Volume 24 (2023): Edition 2 (April 2023)

The transport of temperature sensitive products takes place under special conditions defined by specific agreements and international standards. The only exception to this rule is consumer transport. This transport is carried out by the consumer and takes place on the way home from the shop. The study examined consumers' awareness of the consumer transport of frozen food and analysed this type of transport in terms of the continuity of the cold chain. Such situation affects the deterioration of frozen food quality especially in case of its later storage in the home freezer. It was found that the average distance that customers cover from shop to home was 4.98 km. They usually used a car and covered this distance in an average of 12.85 minutes. During the summer months, this time is sufficient to partially thaw a package of frozen vegetables. Only 33% of the respondents used insulated bags to protect frozen food on the way home. When analysing the transport of frozen raw material carried out by consumers in real conditions, the use of insulated bags was found to be justified. These bags are able to keep the temperature of the packed raw material below −5°C. It was found that the legal imposition of the necessity to use such bags or the introduction by the manufacturer of frozen food of appropriate packaging protecting the food against transport at inappropriate temperatures in the summer months is necessary.

Ego lane detection is one of the key techniques in Ego Lane Analysis System (ELAS) implemented in smart autonomous driving cars for lane detection in roads. This technique has been extensively studied in recent years because of its accurate and robust detection of shape and location of lanes. The conventional methods are less robust and computationally expensive since they have several challenges in localization of lanes due to presence of occlusions on roads. So to avoid these issues, this paper uses a novel 2-stage lane detection method using deep convolutional neural network to detect the lanes and its key-points by optimally fit a curve to the lane to compensate on above mentioned issues. The proposed methodology for lane detection predicts the key-points accurately and it robust under various weather conditions and highway driving scenarios. In terms of performance, this technique is fast and robust with low computational cost and has high performance when deployed on autonomous vehicle-based systems.

The automotive industry is on a continuous transition towards a more sustainable and integrated ecosystem influenced by the fast-paced adoption of Electrical Vehicles (EVs) and the developments of emerging technologies such as Automated Vehicles (AVs). The road transportation sector is also experimenting with the emergent decentralized blockchain technology in various ways ranging from supply chain transparency to insurance and tokenization. Some of the recent use cases are the use of Non-Fungible Tokens (NFTs), unique digital assets designed to be immutable, to certify ownership of a vehicle, the data history of it or just for fan base development. The current paper reviews the literature findings concerning the potential of Non-Fungible Tokens for the automotive industry and proposes a new car ownership and revenue generation model using the ERC-1155 token standard. Our proof-of-concept based on fractional vehicle ownership demonstrates the feasibility of such a model that allows for revenue distribution amongst the vehicle owners according to the percentile invested in the vehicle acquisition.

Previous studies have shown that the level of awareness of SDVs is a deciding factor that affects the public attitude towards this emerging technology; however, none of these studies focuses on understanding the relationship between these two variables. Thus, this study utilizes a questionnaire survey with the objective of drawing the relationship between the public attitude and level of knowledge. A total of 2447 complete responses were revised from participants from the US. The results show that people with prior knowledge about SDVs are more likely to travel on SDVs. However, participants who know a bit about SDVs were the most likely to travel on SDVs when compared with participants who had no knowledge and participants who know a lot about SDVs. In addition, the analysis shows that the relationship between the level of knowledge and the level of acceptance of SDVs is not linear but rather parabolic.

The evolution of vehicles has always been continuous with respect to growth in technology.The concept of the Internet of Vehicles (IoV) is the process of allowing vehicles to interact with each other to provide real-time information. This paper introduces the various aspects of IoV and their components. Despite the fact that there are more and more vehicles connected to the IoV, there are still many unknown issues and potentials that needs to be identified to carry out research. In order to identify and classify the current difficulties in implementing and using IoV in urban cities, various research publications on the topic were analysed in this paper. The limitations of the Internet of Vehicular technology are also described. Additionally, a number of current and potential remedies that address the highlighted problems were briefly covered. The background information and reasons for evolving heterogeneous vehicular networks are thoroughly reviewed in this research. Also highlights the key technologies of IoV, network architecture and comparison of IoV architecture models with focus on different communication models The most modern IoV enabling technologies are also highlighted, along with environmental scope of intelligent internet of vehicles. Finally, the paper has reviewed the open research issues of Intelligent IoV such as Poor Connectivity of on road vehicles and Stability, Hard delay constraints, High reliability requirements, high scalability, Security and privacy, etc. and related solutions.

The publication includes a review of information on the methods of pavement condition recognition using various methods. Measurement system has been presented that allows to determine the condition of the pavement using the Inertial Measurement Unit (IMU) and machine learning methods. Three machine learning methods were considered: random forest, gradient boosted tree and custom architecture neural network (roadNet). Due to the developed system the set of learning and validation data was created on 3 vehicles: Opel Corsa, Honda Accord, Volkswagen Passat. All of the listed vehicles have front wheel drive. The presented machine learning methods have been compared with each other. The best accuracy on the validation set was achieved by the artificial neural network (ANN). The study showed that asphalt condition classification is possible and the developed system fulfils its task.

In many road safety, traffic management, and travel planning analyses, it is useful to classify road sections according to risk level. Such classification is labour-intensive and needs to be reviewed periodically. The authors propose a model for identifying a discrete risk class for road sections based on selected traffic flow parameters, which are available in most measurement systems monitoring current traffic conditions. The Surrogate Safety Measures approach was applied in the model formulated using Principal Components Analysis. As input to the model SSMs are used in the form of a set of hourly average traffic flow parameters. The SSMs used are: the percentage of light vehicles exceeding the speed limit by a value in the range 21 to 30 km/h; the percentage of light vehicles exceeding the speed limit by more than 30 km/h; the traffic volume of light vehicles; the traffic volume of heavy vehicles and the mean speeds of light vehicles and heavy vehicles.

This paper presents results of calculations of risk class obtained from the model for different locations on single-carriageway two-lane roads in Poland. Satisfactory compliance of risk classes designated by the road operator and identified by the model based on current traffic data was achieved. The proposed model can be used as the core of an effective alternative road safety screening method.

Impact of climate change on railway transport manifests in a variety of consequences, such as rail buckling, rail flooding, expansion of swing bridges, overheating of electrical equipment and its damage, bridge scour, failure of earthworks, ground settlement, pavement deterioration, damage to sea walls, coastal erosion of tracks and earthworks, and an increased number of railway accidents in general. Such impacts can cause considerable disruption of railway operations and lead to substantial financial expenses for repair of the railway infrastructure. Therefore, it is crucial to include adaptation strategies already in the design phase of the railway construction to ensure stability and integrity of the railway operations. This paper provides a literature review of adaptation considerations in Canada, China and Sweden and discusses climate change challenges that these countries face in their railway systems. In conclusion, the authors provide recommendations for adaptation approaches based on the reviewed international experience which can be useful for policymakers and managers of railway companies.