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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 EuroMed Regional Transport Project: Road, Rail and Urban Transport (RRU) Symposium on Transport Ramallah, February 2013 Live video conference with Gaza

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Study Objectives To provide a quantitative explanation of the choices of travel modes for work trips in Gaza city. To study the factors affecting the mode choice To specify the most significant factors which affect mode choice. To study the various types of mode choice models. To choose the most suitable model. To calibrate and estimate the chosen mode choice model. To validate the developed models. To develop a mode choice model for work trips in Gaza city

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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.

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Urban Transportation planning PRE-ANALYSIS PHASE Problem/Issue Identification Formulation of Goals and Objectives Data Collection Generation of Alternatives TECHNICAL – ANALYSIS PHASE Urban Transportation Model System. Discrete choice Modeling POST-ANALYSIS PHASE Evaluation of Alternatives. Decision Making. Implementation. Monitoring

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Research Methodology Review the Literature Initial SurveyFinal Survey Calibration of Model Validation of Model Conclusions and Recommendations

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Research Methodology Review the literature Transportation planning process.Types of mode choice models.Model estimation techniques.Sampling and data collection Initial survey Design of initial survey questionnaire. Pilot studyAnalysis of pilot study Final survey Design of final survey questionnaire. Determination sample sizeDistribution and collectionGeneral analysis

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Research Methodology Calibration Calibration of N number of models Comparison between models in terms of a) coeff-Estimators b) t-Statistics C) Stnd error d)Overall fit Validation a) Likelihood ratio test LRTs b) Estimation of prediction ratio Comparison LRTS with critical chi square value at 95% confidence level Conclusions &recommendations Conclude the main findingsRecommendations

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Analysis _ General information General informationFrequencyPercent Gender of respondents Male % Female % Marital Status Married % Single % Jobs of respondents Governmental employee % Private sector employee13825% UN employee7513.6% Business man or special works213.8% Waged worker8715.8% others91.6% Age of respondents years386.9% years % years % years % years8515.3% years559.9% years173% >55 years61.1%

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Analysis _ General information General informationFrequencyPercent Average monthly income Less than 1000 ILS162.9% ILS5610.1% ILS % ILS % ILS % ILS468.3% More than 5000 ILS274.9% Family size 1-6 persons % 7-10 persons % More than 10 persons81.5% Ownership of transportation means Private car9116.5% Motorcycle % Bicycle224.0% No means % Trip length km376.7% km % km % km9316.9% km7713.9% km498.9% More than 6.0 km112.0%

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Analysis _ General information General informationFrequencyPercent The means of transport usually used by respondents Private car7613.8% Shared taxi % Taxi397.1% Motorcycle9016.3% Bicycle132.4% Walking8916.1% Captivity Choice riders % Captive riders %

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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

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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.

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The Selected Revealed Model ParametersModel_8 (MNL) Estimated valuet-statistics Generic Parameters TT TC/PINC Alternative Specific Parameters CONSTANTS_Taxi CONSTANTTaxi CONSTANTMotorcycle CONSTANTBicycle CONSTANTWalking AGEBicycle OWTMS_Taxi DISTWalking FINCMotorcycle FINCWalking Model Statistics Log Likelihood at Zero Log Likelihood at Constants Log Likelihood at Convergence Rho Squared w.r.t. Zero Rho Squared w.r.t Constants Adjusted Rho Squared w.r.t. Zero Adjusted Rho Squared w.r.t Constants Number of Cases368 Number of iterations15 Estimation statusconverged, with contants, with zeros, valid lic.

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The Utility Functions of Revealed Model

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The Selected Stated Preference Model Parametersmodel_S5 (MNL) Estimated valuet-statistics Generic Parameters TT FREQ FARE/PINC Alternative Specific Parameters CONSTANTMinibus CONSTANTBus AGEMinibus AGEBus DISTBus FINCMinibus FINCBus Model Statistics Log Likelihood at Zero Log Likelihood at Constants Log Likelihood at Convergence Rho Squared w.r.t. Zero Rho Squared w.r.t Constants Adjusted Rho Squared w.r.t. Zero Adjusted Rho Squared w.r.t Constants0.318 Number of Cases494 Number of iterations18 Estimation statusconverged, with contants, with zeros, valid lic.

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The Utility Functions of Stated Preference Model

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Model Validation Validation test Test of Reasonableness Likelihood ratio test (LRTS) Prediction Ratio

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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.

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Likelihood Ratio Test (LRTS) This test is conducted using 1/3 rd 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

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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).

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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).

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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.

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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.

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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.

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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.

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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

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