Assignment Model Research Articles

A novel multi-source information fusion method for emergency spatial resilience assessment based on Dempster-Shafer theory

In the midst of today's intricate and ever-changing natural and social landscape, this study is committed to proposing a novel multi-source information fusion method for assessing the spatial resilience of urban emergency evacuation and rescue. Its overarching aim is to uncover latent challenges and contribute to the enhancement of a city's capacity to respond to emergencies. To achieve this, we have devised a comprehensive assessment indicator system, capable of not only quantifying a city's spatial resilience but also offering indispensable guidance for urban emergency evacuation and rescue spatial planning. Furthermore, we have introduced an innovative evaluation methodology framework. This framework includes the establishment of the basic probability assignment (BPA) model, a pioneering method for generating BPA for observed values, and the novel application of hierarchical weighting and fusion techniques. By extending the Dempster-Shafer theory, we have not only enhanced the method's ability to express and integrate uncertain information but also significantly improved the precision of the evaluation. Ultimately, we conducted a quantitative assessment using Shenzhen, China, as a case study, identifying existing issues and proposing highly-targeted improvement strategies. These research findings not only provide robust support for the augmentation of urban emergency capabilities in Shenzhen but also offer pioneering insights for the quantitative assessment and advancement of emergency spatial resilience in cities across the globe.

Limited Sample Radar HRRP Recognition Using FWA-GAN

In radar High-Resolution Range Profile (HRRP) target recognition, the targets of interest are always non-cooperative, posing a significant challenge in acquiring sufficient samples. This limitation results in the prevalent issue of limited sample availability. To mitigate this problem, researchers have sought to integrate handcrafted features into deep neural networks, thereby augmenting the information content. Nevertheless, existing methodologies for fusing handcrafted and deep features often resort to simplistic addition or concatenation approaches, which fail to fully capitalize on the complementary strengths of both feature types. To address these shortcomings, this paper introduces a novel radar HRRP feature fusion technique grounded in the Feature Weight Assignment Generative Adversarial Network (FWA-GAN) framework. This method leverages the generative adversarial network architecture to facilitate feature fusion in an innovative manner. Specifically, it employs the Feature Weight Assignment Model (FWA) to adaptively assign attention weights to both handcrafted and deep features. This approach enables a more efficient utilization and seamless integration of both feature modalities, thereby enhancing the overall recognition performance under conditions of limited sample availability. As a result, the recognition rate increases by over 4% compared to other state-of-the-art methods on both the simulation and experimental datasets.

Impact of Home Assignment on the Achievement of Students in Makawanpur District, Nepal

The main goal of the study is to analyze the impact of home assignment on the achievement of students. The null hypothesis states there is no association between practices of home assignment and the achievement of the students. The study was guided by Achievement Goal Theory of Nicholls as well as descriptive and analytical research design was applied. At the same time, the cross-sectional data collection method was administered. The study was carried out among 386 students of 14 sampled schools of Makawanpur district, Nepal 2024 AD. The students were selected with the stratified random sampling method. The study shows that the students of educated parents spent less time in doing home assignment as compared with the students of illiterate parents. The parental support seems to have positive result on doing home assignment of the students. The location of school and students did not have any effect on doing home assignment. The result shows that there was the positive impact of home assignment on the achievement of the students and statistically, there significant association (p=0.022≤ 0.05) between home assignment and the achievement of students. Home assignment for both the students and parents is a burden. There was a debate on home assignment that it harmed the mental health of the children, at the same time some studies claimed that it had a positive impact on the achievement of the students if administered effectively. The study also emphasized the importance of home assignment for the better achievement of the student in school life. The study helps educational institutions to form a strategic plan and develop the model of home assignment that enhance the achievement of students.

Optimizing energy efficiency in intelligent vehicle-oriented road network design: A novel traffic assignment method for sustainable transportation

In light of the development of intelligent vehicles and the persistent energy crisis, improving the predictive accuracy of macroscopic traffic flow and formulating energy-efficient road network design solutions are essential to promote sustainable development in road transportation. This study introduces a dual-objective impedance function that incorporates considerations of both travel time and energy consumption. The function is developed utilizing the principles of intelligent vehicle energy flow and traffic flow parameters. Then, an enhanced user equilibrium traffic assignment (E-UE) model is formulated. The effectiveness of the E-UE function was validated through road experimental data. The performance of E-UE was analyzed using a planned road network consisting of 28 sections. In comparison with the classical UE model, the E-UE model promotes uniform traffic distribution and alleviates congestion, leading to a 1.1 % reduction in average road energy consumption. This highlights the E-UE model’s effectiveness in accurately capturing the impacts on energy consumption with enhanced predictive precision. Strategies to mitigate potential energy consumption include implementing the E-UE model with realistic weights, adjusting weights for specific roads, employing a design speed of 50 km/h, and utilizing 6-lane roads. This study contributes to the advancement of energy-saving technologies in sustainable transportation.

Modeling and Simulation of UAV Task Assignment and Execution in Designated Area Based on Order Strike Theory

Abstract Since 2023, local conflicts have intensified and local wars have occurred frequently. The term “order strike” has appeared on the battlefield many times and is one of the hot spots in the study of modern warfare. Order strike has the characteristics of small scale, fast response, low cost, etc. It is a distributed combat mode proposed by the US military on the basis of theories of “Kill Chain” and “Mosaic Warfare”, etc. This paper takes the UAV penetration and strike against large targets as research case, and builds a new type of UAV penetration and strike combat platform based on the theory of “order strike”, combined with the technologies of “ StarLink “ satellite network reconnaissance and intelligent UAV control. This combat platform can continuously monitor the designated area and effectively strike the hostile targets invading the area. Finally, we conducted a preliminary simulation and initial effectiveness evaluation of the method proposed in this paper through multi-agent simulation.

The Application of the Piecewise Linear Method for Non-Linear Programming Problems in Ride-Hailing Assignment Based on Service Level, Driver Workload, and Fuel Consumption

Ride-hailing services have grown rapidly, presenting challenges such as increased traffic congestion, inefficient driver workload distribution, and environmental concerns like higher fuel consumption and emissions. This study develops a non-linear ride-hailing assignment model addressing these issues by considering service level, driver workload, and fuel consumption. A piecewise linear method was employed to handle a non-linear programming model, and the method was modified to function autonomously without operator intervention. The model’s performance was evaluated using a publicly accessible dataset of taxi trips in Manhattan, focusing on indicators such as passenger waiting time, driver workload distribution, and fuel consumption. Numerical simulations demonstrated significant improvements: a 15% reduction in average passenger waiting time, a 20% improvement in balancing driver workloads, and a 10% decrease in overall fuel consumption, contributing to reduced emissions and environmental impact. The modified piecewise linear method proved effective in optimizing ride-hailing assignments, providing a more efficient and sustainable solution. The model also showed robustness in handling large datasets, ensuring scalability and applicability to various urban settings. These findings highlight the model’s potential to enhance operational efficiency and promote sustainability in ride-hailing services. By integrating considerations for service level, driver workload, and fuel consumption, the model offers a holistic approach to addressing the key challenges faced by the ride-hailing industry. This study provides valuable insights for future ride-hailing development and implementations of ride-hailing systems, promoting practices that are both efficient and environmentally friendly.

An optimization approach to evaluating accessibility to community pharmacies

ABSTRACT Accessibility to community pharmacy is key to community health and well-being. An optimal community-pharmacy assignment (OCPA) model is developed to measure the geographical accessibility of community pharmacies in a regional population network. The model minimizes a risk-adjusted distance between census tracts and community pharmacies by optimally matching census tracts to single or multiple pharmacies. Social determinants of health (SDOH), such as vehicle ownership, access to public transit, and insurance, are used as risk factors for adjusting the geographic distance. We present a novel definition of ‘pharmacy desert’ based on the distance between census tracts and their matching community pharmacies. Extensive computational experiments are conducted based on a case study in Louisville, Kentucky. Benchmark studies with leading literature and other public health indicators validate the proposed model. Extensive sensitivity analyses suggest our model for identifying pharmacy desert is effective and can be a valuable tool for pharmacy and public health stakeholders.

Two-stage differential evolution with dynamic population assignment for constrained multi-objective optimization

Using infeasible information to balance objective optimization and constraint satisfaction is a very promising research direction to address constrained multi-objective problems (CMOPs) via evolutionary algorithms (EAs). The existing constrained multi-objective evolutionary algorithms (CMOEAs) still face the issue of striking a good balance when solving CMOPs with diverse characteristics. To alleviate this issue, in this paper we develop a two-stage different evolution with a dynamic population assignment strategy for CMOPs. In this approach, two cooperative populations are used to provide feasible driving forces and infeasible guiding knowledge. To adequately utilize the infeasibility information, a dynamic population assignment model is employed to determine the primary population, which is used as the parents to generate offspring. The entire search process is divided into two stages, in which the two populations work in weak and strong cooperative ways, respectively. Furthermore, multistrategy-based differential evolution operators are adopted to create aggressive offspring. The superior exploration and exploitation ability of the proposed algorithm is validated via some state-of-the-art CMOEAs over artificial benchmarks and real-world problems. The experimental results show that our proposed algorithm gained a better, or more competitive, performance than the other competitors, and it is an effective approach to balancing objective optimization and constraint satisfaction.

A Version of Predicate Logic with Two Variables That has an Incompleteness Property

AbstractIn this paper, we consider predicate logic with two individual variables and general assignment models (where the set of assignments of the variables into a model is allowed to be an arbitrary subset of the usual one). We prove that there is a statement such that no general assignment model in which it is true can be finitely axiomatized. We do this by showing that the free relativized cylindric algebras of dimension two are not atomic.

Using traffic assignment models to assist Bayesian inference for origin–destination matrices

Estimation of traffic volumes between each origin and destination of travel is a standard practice in transport engineering. Commonly the available data constitute traffic counts at various locations on the network, supplemented by a survey-based prior estimate of mean origin–destination traffic volumes. Statistical inference in this type of network tomography problem is known to be challenging. Moreover, the difficulties are increased in practice by the presence of a large number of nuisance parameters corresponding to route choice probabilities, for which we have no direct prior information. Working in a Bayesian framework, we determine these parameters using a stochastic user equilibrium route choice model. We develop an MCMC algorithm for model fitting. This requires repeated computation of stochastic user equilibrium flows, and so we develop a computationally cheap emulator. Our methods are tested on numerical examples based on a section of the road network in the English city of Leicester.

Enhancing transportation accessibility: evaluating the impact of route guidance systems and connected autonomous vehicles

Enhancing accessibility in transportation systems is an escalating interest among researchers, fueled by the rising issues with traffic congestion and technological innovations. Accessibility significantly contributes to improving the quality of life, thereby necessitating an examination of how route guidance systems impact it. This study explores the influence of system optimum (SO) and user equilibrium (UE) route guidance systems on accessibility, particularly in the context of connected autonomous vehicles (CAVs) embedded in the network. We employ a hybrid assignment model that enables the concurrent allocation of SO and UE assignments, along with the Gravity model to compute accessibility. The Sioux Falls network, previously leveraged in prior research, was chosen for numerical simulations to permit insightful comparisons. Our study scrutinizes three scenarios: the first scenario examines the repercussions of route guidance systems on human-driven vehicles, while the second and third assess their effects on CAVs. The difference between the second and third scenario is in the way of increasing the capacity in the assignment. Our findings reveal that at lower penetration rates of route guidance systems, accessibility initially dips, and then ascends. When all human-driven users adopt route guidance systems, accessibility increases by 12.75% compared to the initial state (without any intervention). Remarkably, should all users employ autonomous vehicles, accessibility would surge by 220% compared to the initial state (without any intervention). These insights highlight the importance of integrating CAVs and route guidance systems into transportation planning to enhance accessibility and improve the quality of life.

Trip Assignment Modeling for Transportation in Denpasar City Using PTV Visum Software

Mapping and macro transportation modelling are methods used to obtain an overview and analysis of transportation conditions at the macro level, encompassing all road segments in urban areas. Critical constraints on road segments can be identified through this process. Macro transportation modelling serves as a tool for studying the implementation of problem-solving policies, allowing the anticipated outcomes to be analysed through simulations in transportation modelling. This research has a specific goal: to understand the travel patterns of the population in the city of Denpasar. Calibration and validation processes in the PTV Visum modelling are conducted to test the validity of the created macrosimulation model. The trip assignment results are generated through PTV Visum modeling. The research methodology involves quantitative analysis using household interview surveys, traffic counting, correlational studies, and macrosimulation modelling. Calibration is performed by calculating the beta coefficient value (with specific calculated values), time matrices, and distance matrices to measure travel impedance functions. Validation is conducted by evaluating the Generalised Exponential Horn (GEH) value, which is less than 5, to ensure the alignment between travel output in the model and field survey data from traffic counting. From the analysis of a sample of 2,725 households, a total of 2,165,876 daily trips were obtained. Types of trips include movements from internal to internal zones (2,115,602 trips), from internal to external zones (28,799 trips), from external to internal zones (19,319 trips), and from external to external zones (2,159 trips) per day. Roads with the highest traffic volume in a single day include Hasanudin Street (42,524 pcu/hour), Diponogoro Street (42,282 pcu/hour), Sudirman Street (31,062 pcu/hour), Sutoyo Street (30,180 pcu/hour), and Sumatra Street (23,515 pcu/hour).

Optimization of merchandise delivery logistics: case studies at Bejaia port

The object of the study is the logistics of goods delivery by ports. This study presents a methodology designed to improve the efficiency of goods delivery logistics at the Bejaia port (Algeria). It prioritizes the optimization of empty container allocation to the ZEP zone, taking into account the geographical accessibility of the Tixter area, aiming to reduce the high costs linked with goods transportation. At the core of this strategy lies the use of simulation techniques to optimize truck fleets, ensuring maximum utilization rates and effective management of delivery operations by the Bejaia Mediterranean Terminal (BMT) for its clients. Addressing this challenge, the study offers an exhaustive analysis, integrating truck assignment models and accessibility assessments of logistic zones. The results highlight the paramount significance of optimal resource allocation and synchronized client coordination for achieving streamlined goods delivery. It becomes apparent that employing these methodologies can yield substantial productivity improvements, emphasizing their pivotal role in strengthening the port's logistical infrastructure. Via rigorous analysis and insights derived from data, this study elucidates avenues towards achieving operational excellence within the logistical infrastructure of the port. By harnessing innovative strategies to confront persistent challenges, such as optimizing truck fleets and strategically allocating resources, the research anticipates a profound transformation in the efficiency and cost-effectiveness of goods delivery operations. Ultimately, the integration of these methodologies holds the potential to propel the port of Bejaia towards enduring success and a competitive edge in the ever-evolving landscape of global trade. Through extensive efforts, this strategy can be extended to other national and international ports operating under similar conditions, as it provides valuable information and methodologies to optimize logistics and transportation operations.

End-to-end heterogeneous graph neural networks for traffic assignment

The traffic assignment problem is one of the significant components of traffic flow analysis for which various solution approaches have been proposed. However, deploying these approaches for large-scale networks poses significant challenges. In this paper, we leverage the power of heterogeneous graph neural networks to propose a novel end-to-end surrogate model for traffic assignment, specifically user equilibrium traffic assignment problems. Our model integrates an adaptive graph attention mechanism with auxiliary “virtual” links connecting origin–destination node pairs, This integration enables the model to capture spatial traffic patterns across different links, By incorporating the node-based flow conservation law into the overall loss function, the model ensures the prediction results in compliance with flow conservation principles, resulting in highly accurate predictions for both link flow and flow-capacity ratios. We present numerical experiments on urban transportation networks and show that the proposed heterogeneous graph neural network model outperforms other conventional neural network models in terms of convergence rate and prediction accuracy. Notably, by introducing two different training strategies, the proposed heterogeneous graph neural network model can also be generalized to different network topologies. This approach offers a promising solution for complex traffic flow analysis and prediction, enhancing our understanding and management of a wide range of transportation systems.

Bridging multi-level vehicle behavioural patterns with long-term pavement condition: a bidirectional modelling approach for progressively mixed autonomous traffic flow

The increasing of AVs’ market penetration rate (MPR) changes the traffic behavioural pattern and ultimately influences pavement. Pavement condition, in turn, influences microscopic behaviours. The above bidirection influencing mechanism has not been investigated fully yet. This research fill this gap by modelling the linkage between multi-level microscopic behaviour and pavement sustainability. Three levels of behaviour are considered: travel behaviour, microscopic traffic behaviour, and driving behaviour. The latter two are modelled using stochastic lateral movement model, while the former one is captured by a mixed autonomous traffic assignment model. Microscopic behaviours’ influence on pavement is described by a data-driven model, while the reverse counterpart is described by connecting pavement condition with link wandering and disutility. Our analysis results reveal that at link level, AVs can cause extra 50% drop in free flow speed due to pavement deterioration. Neglecting pavement evolution in strategic assignments leads to a 0.2% increase in network cost.

Impact of autonomous vehicles on discretionary activities: an agent-based model with space–time accessibility constraints

AbstractThe extensive development of autonomous vehicles (AVs) is set to revolutionise the way of travelling. Research suggests that the introduction of AVs may affect travel behaviour and choices, resulting in long-term changes in land use. Accessibility is an important concept that connects transportation and land use, providing a holistic performance measure for the transport-land use system. However, this concept has not been adequately capitalised in studies that attempt to understand the impact of AVs on location choice decisions. To explore this knowledge gap, we proposed an agent-based simulation framework that integrates with accessibility constraints to study how AVs influence behavioural and location choices. The framework consists of an activity-based travel demand model with accessibility constraints and a dynamic transport assignment model. The accessibility constraints are derived from individuals’ travel time budgets based on activity-travel survey data. We applied the agent-based simulation framework to Clayton, Australia, and focused on discretionary activity location choices. Various values of travel time and vehicle running costs underpinned by the use of AVs were examined. While most studies have concluded that AVs can significantly increase trip lengths for daily activities, our results demonstrate that even when AVs are used, the movement of individuals is still limited by spatio-temporal constraints of accessibility. As a result, we predict that the increase in discretionary trip lengths and their impact on traffic congestion is modest.

Cooperative multi-task assignment modeling of UAV based on particle swarm optimization

Unmanned Ariel Vehicles (UAVs) are interconnected to perform specific tasks through self-routing and air-borne communications. The problem of automated navigation and adaptive grouping of the vehicles results in improper task completion and backlogs. To address this issue, a Particle Swarm Optimization-dependent Multi-Task Assignment Model (PSO-MTAM) is introduced in this article. The swarms are initialized for the available linear groups towards the destination. This article addressed the subject of UAVs using a multi-task assignment paradigm to increase task completion rates and handling efficiency. The different swarm stages are verified for the task progression, resulting in completion at the final stage. In this completion process, the first local best solution is estimated using the completion and assignment rate of a single task. The second local best solution relies on reaching the final stage. The global solution is identified depending on the convergence of the above solutions in task progression and handling density. The swarm positions are immediately identified, and the synchronous best solutions generate the final global best. The backlog-generating solutions are revisited by reassigning or re-initializing the swarm objects. The proposed model’s performance is analyzed using task handling rate, completion ratio, processing time, and backlogs. Improving the handling rate is essential for this validation, necessitating solution and position updates from the intermediate UAVs. With varying task densities and varying degrees of convergence, the iterations continue until completion. There is an 11% increase in the task handling rate and a 12.02% increase in the completion ratio with the suggested model. It leads to a 10.84% decrease in processing time, a 9.91% decrease in backlogs, and a 12.7% decrease in convergence cost.

Joint Routing and Pricing Control in Bimodal Mixed Autonomy Networks with Elastic Demand and Three-Dimensional Passenger Macroscopic Fundamental Diagram

The interaction between mixed autonomy traffic and public transport vehicles competing for limited road space is a less explored area of research. To evaluate the traffic dynamics in bimodal networks, a three-dimensional (passenger) network fundamental diagram, known as 3D-NFD (3D-pNFD), can be estimated that relates the accumulation of cars (autonomous and human-driven) and buses to the network vehicular (passenger) travel production. In this study, we propose a 3D-pNFD-based congestion pricing scheme considering three vehicle classes of buses, system-optimal (SO) seeking connected and automated vehicles (CAVs), and user-equilibrium (UE) seeking human-driven vehicles (HVs). We develop an iterative tri-level modeling framework for mode choice, pricing, and route choice in a mixed autonomy network. The lower level generates mixed equilibrium traffic flow through an integrated mixed equilibrium simulation-based dynamic traffic assignment model and a transit assignment model. The mid-level finds the optimal toll rates through a 3D-pNFD feedback-based controller. A nested logit-based mode choice model is also applied to capture travelers’ preferences toward three available modes and incorporates elastic demand. To encourage CAVs to follow the SO routing mode, they are provided with a discount on the congestion toll whereas UE-seeking HVs are subject to full price toll. Buses are also entirely exempt from the toll. We explore three scenarios with different discount rates on SO-seeking CAVs to investigate the effect of the incentive plans on the mode choice behaviors of road users in the pricing zone. The performance of the proposed model is evaluated in a large-scale network in Melbourne, Australia.

Evaluating rail transit assignment models in the temporal dimension: The problem and its solution

Passenger flow is the foundation for urban rail transit (URT) operations. However, its calculated results from assignment models may deviate from the actual situation in both spatial and temporal dimensions, which arouses more attention and needs to be evaluated in particular. On the other hand, onboard video data from URT trains provides a potential way for model evaluation. This study defines the evaluation problem and proposes a methodological solution for evaluating rail transit assignment models in the temporal dimension, including qualitative validation and difference quantification. A suitable time granularity is determined for the best effectiveness and onboard video data is used for actual passenger flow extraction. The gap between the actual and the calculated by the model is identified with nonparametric statistical techniques (NPSTs) and quantified with time series similarity measurement (TSSM) methods. A case study on the Shanghai metro demonstrates the performance of the proposed approach, and several practice implications for URT operation agencies are also discussed.

Dynamic Task Allocation for Heterogeneous Multi-UAVs in Uncertain Environments Based on 4DI-GWO Algorithm

As the missions and environments of unmanned aerial vehicles (UAVs) become increasingly complex in both space and time, it is essential to investigate the dynamic task assignment problem of heterogeneous multi-UAVs aiming at ground targets in an uncertain environment. Considering that most of these existing tasking methods are limited to static allocation in a deterministic environment, this paper firstly constructs the fuzzy multiconstraint programming model for heterogeneous multi-UAV dynamic task assignment based on binary interval theory, taking into account the effects of uncertain factors like target location information, mission execution time, and the survival probability of UAVs. Then, the dynamic task allocation strategy is designed, consisting of two components: dynamic time slice setting and the four-dimensional information grey wolf optimization (4DI-GWO) algorithm. The dynamic time slices create the dynamic adjustment of solving frequency and effect, and the 4DI-GWO algorithm is improved by designing the four-dimensional information strategy that expands population diversity and enhances global search capability and other strategies. The numerical analysis shows that the proposed strategy can effectively solve the dynamic task assignment problem of heterogeneous multi-UAVs under an uncertain environment, and the optimization of fitness values demonstrates improvements of 5~30% in comparison with other optimization algorithms.