Modelling of Travel Behaviour of Students Using Artificial Intelligence

• Autorzy: Alex Anu P. , Manju V. S. , Isaac Kuncheria P.

Identyfikatory

DOI

10.5604/01.3001.0013.6159

• Języki publikacji: EN

Abstrakty

EN

Travel demand models are required by transportation planners to predict the travel behaviour of people with different socio-economic characteristics. Travel behaviour of students act as an essential component of travel demand modelling. This behaviour is reflected in the educational activity travel pattern, the timing, sequence and mode of travel of students. Roads in the vicinity of schools are adversely affected during the school opening and closing hours. It enhances the traffic congestion, emission and safety problems around schools. It is necessary to improve the safety of school going children by understanding the present travel behaviour and to develop efficient sustainable traffic management measures to reduce congestion in the vicinity of schools. It is possible only if the travel behaviour of educational activities are studied. This travel behaviour is complex in nature and lot of uncertainty exists. Selection of modelling technique is very important for modelling the complex travel behaviour of students. This leads to the importance of application of artificial intelligence (AI) techniques in this area. AI techniques are highly developed in twenty first century due to the advancements in computer, big data and theoretical understanding. It is proved in the literature that these techniques are suitable for modelling the human behaviour. However, it has not been used in behaviourally oriented activity based modelling. This study is aimed to develop a model system to predict the daily travel behaviour of students using artificial intelligence technique, ANN. These ANN models were then compared with the conventional econometric models developed. It was observed that artificial intelligence models provide better results than econometric models in predicting the activity-travel behaviour of students. These models were further applied to study the variation in activity-travel behaviour, if short term travel-demand management measures like promoting walking for educational activities are implemented. Thus the study established that artificial intelligence can replace the conventional econometric methods for modelling the activity-travel behaviour of students. It can also be used for analysing the impact of short term travel demand management measures.

Słowa kluczowe

EN

travel demand models; travel behaviour; transportation planners; travel management; econometric model

PL

podróże; zachowania podróżujących; planowanie podróży; zarządzanie podróżą; model ekonometryczny

• Wydawca: Warsaw University of Technology, Faculty of Transport
• Czasopismo: Archives of Transport
• Rocznik: 2019
• Tom: Vol. 51, iss. 3
• Strony: 7--19
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Alex Anu P.

• College of Engineering Trivandrum, Kerala, India

autor

Manju V. S.

• College of Engineering Trivandrum, Kerala, India

autor

Isaac Kuncheria P.

• Hindustan Institute of Technology and Science, Chennai, India

Bibliografia

• [1] ABUBAKAR, A. M., KARADAL, H., BAYIGHOMOG, S. W., MERDAN, E., 2018. “Workplace injuries, safety climate and bahaviours: application of an artificial neural network”, International Journal of Occupational safety and Ergonomics. Pp: 1-32
• [2] AMANATIADIS, A., MITSINIS, N., MADITINOS, D. 2014. “A neural network-based approach for user experience assessment”, Behaviour & Information Technology, Volume 34, Issue 3.pp: 304-315.
• [3] ARENTZE,T. A. AND TIMMERMANS, H. 2000. "ALBATROSS - A learning based transportation oriented simulation system", TRB Conference 2000, USA, Issue No. 1706: 136-144.
• [4] BABU, D., BALAN, S., ANJANEYULU, M.V.L.R, 2018. Activity-travel patterns of workers and students: a study from Calicut city, India. Archives of Transport, 46(2), 21-32. DOI: https://doi.org/10.5604/01.3001.0012.2100
• [5] BHAT, C. R. AND SINGH, S. K. 2000. "A Comprehensive Daily Activity-Travel Generation Model System for Workers", Transportation Research Part A: Policy and Practice, 34 (1): 1-22.
• [6] BHAT, C. R., GUO, J.Y., SRINIVASAN, S., AND SIVAKUMAR, A. 2004. "Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns", Transportation Research Record: Journal of the Transportation Research Board, No. 1894: 57-66.
• [7] BINDU, M., MATHEW, T. V. AND DHINGRA, S.L. 2006. "Prototype Time-Space Diary Design and Administration for a Developing Country", Journal of Transportation Engineering, ASCE, 132(6): 489-498.
• [8] BINDU, M., MATHEW,T. V. AND DHINGRA, S. L. 2005. “Development of a Mixed Logit Model to Tour Mode Choice for an Urban Region”, Paper presented at CUPUM 2005, International Conference, London U.K.
• [9] BORIMNEJAD, V., SAMANI, R. E., WRIGHT, L. T., 2016. “ Modelling consumer’s behavior for packed vegetable in Mayadin Management Organization of Tehran using artificial neural network, Cogent Business & Management, Volume 3, Issue 1. Pp: 1-14
• [10] BUSCEMA, M., PIETRALATA, M. M., SALVEMINI, V., INTRALIGI, M., INDRIMI, M. 1998. “Application of Artificial Nueral Net-works to Eating Disorders”, Substance Use & Misuse, Vol. 33-3: 765-791.
• [11] GIBAŁA, Ł., KONIECZNY, J., 2018. Application of artificial neural networks to predict rail-way switch durability. Scientific Journal of Silesian University of Technology. Series Transport. 2018, 101, 67-77. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2018.101.7.
• [12] KITAMURA, R. 1988. An evaluation of activity-based travel analysis. Transportation 15: 9-34.
• [13] MANOJ, M., VERMA, A. 2013. "Analysis and Modelling of Activity -Travel Behaviour of Nonworkers from a City of Developing Country, India", Procedia - Social and Behavioral Sciences 104: 621-629.
• [14] NURUL HABIB, K.M. AND MILLER, E. J. 2009.“Modelling Activity Generation: a Utility Based Model for Activity-Agenda Formation”, Transportmetrica, 5(1) 3-23.
• [15] PENDYALA, R. M., KITAMURA, R., KIKUCHI, A., YAMAMOTO, T. AND FUJII, S. 2005. "Florida Activity Mobility Simulator - Overview and Preliminary Validation Results", Transportation Research Record: Journal of the Transportation Research Board, No. 1921: 123-130.
• [16] POTOGLOU, D., ARSLANGULOVA, B. 2016. “Factors influencing active travel to primary and secondary schools in Wales”, Transportation Planning and Technology, 39(7): 1-21.
• [17] RECKER, W. W. AND MCNALLY, M. G. AND ROOT, G. S. 1986. "A model of complex travel behaviour: part I - theoretical development", Transportation research A, 20A (4): 307-318.
• [18] ROORDA,M. J.,MILLER, E. J. AND HABIB, K. M. N. 2008.“Validation of TASHA: A 24-h activity scheduling microsilulation model”, Transportation Research Part A: 360-375.
• [19] SHARMA, S. K., GAUR, A., SADDIKUTI, V., RASTOGI, A. 2017. “Structural equation model (SEM)-Neural Network (NN) model for predicting quality determinants of e-learning management systems”, Behaviour & Information Technology, Volume 36, Issue 10. Pp:1053-1066.
• [20] SREELA, P.K, MELAYIL,S. AND ANJANEYULU, M.V.L.R. 2013. "Modeling of Shopping Participation and Duration of Workers in Calicut", Procedia - Social and Behavioral Sciences 104: 543 – 552.
• [21] SUBBARAO, S. S. V., RAO, K. V. K. 2014. “Characteristics of household activity and travel patterns in the Mumbai metropolitan region”, Transportation Planning and Technology, 37(5): 484-504.
• [22] SUREKHA, N. 2009. "Microsimulation of Activity-Travel Pattern for Tiruchirappalli City", M. Tech diss., NIT Tiruchirappalli.
• [23] WAINAINA, S. & RICHTER, M., 2002. Stochastic approach in modelling travellers behaviour as a result of activity chains. Archives of Transport, 14( 2), 95-112.