Mode choice prediction using machine learning technique for a door-to-door journey in Kuantan City

A door-to-door journey in a public transportation system is a notable concept that is practically being promoted among users to consider public transport as an important alternative. The door-to-door journey will integrate the travel segments starting from home to destination, including all visible amenities. Users’ preferences on the time travel of these key segments are necessary to be understood. In this case, Machine Learning technique has been seen as a robust computational advancement to forecast their travel mode choice. However, the most convenient model as the best predictor is still questionable. To address this issue, we employed some pre-eminent machine learning models, specifically Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR), k-Nearest Neighbor (kNN) as well as Support Vector Machine (SVM), to compare their travel mode choice prediction performance of users in the city of Kuantan. The data collection was conducted in Kuantan City via Revealed/Stated Preferences (RPSP) Survey between 8:00 AM to 5:00 PM on weekdays. The data collected was split into a ratio of 80:20 for training and testing before evaluating them between the aforesaid models. The results depicted that the Random Forest could provide satisfactory classification accuracies for both training and testing data up to 68.3% and 61.3%, respectively, compared to the other evaluated machine learning models. In summary, Random Forest provides a good result in the training and testing data and is considered as the best predictor in this research to forecast users’ mode choice in the city of Kuantan

Forecasting daily travel mode choice of kuantan travellers by means of machine learning models

In transportation studies, forecasting users’ mode choice in daily commute is crucial in order to manage traffic problems due to high number of private vehicles on the road. Conventional statistical techniques have been widely used in order to study users’ mode choice; however, the choice of the most appropriate forecasting method still remains a significant concern. In this paper, we investigate the application of a number of machine learning models, namely Random Forest (RF), Tree, Naïve Bayes (NB), Logistic Regression (LR), k-Nearest Neighbour (k-NN), Support Vector Machine (SVM), as well as Artificial Neural Networks (ANN) in predicting the daily travel mode choice in Kuantan. The data was collected from a survey of Revealed/Stated Preferences (RPSP) Survey among Kuantan travellers in which eight features were taken into consideration in the present study. The classifiers were trained on the collected dataset by using five-folds cross-validation method to predict the daily mode choice. It was shown from this preliminary study that the RF, as well as ANN classifiers, could provide satisfactory classification accuracies to up to 70% in comparison to the other models evaluated. Therefore, it could be concluded that the evaluated features are rather important in deciding the travel model choice of Kuantan travellers

Travel mode choice modeling: Predictive efficacy between machine learning models and discrete choice model

Background: A complex travel behaviour among users is intertwined with many factors. Traditionally, the exploration in travel mode choice modeling has been dominated by the Discrete Choice model, nonetheless, owing to the advancement in computational techniques, machine learning has gained traction in understanding travel behavior. Aim: This study aims at predicting users’ travel model choice by means of machine learning models against a conventional Discrete Choice Model, i.e., Binary Logistic Regression. Objective: To investigate the comparison between machine learning models, namely Neural Network, Random Forest, Decision Tree, and Support Vector Machine against the Discrete Choice Model (Binary Logistic Regression) in the prediction of travel mode choice amongst Kuantan City. Methodology: The dataset was collected in Kuantan City, Malaysia, through the Revealed/Stated Preferences (RP/SP) Survey. The data collected was split into a ratio of 80:20 for training and testing before evaluating them between the aforesaid models. The hyperparameters of the models were set to default. The performance of the models is evaluated based on classification accuracy. Results: It was shown in the present study that the Neural Network Model is able to attain a higher prediction accuracy as compared to Binary Logistic Regression (Discrete Choice Model) in classifying mode choice of Kuantan users either to choose public transport or private vehicles as daily transportation. Feature importance technique is crucial for identifying the significant features in modelling travel mode choice. It is demonstrated that the Neural Network Model can yield exceptional classification of mode choice up to 73.4% and 72.4% of training and testing data, respectively, by considering the features identified via the feature importance technique, suggesting the viability of the proposed technique in supporting an informed decision. Conclusion: The findings highlight the strengths and limitations of the Machine Learning Technique as well as the Discrete Choice Model in modeling travel mode choice. It was shown that Machine Learning models have the capability to provide better prediction that could assist the urban transportation planning among policymakers. Meanwhile, it could be also demonstrated that the Discrete Choice Model (Binary Logistic Regression) is helpful in getting a better understanding in expressing the inference relationship between variables for improvising the future transportation system

The Classification of Electrooculogram (EOG) through the application of Linear Discriminant Analysis (LDA) of selected time-domain signals

Recently, Human Computer Interface (HCI) has been studied extensively to handle electromechanical rehabilitation aids using different bio-signals. Among various bio-signals, electrooculogram (EOG) signal have been studied in depth due to its significant signal pattern stability. The primary goal of EOG based HCI is to control assistive devices using eye movement which can be utilized to rehabilitate the disabled people. In this paper, a novel approach of four classes EOG has been proposed to investigate the possibility of real-life HCI application. A variety of time-domain based EOG features including mean, root mean square (RMS), maximum, variance, minimum, medium, skewness and standard deviation have been explored. The extracted features have been classified by the linear discriminant analysis (LDA) with the classification accuracy of training accuracy (90.43%) and testing accuracy (88.89%). The obtained accuracy is very encouraging to be utilized in HCI technology in the purpose of assisting physically disabled patients. Total 10 participants have been contributed to record EOG data and the range between 21 and 29 years old

Biohydrogen production from palm oil mill effluent (POME) using immobilized mixed culture (SLUDGE)

The cell immobilization techniques by using immobilized anaerobic sludge as the seed culture were used to generate hydrogen. The substrate carbon source was taken from Palm Oil Mill Effluent (POME). It was revealed that by using the same concentration of POME, the suspended cell reactor was able to produce hydrogen at an optimal rate of 0.349111-12/1 POME at HRT 6 h. However, the immobilized cell reactor was able to exhibit better hydrogen production rate at 0.587 1 I H2/I P0MB h at HRT 2 h. The present of beads enhanced the production of hydrogen during lower HRT without encountering wash out of cell. This could be shown by higher production of hydrogen at HRT 1 h with 0.53 5 I H2/I POME by using immobilized cell reactor. Compared to suspended cell reactor, the production of hydrogen at HRT 1 h was very low with 0.092 1112/1 POME due to wash out of cell during this time. The side products during hydrogen production were soluble metabolites including butyric acid and acetic acid, followed by propionic acid and ethanol

Identification of trigger levels at equilibrium condition for transport mode choice : case study at Klang Komuter Station

This research presents the identification of trigger levels at the equilibrium condition; a condition which users’ decision making on mode choice is balanced when the travel time between bus and car is almost or perfectly equal to each other. The research is conducted at Klang Town where the local authority is planning to construct a new Park and Ride at the Klang Komuter Station. This study chose travel time as an important factor in triggering users’ mode choice either to choose bus or car before embarking on their daily journey and until the users reach their destination. Users often seek for a faster and more convenient method of transportation in getting themselves to reach their destination especially during morning peak hour. The main focus of this research is the trigger levels’ condition that involved in users’ decision making and to investigate the possible choices of decision on the mode of transport whether users will be attracted to switch their mode from car to bus. There are four surveys performed in this study for data collection, namely the Origin-Destination (OD) Survey, Parking Beat Survey, Travel Time Survey, and Revealed Preference/Stated Preference (RP/SP) Survey. The trigger levels were identified by conducting both Graphical and Statistical Analysis. The Statistical Analysis is conducted to validate the results obtained from the Graphical Analysis. The Statistical Analysis was extended to few more steps which are Multiple Linear Regression and Trial and Error Analysis for identification of the trigger levels. There are three equilibrium conditions resulted in this study, which can be applied in order to trigger users to switch mode from car towards bus. The optimum equilibrium condition shows the range of travel time for the bus (26 to 33 minutes) and car (30 to 32 minutes) which indicates the approximately equal of travel time between bus and car. The result of the trigger levels presents the values of travel time for bus and car that almost equal to each other

A preliminary survey on mode choice and its effect in users' satisfaction on their journey to the railway station

This paper focuses on two lines of investigation with regard to mode choice to Klang Komuter Station. Firstly, the profile of the access modes on journeys to the railway station is analysed. Secondly, the relationship of users' mode choice towards overall perception on traveling from home to the railway station is estimated. The data collection was conducted via Revealed Preferences / Stated Preferences (RP/SP) Survey. Meanwhile, the analysis that was implemented in this study was correspondence analysis. This paper discussed more on journey purposes and the effects of distances from home to the railway, users' trip purposes and travel time between car and bus that was found to have an important effect on the users' mode choice and their satisfaction on their journey to the railway station. The results show that users were more satisfied to reach the station by car instead of the bus

Logistic regression approach in studying travel demand during morning peak hour: A case study in Klang Komuter Station, Malaysia

This paper develops a Stated Preference (SP) experiment that provides a way to measure users’ travel demand in public transport. This paper introduces an empirical procedure for optimising the SP experiment. This procedure permits the identification of the choice alternatives defining the experiment by simulating the choices of a user sample. By using the data collected from a survey, a Logit model was calibrated. This model is a way of identifying the importance of service quality attributes on global users’ satisfaction which provides an operationally appealing measure of current or potential service effectiveness. The result shows that out-ofvehicle time gives a great impact towards users’ mode choic