This paper addresses the challenges of improving last-mile logistics delivery satisfaction in urban areas by studying a multi-trip vehicle routing problem with multiple delivery locations (MTVRPMDL). The MTVRPMDL simultaneously decides the visiting order of customers for each vehicle and selects an appropriate delivery location for every customer. The problem exhibits intrinsic spatial and decision symmetries, arising from interchangeable vehicle trips, alternative delivery locations for each customer, and symmetric route permutations that lead to equivalent operational outcomes. A mixed-integer programming model is proposed, aiming to minimize the total vehicle travel time. Within an iterated local search framework, a modified Solomon greedy insertion heuristic suitable for multi-delivery address and multi-trip settings is developed to generate initial solutions. During the iterative search phase, Or-opt and Relocate local search operators are employed, together with random swap perturbations, to enhance solution exploration. Computational experiments confirm the efficiency of the proposed model and algorithm, showing that allowing customers to have multiple delivery locations can significantly reduce overall travel time and improve the flexibility of vehicle routing decisions.

BackgroundMexico is a low malaria transmission setting, however, there has been an increase in the total number of imported cases, associated with greater migratory movement from Central and South America en route to the United States, most of which are linked to passage through the Darién Gap, a high-transmission area in the jungle region between Colombia and Panama.Migration Routes of Imported Malaria Cases, 2024MethodsRetrospective study of all imported malaria cases diagnosed from January 2024 to December 2024 in a tertiary-level hospital in Mexico City. All epidemiological, clinical and laboratory data were obtained from patients’ electronic medical records. All malaria cases were confirmed by thick blood smear examination in accordance with national guidelines. Severe malaria was defined according to the World Health Organization criteria.ResultsA total of 22 imported cases of malaria were admitted, most between May and August 2024 (54%). The patients were predominantly male (68%), with a mean age of 30.54 years. The most common route of arrival was through the Darién Gap (90%), with an average stay of 3.5 days in that region and a mean total travel time of 13.5 days. The most frequent country of origin was Venezuela (63%), followed by Peru (13%), and Chile, Ecuador and Ethiopia (4% each). There were 2 patients who flew directly from Mauritania and Liberia (Image 1). Plasmodium falciparum was found in both cases imported directly from Africa; the rest were identified as Plasmodium vivax. The most frequent clinical and laboratory findings were fever (100%) and thrombocytopenia (86%) respectively (Table 1). A total of 11 patients (50%) were classified as severe malaria, the most common complication being abnormal bleeding (Table 2). The average hospital stay was 7.5 days, and the main treatment was chloroquine in combination with primaquine for Plasmodium vivax (65%), and artesunate for Plasmodium falciparum (100%). The main outcome was recovery in both groups; however, recurrence was observed in patients who received inadequate treatment due to a shortage of antimalarial medication in the country (Table 3).ConclusionImported cases of malaria will continue to rise in Mexico with the current migration crisis; therefore, it is necessary to strengthen knowledge of this diagnosis and address the shortage of antimalarial medications.DisclosuresAll Authors: No reported disclosures

Freeway on-ramp merging zones are critical bottlenecks where frequent lane changes and decelerations often lead to congestion and capacity drops. Although differential variable speed limit (DVSL) strategies have shown potential in mitigating these problems, their effectiveness significantly declines under high-density conditions due to speed limit failure, a situation in which drivers are unable to comply with posted speed limits because of excessive congestion. To address this challenge, this study proposes a multi-zone DVSL (MDVSL) control strategy within a multi-lane cell transmission model framework. The main innovation lies in the introduction of a dynamic upstream buffer zone that is activated when potential speed limit failure is detected. By harmonising speeds across lanes in this buffer zone, the strategy facilitates anticipatory lane changes from the merging lane, improving traffic smoothness and reducing congestion in the primary control zone. A predictive control algorithm is employed to dynamically optimise lane-specific speed limits, aiming to minimise total travel time (TTT). Simulation results demonstrate that the proposed MDVSL strategy effectively reduces the occurrence of speed limit failure, enhances traffic flow stability and achieves a 25.6% reduction in TTT compared to the uncontrolled strategy.

The optimal allocation of patients after a major accident is important because there are constraints such as limited hospital capacity and traffic congestion on the roads connecting the accident location to the hospitals. It is imperative to provide timely treatment to the casualties of such accidents to minimize loss of life. However, most studies that focus on the allocation of patients after accidents do not consider traffic aspects such as congestion on the roads near hospitals or hospital waiting times. This paper proposes a formulation to optimally allocate patients to minimize the sum of the total system travel time and the total waiting and treatment times of patients while considering travel congestion and limited hospital capacities. The developed optimization formulation is nonlinear and therefore a genetic algorithm was developed to solve it. The developed algorithm was tested on a standard transportation network and was found to give close to optimal solutions in a reasonable time. For instance, the proposed genetic algorithm achieved solutions within 4.6% of the optimal value and reduced computation time by an order of magnitude 9 times that of an existing solver when there were 30 hospitals. Sensitivity analysis employing various parameters such as the number of hospitals, hospital capacities, and treatment times, was also performed. The objective function value decreased with the number of hospitals and hospital capacities, but increased with treatment times when the other parameters remained the same. The reasons behind such patterns are discussed.

Integrating connected and autonomous vehicle dedicated lanes (CAVDLs) into existing road networks under mixed traffic conditions presents a complex challenge, often requiring a balance of multiple conflicting objectives. This study develops a dynamic multi-objective optimization framework, formulated as a mixed-integer nonlinear programming problem, to determine the optimal network-wide deployment of CAVDLs. The framework integrates three core components: an endogenous demand model capturing connected and autonomous vehicle (CAV)/human-driven vehicle (HDV) mode choice, a multi-class dynamic traffic assignment model that adjusts lane capacity based on CAV-HDV interactions, and an NSGA-III algorithm that minimizes total system travel time, total emissions, and construction costs. Results of a case study indicate the following: (i) sensitivity analysis confirms that user value of time is the most critical factor affecting CAV adoption; the model’s endogenous consideration of this variable ensures alignment between CAVDL layouts and actual demand; (ii) the proposed Pareto-optimal solution reduces total travel time and emissions by approximately 31% compared to a no-CAVDL scenario, while cutting construction costs by 23.5% against a single-objective optimization; (iii) CAVDLs alleviate congestion by reducing bottleneck duration and peak density by 36.4% and 16.3%, respectively. The developed framework provides a novel and practical decision-support tool that explicitly quantifies the trade-offs among traffic efficiency, environmental impact, and infrastructure cost for sustainable transportation planning.

For the sake of achieving the mensuration of network carrying capacity under regular even congested road conditions with crowded vehicles, passengers or cyclists in a median-scale network, this study examined the ideal travel time of a passenger or cyclist in the hybrid-congested roads (congested and non-congested road accounts for half), being the initial accumulated values for the independent variable in Aggregated Functional Equation. Besides, the various road section’s capacity was examined, together with the total travel time accumulated accordingly, and the capacity limitation took effect to alleviate the heavy load of vehicles on partial road sections. This study proposes a multi-objective bi-level planning model on the basis of the original capacity constraint model to address the potential congested problem, the model optimizes three aspects in the schematic diagram: carrying capacity, average travel time, and expansion cost. This experiment attains 9 effective hyper-paths with traffic flow and travel impedance attributes attached to each hyper-path. The scatter chart results show that the plural modulus of carrying capacity decreases with travel time’s descends; when the road saturation increases from 0.7 to 0.9, the plural modulus of carrying capacity declines by 27.1%. Meanwhile, raising the upper limit of expansion would slightly lift the carrying capacity in the proportion of 15.7%. This research could provide a reference for the majorization of multi-modal cyber flow distribution and also has indirect significance for route planning to a certain extent.

For the sake of achieving the mensuration of network carrying capacity under regular even congested road conditions with crowded vehicles, passengers or cyclists in a median-scale network, this study examined the ideal travel time of a passenger or cyclist in the hybrid-congested roads (congested and non-congested road accounts for half), being the initial accumulated values for the independent variable in Aggregated Functional Equation. Besides, the various road section's capacity was examined, together with the total travel time accumulated accordingly, and the capacity limitation took effect to alleviate the heavy load of vehicles on partial road sections. This study proposes a multi-objective bi-level planning model on the basis of the original capacity constraint model to address the potential congested problem, the model optimizes three aspects in the schematic diagram: carrying capacity, average travel time, and expansion cost. This experiment attains 9 effective hyper-paths with traffic flow and travel impedance attributes attached to each hyper-path. The scatter chart results show that the plural modulus of carrying capacity decreases with travel time's descends; when the road saturation increases from 0.7 to 0.9, the plural modulus of carrying capacity declines by 27.1%. Meanwhile, raising the upper limit of expansion would slightly lift the carrying capacity in the proportion of 15.7%. This research could provide a reference for the majorization of multi-modal cyber flow distribution and also has indirect significance for route planning to a certain extent.

Sustainable transportation and green logistics are becoming increasingly important, and the efficient use of electric vehicles (EVs) plays a critical role. However, efficient path planning for EVs remains a major challenge due to limited driving range and the need for optimised charging strategies. Usually, path recommendations are made based on a single criterion. However, drivers may want to consider multiple criteria for path selection. This study focuses on building a multi-criteria path planning algorithm that incorporates driver preferences by considering total travel time, energy consumption and travelling distance. To obtain the appropriate recommendation, these three criteria are evaluated using the Analytic Hierarchy Process (AHP) and Dijkstra algorithm is used to identify roads that take into account driver preferences. Johnson technique was used to remove negative energy weights due to energy recovery and solved the incompatibility problem of the Dijkstra algorithm with negative edge weights. The results have shown the proposed algorithm can efficiently generate solutions designed based on driver preferences and is suitable for EV routing applications. This study presents a method to increase user satisfaction by aiming at the widespread adoption of EVs and emphasizes the importance of multi-criteria decision making in addressing the unique challenges of EVs.

Urban freight pickup-and-delivery services operate in road networks where travel times are highly variable due to congestion, incidents, and operational restrictions. Such variability threatens the punctuality of deliveries and complicates the design of reliable service schedules. This paper examines an urban pickup-and-delivery vehicle routing problem with soft time windows under travel-time uncertainty and provides an empirical comparison of robust and deterministic planning approaches on a real road network. The problem is formulated as a time-dependent pickup-and-delivery VRP with soft time windows, where link travel times are represented by a finite set of scenarios calibrated from observed network conditions. The objective function combines four components that are central to urban freight operations: total travel time, total distance, and penalties for earliness and lateness relative to customer time windows. This structure captures the trade-off between routing efficiency and service quality. On this basis, a robust model is constructed that optimises tour plans with respect to scenario-based worst-case or risk-aggregated costs, while a standard deterministic model minimises the same objective using nominal (average) travel times only. An empirical study on a real urban network compares the deterministic and robust solutions with respect to delivery punctuality, tour length, and time-window violations across a range of demand and variability settings. The results show that robust routing systematically reduces the frequency and magnitude of late deliveries at the expense of only moderate increases in planned distance and travel time. Although energy use and emissions are not modelled explicitly, the improved reliability and reduced need for reactive re-routing indicate a potential to support more reliable and resource-efficient urban freight operations in the context of sustainable city logistics.

ABSTRACT This paper addresses the capacity-utilization oriented train scheduling problem for busy high-speed railways in China, aiming to enhance transport capacity utilization by increasing the number of scheduled trains. A practical and efficient approach is proposed, integrating differentiated safety time-intervals, train slowdown times, and flexible train stops into an integer programming model. To solve the model, an efficient algorithm is designed to optimize arc penalty costs within the time-space network. Additionally, a flexible train cancellation strategy is introduced to minimize cancellations while ensuring successful scheduling. Numerical analysis on the Shanghai–Hangzhou High-speed Railway demonstrates a 47.66% improvement in scheduled trains, scheduling nearly 160 trains in one direction over a full day, with only a modest 4.72% increase in total travel time, resulting from the conflict resolution strategies employed. This trade-off is acceptable given the significant gain in capacity utilization and scheduling success, making the proposed method an effective solution for enhancing railway capacity.

Variable Speed Limit (VSL) control is essential for managing highway tunnel maintenance work, as it adjusts speed limits based on road conditions to regulate traffic flow. Developing a VSL control strategy that balances traffic efficiency and safety during maintenance can be challenging. This paper addresses this issue by proposing a VSL control strategy based on Model Predictive Control (MPC) that considers the spatial characteristics of traffic flow in a tunnel maintenance work zone. The strategy aims to minimise total travel time, reduce speed variance, and maximise traffic flow through a multi-objective optimisation approach using a Non-dominated Sorting Genetic Algorithm II (NSGA-II). With the Qinling Tiantai Mountain Tunnel selected as the experimental object, a simulation section is constructed based on the SUMO model with the measured data, and a comparative experiment of different speed limit control cycles in the maintenance work zone is designed. The results show that the method of this paper can effectively reduce the total travel time under the influence of maintenance operations by more than 17.5%, reduce the standard deviation of speed by about 22.1%, and enhance the traffic volume by about 7.8%, which can effectively improve the efficiency of road access and safety level.

This study presents a comprehensive comparative analysis of the effect of route set size on stochastic user equilibrium (SUE) traffic assignment, focusing on both logit-based (Multinomial Logit (MNL) and Path Size Logit (PSL)) and weibit-based models (Multinomial Weibit (MNW) and Path Size Weibit (PSW)). The primary objective is to investigate the influence of route set size on traffic patterns and determine the minimum requisite number of routes for flow stabilization within the SUE framework. The analysis, conducted on the Winnipeg network using a customized Self-Regulated Averaging (SRA) scheme, yields three key findings. First, all models successfully converged, but the weibit-based models (MNW and PSW) converged faster than the logit-based models. Second, an analysis of perceived total travel time demonstrated that the majority of efficiency gains from route inclusion diminish after a threshold of approximately maximum 30 routes to 40 routes per O-D pair, indicating this number is sufficient for achieving stable SUE results in both model families. Third, the weibit-based model was found to be more sensitive to route overlap effects, continuing to adjust flow patterns up to maximum 45 routes per O-D pair, and exhibiting a greater tendency to allocate flow to less overlapping outer roads. This highlights the superior capability of the weibit formulation, which accounts for heterogeneous perception variance, to achieve a more behaviorally realistic equilibrium compared to the logit models.

This paper studies an order batching and assignment problem for a warehousing system considering uncertain future orders. Orders that continuously enter a pool are handled in batches, and the core decision of the problem is to categorize the orders in the pool into batches and assign the orders in the current batch to picking stations in the system. When making the decision for the current batch of orders, we consider future orders with uncertain Stock Keeping Units (SKU) requirements and their quantities. Using mixed-integer linear programming, this paper proposes a two-stage stochastic programming model with integer recourses, which is difficult to solve using traditional algorithms. Thus, a hybrid exact algorithm that combines the branch-and-price algorithm, column generation, and the logic-based Benders decomposition is designed and implemented to solve the model. To accelerate the algorithmic solving process, we propose some new cuts and apply parallel computing techniques to solve some of the subproblems embedded in the algorithm. We also conduct experiments to validate the efficiency of the proposed algorithm and derive some potentially useful managerial insights. For example, a counter-intuitive result is that the more picking stations there are, the worse the objective is (i.e., the total travel time of used pods). In addition, the more SKUs are required per order, the worse the objective is, while the more SKUs are stored per pod, the better the objective is. Furthermore, the deployment of picking stations along one short side of the warehouse is the best layout for the system.

The Vehicle Routing Problem with Time Windows (VRPTW) is a critical combinatorial optimization problem in modern logistics, where finding optimal routes is essential for minimizing operational costs and enhancing service reliability. While Genetic Algorithms (GAs) are a powerful tool for solving VRPTW, their effectiveness is often undermined by premature convergence, a phenomenon in which the algorithm stagnates at suboptimal solutions, thus failing to achieve maximum efficiency. This study directly addresses this challenge by systematically evaluating how different reproduction schemes impact GA performance. The primary objective is to identify operator combinations that mitigate premature convergence to achieve superior solution quality, measured by total travel time, while also analyzing the trade-off with computational cost. We investigate combinations of conventional operators, such as Tournament Selection (TS) and Order Crossover (OX), against more advanced schemes, including Split Rank Selection (SRS) and Multi-Parent Order Crossover (MPOX), as well as different mutation methods, namely Scramble Mutation (SM) and Inversion Mutation (IM). Results demonstrate that advanced schemes, particularly the combination of SRS, MPOX, and IM, yield the most robust convergence and the lowest average total travel time of 48,422.8 minutes. However, this superior performance requires the longest computation time at 30.9 h. In contrast, conventional operator combinations are significantly faster, with execution times as low as 8.7 h, but they produce lower-quality solutions and exhibit unstable convergence. This study highlights the crucial role of the reproduction scheme in balancing the trade-off between solution quality and computational efficiency, confirming that a synergistic combination of advanced operators is essential for solving complex VRPTW instances effectively.

Urban congestion causes further increases in travel times, fuel consumption and greenhouse-gas emissions. In this regard, we conduct a systematic study of a Genetic Algorithm (GA) for real-time routing in an urban scenario in Bethlehem City, based on a SUMO microsimulation that has been calibrated using real data from the field. Our work makes four main contributions: (i) the implementation of a reproducible GA framework for dynamic routing with explicit constraints and adaptive termination criterion; (ii) design of a weight sensitivity study for studying a multi term fitness function with travel time and waiting time, and optionally fuel usage; (iii) an edge-assisted distributed architecture on roadside units (RSUs) supported by cloud services; and (iv) specifying and refining the data set description and experimental protocol with a planned statistical analysis. Empirical evidence from the Bethlehem case study shows a consistent decline in total travel time under high congestion cases. Variations in the waiting time between different scenarios are exhibited, reflecting the trade-offs in the fitness weighting scheme. We recognize that we have some limitations, including the manual resolution of data and the inherent problem of differences between simulations and real world, and we are proposing a road-map towards a pilot deployment that handles these issues. Rather than proposing a new GA variant, we present a deployment-oriented framework-an edge- assisted GA with explicit protocols and a latency envelope, and a reproducible multi-objective tuning procedure validated on a city-scale network under severe congestion.

Road closures resulting from construction activities significantly disrupt traffic flow and often divert heavy truck traffic onto alternative routes. These detours, if not properly planned, can funnel trucks onto roads not designed for high axle loads, accelerating pavement deterioration and increasing maintenance needs. To address this challenge, this research proposes a comprehensive bilevel modeling framework that integrates vehicle-type-specific traffic flow prediction with detour signage placement optimization. Employing a user equilibrium traffic assignment model solved by paired alternative segments algorithm, the study predicts traffic distributions before and after road closures, identifying road segments vulnerable to increased truck traffic. A genetic algorithm is utilized to solve the bilevel optimization framework to strategically determine optimal detour signage placement. The primary objective is to minimize a fitness function that incorporates both infrastructure considerations and network efficiency measures, prioritizing the restriction of heavy trucks from vulnerable or prohibited road segments while accounting for network total travel times. The model is demonstrated through application to the entire Minneapolis transportation network, illustrating its capability to handle complex, large-scale real-world scenarios effectively. Results show that the optimized signage placement significantly reduces truck usage of vulnerable roads while maintaining efficient traffic operations, offering valuable insights for policymakers and planners aiming to enhance resilience and sustainability in detour management strategies.

In this study, we explored whether and how area-wide air pollution affected individuals’ activity participation and travel behaviors, and how these effects differed by neighborhood context. Using multi-day travel survey data provided by 390 adults from 223 households in a small urban area in northern Utah, United States, we analyzed a series of 20 activity and travel outcomes. We investigated the associations of three different metrics of (measured and perceived) air quality with these outcomes, separately for residents of urban and suburban/rural neighborhoods, while weighting and controlling for personal/household characteristics and weather. Our regression models detected measurable changes in activity and travel patterns on days with poor air quality. People engaged in more mandatory (work/school) and fewer discretionary activities. The total travel time for urban residents increased, driven by increases in trip-making and travel time by public modes (bus) and increases in travel time by private modes (car). On the other hand, suburban/rural residents exhibited behavior consistent with mode shifts from driving to active transportation, such as: less car travel (distance and time), longer transit distances, more walking/bicycling (trips, distances, and time), and greater odds of being an active mode user. Air quality perceptions also seemed to play a role, with some evidence for increased active transportation and public transit usage on days with worse perceived air pollution. Overall, the results offer more evidence of altruistic than risk-averse travel behavioral responses to episodes of area-wide air pollution, although more research is needed.

This research presents a comprehensive electric vehicle (EV) routing framework designed to address the complex interplay of real-world constraints in EV navigation. The proposed system integrates spectral clustering, fuzzy reinforcement learning, and enhanced pathfinding algorithms to compute optimal routes while considering battery limitations, traffic dynamics, terrain elevation, and charging station delays. Unlike conventional multi-objective EV routing solutions, which typically optimize metrics such as energy, time, and charging delays independently-this work addresses four major gaps in the field: (1) fragmented and isolated optimization lacking dynamic interdependency modeling, (2) limited real-time adaptability to traffic and charging dynamics, (3) inadequate topological modeling with respect to network clustering and geographic scalability, and (4) evaluation restricted to constrained environments. The system introduces four core innovations: (1) a topologically adaptive clustering mechanism using spectral clustering with geodesic distance metrics and elliptical regional modeling;(2) a time-dependent arrival simulation model that predicts charging station occupancy with high accuracy by incorporating temporal demand and station-specific dynamics; (3) a fuzzy reinforcement learning-based charging station evaluator that incorporates spatial density, occupancy trends, and temporal availability; and (4) an enhanced A* algorithm with integrated elevation-aware energy profiling, real-time traffic sensitivity, and adaptive SOC constraint modeling.Experimental evaluations conducted across diverse topographies demonstrate superior performance over baseline and established algorithms including Dijkstra, A*, Hybrid A*, and EVRP + Charging Aware techniques. The proposed method achieves a 22.8% reduction in total journey time (from 877 to 677 minutes), 19.6% improvement in energy efficiency (from 224.5 to 180.5 kWh), and a 63.3% decrease in waiting time (from 34.2 to 12.5 minutes) when compared to the traditional distance-based routing. Additionally, the system achieves a 90.0% reduction in battery violations (from 18.0% to 1.8%), addressing range anxiety through improved SOC-aware planning.The findings confirm that the framework advances beyond both established algorithms and recent multi-objective solutions, offering a more unified and effective approach to EV routing. Performance gains remain consistent across urban, rural, and elevation-intensive routes, with measured improvements of up to 27.2% over conventional routing algorithms. This research directly contributes to SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities) by enabling energy-efficient, reliable EV navigation, thereby supporting the broader vision of clean transportation and smart city integration.

Drivers often engage in aggressive behaviors during time-reduction-goal tasks without fully understanding the actual time saved. This study investigated how such goals influence driving behavior and perception. A total of 99 young male drivers initially completed a survey assessing their beliefs about time-saving performance. Of these, 32 were randomly selected to participate in real driving experiments under both time-reduction and control conditions. Heart rate (HR), skin conductance response (SCR), and driving data were collected. Afterward, the experimental results were shared with all 99 drivers who completed the initial survey, including the 32 experimental participants and 67 non-participants. All drivers then provided cognitive feedback. The findings indicated that: (1) 78% of drivers believed that aggressive driving reduced both traffic light-affected time (TLT) and non-traffic light-affected time (NTLT); (2) Time-reduction goals led to more frequent acceleration and deceleration, reducing total travel time primarily in NTLT segments, while TLT remained stable. HR and SCR showed no significant increase in anxiety; (3) After receiving feedback, 72.7% of drivers, including 85.2% of participants and 69.4% of non-participants, agreed that aggressive driving had limited impact on TLT and expressed a willingness to modify their behavior. This study revealed actual behavioral outcomes under time pressure, assessed the potential of cognitive feedback, and provided insights for promoting safer and more efficient driving.

ABSTRACT Cold chain logistics refers to a special supply chain system with a high demand for low-cost, on-time transportation, high reliability, and environmental friendliness. This study proposes a fuzzy integer nonlinear programming model for the collaborative planning of cold chain transportation routes and timetables, considering service attribute preferences and the uncertainty of the transport cost coefficient, transport time, inventory time, and capacities. Numerical experiments are conducted based on the New Land-Sea Trade Corridor in southwest China. The cold chain multi-service mode achieves a balance between the total cost, reliability, and travel time. Different departure times have a significant impact on transportation plans intended to avoid violating service connection constraints. Preferring particular service attributes does not necessarily increase total transportation costs, enabling improved service quality without increasing total costs.