Intelligent Transportation Systems – Problems and Perspectives

Today’s societies are facing great challenges in transforming living environments in a way better serving people’s demands of the future. A key point in this transformation is reinventing cities as smart cities, where the core services are integrated in a way that ensures a high quality of life while minimizing the usage of resources [Smart cities in Europe. Serie Research Memoranda 0048, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics 1]. Setting up smart cities resp. transforming cities to smart cities includes the development of smart transport systems as a main service all other services rely on. Thinking about current mega trends like Individualization of Products (Mass Customization) in so called Industry grid also known as Industry 4.0 [Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0 2] (Industrie 4.0 describes the industry in the 4th industrial revolution to customized mass production after mechanization, mass production, digitalization [Map ‘n’ tag. Thinking highways 3]), Personalized Medicine or the need of better support for disabled and older people in an aging society, the interconnection of involved bodies is a premise. In virtual world this means integration of data networks and ICTs in the physical world this means establishing individual and personalized transport services that cover individual mobility and individual distribution of goods. To ensure the best utilization of infrastructure while having less employable people in an aging society a high grade of automation and information integration is needed. We call this a smart transport system as the next step in development of today’s intelligent transport systems (ITS) and propose establishing this ITS of the future as an autonomic system to meet all the different requirements and ensure a high reliability of the overall system. Reliability is essential since most other services of the living environment smart city, will rely on transport systems. This chapter gives an overview on state of research based on current literature and recent publications of the authors (see references) and focus on the ICT system needed to manage transportation the autonomic transport management system.

To provide transition to “green economy,” it is necessary to increase the safety and reliability of transport system using Intelligent Transportation Systems. The modern intelligent transportation systems implementation requires communications, command, and control designed originally in vehicles and infrastructure. This paper makes the decision by perfecting vehicle and infrastructure of the transport system. A different way is to use rational management decisions based on the received real-time information. The paper also offers methods to improve the efficiency of the urban transport system.

Traffic congestion understood as transportation crowding is an important factor for increasing inconvenience of life and passing through element of transportation infrastructure in urban areas. Measures aimed at mitigating the negative effects of such situations should be taken at different levels of traffic management: strategic, tactical and operational. This requires a close cooperation and coordination between planners, designers and traffic engineers. The article presents strategies for managing congestion in cities using packages of ITS services. It also presents the concept of an integrated approach to this issue in the context of the theory of traffic flow.

The article presents two short-term forecasting models for determining the traffic flow volumes. The road traffic characteristics are essential for identification the trends in the distribution of the road traffic in the network, determination the capacity of the roads and the traffic variability over the time. The presented model is based on the historical, detailed data concerning the road traffic. The aim of the study was to compare the short-term forecasting models based on Bayesian networks (BN) and artificial neural networks (NN), which can be used in traffic control systems especially incorporated into modules of Intelligent Transportation Systems (ITS). Additionally the comparison with forecasts provided by the Bayesian Dynamic Linear Model (DLM) was performed. The results of the research shows that artificial intelligence methods can be successfully used in traffic management systems.

Transport networks need very effective optimization tool for good utilization of transport line capacities. Planning and dimensioning of capacities can be done in definite planning horizon or to satisfy offered traffic from point to point in the network, crossing multiple lines on the path. Such approach is the crucial part of every Intelligent Transport System (ITS) today. Capacity dimensioning is an important element of resource management and it can be seen as CEP (capacity expansion problem). The mathematical model for optimal capacity sizing of N different transport types (capacity types—commodities) is explained, minimizing the total expansion cost. In the case of CEP for multiple line capacities with mutual traffic correlation such problem could be more demanding. With such approach an efficient heuristic algorithm for three different capacity types is being developed and tested on two different scenarios, for long-term capacity planning and for strategic multi-stop route creation in airline industry.

One of the factors limiting the increase in quality of traffic with the increasing complexity of the freight traffic in Russia is the lack of interaction consistency of the main modes of transport and transport uncommon in areas of direct transport service production and transport units. Lack of practical operating experience of intelligent transport systems in Russian enterprises, poor prevalence of well-known methods of accumulation and analysis of knowledge in transport, decision-making (genetic algorithms, neural networks, knowledge bases, Big Data methods, etc.) require improvement of the existing interaction methodology between industry and transport, in particular transport and technological systems providing direct cargo transport services. This methodology should be based not only on modern progress in technology and organization of rail transport, but also taken into account the economic and informational factors and constraints that arise in the interaction of industrial and mainline rail. This article proposes an approach to the formation and composition of intelligent transport systems in industry. This approach is based on the original combination of analytical and simulation models of transport and technological system realizing the complex of transport and logistic methods of functioning organization of rail transport and technological systems. Intelligent transport system of proposed functional composition is focused on improving the efficiency of interaction between production and transport in terms of complicating the structure of freight traffic and the increasing quality requirements for freight.

The article presents information about the European Rail Traffic Management System (ERTMS). This system is one of important components ensuring interoperability of the European rail system. The structure of the ERTMS has been discussed and its individual components have been described. Train control systems (TCS) are used in many countries in the world. However, for a long time, those were mostly national systems used in individual countries. The system is being implemented in individual European countries (as well as outside Europe), but it will take many years to reach full interoperability of the European rail system. Rail traffic management is an important element of rail transport systems. It contributes considerably to ensuring the expected quality and efficiency of rail services while retaining the required traffic safety level. To meet contemporary safety requirements and growing user expectations, rail traffic management systems are based on technologically and functionally advanced IT solutions. The strategic management system harmonizes all technical and organizational actions to ensure the assumed quality and the required level of rail traffic safety in the respective area. Of course, the ERTMS is not a system used only in Europe (the European Union) but also outside the EU and Europe. The ERTMS is an example of ITS system in rail traffic. It can be noticed that the ITS implementation only shows very good establishment of requirements for the system within the entire European Union. It is the standard best described within the ITS. There are no such accurate guidelines for the entire traffic control and management system in road traffic.

The tendencies of intellectualization in aerospace engineering are considered. The aircraft’s intellectualization subsystems for various purposes are described. The types of intelligent systems in the infrastructure of aerospace engineering for pre-starting procedures and launch of an aircraft, air traffic control, airfield vehicles control are considered. Special attention is given to the intellectualization logistics of air transport.