traffic congestion control for unplanned …...cite this article: metwally g. m. altaher, ahmed...

http://www.iaeme.com/IJCIET/index.asp 528 [email protected]

International Journal of Civil Engineering and Technology (IJCIET)

Volume 10, Issue 06, June 2019, pp. 528-540, Article ID: IJCIET_10_06_050

Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=10&IType=6

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

© IAEME Publication

TRAFFIC CONGESTION CONTROL FOR

UNPLANNED CITIES

Metwally G. M. Altaher

Professor of Highway and Airport Engineering, Faculty of Engineering,

Zagzag University, Egypt

Ahmed Mohamady Abdallah

Associate professor of Highway and Airport Engineering, Faculty of Engineering,


Mohamed Abdelghany Elsayed

Associate professor of Highway and Airport Engineering, Faculty of Engineering,


Abd El-Rahman Baz Abd El-Samii Mahfouz

Assistant Lecturer of Highway and Airport Engineering, Faculty of Engineering,


ABSTRACT

The mean value of average overall running speed (AORS) in Zagazig city main

streets (ZCMS) is about 10 kph. This means that traffic congestion is common

phenomenon all over ZCMS. This leads to more trips delays, traffic accidents, fuel

consumption, air pollution, noise, etc. Many reasons cause traffic congestion

specially the huge number of running vehicles on ZCMS all day time comparing to

their characteristics. This study aims to decrease the running vehicles volume on

ZCMS to its minimum and improve level of service. A comprehensive experimental

program was designed and implemented starting with reviewing past studies then

designing questionnaires to collect data required to define and improve the current

situation. Two questionnaires were designed to define ZCMS current situation and to

forecast the proposed situation in the case of adding new mode facilities. Analyzing

collected data, utility functions for current situation scenario (Scenario 0), adding

public buses system to current modes scenario (Scenario 1), and adding luxury public

buses system to current modes scenario (Scenario 2) are determined. Based on the

calculated utility functions, volume of different commonly types of modes in Zagazig

enough to generated trips implementation are determined. As well as corresponding

real number and types of modes used in implementing Zagazig generated daily trips

(ZGDT). Analyzing study results, it is found that no significant difference is noticed

between real volumes of modes required to implementing ZGDT and the identical

determined using utility functions of Scenario 0. Appling polices of Scenario 1

reducing the volumes of modes required to implementing ZGDT to 44.90% of the

Traffic Congestion Control for Unplanned Cities


original required volumes. Applying Scenario 2 reaches volume reduction to 56.97%.

It’s anticipated that AORS in ZCMS will be increased by 42% applying polices of

Scenario 1 and 64.5% when applying polices of scenario 2.

Key words: Transportation Studies, Modal Split, Mode Choice, Utility Function,

Multinomial Logit Model, Logistic Regression, and Biogem Software.

Cite this Article: Metwally G. M. Altaher, Ahmed Mohamady Abdallah, Mohamed

Abdelghany Elsayed, Abd El-Rahman Baz Abd El-Samii Mahfouz, Traffic

Congestion Control for Unplanned Cities, International Journal of Civil Engineering

and Technology 10(6), 2019, pp. 528-540.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=6

1. INTRODUCTION AND LITERATURE REVIEW

Researchers commonly use utility function to distribute daily generated trips in the studied

area per the most available modes. Utility function is a relation between utility achieved for a

passenger when he uses a specific mode to implement a trip. It is as a dependent variable of a

relation with a group of affecting factors including variables related to each of passenger,

trip, and transportation system. In general, mode choice model was developed using logistics

regression based on maximum utilization theory Georgina Santos, Hanna Maohc, and et al

2013 [1] conducted a study to identify factors influencing modal split for journeys in 112

medium size cities in Europe. It was concluded that, car share increased with car ownership

and capita, whereas motorcycle share decreased with petrol price and increased with

motorcycle ownership. Also, bicycle share increased with the length of the bicycle network in

the city and public transport share increased with resident population and the number of

buses. Finally, the number of students in universities and further education establishments per

1,000 resident populations was positively associated with the shares of public transport,

motorcycle, bicycle, and walking.

H.S.Sathish, H.S.Jagadeesh, and Skanda Kumar 2013 [2] carried out a study to

analyze modal split stage in Bangalore city, India. The study defined utility function as an

ordinal concept that indicated to individual’s arrangement for some available choices. It was

stated that the general form of utility function is as shown in Equation (1).

U = βi – α1 C – α2 T (1)

Where; βi is calibrated mode specific constant, α1 and α2 are constants; C and T are out of

pocket costs and travel time respectively (examples of independent affecting variables).

Tomaž Maher, Irena Strnad, and Marijan Žura 2011 [3] estimated nine types of utility

function parameters including linear utility function for four different modes (private car,

public transport, bike and walking) and five purposes (work, education, shopping, leisure and

other) in city of Ljubljana, Slovenia. The nine estimated types of utility functions were linear

form, EVA (German abbreviation for Erzeugung, Verteilung and Aufteilung meaning

Production, Distribution, and Mode Choice), Schiller, Logit, Kirchhoff, BoxCox, Box-Tukey,

Combined, and Code utility function. The results showed that absolute differences in final

log-likelihood among most types of utility functions are not high despite the different shapes,

which implies that different functions may best describe different variables.

Marwa Elharoun1, Usama Elrawy Shahdah, and Sherif M. El-Badawystudy 2018 [4] stated that for a specified mode choice data, current estimation computer programs can be

used to calibrate a mode choice model, such as, Biogeme), SPSS and Easy Logit Modeling

software. In this study, the Easy Logit Modeling (ELM) software was used for its simplicity,

and for its availability free of charges. Michel Bierlaire 2018 [5] designed Biogeme software

to estimate the parameters of models of various modes in the studied area using maximum

Metwally G. M. Altaher, Ahmed Mohamady Abdallah, Mohamed Abdelghany Elsayed,



likelihood estimation. Sreerag SR, S.N. Sachdeva, and Shri. S. Shameem 2016 [6] used

NLOGIT software to find the utility function.

Binary logit model was used in the case of two modes only available in the studied area. The

used multinomial Logit Model (MNL) is shown in Equation (2).

∑

(2)

Where; Pi is probability that mode (i) is chosen, and e is base of natural logarithms.

The determination of utility function was the objective of many researchers and several

statistical analysis computer programs. Utility function must build to each studied mode. It

consists of some variables and parameters. Some researchers were conducted complete

functions for all modes and constants and some other take the constant equal zero in one

equation. A group of researchers take one mode as reference for other modes and its utility

function was equaled to zero. Mohamed El Esawey and Ahmed Ghareib 2009 [6] construct

the utility function for six modes (Car, bus, minibus, shared taxi, metro, and taxi) in Cairo. In

this study the constant of one mode was taken zero. Milimol Philip, Sreelatha T, and

Soosan George 2013 [7] conducted a study to develop mode choice model for a village in

Ernakulam district of Kerala, India. Mode choice model was developed by applying

multinomial logit model based on maximum utilization theory using SPSS software. The

utility function was created for several travel modes including two wheeler, four wheeler,

three wheeler, school bus, bus, and multi-mode. It was found that two-wheeler was the most

preferred mode, so it was selected as the reference category for mode choice analysis by

taking its utility function equalling zero. The study results indicated that the most popular

variables affecting on utility function were trip cost (TR_CT), trip duration (TR_DRN), trip

waiting time (TR_WTT), trip waking time (TR-WKT) and licence ownership (LICNS). Six

equations were deduced for utility function in the studied area as shown in Figure (1).

Figure 1 Utility functions equations deduced from study [7]

The Easy Logit Modeling (ELM) software was used to build the required model in this

study due to its simplicity and for its availability free of charges. The utility function was

created for available travel modes including privet car, taxi, microbus, walk, and others were

built. The private car was taken as reference mode in the analysis. The study results indicated

that the most popular variables affecting on utility function were Total travel time (TT), Total

travel cost (TC), Gender of respondent (GENDER), Ownership of transport means (OWTM),

Monthly personal income (PINC), Occupational status (WOS), Residency status in Mansoura

city (RES), and driving license holder (LICENSE). Five equations were deduced for utility

function in the studied area as shown in Figure (2)[4].



Figure 2 Utility functions equations deduced from study [4]

Sreerag SR, S.N. Sachdeva, and Shri. S. Shameem 2016 [8] conducted another study

to state the independent variables affecting on utility function and to develop mode choice

model for Thiruvanthpuram city. Two wheeler, bus, and car were the three alternatives

common available modes in the studied area. It was concluded that the multinomial logit

model was recommended to be used to discrete choice model because more than two

alternatives were available for choosing. NLOGIT software was used to find the utility

function parameters for the three considered alternatives. The study results indicated that the

most popular variables affecting on utility function were travel time and travel cost.

Several studies were conducted to control traffic congestion through changing the

characteristics of transportation modes affecting on passenger utility function. Onn Chiu

Chuen, Mohamed Rehan Karim, and Sumiani Yusoff 2014 [9] investigated the effect of

applying different polices on mode choice of Klang Valley population. The percentages basic

distribution of Klang Valley population trips on available modes is shown in Figure (4).The

study suggested three scenarios to shift trips from private cars which representing 45.60% of

total trips to public transportation. The first scenario investigated the effect of increasing of

private transportation cost whereas The second scenario investigated the effect of improving

bus network. The last scenario investigated the effect of increasing of private transportation

cost as well as reducing travel time thought improving bus network. This police led to

decreasing sharing percent of private car to 0.02% and increasing the percent of public

transportation to 99.98%. A study [6] was conducted to predict the potential modal shifts in

greater Cairo region under four hypothetical policy scenarios. The first three scenarios were

increasing the fare of bus, or metro, or shared taxi. The last scenario was increasing individual

person income. The increase in income or mode fare reached to 125% of their origin values.

The study found that the potential for mode shifts was minor even with drastic changes in

network characteristics. It concluded that mode choice in greater Cairo region was inelastic.

Another study [4] was investigated the microbus fare increasing in Mansoura City, Egypt.

The checked increasing cost was 25%, 50%, 75%, 100%, and 125% of microbus base fare.

All other variables were held constant to observe the varying percentage modal split for all

travelling modes with a change in the value of the microbus fare increase. The study

concluded to the probability of walking and using other modes rather than taxi and private car

increased by about 25% when increasing microbus fare by 100%.

Rajat Rastogi 2014 [10] compared between revealed preference and stated preference

information. The study illustrated that the revealed information related to actual behaviour.

Stated information related to hypothetical scenarios.

This study aims to decrease the running vehicles volume on Zagazig city main streets

(ZCMS) to its minimum. This will lead to increasing average overall running speed (AORS)




and improve level of service. To achieve the study objectives, comprehensive experimental

program was designed and implemented.

2. STUDY METHODOLOGY

The study methodology included three main stages as shown as Figure (3). The stages include

Office work, Data collection, and Data analysis. Office work stage that included defining of

problem definition, study objective, then past studies related to the research field was

reviewed. The anticipated scenarios that can be used to find all possible solutions of the

problem statement are then defined. Two hypothecs scenarios were estimated in addition to

current situation (Senario 0) to reduce volume of running vehicles in Zagazig main streets.

The first scenario is adding bus system to the current available travel modes whereas; the

second scenario is adding Luxury bus system instead of bus system. Luxury bus system will

have a fixed time table and no-stand to overcome the crowding problems. To collect the

required data, two questionnaires were designed. The objective of the first is to define the

current situation of the problem, whereas the objective of the second questionnaire is to

forecast the future situation in the case of adding new mode facilities. The design

questionnaires are shown in Figures (4 and 5). To define the current situation, questions

including trip origin, destination, mode, purpose, time, cost, and parking fee (shown in

questionnaire1) were directed to open home sample by five field surveyors along seven

months. The costs were forty thousand Egyptian pounds. The required data were collected

from 3,094 household of 14211 persons. To determine the mode sharing in the case of adding

the bus or luxury bus system, Questionnaires 2 was published on

line(https://docs.google.com/forms/d/e/1FAIpQLSecAM9qJro9BYwDybUhdc5relXA5SaXN

HWkksOlAk0liCuIww/viewform?usp=sf_link). The number of responses reached to seven

hundred and eleven persons. The data collected from questionnaire 1 were then analysed. The

analysis began with classifying the observed trips based on their purpose and modes. Then,

biogeme software was used to construct utility functions for current situation modes (Scenario

0). After that, percentages of contribution of different modes adding each of new transporting

facilities (Scenario 1 and Scenario 2) through analyzing data collected from questionnaire 2.

The volume of different modes needed to perform daily the motorized trips in ZCMS were

determined based on Zagazig trip generation model deduced in study [11]. The volume of

running vehicles on Zagazig city main streets was then converted to passenger car equivalent

(PCE) units and distributed regularly on ZCMS. Consequently, the average overall running

speed in Zagazig city main streets were calculated and evaluated for different study Scenarios

to get the conclusions and necessary recommendations.

https://docs.google.com/forms/d/e/1FAIpQLSecAM9qJro9BYwDybUhdc5relXA5SaXNHWkksOlAk0liCuIww/viewform?usp=sf_link

https://docs.google.com/forms/d/e/1FAIpQLSecAM9qJro9BYwDybUhdc5relXA5SaXNHWkksOlAk0liCuIww/viewform?usp=sf_link



Questionnaire design

Questionnaire 1: investigating

current situation

Questionnaire 2: investigating two

hypothecs scenarios

Figure 3 Study Program flow chart

Experimental Program

Field data collection Office work

Conclusion and Recommendations

Data Analysis

-Classifying the collected data

-Creating utility function for scenario 0 (Current

situation)

-Creating utility function for different scenario 1

-Creating utility function for different scenario 2

- Determining the mode sharing and the volume of

vehicles in PCE for different scenarios

- Calculating average travel speed in study area main

streets for different scenarios

Defining the problem and study objectives

Reviewing the past studies

Defining the possible scenarios of problem solution

- Available travel modes - Travel Times

- Travel Cost

Including




Figure 4 The data collection questionnaire final form

Figure 5 The stated preference questionnaire

3. RESULTS AND DISCUSSION

This section includes the data analysis to find the study objectives. The following sub-titles

summarize the data analysis these consists of common travel modes in the study area, mode

1

2

3

4

2. ال1. ؼ

6

7

8

10

اى

13

14

15

)5(/

البا

)6(/

البا

جاىز

جتىز

ا

يييت الفشاد االسش بيااث انشدالث )حمالث ( ان

جسيت يصش انؼشبيت

يذافظت انششليت

مم انشايهت نذيت انضلاصيك دساست انكهيت انذست - جايؼت انضلاصيك

صي

دقهت

شدان

فت حكه

دقهت

شدان

انؼا )حو االقبت(

صفت

انمائى

بانشدهت

ض غش

هت شد

ان

خش()اخ

اس خظ

اال

صت خا

نه

مظف

12يو انؼطهت االسبػيت ) 1. اىسبج 2. االحذ 3.االث 4. اىثالثبء 5.االسبؼبء 6. اىخس

7. اىجؼ 8. خغشة 9.اخش( أخخش سق ىنو فشد افشاد االسش.

5ع االخظاس

1. ببىشبسع2. جشاج

خبسج اىشبسع

م االسشة حخهك أ يشكبت

9

500 3. 501 حخ يخسظ انذخم انشش نكم فشد )1. اقو 300 جت 2. 301 حخ2000 5. 2001 حخ 5000 6 .امثش 5000 7.شفض( 1000 4. 1001 حخ

ي ببالسش. أخخش سق ىنو فشد االفشاد اىؼب

)3(/

البا

)4(/

البا

)1(/

البا

)2(/

البا

)7(/

البا

)8(/

البا

يييت انؼخاد نكم فشد )سلى( )ثبه: اىزه اى اىؼو سحيت اىؼنس سحيت( خمالث( ان ػذد انشدالث )ان

ػش كم فشد )1. 7 اى 9 2. 10 اى19 3. 20 اى29 4. 30 اى 39

5. 40 اى 49 6. 50 اى59 7. أمبش 60( أخخش سق ىنو فشد افشاد االسش.

11

أسخاس )أ(: انبيااث انضنيت

ظف(: سلى انؼيت )حأل بؼشفت اى

ساػاث انؼم ا انذساست انؼخادة نكم فشد أمخب اىسبػت ىنو فشد افشاد االسش.

ق( فس انذذة انسكيت يذم االلايت )ضغ ػالت √ اب االفشاد اى أفشاد االسشة انمي ب

)2= ع كم فشد )1. رمش 2.أث( ) ثو: اىزج=1 اىزج=2 نزا ىالببء اىزمش=1 االث

( 2. طبىب )اػذاد( 3. طبىب )ثب( 4. طبىب )دبي( ذانيت نكم فشد )1. طبىب )ابخذائ ذانت ان ان

ؼت( 6. ظف 7. سبت زه 8. خقبػذ 9. الؼو 10. أخش( أخخش سق ىنو فشد 5. طبىب )جب

افشاد االسش.

ػذد افشاد االسش اكبش ي 6 ساث )........( -ػذد االطفال الم ي 6 ساث )........(

انخسظ انشش نفاحسة انكشباء )1. اقو 50 جت 2. 51 حخ100 3. 101 حخ

150 4. 151 حخ200 5. 201 حخ 250 6 .امثش 251( أخخش سق ىالسش.

ال حيل ايت )1. شقت حيل 2. شقت اجبس 3. زه حيل 4. زه اجبس 4. ف دذة يذم االل ع ان

5.اخش( أخخش سق ىالسش.

ايت )1. زه اقو خست اداس 2. ػقبس امبش 5 اداس 3.اخش( أخخش سق ىالسش. ع انؼماس يذم االل

سلى حهيف ا اييم ا فيس ا فشد ي االسشة

يكا بذايت انشدهت )حفصيه(

ػذد انشكباثااع انشكباث )الم-حبمس-نشببص-حمخك ........(

هت يكا ايت انشدهت )حفصيه(سي

هت شد

ان

خش()اخ

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

انسياسة

انخاصتحاكس

انيكشباص

)انسشفيس(

انسياسة

انخاصتحاكس

انيكشبا

ص

)انسشفيس(

ػبى جذاالجذالجذػبى جذاالجذالجذ

الجذالجذالجذالجذالجذالجذ

الجذالجذجذالجذالجذجذ

002002

004004

161818.5161818.5

003003

8.2514.528.2514.52

200200

19

20

21

22

23

3

انخاكسانيكشباص

)انسشفيس(

ظاو

حبيس

يذس

ػبى جذاالجذ

اخخاس انسيهت انفضهت انسخخذيت ف انضغ بؼذ انخطيش باضافت ظاو احبيس انؼادة

انبذيم انثا )اضافت ظاو احبيس يذس(

20

044

1818.520

الجذ

الجذالجذجذ

ثببج

3

0

انسياسة

انخاصت

الجذ

الجذ

جذ

0

0

16

0

8.25

2

14.527.5

000

03

انضغ انذان

ظاو

احبيس

ػاد

جذ

الجذ

جذ

0

4

18.5

3

انبذيم االل )اضافت ظاو احبيس ػادة(

يخسظ حكهفت االخظاس نهشدهت انادذ بانسياسة انخاصت )ببىجت(

سلى حهيف ا اييم )اخخبس(

ساػاث انؼم ا انذساست انيييت انؼخادة ) اىسبػ ...اى اىسبػ(

اخخاس انسيهت انفضهت انسخخذيت ف انضغ انذان لبم اضافت االحبيس

اخخاس انسيهت انفضهت انسخخذيت ف انضغ بؼذ انخطيش باضافت ظاو االحبيس انذس

اكخب سيهخك انفضهت انسخخذيت ا نى حك يجدة باالخخياساث )امخبب(

يخسظ صي االخمال ي انسيهت نايت انشدهت )ببىذققت(

يخسظ حكهفت )حزكشة( انشدهت انادذ )ببىجت(

يخسظ حكهفت االخظاس نهشدهت انادذ )ببىجت(

زه اى اىؼو سحيت اىؼنس سحيت( ػذد انشدالث )انخمالث( انيييت انؼخاد )سلى( )ثبه: اى

نشببص )اىسشفس( 4. ت انضلاصيك )1.اىسبسة اىخبصت 2.اىخبمس 3.اى سيهت انصالث )انذاخهيت( انسخخذيت ف انخمالث انيييت ػهي شبكت انطشق داخم يذي

خسنو 7.ش 8.اىؼجيت 9.اخش( حبفظت 5.اىخمخك 6.اى ببص اى

ايت( انشدهت ػذد يشاث حغييش انسيهت )انذاخهيت( نهصل ان ذف )

يخسظ صي انصل ان انسيهت انذاخهيت )ببىذققت(

يخسظ صي انشدهت داخم انسيهت انذاخهيت )ببىذققت(

يخسظ صي اخظاس انسيهت انذاخهيت )ببىذققت(

يخسظ صي االخمال ي انسيهت نايت انشدهت )ببىذققت(

يخسظ حكهفت )حزكشة( انشدهت انادذ بانسيهت انذاخهيت )ببىجت(

سائم انصالث انخادت نهخمم

داالث انذساست

مم انؼاو انخادت ثا اضافت ظاو احبيس يذس ان سائم ان بذيم االل اضافت ظاو احبيس ػادة ان مم انؼاو بانذيت. ان ارا حى الخشاح بذيهي نخطيش ان

بذائم: )اقشأ اىجذه بؼبت اجب ػي االسئيت )19-20-21( اسفيت( ذانيت بؼذ اضافت ان خان يضخ انماست بي سائم انصالث ان بذيت انضلاصيك. انجذل ان

االردداو بانسيهت اثاء انشدهت

ػذد يشاث حغييش انسيهت نهصل انىايت انشدهت

جذل صي ثابج يؼه

فيش ػايم انشادت )حنف - بببث اىشحالث( جذالجذالجذ ح

0

يخسظ صي انصل ان انسيهت )ببىذققت(

يخسظ صي انشدهت داخم انسيهت )ببىذققت(

م حخهك سياسة خاص )1.ال 2-ؼ احذ فقط 3-ؼ اثب 4-ؼ امثش اثب(

2000 5. 2001 حخ 5000 6 .امثش 500 3. 501 حخ 1000 4. 1001 حخ يخسظ انذخم انشش )1. اقو 300 جت 2. 301 حخ

ي فقط. 5000 7.شفض( أخخش سق ىالفشاد اىؼب

) انع )1. رمش 2.أث

59 7. أمبش 60( 29 4. 30 اى 39 5. 40 اى 49 6. 50 اى 19 3. 20 اى انؼش )1. 7 اى 9 2. 10 اى

ػا يذم االلايت

مم انشايهت نذيت انضلاصيكجسيت يصش انؼشبيت دساست ان

ذست - جايؼت انضلاصيك يذافظت انششليت كهيت ان

أخخياس سيهت انصالث بذيت انضلاصيك

ػا انؼم ا انذساس

انذانت االجخاػيت )1. غش خزج 2. خزج (

ؼت( 6. ظف 7. سبت زه 8. ( 2. طبىب )اػذاد( 3. طبىب )ثب( 4. طبىب )دبي( 5. طبىب )جب انذانت انؼهيت انذانيت )1. طبىب )ابخذائ

خقبػذ 9. الؼو 10. أخش(



choice modeling, mode shafting, and number of vehicles in Zagazig main streets, and average

running speed calculations at different study scenarios.

3.1. Common Modes in Study Area

Figure (6) shows the Common modes in Zagazig city and percentages of its using to perform

the Zagazig daily generated trips. The figure shows that walk trips represent 40.37% of

generated trips and bicycle trips represent is 2.35%. The small area of the city as well as it

was constructed on a flat area may be the reason.

The figure also shows that the common modes in Zagazig city are Privet car (PC), Taxi

(TA), microbus (M), motorcycle, and three-wheel trips. Three-wheel trips include trips

performed by tricycle and tuk-tuk. The contribution of microbus representing about 38.94%

of total Zagazig daily generated trips, while private car and taxi trips representing about

8.08% and 6.31% recepectivly. In the same time, the sum of two wheels and three wheels

trips are about 3.95%.

Figure 6 Observed trips percentages

3.2. Mode Choice Modeling

Travel time and travel cost are considered the main independent variables affecting on utility

function. The total travel time includes the walk and waiting times to use public transport

modes. For private cars and taxi, it is the total time for transport from trip origin to its

destination. Average Travel Times for different modes is calculated from collected field data

as the average observed travel time for each trip. Table (2) shows that the average travel times

of private car, taxi, and public microbus are almost equal. This may be due to traffic

congestion in the study area. The fare for public microbus and taxi were surveyed in the data

collection stage. They are 14.5 and 2.00 L.E for taxi and microbus respectively as shown in

Table (1). The average trip cost of private car is 8.25 based on recommendations of JICA [12]

after updating its value depending on dollar price.

Table 1 Average travel time for different modes

Travel mode Private Car Taxi Microbus

Average Travel Times (Min) 16 18 18.5

Trip cost (LE) 8.25 14.50 2.00

Multinomial Logit Model (MNL) was used to modeling the mode choice in Zagazig. The

model was estimated for three modes that include privet car, Taxi, and microbus. Utility (U)

is expressed as function of total travel time (TM) and mode fare cost (C). The likelihood

function describes the probability of individual mode choice that has been observed in the

8.08% 6.31%

1.49%

2.46%

38.94%

2.35%

40.37%

Privit CarTaxiThree WheelMotocyclePublic MicrobusBicycle




actual situation. The utility constants can be estimated by solving the logarithm of likelihood

function [3]. Biogeme software is used to determine the utility function parameters. Table (2)

presents utility function parameters as well as relation parameters (standard error, p-value,

and t-value ) of the current situation (Scenario 0). The table shows that microbus travel time

has insignificant value; 0.89 p-value and very small coefficient (-0.00096). Travel time

parameter of each private car and taxi has coefficient of un-logic positive value. Constant

coefficient of PC is also of small positive value (0.0421). So microbus travel time, travel time

parameter of each private car and taxi, as well as constant coefficient of PC are neglected.

Biogeme program processing is repeated again after discarding all mentioned coefficient and

the new results are shown in Table (3). Equations (3, 4, and 5) show the resulted utility

functions of Zagazig common modes.

Table 2 Utility function parameters for all modes at Scenario 0

Variables Coefficient Std_err -value t-test

Travel Time (PC) 0.0261 0.00636 0.000 4.11

Travel Time (TA) 0.0194 0.00692 0.010 2.81

Travel Time (M) -0.00096 0.0071 0.89 -0.14

Travel Cost (PC) -0.0561 0.0163 0.000 -3.44

Travel Cost (TA) -0.0301 0.00895 0.000 -3.37

Travel Cost (M) -1.4100 0.164 0.000 -8.61

Constant (PC) 0.0421 0.254 0.87 0.17

Constant (TA) 0.000 - - -

Constant (M) 4.4600 0.359 0.000 12.43

Table 3 Utility function significant parameters for all modes at scenario 0

Variables Coefficient Std_err -value t-test

Travel Time (PC) 0.0000 - - -

Travel Time (TA) 0.0000 - - -

Travel Time (M) 0.0000 - - -

Travel Cost (PC) -0.0524 0.01130 0.000 -4.660

Travel Cost (TA) -0.0385 0.00639 0.000 -6.030

Travel Cost (M) -1.4700 0.15700 0.000 -9.330

Constant (PC) 0.0000 - - -

Constant (TA) 0.0000 - - -

Constant (M) 4.0900 0.335 0.000 12.200

UPC = 0.00 + 0.00 * TM - 0.0524* C (3)

UTA = 0.00 + 0.00 * TM - 0.0385 * C (4)

UM = 4.09 + 0.00 * TM - 1.4700 * C (5)

3.3. Mode Shifting

This section discusses the effect of adding new transport facilities to Zagazig city modes

(Scenario 1: bus system) or (Scenario 2: luxury bus system). The results of collecting field

data from Questionnaire 2 is presented Figure (7) which illustrates the sharing percentages of

different modes and Scenarios to perform daily trips. It illustrates that sharing percentages of

private car, taxi, and microbus at current situation (Scenario 0) are 15.15%, 11.83%, and

73.02% respectively. While the corresponding sharing percentages become 7.12%, 5.15%,

and 43.33% as well as 5.49%, 2.13%, 41.92%, for Scenario 1and Scenario 2 respectively. The



sharing percentage of bus system is 44.39% (Scenario 1) whereas, and sharing percentage of

luxury bus system (Scenario 2) is 50.46%. The utility functions of Zagazig current modes

after adding each of bus system or luxury bus system ( Scenario 1 or Scenario 2) are

determined by trial and error after fixing utility functions of current modes (private car, taxi,

and microbus). The deduced utility functions are shown by Equations from 6 to 9 for Scenario

1 and Equations from 10 to 13 for Scenario 2.

Figure 7 mode sharing for different Scenarios

UPC = 0.00 + 0.00 * TM - 0.0524* C (6)

UTA = 0.00 + 0.00 * TM - 0.0385 * C (7)

UM = 4.09 + 0.00 * TM - 1.4700 * C (8)

Ubus = 1.44 + 0.00 * TM - 0.0951 * C (9)

UPC = 0.00 + 0.00 * TM - 0.0524* C (10)

UTA = 0.00 + 0.00 * TM - 0.0385 * C (11)

UM = 4.09 + 0.00 * TM - 1.4700 * C (12)

ULbus = 1.57 + 0.00 * TM – 0.0334 * C (13)

3.4. Number of Vehicles in Zagazig Main Streets

Firstly, Zagazig daily generated trips can be calculated knowing that its population is 569299

persons representing 143640 household based of cense of 2018. Current Zagazig daily

generated trips are1182543 person trip per day based on the following model [11].

Consequently, the motorized total generated trips are 677361 persons trips/day. The average

percent of trips in peak hour is about 16.10%. So, Zagazig daily current trips are109055

motorized person trip / day. Sharing of different modes of Scenarios0, 1, and 2, vehicle

occupancy, and the volume of vehicles in PCE for different Scenarios are shown in Table (4).

The vehicle occupancies of different modes are determined as the average values of

representative samples of the different common modes. The occupancies of bus and luxury

bus for scenario 1 and Scenario 2 are determined as a percent of field occupancy of microbus

related to the number of seats for them. Using collected sample inside the study area. The

volume of vehicles was calculated using Equation (14) [13].

(14)

Analyzing the results shown in Table (4), the total volume of different modes necessary to

perform Zagazig Daily generated trips are 292096, 160945, and 125702 PCE per day for

Scenario 0, Scenario 1, and Scenario 2 respectively. It can be deduced that great reduction is

noticed in daily traffic volume running on ZCMS when using both Scenario 1, and Scenario

Scenario 0 Scenario 1 Scenario 2

Privat Car 15.15 7.12 5.49

Taxi 11.83 5.15 2.13

Microbus 73.02 43.33 41.92

bus 0.00 44.39 0.00

luxury bus 0.00 0.00 50.46

0102030405060708090

100%




2. The reduction reaches 44.90% when using Scenario 1 and increases to 56.97% when using

Scenario 2. This may lead to greatly increasing average overall running speed in ZCMS.

Table 4 Number of vehicles in Zagazig city main streets at different Scenarios

Scenario Mode Trip

Percent Occupancy

Volume of

Vehicles

Volume of Vehicles

in PCE

Scenario 0

Private Car 15.15 1.6 64159

292096 Taxi 11.83 0.70 1270901

Microbus 73.02 7.8 63029

Scenario 1

Private Car 7.12 1.6 30148

160945 Taxi 5.15 0.70 49849

Microbus 43.33 7.8 37631

Bus 44.39 29 10369

Scenario 2

Private Car 5.49 1.6 23233

125702 Taxi 2.13 0.70 20651

Microbus 41.92 7.8 36404

Luxury Bus 50.46 29 11785

3.5. Average Running Speed Calculations at Different Study Scenarios

The volume of traffic by PCE per each main street in Zagazig city can be calculated by

dividing the total daily volume by PCE per number of main streets. Based on the definition

that main street in Zagazig city is the street of widths more than 10 m, the number of Zagazig

main streets is about 35 streets. Consequently the average daily volumes per street are 1344,

741, and 579 for Scenarios 0, 1, and 2 respectively. The average overall running speed can be

determined from equation (15) [14] and shown in Table (5). It can be deduced that great

increasing is noticed in average overall running speed at ZCMS when using both Scenario 1,

and Scenario 2. The increasing reaches 42% when using Scenario 1 and increases to 64.5%

when using Scenario 2. This may lead to greatly improving level of service at all ZCMS as

well as decreasing traffic congestion problems.

( ) (15)

Table 5 Average overall running speed at ZCMS

Scenario Average overall running

speed Increasing percent

Scenario 0 10.70 -

Scenario 1 15.20 42%

Scenario 2 17.60 64.5%

5. THE STUDY CONCLUSIONS AND RECOMMENDATIONS

Analyzing study results the following conclusions and recommendations can be concluded

that:

About 40.37%, 2.35% of Zagazig daily generated trips were performed by walking and by

bicycle respectively. The small area of the city as well as it was constructed on a flat area may

be the reason.

Common modes in Zagazig city are microbus (M), privet car (PC), Taxi (TA), motorcycle,

and three-wheel trips (tricycle and tuk-tuk)



The contribution of microbus representing about 38.94% of total Zagazig daily generated

trips, while private car and taxi trips representing about 8.08% and 6.31% recepectivly. In the

same time, the sum of two wheels and three wheels trips are about 3.95%.

Zagazig daily generated trips for (569299 persons representing 143640 household) population

based of cense of 2018 are1182543 person trip / day

The average travel times of private car, taxi, and public microbus are almost equal. This may

be due to traffic congestion in the study area.

The average travel costs are 14.5 and 2.00 L.E for taxi and microbus respectively. The average

trip cost of private car is 8.25 based on recommendations of JICA [13] after updating its value

depending on dollar price.

The deduced utility function for current situation (Scenario 0) are

UPC = - 0.0524* C, UTA = - 0.0385 * C, and UM = 4.09 - 1.4700 * C


UPC = -0.0524*C, UTA = -0.0385*C, UM =4.09 -1.4700*C, and Ubus= 1.44 - 0.0951*C


UPC = -0.0524*C, UTA=-0.0385*C, UM=4.09 -1.4700*C, and ULbus=1.57-0.0334*C

Total volume of different modes necessary to perform Zagazig Daily generated trips are

292096, 160945, and 125702 PCE per day for Scenario 0, Scenario 1, and Scenario 2

respectively.

Great reductions reaching 44.90% and 56.97% are noticed in daily traffic volume running on

ZCMS when using both Scenario 1, and Scenario 2. This may lead to greatly increasing

average overall running speed in ZCMS.

Great increasing reaching 42% and 64.5noticed in average overall running speed at ZCMS

when using both Scenario 1, and Scenario 2. This may lead to greatly improving level of

service at all ZCMS as well as decreasing traffic congestion problems.

Trying to reaches to clean environment in Zagazig city and to encourage the high percentage

of waking trips (about 40.37% of total trips, and 2.35% bicycle trips), special paths of waking

and bicycle are highly recommended.

REFERENCES

[1] Georgina Santos, Hanna Maoh, Dimitris Potoglou, and Thomas von Brunn “Factors

influencing modal split of commuting journeys in medium-size European cities” Journal

of Transport Geography, Volume 30, pages 127-137, 2013.

[2] H.S.Sathish, H.S.Jagadeesh, and Skanda Kumar “Travel Delay and Modal Split Analysis

– A Case Study” IOSR Journal of Mechanical and Civil Engineering, Volume 5, pages 40-

45, 2013.

[3] Tomaž Maher, Irena Strnad, and Marijan Žura“Estimation of EVA Mode Choice Model

Parameters with Different Types of Utility Functions” Promet – Traffic&Transportation,

Volume 23, pages 169-175, 2011.

[4] Marwa Elharoun1, Usama Elrawy Shahdah, and Sherif M. El-Badawystudy “developing a

mode choice model for Mansoura city in egypt” International Journal for Traffic &

Transport Engineering, Volume 8, pages 528-542, 2018.

[5] Michel Bierlaire “PandasBiogeme: a short introduction” Report TRANSP-OR 181219

Transport and Mobility Laboratory School of Architecture, Civil and Environmental

Engineering Ecole Polytechnique F´ed´erale de Lausanne, 2018.




[6] Mohamed El Esawey and Ahmed Ghareib“Analysis of Mode Choice Behavior in Greater

Cairo Region” Annual Meeting of Transportation Research Board, No 09-0168, 2009.

[7] Milimol Philip, Sreelatha T, and Soosan George“Activity Based Travel Behavioural

Study and Mode Choice Modelling” International Journal of Innovative Research in

Science, Engineering and Technology, Volume 2, pages 181-190, 2013.

[8] Sreerag SR, S.N. Sachdeva, and Shri. S. Shameem “Mode Choice Modelling for Work

Trips in Thiruvanthpuram City” Journal of Basic and Applied Engineering Research,

Volume 3, pages 381-384, 2016.

[9] Onn Chiu Chuen,Mohamed Rehan Karim, and Sumiani Yusoff “Mode Choice between

Private and Public Transport in Klang Valley, Malaysia” The Scientific World Journal,

Volume 2014, Article ID 394587, 14 pages, http://dx.doi.org/10.1155/2014/394587, 2014.

[10] Rajat Rastogi “Stated Preference Survey: Developing Country” Un published report, Civil

Engineering Department, Engineering College, KOTA, India.

https://www.researchgate.net/profile/Rajat_Rastogi3/publication/228513931_Stated_Prefe

rence_Survey_Experience_From_Developing_Country/links/0c96053637b8cc40a500000

0.pdf

[11] Metwally G. M. Altaher, Ahmed Mohamady Abdallah, Mohamed Abdelghany Elsayed,

Abd El-Rahman Baz Abd El-Samii Mahfouz “Creating Trip Generation Models for

Unplanned Cities” International Journal of Scientific & Engineering Research, Volume

10, pages 396-406, 2019.

[12] JICA “Cairo Regional Area Transportation Study )CREATS( ” JICA for Ministry of

Transport, Cairo, Egypt, Phase I Final Report, 2002.

[13] William A. Martin and Nancy A. Mcguc-kin “Travel Estimation Techniques for Urban

Planning” National Academy Pre-ss, Washington, Report 365, 1998.

[14] Hemant Kumar Sharma, Mansha Swami, Bajrang Lal Swami “Speed-Flow Analysis for

Interrupted Oversaturated Traffic Flow with Heterogeneous Structure for Urban Roads”

International Journal for Traffic and Transport Engineering, Volume 2, pages 142-152,

2018.

traffic congestion control for unplanned …...cite this article: metwally g. m. altaher, ahmed...

Documents