Transportation plays a significant role in the global economy and society and takes part in a lot of different processes such as mass transportation and the supply chain. Therefore, it is crucial to introduce modern technologies in this area of the economy in the context of Industry 4.0. The main scope of this study is to develop a model that supports analyzing last-mile logistics modern solutions using the latest technologies such as road autonomous delivery robots (RADRs), civil drones, or smart bikes, and compare them to conventional solutions (delivery vehicles). Multi-criteria decision analysis (MCDA) was applied to build a formal comparison model that scores the solutions and weights different criteria according to decision-makers and placeholders, to rank the solutions from the most crucial option to the weakest in a predetermined scenario with set parameters and conditions (three varied scenarios were included in the present investigation). The results of the model were in favor of using civil drones or smart bicycles to perform light deliveries in small urban areas (these key findings support the assumptions that are often manifested in speech in the context of the use of new technologies). The modern solutions scored almost 40–80% higher in total in the conglomeration of assessment criteria (such as safety, economy, laws and regulations, operation time for the delivery, environment, and payload) than the conventional solution, which indicates the importance of studying the implementation of such technologies. An interesting result of the study is the operational cost reduction by ca. 60–74% in favor of autonomous delivery robots, 89–93% in favor of civil delivery drones, and 87–90% in favor of smart bikes vs. conventional delivery trucks/vans. Yet, it should be underlined that the results may vary with different assumptions within the MCDA method.

Low-cost Global Navigation Satellite System (GNSS) sensors have been successfully applied in commercial vehicles’ position monitoring, and they continually raise interest among research audiences both in theoretical and practical aspects. While numerous studies have applied simulations and numerical methods to evaluate the accuracy of the sensors, this paper presents an analysis, supported by actual measurements collected under diversified conditions. The measurements were collected under a variety of conditions, including urban and suburban routes of considerable length, and in accordance with the position in lane applied in most European countries, which is considerably related to the sustainability of road transport. The measurements were collected during driving of three different passenger vehicles, and the response of the measurements to correct, partially correct and incorrect vehicle positions was recorded. Differentiated kinematic conditions and actual dynamic performance during driving were analyzed. This research compared the position accuracy of a low-cost GNSS sensor and a dual-antenna GNSS/INS sensor for vehicle dynamics monitoring. Both types of sensors were operated on all the passenger vehicles and with the same measurement conditions. Statistical hypothesis tests have been considered to compare the results, in accordance with the latest guidelines for carrying out such tests. Studies have indicated that a low-cost GNSS sensor also has satisfactory accuracy. However, this paper points out additional considerations and conclusions. Both the positive and negative results are described and commented on in the paper, including research limitations and suggestions for future measurement and future research agendas, both by the authors and as an inspiration for other researchers.