Periodica Polytechnica Transportation Engineering 

About: Periodica Polytechnica Transportation Engineering is an academic journal published by Budapest University of Technology and Economics. The journal publishes majorly in the area(s): Computer science & Engineering. It has an ISSN identifier of 0303-7800. It is also open access. Over the lifetime, 649 publications have been published receiving 3227 citations. The journal is also known as: Periodica polytechnica. Transport & PP. Periodica polytechnica. Transportation engineering (Online).

Public Transport Accessibility: A Literature Review

Abstract: Accessibility is one of the most important outcomes of the transportation system. Public transport can be more attractive by providing "Door to door mobility" and development of transportation services is an important factor of social quality. Public transport accessibility has gained vital importance in designing and evaluating the transit system in terms of mobility and sustainability. Apart from the transport system itself, public transport accessibility has the considerable impact on life satisfaction in the form of perceived accessibility. Moreover, researchers have revealed several impact and correlation of provision of public transport accessibility to the environment and daily life which would have a noticeable impact on public health and other aspects of public daily life. Furthermore, the correlation between public transport accessibility and job opportunities has attracted the researchers' attention in the literature. Also, public participation in social activities has been investigated based on public transport accessibility and their close connection has been revealed under the topic of social exclusion. In this paper, the available literature on public transport accessibility has been reviewed. As a conclusion, it should be highlighted that not just the performance of public transportation but its impact on other social aspects should be considered while planning the public transport facilities.

Development of a Test Track for Driverless Cars: Vehicle Design, Track Configuration, and Liability Considerations

Abstract: The Research Center for Autonomous Road Vehicles (RECAR) was founded in 2015 upon the initiative of the Faculty of Transportation Engineering and Vehicle Engineering of Budapest University of Technology and Economics. The research center is supported by industrial partners and other academic partners targeting research and educational purposes. In complement to this project, the construction of a new automotive test track is also under development especially for autonomous road vehicle testing serving as automotive proving ground in Zalaegerszeg, Hungary. Accordingly, an intensive research has been started in RECAR center in the field of autonomous vehicle technology. The paper’s goal is to share the main practical and methodological experiences with the scientific audience as well as the industrial sector. Based on the initial research actions we intend to enlighten the upcoming research challenges of driverless vehicles and automated intelligent transport system. Basically, three main topics are concerned. Firstly, the main issues concerning autonomous vehicle research are summarized. Secondly, the requirements for autonomous test track design are concluded. Thirdly, the legal questions that emerge with the appearance of driverless vehicles are investigated, especially concerning liability.

Transfer Learning Based Traffic Sign Recognition Using Inception-v3 Model

Abstract: Traffic sign recognition is critical for advanced driver assistant system and road infrastructure survey. Traditional traffic sign recognition algorithms can't efficiently recognize traffic signs due to its limitation, yet deep learning-based technique requires huge amount of training data before its use, which is time consuming and labor intensive. In this study, transfer learning-based method is introduced for traffic sign recognition and classification, which significantly reduces the amount of training data and alleviates computation expense using Inception-v3 model. In our experiment, Belgium Traffic Sign Database is chosen and augmented by data pre-processing technique. Subsequently the layer-wise features extracted using different convolution and pooling operations are compared and analyzed. Finally transfer learning-based model is repetitively retrained several times with fine-tuning parameters at different learning rate, and excellent reliability and repeatability are observed based on statistical analysis. The results show that transfer learning model can achieve a high-level recognition performance in traffic sign recognition, which is up to 99.18 % of recognition accuracy at 0.05 learning rate (average accuracy of 99.09 %). This study would be beneficial in other traffic infrastructure recognition such as road lane marking and roadside protection facilities, and so on.

Impacts of Autonomous Cars from a Traffic Engineering Perspective

Abstract: The era of autonomous vehicles infer new challenges in several fields. When autonomous vehicles take over the road in a large volume, consumer preferences around car-ownership will transform, traffic modeling and control will need correction, and moreover hackers will appear. These are just a few impacts which are expectable in the near future. Accordingly, the paper’s aim is to enlighten trends and upcoming challenges of driverless vehicles and automated transportation system from a transportation engineering perspective.

Nonlinear model-building of a low-power gas turbine

Abstract: The development of a nonlinear gas turbine model is presented in this paper for loop-shaping control purposes. The nonlinear dynamic equations of the gas turbine are based on the first engineering principles. In order to complete the model, constitutive algebraic equations are also needed. These equations describe the static behavior of the gas turbine at various operating points. The complete, substituted nonlinear model is presented along with its model verification results based on a simulator and measured data.