ME7711A Research Methodology
Advances Transportation Technologies
In this digital era, the graph of progress of science and technology is rising more critically than ever before. In the transportation system, the world is also progressing with a motto of achieving higher speed day by day. Achieving the speed of transportation by maintaining all the safety issues, rules and regulations of the existing society is the actual challenge in this industry. However, humans achieved Roads, Air, Water, Underwater, even the Deep Space, as the medium of transportation. This assessment is focusing on the two journals on Advanced Transportation Technology and critical analysis of the research methodologies, which have taken during the making of these two journals.
For this research, need to find two research papers on Mechanical Engineering, by following all the required areas given in the assessment details. First, need to visit the website scimago.com as mentioned in the assessment details, this is the site, the quality of journals has discussed. From this site, there have option to choose two journal papers, which have published in the first and second quartile, as mentioned in the assessment details.
Figure 1: Home page of scimago.com
After opening the website, there are some sections in the above search section and need to choose the “Journal Rankings” there. Then it will open a portal of several articles, where the required article can found. Next, there are more than five options for sorting the required journal, and these should followed systematically.
Figure 2: Sorting the journal
From the very left side, first, have to choose the Subject “Engineering” from “All subject areas”, which will sort the journals, which are only engineering journals and articles. Next, I need to choose “Mechanical Engineering” from “All subject categories”, which will sort all the journals and articles from the Mechanical Engineering section. Then, there have an option to choose “The regions/countries”, for finding the specific country or region, the required journal was published from. From the “All Types” section, have to choose, whether the required paper is journal or book or anything else, if journal, then select “journal”. In the very right of the section, there can found a section named as “Year”, where the specific year can chose for the required journal. After all of these, in the beneath of the discussed section, there can be found three checkboxes, which will help to find the required open source journal from this site.
Figure 3: After going through all the steps
Now in the last step, the sorted journals can found in the result, and from there two journals have selected based on its originality, quartile standards and it has noticed so that every article must be peer review. After choosing the required topic of the journal, the journal can found in Google Scholar, by simply copying and pasting the name of the journal. By following all these steps, the research papers have found for analyzing and comparing in this assessment.
- Jia, Y., Wu, J. and Xu, M., 2017. Traffic flow prediction with rainfall impact using a deep learning method. Journal of advanced transportation, 2017.
- Toruń, A., Burniak, C., Biały, J., Tomaszewska, J., Grzesik, N., Hoskova-Mayerova, S., Woch, M., Zieja, M. and Rurak, A., 2018. Challenges for air transport providers in Czech Republic and Poland. Journal of advanced transportation, 2018.
In this journal, the researcher explains the methods of using the advanced technology in the Traffic System, to enhance the speed of the journey, through controlling and predicting the traffic in real-time. This prediction of traffic will provide extra information and by using a deep learning method, transportation can be faster as well as optimized gradually. Especially in the time of rain, the traffic becomes more congested and slower, which is the result of the data collected from Beijing. This implementation of deep learning method technology in the traffic system will help to control the traffic smartly in different weather conditions (Jia et al. 2017).
For this research, the researcher took the data on the rate of rainfall as well as the intensity of rainfall, using various types of sensors and other methods. After that, they took the data of flowrate of the traffic during rainfall as well as they took the report on the impacts of traffic due to the rate of rainfall from time to time. In this way, they followed the quantitative data collection method and Conference interpreting process in this research. They studied all the parameters regarding the report coming from these data collection methods and they uploaded it on the stack of RBM, which has developed for faster data learning method (Jia et al. 2017).
They took five steps to process all data and to build a smart algorithm using the deep learning method. Firstly, they used traffic data and rainfall data for preparing and testing data sets for the next 10 to 30 minutes of prediction. As mentioned by Jia et al. (2017), next, they started to analyses the used architecture optimization data set to create a decision on the parameters of R-DBN architecture along with the dimension of input data, size of layers and the size of hidden units per layer.
After this step, they started giving the training to R-DBN and R-LSTM based architectures using the training data set developed from the previous step. After all, of these steps, they compared all of the trained architecture, which are R-DBN, R-LSTN with the general architectures, which are DBN, LSTN and others. They also created a report based on the benchmark they got after the comparison of algorithms for optimizing the traffic congestion during rainfall (Jia et al. 2017).
This research paper is to display the challenges of Air Transport providers as well as the methods to optimize those challenges by providing more information to the operators. In the research paper, the researchers discussed the methods of data analysis and data collection. All those data used to analyze, collected from the Eurostat webpage free of cost via the internet. As per the said by Toruń et al. (2018), all of the data collected for analyzing has collected from two specific countries, which are Czech and Poland.
As mentioned by Li et al. (2016), the research aims to build a relationship between the usage of the air transport system and the economic condition of the people in specific countries. As per said by Park et al. (2017), all the dataset collected in a form of the navigation tree and in a table format. The table format has distinguished from the multidimensional datasets using an interactive tool.
The data of about 31 European countries have analyzed from the year of 2004-2016, for this research purpose. As per the view of Lavieri et al. (2017), this quantitative data collection process has applied in this research carefully to manage all the data for analysis. This research also aimed to find the indicators, which will influence the factors and those will define the number of passengers of Air transport system, in future (Toruń et al. 2018).
They analyzed the total number of passengers, who travelled by plane and divided them into different groups, in the terms of a specific time. This division helped them to create a graph and a relation between the number of passengers increasing or decreasing in terms of time. In this way, the research will find all the influencing indicators, which will show the rate of using air transport system in different places (Toruń et al. 2018).
Every research process goes through a process or method, which set a goal for researches to complete in a systematic process (Ab Rahman et al. 2017). As per the view of Alam et al. (2018), the most important area of the whole research process is the data collection process, there have many methods to follow. There are several types of methods and process available in the history of research, which can used simultaneously or individually.
According to Auld et al. (2017), the choosing of the right method is the most important thing, and it depends on the research topic. Sometimes the data need to be qualitative, sometimes data need to be quantitative, depends on the research topic. As mentioned by Bridgelall and Tolliver (2020), data collection from review or interview has known as qualitative data collection method and when the numbers come to define as the whole data in research, it has known as quantitative data collection method. As per said by Davoudi et al. (2016), choosing the proper data collection method gives a huge boost in the whole research period and thus the researcher should focus on choosing the proper way of starting the research.
Data collection is not the only important thing need to focus, there has a lot more work to do, like Analyzation of all the data, processing of them and finding the result from them. According to Fazekaš et al. (2017), after following all of these processes, there has an analyzation method to define the research work as successful or failure. As per said by Ioannou (2016), failure of research explains the choosing wrong methods during the whole research work. Thus, the need to focus on choosing the methods in every step and to process the areas of research is too much important (Khaliq et al. 2016).
Two of these articles used different methods of research and different methods of the data collection process. However, both of the research papers are focusing on the advancement and progress of the transport system in future. As per the opinion of Kouvelas et al. (2018), the first research paper mainly focused on the advancement of traffic system using the latest technology and algorithm. The second one has focused on finding the key indicators for predicting the number of passengers, who will use the air transport system in the future.
Two of research paper has mentioned as Paper A and Paper B, for making the comparison easier. Paper A has focused on using higher technologies, whereas Paper B does not have to use the technologies. Paper A and B both approached the quantitative data collection method, where Paper A, used the real-time recent information. Paper B, used the previous data of 2004-2016 of specific countries Czech and Poland from a website via the internet.
Paper A has collected data on the rate of rainfall and the impact of traffic in various phase of rainfall in Beijing. It analyzed all the collected data in the RBM to learn using deep machine learning process and they created an algorithm, which will predict the traffic rate as well as control them to make transportation faster. Here in Paper B, the researcher tried to focus on finding the key areas, which have influenced the people of Europe countries for these long 12 years, in travelling via the air transport system. They both successfully completed their research work and it will help the future generation in a different way (Lam et al. 2016).
This assessment has focused on the research methodologies and the method two groups of researchers used different research methodologies for their different topics of research. This assignment also highlighted the process of searching two papers from the mentioned website scimag.com. The step by step process of sorting and finding research papers and lastly, downloading them with the using of Google scholar has mentioned in this assessment. Next, both of the paper has analyzed in this assessment using in-depth knowledge and the technologies they used. The used methodologies and steps used by the researchers during those researches also briefly discussed in this assessment. The importance of choosing and using proper methods during any research work has also explained in the assessment. In the assessment, the comparison of both journal and the need for using different methods in different topics have also discussed and concluded.
Ab Rahman, A., Hamid, U.Z.A. and Chin, T.A., 2017. Emerging technologies with disruptive effects: a review. Perintis eJournal, 7(2), pp.111-128.
Alam, M., Moroni, D., Pieri, G., Tampucci, M., Gomes, M., Fonseca, J., Ferreira, J. and Leone, G.R., 2018. Real-time smart parking systems integration in distributed ITS for smart cities. Journal of Advanced Transportation, 2018.
Auld, J., Sokolov, V. and Stephens, T.S., 2017. Analysis of the effects of connected–automated vehicle technologies on travel demand. Transportation Research Record, 2625(1), pp.1-8.
Bridgelall, R. and Tolliver, D.D., 2020. A cognitive framework to plan for the future of transportation. Transportation Planning and Technology, 43(3), pp.237-252.
Davoudi, A., Zou, Y., Camacho, I.C. and Hu, X., 2016. Guest editorial modeling and control of electrified vehicles and transportation systems. IEEE Transactions on Transportation Electrification, 2(2), pp.115-118.
Fazekaš, T., Bobera, D. and Ćirić, Z., 2017. Ecologically and Economically Sustainable Agricultural Transportation Based on Advanced Information Technologies. Economics of Agriculture, 64(2), pp.739-751.
Ioannou, P.A., 2016. Transportation Activities at the University of Southern California [ITS Research Lab]. IEEE Intelligent Transportation Systems Magazine, 8(3), pp.88-91.
Jia, Y., Wu, J. and Xu, M., 2017. Traffic flow prediction with rainfall impact using a deep learning method. Journal of advanced transportation, 2017.
Khaliq, K.A., Qayyum, A. and Pannek, J., 2016. Synergies of advanced technologies and role of VANET in logistics and transportation. International Journal of Advanced Computer Science and Applications (IJACSA), 7(11), pp.359-369.
Kouvelas, A., Chow, A., Gonzales, E., Yildirimoglu, M. and Castelan Carlson, R., 2018. Emerging information and communication technologies for traffic estimation and control. Journal of Advanced Transportation, 2018.
Lam, S., Taghia, J. and Katupitiya, J., 2016. Evaluation of a transportation system employing autonomous vehicles. Journal of Advanced Transportation, 50(8), pp.2266-2287.
Lavieri, P.S., Garikapati, V.M., Bhat, C.R., Pendyala, R.M., Astroza, S. and Dias, F.F., 2017. Modeling individual preferences for ownership and sharing of autonomous vehicle technologies. Transportation research record, 2665(1), pp.1-10.
Li, L., He, S., Zhang, J. and Ran, B., 2016. Short‐term highway traffic flow prediction based on a hybrid strategy considering temporal–spatial information. Journal of Advanced Transportation, 50(8), pp.2029-2040.
Park, C., Park, J. and Choi, S., 2017. Emerging clean transportation technologies and distribution of reduced greenhouse gas emissions in Southern California. Journal of Open Innovation: Technology, Market, and Complexity, 3(2), p.8.
Spurlock, C.A., Sears, J., Wong-Parodi, G., Walker, V., Jin, L., Taylor, M., Duvall, A., Gopal, A. and Todd, A., 2019. Describing the users: Understanding adoption of and interest in shared, electrified, and automated transportation in the San Francisco Bay Area. Transportation Research Part D: Transport and Environment, 71, pp.283-301.
Toruń, A., Burniak, C., Biały, J., Tomaszewska, J., Grzesik, N., Hoskova-Mayerova, S., Woch, M., Zieja, M. and Rurak, A., 2018. Challenges for air transport providers in Czech Republic and Poland. Journal of advanced transportation
Know more about UniqueSubmission’s other writing services: