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Development of mode choice model for work trips in Gaza city

State of Palestine Ministry of Transport. Sadi I. S. AL- Raee. Development of mode choice model for work trips in Gaza city. EuroMed Regional Transport Project: Road, Rail and Urban Transport (RRU) Symposium on Transport Ramallah, 13-14 February 2013 Live video conference with Gaza.

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Development of mode choice model for work trips in Gaza city

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  1. State of Palestine Ministry of Transport Sadi I. S. AL-Raee Development of mode choice model for work trips in Gaza city EuroMed Regional Transport Project: Road, Rail and Urban Transport (RRU) Symposium on Transport Ramallah, 13-14 February 2013Live video conference with Gaza

  2. Study Objectives

  3. Problem Statement • Gaza city is currently facing urbanization and economic growth, with this, demand for private and public transport have been increasing. • To meet the increasing of travel demand without increasing the congestion problem there is a need to adopt suitable transport policies. And this is couldn't be achieved without understanding the travelers’ needs and preference of using the modes • Developing countries including Gaza Strip often use the mode choice models that are developed by the developed countries. These models are not suitable to be used as the original form because of the different conditions and circumstances in developing countries. • Therefore, there is a need to develop mode choice model for Gaza in order to help in predicting the future demand for each mode of transport and adopting the suitable transport policies to solve the congestion problem.

  4. Urban Transportation planning

  5. Research Methodology

  6. Research Methodology

  7. Research Methodology

  8. Analysis _ General information

  9. Analysis _ General information

  10. Analysis _ General information

  11. Calibration of Model • Multinomial Logit model was used • Maximum likelihood function was used for determining the estimators. • The Easy Logit Model (ELM) software was used for estimation of the models

  12. Calibration Criteria • Wrong sign coefficient variables were dropped from the model. • Variables with insignificant coefficients were dropped from the model except the level of service variables (travel time and travel cost). • Some variables with insignificant coefficient were considered based on its improving the statistics of the model. • The level of service variables were considered in different forms (strait forward as cost and travel time) or in ration form such as cost over income. • Some of intuitively important variables which have been dropped from the model were reconsidered. • The mode specific constants were considered in spite of the significance of coefficients of the variables.

  13. The Selected Revealed Model

  14. The Utility Functions of Revealed Model THE UTILITY FUNCTIONS FOR THE SELECTED REVEALED MODEL CAN BE WRITTEN AS THE FOLLOWING

  15. The Selected Stated Preference Model

  16. The Utility Functions of Stated Preference Model THE UTILITY FUNCTIONS FOR THE SELECTED STATED PREFERENCE MODEL CAN BE WRITTEN AS THE FOLLOWING

  17. Model Validation

  18. Test of reasonableness • This test is performed during the calibration process depending on the expected sign of estimators. • All the models with wrong signs of estimators would not considered as a valid models. • Based on this criterion, The selected revealed and stated preference models are considered as a valid models because all the variables for these models have correct signs of estimators.

  19. Likelihood Ratio Test (LRTS) • This test is conducted using 1/3rd of data sets. represents the likelihood ratio test statistics which restricts the parameters estimated from data j to be used to predict mode share in data i for same specifications is log likelihood ratio value when the parameters are restricting in data j is log likelihood ratio value when the parameters are unrestricted in data j

  20. LRTS for Revealed Model • The calculated chi square value for the Selected revealed model is • the calculated chi square value can’t lead to reject the null hypothesis stated that there is no difference between the predicted and observed behavior because the calculated chi square value is less than critical chi square value at 95% confidence level and twelve degrees of freedom (21.026).

  21. LRTS for Stated Preference Model • The calculated chi square value for the chosen stated preference model is • the calculated chi square value can’t lead to reject the null hypothesis stated that there is no difference between the predicted and observed behavior because the calculated chi square value is less than critical chi square value at 95% confidence level and ten degrees of freedom (18.31).

  22. Prediction Ratio • The last phase for validation process is calculated the prediction capability of the calibrated model. • The calculated prediction value for revealed model is 0.69 which means that the model is capable to predict about 69% of the choices of the trip makers’ correctly. • The calculated prediction value for the stated preference model is 0.80 which means that the model is capable to predict about 80% of the choices of the trip makers’ correctly.

  23. Conclusions • For revealed model, the total travel time, total travel cost divided by personal income, ownership of transport means, age, distance and average family monthly income are the factors that affect the mode choice for workers in Gaza city. While the gender and out of vehicle time are statistically insignificant at 90% confidence level so they are excluded from the model • For stated preference model, the travel time, fare over personal income, frequency of service, age, average monthly family income, and distance have an effect on mode choice decision of workers as they are statistically significant at 95% confidence level while the gender variable has no effect on mode choice decision as it is statistically insignificant even at 90% confidence level.

  24. Conclusions contd., • The developed revealed at stated preference models are able to predict the choice behavior of the workers in Gaza city as the two models are valid at 95% confidence level. • There are six factors affect the captive riderships which are gender, job, private car ownership, motorcycle ownership, bicycle ownership, and distance.

  25. Recommendations • Using the developed revealed model in travel demand analysis and in developing transport policies for Gaza city. • Using the developed stated preference model in studying the possibility and the feasibility of introducing the bus services for transport system in Gaza city. • Using the developed stated preference model in establishing the time table and in determining the appropriate fare for bus services in Gaza city. • Awareness campaigns should be implemented to encourage young people for using a bicycle mode.

  26. Recommendations contd., • In case on introducing a bus service to transport system in Gaza city awareness campaigns may be needed to encourage the young people’s for using bus modes. • Further study for developing mode choice models for trips other than work trips such as social, recreational and study trips. • Studying the effect of captive travelers on mode choice models. • Calibrating the mode choice using probit and generalized extreme model and comparing them with logit model

  27. Thank you for your attention

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