When Will The Future In Public Transport ?

When Will The Future In Public Transport ?

As a result of the rapid development in information technologies and the widespread use of mobile devices, navigation systems have become an important part of social life. Mobile devices, which previously only had navigation functions, are gradually being replaced by more functional and multifunctional smart devices. In addition to this hardware development, some software innovations have been added to the navigation systems; Especially in urban route assignment, routes have begun to be calculated based on instant traffic data. In recent years, unlike the navigation systems developed for vehicle routing, applications developed for travel planning on tolpu transport networks are rapidly becoming widespread. In addition, applications that show only the instant traffic situation of the city are also popular by local authorities.

These types of applications to be developed aim to make optimum travel planning according to the expectations and wishes of the people who make their trips by public transportation and as a result, the use of public transportation vehicles will increase. In addition, increasing the efficiency of using public transportation vehicles will be another important output of these projects.

Due to all these developments, processing traffic data has become a necessity for all travel planning systems in general. When looking at the "Annual Motor Vehicle Increase Rate" table published by TUIK, it can be said that the traffic problem will continue to increase in the coming years. In this context, it is inevitable for journey planning systems to produce solutions based on traffic data to ensure the usability of the solutions.

The problem of timing deviations encountered in our cities constitutes a starting point for new projects. For these new generation projects to be successful;

  • First of all, the statistical distribution parameters (expected value, standard deviation etc.) of the arrival times of public transportation vehicles should be estimated.
  • Low-risk solutions should be brought to the fore by calculating the risk of not applying the solution proposals produced by the application with the statistics obtained.
  • Routes with high deviation should be excluded from the solution proposals.
  • Variables in the arrival time of the vehicles should be determined by statistical methods.
  • Prediction success should be increased by making predictions with Artificial Neural Network models.
  • The main reason I consider the priority target as the risk calculation is the ease of calculation. Because long calculation times make trip planning systems useless. In addition, this model can be easily applied in cities with characteristic traffic density (such as the beginning and end of working hours, such as the traffic density at certain hours) by arranging it with deterministic variables to be determined for the relevant hours (such as increasing the risk to a certain extent at certain hours).

    The purpose of using forecasting models in the project can be explained as follows: It is evaluated that instantaneous traffic data alone will not be sufficient for journey planning systems to produce appropriate solutions. Especially in cities where residential areas are spread over a wide geography, urban transportation times can reach 2-3 hours depending on the traffic density. It is clear that the instant traffic situation of a route can change even in a shorter time. For this reason, in navigation systems and instant traffic information systems, it would be a more correct approach to inform the driver of a moving vehicle according to the intensity forecast to be calculated depending on the location. In addition, in travel planning systems over public transportation networks, the user may want to plan his journey a few hours in advance. In this case, the system must make a calculation according to the density prediction.

    As a result, in these new generation projects; It is aimed to increase the usability of the traffic data obtained from vehicle tracking and traffic control systems (camera and sensor based systems, etc.) by travel planning systems in navigation, traffic information and public transportation networks and to make these systems data efficient. Thus, it is predicted that traffic density, air pollution caused by exhaust gases and energy consumption will decrease due to the decrease in travel times. In a study conducted in 2006, it was calculated that the cost of traffic to our country in terms of fuel and time waste was 3.12 billion dollars [2]. Considering the increase in the number of vehicles in traffic, this number is considered to be much higher today. In another study conducted in 2014, it was argued that "a good traffic analysis can only be possible by creating a quality, reliable and complete archive of traffic flow variables" [3]. In this context, it is thought that such projects will increase the awareness of the relevant authorities in this field. In addition, the project includes a travel proposal with traffic information systems in accordance with this basic definition of Intelligent Transportation Systems, which is defined as "integrating information technologies and advanced transportation methods, taking into account technology, human behavior, socio-economic and political systems in order to solve transportation problems." It aims to integrate systems. More effective travel planning systems over the public transportation networks are among the future targets of the Istanbul Metropolitan Municipality, as a result of increasing mobility and accessibility in the city by reducing the motor vehicle traffic, improving the public transport infrastructure, promoting traffic demand from private vehicles to public transport It will contribute to the goal of creating an urban environment ”[5].

    [1]Motor Vehicle Statistics Turkey Statistical Institute in Ankara Police Headquarters 2013 Page 2.

    [2] Ergün G., Şahin, N., Development Of Traffic Congestion Management Strategies: Strategic Plan Studies, Istanbul, 2006.

    [3] İmo Teknik Magazine, 2014 6655-6678, Article 414 Traffic Management Strategies: Fsm Bridge Maintenance Example * Ilgın Gökaşar Ömer Faruk Aydın

    [4] Leung H., El Faouzi N.E., Kurian A., "Intelligent Transportation System (Its)", Information Fusion, 01/2011; 12: 2-3, 2011.

    [5] Istanbul Metropolitan Area Urban Transport Master Plan (Iuap) Istanbul May-2011

    • EREN K., 11 Jan, 2021