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The application of SON algorithms for automating the processes of operating fourth-generation mobile networks based on the networks of operation, administration and management of OAM (Operation and Maintenance) is considered. The features of the tasks at the stages of self-optimization and self-configuration of the network for the various stages of 4G mobile network life cycle are shown. Criteria and approaches to assessing the effectiveness of solving problems by the SON network are proposed. The technical requirements are also formulated for SON algorithms. The experimentally achieved values of the selected performance exponents depending on the duration of the test cluster self-optimization time of the 4G network are shown.

This paper proposes a method of automatic speaker-independent recognition of human psycho-emotional states by analyzing the speech signal based on Deep Learning technology to solve the problems of aviation profiling. For this purpose, an algorithm to classify seven human psycho-emotional states, including anger, joy, fear, surprise, disgust, sadness, and neutral state was developed. The algorithm is based on the use of Mel-frequency cepstral coefficients and Mel spectrograms as informative features of speech signals audio recordings. These informative features are used to train two deep convolutional neural networks on the generated dataset. The developed classifier testing on a delayed verification dataset showed that the metric for the multiclass fraction of correct answers’ accuracy is 0.93. The solution proposed in the paper can be in demand in human-machine interfaces creation, medicine, marketing, and in the field of air transportation.

Internet of Vehicles (IoV) is a network of vehicles communicating with each other by exchanging road traffic information via radio access technologies. Two potential technologies of V2X that have gained attention over the past years are DSRC and cellular networks such as 4G LTE and 5G. DSRC is suitable for low latency communications, however provides a shorter coverage range whereas, 4G LTE offers a wide coverage range but has high transmission time intervals. In contrast, 5G offers higher data rates, low latencies but prone to blockages. Single technology might not fully accommodate the requirements of vehicular communications. Hence, it is required to interwork with more than one radio access network to satisfy the requirements of safety vehicular applications. One issue identified when working with multiple radio access networks is the selection of the most appropriate network for vertical handover. Usually, in the previous works, the network is selected directly or will be connected to the available network due to which the handover had to take place frequently resulting in unnecessary handovers. Hence, in the existing state-of-the-art, the need for handover is not validated. In this paper, we have proposed a dynamic Q-learning algorithm to validate the need for handover, and then, appropriate selection of network would take place by using a fuzzy convolutional neural network. Besides, a modified jellyfish optimization algorithm is proposed to select the shortest paths by forming V2V pairs that take into account channel metrics, vehicle metrics, and vehicle performance metrics. The proposed algorithms are then evaluated using OMNET++ and compared with the existing state-of-the-art concerning mean handover, HO failure, throughput, delay, and packet loss as the performance metrics.

The article describes a project to use PLC technology in railway light signs. The proposal presents a solution for the separation of supplying circuits with the use of filters. The necessity of separation is a result of long distance data transmission between sign controllers and LED railway signs using PLC modems which work on the same frequency. Problems in the selection of filter parameters are presented due to the fact that there is no specification for the characteristic of the separation filter, there are only requirements for the reliability communication system. To meet these requirements, the circuits separation efficiency must be assessed. The paper presents a quick method to evaluate separation efficiency. Structure of the network, as well as the set of devices which realize this specific kind of wired sensor network for supervising railway LED sign network and maintenance parameters are also presented in this article.

Autonomous navigation is important not only in autonomous cars but also in other transportation systems. In many applications, an autonomous vehicle has to follow the curvature of a real or artificial road or in other words lane lines. In those application, the key is the lane detection. In this paper, we present a real-time lane line tracking algorithm mainly designed to mini vehicles with relatively low computation capacity and single camera sensor. The proposed algorithm exploits computer vision techniques in combination with digital filtering. To demonstrate the performance of the method, experiments are conducted in an indoor, self-made test track where the effect of several external influencing factors can be observed. Experimental results show that the proposed algorithm works well independently of shadows, bends, reflection and lighting changes.

The technical conditions and service life of steering elements of vehicles are an important factor directly affecting road safety. Therefore, high reliability of such kind’s components is required. In the paper, on the basis of the stand test, the fatigue durability of a ball joint of a steering tie rod is determined. It is elaborated together with a prediction for the further number of cycles, enabling to determine the technical state of the tested component containing its service life. The aim of the article is to select an appropriate mathematical model with respect to describing the relationship between the moment of force and the fatigue cycles performed for the ball joint of a steering rod of a vehicle with a GVW above 3.5 tonnes, and identifying the model’s parameters. As a result, the limit number of loading cycles after which the examined joint does not meet safety requirements is estimated.

The main objective of this work is to develop a perception algorithm for self-driving cars which is based on pure vision data or camera data. The work is divided into two major parts. In part one of the work, we develop a powerful and robust lane detection algorithm which can determine the safely drive-able region in front of the car. In part two we develop and end to end driving model based on CNNs to learn from the drivers driving data and can drive the car with only the camera data from on-board cameras. Performance of the proposed system is observed by the implementation of the autonomous car that can be able to detect and classify the stop signs and other vehicles.

The paper considers an approach to building various mathematical models for homogeneous groups of intersections manifested through the use of clustering methods. This is because of a significant spread in their traffic capacity, as well as the influence of several random factors. The initial data on the traffic flow of many intersections was obtained from real-time recorders of the convolutional neural network. As a result of the analysis, we revealed statistically significant differences between the groups of intersections and compiled their linear regression models as a basis for the subsequent formation of generic management decisions. To demonstrate visually the influence of random factors on the traffic capacity of intersections, we built distribution fields based on the fuzzy logic methods for one of the clusters consisting of 14 homogeneous intersections. Modeling was based on the Gaussian type of membership functions as it most fully reflects the random nature of the pedestrian flow and its discontinuity.

Unlike literature and studies coming from high-income or Western countries, the existing conducted on the Middle East and North Africa fail to draw a nearly complete image of the characteristics of passenger travel behaviors in the urban areas of the region. This gap necessitates a holistic review of the previous studies and comparing their results of those of the international findings. This paper summarizes the status of urban travel behavior studies on the MENA region under eight categories of socioeconomics, land use, perceptions and attitudes, urban sprawl, neighborhood design, public transportation use, active mobility, and new technologies and concepts. Descriptive literature review and desk research depicts both lack of research results or data and differences between the behaviors in the MENA region and the Western countries. Moreover, based on the background review, this paper provides a list of recommendations for having more sustainable mobility in the MENA region.

Speed is a crucial factor in the frequency and severity of road accidents. Light and heavy vehicles speed in free-flow traffic at six locations on Poland’s national road network was analyzed. The results were used to formulate two models predicting the mean speed in free-flow traffic for both light and heavy vehicles. The first one is a multiple linear regression model, the second is based on an artificial neural network with a radial type of neuron function. A set of the following input parameters is used: average hourly traffic, the percentage of vehicles in free-flow traffic, geometric parameters of the road section (lane and hard shoulder width), type of day and time (hour). The ANN model was found to be more effective for predicting the mean free-flow speed of vehicles. Assuming a 5% acceptable error of indication, the ANN model predicted the mean free-flow speed correctly in 84% of cases for light and 89% for heavy vehicles.

One of the drawbacks of the currently used tonal rail chains without insulating joints is that when the train approaches the controlled section, the train begins to shunt this section at some distance before entry on him. In connection with the traffic light moves on this distance. But being that this distance depends on many variables, it constantly changes these variables. For eliminate this drawback, instead of the traditional potential receiver it is proposed to use electric current track receiver, which has a clear boundary fixation entry of train on the controlled section. By now developed and used methods for determining the main analytical expressions for analysis and synthesis tonal rail circuits with potential receivers in various operating modes, and the theoretical issues of tonal rail chains with current receivers were practically not considered, in this connection, the article considers the development of mathematical models for determining the absolute shunt sensitivity of tonal rail chains without isolating joints with current receivers and methods for calculating determination of the critical location of the minimum shunt sensitivity. The obtained expressions differ significantly from the known equations of absolute shunt sensitivity and the critical location of the minimum value of the shunt sensitivity and will allow to spend analyzing, synthesis and design of tonal rail chains without isolating joints with current receivers, that will allow to increase the safety of train traffic.

This paper describes trajectory planning for an Autonomous Vehicle (AV) in the freeway path. Three types of driving modes are analyzed. First was free flow, this constitutes that moving at the desired speed is determined at the beginning of the movement. Second case was car following, when overtaking or lane-change was impossible, distance or speed adaptation is executed using the variable acceleration/deceleration strategy. Third case was lane change or overtaking. For lane change or overtaking paths, the 5th degree polynomial is used to create a curvilinear path to changes its path to the left lane and then returns to its default lane. The velocity and relative distances of cars are main factors for decision making. All proper driving decisions algorithm is introduced. According to autonomous car desired velocity, in the two driving cases (fast and slow desired velocity for AV) are studied by simulation and their results analyzed and compared with together.

Small and medium-sized ports (SMSPs) in the Baltic Sea Region (BSR) are caught in a dilemma of less financial support by the European funding programmes due to their peripheral position in the TEN-T Core Network. Most of the BSR ports belong to the TEN-T Comprehensive Network, which is rationally less important in the topical financial, infrastructural and policy discourses. Despite this, BSR ports are of a colossal importance for the regional economic development serving as gateways for the individual regions and drivers of socio-economic and environmental transition. In addition, ports are essential in pursuing new innovation avenues. The environmental targets published by the European Commission for maritime sector in 2030 and 2050 are applicable for SMSPs as well, creating further obstacles and future challenges on the one hand, but opening new horizons to grow and innovate on the other one. A successful environmental and digital transformation going hand in hand in SMSPs enable sustainable boost of sustainable development. Thus, as a first step, the assessment of sustainable readiness in SMSPs is incumbent in order to implement tailor-made solutions on individual basis, by ensuring efficient usage of available resources and capabilities. In line with the Connect2SmallPorts project, part-financed by the INTERREG South Baltic Programme, 38 SMSPs in the BSR were approached to mapping their digital readiness according to the methodology of the Digital Readiness Index for Ports (DRIP), published within the project in 2020. Building upon this, this paper introduces the idea of digital and environmental twinning to conclude on sustainable development potentials in SMSPs with an adaptation of the DRIP score. Hence, the research contributes to the sustainable port concept and illustrates the positioning of SMSPs in the progress of sustainable development.

The paper describes the concept of a Digital Twin (DT) creation, which could be used as part of a centralized ground traffic control system in the airport’s apron area. The work is focused on the automatic control systems expected in the future, which will completely replace the ground traffic controllers that solve the centralized control’s tasks in our days. It is assumed that the developed DT can be used both at the stage of training the control system and at the stage of its use. A special feature of the proposed DT concept is the clearly formulated principles of the observed process’ time discretization and the space in which the movement of ground vehicles and aircraft occurs. The experimental verification of the developed concept was carried out using a special simulation model designed to generate data streams on the airport’s transport network’s traffic participants’ location.

The paper discusses the method of improvement of transmission properties of ICT networks by setting the correct distribution of transmission resources in order that it is possible to increase the resulting global probability of supporting the calls generated in the nodes included in the network. The method uses some parts of the graph theory. A minimum length path searching algorithm was used which made it possible to calculate the volume of movement transferred by individual edges of the graph describing the network. The coefficient of the distribution of transmission resources was determined and used to calculate the value of the resources in reference to a particular link, both in the case of equal and diverse movement generated in individual network nodes. A piece of software was developed which uses the results of applying the above-mentioned algorithm to prepare the files describing the analyzed structures in order to carry out simulation tests. The views and analyses are illustrated by examples, and the resulting effects of the influence of the introduction of the method of dividing network resources into transmission properties of a network modelled by graphs are shown in the charts included.

The work deals with the mathematical modeling of traffic phenomena. The submitted model is based on a prospective analogy of some described phenomena with particle sedimentation. Both the qualitative analysis of the model and the numerical experiment is carried out. Qualitative results of the research have been compared with the known data of supervision of the traffic on city highways. As a result, the main control parameters which can use for optimal traffic management are highlighted and justified.

In this article we focus on multi-compartment vehicles that have to pick-up goods from suppliers and deliver them to various customers. Usually these goods cannot be transported by single-compartment vehicles due to the fact that some products are harmful to others: incompatibility between products. It is therefore a question of satisfying a set of customers while respecting the constraints linked to the capacity of each vehicle compartment, each type of product and ensuring that each supplier is visited before his customer. Our work consists firstly of mathematically modelling the problem described and secondly of solving it using methods due to its complexity. In this case we use the genetic algorithm to solve the pick-up and delivery problem of goods with a time window provided by multi-compartment vehicles. Our model allows to find a minimum distance and a minimum cost in the tour carried out by a reasonable number of vehicles.

In today’s daily traveller world, not only the time and money became important, but climate change and pandemic raised the importance of safety and sustainability of future trip plans. In order to provide such a wide coverage for a variety of important information, a sustainable trip planner needs to receive a lot of data from a variety of differentiated data sources. Provided review of related works shows that a lot of started activities in this aspects gives all of us promises and hope that available Big Data sources will be wisely used in order to bring added value not only to individual travellers, but also society, the transportation services will become better structured and information will be easily available for smart and safe decision making, that gradually will increase life quality. The study’s main goal is analysis of open data sources for the trip planner development. Authors analysed availability of data for Riga transport system and data usage for sustainable trip planner.

Nowadays, traffic engineers employ a variety of intelligent tools for decision support in the field of transportation planning and management. However, not a one available tool is useful without precise travel demand information which is actually the key input data in simulation models used for traffic prediction in urban road areas. Thus, it is no wonder that the problem of estimation of travel demand values between intersections in a road network is a challenge of high urgency. The present paper is devoted to this urgent problem and investigates its properties from computational and mathematical perspectives. We rigorously define the travel demand estimation problem as directly inverse to traffic assignment in a form of a bi-level optimization program avoiding usage of any pre-given (a priori) information on trips. The computational study of the obtained optimization program demonstrates that generally it has no clear descent direction, while the mathematical study advances our understanding on rigor existence and uniqueness conditions of its solution. We prove that once a traffic engineer recognizes the travel demand locations, then their values in the road network can be found uniquely. On the contrary, we discover a non-continuous dependence between the travel demand locations and absolute difference of observed and modeled traffic values. Therefore, the results of the present paper reveal that the actual problem to be solved when dealing with travel demand estimation is the problem of recognition of travel demand locations. The obtained findings contribute in the theory of travel demand estimation and give fresh managerial insights for traffic engineers.

Several European road operators and authorities joined the C-Roads Platform with the aim of harmonising the deployment activities of cooperative intelligent transport systems (C-ITS). C-ITS research is preliminary to future automated-driving vehicles. The current conventional highways were designed on traditional criteria and models specifically developed for traffic flows of manually guided vehicles. Thus, this article describes some new criteria for designing and monitoring road infrastructures on the basis of performance features of autonomous (or self-driving) vehicles. The new criteria have been adopted to perform an accurate conformity control of the A22 Brenner motorway, included in the C-Roads Platform, and also to ascertain whether in future it may be travelled by automated vehicles in safety conditions. Always in accordance with the technical and scientific insights required by the C-Roads Platform, a traffic model has been implemented to estimate how the A22 capacity increases compared to current values, by taking various percentages of automated or manual vehicles into consideration. The results given by theoretical models indicate that the highway will be able to be travelled by automated vehicles in safety conditions. On the other hand, the lane capacity is due to increase up to 2.5 times more than the current capacities, experimentally determined through traffic data collected from 4 highway sections by means of Drake’s flow model.

The object of this study is a multicriteria transport problem, being stated for availability of several means of cargo delivery, meaning a multimodal transport problem. The optimization criteria of the multimodal transport problem described above are two objective functions of minimizing total transportation costs and level of transport risks. Three types of transport were selected for research: automobile, rail and river (inland waterway). The results of the study lay the foundation for development of a new valid algorithm for solving multimodal transport problems like multi-criteria optimization ones. The main advantage of such an algorithm lies in its higher potential convergence rate compared to classical numerical optimization methods, which now are predominantly used to solve the problems of this type. This advantage may not be decisive, but it appears to be at least quite an important argument when choosing the method of realization for two-criteria multimodal transport problems earlier considered, especially, in case of a large dimension. Moreover, the algorithm described in the work can be applied to similar problems with any number of types of transport and optimization criteria.

Increasing requirements in the field of security, in particular in the transport sector, the rescue work, the inviolability of private property and others urged to research work in the field of radar monitoring people, vehicles or other objects in the environment that not allow make so using the most popular and available technology for production and analysis of video images. These conditions of poor visibility, or even lack thereof, are darkness, bad weather, smoke, dust, wall (roof) buildings and the vehicle body. Existing instruments and special equipment occupy a certain niche in this area, mainly for counter-terrorism operations. However, such equipment is not readily available and extremely high price. In the paper presented research is development of the group’s existing radar technology in the field of location through opaque obstacles.

Recent years have witnessed a colossal increase of vehicles on the roads; unfortunately, the infrastructure of roads and traffic systems has not kept pace with this growth, resulting in inefficient traffic management. Owing to this imbalance, traffic jams on roads, congestions, and pollution have shown a marked increase. The management of growing traffic is a major issue across the world. Intelligent Transportation Systems (ITS) have a great potential in offering solutions to such issues by using novel technologies. In this review, the ITS-based solutions for traffic management and control have been categorized as traffic data collection solutions, traffic management solutions, congestion avoidance solutions, and travel time prediction solutions. The solutions have been presented along with their underlying technologies, advantages, and drawbacks. First, important solutions for collecting traffic-related data and road conditions are discussed. Next, ITS solutions for the effective management of traffic are presented. Third, key strategies based on machine learning and computational intelligence for avoiding congestion are outlined. Fourth, important solutions for accurately predicting travel time are presented. Finally, avenues for future work in these areas are discussed.

The paper presents issues associated with the impact of electromagnetic interference on static converters, which exploitation in a railway transport environment. The measurements of an electromagnetic field emitted by a static converter were shown. Designs of this kind are exploitation in railway facilities, therefore, they should not disturb the functioning of other equipment, the rail traffic control systems, in particular. As a result of the EMC tests, it was concluded that the permissible values of conducted interference emissions were exceeded. An analysis of the obtained results enabled developing a research model, and a further reliability and exploitation analysis, taking into account electromagnetic interference. This, in turn, enabled determining a relationship allowing to determine the probability of a static converter staying in a state of full ability. The presented discussions regarding a static converter, taking into account electromagnetic interference, allow for the numerical assessment of different types of solutions (technical and organizational), which can be implemented in order to mitigate the impact of electromagnetic interference on a system’s functioning.

This study provides with a safety assessment of the pedestrian’s crash data in one of the largest cities of the state of Michigan, Grand Rapids. Crash data reviewed included a 9-year period between years 2010 and 2018. Crash clusters with largest number of accidents were selected to perform analysis based on the normalization of crash with population (using Census Bureau information). Geographic Information System (GIS) software was used to gather this data using a 250-feet buffer around the clusters. Also, GIS was used to identify the infrastructure design and locations nearby the studied area (e.g. schools and hospitals) to understand the crash environments. Observation of the associated factors with pedestrian crashes were studied at the location of interest. An analysis of all safety efforts was completed and a list of recommendations and possible implementation strategies (e.g. pedestrian countermeasures). Finally, it was found that four types of pedestrian crashes were most representative that crashes involved left-turning vehicle, crashes involved right-turn vehicle, crashed involved pedestrian in crosswalk and through traffic, and pedestrian were not cross at designated cross location

This article considers various aspects of the impact of climate change on the railway infrastructure and operations. A brief international overview and the importance of this issue for Russia are given. Temperature effects, permafrost thawing, strong winds, floods and sea level rise, long-term effects, and adaptation measures are discussed. In conclusion, the authors give several recommendations on further research in this area, and highlight that special attention should be given to the areas in the Russian Federation which already face or might soon experience damage from storm events or flooding and sea level rise, namely Kaliningrad Region on the Baltic Sea, the area between Tuapse and Adler in Krasnodar Region on the Black Sea, and on Sakhalin Island from the side of the Sea of Japan.

The paper describes a mixed reality environment for testing highly automated vehicle functions. The proposed Scenario-inthe-Loop test system connects real-time computer simulation with real elements being applicable at automotive proving ground. The paper outlines necessary hardware and software requirements and proposes basic system architecture. The limitation and bottleneck of the system are also identified: latency of the wireless communication constraints the accuracy of the test system. However, the presented framework can contribute to efficient development, testing and validation of automated cars. The Scenario-In-The-Loop architecture has also been justified by real-world demonstration using an experimental 5G New Radio network technology.

The logistics performance has a crucial role in the industrial and economic development of countries. This study aims to underline implications for policy makers in improving the logistics performance of countries in terms of Industry 4.0. For this purpose, the effect of digitalisation on logistics performance is analysed by using correlation and multiple regression analysis. The empirical study builds upon dimensions and indicators of the Digital Economy and Society Index (DESI) and Logistics Performance Index (LPI) of the World Bank. The results indicate that governmental policies should target to deliver sound framework conditions for the generation of human capital (here: ICT specialists), sustainable usage of internet services (e.g. professional social networks, online sales, etc.), integration of digital technologies (e.g. Big Data, Cloud computing, etc.), as well as digital connectivity (here: fixed broadband and 4G coverage) in order facilitate improvement of logistics performance.

This article proposes a model of systems dynamics for the analysis and study of physical flows in a distribution logistics chain. The proposed model is a decision support tool that allows testing several scenarios in order to study the behavior of physical flows within a supply chain depending on the inventory and transport costs and taking into consideration the environmental issues through the integration of a CO2 tax in transport costs. The developed model can be used to analyze various scenarios and perform various “what-if” analyzes, as well as to answer questions about the long-term operation of distribution chains. The results show that logistic decisions and strategies taken at the supply chain level influence inventory and transport costs. They show also that the model is a well decision-support tool for calculating and analyzing transport and inventory costs. We first expose the methodology and the literature review. Next, we present in detail the structure of the model consisting of the causal loops diagram and stock and flow diagram. Finally, we discuss the results of the model.

Using a qualitative instrumental case study research design, this study examines the strategies and carbon reduction measures implemented by Sydney Airport to achieve their goal of being a carbon neutral airport by 2025. The study period was from 2013 to 2019. The qualitative data was analyzed using document analysis. Sydney Airport has implemented a wide range of carbon reduction measures that underpin its strategy to become a carbon neutral airport. Sydney Airport’s annual emissions intensity per passenger declined in each year examined in study. Sydney Airport has participated in the Airports Council International Airport Carbon Accreditation Program since 2014 and currently holds Accreditation Level 3: Optimization. Sydney Airport’s goal is to be awarded Accreditation Level 3+: Carbon Neutrality by 2025. Sydney Airport has a carbon offsets agreement in place with a not-for-profit organization.

Vehicular queue length measurement is an important parameter to detect the traffic congestion, which is resulted from several issues such as traffic lights, accidents, and poor roads infrastructures. In this paper, a system in real-time is proposed to detect and measure the vehicular queue length at intersections. The proposed system consists of two main steps: the first step is the detection of queue by using frames differencing method to detect the motion in the target areas. If there is no a motion, then the second step is implemented to detect the vehicles in these areas by using Single Shot Multibox Detector (SSD) algorithm. If there are vehicles, that means the queue exists and the measurement process begins. Some modifications are applied on SSD algorithm to fit with in our system and to improve the accuracy of the vehicle detection process. The system is applied on videos obtained by stationary cameras. The experiments demonstrate that this system is able to accurately detect and measure the vehicular queue length.

After the implementation of Emission Control Areas (ECA) in Northern Europe in 2015, ship operators have to decide for new compliance methods. Among different abatement technologies, also the use of LNG as maritime fuel is one option. Hence, the number of LNG-fueled vessels plying in North Sea and Baltic Sea is growing, so ports have to cope with the increasing demand for LNG. Until now, German ports have been a little bit delayed in the installation of LNG infrastructure. Currently, a new LNG-terminal at Brunsbüttel, the western entrance to the Kiel Canal, is under construction. The distribution of LNG from the new LNG-hub to the other German ports can be modelled as an Inventory Routing Problem (IRP) that is usually solved by combinatorial optimization methods. This paper investigates the research question, how the distribution can be modeled as an IRP, which distribution mode is the most economic for the German ports and which modal mix for the LNG supply leads to the greenest distribution. The results of this paper are empirically validated by data that were collected in several EU-projects on sustainable supply chain management and green logistics.

The operating condition of bus transit system has not been efficient in most cities of Iran, and many management methods such as regular bus scheduling, assigning exclusive bus lanes, etc., which are necessary for increasing the efficiency of this system, were not regarded enough. Thus, achieving a method for locating the bus stops and optimizing the number of such stops based on a non-homogeneous spatial and temporal distribution of passengers as well as the local traffic patterns are important to be investigated. As such, the present study aims to investigate the modeling of a bus transit system corridor according to the non-homogeneous spatial and temporal distribution of passengers throughout the route aiming at optimization of the number of attracted passengers to the bus. For this purpose, the 8-km route from Vali-e-asr roundabout to Gas roundabout in the city of Rasht in the north of Iran is selected for modeling. Hammersley sampling method, as well as two heuristic optimization techniques, including a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm, are used for generating a non-uniform population and solving the optimization model. Therefore, the results of this analysis are compared to the optimization results by using the probabilistic analysis without considering the reference uncertainty. Finally, the PSO is selected as the superior algorithm for modeling and locating the bus stops due to its results in less travel time, and the validity of robust optimization model is shown due to its higher accuracy and adaptation to the real-world environment. Overall, although the optimization results based on indeterminate analysis in comparison to determinate analysis brought about more average travel time, more population sets were covered by the new introduced stops during 18 active hours of the bus transit system.

A sustainable transportation system is possible only through an efficient evaluation of transportation network performance. The efficiency of the transport network structure is analyzed in terms of its connectivity, accessibility, network development, and spatial pattern. This study primarily aims to propose a methodology for modeling the accessibility based on the structural parameters of the urban road network. Accessibility depends on the arrangement of the urban road network structure. The influence of the structural parameters on the accessibility is modeled using Multiple Linear Regression (MLR) analysis. The study attempts to introduce two methods of Artificial Intelligence (AI) namely Artificial Neural Networks (ANN) and Adaptive network-based neuro-fuzzy inference system (ANFIS) in modeling the urban road network accessibility. The study also focuses on comparing the results obtained from MLR, ANN and ANFIS modeling techniques in predicting the accessibility. The results of the study present that the structural parameters of the road network have a considerable impact on accessibility. ANFIS method has shown the best performance in modeling the road network accessibility with a MAPE value of 0.287%. The present study adopted Geographical Information Systems (GIS) to quantify, extract and analyze different features of the urban transportation network structure. The combination of GIS, ANN, and ANFIS help in improved decision-making. The results of the study may be used by transportation planning authorities to implement better planning practices in order to improve accessibility.

Recent advances in green and smart mobility aim to reduce congestion and foster greener, cheaper and with less delay transportation. The reduction of fuel consumption and CO2 emissions have worked on light-duty vehicles. However, the reduction of emissions and consumables without sacrificing on emission standards is an important challenge for heavy-duty vehicles. The paper introduces a big data system architecture that provides an on-demand route optimization service reducing NOx emissions of heavy-duty vehicles. The system utilizes the information provided by the navigation systems, big data analytics such as predictive traffic and weather conditions, road topography and road network and information about vehicle payload, vehicle configuration and transport mission to develop a strategy for the best route and the best velocity profile. The system was proven efficient during the performance evaluation phase, since the cumulative engine-out NOx has been decreased more than 10%.

This work apply a deep learning artificial neural network model – the Multilayer Perceptron – as a regression model to estimate the demand of bus passengers. Transit bus ridership and weather conditions were collected over a year from a medium-size European metropolitan area and linked under the assumption: individuals choose the travel mode based on the weather conditions that are observed during (a) the departure hour, (b) the hour before or (c) two hours prior to the travel start. The transit ridership data were also labelled according to the hour of the day, day of the week, month, and whether there was a strike and/or holiday or not. The results show that the prediction error of the model decrease by ~9% when the weather conditions observed two hours before travel start is taken into account. The model sensitivity analyses reveals that the worst performance is obtained for a strike day of a weekday in spring (typically Wednesdays or Thursdays).

Smart Cities promise to their residents, quick journeys in a clean and sustainable environment. Despite, the benefits accrued by the introduction of traffic management solutions (e.g. improved travel times, maximisation of throughput, etc.), these solutions usually fall short on assessing the environmental impact around the implementation areas. However, environmental performance corresponds to a primary goal of contemporary mobility planning and therefore, solutions guaranteeing environmental sustainability are significant. This study presents an advanced Artificial Intelligence-based (AI) signal control framework, able to incorporate environmental considerations into the core of signal optimisation processes. More specifically, a highly flexible Reinforcement Learning (RL) algorithm has been developed towards the identification of efficient but-more importantly-environmentally friendly signal control strategies. The methodology is deployed on a large-scale micro-simulation environment able to realistically represent urban traffic conditions. Alternative signal control strategies are designed, applied, and evaluated against their achieved traffic efficiency and environmental footprint. Based on the results obtained from the application of the methodology on a core part of the road urban network of Nicosia, Cyprus the best strategy achieved a 4.8% increase of the network throughput, 17.7% decrease of the average queue length and a remarkable 34.2% decrease of delay while considerably reduced the CO emissions by 8.1%. The encouraging results showcase ability of RL-based traffic signal controlling to ensure improved air-quality conditions for the residents of dense urban areas.

Late research has established the critical environmental, health and social impacts of traffic in highly populated urban regions. Apart from traffic monitoring, textual analysis of geo-located social media responses can provide an intelligent means in detecting and classifying traffic related events. This paper deals with the content analysis of Twitter textual data using an ensemble of supervised and unsupervised Machine Learning methods in order to cluster and properly classify traffic related events. Voluminous textual data was gathered using innovative Twitter APIs and managed by Big Data cloud methodologies via an Apache Spark system. Events were detected using a traffic related typology and the clustering K-Means model, where related event classification was achieved applying Support Vector Machines (SVM), Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks. We provide experimental results for 2-class and 3-class classification examples indicating that the ensemble performs with accuracy and F-score reaching 98.5%.

The origin-destination (OD) demand estimation problem is a classical problem in transport planning and management. Traditionally, this problem has been solved using traffic counts, speeds or travel times extracted from location-based sensor data. With the advent of new sensing technologies located on vehicles (GPS) and nomadic devices (mobile and smartphones), new opportunities have emerged to improve the estimation accuracy and reliability, and more importantly to better capture the dynamics of the daily mobility patterns. In this paper we frame this new data in a comprehensive framework which estimates origin-destination flows in two steps: the first step estimates the total generated demand for each traffic zone, while the second step adjusts the spatial and temporal distribution on the different OD pairs. We show how mobile data can be used to obtain OD matrices that reflect the aggregated movements of individuals in complex and large-scale instances, while speed information from floating car data can be used in the second step. We showcase the added value of big data on a realistic network comprising Luxembourg’s capital city and its surrounding. We simulate traffic by means of a commercial simulation software, PTV-Visum, and leverage real mobile phone data from the largest telco operator in the country and real speed data from a floating car data service provider. Results show how OD estimation improves both in solution reliability and in convergence speed.

Inventory control has been a major point of discussion in industrial engineering and operations research for over 100 years. Various advanced numerical methods can be used for solving inventory control problems, which makes it a highly multidisciplinary filed attracting researchers from different academic disciplines. This fact makes it a daunting task to subsume the gargantuan spectrum of literature related to inventory control theory in one treatise. In light of this fact, this paper focuses on the timeline of inventory control models with respect to methodologies behind deriving optimal control parameters. Such methodologies include analytical approaches, optimal control theory, dynamic programming, simulation-based optimization and metamodel-based optimization.

Trends in the digitalization of business open up opportunities for the use of fundamental approaches to the development of enterprise architecture in the creation of appropriate methodologies. The article discusses the approach to the use of adapted Zachman framework for enterprise architecture as a basis for the systematization and structuring of the industry methodologies of integrated supply chain planning based on SCOR model. A practical example of using the proposed approach for description of one of the target processes - tactical supply chain planning is considered.

Modern societies are built on good road infra-structure and efficient transport system. Safety is a high-priority consideration in development of road traffic systems. In recent years the weather information has become becomes very vital for road traffic safety because slippery roads are the key source of road accidents in northern regions of Europe, America and Canada as well. In this article we are presenting the test experiences and pilot road weather related services by executing a set of Vehicle to Infrastructure (V2I) communication scenarios by using IEEE 802.11p and 5G test networks. We have made an effort to evaluate the performance of IEEE 802.11p and 3GPP (3rd Generation Partnership Project) 5G test network. We also analyzed the performance of IEEE 802.11p with and without safety feature for secure and reliable vehicular communication. The combination of IEEE 802.11p with 5G test network cellular network makes the traffic system heterogeneous for traffic safety. This heterogeneous system provides the opportunity to exploit the vehicle-based actuators, sensor, and observation data in order to produce the intelligent service platform and up-to-date real time services for vehicles. In this article we have also made a comparison by using an IEEE 802.11p system having safety feature of SafeCOP (Safe Co-operating Cyber-Physical Systems using Wireless Communication) project. SafeCOP is a European project that aims cyber-physical systems-of-systems relying on wireless communication for safe and secure cooperation. This safety feature will help to decrease the amount of road accidents (Car crashes, injuries and fatalities) by offering safe and secure V2V and V2I co-operation. The fundamental advantage of this kind of performance analysis is that the communication between Vehicle-to-Road Weather station (V2RWS) can be exchanged safely and reliably, at the cost of network resources consumed by a safety feature in IEEE 802.11p. It’s clearly presented in this paper, that the use of heterogeneous network and SafeCOP feature for vehicular networking has a clear potential in near future for vehicle’s safety and security of vehicular network.

The ever-increasing tendency of people to travel by motorized vehicles contributes significantly to air pollution and traffic problems. Active travel, namely walking and cycling, seems to be a feasible solution to the current situation in urban mobility. The present paper aims at investigating the effects of active travel in health and quality of life and determine those factors that affect travel behavior. A structured literature review was carried out, which revealed the strong association of walking/cycling with the containment of noncommunicable diseases and the invigoration of wellbeing and self-confidence. In addition, a questionnaire survey was conducted in Greece, addressing the attitudes and perceptions of 507 people towards active travel, health and quality of life. Results showed that participants have a more positive attitude about walking compared to cycling, while characteristics such as age, gender, body mass index, physical and health conditions determine active commuting and consequently life satisfaction.

Vehicular networks that deal with sharing of information among vehicles are gaining popularity among the automobile industry as well as the researchers. These networks are prevalent under the umbrella of Intelligent Transportation Systems (ITS) and deal with data that belongs to either the emergency category or the entertaining category. In case of emergency services, it is clear that - earlier the reception of information, lesser the commotion. The objective of this work thus has been the reduction of the end to end delay when video files are exchanged among vehicles during intersessions. The set objective is accomplished through the design and development of the technique “Instantly Decodable RaptorQ Inter-Sessions” (IDRQIS) for Vehicular Adhoc Networks and the results obtained show that this outperforms the existing popular techniques – the Network Coding and RaptorQ when applied independently to the same environment. This technique can also be applied to the upcoming unmanned vehicles.

Smart devices and their connections to the Internet of Things (IoT) have been the subject of many papers in the past decade. In the context of IoT in transportation, one feature is the smart junction. This research deals with this junction, where several cars approach the intersection from different directions, and a smart traffic light must decide regarding the time intervals of red and green light in each direction. Out novel approach is based not only on the number of vehicles in each lane, but also on the social characteristics of the passengers (e.g. a handicapped person, a driver with no previous traffic violations). These factors will be gleaned from IoT network sources on cars, traffic lights, individuals, municipality data, and more. In this paper, we suggest using a VCG (Vickrey-Clarke-Groves) auction mechanism for the intersection scheduling, combining the social characteristic with a benefit parameter that expresses the passenger’s subjective perception of the importance of crossing the intersection as soon as possible. Our simulation results show the efficiency of the suggested protocol and demonstrate how the intersection scheduling depends on the passengers’ preferences, as well as on their social priorities.

The objective of this study was to investigate the carbon dioxide (CO2) emissions of an airport, to determine if strategies are helping to achieve sustainability targets. Kansai International Airport was selected as the case study, and it is Japan’s third largest airport and there was readily available comprehensive data to enable a study to be undertaken. The airport has a dedicated environmental division and has implemented various initiatives over the past decade or so to reduce the airport’s impact on the surrounding environment, especially since it is in Osaka Bay. The research used an exploratory design, with an initial qualitative case study, followed by a quantitative longitudinal study, utilizing correlation to assess trends over time. Results showed statistically significant reductions in carbon dioxide (CO2) emission from the three facets of airport operations, both in terms of the number of passengers and number of aircraft serviced by the airport. As a result, the initiatives undertaken at Kansai International Airport could be adapted and used by other airports to help reduce their carbon dioxide emissions.

Research is based on wholesale and distribution operations of real-life case company, and in this setting, the most critical part of company’s supply chain is the inventory replenishment to warehouse (Distribution Center) as well as fulfilling and delivering customers’ orders. Different Economic Order Quantity (EOQ)-based models have been considered (Reorder Point, Reorder Point with pipeline on order inventory, and “pulse train”). Simulation system evaluates annual total logistics costs. Results show that in an environment, where local warehouse inventory levels are rather high and replenishment order quantity is rather small, it is important have frequent shipments divided in suitable intervals. In simulation model, this could be done e.g. with the use of “pulse train” function or incorporating pipeline on order inventory in order decision. The research findings are valid for a small-scale supply chain servicing small and geographically limited markets with clients assuming high customer service levels (e.g. 24-hours lead time). For bigger markets, the cross-docking based supply chain models are worth considering in simulations.

This research proposes a background subtraction method with the truncate threshold to improve the accuracy of vehicle detection and tracking in real-time video streams. In previous research, vehicle detection accuracy still needs to be optimized, so it needed to be improved. In the vehicle detection method, there are several parts that greatly affect, one of which is the thresholding technique. Different thresholding methods can affect the results of the background and foreground separation. Based on the results of testing the proposed method can improve accuracy by more than 20% compared to the previous method. The thresholding method has a considerable influence on the final result of vehicle object detection. The results of the average accuracy of the three types of time, i.e. morning, daytime, and afternoon reached 96.01%. These results indicate that the vehicle counting accuracy is very satisfying, moreover, the method has also been implemented in a real way and can run smoothly.

The Baltic Sea Region (BSR) stands for a flagship maritime region in Europe with dominating SME sector. Nevertheless, compared with other European regions, the cooperation and promotion activities of companies that belong to the Blue Economy in South Baltic Sea Region (SBSR) are not sufficient. As a response to this, the EU-project INTERMARE South Baltic aims to support the maritime economy in the SBSR by the creation of a network of companies and stakeholders. In line with the project, this study aims to analyse the future potential of the maritime economy and to identify trends that impact the sustainable development of the blue sector in SBSR. Based on primary data from a SBSR wide survey, descriptive statistical analysis is applied and Compound Annual Growth Rate is used as an indicator. The findings reveal need for actions regarding the sub-sectors Transport, Offshore oil & gas, Aquaculture, Fishery, Mineral resources and Biotechnology.

Currently, in many cities around the world there is a significant increase in the number of vehicles, which leads to an aggravation of problems and contradictions in the road and transport system. This is especially true of traffic congestion, since the presence of the congestion leads to a number of negative consequences: an increase in travel time, additional fuel consumption and vehicle wear, stress and irritation of drivers and passengers, environmental poisoning and others. To solve the problem of congestion, it is necessary to have a reliable system for collecting information about the situation on the roads and a well-developed method for analyzing the collected information. The paper discusses the possibilities of collecting the required information using multi-touch video cameras and ways to improve them. A distinctive feature of this study is the registration of pedestrians crossing the road at the intersection. The aim of the work is to develop methods for collecting information using road sensor video surveillance systems in a traffic congestion and data processing using statistical methods such as: multiple regression analysis, cluster analysis, multidimensional scaling methods and others. The tasks were set: 1) to identify the most significant factors affecting the intensity of movement of vehicles at intersections in a congestion; 2) divide congestion into clusters with the identification of their characteristics; 3) to give a visual representation of multidimensional statistical information obtained with the help of multi-touch road video cameras.