The external cordon line should -be continuous and uniform in its courses so that movements cross it only once. The line should intersect roads where it is safe and convenient for carrying out traffic survey.
The external cordon line should be compatible with the previous studies of the areas studies planned for the future. Zoning The defined study area is sub-divided into smaller areas called zones or traffic zones. Subdivision into zones further helps in geographically associating the origins and destinations of travel. Zones can themselves be sub divided into sub- zones depending upon the type of land use.
One such system is to divide the study area into 9 sectors. The central sector CBD is designated 0, and the remaining eight are designated from 1 to 8 in clockwise manner. The prefix 9 is reserved for the external zones. Each sector is subdivided into 10 zones bearing numbers from 0 to 9. It would be helpful, if the following points are kept in view when dividing the area into Zones: 1. The zones should have a homogenous land use so as to reflect accurately the associated trip making behavior.
Anticipated change in land use should be considered when sub- dividing the study area into zones. It would be advantages, if the subdivision follows closely that adopted by other bodies e.
This will facilitate correlation of data. The zones should not too large to cause considerable errors in data. At the sometime, they should not be too small either to cause difficulty in handling and analyzing the data. In residential areas, the zones may accommodate roughly households.
The zones should preferably have regular geometric form for easily determining the centroid, which represent the origin and destination of travel. The sectors should represent the catchment of trips generated on a primary route. Zones should be compatible with screen lines and cordon lines. Zone boundaries should preferably be watersheds of trip making. Natural or physical barriers such as canals, rivers, etc. In addition to the external cordon lines, there may be a number of internal cordon lines arranged as concentric rings to check the accuracy of survey data.
Screen lines Running through the study area are also established to check the accuracy of data collected from home- interview survey. Screen lines can be convenitally located along physical or natural barriers having a few crossing points. Examples of such barriers are river, railway lines, canals, etc. Types of Movements The basic movements for which survey data are required are: 1.
Internal to internal. External to internal. Internal to external. External to external. For large urban areas, the internal to internal travel is heavy whereas for small areas having a small population say less than the internal to internal travel is relatively less.
Most details of internal to internal travel can be obtained by home interview survey. The details of internal- external, external internal and external- external travels can be studied by cordon surveys. Types of source of data Data Collection: The data can be collected: 1. At home. During the trip end. At the destination of the trip.
When collected at home, the data can be wide ranging and can over all the trips made during a given period. The data collected during the trip is necessary of limited scope since the procedure yields data only on the particular trip intercepted. At the destination end, the direct interview types of surveys provide data on demand for parking facilities and or the trip ends at major traffic attraction centers such as factories, offices and commercial establishments.
The following are the surveys that are usually carried out: 1. Home- interview survey. Commercial vehicles surveys. Intermediate public transport surveys. Public transport surveys. Road —side — interview surveys.
Post- card- questioner surveys. Registration- number surveys. Tag- on- vehicle surveys. The information to be collected from home-interview survey can be broadly classified in two Groups: 1.
Household information. Journey or trip data. The household information needs to contain data with regard to: a- Size of household. Journey data will contain information all trips made during the previous 24hr. Origin and destination of trip. Purpose of trip. Modes of travel. Time at start of trip.
Time at finish of trip. Inventory of Transport Facilities The inventory of existing transport facilities should be undertaken to identify the deficiencies in the present system and the extent to which they need to be improved.
The inventory consists of: - Inventory of streets forming the transport network. Link width length, no. Nodes complete geometric of intersection. Inventory of Land Use and economic Activities 1. Inventory of Land Use Since travel characteristics are closely related to the land use pattern, it is of utmost important that an accurate inventory of land-use be prepared.
Inventory of Economic Activities Aggregate data on demographic and socioeconomic activities should be collected other sources to include the following: - Population of the planning area and various zones. Road side Interviews These provide trips not registered in a household survey, especially external-internal trips. This involves asking questions to a sample of drivers and passengers of vehicles crossing a particular location.
Unlike household survey, the respondent will be asked with few questions like origin, destination, and trip purpose. Other information like age, sex, and income can also be added, but it should be noted that at road-side, drivers will not be willing to spend much time for survey. Home interview surveys Home-interview survey is one of the most reliable type of surveys for collection of origin and destination data.
The survey is essentially intended to yield data on the travel pattern of the residents of the household and the general characteristics of the household influencing trip making. The information on the travel pattern includes number of trips made, their origin and destination, purpose of trip, travel mode, time of departure from origin and time of arrival at destination and so on.
The information on household characteristics includes type of dwelling unit, number of residents, age, sex, race, vehicle ownership, number of drivers, family income and so on.
Based on these data it is possible to relate the amount of travel to household and zonal characteristics and develop equations for trip generation rates. It is impractical and unnecessary to interview all the residents of the study area. Since travel patterns tend to be uniform in a particular zone. The size of the sample is usually determined on the basis of the population of the study area. And the standards given by the Bureau of Public Roads as shown in below table.
Standard Practice now is instead to calculate the sample size which will achieve the desired precision for key indicators at the required level of confidence. One such equation is given by Traffic Appraisal manual. The usual procedure is for an interviewer to call on a household on a scheduled data and to leave a copy of the home interview questionnaire. This questionnaire is broadly divided into : a General household characteristics — number of residents, vehicles owned, income, dwelling type.
Commercial vehicle surveys A similarly styled survey of non-residential land uses could be designed to collect information on goods movements, but transport resources are rarely allocated to such an ambitious project. Instead, urban freight flows are usually measured indirectly from commercial vehicle survey. Commercial vehicle surveys are conducted to obtain information on journeys made by all commercial vehicles based within the study area.
The addresses of the vehicle operators are obtained and they are contacted. Forms are issued to drivers with a request that they record the particulars of all the trips they would make. How is it gathered? How do we ensure its accurate? Is the data reliable? Is it representative of the population from which it was drawn?
This chapter explores some of these issues. Drawing three names from a hat containing all the names of the students in the class is an example of a simple random sample: any group of three names is as equally likely as picking any other group of three names. Sampling Errors.. Random and we have no control over.
Non-Sampling Errors… Non-sampling errors are more serious and are due to mistakes made in the acquisition of data or due to the sample observations being selected improperly. Most likely caused be poor planning, sloppy work, act of the Goddess of Statistics, etc. Expansion factors Sample expansion the second step in the data preparation is to amplify the survey data in order to represent the total population of the zone. This is done with the help of expansion factor which is defined as the ratio of the total number of household addressed in the population to that of the surveyed.
A simple expansion factor Fi for the zone i could be of the following form. These are allocated to the study area, and then the totals are distributed to each zone. This process can be accomplished by using either a ratio technique or small- area land-use allocation models.
The population and economic data usually will be furnished by the agencies responsible for planning and economic development, whereas providing travel and transportation data is the responsibility of the traffic engineer. For this reason, the data required to describe travel characteristics and the transportation system are described as follows. The transportation facility inventories provide the basis for establishing the networks that will be studied to determine present and future traffic flows.
In other instances, some data may be more essential than others. A careful evaluation of the data needs should be undertaken prior to the study. Household characteristics This section includes a set of questions designed to obtain socioeconomic information about the household.
Relevant questions are:number of members in the house, no. Unit 3 Trip generation and distribution: UTPS approach The first phase of the transportation planning process deals with surveys, data collection and inventory. The analysis and model building phase starts with the step commonly known as Trip Generation.
Trip Generation: Trip generation is a general term used in the transportation planning process to calculate the number of trip ends in a given area. Trip Types: Trips can be defined based on the nature of movement between the zones and across the cordon lines. They are also categorized based on the location of trip ends. The trip ends are classified into productions and attractions. Trip Production: A production is the home end of any trip that has one end at the home i.
This points towards one aspect related to trips generated, i. Trip Time: Trip time is the time taken while moving between a set of origin and destination. Trip Length: Trip length is the distance traveled between a set of origin and destination. The number of workers in a household, and 2. The household income or some proxy of income, such as the number of cars per household. Various factors that create an effect on the production of the trips are discussed below: Population and its characteristics: The size of the population in an area obviously has an effect on the total number of trips supposed to be produced from that area.
This further can be looked at in terms of: a. Number of households in an area: More are the households more will be the trips. Size and composition of the household: Bigger is the size of the household, it is possible that more trips will be produced by that household. The composition defines the members of a household involved in different activities that necessitates travel.
This may be related to work, education, shopping, recreation, etc and thus produces different types of trips from a single household. Population density: This is one factor that is discussed in detail with respect to the use of different modes or travel. In general, if the density is more, more will be the trips from that area. Household Income: Disposable household income will define the possibility of trip making by the members of a household.
Factors influencing attraction rates 1. Land use activities: Land use activities in an area define the type of the trips that will be attracted towards that area. If the area is homogeneous in nature then the trips made to that area will be of same nature but heterogeneous activities will attract different types of trips. Employment opportunities: The employment potentiality of any area is defined by the type of activity undertaken in that area. The industrial, shopping unit or an office establishment directly governs the trip attraction rate.
Floor area allotted for the activities: Another factor to which the trip attraction rate can be related is the floor space in the premises of industries, shops and offices. This will allow the estimation of different trips which can be made to an area. Factors affecting trip making patterns 1. Study Area characteristics: Certain characteristics of the study area affect the trip making pattern in that area. These characteristics are discussed below. Location of an activity: The distance of the activity zone from the household is an important determinant of the amount of travel that people might like to do if needed.
The further the activity centre, the less the number of trips are likely to be to that activity. Accessibility of an activity: Accessibility to an activity is governed by the type of network facilities available in that area and the affordability of those facilities to the masses. Commercial trip rates Bypass Trips Trip generation analysis Methods of trip generation There are three main methods of generating trips from the study area.
Due to fast changing scenario and the complexities involved in the estimation of future values, the use of this method is confined to short term forecasting in small urban or rural areas. The method was developed by Wotton and Pick and has been used in some transportation studies in U. A multidimensional matrix defines the categories, each dimension in the matrix representing one independent variable.
The independent variables themselves are classified into a definite number of discrete class intervals. Category analysis may also be used for estimating trip attractions.
Assumptions The technique is based on the following assumptions: i The household is the fundamental unit in the trip generation process, and most journeys begin or end in response to the requirements of the family.
Trip attraction Trip attraction rates can be made by analyzing the urban activities that attract trips. Trips are attracted to various locations, depending on the character of, location, and amount of activities taking place in a zone. Three tools are used for this end too, but obviously types of independent variables used are different.
Zonal Models A Sample Zonal Attraction Model The sample model estimate relative attractiveness by regressing factored values of sample trips aggregated to the zone level on relevant zonal characteristics. The choice of explanatory variables is constrained in a manner similar to trip productions models - model significance, policy sensitivity, and forecastability. These models are summarized in Box 1.
One problem with this approach is that zonal averages may be deceptive depending on the distribution of a given parameter. For example, two zones with the same average income could have very different income distribution and presumable different travel performances.
Category variables are selected based on ability to significantly discriminate trip rates between categories, general policy sensitivity, and the availability of future data. Category models are less restrictive than regression models but require that the joint distribution of population variables be forecast.
A variety of methods have been used with iterative proportional fitting perhaps the most direct. The role of activity system forecasts is clear, as is the need for quality forecasts of automobile ownership since this variable is typically most highly correlated with total trips per household.
The resulting estimated trip rates are displayed in Table 4 to simplify presentation, rates from Martin and McGuckin are utilized. Aggregation proceeds directly since the model is linear. Once the joint distribution of households is known for the base or forecast year, the cell counts are multiplied by the estimated trip rates to obtain the total number of trips per zone. Trip Rate Analysis Trip rate is estimated on characteristics of the trip generators with in the zone.
Production rates are determined using the characteristics of the residential land uses and attraction rates using the characteristics of the nonresidential land uses Trip Distribution Introduction Once estimate of the trips generated, i. The number of trips produced in any zone of the study area has to be apportioned to the zones to which these trips are attracted. This can be represented in a matrix form as given below: The horizontal axis of the trip matrix shown represents the zones of attractions i.
Therefore, Based on the future land uses in the zones and the area, the future trips going to be produced or attracted from or to any zone are computed. These may be denoted as Pi and Aj respectively.
Methods of trip distribution There are two categories of trip distribution methods, namely, i Growth factor methods ii Synthetic methods Growth factor methods have been used in earlier studies and have yielded now to the more rational synthetic models. Both of these categories are further divided as follows: Growth factor methods: i Uniform factor method ii Average factor method iii Detroit Method iv Fratar method v Furness method vi Time function iteration method Synthetic methods: i Gravity Model ii Tanner Model iii Intervening opportunities model iv Competing opportunities model Growth Factor Methods The growth factor methods are based on the assumption that the present travel patterns can be projected to the design year in the future by using expansion factors.
A single growth factor, F, for the entire study area is calculated by dividing the future estimated number of trip ends for the design year by the trip ends in the base year. The future trips produced and attracted from or towards these zones are , and respectively. It is required to distribute the future trips among these zones. As the new growth factor is equal to 1. But the total number of trips generated from each zone, as calculated, do not tally with the known future trip values for those zones.
This is because of the assumption of a uniform growth rate for all the zones. The method, therefore, suffers from the following disadvantages: i The assumption of a uniform growth rate for the entire study is not correct, because each zone will have its own growth rate and the rate of growth of traffic movement between any two zones will be different. This may rarely be the case in reality.
Average growth factor method In this method, an average growth factor between the two zones related to two trip ends is calculated based on the growth factors of the zones at both the ends of the trip.
This factor thus represents theaverage growth associated both with the origin and the destination zones. But if it is not so, then new growth factors for each zone are computed and a new iteration is started. The process continues till the zonal growth factors come out to be either 1. As the growth factors of zones are not either 1. The average factor method has the same disadvantages of the uniform factor method.
The multiplying factor has no real significance and is only a convenient tool to balance the movements. There is no explanation of the movement between zones and the factors causing the movement. It has the additional disadvantage that a large number of iterations are required. Because of these drawbacks the method is rarely used except for updating existing table and for quick results. Detroit method This method is the further improvement on average factor method and takes into account the growth factor for zones and average growth factor for the entire study area.
This relative attractiveness is considered in the form of Locational factor L. The procedure is laborious except for simple problems, but can be conveniently tackled by a computer. It has the same drawbacks as observed in other growth factor methods.
It does not take into account the effect of changes in accessibility for various zones of the study area. Furness method The method requires the estimates of future traffic originating and terminating at each zone, thus yielding origin growth factor and destination growth factors for each zone.
Thus, Time Function iteration models This method assumes that the trip distance is influenced by the journey time and row and column totals are nothing but trip ends. The procedure after this remains the same as that of Furness method.
Final trip matrix is then converted into travel time index matrix, which provides the effect of travel time between the zones. The method can be understood from the example taken below.
The procedure will continue till equilibrium is achieved. Trips computed for different travel times are as follows: The trip frequency data, observed and calculated, is presented as a frequency polygon. Accordingly it is heuristically derived for synthesizing trip interchanges.
This may take different functional forms. It shows following shortcomings: a. If population is doubled, the trips produced will quadruple. No constraint is taken in the analysis. Aggregation of zone is considered but characteristics of the zone are not considered.
Modification was made to the above unconstrained Gravity Model by introducing the employment opportunities of the destination zone to make it synonymous with trip attractions. So the modified unconstrained Gravity Model is represented as: Further, the constrained gravity models were proposed so as to take into consideration the effect of production or attraction zones.
Accordingly, these were termed as Production constrained Gravity model or attraction constrained gravity model. The formulation is given below: Calibration Process: 1. Now calculate Ai.
These two criteria are: agreement between observed and simulated trip length constraint equation. A principal difficulty wit this calibration procedure is that the travel time factor function and associated trip length frequency distribution are assumed to be constant for each zone of a study area.
Opportunity Models Opportunity model are based on the statistical theory of probability as the theoretical foundation. The concept has been pioneered by Schneider and developed by subsequent studies. The two well known models are: i The intervening opportunities models ; ii The competing opportunities model. Schneider Modification Modified hypothesis states that the probability that a trip will terminate in some destination point is equal to the product of the probability that the destination met is acceptable and the probability that an acceptable destination closer to the origin has not been found.
The probability function in above may then be expressed as the difference between the probability that the trip origins at i will find a suitable terminal in one of the destinations, ordered by closeness to i, up to and including j, and the probability that they will find a suitable terminal in the destinations up to but excluding j. This is because of the key role played by public transport in policy making. Public transport modes make use of road space more efficiently than private transport.
Also they have more social benefits like if more people begin to use public transport, there will be less congestion on the roads and the accidents will be less. Again in public transport, we can travel with low cost. In addition, the fuel is used more efficiently. Main characteristics of public transport is that they will have some particular schedule, frequency etc. On the other hand, private transport is highly flexible.
It provides more comfortable and convenient travel. It has better accessibility also. The issue of mode choice, therefore, is probably the single most important element in transport planning and policy making. It affects the general efficiency with which we can travel in urban areas. It is important then to develop and use models which are sensitive to those travel attributes that influence individual choices of mode.
Mode choice behavior Mode choice is a key aspect of travel demand modeling. The choice of travel mode is a complicated behavioral process and as such is a core focus in Travel Behavior Factors influencing the choice of mode The factors may be listed under three groups: 1. Characteristics of the journey: Mode choice is strongly inuenced by: a The trip purpose; for example, the journey to work is normally easier to undertake by public transport than other journeys because of its regularity and the adjustment possible in the long run; b Time of the day when the journey is undertaken.
Characteristics of the transport facility: There are two types of factors. One is quantitative and the other is qualitative. Types of modal split models Trip-end modal split models Traditionally, the objective of transportation planning was to forecast the growth in demand for car trips so that investment could be planned to meet the demand.
When personal characteristics were thought to be the most important determinants of mode choice, attempts were made to apply modal-split models immediately after trip generation.
Such a model is called trip-end modal split model. In this way different characteristics of the person could be preserved and used to estimate modal split. The modal split models of this time related the choice of mode only to features like income, residential density and car ownership. The advantage is that these models could be very accurate in the short run, if public transport is available and there is little congestion. Limitation is that they are insensitive to policy decisions example: Improving public transport, restricting parking etc.
Trip-interchange modal split models This is the post-distribution model; that is modal split is applied after the distribution stage. This has the advantage that it is possible to include the characteristics of the journey and that of the alternative modes available to undertake them.
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