This document describes transportation modeling and planning using the four-step model. It discusses data collection and characterization for the study area of North Cyprus. The four steps of the model are then explained: trip generation calculates the number of trips produced and attracted in each zone based on household surveys; trip distribution develops an origin-destination matrix to show trip flows between zones; modal split determines the proportion of trips using each transportation mode; and trip assignment allocates the trips to the transportation network. The document provides examples of applying each step of the four-step model to the study area.
Origin and Destination ( O-D) Study. defined all types very well with advantages and disadvantages. Introduction of OD, Objective of OD Study
Information required for OD
OD Survey Types
Methodology
Road Side Interview Method
License Plate Method
Tag on Car method
Home Interview method
postal method
online survey method
commercial and public vehilce method survey
OD MATRIX
Desire line diagram and Flow Line diagram
Conclusion and Reference.
Origin and Destination ( O-D) Study. defined all types very well with advantages and disadvantages. Introduction of OD, Objective of OD Study
Information required for OD
OD Survey Types
Methodology
Road Side Interview Method
License Plate Method
Tag on Car method
Home Interview method
postal method
online survey method
commercial and public vehilce method survey
OD MATRIX
Desire line diagram and Flow Line diagram
Conclusion and Reference.
Transportation planning is an integral part of overall urban planning and needs systematic approach.
Travel demand estimation is an important part of comprehensive transportation planning process.
However, planning does not end by predicting travel demand.
The ultimate aim of urban transport planning is to generate alternatives for improving transportation system to meet future demand and selecting the best alternative after proper evaluation.
Urban transportation system meaning ,travel demand functions with factors, design approaches & modeling , types of mass transit system with advantages -disadvantages or limitations , opportunities in mass transport , integrated approach for transit -transportation system
Detailed description of Capacity and Level of service of Multi lane highways based on Highway Capacity Manual (HCM2010) along with one example for finding LOS of a highway
Capacity & Level of Service: Highways & Signalized Intersections (Indo-HCM)Vijai Krishnan V
This presentation gives a glimpse on estimating the capacity and Level of Service (LOS) of highway midblock sections and signalized intersections under heterogeneous traffic conditions using the Indo-HCM 2017 Manual. It also compares the Indo-HCM LOS estimation methods with US-HCM. Some practice questions are also included.
I acknowledge the co-author Ms. Sethulakshmi G (Ph. D. Scholar, NIT Surathkal) for her valuable contribution to this presentation.
In today’s world with the ever increasing traffic it is inherent that we immediately find an optimum solution for it so that we can move on from being a developing nation to a super power.
There is a great need to resolve our transportation issues at the earliest as connectivity is of grave importance. Finding a systematic and organized way around the current situation is only going to benefit us in the long run. Better connectivity reduces transportation costs immensely and saves time in traveling.
This presentation talks about the process of Traffic & Transportation surveys, the bases of delineating Traffic Analysis Zones and the various surveys required to be carried out to understand the traffic behavior of the city.
Risk governance for traffic accidents by Geostatistical Analyst methodsIJRES Journal
Geographical Information Systems (GIS) are indispensable tool for administrating big datasets based on location of measured point. The values related to space may vary with both time and location. GIS-supported Geostatistical Analyst (GA) can evaluate datasets by analysing the locations of points. Maps produced using probability and prediction methods must be the base products for city planning. This study develops methods to obtain maps to determine traffic hot zones in Konya, Turkey, by applying GA supported by GIS. By applying GA, this study differs from previous studies which have determined the hot spots using linear analysis. In this study, unlike preceding studies, the aim is to determine new safe routes and zones with the help of GA.
Another, different aim is to map and determine graduated hot or safe zones using number of mortalities criterion (AC1), number of injured people criterion (AC2), number of accidents with damage only criterion (AC3), and total number of accidents criterion (AC4).
Transportation planning is an integral part of overall urban planning and needs systematic approach.
Travel demand estimation is an important part of comprehensive transportation planning process.
However, planning does not end by predicting travel demand.
The ultimate aim of urban transport planning is to generate alternatives for improving transportation system to meet future demand and selecting the best alternative after proper evaluation.
Urban transportation system meaning ,travel demand functions with factors, design approaches & modeling , types of mass transit system with advantages -disadvantages or limitations , opportunities in mass transport , integrated approach for transit -transportation system
Detailed description of Capacity and Level of service of Multi lane highways based on Highway Capacity Manual (HCM2010) along with one example for finding LOS of a highway
Capacity & Level of Service: Highways & Signalized Intersections (Indo-HCM)Vijai Krishnan V
This presentation gives a glimpse on estimating the capacity and Level of Service (LOS) of highway midblock sections and signalized intersections under heterogeneous traffic conditions using the Indo-HCM 2017 Manual. It also compares the Indo-HCM LOS estimation methods with US-HCM. Some practice questions are also included.
I acknowledge the co-author Ms. Sethulakshmi G (Ph. D. Scholar, NIT Surathkal) for her valuable contribution to this presentation.
In today’s world with the ever increasing traffic it is inherent that we immediately find an optimum solution for it so that we can move on from being a developing nation to a super power.
There is a great need to resolve our transportation issues at the earliest as connectivity is of grave importance. Finding a systematic and organized way around the current situation is only going to benefit us in the long run. Better connectivity reduces transportation costs immensely and saves time in traveling.
This presentation talks about the process of Traffic & Transportation surveys, the bases of delineating Traffic Analysis Zones and the various surveys required to be carried out to understand the traffic behavior of the city.
Risk governance for traffic accidents by Geostatistical Analyst methodsIJRES Journal
Geographical Information Systems (GIS) are indispensable tool for administrating big datasets based on location of measured point. The values related to space may vary with both time and location. GIS-supported Geostatistical Analyst (GA) can evaluate datasets by analysing the locations of points. Maps produced using probability and prediction methods must be the base products for city planning. This study develops methods to obtain maps to determine traffic hot zones in Konya, Turkey, by applying GA supported by GIS. By applying GA, this study differs from previous studies which have determined the hot spots using linear analysis. In this study, unlike preceding studies, the aim is to determine new safe routes and zones with the help of GA.
Another, different aim is to map and determine graduated hot or safe zones using number of mortalities criterion (AC1), number of injured people criterion (AC2), number of accidents with damage only criterion (AC3), and total number of accidents criterion (AC4).
Webinar: Using smart card and GPS data for policy and planning: the case of T...BRTCoE
2014/08/28 webinar by Marcela A. Munizaga
See more in:
http://www.brt.cl/webinar-using-smart-card-and-gps-data-for-policy-and-planning-the-case-of-transantiago/
EDSP 557 Phonological Awareness Activity Part I Grou.docxjack60216
EDSP 557
Phonological Awareness Activity
Part I: Grouping Students for Instruction
DIBELS Information
Code: PSF1, PSF2 = 1st Grade Phoneme Segmentation Time 1, 2 (Fall, Winter)
Activity:Use the student scores to determine instructional groups. What students would
benefit from additional explicit instruction in phonemic awareness in fall and winter?
Benchmarks
Levels Beginning of the Year Middle of the year
At Risk < 10 < 10
Emerging Between 10-34 Between 10-34
Established > 35 > 35
Student Name PSF1 PSF2
Amy 12 48
Brian 33 47
Charlie 7 8
David 38 47
Eduardo 9 14
Fernando 13 20
Garrett 30 36
Halley 35 49
Ignacio 29 35
Justin 9 7
Kari 12 15
Lani 33 49
Marisol 9 13
Groups
Benchmark Instruction Strategic Instruction Intensive Instruction
Part II: Phonological Awareness Intervention Instruction
Once we have identified students experiencing early difficulty with this “big idea,” we
can plan instruction to meet students’ needs. Intervention would likely take place in a
small group with students who have similar needs. For this activity, we will look at one
student who is at high risk of reading difficulty. Look at some of Charlie’s responses on
the PSF assessment at Time 2:
Charlie PSF2:
bad /b/ /a/ /d/ lock /l/ /o/ /k/
that /TH/ /a/ /t/ pick /p/ /i/ /k/
mine /m/ /ie/ /n/ noise /n/ /oi/ /z/
Diagnostic Questions:
1. What does Charlie know? What are his phonological strengths? This will tell us
the starting place for instruction.
2. Do you see a trend or pattern in his segmentation performance? What types of
errors are prevalent in Charlie’s performance?
Instructional Questions
1. If you wanted to start with a phonological task that Charlie could have some
success with, what would it be?
2. What would be a logical progression of segmenting instruction for Charlie?
3. Assuming that Charlie’s phonological performance is similar to others in his
intervention group, select and describe three activities that would be appropriate
for building skills in this area and helping these students reach the target.
t
transport/BASE.xls
Sheet1BASE YEAR LAND USE AND TRAVEL DATAZone NoPopulationLabour ForceCars OwnedIncomeComm EmpMan EmplOther EmpTotal trips producedTotal trips attracted1163964943144233573487764781185223529456845300426291071826104335355234220136764129229420421872431437801592157581945644402155514755162641228493132178101434089661733719115963444226255110345375653208414126558754043121373737828371441114074171670314014928599458318331338706335419131917624141026289296658385180302063141051190046330466002018020468168122645106974643792054208735407766959136965278720985433145497282117962141009637493301595782413768123769300215596923881759430524929124351554216416490419641121503410718071820112828691771252851199852402511645642245425371833421351103067631191434236130325011983303332282561321551819127527402047424313960001181 ...
An Empirical Study of the Environmental Kuznets Curve for Environment Quality...ijceronline
This paper attempts to examine the determinants of environmental degradation within the framework of Environment Kuznets Curve (EKC) hypothesis using China's city-level panel data from 2003 to 2012. The population agglomeration as well as three types of cities such as municipalities, sub-provincial city and prefecture-level city are considered in our paper. Our empirical results with the whole sample data verified the theory of the EKC hypothesis, which shows a reverse "U" shape between economic growth and environmental pollution. In addition, the effect of population on environmental pollution is quite different among the various types of cities. The results of this study can serve as a useful reference for policy makers in terms of achieving economic and environmental sustainability.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
3. 1 | P a g e
Contents
1. Introduction: .................................................................................................................... 2
2. Purpose of Study.............................................................................................................. 2
3. Methodology:................................................................................................................... 3
3.1. Data collection ......................................................................................................... 3
3.2. Data Characterization .............................................................................................. 3
3.3. Data preparation...................................................................................................... 3
3.1. Growth factor modeling........................................................................................... 4
3.2. Defining Study Area, And Zoning ............................................................................. 4
4. The Four-Stage Model...................................................................................................... 6
4.1. Trip Generation:....................................................................................................... 6
4.2. Trip Distribution ....................................................................................................... 8
4.3. Modal split ............................................................................................................. 10
4.4. Trip assignment...................................................................................................... 12
5. Conclusion:..................................................................................................................... 14
6. Reference:...................................................................................................................... 15
4. 2 | P a g e
1. Introduction:
The demand for transport is derived, it is not an end in itself. With the possible
exception of sightseeing, people travel in order to satisfy a need (work, leisure,
health) undertaking an activity at particular locations (Ortuzar and Willumsen,
2002).
Travel Demand Forecasting is a key component of the transportation engineer's
technical repertoire. It allows the engineer to predict the volume of traffic that will
use a given transportation element in the future, whether that element is an existing
highway or a potential light-rail route. The history of demand modeling for person
travel has been dominated by the approach that has come to be referred to as the
four-step model (FSM) (Hensher, 2008). The FSM is the primary tool for forecasting
future demand and performance of a transportation system, typically defined at a
regional or sub-regional scale (smaller scales often apply simplified models)
(Knoflacher et. al., 2017).
The Four-Step Model is four separate sub-models that are performed sequentially.
The four steps are trip generation, trip distribution, modal split, and trip assignment.
Each step in the model requires information from the previous step (s). The end
product of the Four-Step Model is the demand ( the number of forecasted trips on the
network) on each link of the network (Meyer and Miller 2001).
Figure 1.1 Components of the four-step model ( Mintesnot and G. Woldeamanuel, 2016).
2. Purpose of Study
The objectives of the course-work are: To understand how to collect data and
analysis it by using household survey for FSM. To gain knowledge of the needs for
transport demand model in transport projects evaluation. To gain knowledge of the
various techniques applied in transport demand modeling.
5. 3 | P a g e
3. Methodology:
3.1. Data collection
According to HTM (2005), the Minimum sample size [%] is 10% for population
in analyzed area below 50,000. In this study 13.1% has been used. The study area
consists of 320 apartments and 83 single-family detached home. This survey has
been conducted in Hasplaot region, has collected data on more than 53 households.
Data are divided as follows; 35 apartments, 12 houses, 3 with no response and 3
classified as unrealistic data.
3.2. Data Characterization
The arithmetic mean of a population in flats is 3.15 and in houses are 3.67
persons. 61% are men and 39% are women. The average auto occupancy is 1.6 in
single-family detached homes and 0.66 in flats. 45% of population is student, during
survey it is noticed that most population at flats are students where there are only 16
families in whole campus. Full-Time status is about 23% and 8% is retired, if it is
compared with present of people more than 60 years, it is reasonable and indicates
the extent of homogeneity of data.
3.3. Data preparation
Sample expansion
Once the data have been collected it is necessary to expand them in order to
represent the total population. The following expression is fairly general in this
sense:
= = 8.76
Where A is the total number of addresses (Household) in the original population
list, B is the total number of addresses selected as the original sample, and D is the
number of samples where no response was obtained.
34%
42%
15%
9%
Age Ranges
<20
20-40
40-60
>60
23%
5%
45%
12%
7%
8%
Working Status
Full work
Part work
Full study
Pre school
unemployed
Retired
6. 4 | P a g e
Validation of results
The current research study has been used simple method based on site checks of
the completeness and coherence of the data. For example, one of the household when
he asked for mode of trip, he said by using Airplane, so it is classified as non-
eligible. The other factor used in this study to check the reality is manual count. The
MC has conducted between 8:30 a.m. and 9:00 a.m.
3.1. Growth factor modeling
Growth factor modes tries to predict the number of trips produced or attracted by
a house hold or zone as a linear function of explanatory variables. The models have
the following basic equation:
=
Where is the number of future trips in the zone and is the number of current
trips in that zone and is a growth factor. The growth factor depends on the
explanatory variable such as population (P) of the zone, average house hold income
(I), average vehicle ownership (V). The simplest form of is represented as follows:
Where the subscript "d" denotes the design year and the subscript "c" denotes the
current year. In this report assuming that in the future, auto ownership will Increase
80% to meet forecast demand, assuming that the population and income remains
constant.
3.2. Defining Study Area, And Zoning
Study area: Once the nature of the study is identified, the study area should be
defined such that majority of trips have their origin and destination in the study area
and should be bigger than the area-of-interest covering the transportation project. In
this study Nicosia city are selected as a study area based on the significant of the
town and needs of traffic studies. Fig 1.2 have shown the location of study area.
Zoning: Once the study area have selected, the next step is defining the origin
and destination of passenger and freight. In order to reduce the number of origin
and destination of travellers, the travellers' locations are grouped into zones such as
city blocks. In current study, the zone defined based on shape, main roads, and
nature border of the area. The zone shape is a triangle. It is bordered on the north
corner by Haspolat roundabout, the south leg by Dr. Fazıl Küçük Cd, from the east
leg by Bülent Ecevit Cd. Street, and on the west side by Zafer CD. The zone consist
of two types of residential buildings, about 83 houses and 20 building (each building
has 16 flats). Fig 1.2 presents the selected zone and future work zoning.
7. 5 | P a g e
Figure 1.2 shows the selected zone and future work zoning.
8. 6 | P a g e
4. The Four-Stage Model
4.1. Trip Generation:
The first step in four step model is forecasting trip generation. The final product
of this step is two forecast numbers for each zone: a number of trips produced and a
number of trips attracted. One of the methods for calculating these numbers is cross-
classification.
Table 1.1 have shown trip rates.
Time
# of trips Trip rate hour / Home
in out In out Total
6:30-7:30 0.00 2.00 0.00 0.04 0.04
7:30-8:30 0.00 22.00 0.00 0.48 0.48
8:30-9:30 1.00 10.00 0.02 0.22 0.24
9:30-10:30 0.00 8.00 0.00 0.17 0.17
10:30-11.30 0.00 5.00 0.00 0.11 0.11
11.30-12:30 3.00 1.00 0.07 0.02 0.09
12:30-13:30 1.00 1.00 0.02 0.02 0.04
13:30-14:30 7.00 1.00 0.15 0.02 0.17
14:30-15:30 1.00 1.00 0.02 0.02 0.04
15:30-16:30 5.00 0.00 0.11 0.00 0.11
16:30-17:30 13.00 1.00 0.28 0.02 0.30
17:30-18:30 7.00 0.00 0.15 0.00 0.15
18:30-19:30 3.00 0.00 0.07 0.00 0.07
19:30-20:30 2.00 1.00 0.04 0.02 0.07
20:30-21:30 2.00 0.00 0.04 0.00 0.04
21:30-22:30 4.00 0.00 0.09 0.00 0.09
22:30-23:30 1.00 0.00 0.02 0.00 0.02
23:30-00:30 1.00 0.00 0.02 0.00 0.02
00:30-01:30 0.00 0.00 0.00 0.00 0.00
01:30-02:30 0.00 0.00 0.00 0.00 0.00
02:30-03:30 1.00 0.00 0.02 0.00 0.02
∑ 52.00 53.00 1.130 1.152 2.28
From table above the trip rate for the zone = 2.30 trip per home, and the total
number of trip is 105 for the sample. The approximately numbers of trip of trip can
be estimated as following:
Total trips (produce) per day = Number of trips (sample) x expansion factor.
= 105x8.76=919.8 trips/day
9. 7 | P a g e
Figure 1.2 shows peak Spreading.
a) Characterization of Journeys:
By Purpose
It has been found in practice that a better understanding of travel and trip
generation models can be obtained if journeys by different purposes are identified
and modeled separately (See table 1.2). The work and education trips are usually
called compulsory (or mandatory) trips and all the others are called discretionary (or
optional) trips.
By Time of Day
Trips are sometimes classified into peak and off-peak period trips; the proportion
of journeys by different purposes usually varies greatly with time of day. Table 1.2
presents characterization of Journeys by time of day, the morning (AM) peak period
occurred between 7:00- 9:00 (the evening peak period is sometimes assumed to be its
mirror image) and the representative off-peak period was taken between 10:00 -
12:00.
Table 1.2 Characterisation of Journeys By Time of Day
Purpose
AM Peak (7:00-
9:00)
AM Off Peak
(10:00-12:00)
No % No %
Home/Hotel 1 4% 0.022 1 13%
Place of work 8 31% 0.174 ‒ 0%
Shopping 1 4% 0.022 ‒ 0%
Education 16 62% 0.348 7 88%
Leisure/Religious ‒ 0% ‒ ‒ 0%
Visit family/friends ‒ 0% ‒ ‒ 0%
Business ‒ 0% ‒ ‒ 0%
0.00
0.10
0.20
0.30
0.40
0.50
0.60
Triprate
Time
10. 8 | P a g e
One comment are in order with respect to this table 1.2. The numbers of trip
generation for peak period are 26 trips. Secondly, note that although the vast majority
(92%) of trips in the AM peak are compulsory (i.e. either to work or education), this
is not the case in the off-peak period.
b) Trip generation forecasting example:
Table 1.3 Base year number of households and trips by HH size and auto
ownership.
Auto ownership
Hosehould
(HH) size
0 car 1 car 2+ car
# of
HH's
# of
trips
# of
HH's
# of
trips
# of
HH's
# of
trips
1 person 1 2 1 2 0 0
2 Persons 9 18 5 12 0 0
3+ Persons 9 20 10 24 12 30
Total trips (produce) per day = Number of trips (sample) x expansion factor.
= 105x8.76=919.8 trips/day
Table 1.4 Base year trip rates by HH size and auto ownership.
Hosehould
(HH) size
Auto ownership
0 car 1 car 2 car
1 person 2 2 0
2 Persons 2 2.4 0
3+ Persons 2.22 2.4 2.5
The trip rate for the zone = 2.30. For total number of trips expected to be
generated in the feature, the current trips must be multiply by the growth factor = 1.8
Total trips (produce) in future = Number of trips (current) x growth factor.
= 919.8x1.8=1576.8 trips/day
4.2. Trip Distribution
The second step is modeling the microscopic flow of forecasted trips from each
zone to every other zone. The trip generation step estimates the number of trips
produced and attracted to each zone. Trip generation is commonly expressed using
an O-D (Origin-Destination) matrix. An O-D matrix operates as an input-output
table. Where trip origins in each zone are inputs and trip destinations to each zone
are outputs, which spatially expresses the interaction of trips throughout the study
area. The O-D matrix is the final product of the trip distribution step (Ben-Akiva
2008).
12. 10 | P a g e
4.3. Modal split
The third stage in travel demand modeling is modal split. The modal spilt model
disaggregates the trip distrusted by travel mode (care, bus, train walk…etc.). In other
words, the step estimates the proportion of trips originating and arriving in each zone
that made by passengers using different mode. Table 3.1 presents trip rates by mode
per hour/home.
Table 3.1 trip rates by mode per hour/home.
Time
Trip rates by mode per hour/Home
Vehicle occupation Passenger Bus Pedestrian
6:30-7:30 0.043 0.000 0.000 0.000
7:30-8:30 0.348 0.000 0.000 0.130
8:30-9:30 0.065 0.043 0.065 0.065
9:30-10:30 0.065 0.000 0.022 0.087
10:30-11.30 0.022 0.043 0.000 0.043
11.30-12:30 0.022 0.022 0.043 0.000
12:30-13:30 0.043 0.000 0.000 0.000
13:30-14:30 0.109 0.000 0.022 0.043
14:30-15:30 0.022 0.000 0.000 0.022
15:30-16:30 0.000 0.000 0.000 0.109
16:30-17:30 0.174 0.000 0.000 0.130
17:30-18:30 0.065 0.022 0.022 0.043
18:30-19:30 0.043 0.022 0.000 0.000
19:30-20:30 0.022 0.000 0.000 0.043
20:30-21:30 0.022 0.000 0.022 0.000
21:30-22:30 0.043 0.022 0.000 0.022
22:30-23:30 0.000 0.000 0.000 0.022
23:30-00:30 0.022 0.000 0.000 0.000
00:30-01:30 0.000 0.000 0.000 0.000
01:30-02:30 0.00 0.000 0.000 0.000
02:30-03:30 0.02 0.000 0.000 0.000
Total 1.152 0.174 0.196 0.761
Percentage 50% 8% 9% 33%
Figure 3.1 shows the relative size of modes split of zone. The pie charts (Figure
3.1 a. b) have shown that the pedestrian percentage (53%) increases in peak period
morning because majority of population are students (45%) and most of them
traveling to CIU by walking. In addition, the public transport has increased from
(9%-23%).
13. 11 | P a g e
The bar chart (see Figure 3.1 c.) shows that the car drivers in houses is higher
than in flats, this can be explained by the high rates of average vehicle occupancy in
houses (1.6 car per house and 0.66 per flat) and also it is due to the majority of
population in flats are student as well as economic factors . The second thing to
notice is percentage of walk and bus modes are 34% and 25% respectively and fully
reflected in houses with 0%.
Figure 3.1 Modes split of transport for trip making
58%
9%
33%
(a) Mode split for all day (in+out).
Vehicle occupation
Public transport
Pedestrian
53%
27%
20%
(b) Mode split for peak period
Vehicle occupation
Public transport
Pedestrian
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Car Driver Passenger Bus Walk L.G Vehicle
(c) Mode split for for different type of development.
Flats Single-Family home
14. 12 | P a g e
4.4. Trip assignment
Network assignment or trip assignment is the final stage of the four-step model.
Trip assignment models aim to determine the number of trips on different links (road
sections) of the network given the travel demand between different pairs of nodes
(zones). The trip assignment involves assigning traffic to a transportation network
such as roads and streets or a transit network. Network assignment applies the trip
distribution O-D matrix and mode data to the transportation network. There are many
used methods for trip assignment such as all-or-nothing. In all-or-nothing technic all
trips between an origins and a destination assumed to take shortest way.
An example for as all-or-nothing method
It is unrealistic to produce a plan for traffic assignment without knowing all
information about whole region (all zones) to understand volumes and trips
distribution for these zones. In this example we assumed available information just
for practicing. By using O-D matrix (Table 2.1) to assign the trips to the network
given in Figure 4.1 using all-or-nothing method. The output will be the list of links in
the network and their corresponding traffic volume.
15. 13 | P a g e
As discussed early in this method we used short way. For example, all of the 35
vehicles that travel between nodes Z1 and Z12 will travel via Z7-Z11 (shortest travel
time). The table shown below indicates the route that we selected from Haspolat
zone to other destination.
Table 4.1 choosing the shortest path between Haspolat zone (orgin) and other destination.
From Node To Node Link/road Travel time (min)
1 2 1‐2 3
3 1‐3 3
4 1‐4 10
5 1‐4,4‐5 22
6 1‐6 10
7 1‐6,6‐7 25
8 1‐6,6‐8 24
9 1-6,6-11,11‐9 29
10 1‐6,6‐11,11‐9,9‐10 41
11 1‐6,6‐11 20
12 1‐6,6‐11,11‐12 28
16. 14 | P a g e
5. Conclusion:
Travel Demand Forecasting is a key component of the transportation
engineer's technical repertoire.
The four-step model (FSM) is the primary tool for forecasting future demand
and performance of a transportation system, typically defined at a regional or
sub-regional scale
The trip rate for the zone = 2.30 trip per home, and the total number of trip is
105 for the sample.
The numbers of trip generation for peak period are 26 trips. Secondly, note
that although the vast majority (92%) of trips in the AM peak are compulsory
(i.e. either to work or education), this is not the case in the off-peak period.
The pedestrian percentage (53%) increases in peak period morning because
majority of population are students (45%) and most of them traveling to CIU
by walking. In addition, the public transport has increased from (9%-23%).
The car drivers in houses is higher than in flats, this can be explained by the
high rates of average vehicle occupancy in houses (1.6 car per house and 0.66
per flat) and also it is due to the majority of population in flats are student as
well as economic factors . The second thing to notice is percentage of walk
and bus modes are 34% and 25% respectively and fully reflected in houses
with 0%.
17. 15 | P a g e
6. Reference:
1) Aldian, A. (2006). On the development of evaluation system and transport
demand model for road network planning in developing countries:(a case
study of Indonesia) (Doctoral dissertation, University of South Australia).
2) Butler, M. N. (2012). An assessment tool for the appropriateness of
activity-based travel demand models(Doctoral dissertation, Georgia
Institute of Technology).
3) Hensher, D. A., & Button, K. J. (Eds.). (2007). Handbook of transport
modelling. Emerald Group Publishing Limited.
4) Knoflacher, H., & Ocalir-Akunal, E. V. (Eds.). (2017). Engineering Tools
and Solutions for Sustainable Transportation Planning. IGI
Global.Chicago.
5) Ortuzar, Juan de Dios, and Luis G. Willumsen. Modelling transport. Vol.
3. 2002.
6) United Nations. Statistical Division. (2008). Designing household survey
samples: practical guidelines (Vol. 98). United Nations Publications