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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,
Zagzag University, Egypt
Mohamed Abdelghany Elsayed
Associate professor of Highway and Airport Engineering, Faculty of Engineering,
Zagzag University, Egypt
Abd El-Rahman Baz Abd El-Samii Mahfouz
Assistant Lecturer of Highway and Airport Engineering, Faculty of Engineering,
Zagzag University, Egypt
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
http://www.iaeme.com/IJCIET/index.asp 529 [email protected]
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,
Abd El-Rahman Baz Abd El-Samii Mahfouz
http://www.iaeme.com/IJCIET/index.asp 530 [email protected]
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].
Traffic Congestion Control for Unplanned Cities
http://www.iaeme.com/IJCIET/index.asp 531 [email protected]
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)
Metwally G. M. Altaher, Ahmed Mohamady Abdallah, Mohamed Abdelghany Elsayed,
Abd El-Rahman Baz Abd El-Samii Mahfouz
http://www.iaeme.com/IJCIET/index.asp 532 [email protected]
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.
Traffic Congestion Control for Unplanned Cities
http://www.iaeme.com/IJCIET/index.asp 533 [email protected]
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
Metwally G. M. Altaher, Ahmed Mohamady Abdallah, Mohamed Abdelghany Elsayed,
Abd El-Rahman Baz Abd El-Samii Mahfouz
http://www.iaeme.com/IJCIET/index.asp 534 [email protected]
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 (
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خقبػذ 9. الؼو 10. أخش(
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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
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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
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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%
Metwally G. M. Altaher, Ahmed Mohamady Abdallah, Mohamed Abdelghany Elsayed,
Abd El-Rahman Baz Abd El-Samii Mahfouz
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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)
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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
The deduced utility function for current situation (Scenario 1) are
UPC = -0.0524*C, UTA = -0.0385*C, UM =4.09 -1.4700*C, and Ubus= 1.44 - 0.0951*C
The deduced utility function for current situation (Scenario 2) are
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.
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