Total Travel Research Articles

Heterogeneous multi-task allocation algorithm based on multi-round distributed auction

Aiming at the distributed decision-making needs of different combat tasks such as multi-machine collaborative detection, attack, and evaluation, a multi-round distributed auction algorithm for heterogeneous tasks with time constraints is established. The algorithm flexibly solves the distributed collaborative task allocation problem with complex constraints between drones with different capabilities. In the distributed task allocation framework, each drone can realize distributed asynchronous computing and asynchronous communication, and can ensure good solution efficiency and accuracy. The task allocation principle is mainly based on the nearest allocation, while taking into account the target value and time constraints, realizing the priority allocation of important targets under limited resources and making the total journey as short as possible. Simulation results show that: given any number of drones and targets and the configuration of drone combat capabilities, the heterogeneous multi-round distributed auction algorithm can obtain good allocation results.

Analysis of COVID-19 Virus Spread in Jakarta Using Multiple Linear Regression

COVID-19, first identified in Wuhan, China in December 2019, quickly spread worldwide and was declared a pandemic by WHO in March 2020. Indonesia reported its first case on March 2, 2020, and the pandemic has had a significant impact on the country's economic, social, and health sectors. This study aims to predict the death rate due to COVID-19 in Jakarta using multiple linear regression method. The dataset collected from Andra Farm - Go Green website includes COVID-19 cases recorded in all sub-districts in Jakarta on November 1, 2023. Pre-processing was performed to improve the quality and accuracy of the model. The method used was multiple linear regression. The analysis results show that variables such as total travel and discarded trip have a significant influence in predicting the number of positive cases. The study found that lowering the correlation threshold for selecting independent variables reduced the mean squared error (MSE) and improved model performance, highlighting the importance of variable selection in developing accurate predictive models. These findings provide important insights for the government in making informed decisions regarding post-pandemic healthcare. This research underscores the value of robust data processing and variable selection techniques in enhancing predictive accuracy for public health planning.

Optimizing electric vehicles charging for enhancing environmental sustainability and reducing carbon emissions of freight transport: case of Czech Republic

The limited infrastructure of charging stations, which is crucial in route planning and total journey time and creates uncertainty in efficiency and operating costs, calls for new economic and statistical methods in sustainability development and environmental economics. This paper aims to examine the challenges of integrating electric vehicles into freight transport to improve distribution logistics’ environmental sustainability, which represents one of the pathways for reducing environmental risk. The analysis results underscore the inadequacy of the truck charging station network in the Czech Republic. This insufficiency presents an opportunity to enhance environmental sustainability and reduce carbon emissions through strategic analysis and optimizing charging station locations. The difficulty of identifying optimal locations for these stations, given truck availability, requires using multi-criteria decision-making techniques such as the Analytical Network Process (ANP). Municipalities with limited access to existing logistics facilities were considered during the simulation. This way, 15 new locations were identified for municipalities with insufficient distance to a charging station.By implementing the ANP method, the study contributes to a more environmentally sustainable transportation infrastructure, highlighting the potential for significant reductions in carbon emissions through improved charging station networks. These results apply to other countries and can provide novel insights on optimizing charging station locations for sustainable economic development and reducing freight transport’s carbon emissions and environmental risks.

Integrating Whale Optimization Algorithm and Machine Learning for Efficient Traffic Management

In order to protect extra retort teams, building labours, and the common community, road needs managing vehicle and pedestrian to manage traffic flows and offer guidance regarding traffic congestion, municipal or state roadway authorities may also utilize CCTV and other methods of traffic surveillance. One optimization framework is used to provide prompt and dependable decision-making by combining the dependable Whale Optimization Algorithms (WOA) and the quick-running Machine Learning (ML) algorithms. First, a standard WOA algorithm is used as a decision variable, with the network’s total journey time serving as the unbiased role. In order to select the good performing model for additional hyper-tuning, multiple regression models are trained to estimate the overall journey interval in the traffic network under study. Finally, a good performance regression model, the extreme-gradient decision tree (XGBT), and WOA algorithm are clustered into a lone optimization outline.

A series of the first 13 combined Magseed and Magtrace cases in a single centre in regional Victoria.

The standard journey for a patient with impalpable breast cancer wishing to undergo breast-conserving surgery involves placement of a guidewire and lymphoscintigraphy pre-operatively. Access to these procedures is limited in the regional centres, this scheduling can require overnight stays away from home and can delay theatre which can increase patient distress. The Sentimag technology uses magnetism for localisation of preoperatively inserted Magseeds (for impalpable breast lesions) and Magtrace (for sentinel node biopsy), avoiding guidewire placement and nuclear medicine. This study evaluates the first 13 cases using this combined technique by a single specialist breast surgeon in a regional centre. Thirteen consecutive patients were enrolled with ethics approval. Magseeds were placed under ultrasound guidance preoperatively, and Magtrace was injected at the time of pre-operative consultation. The median age of patients was 60 (range 27-78). The average distance from hospital was 81.63 km (2.8-238 km). The average operating time was 1h54m (range 1 h 17 m-2 h 39 m) and the mean total journey time was 8h54m (range 6-23 h). The earliest time-out was 8:40 am. Re-excision rate was 23% (n= 3), however, in each re-excision case the lesions were in the axilla, were small (<15 mm) and were in patients with dense breasts on mammography. There were no significant adverse outcomes. In this preliminary study Sentimag localisation appears to be safe and reliable when used in combination. Re-excision rates were only slightly higher than reported in the literature and predicted to downtrend with ongoing learning curve.

Characteristics of Light Rail Travel Time: Examples from France

The total journey time of Light Rail Vehicles (LRVs) is made up of running time, dwell time (station stops for passenger boarding and alighting), and signal delay (delay of LRVs being stopped by the regular traffic signals). Data from operational surveys of six modern light rail systems in France has shown that LRV running time was 65-71 percent of the total journey time and dwell time was 22-27 percent, while signal delay was 7 to 8 percent of the total journey time. The average operating speed of the Light Rail Transit (LRT) ranged from 17.7 to 22.8 km/h and has an approximate linear relationship to passenger stop frequency (stops/km).Light rail dwell time has been found to follow a log-normal distribution, although the values differed significantly between different LRT systems. The means of the dwell time distribution have the range of 16 to 31 seconds in off-peak periods and 21 to 37 seconds in peak periods. Factors that influence light rail dwell time include the number of passengers at the stops, the number of standees in the vehicles, vehicle design (number of doors, door size, low floor or high floor vehicle, etc.), fare collection system, and the location of LRT stops.The findings in this paper could be used by LRT planners and operators directly in developing and assessing operating and service changes and in providing input to long-range planning procedures. The results can also be used in microscopic simulation modeling studies of LRT in an urban network, such as the TRGMSM model.

Work flexibly, travel less? The impact of telework and flextime on mobility behavior in Switzerland

There is an ongoing discussion about the impact of flexible forms of work on travel behavior. Though it is generally accepted that telework decreases distance commuted, there are mixed conclusions about the notion that non-work-related journeys could be offsetting any saved commute. This paper investigates the influence of two flexible working arrangements – namely telework and flextime – on commutes, non-work traffic, and peak-period travel in Switzerland. Using the 2015 Swiss Mobility and Transport Microcensus (MTMC), this study analyzes flexible working arrangements with respect to their effects on traffic. The results show that people who work partly from home – compared to those who never telework – do indeed commute less; however, their non-work travel increases. This rebound effect completely offsets the saved commutes, resulting in a zero impact on the total distances covered. Only people who work exclusively remotely show less total mobility compared to those who never telework. However, only a small minority of people work only from home, with most teleworkers combining working on-site with some degree of working from home. Moreover, this study finds only slight potential for relieving traffic congestion through flexible working arrangements: Whereas teleworkers are less likely to commute during evening peak periods, people working flextime are even more likely to commute during morning rush hours. Hence, the distinction between morning and evening peak periods should be taken into account in future studies. Furthermore, research on flexible working arrangements and travel behavior benefits from the consideration of both non-work travel and total travel as well as the separation of part-time from full-time telework.

667-P: Impact of Direction of Long-Haul Travel on Glycemic Control in Adults with Type 1 Diabetes (T1D) Using Multiple Daily Injections (MDI) of Insulin

Long-haul travel (flights &amp;gt;6 hours) can be very disruptive for people living with type 1 diabetes (T1D) . As part of a study comparing long-acting basal insulins during long-haul travel (Bevier &amp; Castorino, et al) , we evaluated the impact of direction of travel on glucosecontrol (Abbott Freestyle Libre Pro) . Each journey began in Honolulu (HI) with a non-stop flight to New York (NY) lasting hours with a 6-hour time difference. After 72 hours, participants returned non-stop to HI. Each subject completed round trips (using either Glargine or Insulin Degludec as the basal insulin) . Participants traveled in the same direction for either basal insulin. To assess the impact of direction of travel, data using either basal insulin were combined. Overall, 21 participants (13 women, age 34.2±15.2 years, HbA1c 7.5±1.2%) completed both journeys. CGM-derived metrics comparing (a) initial 72 hours at each destination, (b) during each flight and (c) for the total journey are shown below. During sleep, TIR70-180 was 56±26% travelling East to West and 66±26% going from West to East. Although there were no significant differences in CGM-derived metrics irrespective of direction of travel, the data suggest that achieving the recommended time in range between 70-180 mg/dl is elusive for adults with T1D using MDI when travelling across multiple time zones. Disclosure W.C.Bevier: Research Support; Abbott Diabetes. H.Liu: Employee; Abbott Diabetes. K.N.Castorino: Consultant; Lilly Diabetes, Research Support; Abbott Diabetes, Dexcom, Inc., Drawbridge Health, Inc., Eyenuk, Inc., Laxmi Therapeutic Devices, Medtronic, National Institute of Diabetes and Digestive and Kidney Diseases, Novo Nordisk, Speaker's Bureau; Dexcom, Inc. D.Kerr: Advisory Panel; Abbott Diabetes, Novo Nordisk A/S, Sanofi, Consultant; Evidation Health, Research Support; Novo Nordisk A/S, Stock/Shareholder; Glooko, Inc., Hi.Health. Funding Novo Nordisk Investigator Sponsored Study - ISS-001227

Modified Camel Algorithm for Optimizing Green Vehicle Routing Problem with Time Windows

In recent years, the issue of fuel depletion has become a significant problem in the world. The logistics sector is one of the sectors with an increase in fuel consumption. Therefore, route optimization is one of the attempts to solve the problem of minimization fuel consumption. In addition, this problem generally also has time windows. This study aimed to solve the Green Vehicle Routing Problem with Time Windows (GVRPTW) using the Camel Algorithm (CA). The objective function in this problem was to minimize the total cost of distribution, which involves the cost of fuel consumption and the cost of late delivery. The CA parameter experiment was conducted to determine the effect of the parameter on distribution cost and the computation time. In addition, this study also compared the CA algorithm's performance with the Local search algorithm, Particle Swarm Optimization, and Ant Colony Optimization. Results of this study indicated that the use of Camel population parameters and the total journey step affected the quality of the solution. Furthermore, the research results showed that the proposed algorithm had provided a better total distribution cost than the comparison algorithm.

Maximum movement and cumulative movement (travel) to inform our understanding of secondary spinal cord injury and its application to collar use in self-extrication

BackgroundMotor vehicle collisions remain a common cause of spinal cord injury. Biomechanical studies of spinal movement often lack “real world” context and applicability. Additional data may enhance our understanding of the potential for secondary spinal cord injury. We propose the metric ‘travel’ (total movement) and suggest that our understanding of movement related risk of injury could be improved if travel was routinely reported. We report maximal movement and travel for collar application in vehicle and subsequent self-extrication.MethodsBiomechanical data on application of cervical collar with the volunteer sat in a vehicle were collected using Inertial Measurement Units on 6 healthy volunteers. Maximal movement and travel are reported. These data and a re-analysis of previously published work is used to demonstrate the utility of travel and maximal movement in the context of self-extrication.ResultsData from a total of 60 in-vehicle collar applications across three female and three male volunteers was successfully collected for analysis. The mean age across participants was 50.3 years (range 28–68) and the BMI was 27.7 (range 21.5–34.6). The mean maximal anterior–posterior movement associated with collar application was 2.3 mm with a total AP travel of 4.9 mm. Travel (total movement) for in-car application of collar and self-extrication was 9.5 mm compared to 9.4 mm travel for self-extrication without a collar.ConclusionWe have demonstrated the application of ‘travel’ in the context of self-extrication. Total travel is similar across self-extricating healthy volunteers with and without a collar. We suggest that where possible ‘travel’ is collected and reported in future biomechanical studies in this and related areas of research. It remains appropriate to apply a cervical collar to self-extricating casualties when the clinical target is that of movement minimisation.

Medical data integration using HL7 standards for patient's early identification.

Integration between information systems is critical, especially in the healthcare domain, since interoperability requirements are related to patients’ data confidentiality, safety, and satisfaction. The goal of this study is to propose a solution based on the integration between queue management solution (QMS) and the electronic medical records (EMR), using Health Level Seven (HL7) protocols and Extensible Markup Language (XML). The proposed solution facilitates the patient’s self-check-in within a healthcare organization in UAE. The solution aims to help in minimizing the waiting times within the outpatient department through early identification of patients who hold the Emirates national ID cards, i.e., whether an Emirati or expatriates. The integration components, solution design, and the custom-designed XML and HL7 messages were clarified in this paper. In addition, the study includes a simulation experiment through control and intervention weeks with 517 valid appointments. The experiment goal was to evaluate the patient’s total journey and each related clinical stage by comparing the “routine-based identification” with the “patient’s self-check-in” processes in case of booked appointments. As a key finding, the proposed solution is efficient and could reduce the “patient’s journey time” by more than 14 minutes and “time to identify” patients by 10 minutes. There was also a significant drop in the waiting time to triage and the time to finish the triage process. In conclusion, the proposed solution is considered innovative and can provide a positive added value for the patient’s whole journey.

Towards a Distributed SMA-based Solution for the Interoperability of Hospital Information Systems for Better Routing of Emergency Ambulances

Multi-agent systems MASs have been widely used to interoperate hospital information systems (HISs). The use of MASs for HISs interoperability has become a central solution, especially in the field of emergency medicine. In emergencies, the notion of delay is relative, because responders only have a few minutes to react. This emergency response time has an important role in the event that an accident occurs on the road. Existing procedures for the emergency ambulance (EA) dispatch strategy are based on manual dispatch. In this work, we are introducing a distributed emergency ambulance (DEA) routing system to control emergency latency time, which includes driving route planning to guide emergency vehicles and the allocation of distributed emergency resources (emergency ambulances and hospitals) to reduce the EA response time caused by traffic or the wrong human decision to transport ambulance to the accident site. The allocation of resources (hospitals) is ensured through a recommendation system based on the interoperability of several interconnected HISs using a multi-agent system. The proposed solution takes into consideration dynamic traffic flow information during the day to build dynamic paths for EA. The improved method is based on a distributed architecture to calculate and find the optimal pathway for a set of emergency vehicles based on ACO ant colony optimization techniques. The results of the simulation show that the proposed method can decrease the total travel time of the ambulance to reach the accident position compared to conventional methods that use lights and sirens to warn other vehicles to free up the road for the ambulance or use a traditional approach based on the vision/reflection of the driver to choose in a random way the paths to take. Based on such a solution, ambulance staff will be able to save lives by optimizing the total journey with the minimum travel.

Integration of simulation and dispatch modelling to predict fleet productivity: an open-pit mining case

ABSTRACT Predicting the fleet requirement based on fleet productivity estimation is one of the critical parts of a robust long-term mine plan. The dispatch logic that determines the return destination of the empty trucks is significantly important in the overall full and empty travel distances and trucks’ productivity. In this paper, a Monte-Carlo simulation model is presented to mimic the real truck-and-shovel operations and measure trucks’ productivity in terms of Tonne Per Gross Operating Hour (TPGOH). A linear programming model is integrated into the simulation model to optimize the dispatch decision through distance minimization subject to the mine’s production schedule. The historical data records of oil sands mining operations are used to validate model’s performance. The results show significant improvement over the existing mine site’s method with closely matching the real TPGOH and better estimation of the total empty travel distance, as a result of new dispatch model implementation.

Optimization of Stop Plan for Skip-Stop Operation on Suburban Railway Line

A combination of express and local trains (E/L mode) is generally used to operate a suburban rail service, it can meet the rapid and direct service needs of long-distance travelers as well the needs of short-distance travelers. Generally, a stop plan is the core of the E/L mode. A stop plan optimization model in E/L mode, which aims to minimize the total passenger travel time and the number of operating trains during the peak period with the safe headway and departure frequency constraints, is proposed in this study. Meanwhile, an algorithm based on a genetic algorithm is designed to solve the proposed model. A case study of the Jiangjin Line, a suburban railway in Chongqing, China, is carried out. The results show the efficiency and feasibility of the proposed method. The calculation results also show that the total passenger travel time under E/L mode with the overtaking condition is significantly reduced compared with the all-stops (AS) mode and E/L mode without overtaking condition. The superiority of the E/L mode can be enhanced by reducing the dwell time at stations and adopting the overtaking condition.

Mixed Scheduling Model for Limited-Stop and Normal Bus Service with Fleet Size Constraint

Limited-stop service is useful to increase operation efficiency where the demand is unbalanced at different stops and unidirectional. A mixed scheduling model for limited-stop buses and normal buses is proposed considering the fleet size constraint. This model can optimize the total cost in terms of waiting time, in-vehicle time and operation cost by simultaneously adjusting the frequencies of limited-stop buses and normal buses. The feasibility and validity of the proposed model is shown by applying it to one bus route in the city of Zhenjiang, China. The results indicate that the mixed scheduling service can reduce the total cost and travel time compared with the single scheduling service in the case of unbalanced passenger flow distribution and fleet constraints. With a larger fleet, the mixed scheduling service is superior. There is an optimal fleet allocation that minimizes the cost for the system, and a significant saving could be attained by the mixed scheduling service. This study contributed to the depth analysis of the relationship among the influencing factors of mixed scheduling, such as fleet size constraint, departure interval and cost.

Implementation of Triage System and Shortening Patient Journey Time to Prevent COVID-19 Transmission in a University Hospital during a Pandemic.

To explore the characteristics of the patient under investigation (PUI), and the routes and the patient journey time in our outpatient service, we examined the demographic data, presenting symptoms, risks of contact with COVID-19 cases, and the results of real-time polymerase chain reaction (PCR) tests in PUI cases from March to May 2020. The contact time, transfer time and total journey time of patient journey routes in our hospital were also explored. The results were shown in numbers, percentages and medians (interquartile range, IQR). A total of 334 PUI cases were identified from our triage system. The median (IQR) age was 35 (27, 47) years. Cough was the most common presenting symptom (56.2%), while fever (≥37.5 °C) was found in only 19.8% of the cases. The median (IQR) time of onset of the presenting symptoms was 3 (1, 5) days. The most common risk of contact with COVID-19 cases found during the triage was living in or returning from an outbreak area. Fifteen (4.5%) of the PUI cases had positive real-time PCR tests. The contact time and transfer time were longest in the PUI ward and from the Emergency Department (ED) to the PUI ward, respectively. Plans and actions to shorten the transfer time between the ED and the PUI ward and the total journey time should be developed.

939-P: Prevalence of Celiac Disease, Undiagnosed Celiac Disease, and Coexisting Autoimmunity in Youth with Type 1 Diabetes: The SEARCH for Diabetes in Youth Study

Type 1 diabetes (T1D) and celiac disease (CD) are autoimmune diseases with shared genetic risk and are increasing in prevalence in the US. As CD is often under-diagnosed in the setting of T1D, additional data on the prevalence and characteristics of CD and potentially undiagnosed CD in youth with T1D is needed.In 2,442 youth with T1D in the SEARCH for Diabetes in Youth study we assessed self-reported CD via questionnaire and potentially undiagnosed CD by IgA tissue transglutaminase autoantibody assays (TGA). Groups were defined as 1) CD (based on self-reported CD), 2) potentially undiagnosed CD, (no self-reported CD and elevated TGA), 3) no CD (T1D controls). Patient characteristics were compared between CD and controls, and between potentially undiagnosed CD and controls. Coexisting autoimmune diseases were also assessed.The prevalence of self-reported CD was 6.9% and potentially undiagnosed CD was 4.1%. Youth with T1D and CD or potentially undiagnosed CD were more likely to be younger at T1D diagnosis and female compared with controls. Youth with T1D and CD were more likely to be Non-Hispanic white and to have co-existing autoimmune diseases compared with controls, all p<0.05 (Table).CD and, importantly, undiagnosed CD are seen in youth with T1D. Youth with combined disease are at high risk for additional co-autoimmune diseases.View largeDownload slideView largeDownload slide DisclosureR. Brady: None. A. Honor: None. D. J. Pettitt: None. L. Merjaneh: None. D. Dabelea: None. A. S. Shah: None. E. T. Jensen: None. J. Rigdon: None. N. A. Crimmins: None. D. Mallon: Other Relationship; Self; Total Travel and Events. L. M. Dolan: None. G. Imperatore: None. A. Kahkoska: Other Relationship; Self; Novo Nordisk A/S. A. K. Mottl: Advisory Panel; Self; Bayer U. S.

61 Real-time Burn Outpatient Virtual Visits in the Home During the Era of COVID-19

IntroductionThe majority of burn injured patients travel long distances to receive burn care from regional burn centers, creating a burden on families and impairing outcomes. Recent federal policies in response to the COVID-19 pandemic have relaxed some of the barriers to virtual visits in the non-health care setting. We sought to review the experience of a comprehensive burn program in managing burn patients with a virtual platform.MethodsA clinical quality database was maintained to evaluate virtual videoconference and in-person clinic visits for a comprehensive adult and pediatric burn program during the COVID-19 pandemic (March 2020 to August 2020). Virtual visits utilized a telemedicine platform that employs real-time audio and video communication. Demographic, burn severity, and visit quality data were recorded. Zip code data was also collected and then used to calculate the following estimated savings for the patient and their family: total miles, travel hours, driving costs, and wages.ResultsA total of 145 patients were included in this study with 96 (66.2%) male and 49 (33.7%) female. 91 (62.8%) were pediatric patients with a mean age of 6.2 ± 0.5 years and 54 (37.2%) were adult patients with a mean age of 40.4 ± 2.5 years. There were 320 total burn outpatient follow-up visits with 199 pediatric visits (40 virtual and 159 in-person) and 121 adult visits (24 virtual and 97 in-person). The majority of patients (73.1%) were treated as in-person visits while 6.9% had purely virtual visits, and 20.0% of patients had both virtual and in-person visits. The following savings were associated with virtual visits: 8562.6 total miles (average 133.8 ± 42.4), $6789.29 total driving cost (average $106.08 ± 33.61), 161.5 total travel hours (average 2.5 ± 0.7), and $4758.42 total wages lost to travel (average $74.35 ± 21.43). Technical issues were only reported in 14% of total visits (2.5% of pediatric virtual visits and 33.3% of adult virtual visits).ConclusionsOutpatient virtual visits for burn care are a new frontier, driven by improvements in technology and reduced barriers to reimbursement. This study demonstrates that virtual visits are associated with major financial and temporal benefits for patients and their families. Technical issues remain an important barrier, particularly in the adult population. A clear understanding of this and other barriers may improve implementation of this new healthcare delivery paradigm.

Impact of travel by walk and road on testicular hormones, oxidants, traces minerals, and acute phase response biomarkers of dromedary camels

This study aimed to compare the effect of truck transport and walk travel on testicular hormones, oxidants, antioxidants and acute-phase responses of camels’ walked from Sudan to the Egyptian quarantine and were transported from the quarantine to the slaughterhouses by trucks. Blood samples were collected from walked camels (N ≤ 30) just arrived at the quarantine (Walk), unloaded (N ≤ 12) from the truck (Truck), and control camels (N ≤ 20). Animals were statistically categorized into Walk travel, Truck transport, and Control, then Total travel (Walk + truck transport) was compared to control. Haptoglobin, fibrinogen, superoxide dismutase (SOD), glutathione peroxidase (GPx), nitric oxide (NO), ascorbic acid, glucose, cholesterol, testosterone, estradiol, iron, copper, ALT, AST, alkaline phosphatase (ALP), total proteins, albumin, and creatinine were measured. Results showed that the travel by walk and truck increased haptoglobin (P ≤ 0.0001), fibrinogen (P < 0.05), ALT (P < 0.05), and creatinine (P ≤ 0.0001) but decreased NO (P ≤ 0.0001), albumin (P < 0.05), Ascorbic acid (P < 0.05), testosterone (P ≤ 0.0001), ALP (P < 0.0001), and glucose (P ≤ 0.0001). The declined NO (P ≤ 0.0001), Ascorbic acid (P ≤ 0.0001), iron (P ≤ 0.005), copper (P ≤ 0.023), cholesterol (P > 0.05), total proteins (P ≤ 0.0001), albumin (P ≤ 0.018), globulins (P ≤ 0.001), with increased haptoglobin (P ≤ 0.0001), AST (P ≤ 0.0001), ALP (P ≤ 0.0001), and testosterone (P ≤ 0.0001) was evident in camels transported by truck compared to walk transport. In conclusion, transport enhanced the acute phase proteins, retarded kidney function, antioxidant status, and energy but truck produced a significant acute-phase response and adversely affected the oxidant-antioxidant balance, destructed proteins kidney, and liver functions than the long travel by walk.

Mathematical Model for Cargo Allocation Problem in Synchromodal Transportation

Synchromodality is a freight transport process in which information is exchanged expeditiously in order to maximize the benefits of different modes of transport and transport nodes in terms of efficiency and environmental impact. The aim of the study is to analyze the problems of synchronized intermodal traffic management between the main port and inland transport nodes in European transport corridors and to find reliable solutions to these problems. Therefore, the main purpose of this article is to investigate the problem of the distribution of containers transported by rail between two transport terminals in a synchronous transport network. A specific optimization model is presented in this article. This optimization task is formulated as a stochastic integer programming model between the terminals located in Vilnius and Klaipeda Seaport, the essence of which is as follows: (a) to minimize the waiting time for container cargo at the location—terminal No. 1; (b) to minimize the total journey time of the train; (c) to minimize the waiting time for containerized cargo at the point of arrival—terminal No. 2.