Transportation Forecasting Research Articles

Road Freight Demand Forecasting Using National Accounts’ Data—The Case of Cereals

This paper investigates the potential of utilising historical agricultural production data for enhancing road freight transport forecasting, focusing on cereal production. This study applies a multiple linear regression analysis using national statistical accounts and secondary data. The data were sourced from Finland’s Statistics Agency and the Natural Resources Institute. The analysis identifies an observable correlation between agricultural production and road freight volumes, although this correlation is not statistically significant. The highest adjusted R² observed in the models was 0.62. The analysis reveals that previous years’ production data can help forecast future road freight volumes, with vehicle mileage estimable from recent production and stock levels. Additionally, annual percentage changes in the volume of transported cereals can be partially predicted by the changes in total available cereals and opening stocks from two years prior. This exploratory research highlights the untapped predictive potential of agricultural production variables in forecasting road freight demand, suggesting areas for further forecasting enhancement.

Improving fault zones hydrodynamic characterization and simulation in karstified carbonate environments with GLM and IES invers methods

A quantitative and qualitative estimation of groundwater resources is a challenge for water management in the world, especially in carbonated and karstified aquifers. Within these aquifers, fault zones exert a significant influence on groundwater hydrodynamics and transport. Improving the management of this type of aquifer involves better assessment of its hydrodynamics properties. Sustainability and management solutions can be estimated through numerical modeling. In this study hydrodynamic parameter of fault zone in karstified carbonate environments are estimated from cross-hole pumping test data. These estimations allow the simulation and forecasting of fault zone flow and transport. For this purpose, a flow and transport model has been set up using MODFLOW6 and MODPATH7 codes. Inverse modeling of hydrodynamic parameters is performed with the PEST++ code suite, with both the Gauss-Levenberg-Marquardt algorithm (GLMA) and the iterative ensemble smoother (IES) which is a recent approach with few applications so far. Simulation, parameter estimation, and forecasts of drawdown and pollutant travel time through the fault zone have been first evaluated on a synthetic fault zone and are eventually applied to a real-world fault zone of interest in karst carbonate environments. A comprehensive comparison and analysis of GLMA and IES results are performed for parameter estimation, parametric and predictive uncertainties, ensuring a thorough assessment of results. The findings reveal satisfactory levels of uncertainty associated with the hydraulic conductivity field and both flow and transport forecasts. This approach demonstrates its ability to estimate the hydrodynamic parameter field with a high level of accuracy within the internal structures of the fault zone. This study constitutes valuable and reproducible insights into the simulation and forecasts of fault zones in karst carbonate aquifers. The findings are a step forward in groundwater flow and transport simulation of fault zones and provide relevant practical help for the management and protection of water resources in these critical areas of interest.

ADCT-Net: Adaptive traffic forecasting neural network via dual-graphic cross-fused transformer

The rapid development of road traffic networks has provided a wealth of research data for intelligent transportation systems. We are faced with vast high-dimensional traffic flow data, characterized by complex spatio-temporal dependencies, waiting for exploration of their internal relationships. Accurately representing these spatiotemporal relationships and improving the accuracy of spatiotemporal traffic prediction are critical challenges in current intelligent transportation forecasting. To tackle this issue, we propose an intelligent prediction framework for traffic flow based on the adaptive dual-graphic transformer with a cross-fusion strategy. Our aim is to uncover latent graphic feature representations that transcend temporal and spatial limitations. Furthermore, we establish a traffic spatiotemporal prediction model using a cross-fusion attention mechanism to capture dependency relationships represented by adaptive graphs. Extensive experiments demonstrate that our proposed model achieves superior prediction performance on practical urban traffic flow datasets compared to benchmarks, particularly for long-term predictions. Further analysis confirms its strength in balancing reliability and practicality, making it well-suited for applications in intelligent transportation systems.

Soil salinity simulation based on electromagnetic induction and deep learning

In pursuit of fine modeling and forecasting of water-salt transport in arid zones, this study uses the rapid electromagnetic induction survey as the main tool to establish an empirical relationship between soil volumetric water content, salinity and electrical conductivity based on on-site measurement data. By using deep learning to set up a surrogate model for electromagnetic induction inverse problem, the vertical distribution of soil salinity during salt leaching at non-growth season is revealed. HYDRUS-1D was used to perform inversions of soil hydraulic parameters to obtain accurate numerical solutions. The results show that the surrogate of electromagnetic induction inversion using a deep convolutional encoding-decoding network (NET) with a learning rate of 0.0006 and 1500 sets of training samples has high accuracy, with an R2 of 0.9930 and RMSE of 0.2585 mg/cm3. The HYDRUS inverse modeling was performed using synthetic ECa data, water content and soil salinity observation data, and the combination of ECa and shallow ground (10 cm deep) water content respectively. It was shown that although the accuracy of the water-salt transport model obtained by using ECa data is slightly lower than that of the inversion model using traditional observation data, it can still accurately simulate the water-salt distribution and has the incomparable advantages of being fast, convenient and low-cost, and the accuracy of the model is improved to some extent by incorporating shallow ground water content data into the inversion based on ECa data. Therefore, the water-salt transport simulation framework incorporating soil apparent electrical conductivity proposed in this study can accurately simulate salt transport and can provide strategies and methods for the simulation and prediction of soil water-salt transport using electromagnetic induction technology.

The Forecasting of Dangerous Goods Transport by Railin Poland in Terms of Environmental Security

The Forecasting of Dangerous Goods Transport by Railin Poland in Terms of Environmental Security

Traffic Request Generation through a Variational Auto Encoder Approach

Traffic and transportation forecasting is a key issue in urban planning aimed to provide a greener and more sustainable environment to residents. Their privacy is a second key issue that requires synthetic travel data. A possible solution is offered by generative models. Here, a variational autoencoder architecture has been trained on a floating car dataset in order to grasp the statistical features of the traffic demand in the city of Rome. The architecture is based on multilayer dense neural networks for encoding and decoding parts. A brief analysis of parameter influence is conducted. The generated trajectories are compared with those in the dataset. The resulting reconstructed synthetic data are employed to compute the traffic fluxes and geographic distribution of parked cars. Further work directions are provided.

Road Freight Transport Forecasting: A Fuzzy Monte-Carlo Simulation-Based Model Selection Approach

Road Freight Transport Forecasting: A Fuzzy Monte-Carlo Simulation-Based Model Selection Approach

Potential Impact of Soil Inner Flow of the Loess Plateau Gully

As an important process of soil water cycle, Soil Inner Flow plays an important role in the occurrence of runoff, the influence of soil structure, soil nutrient transport, slope stability and disaster forecasting. Based on previous research results, this article summarizes the three potential effects of mid-soil flow: the impact on soil structure, soil nutrient transport and slope stability, in order to seek advantages and avoid disadvantages, and strengthen the control of mid-soil flow. The study of systems and control measures provides a reference for the development and utilization of soil midstream.

Time series forecasting of domestic shipping market: comparison of SARIMAX, ANN-based models and SARIMAX-ANN hybrid model

Seaborne transport forecasting has attracted substantial interest over the years because of providing a useful policy tool for decision-makers. Although various forecasting methods have been widely studied, there is still broad debate on accurate forecasting models and preprocessing. The current paper aims to point out these issues, as well as to establish the forecasting model of the domestic cargo volumes using SARIMAX, MLP, LSTM and NARX and SARIMAX-ANN hybrid models. Based on the domestic cargo volumes of Turkey, findings suggest that SARIMA-MLP models can be considered as an appropriate alternative, at least for time series forecasting of shipping. Pre-processed data provides a significant improvement over those obtained with unpreprocessed data, with the accuracy of the models found to be significantly boosted with the Fourier term of decomposition. The results indicate that SARIMAX-MLP, with a mean absolute percentage error (MAPE) of 4.81, outperforms the closest models of SARIMAX, with a MAPE of 6.14 and LSTM with Fourier decomposition with a MAPE of 6.52. Findings have implications for shipping policymakers to plan infrastructure development, and useful for shipowners in accurately formulating shipping demand.

The role of transport planning in the sustainable development of the Irkutsk agglomeration

The lack of integrated territorial and transportation planning in Irkutsk and Irkutsk agglomeration has led to the degradation of transport infrastructure. A number of measures are proposed to improve the efficiency of the transport system, including the elaboration of a development plan for sustainable mobility with a focus on public transportation services for urban areas. The use of modern transport modeling tools allows to take into account the negative consequences of reducing transport accessibility, but requires the integration of accumulated information about the characteristics of the service areas to increase the accuracy of transport forecasts.

Variance decomposition of forecasted sediment transport in a lowland watershed using global climate model ensembles

Forecasting change in sediment transport using global climate model ensembles is under-developed in the hydrology community due to a lack of knowledge regarding the variance structure of predictions. We investigate the uncertainty of forecast sediment transport from sources, including global climate model realizations, global climate model ensemble design, forecasted hydrologic inputs, and hydrologic modeling parameterizations. We then forecast sediment transport for the gently rolling watershed in Kentucky, USA. Contrary to past research forecasting hydrology with global climate models, hydrologic model parameterization was the most significant source of variance impacting forecasted sediment transport variables. Forecast of sediment transport responses shows the propagation of uncertainty from the hydrologic model parameterization was over two times greater than the uncertainty from the selected global climate model realizations. This result emphasizes researchers focused on forecasting sediment transport with global climate models may need to give as much, or more, consideration to their water–sediment linkages as climate-water–sediment linkages. Hydrologic inputs from climate change, including forecast precipitation, temperature, relative humidity, solar radiation, and wind speed, impacted sediment transport. Considering changes in precipitation and temperature alone under-predicts streamflow and sediment transport by 49% and 35%, respectively, compared to including all meteorological variables inputs. Variance introduced across the three different global climate model ensembles was a relatively small source of variance impacting forecasted streamflow or sediment yield. The results suggest a quantitative effort by the researcher to design the global climate model ensemble by considering representativeness, historical performance, and independency will lead to robust results. Ensemble average forecasts forecast streamflow and sediment yield to increase by 19% and 14%, respectively, for the lowland watershed. The sediment transport forecasts reflect the shear-limited landscape of the watershed and the transport-limited conditions in the stream channel.

Land use uncertainty in transportation forecast

Using an integrated land use and travel model system implemented for the Puget Sound region in Washington state, a Bayesian Melding technique is applied to represent variations in land use outcomes, and is propagated into travel choices across a multi-year agent-based simulation. A scenario is considered where zoned capacity is increased around light rail stations. Samples are drawn from the posterior distribution of households to generate travel model inputs. They allow for propagation of land use uncertainty into travel choices, which are themselves assessed for uncertainty by comparing against observed data. Resulting travel measures of zonal vehicle miles traveled (VMT) per capita and light rail station boardings indicate the importance of comparing distributions rather than point forecasts. Results suggest decreased VMT per capita in zones near light rail stations and increased boardings at certain stations with existing development, and less significant impacts around stations with lower initial development capacity. In many cases, individual point level comparisons of scenarios would lead to very different conclusions. Altogether, this finding adds to a line of work demonstrating the policy value of incorporating uncertainty in integrated models and provides a method for assessing these variations in a systematic way.

Entropy as an Objective Function of Optimization Multimodal Transportations.

This article considers the use of the entropy method in the optimization and forecasting of multimodal transport under conditions of risks that can be determined simultaneously by deterministic, stochastic and fuzzy quantities. This will allow to change the route of transportation in real time in an optimal way with an unacceptable increase in the risk at one of its next stages and predict the redistribution of the load of transport nodes. The aim of this study is to develop a mathematical model for the optimal choice of an alternative route, the best for one or more objective functions in real time. In addition, it is proposed to use this mathematical model to estimate the dynamic change in turnover through intermediate transport nodes, forecasting their loading over time under different conditions that also include long-term risks which are significant in magnitude. To substantiate the feasibility of the proposed mathematical model, the analysis and forecast of cargo turnover through the seaports of Ukraine are presented, taking into account and analysing the existing risks.

A Summary of GFS Ensemble Integrated Water Vapor Transport Forecasts and Skill along the U.S. West Coast during Water Years 2017–20

AbstractThe ability to provide accurate forecasts and improve situational awareness of atmospheric rivers (ARs) is key to impact-based decision support services and applications such as forecast-informed reservoir operations. The purpose of this study is to quantify the cool-season water year skill for 2017–20 of the NCEP Global Ensemble Forecast System forecasts of integrated water vapor transport along the U.S. West Coast commonly observed during landfalling ARs. This skill is summarized for ensemble probability-over-threshold forecasts of integrated water vapor transport magnitudes ≥ 250 kg m−1 s−1 (referred to as P250). The P250 forecasts near North-Coastal California at 38°N, 123°W were reliable and successful at lead times of ~8–9 days with an average success ratio > 0.5 for P250 forecasts ≥ 50% at lead times of 8 days and Brier skill scores > 0.1 at a lead time of 8–9 days. Skill and accuracy also varied as a function of latitude and event characteristics. The highest (lowest) success ratios and probability of detection values for P250 forecasts ≥ 50% occurred on average across Northern California and Oregon (Southern California), whereas the average probability of detection of more intense and longer duration landfalling ARs was 0.1–0.2 higher than weaker and shorter duration events at lead times of 3–9 days. The potential for these forecasts to enhance situational awareness may also be improved, depending on individual applications, by allowing for flexibility in the location and time of verification; the success ratios increased 10%–30% at lead times of 5–10 days allowing for flexibility of ±1.0° latitude and ±6 h in verification.

A Fuzzy ARIMA Correction Model for Transport Volume Forecast

In recent years, few water transportation forecasting studies conduct relative to transportation forecasting. As a neglected area, the inland waterway volume prediction is an important indicator for investment management and government policymaking. Considering the time-series forecasting, some researchers try to narrow the predicted value interval. However, certain limitations detract from their popularity. For instance, if the prediction length is more than ten, the result would not be acceptable. Therefore, we propose a hybrid model that combines both of their unique properties’ advantages to provide more accurate traffic volume forecasts. Also, the forecasting process will be more straightforward. The empirical results present the proposed model and improve the long-term predictive accuracy at waterway traffic volume.

Calibration of river hydrodynamic models: Analysis from the dynamic component in roughness coefficients

Hydrodynamic modelling is an important tool for several studies, such as flood forecasting and analysis of energy and mass transport. In rivers, the most common calibration parameter is the roughness coefficient, that accounts for the friction term. However, while uncertainties are associated to this parameter definition, it is often defined as constant over time or space. Even though sophisticated techniques are available for model calibration, unsteady flow routing applications usually involve simplified methods, such as trial-and-error and Monte Carlo strategies. These practices can be subjective, time consuming and dependent on user experience; on the other hand, automatic approaches may generate unrealistic values and require large computational efforts. This study introduces a new strategy to calibrate one-dimensional hydrodynamic models, accounting for temporal and spatial variation while maintaining computational thrift. Among multiple random roughness coefficients, a time series is generated according to a linear relationship with observed water depths or flow for five control sections; the resulting daily series of parameters are the set for simulation inputs. The algorithm was combined with the hydrodynamic module in the SihQual model (Hydrodynamic and Water Quality Simulation); when compared to observed data, it showed to be suitable to predict discharges and water levels for different years over 85 km of the Iguaçu river, located in Paraná (Brazil). Compared to Monte Carlo and trial-and-error techniques for calibration, the HTC method (Hydrodynamic Temporal Calibration) was able to match their results or even improve simulations in some cases, requiring less user interaction than the other approaches. The study highlights the relevance of including a temporal component in model calibration, aiming to reduce the overall inherent uncertainty of this process.

A critical review on marine litter in the Adriatic Sea: Focus on plastic pollution.

Marine litter affects various habitats across the world. This review focuses on the Adriatic region, considering the presence of marine litter as well as microplastics (mPs) and macroplastics (MPs) in different environments (water, beach, seabed and biota). Data from 53 scientific papers were critically analysed, providing a snapshot of this type of contamination, and evidencing critical issues. The final part of the review provides considerations on spatial and temporal trends, comparing data with the available information provided by transport forecasting models. It emerges that the most investigated areas are those most subjected to the contribution of rivers, tourism or have the greatest relevance to nature conservation. Our analysis also reveals that, even though many international research projects have played a fundamental role in the creation of shared methods and protocols, currently available data are difficult to compare. Nevertheless, our results enhance knowledge of the state of the art in the research carried out so far, and on the situation regarding pollution due to the marine litter in the Adriatic Sea, as well as highlighting avenues for future investigation.

Моделирование взаимодействия регионов России и Республики Беларусь в целях прогнозирования дополнительных транспортных потоков

The article proposes a methodological approach using the spatial econometrics tool for development of transport and economic forecasting systems and reflection of major structural political decisions in its results on the example of the influence of economic integration of Russia and Belarus on transport flows in the intersectoral interterritorial systems of the Union State. To calculate the studied indicators, data from the official statistical websites of Russia and Belarus were used. The analysis of the spatial distribution of output in the processing sector of the regions of the Russian Federation and the Republic of Belarus is carried out to assess the possibility of interaction between these regions in this sector of the economy. When modeling, the Republic of Belarus is considered as a separate region within the framework of the Union State. Calculations of the global and local Moran indices were made and possible spatial autocorrelations were determined both between the subjects of the Russian Federation and between the regions of the Russian Federation and the Republic of Belarus. In the current study, a standard inverse distance weight matrix was used to assess the degree of interaction between regions. The problem is considered, first of all, from the transport side on the basis of the analysis of relations in heavy industry as the main cargo-forming sphere. The influence of economic and infrastructural factors on the indicator characterizing the degree of possible interaction between the regions of the Russian Federation and the Republic of Belarus in the field of industry and transport is studied. The results of the work make a step towards the systematic application of spatial econometrics in methodology and technology of transport and economic forecasting and can be used in preparation of strategies, programs and schemes for placement and development of industries, taking into account the potential of a new level of integration of the economies of Russia and Belarus.

Assessing the Anthropogenically Caused Pollution of Water Bodies Within the Selenga River Basin on the Territory of Mongolia and Russia

Presented are the results from studying the current state, modeling and forecasting of the transboundary transport of pollutants with the flow of the rivers within the Selenga river basin. The method for calculating the pollution coefficient was used to assess the degree of pollution of the Selenga river basin on the territories of Mongolia and the Republic of Buryatia (RF) on the linear sections of the river and its tributaries within the boundaries of the adjacent river sections according to data of state monitoring observation in both countries. Results from assessing the impact of economic entities located within the transboundary Selenga river basin on the streams according to the α-indicator of the pollution coefficient and subsequent ranking by four levels of pollution indicate that the catchment and Lake Baikal itself continue to be polluted by insufficiently treated wastewater from industrial enterprises and housing and communal services. Calculations showed that the aquatic ecosystems of the Kyakhtinka (Khiagt), Modonkul’ Selenga rivers in the border zone along the border of Russia and Mongolia in the area of Naushki – Sukhbaatar and Kyakhta ‒ Altanbulag, and in the area of the settlement of Novoselenginsk are most disturbed. On the territory of the Russian part of the basin, the Selenga river downstream, the degree of water pollution is increasing in the areas of industrial centers, such as Gusinoozersk and Ulan-Ude. The average indicator of the pollution coefficient as calculate for the Russian part of the Selenga river basin was higher than that for the Mongolian part, which suggests the conclusion that the degree of pollution by the pollutants under consideration is higher on the Mongolian territory than on the Russian territory.

Sediment yield error correction by dynamic system response curve method in real-time flood forecasting

Abstract Real-time flood forecasting requires accurate and reliable estimates of the uncertainty to make efficient flood event management strategies. However, the accuracy of flood forecasts can be severely affected by errors in the estimates of sediment yield in the loess region. To improve the accuracy of sediment-laden flood forecasts generated using streamflow-sediment coupled (SSC) model, an error feedback correction method based on the dynamic system response curve (DSRC) is proposed. The physical basis of the system response curve is the sediment concentration of the hydrological model. The theoretical basis of the method is the differential of the system response function of the sediment yield time series. The effectiveness of DSRC method is evaluated via an ideal case and three real-data cases with different basin scales of the Yellow River. Results suggest that the DSRC method can effectively improve the accuracy and stability of sediment transport forecasts by providing accurate estimates of the sediment yield errors. The degree of forecast improvement is scale dependent and is more significant for larger basins with lower rain gauge densities. Besides, the DSRC method is relatively simple to apply without the need to modify either the model structure or parameters in real-time flood forecasting.