Transportation Engineering on Social Question and Answer Websites: An Empirical Case Study

  • Mohammad NoaeenEmail author
  • Zahra Shakeri Hossein Abad
  • Guenther Ruhe
  • Behrouz Homayoun Far
Part of the Studies in Big Data book series (SBD, volume 27)


In the last decade, the area of Transportation Engineering (TE), and its underlying disciplines such as public transit, connected vehicles, road planning, and air traffic management, has become increasingly prominent. A better understanding of what the most challenging topics related to TE are among practitioners will greatly help to identify the areas of TE that may require extra attention by researchers and project managers. However, there has been very little experimental work in regards to identify true practitioner’s needs on the implementation and understanding of TE activities and tasks. Therefore, in this paper, we use data from the popular social Q&A sites (e.g. Stack Overflow and Engineering Exchange), and analyze 2457 questions and answers in order to examine the needs of transportation engineers, and their concerns and questions. We applied Latent Dirichlet Allocation-based (LDA) topic models and statistical analysis to explore the main related topics to TE.

Our findings show that practitioners are question the application of GIS tools, such as QGIS and ArcGIS for managing and implementing road planning. Further, we determined the popularity of each topic by conducting statistical analysis. Our findings help highlight the challenges facing transportation engineers, which require more attention from the Civil Engineering, Software Engineering, and specifically Data Analysis research communities and establish a novel approach for analyzing the content of social Q&A websites.


  1. 1.
    Barua A, Thomas SW, Hassan AE. What are developers talking about? an analysis of topics and trends in stack overflow. Empir Softw Eng. 2014;19(3):619–54.CrossRefGoogle Scholar
  2. 2.
    Rosen C, Shihab E. What are mobile developers asking about? a large scale study using stack overflow. Empir Softw Eng. 2016;21(3):1192–1223.CrossRefGoogle Scholar
  3. 3.
    Ibrahim H, Far BH. Simulation-based benefit analysis of pattern recognition application in intelligent transportation systems. In: 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE; 2015. p. 507–12.Google Scholar
  4. 4.
    Mohammed EA, Aulakh C, Krishnamurthy D, Naugler CT, Far BH. Short-term travel time estimation: a case study. In: 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE; 2015. p. 489–96.Google Scholar
  5. 5.
    Bajaj K, Pattabiraman K, Mesbah A. Mining questions asked by web developers. In: Proceedings of the 11th Working Conference on Mining Software Repositories. New York: ACM; 2014. p. 112–21.Google Scholar
  6. 6.
    Abad ZSH, Shymka A, Pant S, Currie A, Ruhe G. What are practitioners asking about requirements engineering? an exploratory analysis of social Q&A sites In: IEEE International Requirements Engineering Conference Workshops (REW); 2016. p. 334–43.Google Scholar
  7. 7.
    Shapiro FR, Pearse M. The most-cited law review articles of all time. Mich Law Rev. 2012;110:1483–520.Google Scholar
  8. 8.
    Keshav S. How to read a paper. In: SIGCOMM Computer Communication Review, vol. 37(3); July 2007.Google Scholar
  9. 9.
    Wallach HM. Topic modeling: beyond bag-of-words. In: Proceedings of the 23rd International Conference on Machine Learning, Series ICML. New York: ACM; 2006, p. 977–84.CrossRefGoogle Scholar
  10. 10.
    Blei D, Carin L, Dunson D. Probabilistic topic models. IEEE Signal Process Mag. 2010;27(6):55–65.Google Scholar
  11. 11.
    Porteous I, Newman D, Ihler A, Asuncion A, Smyth P, Welling M. Fast collapsed gibbs sampling for latent Dirichlet allocation. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Series KDD ’08. New York: ACM; 2008, p. 569–77.Google Scholar
  12. 12.
    Darling WM. A theoretical and practical implementation tutorial on topic modeling and Gibbs sampling. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies; 2011. p. 642–7.Google Scholar
  13. 13.
    Chang J, Gerrish S, Wang C, Boyd-Graber JL, Blei DM. Reading tea leaves: how humans interpret topic models. In: Advances in Neural Information Processing Systems; 2009. p. 288–96.Google Scholar
  14. 14.
    Ramezani M, Geroliminis N. Exploiting probe data to estimate the queue profile in urban networks. In: 2013 16th International IEEE Conference on Intelligent Transportation Systems-(ITSC). IEEE; 2013. p. 1817–22.Google Scholar
  15. 15.
    Ramezani M, Geroliminis N. Queue profile estimation in congested urban networks with probe data. Comput. Aided Civ. Inf. Eng. 2015;30(6):414–432.CrossRefGoogle Scholar
  16. 16.
    Ramezani M, Burgener R, Geroliminis N. Optimization of traffic signals for transit priority in arterials with dedicated bus lanes and stochastic arrivals: a system-oriented approach. In: Transportation Research Board 94th Annual Meeting (No. 15-2069); 2015.Google Scholar
  17. 17.
    Noaeen M, Homayoun Far B. Let’s hear it from RETTA: a requirements Elicitation tool for traffic management systems. In: The 35th IEEE International Conference on Requirements Engineering (RE). Lisbon: IEEE; 2017.Google Scholar
  18. 18.
    Noaeen M, Rassafi AA, Far BH. Traffic signal timing optimization by modelling the lost time effect in the shock wave delay model. In: International Conference on Transportation and Development. 2016; p. 397–408.Google Scholar
  19. 19.
    Noaeen M, Rassafi AA, Homayoun Far B. Exploring the residual queue length equation in the shock wave model. In: 51st Annual Conference of the Canadian Transportation Research Forum; 2016.Google Scholar
  20. 20.
    Shakeri Hossein Abad Z, Karras O, Ghazi P, Glinz M, Ruhe G, Schneider K. What works better? A study of classifying requirements. In: Proceeding of the 25th IEEE International Conference on Requirements Engineering (RE’17); 2017.Google Scholar

Papers Used for Frequency Data Analysis (Table 1)

  1. [P1]
    Bajaj K, Pattabiraman K, Mesbah A. Mining questions asked by web developers. In: Proceedings of the 11th Working Conference on Mining Software Repositories. New York: ACM; 2014, p. 112–121.Google Scholar
  2. [P2]
    Allamanis M, Sutton C. Why, when, and what: analyzing stack overflow questions by topic, type, and code. In: Proceedings of the 10th Working Conference on Mining Software Repositories. New York: IEEE Press; 2013, p. 53–56.Google Scholar
  3. [P3]
    Barua A, Thomas SW, Hassan AE What are developers talking about? an analysis of topics and trends in stack overflow. Empir Softw Eng. 2014;19(3):619–54.CrossRefGoogle Scholar
  4. [P4]
    Parnin C, Treude C, Grammel L, Storey M-A. Crowd documentation: exploring the coverage and the dynamics of api discussions on stack overflow. Georgia Institute of Technology, Technical Report, 2012.Google Scholar
  5. [P5]
    Anderson A, Huttenlocher D, Kleinberg J, Leskovec J Steering user behavior with badges. In: Proceedings of the 22nd International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2013, p. 95–106.Google Scholar
  6. [P6]
    Pal A, Chang S, Konstan JA. Evolution of experts in question answering communities. In: ICWSM, 2012.Google Scholar
  7. [P7]
    Riahi F Finding expert users in community question answering services using topic models 2012.Google Scholar
  8. [P8]
    Xia Y, Zhang L, Liu Y Special issue on big data driven intelligent transportation systems. Neurocomputing 2016;181(C):1–3.CrossRefGoogle Scholar
  9. [P9]
    Xia Y, Chen J, Lu X, Wang C, Xu C. Big traffic data processing framework for intelligent monitoring and recording systems. Neurocomputing 2016;181:139–46.CrossRefGoogle Scholar
  10. [P10]
    Ke H, Li P, Guo S, Guo M. On traffic-aware partition and aggregation in mapreduce for big data applications. IEEE Trans Parallel Distrib Syst. 2016;27(3):818–28.CrossRefGoogle Scholar
  11. [P11]
    Lv Y, Duan Y, Kang W, Li Z, Wang F-Y Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst. 2015;16(2):865–73.Google Scholar
  12. [P12]
    Khan Z, Anjum A, Soomro K, Tahir MA Towards cloud based big data analytics for smart future cities. J Cloud Comput. 2015;4(1):1.CrossRefGoogle Scholar
  13. [P13]
    Shi Q, Abdel-Aty M. Big data applications in real-time traffic operation and safety monitoring and improvement on urban expressways. Transp Res Part C Emerg Technolog. 2015;58:80–94.Google Scholar
  14. [P14]
    Al Nuaimi E, Al Neyadi H, Mohamed N, Al-Jaroodi J. Applications of big data to smart cities. J Internet Serv Appl. 2015;6(1):1.CrossRefGoogle Scholar
  15. [P15]
    Dong H, Wu M, Ding X, Chu L, Jia L, Qin Y, Zhou X. Traffic zone division based on big data from mobile phone base stations. Transp Res Part C Emerging Technol. 2015;58:278–91.CrossRefGoogle Scholar
  16. [P16]
    Zhang J, Li H, Gao Q, Wang H, Luo Y. Detecting anomalies from big network traffic data using an adaptive detection approach. Inf Sci. 2015;318:91–110.CrossRefGoogle Scholar
  17. [P17]
    Cheng B, Longo S, Cirillo F, Bauer M, Kovacs E. Building a big data platform for smart cities: Experience and lessons from santander. In: 2015 IEEE International Congress on Big Data. New York: IEEE; 2015. p. 592–9.CrossRefGoogle Scholar
  18. [P18]
    Xu J, Deng D, Demiryurek U, Shahabi C, van der Schaar M. Mining the situation: Spatiotemporal traffic prediction with big data. IEEE J Sel Top Sign Proces. 2015;9(4):702–15.CrossRefGoogle Scholar
  19. [P19]
    Jara AJ, Genoud D, Bocchi Y. Big data for smart cities with knime a real experience in the smartsantander testbed. Softw Pract Exp. 2015;45(8):1145–60.CrossRefGoogle Scholar
  20. [P20]
    Shearmur R. Dazzled by data: big data, the census and urban geography. Urban Geogr. 2015;36(7):965–8.CrossRefGoogle Scholar
  21. [P21]
    Kitchin R. The real-time city? big data and smart urbanism. GeoJournal 2014;79(1):1–14.CrossRefGoogle Scholar
  22. [P21]
    Pijanowski BC, Tayyebi A, Doucette J, Pekin BK, Braun D, Plourde J. A big data urban growth simulation at a national scale: configuring the gis and neural network based land transformation model to run in a high performance computing (HPC) environment. Environ Model Softw. 2014;51:250–68.CrossRefGoogle Scholar
  23. [P23]
    Liu J, Liu F, Ansari N. Monitoring and analyzing big traffic data of a large-scale cellular network with hadoop. IEEE Netw. 2014;28(4):32–9.CrossRefGoogle Scholar
  24. [P24]
    Bettencourt LM. The uses of big data in cities. Big Data 2014;2(1):12–2.CrossRefGoogle Scholar
  25. [P25]
    Cai H, Jia X, Chiu AS, Hu X, Xu M. Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet. Transp Res Part D Transp Environ. 2014;33:39–46.CrossRefGoogle Scholar
  26. [P26]
    Rabari C, Storper M. The digital skin of cities: urban theory and research in the age of the sensored and metered city, ubiquitous computing and big data. Camb J Regions Econ Soc. 2015;8(1):27–42.CrossRefGoogle Scholar
  27. [P27]
    Klauser FR, Albrechtslund A. From self-tracking to smart urban infrastructures: towards an interdisciplinary research agenda on big data. Surveill Soc. 2014;12(2):273.Google Scholar
  28. [P28]
    Bär A, Finamore A, Casas P, Golab L, Mellia M. Large-scale network traffic monitoring with dbstream, a system for rolling big data analysis. In: 2014 IEEE International Conference on Big Data (Big Data). New York: IEEE; 2014. p. 165–70.CrossRefGoogle Scholar
  29. [P29]
    Glatz E, Mavromatidis S, Ager B, Dimitropoulos X. Visualizing big network traffic data using frequent pattern mining and hypergraphs. Computing 2014;96(1):27–38.CrossRefGoogle Scholar
  30. [P30]
    Abbass H, Tang J, Amin R, Ellejmi M, Kirby S. The computational air traffic control brain: computational red teaming and big data for real-time seamless brain-traffic integration. J Air Traffic Control 2014;56(2):10–7.Google Scholar
  31. [P31]
    Zheng Y, Liu F, Hsieh H-P. U-air: when urban air quality inference meets big data. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM; 2013, p. 1436–44.CrossRefGoogle Scholar
  32. [P32]
    Batty M. Big data, smart cities and city planning. Dialogues Hum Geogr. 2013;3(3):274–9.CrossRefGoogle Scholar
  33. [P33]
    Vilajosana I, Llosa J, Martinez B, Domingo-Prieto M, Angles A, Vilajosana X. Bootstrapping smart cities through a self-sustainable model based on big data flows. IEEE Commun Mag. 2013;51(6):128–34.CrossRefGoogle Scholar
  34. [P34]
    Khan Z, Anjum A, Kiani SL. Cloud based big data analytics for smart future cities. In: Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing. New York: IEEE Computer Society; 2013. p. 381–6.CrossRefGoogle Scholar
  35. [P35]
    Yu J, Jiang F, Zhu T. Rtic-c: a big data system for massive traffic information mining. In: 2013 International Conference on Cloud Computing and Big Data (CloudCom-Asia). New York: IEEE; 2013, p. 395–402.CrossRefGoogle Scholar
  36. [P36]
    Fiosina J, Fiosins M, Müller JP. Big data processing and mining for next generation intelligent transportation systems. J Teknol. 2013;63(3):21–38.Google Scholar
  37. [P37]
    Park HW, Yeo IY, Lee JR, Jang H. Study on big data center traffic management based on the separation of large-scale data stream. In: 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS). New York: IEEE; 2013, p. 591–4.CrossRefGoogle Scholar
  38. [P38]
    Koonin S. Smart cities will need big data. Phys Today 2013;66(9):19.CrossRefGoogle Scholar
  39. [P39]
    Hu W, Sun W, Jin Y, Guo W, Xiao S. An efficient transportation architecture for big data movement. In: 2013 9th International Conference on Information, Communications and Signal Processing (ICICS). New York: IEEE; 2013, p. 1–5.Google Scholar
  40. [P40]
    Sacco D, Motta G, You L, Bertolazzo N, Chen C. Smart cities, urban sensing and big data: mining geo-location in social networks. In: AICA, Salerno; 2013.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Mohammad Noaeen
    • 1
    Email author
  • Zahra Shakeri Hossein Abad
    • 2
  • Guenther Ruhe
    • 2
  • Behrouz Homayoun Far
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of CalgaryCalgaryCanada
  2. 2.Department of Computer ScienceUniversity of CalgaryCalgaryCanada

Personalised recommendations