Complex Transportation Networks Research Articles

AGV Scheduling for Optimizing Irregular Air Cargo Containers Handling at Airport Transshipment Centers

Airport transshipment centers play a pivotal role in global logistics networks, enabling the swift and efficient transfer of cargo, which is essential for maintaining supply-chain continuity and reducing delivery times. The handling of irregularly shaped air cargo containers presents new constraints for automated guided vehicles (AGVs), as these shapes can complicate loading and unloading processes, directly impacting overall operational efficiency, turnaround times, and the reliability of cargo handling. This study focuses on optimizing the scheduling of AGVs to enhance cargo-handling efficiency at these hubs, particularly for managing irregular air cargo containers. A mixed-integer linear programming (MILP) model is developed, validated for feasibility with the Gurobi solver, and designed to handle large-scale operations. It incorporates a novel approach by integrating a simulated annealing optimized genetic algorithm (GA). The experimental results demonstrate that the designed algorithm can solve models of considerable size within 8 s, offering superior time efficiency compared to the solver, and an average solution quality improvement of 12.62% over the genetic algorithm, significantly enhancing both the model’s efficiency and scalability. The enhanced AGV scheduling not only boosts operational efficiency but also ensures better integration within the global logistics framework. This research provides a robust foundation for future advancements in logistics technology, offering both theoretical and practical insights into optimizing complex transportation networks.

Model of Sustainable Household Mobility in Multi-Modal Transportation Networks

Nowadays, urban and suburban areas face increasing environmental pressures, and encouraging sustainable transportation behaviors at the household level has become crucial. This paper presents a model of a decision support system (DSS) for promoting sustainable household mobility choices in multi-modal transport networks. The system was modeled using an enhanced Petri Net approach, allowing for the dynamic representation of complex transport networks and multi-modal journey options. The model incorporated various sustainability factors. These were combined into a single environmental impact score, which was considered alongside travel time and cost in the route optimization process. Simulation results demonstrated the DSS’s capability to guide users toward more sustainable mobility choices. The model also showed potential as a tool for policymakers to assess the impact of various sustainable transportation initiatives and infrastructure investments. This paper discussed the versatile applications of the system. It also addressed the limitations of Petri Net models in transportation systems and suggested future research directions.

Disproportional fraction of inactive components leads to the variation in metabolic scaling

In biological systems, solitary organisms or eusocial groups, the metabolic rate often scales allometrically with systems’ size, when they are inactive, and the scaling becomes nearly isometric when the systems are active. Here I propose a hypothesis attempting to offer a departing point for a general joint understanding of the difference in the scaling powers between inactive and active states. When the system is inactive, there exist inactive components, which consume less energy than the active ones, and the larger the system is, the larger the fraction of the inactive components, which leads to sublinear scaling. When the system is active, most inactive components are activated, which leads to nearly isometric scaling. I hypothesize that the disproportional fraction of the inactive components is caused by the diffusants screening in the complex transportation network. I.e., when metabolites or information diffuses in the system, due to the physical limitation of the network structure and the diffusant’s physical feature, not all the components can equally receive the diffusants so that these components are inactive. Using the mammalian pulmonary system, ant colonies, and other few systems as examples, I discuss how the screening leads to the allometric and isometric metabolic scaling powers in inactive and active states respectively. It is noteworthy that there are a few exceptions, in which the metabolic rate of the system has an isometric scaling relationship with size at rest. I show that these exceptions not only do not disapprove the hypothesis, but actually support it.

Assessing vehicle interdiction strategies on a complex transportation network: A simulation-based study

The escape interdiction problem within the context of attacker activities on a transportation network is addressed in this study. In the absence of traffic within the network, the attacker attempts to flee the city by choosing one of the shortest paths from the crime scene to a randomly selected exit point. However, in the presence of traffic, the attacker strategically selects the optimal route that minimizes his time to reach a randomly selected exit point. On the other side, defenders try to interdict the attacker on his escape route. Defenders face the daunting challenge of interdicting the attacker’s escape route while operating under limited resources. Dealing with a real city road network further adds complexity to the scenario. A simulation-based model is proposed for the optimal allocation of resources to tackle this issue. The focus then shifts to the development of an advanced search strategy that involves routing with optimal resource allocation. This paper presents the first comparative study for escape interdiction problems within a simulation environment, explicitly focusing on solution methodologies. An optimal resource allocation approach is proposed in the presence of traffic, constituting a novel contribution that has not been previously implemented in escape interdiction problems. In addition, the paper introduces a Genetic Algorithm (GA)-based meta-heuristic approach within a simulation environment. This approach generates optimal paths for defenders, wherein each node is associated with a fixed time window, representing the defender’s waiting time. In this proposed methodology, defenders undertake a tour of the network rather than remaining stationary at a single location. This approach expands the network search capabilities, thereby requiring optimization to ascertain the optimal routes and schedules for the defender vehicles. A case study is conducted using the map of IIT Kharagpur, India, to evaluate the effectiveness of this approach. By employing this approach and conducting in-depth analyses, the aim is to provide valuable insights into the efficiency and practicality of the developed methods on real-world transportation networks.

Solving Dynamic Full-Truckload Vehicle Routing Problem Using an Agent-Based Approach

In today’s complex and dynamic transportation networks, increasing energy costs and adverse environmental impacts necessitate the efficient transport of goods or raw materials across a network to minimize all related costs through vehicle assignment and routing decisions. Vehicle routing problems under dynamic and stochastic conditions are known to be very challenging in both mathematical modeling and computational complexity. In this study, a special variant of the full-truckload vehicle assignment and routing problem was investigated. First, a detailed analysis of the processes in a liquid transportation logistics firm with a large fleet of tanker trucks was conducted. Then, a new original problem with distinctive features compared with similar studies in the literature was formulated, including pickup/delivery time windows, nodes with different functions (pickup/delivery, washing facilities, and parking), a heterogeneous truck fleet, multiple trips per truck, multiple trailer types, multiple freight types, and setup times between changing freight types. This dynamic optimization problem was solved using an intelligent multi-agent model with agent designs that run on vehicle assignment and routing algorithms. To assess the performance of the proposed approach under varying environmental conditions (e.g., congestion factors and the ratio of orders with multiple trips) and different algorithmic parameter levels (e.g., the latest response time to orders and activating the interchange of trip assignments between vehicles), a detailed scenario analysis was conducted based on a set of designed simulation experiments. The simulation results indicate that the proposed dynamic approach is capable of providing good and efficient solutions in response to dynamic conditions. Furthermore, using longer latest response times and activating the interchange mechanism have significant positive impacts on the relevant costs, profitability, ratios of loaded trips over the total distance traveled, and the acceptance ratios of customer orders.

Multistage dynamic optimisation of ore flow for underground metal mines

ABSTRACT The purpose of transportation dispatching in underground mines is to achieve efficient connection and operation cycles between different stopes while ensuring efficient ore transportation from stopes to a mill. The transportation system of large underground mines is a typical complex transportation network with large-scale transportation equipment, which involves both continuous and batch transportation succession and the blending of ore with different grades. The equipment dispatching has an important impact on the ore flow rate, grade, and transportation costs. Therefore, the ore flow needs to be optimally controlled from the perspective of multistage dynamic ore blending. A new model is presented to optimise the ore transportation process considering the ore flow rate, grade, equipment, and transportation network constraints, which aims at minimising transportation costs. The model is verified through a case study of the Sanshandao gold mine in China.

HD‐Net: A hybrid dynamic spatio‐temporal network for traffic flow prediction

AbstractAccurately predicting traffic flow is crucial for intelligent transportation systems (ITS). In recent years, many deep learning‐based prediction models have been widely applied in traffic flow prediction, and various spatio‐temporal networks have been proposed. However, most of the existing models follow a general technical route to extract the spatio‐temporal features, which lack the capacity of extracting the important historical information with the high spatial and temporal correlations dynamically and deeply. How to develop a well‐performance traffic flow prediction model for a complex transportation network is still facing some challenges. In this paper, a hybrid dynamic spatio‐temporal network (HD‐Net) for traffic flow prediction is proposed. In HD‐Net, the authors first extract the dynamic spatio‐temporal features using dynamic graph convolution and bidirectional gate recurrent uni (BiGRU). Subsequently, the authors extract the important features with high spatial and temporal correlations from the obtained dynamic spatio‐temporal features using an auto‐correlation mechanism from a local perspective, and self‐attention mechanism from a global perspective, respectively. Extensive experiments have been conducted on two real‐world traffic datasets. The experimental results demonstrate that the proposed HD‐Net outperforms the baselines in the field of capturing the dynamic and important spatio‐temporal features with high correlations.

A complex network regulating malate contents during fruit ripening in climacteric fruits.

This article is a Commentary on Wang et al. (2023), 239: 1014–1034.

Non-smooth dynamic analysis of curved bridges considering the earthquake-induced end-pounding

Curved beam bridges are widely used to accommodate the needs of complex transportation networks. However, these bridges are characterized by a complex dynamic response under earthquake excitation, and it is common to observe pounding-induced damages at the interfaces between adjacent beams or between the beam and the abutment after high-intensity earthquakes. The identification of pounding occurrences is a challenging task and highly dependent on the computational strategy used. The present paper addresses this problem by proposing and analyzing the suitability of an innovative non-smooth approach to solve the rigid bodies’ dynamic response and contact interactions problem of curved beam bridges subjected to seismic ground motions. To this end, the dynamic equations of motions, including the pounding effects, are first formulated with the introduction of the Lagrange multipliers approach. The unilateral constraints are converted into a linear complementarity problem (LCP) formulation related to the velocity and impulse in the normal and transverse directions. The proposed algorithm of the differential equations in LCP form is successively implemented in Matlab to identify the collision and motion states by accessing the stepwise acceleration time history. A two-span curved beam bridge with an intermediate pier and two adjacent abutments under a single ground motion record is considered for case study purposes to investigate the suitability of the proposed procedure. The accuracy of the proposed method is verified by comparison with finite element (FE) analysis. The analysis results include the bridge’s displacement time history and the motion states of all observed pounding occurrences. Finally, three validation methods, including the LCP relationship, the geometric relationship, and the momentum theorem, are evaluated to examine the validity of the proposed method. The results show that the proposed non-smooth dynamic formulation represents a new rigorous approach to identify and analyze the complex problem of end-pounding occurrence in curved bridges.

Improving multimodal passenger transportation technology under the wartime conditions in Ukraine

Abstract. Problem. Ukraine has a rather complex transport network, so the mismatch between the work of carriers when transferring from one mode of transport to another can create a large number of inconveniences for passengers. Issuance of various travel documents, the complexity of processing luggage and its movement between vehicles, a more thorough check of documents and luggage by employees of law enforcement agencies at border crossings and transfer points lead to an increase in travel time. On the other hand, ensuring the demand for some main directions of transportation can only be through the use of several modes of transport, so there is a need to improve the existing logistics technology for multimodal passenger transportation.
 Goal. The goal of this publication is to improve the technology of multimodal passenger transportation under martial law Methodology. Analytical research methods were used to form the relationship between the train delay at the border station and the number of passengers in it. Mathematical modulation methods were used in the formation of a new agreed schedule for the arrival of international passenger trains at the Kyiv-Passenger transport hub. Results. The research has shown that even under the current wartime conditions, it is possible to improve the operation of various types of passenger transport and the consistency of their traffic schedules to better meet the needs and quality of passenger transportation. The authors of the article analyzed the state and causes of delays in passenger trains at the Mostyska-2 border crossing, through which the largest railway passenger flows between Ukraine and the EU countries currently follow. Based on the analysis, the dependence of the train delay time at the border station on the number of passengers in the train was revealed, and in order to reduce the duration of these delays, it was proposed to improve the existing technology for calculating the travel time of a passenger train by introducing and using value F, corresponding to the train delay time at the border station per passenger. Originality. It is suggested to improve the model for organizing the interaction of different types of transport in the transport interchange Kyiv-passenger hub, by introducing a new value φ based on the dependence of the train delay time at the border station on the number of passengers transported in it, taking into account the specifics of the introduction of martial law in Ukraine. Practical value. The introduction and integration of the proposed technology into the existing mathematical models for the formation of a rationally coordinated schedule of multimodal passenger transportation will significantly reduce the waiting time for passengers at transfer points, rationalize the capacity and population of the rolling stock and increase the financial profit for carriers, it can also be used for the development of the system, information technologies and the use of a "single ticket".

Entropy and a sub-group of geometric measures of paths predict the navigability of an environment

Despite extensive research on navigation, it remains unclear which features of an environment predict how difficult it will be to navigate. We analysed 478,170 trajectories from 10,626 participants who navigated 45 virtual environments in the research app-based game Sea Hero Quest. Virtual environments were designed to vary in a range of properties such as their layout, number of goals, visibility (varying fog) and map condition. We calculated 58 spatial measures grouped into four families: task-specific metrics, space syntax configurational metrics, space syntax geometric metrics, and general geometric metrics. We used Lasso, a variable selection method, to select the most predictive measures of navigation difficulty. Geometric features such as entropy, area of navigable space, number of rings and closeness centrality of path networks were among the most significant factors determining the navigational difficulty. By contrast a range of other measures did not predict difficulty, including measures of intelligibility. Unsurprisingly, other task-specific features (e.g. number of destinations) and fog also predicted navigation difficulty. These findings have implications for the study of spatial behaviour in ecological settings, as well as predicting human movements in different settings, such as complex buildings and transport networks and may aid the design of more navigable environments.

Transit Itinerary Calculation on the Web: Based on a Transit User Information System

Transit path calculation is not yet widely available on transit authority websites. This is because the calculation of complete transit itineraries requires the integration of underlying components such as Geographic Information Systems (GIS) and operational database and, unlike a simple road application, involves a complex transportation network. In this article, a hybrid algorithm based on heuristics and optimization is presented for the calculation of urban transit itineraries including information on pedestrian access and egress paths, route sequences, schedules, and stops. The use of the Transit User Information System (TUIS) to support the calculations is emphasized. The TUIS uses the Totally Disaggregate Approach (TDA) and Transportation Object-Oriented Modeling (TOOM) in transportation to gather data on territory (for origin and destination specifications and for the pedestrian network) and transit operation (route geometry, schedules). Websites that have been implemented are referenced to demonstrate the applicability of the hybrid algorithm. These websites make use of some special techniques for disseminating user information over the Internet.

Estimating Infection-Related Human Mobility Networks Based on Time Series Data of COVID-19 Infection in Japan

Background: Comprehensive and evidence-based countermeasures against emerging infectious diseases have become increasingly important in recent years. COVID-19 and many other infectious diseases are spread by human movement and contact, but complex transportation networks in the 21st century make it difficult to predict disease spread in rapidly changing situations. It is especially challenging to estimate the network of infection transmission in countries where traffic and human movement data infrastructure is not yet developed. Methods: In this study, we devised a method utilizing an ordinary and partial differential equations-based mathematical model and a modified mathematical optimization method to estimate the network of transmission of COVID-19 from the time series data of its infection and applied it to determine its spread across areas in Japan. Furthermore, utilizing the estimated human mobility network, we predicted the spread of infection using the Tokyo Olympics as a model. Findings: We incorporated the effects of soft lockdowns, such as the declaration of a state of emergency, and changes in the infection network due to government-sponsored travel promotion, and revealed that the estimated effective distance captured human mobility changing dynamically in the different stages of the pandemic. The model predicted that the Tokyo Olympic and Paralympic Games would increase the number of infected cases in the host prefectures by up to 80%. Interpretation: The models used in this study are available online, and our data-driven infection network models are scalable, whether it be at the level of a city, town, country, or continent, and applicable anywhere in the world, as long as the time-series data of infections per region is available. These estimations of effective distance and the depiction of infectious disease networks based on actual infection data are expected to be useful in devising data-driven countermeasures against emerging infectious diseases worldwide.

Analyses of lattice hydrodynamic area occupancy model for heterogeneous disorder traffic

In developing countries, traffic not only consists of a wide range of vehicles, including automobiles, trucks, buses, motorbikes, etc. but is also disordered. Controlling and managing increasingly complex transport networks depend heavily on modeling the mechanics of mixed (heterogeneous) traffic. In heterogeneous disordered traffic, every vehicle has its size and speed, and even they are different from each other in the case of occupying the area on the road, so these can affect the overall movement of vehicular flow. Therefore, a new lattice model is designed by considering the area occupancy of different vehicles in a heterogeneous disorder traffic system with a variable proportion of slow-moving to fast-moving automobiles. In addition, stability analysis is done to investigate the ability of a heterogeneous traffic model. The mixed traffic phase diagrams show a link between traffic stability and the fraction of vehicles. Moreover, a reduction perturbation approach is used to explore the behavior of the disordered traffic, and the mKdV equation is achieved near the critical point. Furthermore, numerical simulations are performed to verify the consistency of theoretical analysis. Results portray that a higher fraction of small vehicles is beneficial for stabilizing the traffic flow.

A new curb lane monitoring and illegal parking impact estimation approach based on queueing theory and computer vision for cameras with low resolution and low frame rate

The rapid development of the internet of things (IoT), sensing technologies, and machine learning and deep learning techniques, along with the growing variety and volume of data, have yielded new perspectives on how novel technologies can be applied to obtain new sources of curb data to achieve cost-effective curb management. This study presents a new computer vision–based data acquisition and analytics approach for curb lane monitoring and illegal parking impact assessment. The proposed “rank, detect, and quantify impacts” system consists of three main modules: 1) hotspot identification based on rankings generated by local spatial autocorrelation analysis, 2) curb lane occupancy estimation leveraging traffic cameras and computer vision techniques, and 3) illegal parking traffic impact quantification using an M/M/∞ queueing model. To demonstrate the feasibility and validity of the proposed approach, it was tested and empirically validated using field data collected from three case study sites in Midtown Manhattan, New York City (NYC)—one of the most complex urban transportation networks in the world. Different types of curb lane occupancy, including parking and bus lanes, and different frequencies of illegal parking (high, moderate, low) were investigated. The proposed method was proven to be effective even for low resolution and discontinuous video feeds obtained from publicly available traffic cameras. All three case study sites achieved good detection accuracy (86% to 96%) for parking and bus lane occupancy, and acceptable precision and recall in detecting illegal parking events. The queueing model was also proven to effectively quantify link travel time with the appearance of illegal parking events of different frequencies. The proposed “rank, detect, and quantify impacts” system is friendly for large-scale real-time implementation and is highly scalable to help evaluate the impact of other modes such as bike or mobility-on-demand (MOD) services. It can also be easily adopted by other cities to provide transportation agencies with effective data collection and innovative curb space management strategies.

Lessons from the Criticality of the Spanish High Capacity Road Network on Direct, Representative Democracies and Technocracies

This paper aims to shed some light on an issue that has not been analyzed enough in previous studies on complex transportation networks. The financial crisis and disrupting events like the COVID-19 pandemic episode are affecting how governments make crucial decisions regarding policymaking paying more attention to experts’ opinions. The impacts of disruptive events that could affect each road section will be analyzed using the criticality of the high capacity road network in Spain under different policymaking scenarios, such as direct democracy based on pure provincial decentralization (federal or cantonal vision), representative democracy in which decisions are taken by the National Parliament, and the governance based on technocracy stimulated by ‘what matters is what works’ (Southern Local Economy, 16(4), 264-271, 2001) using an Evidence-Based Policy Making (EBPM) case study based on a Data Envelopment Analysis applied to four accessibility indicators. We will complement our analysis with the different results obtained by the different national parties that were represented in the past election (November, 2019): PSOE, PP, VOX, Unidas Podemos, Ezquerra Republicana de Catalunya, Ciudadanos, Junts per Catalunya, Partido Nacionalista Vasco and Euskal Herria Bildu, exploring in-depth the obtained differences between the technocrat solution and the direct and representative democracy results. Important insights and lessons for the future will be obtained from the different party visions observed among the regionalist (nationalist) and the state-wide parties. The spillover effects created by the networks are so important that the room for federalist solutions might be very limited.

Hybrid modeling of collaborative freight transportation planning using agent-based simulation, auction-based mechanisms, and optimization

The sharing economy is a peer-to-peer economic model characterized by people and organizations sharing resources. With the emergence of such economies, an increasing number of logistics providers seek to collaborate and derive benefit from the resultant economic efficiencies, sustainable operations, and network resilience. This study investigates the potential for collaborative planning enabled through a Physical Internet-enabled logistics system in an urban area that acts as a freight transport hub with several e-commerce warehouses. Our collaborative freight transportation planning approach is realized through a three-layer structured hybrid model that includes agent-based simulation, auction mechanism, and optimization. A multi-agent model simulates a complex transportation network, an auction mechanism facilitates allocating transport services to freight requests, and a simulation–optimization technique is used to analyze strategic transportation planning under different objectives. Furthermore, sensitivity analyses and Pareto efficiency experiments are conducted to draw insights regarding the effect of parameter settings and multi-objectives. The computational results demonstrate the efficacy of our developed model and solution approach, tested on a real urban freight transportation network in a major US city.

ЗАДАЧА ОПТИМИЗАЦИИ АВТОБУСНОГО МАРШРУТА С ПРИМЕНЕНИЕМ ГЕОИНФОРМАЦИОННОЙ СРЕДЫ

The public transport system is characterized by a complex transport network, intersecting routes and fragmented stopping points. In this regard, the functioning of the transport system to ensure optimal planning of bus transportation is an urgent task. A solution to this problem can be a multi-level model that allows you to represent network objects at several spatial levels, calculate the exact time delay, and meet the needs of planning bus trips with various optimization criteria

Analysis of the Spatio-Temporal Dynamics of COVID-19 in Massachusetts via Spectral Graph Wavelet Theory

The rapid spread of COVID-19 disease has had a significant impact on the world. In this paper, we study COVID-19 data interpretation and visualization using open-data sources for 351 cities and towns in Massachusetts from December 6, 2020 to September 25, 2021. Because cities are embedded in rather complex transportation networks, we construct the spatio-temporal dynamic graph model, in which the graph attention neural network is utilized as a deep learning method to learn the pandemic transition probability among major cities in Massachusetts. Using the spectral graph wavelet transform (SGWT), we process the COVID-19 data on the dynamic graph, which enables us to design effective tools to analyze and detect spatio-temporal patterns in the pandemic spreading. We design a new node classification method, which effectively identifies the anomaly cities based on spectral graph wavelet coefficients. It can assist administrations or public health organizations in monitoring the spread of the pandemic and developing preventive measures. Unlike most work focusing on the evolution of confirmed cases over time, we focus on the spatio-temporal patterns of pandemic evolution among cities. Through the data analysis and visualization, a better understanding of the epidemiological development at the city level is obtained and can be helpful with city-specific surveillance.

Interterminal Truck Routing Optimization Using Cooperative Multiagent Deep Reinforcement Learning

Many ports worldwide continue to expand their capacity by developing a multiterminal system to catch up with the global containerized trade demand. However, this expansion strategy increases the demand for container exchange between terminals and their logistics facilities within a port, known as interterminal transport (ITT). ITT forms a complex transportation network in a large port, which must be managed efficiently given the economic and environmental implications. The use of trucks in ITT operations leads to the interterminal truck routing problem (ITTRP), which has been attracting increasing attention from researchers. One of the objectives of truck routing optimization in ITT is the minimization of empty-truck trips. Selection of the transport order (TO) based on the current truck location is critical in minimizing empty-truck trips. However, ITT entails not only transporting containers between terminals operated 24 h: in cases where containers need to be transported to a logistics facility within operating hours, empty-truck trip cost (ETTC) minimization must also consider the operational times of the transport origin and destination. Otherwise, truck waiting time might be incurred because the truck may arrive before the opening time of the facility. Truck waiting time seems trivial, but it is not, since thousands of containers move between locations within a port every day. So, truck waiting time can be a source of ITT-related costs if it is not managed wisely. Minimization of empty-truck trips and truck waiting time is considered a multiobjective optimization problem. This paper proposes a method of cooperative multiagent deep reinforcement learning (RL) to produce TO truck routes that minimize ETTC and truck waiting time. Two standard algorithms, simulated annealing (SA) and tabu search (TS) were chosen to assess the performance of the proposed method. The experimental results show that the proposed method represents a considerable improvement over the other algorithms.