In the context of the transition to human-centric production systems and the high variability of truck configurations, the problem of ensuring the ergonomics and manufacturability of assembly processes during the production preparation phase is critical. This article analyzes the application of Virtual Reality (VR) technologies for designing and optimizing truck assembly processes. Key problems are considered, including the complexity of mixed-model balancing, the assembly of large-sized units, and the high load involved in mounting cable harnesses. The authors analyze modern approaches, demonstrating how VR simulation combined with digital human modeling allows for the identification of design errors, conducting ergonomic assessments of working postures (using RULA, NIOSH methods), and staff training. A prototype solution is presented, developed on the basis of the Unreal Engine 5 (UE5) game engine using an HTC Vive Pro 2 headset, for the realistic simulation of the cooling unit installation operation onto the frame. It is shown that the implementation of VR technologies can significantly reduce costs, improve quality, and reduce the risk of injury by optimizing workplaces and technological routes even before the start of mass production.

Despite the efforts of scientists and practitioners, pedestrian safety remains one of the world's most acute problems. According to research by scientists and road safety experts, people (both drivers and pedestrians) often create road hazards. Therefore, it is essential to reduce the impact of human error. To this end, increasingly intelligent vehicles and road infrastructure are being created, along with passive safety measures for pedestrians. Furthermore, intelligent systems are being developed to alert pedestrians, monitor their attention, and analyze pedestrian flows, all of which can warn drivers of dangerous behavior and the risk of accidents. Experience shows that only comprehensive solutions can address strategic challenges, including road safety improvement. We propose an upgrade to the pedestrian push-button traffic light, making it “smarter” through dynamic traffic light phase control based on real-time road conditions. This is enabled by a pedestrian classification system we developed, which categorizes individuals based on characteristics such as age, behavior patterns, and mitigating factors. We believe that a smart traffic light system tailored to pedestrian flow patterns will enhance pedestrian safety and optimize traffic flow parameters.

This article explores the potential for addressing issues related to improving the sustainability and safety of a city's transportation system. The study demonstrates the need for a comprehensive approach that considers not only the direct but also the indirect effects of potential solutions, selecting those that enhance the overall sustainability and safety of the system. The article presents a conceptual model of such a system and illustrates implementation methods using a large city as an example. Furthermore, it demonstrates how simulation modeling can address urban transportation network issues and quantifies the impact of implementing the proposed solutions.

This article investigates methods for optimizing traffic light control parameters with the aim of improving urban traffic efficiency. Modern approaches to ensuring the sustainability of urban transport systems are discussed. The study concludes that simulation modeling serves as a leading methodology for analyzing transportation systems and identifying optimal solutions to diverse challenges, which ultimately enhances traffic flow characteristics and increases the resilience of the transport network. An optimized simulation model of a major city intersection has been developed, an optimization experiment carried out, and optimal values for traffic light signal phases have been determined.

Competition in the e-commerce system due to its rapid development forces companies to search for solutions to improve efficiency. The most important issue in this case is logistics operations, which determine the satisfaction of customer requirements. The emergence of intelligent technologies allows you to rebuild the system of delivery of online orders, making it more flexible and convenient. The article considers one of the ways to solve this problem. Using the example of building a two-link delivery system from an online store, it is shown that the proposed system will not only speed up the delivery process, but also reduce the load on the city’s transport network both in terms of traffic and from an environmental point of view. Ultimately, the proposed delivery option will contribute to the implementation of the ESG concept. The proposed system uses simulation models that have proven themselves well for solving such problems. Various options for courier deliveries of the “last mile” are recommended

Transport logistics in modern conditions includes more and more intelligent elements that improve both physical objects and optimize processes, using technologies of communication means, the Internet of things, and data analysis,. One of the processes that is implemented in the logistics chain is the loading and unloading process. The implementation of this process is especially relevant when delivering goods to shopping centers located in cities. To optimize loading and unloading processes, simulation models are used that allow not only to reduce the waiting time of trucks in the queue for unloading, but also to increase the efficiency of the entire process, as well as reduce the impact on the environment, which is extremely important in cities, and especially megacities. The article provides a practical example of using such a model, an optimization experiment was conducted and the achieved efficiency is shown.

One of the most significant factors in environmental degradation is the increase in motorization. Currently, road transport is the largest source of environmental pollution in urban areas. The article analyzes the environmental impact of chemical (emissions) and physical (noise) factors from road transport. It is found that these factors have a strong relationship. Considering that continuous simultaneous monitoring of air quality and noise level requires significant investments, using the ratios obtained in the article based on linear regression, you can get quite reliable data on the degree of environmental impact of one of the factors (for example, volume emissions) by monitoring another factor (for example, noise).

The article describes a developed factor tree that determine the risk of an accident. The proposed factor tree in addition to the categories “driver”, “automobile”, “road”, “environment” is also supplemented with factors of the categories “pedestrian” and “traffic flow”. In contrast to the accepted practice of analyzing road accidents enlarged throughout the Russian Federation by one factor, an analysis of the statistics of the average settlement in the context of several factors was carried out. As a method of analysis, the construction of frequencies polygons has been chosen. Patterns of accidents that occurred in 2017-2018 on the roads of Yelabuga in the context of the day of the week, time of day, type of vehicle, type of accident, driver experience and gender were identified.

This paper proposes a group annotation approach to interactive semantic labeling of data and demonstrates the idea in a system for labeling objects in 3D LiDAR scans of a city. In this approach, the system selects a group of objects, predicts a semantic label for it, and highlights it in an interactive display. In response, the user either confirms the predicted label, provides a different label, or indicates that no single label can be assigned to all objects in the group. This sequence of interactions repeats until a label has been confirmed for every object in the data set. The main advantage of this approach is that it provides faster interactive labeling rates than alternative approaches, especially in cases where all labels must be explicitly confirmed by a person. The main challenge is to provide an algorithm that selects groups with many objects all of the same label type arranged in patterns that are quick to recognize, which requires models for predicting object labels and for estimating times for people to recognize objects in groups. We address these challenges by defining an objective function that models the estimated time required to process all unlabeled objects and approximation algorithms to minimize it. Results of user studies suggest that group annotation can be used to label objects in LiDAR scans of cities significantly faster than one-by-one annotation with active learning.