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Mode choice analysis of school trips using random forest technique

D'Cruz Jinit J.M., Alex Anu P., Manju V. S.

Archives of Transport

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2022

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Vol. 62, iss. 2

39--48

EN

Mode choice analysis of school trips becomes important due to the fact that these trips contribute to the second largest share of peak hour traffic. This scenario is more relevant in India, which has almost 265 million students enrolled in different accredited urban and rural schools of India, from Class I to XII as per the UDISE report of 2019-20. Thus, it becomes necessary to understand what mode of transport will be mostly used for school trips in order to design an efficient transportation system. Modal attributes and socio-economic characteristics are mostly considered as explanatory variables in travel mode choice models. Multinomial Logit (MNL) model is one of the classic models used in the development of mode choice models. These logistic regression models predict outcomes based on a set of independent variables. With the recent advances in machine learning, transportation problems are getting a wide arena of methods and solutions. Among them the method of ensemble learning is finding a prominent place in contemporary modelling. This study explores the potential of using ensembles of random decision trees in mode choice analysis by Random Forest Technique with a comparative analysis on conventional method. It was observed that Random Forest method outperforms MNL method in predicting the mode choice preference of students. The high accuracy of machine learning models is mainly due to its ability to consider complex nonlinear relationship between socio-economic attributes and travel mode choice. These models can learn and identify pattern characteristics extracted from sample data and form adaptive structures through computational process thereby offering insights into the relationships between variables that random utility models cannot recognize. This study considered activity -travel information, personal data and household characteristics of students as attributes for model development and observed that the age of the student and distance of school from home plays a significant role in deciding the mode choice of school trips.

2

Impact assessment of short-term management measures on travel demand

D'Cruz Jinit J.M., Alex Anu P., Manju V. S., Peter Leema

Archives of Transport

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2020

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Vol. 53, iss. 1

37--52

EN

Travel Demand Management (TDM) can be considered as the most viable option to manage the increasing traffic demand by controlling excessive usage of personalized vehicles. TDM provides expanded options to manage existing travel demand by redistributing the demand rather than increasing the supply. To analyze the impact of TDM measures, the existing travel demand of the area should be identified. In order to get quantitative information on the travel demand and the performance of different alternatives or choices of the available transportation system, travel demand model has to be developed. This concept is more useful in developing countries like India, which have limited resources and increasing demands. Transport related issues such as congestion, low service levels and lack of efficient public transportation compels commuters to shift their travel modes to private transport, resulting in unbalanced modal splits. The present study explores the potential to implement travel demand management measures at Kazhakoottam, an IT business hub cum residential area of Thiruvananthapuram city, a medium sized city in India. Travel demand growth at Kazhakoottam is a matter of concern because the traffic is highly concentrated in this area and facility expansion costs are pretty high. A sequential four-stage travel demand model was developed based on a total of 1416 individual household questionnaire responses using the macro simulation software CUBE. Trip generation models were developed using linear regression and mode split was modelled as multinomial logit model in SPSS. The base year traffic flows were estimated and validated with field data. The developed model was then used for improving the road network conditions by suggesting short-term TDM measures. Three TDM scenarios viz; integrating public transit system with feeder mode, carpooling and reducing the distance of bus stops from zone centroids were analysed. The results indicated an increase in public transit ridership and considerable modal shift from private to public/shared transit.

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Modelling of Travel Behaviour of Students Using Artificial Intelligence

Alex Anu P., Manju V. S., Isaac Kuncheria P.

Archives of Transport

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2019

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Vol. 51, iss. 3

7--19

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.