The current methods that aim at monitoring the structural health status (SHS) of road pavements allow detecting surface defects and failures. This notwithstanding, there is a lack of methods and systems that are able to identify concealed cracks (particularly, bottom-up cracks) and monitor their growth over time. For this reason, the objective of this study is to set up a supervised machine learning (ML)-based method for the identification and classification of the SHS of a differently cracked road pavement based on its vibro-acoustic signature. The method aims at collecting these signatures (using acoustic-sensors, located at the roadside) and classifying the pavement’s SHS through ML models. Different ML classifiers (i.e., multilayer perceptron, MLP, convolutional neural network, CNN, random forest classifier, RFC, and support vector classifier, SVC) were used and compared. Results show the possibility of associating with great accuracy (i.e., MLP = 91.8%, CNN = 95.6%, RFC = 91.0%, and SVC = 99.1%) a specific vibro-acoustic signature to a differently cracked road pavement. These results are encouraging and represent the bases for the application of the proposed method in real contexts, such as monitoring roads and bridges using wireless sensor networks, which is the target of future studies.

Multimodal transport terminals are the elements of transport systems that ensure the interaction of enterprises of various modes of transport. The effective functioning of transport terminals significantly affects the efficiency of the material flow servicing in a supply chain and the sustainability of the whole transport system. The paper proposes an approach to determine the optimal parameters of production resources in multimodal transport terminals, based on numerical computer simulations of technological operations in a transport terminal for the given parameters of incoming and outgoing material flows. The practical use of the proposed approach is shown on the example of the Amur Harbor cargo area of the Dnipro River Port.

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Electric cargo bicycles have become a popular mode of transport for last-mile goods deliveries under conditions of restricted traffic in urban areas. The indispensable elements of the cargo bike delivery systems are loading hubs: they serve as intermediate points between vans and bikes ensuring loading, storage, and e-vehicle charging operations. The choice of the loading hub location is one of the basic problems to be solved when designing city logistics systems that presume the use of electric bicycles. The paper proposes an approach to justifying the location of a loading hub based on computer simulations of the delivery process in the closed urban area under the condition of stochastic demand for transport services. The developed mathematical model considers consignees and loading hubs as vertices in the graph representing the transport network. A single request for transport services is described based on the set of numeric parameters, among which the most significant are the size of the consignment, its dimensions, and the time interval between the current and the previous requests for deliveries. The software implementation of the developed model in Python programming language was used to simulate the process of goods delivery by e-bikes for two cases—the synthetically generated rectangular network and the real-world case of the Old Town district in Krakow, Poland. The loading hub location was substantiated based on the simulation results from a set of alternative locations by using the minimum of the total transport work as the efficiency criterion. The obtained results differ from the loading hub locations chosen with the use of classical rectilinear and center-of-gravity methods to solve a simple facility location problem.

To increase the efficiency of transport nodes functioning taking into account the logistics management principles it is necessary to optimize the structure and capacity of transport nodes' production resources, and to develop such a method for calculating of joint schedules for vehicles and freight hubs of the transport node, which takes into account stochastic nature of the parameters of material and informational flows. For solving the problems of optimal management of transport nodes functioning processes it is proposed to use the specific efficiency indicator, which is determined as a ratio of total costs of clients servicing in transport node to the costs of production resources used while servicing. According to the used approach for formalization of the transport node internal processes, development of the simulation model was implemented on the base of object-oriented programming principles. TransportNode.dll class library has been used as basic tool for simulations. The model implemented on the base of the library allows to take into account stochastic nature of demand and probabilistic nature of technological processes in transport nodes. Some results of numeric simulations for the loading area “Amur-Gavan” of Dnipropetrovsk River Port have been described in the paper.

For the developed system of public transport, the passengers, as the customers, have a variety of alternatives when choosing the transport mode or even the route for the given mode of public transport. The estimation of the passengers’ preference is the key task for transportation planners for solving the wide range of optimization problems in the field of public transport. A methodology for estimation of the passengers’ preference when choosing the bus line within a public transport system is developed in this paper. The proposed approach is based on the fuzzy-logic mathematical apparatus and uses the surveys’ data to calculate the membership functions defining the passengers’ preference. The case study of the passengers’ survey, held in Talas (Kazakhstan), is used to illustrate the developed methodology.

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