Transportation Management Research Articles

Roadmap for a European Open Science Alliance for ATM Research

To meet the future challenges of air transport and air traffic management (ATM), higher levels of transparency and access to the underlying data will be a key enabler. We here propose an Open Science Alliance for ATM, advocating for open data in such a framework. The benefits of adopting an open science approach are to be found, inter alia, through the independent verification and validation of reported impacts/results and achieved performance levels, i.e., through reproducibility. We consider that this can only be achieved through: (1) open access to scientific methods and data utilised; (2) open access to (analytical) code and methods; (3) open review of reported analyses/research. The proper application of such practices will reduce the innovation cycles in ATM, which is much needed by industry and society. Steps for forming an Open Science Alliance for ATM are described. We propose further initial, specific recommendations for supporting open data and improved access for research.

Logistics Management Practices and Performance of Pharmaceutical Manufacturing Firms in Nairobi City County, Kenya

The study sought out to determine the influence of logistics management on the performance of pharmaceutical manufacturing firms in Nairobi City County, Kenya. The objectives included to determine the influence of logistics management practices, logistics planning, inventory management, warehouse management, and transportation management on performance. The study is anchored on theory of constraints, lean theory, game theory, and queuing theory. The study employed an explanatory research design and mainly concentrated on all 77 pharmaceutical manufacturing firms within Nairobi City County. The respondents were chosen based on the sample frame. The heads of warehouse, sales and marketing, production and transport departments in each of the firms were employed as unit of analysis. The primary data were collected through self-administered questionnaires. Out of 168 questionnaires that were distributed, 146 responses were obtained, which gives a 93.06% response rate. Statistical Package for the Social Sciences (SPSS version 24) was used to analyze the data. The Cronbach’s Alpha value was greater than 0.7 which indicated that the questionnaire met the minimum acceptable threshold. The study findings showed that logistics management practices namely logistics planning, inventory management, warehouse management and transportation management significantly influence the performance of pharmaceutical manufacturing firms in Nairobi City County, Kenya. The study concluded that performance was predicted to improve for every unit increase in these practices. The study recommends that the management of the firms should invest in advanced software for logistics planning to further enhance efficiency and effectiveness in managing supply chain operations

A study of short-term passenger flow forecasting for public transportation based on the time series method

Abstract. In order to solve the conflicts between the supply and demand of urban transport, improve the living standard of urban residents, raise the level of urbanization, and promote the sustainable socio-economic development of the whole city, it is necessary to increase the pace of urban transport construction, planning and management in China. And at the same time, its necessary to take various measures to regulate the structure of urban transport and guide urban transport to develop in the direction of taking public transport as the mainstay. The experience of the development of urban transportation in China and abroad also proves that prioritizing the development of public transportation is the fundamental way to solve the problem of urban transportation. Public transport passenger flow data is the basis for optimizing public transport operations and scheduling. Accurate passenger flow forecasts on public transport routes can effectively guide public transport operation decisions, formulate operation scheduling plans and effectively improve the operational efficiency of the public transport system. The following are some of the key factors that can be taken into consideration when making passenger flow forecasts. The main contribution of this article is to propose the use of data mining technology, use bus IC card data and bus GPS data to integrate and organize them, conduct correlation analysis on the data, and on this basis, obtain the main distribution characteristics of bus passenger flow, and provide short-term Passenger flow forecast provides relevant ideas.

Investigating the factors determining service quality in Bhopal’s multimodal public transport system: A factor analysis

Multimodal public transport systems are crucial for sustainable urban mobility in growing cities like India. This study investigates the factors determining service quality in Bhopal’s multimodal public transport system using factor analysis. A survey of 650 regular users was conducted to assess service quality perceptions. Exploratory factor analysis revealed six significant factors: integration (22.12 % of variance explained), reliability (14.30 %), comfort (9.38 %), safety (8.07 %), accessibility (6.85 %), and customer service (5.74 %). The factors demonstrated high internal consistency reliability (Cronbach’s alpha > 0.8). Satisfaction levels varied across factors, with accessibility and comfort receiving higher ratings than reliability. Demographic comparisons revealed significant differences based on gender and age. The findings provide insights into the critical factors shaping service quality perceptions and offer a framework for assessment. The results highlight the need for targeted interventions, particularly in areas with lower satisfaction levels. This research contributes to the literature on public transport service quality in developing cities and underscores the importance of considering passenger perspectives in multimodal transport planning and management. This research contributes to the growing body of literature on public transport service quality in developing cities and underscores the importance of considering passenger perspectives in the planning and management of multimodal transport systems.

Petrol Pumps to Power Plugs: Is the Surge in Electric Two-Wheeler Adoption in Kathmandu Due to Unstable Petroleum Prices?

Since the dawn of the 21st century, the awareness towards environmental concern and sustainability slowly began to rise and the “Electric Wave” truly encompassed the world with electric automobiles. Nepal was no exception to this global electric wave. This research explores a key determinant behind this transition, the influence of fluctuating petroleum price. Using secondary data obtained from registrations of electric and petroleum two-wheelers at the Transport Management Office in Kathmandu, under the Ministry of Labor, Employment, and Transport as well as historical petrol prices over the past 4 years from 2019 to 2023, this research seeks to offer valuable insights into the connection between petrol prices and the adoption of electric vehicles in Kathmandu, Nepal. Using SPSS software, a linear regression analysis was conducted and it revealed that while petroleum prices played a role, they were not the sole determinant affecting adoption. This study contributes to the growing body of knowledge on sustainable transportation choices and their drivers. It provides valuable insights for policymakers, consumers, and industry stakeholders, as it underscores the influence of fuel prices on consumer choices and the potential for electric two-wheelers to become a viable alternative in the context of Kathmandu’s urban transport.

Assessing braking performance on wet-road through water-depth estimation and vehicle-pavement dynamic simulation

ABSTRACT The presence of water on wet roads due to rainfall has emerged as a critical factor influencing driving safety. During severe weather, the non-uniform water distribution on the road can significantly diminish pavement friction, elevating the risk of traffic accident. Furthermore, the tire-pavement friction coefficient varies with vehicle velocity, rendering wet braking analysis complex. Conventional braking risk analysis often assumes a fixed friction coefficient, neglecting the impact of uneven pavement-induced water depth irregularities. This paper introduces a novel braking performance assessment method based on water-depth estimation and vehicle-pavement simulation. The uneven water-depth distribution is first estimated using LiDAR-measured pavement geometry. A co-simulation framework is then proposed to analyze the tire-pavement friction and study the dynamic braking performance. The 85th percentile stopping distance is adopted as the evaluation index for quantifying braking risk. Results from case studies highlight the substantial influence of rainfall intensity and vehicle velocity on braking risk. Additionally, pavement rutting accumulates deeper water depth, thereby elevating braking safety risks. The proposed model offers a novel perspective on vehicle braking risk analysis and can serve as a valuable safety indicator for highway transportation management and decision-making in driving strategies during wet weather conditions.

Comparative transcriptomic analyses of diploid and tetraploid citrus reveal how ploidy level influences salt stress tolerance

IntroductionCitrus is an important fruit crop for human health. The sensitivity of citrus trees to a wide range of abiotic stresses is a major challenge for their overall growth and productivity. Among these abiotic stresses, salinity results in a significant loss of global citrus yield. In order to find straightforward and sustainable solutions for the future and to ensure citrus productivity, it is of paramount importance to decipher the mechanisms responsible for salinity stress tolerance. Thisstudy aimed to investigate how ploidy levels influence salt stress tolerance in citrus by comparing the transcriptomic responses of diploid and tetraploid genotypes. In a previous article we investigated the physiological and biochemical response of four genotypes with different ploidy levels: diploid trifoliate orange (Poncirus trifoliata [L.] Raf.) (PO2x) and Cleopatra mandarin (Citrus reshni Hort. Ex Tan.) (CL2x) and their respective tetraploids (PO4x, CL4x).MethodsIn this study, we useda multifactorial gene selection and gene clustering approach to finely dissect the influence of ploidy level on the salt stress response of each genotype. Following transcriptome sequencing, differentially expressed genes (DEGs) were identified in response to salt stress in leaves and roots of the different citrus genotypes.Result and discussionGene expression profiles and functional characterization of genes involved in the response to salt stress, as a function of ploidy level and the interaction between stress response and ploidy level, have enabled us to highlight the mechanisms involved in the varieties tested. Saltstress induced overexpression of carbohydrate biosynthesis and cell wall remodelling- related genes specifically in CL4x Ploidy level enhanced oxidative stress response in PO and ion management capacity in both genotypes. Results further highlighted that under stress conditions, only the CL4x genotype up- regulated genes involved in sugar biosynthesis, transport management, cell wall remodelling, hormone signalling, enzyme regulation and antioxidant metabolism. These findings provide crucial insights that could inform breeding strategies for developing salt-tolerant citrus varieties.

A Study of Heavy Road Freight Transport in Poland in the Context of the Pursuit of Sustainable Road Transport

The efficiency of road freight transport determines—to a significant degree—the total environmental footprint and the amount of greenhouse gases and other pollutants released into the atmosphere by inland transport. The rate of empty or partially empty vehicles is one of the key metrics for improvement of the environmental performance of freight road transportation. This paper presents the characteristics of road freight transport in Poland on the basis of data collected by weigh-in-motion stations. Data aggregation for environmental analysis represents a novel aspect of the work. Indicators describing the degree of loading, the share of empty vehicles in traffic, and the share of vehicles of maximum permissible total weight in traffic were determined for a representative group of heavy goods vehicles. Based on the representative load factor value (LFA), a classification of the road section into four groups was proposed. The results obtained show a clear differentiation of the values of the indicators analyzed for individual groups and their variability in the three-year period covered by the analysis. The aggregation method presented can be used to identify the nature of the distribution of the weight categories of the heavy goods vehicles and provide input information for targeted analyses relating to sustainable road transport management and environmental protection. Finally, the grouped LFA values were linked with indicators of energy consumption (ECI) and on-road emissions (EI).

Enhancing Business Operations through Strategic Warehouse Location: Methodologies and Implications for Supply Chain Efficiency

For supply chain management to be efficient and productive, a warehouse's ideal location is essential. Here are 10 crucial ideas for a warehouse's introduction and ideal location. The cost and efficiency of product distribution are strongly influenced by the location of a warehouse. A well-placed warehouse speeds up deliveries, improving customer satisfaction. A crucial factor is proximity to transportation networks including highways, railroads, and ports. The availability of transportation infrastructure and access to a skilled labour pool are important considerations. To choose acceptable sites, market demand and client distribution patterns should be examined. The placement of the warehouse should support corporate objectives like cost containment and operational effectiveness Geographical elements such as climate, natural calamities, and local laws should be considered. A centralised position allows for efficient routing and lower transportation expenses. To serve various markets and improve inventory management, several warehouses may be needed. Warehouse location analysis may be performed using cutting-edge technology like geographic information systems (GIS).These bullet points offer a succinct summary of the significance and factors to be taken into account when choosing the best location for a warehouse. The effectiveness of the supply chain and customer satisfaction are directly impacted by the best warehouse location. Proximity to transportation networks and access to skilled labour are crucial factors. Finding ideal sites benefits from analysis of consumer distribution patterns and market demand. It is important to evaluate geographic elements including the climate, laws, and natural catastrophes. Advanced technology and centralised sites can improve routing while lowering costs and optimising inventory control Studying the best place for a warehouse has research implications since it may enhance supply chain management and commercial operations. The importance of the research is highlighted by the following 5 points. Cost cutting Finding the best place for a warehouse may result in savings on labour, transportation, and inventory management, which can boost profitability. Customer satisfaction: Quicker delivery times, improved customer satisfaction, and increased customer loyalty can all contribute to corporate success. Competitive edge: Choosing the best warehouse site might provide you an advantage over rivals by enabling quicker responses to market needs and more effective distribution. Sustainability and environmental impact: Investigating the best warehouse sites may help you make decisions that are good for the environment by lowering transportation costs and emissions, boosting sustainability, and decreasing your ecological imprint. Making strategic choices: Businesses may better align their operations with long-term development objectives by understanding the impact of warehouse location before making choices about distribution centre expansion, consolidation, or the creation of new facilities. Research into the best spot for a warehouse is important overall because it may increase operational effectiveness, cost savings, customer happiness, and sustainable business practices—all of which are necessary for success in a cutthroat market. The following phases are often included in the technique for locating a warehouse in the best possible location:data gathering Obtain pertinent information on market demographics, customer distribution patterns, transportation systems, labour availability, and location-specific costsSite-selection standards: Define the criteria in accordance with the particular requirements of the company, such as closeness to suppliers, consumers, transportation systems, and labour markets. Depending on each criterion's proportional value, give it a weight. Geographic analysis: Use geographic information systems (GIS) to visualise aspects like traffic patterns, population density, and competitor locations while analysing spatial data. To find suitable warehouse locations that match the given requirements, do a geographical analysis. Analyse the costs connected with each prospective site, including the purchase of land or buildings, the price of construction or refurbishment, taxes, utilities, labour, and transportation. Think of long-term elements like scalability and possible growth. Risk assessment: Examine possible hazards and vulnerabilities related to each place, including weather-related catastrophes, political stability, regulatory restrictions, and regulatory restraints. Assess the effects on operations and put risk-reduction plans in place. Decision-making: Compare and order the prospective warehouse sites based on the data that has been gathered, the analysis, and the cost factors. Choose the greatest location for your company, taking into account aspects like cost-effectiveness, market reach, and operational efficiency. Warehouse A, Warehouse B, Warehouse C, Warehouse D. Stock holding capacity (unit) {SHC}, Movement flexibility, Average distance to shops (km), Average distance to main suppliers (km), Unit price {UP} shows the rank of Alternatives Warehouse A, Warehouse B Warehouse C, Warehouse D. In order to achieve supply chain efficiency, customer happiness, and overall business success, a warehouse's ideal location is essential. Businesses may choose the best location for their warehouse operations using a structured technique that includes data collecting, analysis, and decision-making.

Short-term freeway traffic speed multistep prediction using an iTransformer model

Accurate prediction of the freeway traffic speed is important for enhancing intelligent transportation management and assisting with route planning. Current traffic speed prediction studies usually neglect predictions extending to an hour or longer. Thus, to address this gap, the inverted Transformer (iTransformer) model is utilized to predict freeway speeds across intervals from 5 to 150 minutes. The iTransformer model provides a global view by encapsulating the entire time series from each detector into variate tokens, allowing it to capture the complex patterns and dependencies that change over time. Additionally, a multi-head attention mechanism is employed to identify short-term and long-term speed patterns. This study validates the prediction performance of iTransformer by using the traffic speed data from an interstate freeway in Minnesota, comparing it against traditional traffic prediction methods and two other Transformer models. Results indicate that the iTransformer model outperforms these benchmark approaches, particularly when predicting speed over 60 minutes for peak hour periods.

Physical Distribution Practices and Marketing Performance of Selected Fast Moving Consumer Goods Companies in South-South Region of Nigeria

This study was designed to investigate the influence of physical distribution practices (transportation management, warehousing management, communication and information systems, and order processing) on the marketing performance of selected fast-moving consumer goods (FMCG) companies in the south-south region of Nigeria. The survey research design approach was adopted using a structured questionnaire to collect data from 247 selected FMCG sales representatives involved in the distribution of FMCG products in the south-south region of Nigeria. Stratified random sampling was employed. The data gathered was analysed using percentages and frequency tables. The formulated hypotheses were tested using a multiple regression approach. Findings from the study revealed a strong positive influence between the independent variables (transportation management, warehouse management, communication and information systems, and order processing management) and the dependent variable (marketing performance). The researchers suggest that FMCG companies should prioritise transportation management to facilitate better and more efficient movement of goods and people, thereby improving their marketing performance. Given the limitations of this study, the researchers presented practical implications and directions for future research.

Exploring Trends in Transportation Management: A Review of Scientific Papers on Dimension AI Database from 2020-2024

This qualitative study explores contemporary trends and advancements in sustainable transportation management practices, focusing on articles published between 2020 and 2024 and sourced from sustainability journals. A comprehensive search strategy using the Dimension AI database with the keywords "Transportation" and "Management". Articles were first screened by their title, abstract, and keywords, from which 63 manuscripts were chosen for thematic analysis. The key findings were the importance of sustainable traffic management systems, including congestion measures, infrastructure resilience, and environmental impact assessment. Another strand that emerged was data-driven approaches for smart city traffic management, which included novel techniques for effective decision-making. The study has especially highlighted the requirements of strategies that would set up the innovation in modeling techniques and resilience frameworks for transportation management. This study offers significant insight into modern trends in sustainable transportation management, which should be of immense value to policymakers, urban planners, and transportation professionals in seeking ways to overcome the problems associated with modern urban mobility while ensuring environmental sustainability.

Sustainable mobility challenges – case study of the offshore center in Gdansk transport accessibility

Urban mobility planning is one of the key elements in building a sustainable future. Strategic management of urban transport traffic not only reduces congestion and minimises the negative impact on the environment but also positively affects the improvement of residents' quality of life. Furthermore, following the Corporate Sustainability Reporting Directive approved by the European Union in 2023, companies are obliged to submit annual reports on their environmental impact. One of its elements is the calculation of the organization's carbon footprint, which includes emissions caused by means of transport, including emissions from commuting to work. This case study investigates the impact of transport accessibility on employee preferences and behaviour in the context of changing the premises location, using the example of the Maritime Institute of Gdynia Maritime University (GMU), located at the newly opened Offshore Center in Gdansk seaport. The research hypothesis states that the relocation to new premises with limited transport accessibility may, as a consequence, influence the choice of commuting transportation methods among employees, potentially leading to a shift towards less sustainable means of transport. The results of the research survey prove that there is a complex interconnection between transport accessibility, commuting behaviours, and sustainable mobility initiatives. This pilot study aims to contribute to urban mobility planning by exploring how to transport accessibility influences employee behaviour and by proposing strategies to improve commuting conditions and promote more sustainable solutions.

Relationship between urban traffic crashes and temporal/meteorological conditions: understanding and predicting the effects

Urban traffic accidents pose significant challenges to public safety and transportation management. Previous studies have revealed that temporal and meteorological factors are the key contributors to accident rate. Besides the inconsistent observations or lack of exploration in some aspects such as snowfall, fog, wind and daily temperatures, it has been shown that these factors are essentially entangled. Furthermore, existing methodologies of analysis or prediction have been limited to relative risk or traditional models. Hence, this study is centered on understanding the detailed correlations between temporal and meteorological factors and accident rate of two types of crashes – moving vehicle and fixed-object crashes using the traffic accident data from Dalian. Further, by incorporating a diverse set of the features, a prediction model leveraging the random forest algorithm is proposed and proved effective in anticipating accident occurrences on the district level. The feature importance analysis has shown that time period and factors such as holiday, temperature and season are most important factors. The rate is higher on working days and in spring, and collisions of both types peak at 6–7 am. When the highest daily temperature is 27 °C and the lowest is 20 °C or -8 °C, the incidence is relatively higher. On the basis, the recommendations are aimed at optimizing transportation systems, mitigating accident risks, and enhancing public safety in urban environments.

Machine learning and multisource data analysis approach towards traffic accident risk prediction

Traffic accidents are continually a concern across the world bringing with them huge economic costs and high rates of fatality and injuries each single year. Predictive modeling can lessen such risks if the accident hotspots and timing can easily be ascertained. This study collects various data including historical accident reports, water and meteorological conditions, road characteristics, and telematics data, to form traffic accident risk predictive models. To construct and analyze these predictive models, we use various machine learning techniques such as logistic regression, decision trees, and ensemble methods (Random Forest and Gradient Boosting). The results bear out as efficient and effective, ensemble models are streets ahead than regression-based techniques with Gradient Boosting achieving AUC-ROC scores of up to 0.89. The results of this research can also assist in applying effective safety measures in a more cost-effective manner as well as facilitate the development of efficient transport management systems and policies.

Post Harvest Management Techniques of Few Selected Crops in Assam, India

Reducing agricultural loss or waste is the ultimate goal of post-harvest management techniques. Post-harvest management has become crucial in the present environment of growing population and declining agricultural land and other resources. Ensuring food security in a manner that is safe for both humans and the environment is currently the world's top priority. The FAO estimates that in order to feed the nine billion people on the planet by 2050, food production would need to rise by seventy percent. Therefore, a deep comprehension of the roles played by agro-ecosystems is necessary. A vast number of agricultural products has been produced in recent years, yet there has been a lack of advancement and uptake of post-harvest technologies, leading to significant post-harvest losses. Agricultural crops have 16–36% post-harvest losses each year due to microbiological, physiological, and mechanical losses. Because most products are highly perishable, they require much more care and strategies to ensure that value addition and processing are encouraged. The value chain for processing has become essential to enhancing national food security and promoting food safety. The value chain in agriculture crops post-harvest management includes pre-harvest variables, harvesting, market preparation (pre-cooling, sorting, grading, packaging, and on-farm storage), transportation, storage, value addition/processing, and by-product waste management. The chapter focuses mostly on few selected fruit, vegetable, and cereal crops while discussing current advancements in post-harvest technology and food crop value addition.

The Enactment of Sustainable Development Goals at a Local Scale Leads to Various Implications

The hallmark of global sustainable development efforts is the Millennium Development Goals (MDGs) initiative, so the SDGs were proposed after this. Early initiatives highlighted the need for a more comprehensive and globally applicable set of goals, thus laying the groundwork for the SDGs. Among them are sustainability, ecological environment protection and so on. This article explores the local implementation and impact of the United Nations Sustainable Development Goals (SDGs). The main questions are how to effectively implement SDGs policies in different social cultures, and the role of local government and community organizations is carefully analyzed. These are also areas where SDGs can be effectively promoted at the local level through muti-stakeholder collaboration, inclusive policies and innovative approaches. And there are three field examples to prove its effectiveness. For example, sustainable transport and energy management in Barcelona, participatory planning in Kerala and community conservation in Kenya. The significance of the study highlights the key role of local action in achieving the global Sustainable Development Goals, and provides concrete cases for analysis.

Strengthening Collaborative Governance in the Department of Transportation: Implementation and Enforcement Levies

This article explores the strengthening of collaborative governance within Indonesia's Department of Transportation, focusing on the implementation and enforcement of levies. It highlights the integration of diverse stakeholders, including government bodies, private sector entities, and civil society, to create a more effective levy management system. However, a lack of collaboration between the Palembang city government and the local community has hindered the effectiveness of levies. Key strategies for improvement include promoting transparency, shared decision-making, and the use of innovative technologies. The study underscores the importance of trust and accountability among stakeholders to enhance compliance and enforcement. In Palembang, the capital of South Sumatra Province, transportation and traffic management challenges necessitate the effective implementation of levies. Employing a qualitative approach through in-depth interviews, observations, and documentation, the research reveals that collaborative efforts between local governments, the private sector, and the community are essential, despite challenges such as limited resources and community resistance. The findings suggest that while collaborative governance can improve the effectiveness of levy enforcement, it requires commitment and active participation from all parties. Recommendations include enhancing human resource capacity, strengthening regulatory frameworks, and raising public awareness of the importance of levies for regional revenue. This study also aligns with the Sustainable Development Goals (SDGs), particularly in improving infrastructure, fostering sustainable cities (SDG 11), and promoting effective partnerships (SDG 17).

Управление логистическими процессами в трансграничной электронной коммерции

In the context of the globalization of the economy and the development of cross-border e-commerce, the importance of cross-border logistics is increasing, allowing for an effective process of product distribution. The issues of organizing transport and logistics processes during the delivery of goods within the framework of the functioning of cross-border e-commerce are discussed. The main aspects of the development of e-commerce logistics and its impact on the trading business have been studied. An analysis of the cross-border e-commerce market in Russia is carried out. It is noted that currently the global logistics industry is in an unstable environment. The main directions and trends in the development of logistics services in modern conditions are considered and the specifics of adapting the process of goods distribution within the framework of cross-border e-commerce to changes in the external environment are investigated. The distinctive features of e-commerce logistics have been studied in the context of its impact on the trading business, which allows for increased efficiency of logistics processes such as procurement, transportation, order processing, inventory and warehouse management, packaging and labeling. It has been proven that with the development of information technology and telecommunication networks, as well as increasing demand in the field of online trading, the need for effective logistics support is significantly increasing, and delivery speed is becoming increasingly important to provide quality service to consumers. The advantages of e-commerce are substantiated from the perspective of subjects of trade and logistics activities, which include the global nature of customer coverage and the convenience of inter-subject interaction, reduction of operating costs of trade and logistics activities, expanded opportunities for marketing and advertising support, increased level of automation of e-commerce logistics management processes.

Research and development of automation subroutine for forecasting the number and load of cargo vehicles depending on the stochastic formation of dangerous goods collection centers

The issues of forecasting the load of vehicles, route networks, formation of collection centers for cargo flows, including dangerous goods, in territorial planning systems determine the need for transition from deterministic models to dynamic models with the possibility of taking into account the influence of the external environment. Existing information transportation systems in the sphere of work with dangerous goods are based on deterministic models and allow to study the problems of forecasting changes to a limited extent. Models of routing networks construction on cargo vehicles are limitedly applicable when there is a task of dangerous goods transportation taking into account the presence of a distributed system of accumulation centers both in the city and, for example, within a large transport hub, seaport with different terminals. The paper presents routing models adapted to the conditions of dangerous goods transportation. Determining transport efficiency and the main costs of transport enterprises is the scheme of traffic flows, fixed in the territorial scheme. The task of the study is to develop a separate automation toolkit for decision making on the number and type of specialized transport (depending on the load capacity) to work with dangerous goods and large-sized objects within the existing flow scheme. On the basis of the research of the group of companies the conclusion is presented that today the task of choosing the optimal amount of transport by carriers is solved, as a rule, not systematically, but by operative construction of a set of routes, and does not involve primary analysis of the territory and parameters of accumulation sites. The presented study considers a new approach to the analysis of the system of transportation of hazardous waste, associated large cargoes, in terms of assessing the necessary list of universal structured input data, which are primary principled and characterize the complexity of work in the selected facility, terminal. To solve the problem of forecasting the load of vehicles is offered developed a new automation subprogram based on the organization of client-server access to data, and implemented in the internal programming language “1C. Management of motor transport” (version ММT 8.2). The presented automation subprogram provides data connectivity on volumes of dangerous goods cargo flows, cargo flow collection centers, available data on vehicles and planned data on removal schedules. Work of the subprogram provides integration of work with other databases of transport companies and allows to carry out modeling of various variants of placement of centers of collection of dangerous goods, centers of cargo flows with high accuracy. The obtained results allow to determine the required number of vehicles, their load taking into account different types and technical capabilities, as well as to determine the required number of vehicle trips in the serviced area taking into account the dynamic influence of the external environment. The presented methodology of working with data, performed in a separate automation subprogram, presented routing models, adapted to the transportation of dangerous goods, can be applied to various transport systems, including sea cargo ports and terminals, which determines the universality of the presented approach.