Minimum Travel Time Research Articles

A reinforcement learning-based hyper-heuristic for AGV task assignment and route planning in parts-to-picker warehouses

Globally, e-commerce warehouses have begun implementing robotic mobile fulfillment systems (RMFS), which can improve order-picking efficiency by using automated guided vehicles (AGVs) to realize operations from parts to pickers. AGVs depart from their initial points, move to a target rack position, and subsequently transport racks to picking stations. The AGVs return the racks to their original positions after the workers pick them up. When all tasks are completed, the AGVs return to their starting point. In this context, the main challenge is the task assignment and route planning of multiple AGVs to minimize travel times. We formulate a mixed-integer linear programming (MILP) model with valid inequalities to solve small problem instances optimally. We introduce a reinforcement learning (RL)-based hyper-heuristic (HH) framework to solve large instances to near-optimality. A typical HH framework comprises two levels: high-level heuristics (HLH) and low-level heuristics (LLH). The framework starts from an initial solution and improves iteratively through LLHs, while the HLH invokes a selection strategy and an acceptance criterion to generate a new solution. We propose a novel selection strategy based on the improved Multi-Armed Bandits algorithm called Co-SLMAB and Exponential Monte Carlo with counters (EMCQ) as the acceptance criterion. The corresponding collision avoidance rules are then formulated for different conflicts to construct a conflict-free traveling route for AGVs. Besides testing the proposed framework’s effectiveness in real-life warehouse layouts, we perform extensive computational experiments and a thorough sensitivity analysis. The results show that (i) the proposed valid inequalities aid in obtaining better lower bounds and significantly speed up the solution process; (ii) the Co-SLMAB-HH framework is quite competitive compared to CPLEX, outperforming the other tested hyper-heuristics and the problem-specific heuristic regarding convergence and computation time; and (iii) a pool of LLHs consisting of a wide range of different operators is advantageous over a limited set of simple operators while solving problems using hyper-heuristics.

Research on the assignment of elderly care service personnel with time window constraints

As the population continues to age, there is a growing need for elderly care services. In China, home care is widely embraced as a preferred method of caring for the elderly, and it has a promising commercial outlook. The strategic allocation of Elderly Care Service Personnel (ECSP) is a crucial component of the operational procedures for home care services. By strategically assigning elderly service personnel, it is possible to enhance the satisfaction and simultaneously minimize expenses. The issue of rationalizing the assignment of ECSP in the face of restricted resources is a genuine challenge that requires a solution, given the many types of elderly service demands and the skill requirements and time limits of certain projects. This study presents a strategy for assigning personnel on an hourly basis, taking into account time frame limits. This method involves several steps. Firstly, it involves assessing the specific service needs of the elderly, including the required door-to-door service time, service level, and gender preferences. Secondly, it calculates the service satisfaction and service operation cost of the Home Care Service Platform (HCSP) separately. Finally, it constructs a multi-objective elderly service personnel assignment method. This method aims to minimize the ECSP 's travel time and wasted time, maximize the satisfaction of the elderly by considering priority levels and ECSP scores, and minimize the operation cost of the HCSP. A model is developed to assign ECSP for elderly individuals, with the goal of minimizing travel time and wasted time, maximizing elderly satisfaction by considering priority and service personnel rating, and minimizing operating costs for the HCSP. Additionally, if there are unserved elderly individuals, an optimized path model is constructed using a cross-modified genetic algorithm to obtain the optimal matching result. Ultimately, an arithmetic example is employed to demonstrate the practicality and efficiency of the strategy put forth in this research. The findings demonstrate that the model presented in this research possesses distinct advantages in comparison to other conventional models.

Driving Risk Identification of Truck Drivers Based on China’s Highway Toll Data

Dangerous or illegal driving may disrupt the traffic safety management of public security organs, damage road infrastructure, lead to traffic accidents, or result in economic losses. This paper proposes a framework based on China’s highway toll data to identify dangerous or illegal driving risks, such as unfamiliarity with road conditions, overload, driving over the speed limit, fatigued driving, fake license plates, and other risks. The unfamiliarity with road conditions is identified with the frequency of driving routes. When the total weight of a vehicle and its cargo is greater than the upper limit of the total weight of the vehicle and its cargo, the vehicle can be judged as overloaded. When the actual travel time is less than the minimum travel time, it can be inferred that the vehicle has a risk of fatigued driving, driving over the speed limit, a fake license plate, or other risks. Two accidents are used to demonstrate the process of the proposed framework for identifying driving risks based on China’s highway toll data. Additional analysis proves that the proposed framework can be used to identify dangerous or illegal driving risks, and it provides a scientific basis for the traffic safety management of public security organs, reducing infrastructure damage, and avoiding the loss of national taxes and fees.

Multi-objective sustainability tourist trip design: An innovative approach for balancing tourists' preferences with key sustainability considerations

This study introduces an innovative approach to sustainable tourist trip planning that aligns tourists’ preferences with vital sustainability factors. Traditional methods often prioritize singular objectives, such as minimizing travel time or cost, thereby overlooking the broader sustainability implications associated with travel. To bridge this gap, we propose a multi-objective sustainability tourist trip design model that optimizes travel itineraries across various dimensions, including CO2 emissions, environmental impacts, and socio-economic benefits. To address this problem, we developed a mixed-integer programming model. Subsequently, a novel Artificial Multiple Intelligence System (AMIS) was employed. The AMIS integrates multiple intelligence systems and employs meticulously designed and effective improvement methods facilitated by adaptive heuristic learning selection procedures. We subjected the model to rigorous testing using real-world data, and our analysis underscored the superiority of our approach. It resulted in a noteworthy 22.46%–27.95% reduction in CO2 costs and a substantial 14.20% reduction in waste generation, along with a noteworthy 6.46% increase in community ownership and a 7.07% increase in cultural heritage value, compared to existing models and methods based on differential evolution algorithms, ant colony optimization, and genetic algorithms. This study contributes significantly to enhancing our understanding of the challenges and prospects inherent in integrating sustainability considerations into the design of travel itineraries.

Optimizing maintenance responsibility distribution in real estate management: A complexity-driven approach for sustainable efficiency

Efficient route management is critical for optimizing maintenance activities in real estate management. This study delves into the intricate task of allocating maintenance duties among building surveyors, a pivotal concern for property management firms. The primary goal of route planning is to enhance operational efficiency by minimizing travel time. To achieve this, the study explores three distinct algorithms: Saving, Nearest Neighbor, and an Evolutionary Algorithm (EA) customized with Dual Response Surface Optimization (DRSO). The integration of DRSO and EA enhances adaptability, allowing for dynamic responses to changes and improved allocation of maintenance tasks. Practical limitations, such as time and capacity, are considered through a case study involving a well-established building management organization. Results indicate that the Nearest Neighbor Algorithm generates 16–18 routes, the Saving Algorithm produces 18 routes, and the DRSO-driven EA also yields 18 routes. Significantly, the DRSO-driven EA consistently outperforms traditional methods, achieving a remarkable 17.7% reduction in route distance and an 18.8% reduction in journey time. In specific scenarios with 50 and 80 locations in Northeast and Central Bangkok, the DRSO-driven EA demonstrates practicality and efficacy. The algorithm's ability to address real-world challenges is underscored by these examples, showcasing its potential for broader implementation in real estate management. This study contributes significantly to the optimization of maintenance routes, presenting a clear roadmap for enhancing operational efficiency and customer satisfaction. Furthermore, it addresses the challenges posed by capitalism's growth constraints, offering insights for the adoption of sustainable economic practices in management, economics, and engineering.

Cooperative Eco-Driving Control of Connected Multi-Vehicles With Spatio-Temporal Constraints

In this article, we propose a novel time-energy optimal control approach with applications in cooperative eco- driving of connected and automated vehicles (CAVs) in urban traffic networks. Safely approaching and departing signalized intersections requires the satisfaction of both spatial equality constraints determined by intersection locations and temporal inequality constraints in compliance with the green light phases. To generate time- and energy-optimal trajectories, the optimal crossing times at intersections are firstly treated as characteristic time constraints, which makes the problem tractable. Then the di- rect multiple shooting method and time transformation technique are applied to find a numerical solution. The contribution of this article is twofold. The first one is the development of a novel time- and energy-optimal control approach that ensures a trade- off between minimizing energy and time for a general class of optimal control problems with multiple characteristic times. The second contribution is the application of the proposed method to the challenging problem of multi-CAVs’ cooperative eco-driving control, in which multiple vehicles must simultaneously minimize travel time and energy consumption in the presence of spatio- temporal constraints. Simulation analysis over real-world urban route scenarios shows that the proposed eco-driving control strategy can save up to 8.2% of energy or reduce up to 6.7% of travel time compared to a baseline method. Furthermore, hardware-in-the-loop (HiL) experimental results indicate that the proposed strategy can be implemented in real-time.

Eco-approach at an isolated actuated signalized intersection: Aware of the passing time window

Isolated actuated signalized intersection is a pressing challenge for conventional eco-approach methods, due to the ever-changing signal timing strategy. This research proposes an optimal control based eco-approach method tailored to tackle this challenge. The proposed method bears the following features: i) capable of predicting the ever-changing actuated signal timing; ii) with enhanced fuel efficiency via proactively catching a feasible passing time window; iii) with real-time computation efficiency for implementation. Simulation results demonstrate that the proposed method enhances fuel efficiency by 9.1%, reduces stop count by 14.8%, and enhances safety performance by 317.14%, compared to conventional human-driven vehicles. The passing time window prediction capability is confirmed with an accuracy of 3.1 s. All the aforementioned benefit is at a cost of a minimal travel time increase of 5.5 s. Moreover, the average computation time of the proposed method is 12 ms, demonstrating its readiness for field implementation.

Max-Pressure Control utilizing Total Movement Delay

Traffic signal control algorithms utilizing max-pressure principles can rely on feedback obtained from either queue lengths or travel times. This paper considers max-pressure algorithms based on travel times, aiming to address traffic-related challenges, including (i) unbounded link capacity, (ii) mitigating the “the Last Packet effect”, and (iii) enhancing queue modeling to account for spatial queue distribution. Hence, we propose a new max-pressure algorithm based on total movement delays, which requires local information only to determine appropriate phase activation, and aims at minimizing delays. First, we introduce a new traffic model based in queuing theory. Unlike previous approaches that rely on the store-and-forward model, our model accurately depicts vehicle travel time delays. Second, based on the developed model, we introduce a new traffic oriented max-pressure algorithm. The objective of maximizing throughput is compared to minimizing travel time delays, through the comparison of different a-cyclic time-based max-pressure control schemes with existing a-cyclic queue-based algorithms control schemes.

Partially observable Markov decision process for perimeter control based on a stochastic macroscopic fundamental diagram

The use of a macroscopic fundamental diagram (MFD) for perimeter control is an effective strategy for managing traffic flow on regional road networks. However, existing methods either assume a MFD with low scattering or assume complete availability of information, which is not realistic. In order to address these limitations, we propose a novel approach that incorporates a stochastic evolution dynamic into the MFD and introduces a partially observable Markov decision process for perimeter control. Our approach assumes that the MFD is randomly distributed between upper and lower boundaries, characterized by a physically interpretable parameter, and that real-time observations represent cumulative data in specific regions. We focus on three sub-objectives: minimizing travel time, minimizing capacity loss due to hysteresis, and preventing gridlock. By utilizing the Bellman equation, we solve the problem and demonstrate that our proposed control method enhances trip completion rates by 7.2% compared to the benchmark case without perimeter control. Furthermore, the inclusion of stochasticity leads to an additional 1.2% improvement in trip completion rates. Through extensive numerical tests, we establish the effectiveness of our approach in optimizing traffic flow regulation on regional road networks.

Модель региональной транспортной сети для построения рациональных мультимодальных маршрутов пассажирских перевозок

Purpose: Adaptation of methods of constructing models of transport networks for the development of rational multimodal routes of regional passenger transportation, as well as the creation of a software product for the practical implementation of the proposed algorithms. Methods: Mathematical modeling; graph theory for solving regional transportation problems with construction of two-level oriented weighted multigraphs; Analysis of existing algorithms for optimizing the search for graph solutions; Dijkstra’s algorithm. Results: The model of the regional passenger transport network has been implemented in the form of a calculation program developed by the authors. In this program, the schedule of the movement of all transport points under consideration is the initial data, and the result is the construction of rational multimodal routes. The calculation program is compiled from several modules that implement the following functionality in general: reading input data from files; the formation of an array of connections for the problems under consideration; search for a rational solution and output of the received data while saving it to the file. The optimization criterion when choosing a route can be the minimum travel time, distance, or cost of the trip, depending on the task and input data provided. The resulting array of data with the results has been further analyzed using the MS Excel table editor. This approach is selected due to sufficient flexibility when performing a variety of data analysis and the convenience of graphic presentation of the results. Practical significance: The development of rational multimodal routes, relevant primarily for cases of abrupt changes in transport relations, in which the extrapolation methods of existing passenger flow do not provide reliable forecast data. Justification of the adjustment of the schedules of regional passenger routes based on the results of the analysis of the demand and the load of their specific segments.

Solving dynamic vehicle routing problem with time windows by ant colony system with bipartite graph matching

Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) is an extension of the traditional VRPTW by considering dynamic customer characteristics. In this problem, new customers with specific time windows are received dynamically as time progresses and must be incorporated into the evolving schedule. The goal of DVRPTW is to use the minimum number of vehicles to fulfill all the customer demands with the minimum total traveling time. Little work has been done for solving this dynamic variant. In this paper, an evolutionary algorithm using Ant Colony System and Kuhn-Munkres bipartite graph matching, named ACS-KM, is proposed. It can tackle the dynamic issues during the evolutionary process. Experiments show that the proposed algorithm is very effective and has fast convergence. It achieves the best results on 40 out of 48 benchmark instances. For the DVRPTW instances with different levels of dynamicity, the algorithm can always produce better solutions than the previous works.

Seamless Multimodal Transportation Scheduling

Ride-hailing services have expanded the role of shared mobility in passenger transportation systems, creating new markets and creative planning solutions for major urban centers. In this paper, we consider their use for the first-mile or last-mile passenger transportation in coordination with a mass transit service to provide a seamless multimodal transportation experience for the user. A system that provides passengers with predictable information on travel and waiting times in their commutes is immensely valuable. We envision that the passengers will inform the system of their desired travel and arrival windows so that the system can jointly optimize the schedules of passengers. The problem we study balances minimizing travel time and the number of trips taken by the last-mile vehicles, so that long-term planning, maintenance, and environmental impact are all taken into account. We focus on the case where the last-mile service aggregates passengers by destination. We show that this problem is NP-hard, and we propose a decision diagram–based branch-and-price decomposition model that can solve instances of real-world size (10,000 passengers spread over an hour, 50 last-mile destinations, 600 last-mile vehicles) in computational time (∼1 minute) that is orders of magnitude faster than the solution times of other methods appearing in the literature. Our experiments also indicate that aggregating passengers by destination on the last-mile service provides high-quality solutions to more general settings. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods and Analysis. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2019.0163 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2019.0163 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Review of the Literature Referring to a Method to Achieve Active Electrical Energy Savings - Single-Phase 220 (VAC) and 50 (Hz) - in Synchronous Ventilation Motors, Greater than that Obtained with the "Fan Law"

It is a mechatronic method to achieve savings in single-phase active energy, greater than that obtained with the “Fan Law” in electrical machines applied to ventilation. The quantitative analysis methods were based on electrotechnical techniques, practiced with the corresponding laboratory instruments on the work materials (three prototypes of electrical machines). The results found from the experimentation on the test bench were expressed in tables that collect data on formulas, values and physical units. The discussion carries out a complete comparative study; mainly between power (watts), active energy consumption (kwh) and rotation speed (RPM). The PMSM type synchronous motor with the coupling of an RL mechatronic circuit design performs mechanical work at its maximum speed of 3000 (RPM) with only 6.3 (Watts), this is only 25.2% of the active power required by the single-phase asynchronous induction motor or shaded-pole motor that needed 25 (Watts) to rotate at 1690 (RPM). This translates into 75% lower active power, with a 44% superiority in speed, which translates into a 75% saving in single-phase active energy (kWh). The same thing also happens if we compare the universal AC motor with carbon and wound rotor, to maintain a speed at 3000 (RPM); given that it will consume 64.8 (Watts), that is, 90.3% more single-phase active energy than that required to match the same speed of the PMSM type synchronous motor. All with the same diameter of the impeller blades and the same conditions of temperature and atmospheric air pressure.

A bi-objective optimization model to decide the lines attending a bus-stop with high passenger demands

A public transportation network must change dynamically along the time due to the variation of the mobility of the population in interurban areas. This research addresses the challenge of serving newly detected zones with high passenger demand. We present a bi-objective optimization model to determine the feasibility of meeting new demands within the existing lines of the public transportation network. The two objectives considered are to minimize passenger overload on the selected lines and minimize travel time increases for these lines. Subsequently, a polynomial-time algorithm utilizing network flow techniques is introduced to solve the bi-objective problem. The algorithm is then incorporated into the decision-making system of Transportes Interurbanos de Tenerife, S.A. We also explore the company’s utilization of this tool and demonstrate its current implementation in a real case scenario where a high-demand area is detected.

Optimized Flexible Lane Assignment Using Simulation and Metaheuristic Algorithms

Technological advances with connected and autonomous vehicles (CAVs) have opened opportunities to increase the efficiency of transportation networks. A novel control framework called combined flexible lane assignment and reservation-based intersection control (CFLARIC) system has been recently proposed for better management of directionally unrestricted CAVs traffic flows in an urban environment. CFLARIC offers a full spectrum of lane assignment possibilities in combination with the appropriate reservation-based intersection control. In CFLARIC, vehicles position themselves in a proper lane before they reach the downstream intersection, which enables resolution of vehicular conflicts both between intersections and within the intersection boxes. Although CFLARIC has shown promising results, only a limited number of lane assignment scenarios have been designed and tested using some preset rules and based on the previous research objectives. Therefore, the optimality of their performance has never been studied. The objective of this study is to address the flexible traffic lane assignment in such a system as a network optimization problem, in which an optimal lane assignment schema is achieved using metaheuristic optimization algorithms. To this end, a combination of NetLogo and the BehaviorSearch tool is utilized for the simulation modeling and optimization process using random search and brute-force as the search algorithms. The output of the optimization process is the lane assignment that leads to a minimum total travel time for a given network geometry and traffic volumes. Results indicate that a flexible control concept such as optimized CFLARIC has great potential to improve the efficiency of traffic control strategies with CAVs.

Exploring Regional Reduction Pathways for Human Exposure to Fine Particulate Matter (PM2.5) Using a Traffic Assignment Model.

An exposure-based traffic assignment (TA) model is used to quantify primary and secondary fine particulate matter (PM2.5) exposure from on-road vehicle flow on the Chicago Metropolitan Area regional network. PM2.5 exposure due to emissions from light-duty vehicles, heavy-duty trucks, public transportation, and electricity generation for electric vehicle charging and light-rail transportation is considered. The model uses travel demand data disaggregated by time-of-day period and vehicle user class to compare the exposure impacts of two TA optimization scenarios: a baseline user equilibrium with respect to travel time (UET) and a system optimal with respect to pollutant intake (SOI). Estimated baseline PM2.5 exposure damages are $3.7B-$8.3B/year. The SOI uses exposure-based vehicle rerouting to reduce total damages by 8.2%, with high-impacted populations benefiting from 10% to 20% reductions. However, the SOI's rerouting principle leads to a 66% increase in travel time. The model is then used to quantify the mitigation potential of different exposure reduction strategies, including a bi-objective optimization formulation that minimizes travel time and PM2.5 exposure concurrently, adoption of a cleaner vehicle fleet, higher public transportation use, particle filtration, and exposure-based truck routing. Exposure reductions range between 1% and 40%, but collective adoption of all strategies would lead to reductions upward of 50%.

On gap matching for lane-changing vehicles: Coordinated strategies to improve traffic flow at weave bottlenecks

The weaving section on the freeway will be an active bottleneck due to vehicles’ mandatory or discretionary lane-changing (LC) conflicts. In the early stages of the development of intelligent connected transportation, destination information collection of weaving vehicles is possible, which provides an opportunity to improve performance in weaving traffic. In this paper, a simulation system with coordinated strategies embedded to satisfy the LC needs is presented under the weaving traffic flow. First, an algorithm is proposed to evaluate the balance between the demand for LC vehicles and the available gaps provided in weaving traffic, which can be adjusted by variable speed limit (VSL) and ramp metering strategies. Second, to validate the proposed algorithm, a typical weave section with a merge and a diverge connected through a single auxiliary lane is researched, and a simulation platform based on cellular automata has been developed. The optimal control strategies are obtained by model predictive control (MPC) and taking the minimum travel time as an objective. Third, the advantages of coordinated control strategies are studied and discussed in traffic efficiency improvement and the success of LC proportions under different combinations of weaving ratios and large vehicle traffic mix.

Wildfire exposure and risk in pulp paper companies’ plantations under extreme weather conditions: A case study in North-Western Portugal

Since the 1960's, Portugal has been affected by profound land use/land cover changes, which have boosted fuel continuity and fire-proneness nationwide. Within this context, the assets of pulp paper companies are exposed to large wildfires, which may cause severe economic losses. Wildfire simulations can help to scenarize and anticipate fire spread, contributing to efficiently allocate resources to protect values at risk.To test this framework, we assessed wildfire hazard in a 115,000 ha landscape, including 3,720 ha owned by two companies, with the aim of identifying the management units most exposed to large wildfires and the ones most at risk. We used Minimum Travel Time algorithm to estimate Burn Probability (BP), Fire Size (FS) and Conditional Flame Length (CFL). Wildfire hazard and exposure were assessed at the landscape-level; risk was estimated in production eucalypt plantations, by combining the annual BP with potential economic losses, based on management costs, loss of revenue and salvage wood.Our results showed that eucalypt plantations not managed by the companies represent a major threat to their patrimony. Industrial plantations with ages between 6 and 9 years have proven the most exposed. Very large wildfires (≥5,000 ha) can potentially spread in 53 % of the landscape, affecting 75 % of young eucalypt (<5 years) area. The 90th percentile of the risk ranges from 213 to 429 €ha−1 year−1 (9.6 % of production eucalypt area). This work resulted in a flexible methodology, replicable in Mediterranean regions with similar landcover. The information produced can provide additional guidance for wildfire prevention and risk management.

Pareto multi-objective optimization of metro train energy-saving operation using improved NSGA-II algorithms

Quantitative analysis of the factors affecting the energy consumption of metro trains and finding out the breakthrough points of energy conservation is of great practical significance for reducing transport costs and improving energy utilization. Train operation curve optimization is one of the main methods to reduce the energy consumption of train operation. This paper introduces the metro train energy-saving operation optimization model with fixed travel time. Firstly, taking the energy consumption index and punctuality index as the objectives, a train energy-saving operation optimization model is established. Then, the solution method based on the improved NSGA-II algorithm is developed. Finally, the developed optimization model is applied to Guangzhou Metro Line 7 to verify the performance. The results show that the energy consumption of the developed operation strategy is 24.4 % less than that of the minimum travel time strategy, and the total operation time meets the punctuality requirements. Meanwhile, the improved NSGA-II algorithm has less energy consumption than GA and PSO algorithms in the application of Guangzhou Metro Line 7.

Assessing cross-boundary wildfire hazard, transmission, and exposure to communities in the Italy-France Maritime cooperation area

The growing threats posed by wildfires in Southern Europe are calling for the development of comprehensive and sound management and risk assessment strategies. In this work, we present the application of wildfire simulation modeling based on the minimum travel time (MTT) algorithm to assess fine-scale (100-m resolution) wildfire hazard, transmission, and exposure to communities in the Italy-France Maritime cooperation area (Sardinia, Corsica, Tuscany, Liguria and Provence-Alpes-Côte d’Azur), which cover about 72,000 km2 of land. We simulated thousands of wildfires considering the current landscape and characterized and measured fine-scale wildfire risk factors and profiles by taking into account historical fire regimes, fuels, winds and fuel moisture conditions associated with the occurrence of the largest wildfires (&amp;gt;100 ha) that affected the study area in the last 20 years. Individual fires were simulated at 100-m resolution, consistent with the input files. Modeled annual burn probability and ignition probability revealed that Sardinia was the Region most affected by wildfires. The wildfire simulation outputs were then combined with main land uses and building footprint locations to characterize wildfire transmission and exposure to communities, and were summarized for main vegetation types and Regions. This study presents a cross-boundary and standardized approach based on wildfire spread modeling to analyze and quantify wildfire risk profiles in Southern Europe. The stochastic wildfire modeling systems we implemented used harmonized sets of data for a vast, fire-prone Mediterranean area, where previous similar studies were conducted at coarser resolutions and covered lower extent of lands. The approach presented in this work can be used as a reference pillar for the development and implementation of a common wildfire risk monitoring, management, and governance plan in the study area. The methods and findings of this study can be replicated in neighboring Mediterranean and other regions threatened by wildfires.