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The use of information technologies such as Transportation Management System (TMS) is crucial to improve the transportation process in a company, offering potential results that include optimization of shipments, an increase in cost savings and customer satisfaction. The TMS, along with data analysis techniques such as Machine Learning (ML) and/or Data Mining (DM) are useful tools for demand forecasting, facilitating strategic, tactical and operational decision making, which ultimately improves the logistics distribution performance. For this, this work proposes a methodology that will compare three prediction methods (traditional statistical method, hybrid method and artificial neural network), which will be tested through the Mean Squared Error (MSE) indicator to determine which one has more accurate results in the forecast of demand for freight transport. The experimental results obtained in this research show that the historical data saved in a TMS can be used for reliably freight transportation demand forecasting. Each of the three methods proposed in this paper has been an effective tool in resolving time series forecasting problems, and when Artificial Neuronal Network (ANN) was used, better results are yielded.

Dennis Rondinelli and Michael Berry, both of the Kenan-Flagler Business School, University of North Carolina, argue that proactive environmental management is vital as multimodal transportation and intermodal logistics services increasingly impact the environment. Regulatory compliance is not enough.

Transportation Management Systems (TMSs) play a pivotal role in streamlining logistics operations, yet selecting the most suitable TMS software remains a complex, multi-criteria decision-making problem. This study introduces a hybrid evaluation framework combining the Bayesian Best–Worst Method (BBWM) and TOPSIS to identify, weigh, and rank software selection criteria tailored to the logistics business. Drawing on insights from 13 logistics experts, five main criteria—technological competence, service, functionality, cost, and software developer (vendor)—and 16 detailed sub-criteria are defined to reflect business-specific needs. The core novelty of this research lies in its systematic weighting of TMS software criteria using the BBWM, offering robust and expert-driven priority insights for decision makers. Results show that functionality (26.6%), particularly load tracking (35.8%) and cost (22.7%), mainly software license cost (39.8%), are the dominant decision factors. Beyond operational optimization, this study positions TMS software selection as a strategic entry point for sustainable digital transformation in logistics. The proposed framework empowers business to align digital infrastructure choices with sustainability goals such as emissions reduction, energy efficiency, and intelligent resource planning. Applying TOPSIS to a real-world case in Türkiye, this study ranks software alternatives, with “ABC” emerging as the most favorable solution (57.2%). This paper contributes a replicable and adaptable model for TMS software evaluation, grounded in business practice and advanced decision science.

With the wide variety of information systems and applications for motor transport and transport logistics control we have today, one may think we are already living in the digital era of general welfare, and digital tools would easily ensure sustainable development and prosperity of businesses. However, the experience of deployment and introduction of such solutions shows that their value for transport business is significantly lower than expected. Moreover, in some projects, business performance of transport companies had no correlation with introduction of information systems. In the best-case scenario, they provided for a slight decrease in document flow transaction costs. The change of the strategic status of a company in the transportation service market is a fairly complicated task, which, as analysis of literary sources shows, is achievable for few enterprises, primarily small and medium-sized businesses. Such situations show that information solutions were introduced without analyzing or assessing the business models of certain companies which could be used a basis for digital landscape of business as a whole. In recent years, the basic concept of forming a single information space of an enterprise has been the enterprise architecture. It provided for coordination between all the business processes in order to achieve a company’s strategic goals. The fundamentals of the concept were developed by J. Zachman in his famous Zachman Framework, and it was later developed with numerous models of enterprise architecture (e.g., TOGAF (Department of Defense Architecture Framework), GERAM (Generalised Enterprise Reference Architecture and Methodology), DoDAF (Department of Defense Architecture Framework)). However, currently some researchers note that sustainable corporate development should stem not only from a “correct assembly” of all its business elements, which was the purpose of enterprise architecture, but also from the interaction of these elements when reaching the emergence effect. In this context, one should pay attention to comprehensive activity analysis of a transport and logistics business using ontological and architecture approaches.

The article proposes a novel two-stage network traffic anomaly detection method for the railway transportation critical infrastructure monitored using wireless sensor networks (WSN). The first step of the proposed solution is to find and eliminate any outlying observations in the analyzed parameters of the WSN traffic using a simple and fast one-dimensional quartile criterion. In the second step, the remaining data is used to estimate autoregressive fractional integrated moving average (ARFIMA) statistical models describing variability of the tested WSN parameters. The paper also introduces an effective method for the ARFIMA model parameters estimation and identification using Haslett and Raftery estimator and Hyndman and Khandakar technique. The choice of the “economically” parameterized form of the model was based on the compromise between the conciseness of representation and the estimation of the error size. To detect anomalous behavior, i.e., a potential network attack, the proposed detection method uses statistical relations between the estimated traffic model and its actual variability. The obtained experimental results prove the effectiveness of the presented approach and aptness of selection of the statistical models.

The rapid growth in computer technology provides supply chain managers with valuable tools to better coordinate and control their operations. This book seeks to describe systems available to give supply chains information system support, demonstrating key tasks with demonstrated analytic techniques. This second edition is basically the same as the first edition, but with newer cases to demonstrate concepts. The target market for this book is practitioners in the supply chain management field, one of the fastest growing fields in our economy.

In the rapid process of urbanization, urban agglomerations have become a key driving factor for regional development and spatial reorganization. The formation and development of urban agglomerations rely on communication between cities. However, the spatiotemporal characteristics of intercity travelers are not fully grasped throughout the entire trip chain. This study proposes a spatiotemporal analysis method for intercity travel in urban agglomerations by constructing origin-to-destination (OD) trip chains using smartphone data, with the Beijing–Tianjin–Hebei urban agglomeration as a case study. The study employed Cramer’s V and Spearman correlation coefficients for multivariate feature selection, identifying 12 key variables from an initial set of 20. Then, optimal cluster configuration was determined via silhouette analysis. Finally, the K-prototypes algorithm was applied to cluster 161,797 intercity trip chains across six transportation corridors in 2019 and 2021, facilitating a comparative spatiotemporal analysis of travel patterns. Results show the following: (1) Intercity travelers are predominantly males aged 19–35, with significantly higher weekday volumes; (2) Modal split exhibits significant spatial heterogeneity—the metro predominates in Beijing while road transport prevails elsewhere; (3) Departure hubs’ waiting times increased significantly in 2021 relative to 2019 baselines; (4) Increased metro mileage correlates positively with extended intra-city travel distances. The results substantially contribute to transportation planning, particularly in optimizing multimodal hub operations and infrastructure investment allocation.

Radio frequency identification or RFID has received much press of late, mainly due to the recent compliance mandates by many of the world's largest retailers (Wal-Mart, Tesco, Marks and Spencer, Target, etc.) and Gillette's reported purchase of 500 million units last year. The technology has been proclaimed to 'lead to an entirely new relationship between people and things' (J. D. Markman, 'Invest in the Greatest Thing since the Bar Code', MSN Money - SuperModels, 25th June, 2003; http: moneycentral.msn.com content P50823.asp). Others have said 'we think it will be bigger than the Internet. All the Web did was connect computers to computers. That's not as big as connecting things to computers' (M. Roberti, publisher of RFID Journal, in interview with Markman, above). Promoters describe a supply chain where all assets are in perfect visibility through production, distribution, retail and consumption. According to one analyst, the world will need about half the warehouse space it needs today (P. Jilek, 'Corporate Sector Focus, A Killer App?' CSFB Investment Strategy, 17th June, 2003). This paper introduces RFID technology and its potential implications. Although the technology is compelling, there are serious nearterm challenges. Finally, the paper looks at the impact RFID could have on supply chain facilities and the future demand for industrial real estate.

This article develops a Sustainability Informatics Framework, a framework that connects Information Technology with sustainability and is based on the Belief-Action-Outcome and Energy Informatics frameworks. The triple-bottom line (People, Planet, and Profit) is adopted as a criterion for measuring sustainability and three sustainability indices are first formulated and then visualized using Sustainability Dashboards. This framework is then used to analyze transportation systems. This leads to the development of research propositions that can expand the role of the Information Systems discipline to research on areas of sustainable nation building.

Today, the world has experienced a new trend with regard to data system management, traditional database management tools have become outdated and they will no longer be able to process the mass of data generated by different systems, that's why big data is there to process this mass of data to bring out crucial information hidden in this data, and without big data technologies the treatment is very difficult to manage; among the domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In this article, we establish in the first step a method that allow to detect anomalies or accidents within the road and compute the time spent in each road section in real time, which permit us to obtain a database having the estimated time spent in all sections in real time, this will serve us to send to the vehicles the right estimated time of arrival all along their journey and the optimal route to attain their destination. This database is useful to utilize it like inputs for machine learning to predict the places and times where the probability of accidents is higher. The experimental results prove that our method permits us to avoid congestions and apportion the load of vehicles in all roads effectively, also it contributes to road safety.