DEVELOPMENT OF MODE CHOICE MODEL FOR WORK TRIPS IN GAZA

CITY

State of PalestineMinistry of Transport

Sadi I. S. AL-Raee

EuroMed Regional Transport Project: Road, Rail and Urban Transport (RRU)

Symposium on TransportRamallah, 13-14 February 2013Live video conference with Gaza

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

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.

Urban Transportation planningPRE-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

Research Methodology

Research Methodolo

gy

Review the Literature

Initial Survey

Final Survey

Calibration of Model

Validation of Model

Conclusions and

Recommendations

Research MethodologyReview 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 study

Analysis of pilot study

Final survey

Design of final survey questionnaire.

Determination sample size

Distribution and collection

General analysis

Research Methodology

Calibration

Calibration of N number of models

Comparison between

models in terms of

a) coeff-Estimatorsb) t-StatisticsC) Stnd errord)Overall fit

Validationa) Likelihood ratio test LRTsb) Estimation of prediction ratio

Comparison LRTS with critical chi

square value at 95%

confidence level

Conclusions &recommendat

ionsConclude the main

findings

Recommendations

Analysis _ General information

General information Frequency Percent

Gender of respondents

Male 378 68.5%

Female 174 31.5%

Marital Status

Married 437 79.2%

Single 115 20.8%

Jobs of respondents

Governmental employee 222 40.2%

Private sector employee 138 25%

UN employee 75 13.6%

Business man or special works 21 3.8%

Waged worker 87 15.8%

others 9 1.6%

Age of respondents

18-25 years 38 6.9%

26-30 years 107 19.4%

31-35 years 114 20.7%

36-40 years 130 23.6%

41-45 years 85 15.3%

46-50 years 55 9.9%

51-55 years 17 3%

>55 years 6 1.1%

Analysis _ General information

General information Frequency PercentAverage monthly incomeLess than 1000 ILS 16 2.9%1001-1500 ILS 56 10.1%1501-2000 ILS 111 20.1%2001-3000 ILS 181 32.8%3001-4000 ILS 115 20.8%4001-5000 ILS 46 8.3%More than 5000 ILS 27 4.9%Family size1-6 persons 386 69.9%7-10 persons 158 28.6%More than 10 persons 8 1.5%Ownership of transportation meansPrivate car 91 16.5%Motorcycle 112 20.3%Bicycle 22 4.0%No means 327 59.2%Trip length 0.3-1.0 km 37 6.7%1.1-2.0 km 121 21.9%2.1-3.0km 164 29.7%3.1-4.0 km 93 16.9%4.10-5.0 km 77 13.9%5.1-6.0 km 49 8.9%More than 6.0 km 11 2.0%

Analysis _ General information

General information Frequency Percent

The means of transport usually used by respondents

Private car 76 13.8%

Shared taxi 245 44.4%

Taxi 39 7.1%

Motorcycle 90 16.3%

Bicycle 13 2.4%

Walking 89 16.1%

Captivity

Choice riders 396 71.7%

Captive riders 156 28.3%

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

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.

The Selected Revealed Model

Parameters Model_8 (MNL)    Estimated value t-statisticsGeneric ParametersTT -0.1299 -3.264TC/PINC -227.5075 -2.7647Alternative Specific ParametersCONSTANT S_Taxi 1.0773 1.4379CONSTANT Taxi -0.5826 -0.987CONSTANT Motorcycle 4.9794 2.7175CONSTANT Bicycle -15.3013 -2.2932CONSTANT Walking 7.366 4.2017AGE Bicycle 0.3979 2.4455OWTM S_Taxi -1.0626 -1.9061DIST Walking -2.16 -4.3961FINC Motorcycle -0.0021 -2.6153FINC Walking -0.001 -2.7697Model StatisticsLog Likelihood at Zero -190.9438  Log Likelihood at Constants -144.6343  Log Likelihood at Convergence -105.8891  Rho Squared w.r.t. Zero 0.4454  Rho Squared w.r.t Constants 0.2679  Adjusted Rho Squared w.r.t. Zero 0.3826  Adjusted Rho Squared w.r.t Constants 0.2122  Number of Cases 368  Number of iterations 15  Estimation status converged, with contants, with zeros, valid lic.

The Utility Functions of Revealed Model

THE UTILITY FUNCTIONS FOR THE SELECTED REVEALED MODEL CAN BE WRITTEN AS THE FOLLOWING

The Selected Stated Preference Model

Parameters model_S5 (MNL)  Estimated value t-statistics

Generic ParametersTT -0.3213 -2.5138FREQ -0.0518 -2.127FARE/PINC -372.5231 -2.003Alternative Specific ParametersCONSTANT Minibus 1.8179 2.7047CONSTANT Bus 1.1165 0.7961AGE Minibus 0.0493 2.8485AGE Bus 0.069 2.2217DIST Bus 0.6234 3.6936FINC Minibus -0.0018 -7.6099FINC Bus -0.0039 -6.6471Model StatisticsLog Likelihood at Zero -542.7145  Log Likelihood at Constants -380.0125  Log Likelihood at Convergence -250.54  Rho Squared w.r.t. Zero 0.5384  Rho Squared w.r.t Constants 0.3407  Adjusted Rho Squared w.r.t. Zero 0.5199  Adjusted Rho Squared w.r.t Constants 0.318  Number of Cases 494  Number of iterations 18  Estimation status converged, with contants, with zeros, valid lic.

The Utility Functions of Stated Preference Model

THE UTILITY FUNCTIONS FOR THE SELECTED STATED PREFERENCE MODEL CAN BE WRITTEN AS THE FOLLOWING

Model Validation

Validation test

Test of Reasonablen

ess

Likelihood ratio test

(LRTS)Prediction

Ratio

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.

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 specificationsis 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

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).

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).

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.

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.

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.

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.

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

Thank you for your attention