of these two technologies can result in the development of a highly efficient system for assessing environmental conditions by analyzing the interplay between texture, size, pattern, and process. This viewpoint has gained appeal across various academic disciplines. GIS heavily relies on processors, especially for tasks such as 3D computations, map rendering, and route calculation. In contrast, DL has the capability to efficiently analyze vast quantities of data. DL has garnered significant attention in recent times due to its potential for delivering a wide range of promising outcomes. Moreover, there is clear evidence of the increasing utilization of deep learning techniques across various fields, including GIS. The objective of this study is to provide an overview of the application of DL techniques in the field of GIS. This paper presents a concise review of the fundamental DL ideas that are pertinent to GIS, with a focus on the most current research findings. The present study investigates the various uses and technology of remote sensing in diverse domains, including mapping, hydrological modeling, disaster management, and transportation route planning. This study offers insights into contemporary framework approaches and proposes avenues for further research.

Safety map: Disaster management road network for urban resilience

This study aims to select disaster resistance roads in urban areas which require management through their topological relationship and by the consideration of the roads’ durable and resilient attributes against disaster. Disaster resistance road network means a network of safe roads that are used dynamically according to various disaster scenarios. The network requires high accessibility and disaster resistance. In some cases, roads are used as an evacuation route to secure the safety of the citizens, to preserve human resources, and to transport emergency supplies to disaster areas. Upon the occurrence of a disaster, using the road infrastructure resistant to various types of disaster can help maintain the functions of cities and can contribute to the increase of urban resilience. In this paper, the process of reconstructing road network entails the extraction of road layers through ERAM (Eigenvector Ratio of Adjacency Matrix) model, both the road layers with high disaster resilience from the public road data, and the road layers with high connectivity in the topological relationship between roads. Subsequently, the network combines the layers based on high disaster resistance and the layers based on topological relationship, and leads to a disaster resistance road network. Verification will be performed through a case study comparing the extracted disaster resistance road network and the traffic counting points that signify the traffic characteristics. The proposed research results will be used as geographical information referred to for evacuation route planning and emergency transport route planning in disaster situations and will serve as basic study material for disaster plan.

Research on emergency logistics location-routing problem for public health emergencies

At present, the world's public health emergencies are becoming more and more frequent, bringing great threats to people's life and health safety and economic development. In the new crown pneumonia epidemic that affected the world in 2020, due to the shortage of medical resources and poor transportation of materials, the comprehensive control of the epidemic encountered great difficulties. In order to minimize the impact and loss caused by public health emergencies, it is necessary to study the location of emergency logistics centers and transportation route planning. This paper studies the positioning-routing problem in the emergency logistics system after public health emergencies, applies genetic algorithm and the Traveling Salesman Problem (TSP) to solve the positioning-routing problem in this paper, takes the current Wuhan map as an example, combines the p-center theory to take the location of each new coronary pneumonia directional receiving hospital as the basis for determining the material distribution center and transportation route, and uses actual cases to study the positioning-route problem.

Uncertain Multi-Objective Hazardous Materials Transport Route Planning Considering Resilience and Low–Carbon

Hazardous material transport accidents are events with low probability and high consequence risk. With the increase in the proportion of hazardous materials transported on domestic roads, increasing number of scholars have begun to study this field. In this study, a multi-objective hazardous materials transport route planning model considering road traffic resilience and low carbon, which considers the uncertainty of demand and time and is under the limit of the time window. It transports many types of hazardous materials from multiple suppliers to multiple retails with the three goals (transportation cost, risk and carbon emission). This model fills the gap in the research on hazardous materials transportation in the field of low carbon, and this is the first time that road traffic resilience is considered in the transport of hazardous materials as one of the weight factors of risk calculation. We designed a improved ant colony optimization algorithm (ACO) to obtain the pareto optimal solution set. We compare the improved ACO with genetic algorithm and simulated annealing algorithm. The results show that the improved ACO has better solution quality and solution space, which verifies the validity and reliability of the improved ACO.

Approximation algorithms for some extensions of the maximum profit routing problem

In this paper, we consider extensions of the Maximum-Profit Public Transportation Route Planning Problem, or simply Maximum-Profit Routing Problem (MPRP), introduced in Armaselu and Daescu (Approximation algorithms for the maximum profit pick-up problem with time windows and capacity constraint, 2016. arXiv:1612.01038, Interactive assisting framework for maximum profit routing in public transportation in smart cities, PETRA, 13–16, 2017). Specifically, we consider MPRP with Time-Variable Supply (MPRP-VS), in which the quantity q_i(t) supplied at site i is linearly increasing in time t, as opposed to the original MPRP problem, where the quantity is constant in time. For MPRP-VS, our main result is a 5.5 log {T} (1 + epsilon ) left( 1 + frac{1}{1 + sqrt{m}}right) ^2 approximation algorithm, where T is the latest time window and m is the number of vehicles used. We also study the MPRP with Multiple Vehicles per Site, in which a site may be visited by a vehicle multiple times, which can have 2 flavors: with quantities fixed in time (MPRP-M), and with time-variable quantities (MPRP-MVS). Our algorithmic solution to MPRP-VS can also improve upon the MPRP algorithm in Armaselu and Daescu (2017) under certain conditions. In addition, we simulate the MPRP-VS algorithm on a few benchmark, real-world, and synthetic instances.

Dynamic Maritime Traffic Pattern Recognition with Online Cleaning, Compression, Partition, and Clustering of AIS Data.

Maritime traffic pattern recognition plays a major role in intelligent transportation services, ship monitoring, route planning, and other fields. Facilitated by the establishment of terrestrial networks and satellite constellations of the automatic identification system (AIS), large quantities of spatial and temporal information make ships’ paths trackable and are useful in maritime traffic pattern research. The maritime traffic pattern may vary with changes in the traffic environment, so the recognition method of the maritime traffic pattern should be adaptable to changes in the traffic environment. To achieve this goal, a dynamic maritime traffic pattern recognition method is presented using AIS data, which are cleaned, compressed, partitioned, and clustered online. Old patterns are removed as expired trajectories are deleted, and new patterns are created as new trajectories are added. This method is suitable for processing massive stream data. Experiments show that when the marine traffic route changes due to the navigation environment, the maritime traffic pattern adjusts automatically.

PassGoodPool: Joint Passengers and Goods Fleet Management With Reinforcement Learning Aided Pricing, Matching, and Route Planning

In this paper, we present a dynamic, demand aware, and pricing-based matching and route planning framework that allows efficient pooling of multiple passengers and goods in each vehicle. This approach includes the flexibility for transferring goods through multiple hops from source to destination as well as pooling of passengers. The key components of the proposed approach include (i) Pricing by the vehicles to passengers which is based on the insertion cost, which determines the matching based on passenger's acceptance/rejection, (ii) Matching of goods to vehicles, and the multi-hop routing of goods, (iii) Route planning of the vehicles to pick. up and drop passengers and goods, (i) Dispatching idle vehicles to areas of anticipated high passenger and goods demand using Deep Reinforcement Learning, and (v) Allowing for distributed inference at each vehicle while collectively optimizing fleet objectives. Our proposed framework can be deployed independently within each vehicle as this minimizes computational costs associated with the gorwth of distributed systems and democratizes decision-making to each individual. The proposed framework is validated in a simulated environment, where we leverage realistic delivery datasets such as the New York City Taxi public dataset and Google Maps traffic data from delivery offering businesses.Simulations on a variety of vehicle types, goods, and passenger utility functions show the effectiveness of our approach as compared to baselines that do not consider combined load transportation or dynamic multi-hop route planning. Our proposed method showed improvements over the next best baseline in various aspects including a 15% increase in fleet utilization and 20% increase in average vehicle profits.

A Brief Review of Recent Developments in the Integration of Deep Learning with GIS

The interaction of Deep Learning (DL) methods with Geographical Information System (GIS) provides the opportunity to obtain new insights into environmental processes through the spatial, temporal and spectral resolutions as well as data integration. The two technologies may be connected to form a dynamic system that is incredibly well adapted to the evaluation of environmental conditions through the interrelationships of texture, size, pattern, and process. This perspective has acquired popularity in multiple disciplines. GIS is significantly dependant on processors, particularly for 3D calculations, map rendering, and route calculation whereas DL can process huge amounts of data. DL has received a lot of attention recently as a technology with a plethora of promising results. Furthermore, the growing use of DL methods in a variety of disciplines, including GIS, is evident. This study tries to provide a brief overview of the use of DL methods in GIS. This paper introduces the essential DL concepts relevant to GIS, the majority of which have been published in recent years. This research explores remote sensing applications and technologies in areas such as mapping, hydrological modelling, disaster management, and transportation route planning. Finally, conclusions on contemporary framework methodologies and suggestions for further studies are provided.

<b>Conceptual Framework of Environmentally Sustainable Transportation in Local Public Transport Route Planning</b>

Sustainability balances the economy, society, and environment with “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” as cited in the Brundtland Report. Research acknowledges there is no single definition of 'sustainability, and it is challenging to have it since the term is defined based on what is desirable, convenient, and measurable for the user group. This reasoning suggests there can be many definitions of sustainability, and this idea is not necessarily wrong. The concept of sustainability can and should be adapted to the local situation. Research also suggests differences in capacities of developing countries, which adds value to studying the case of developing countries.

Research on long-distance cold chain logistics route optimization considering transport vibration and refrigerant carbon emission

The long-distance cold chain transportation of fresh products from the place of origin to sale is costly due to the high probability of spoilage and high carbon emissions. The potential factors for the high cost of long-distance cold chain transportation are the spoilage of fresh goods caused by transportation vibration and the carbon emission caused by refrigerant use. How to minimize the impact of these factors through optimal transportation route planning is the current problem to be solved. Firstly, a longdistance cold chain logistics route planning model including goods spoilage cost and carbon emission cost is developed, considering the goods spoilage cost caused by transportation vibration and the carbon emission cost generated by refrigerant as well as several other factors. Secondly, an improved ant colony algorithm is used to solve the optimal solution of the model, and the algorithm is redesigned for the characteristics of the model. Thirdly, the pheromone update mechanism in the ant colony algorithm is improved by adding pheromone coefficients to accelerate the convergence speed of the algorithm. Finally, the validity of the model is verified by an arithmetic example. The model and method proposed in this paper help to reduce the cost in the process of long-distance cold chain transportation and bring practical application value.

Quasi-dynamic operation and maintenance plan for photovoltaic systems in remote areas: The framework of Pantanal-MS

Off-grid photovoltaic energy systems (PESs) are increasingly being used for the electrification of remote areas in Brazil, owing to the need for carbon emission avoidance, potential approval of solar resources, and cost-effective expansion limits for conventional distribution systems. However, the complex environment, social, economic, and geographical aspects turn the operation and maintenance (O&M) of PES into a logistic and technical challenge in remote areas. Therefore, this study presents an O&M plan for rural electrification of the Pantanal of the Mato Grosso do Sul (MS) region in Brazil. A quasi-dynamic heuristic approach is proposed, which considers not only the PES requirements but also the environmental constraints, given by the consumer locations, the flood period variation, and the different logistical strategies for off-road assessment. The quasi-dynamic O&M plan was designed based on space, time, and costs, with five main goals: task scheduling, transport route planning, team dispatch, inventory, and vehicle estimation.