UNDP Training Module Subject Module Volume 1 - Training Manual Demand Assessment Planning

1. UNDP Training Module Subject ModuleVolume 1 - Training ManualDemand Assessment Planning Subject Lecture Material (90 minutes)

2. Objectives of the Module • Educate the participants on • The need for demand assessment to make transportation decisions • Scientific basis for picking and planning projects • The time and money and data it takes to develop good forecasts and how to choose tools based on time and money • Cannot get the best if time and money are less • Assess the reasonableness of model assumptions and forecasts • Ensure that the model is fully validated against observed data • How to review traffic forecasts and check for reasonableness

3. Who Will the Users of this Module Be? • Policymakers, senior and mid-level staff at the national, state, and city levels, working on transport and related areas such as: land use and development, urban planning and design, environment, road safety, housing, and urban poverty alleviation. • NGOs

4. What is Demand Assessment Planning? • Assessing the demand for a transportation option in a region • Develop traffic forecasts for 10-25 year periods • Use the forecasts to develop a sustainable transportation system • Involves a complicated task of collecting existing transportation data to analyzing demand and forecast future needs • Demand Assessment is very common in private companies • They will never invest millions without assessing the demand • Use demand forecasts to come up with a production/operations plan

5. Why is Demand Assessment Planning Important? • Planning helps create a sustainable transportation system that results in increased economic activity • Failure to plan results in non-optimal choices that reduce mobility and increase vehicle ownership, leading to severe congestion • Essential to design a transportation system, plan operations, and forecast financial viability • Demand Assessment identifies corridors with the most potential users. • Planning a new system where there is little to no demand is a waste of public money. Planning a system with less capacity will make it crowded and not as attractive.

6. Case Studies of Inaccurate Demand Assessment • Bangkok BTS Skytrain (Cost: 1.5 billion USD; took 5 years to build (1994-1999)) • A ridership of 6,00,000 riders per day was projected in the first year of operation. • Assumptions made were aggressive: such as diversion of 60% of bus riders to rail during peak hours. • In the first year of operation, a ridership of only 1,50,000 was recorded. • In 2006, after six years of operation, a daily ridership of 3,50,000 has been recorded.

7. Case Studies of Inaccurate Demand Assessment • Delhi – Gurgaon Expressway (Cost: 10 billion rupees; opened to traffic in January 2008) • It carries more than 180,000 PCUs per day, which is much higher than the traffic estimates for the project by 130,000 to 150,000 PCUs per day (the traffic is growing at 9% per year). • One of the reason for this under-estimation was that NHAI relied on an outdated traffic study conducted in 1998 at the time of project procurement.

8. Common reasons for bad forecasts • Highly Optimistic Growth Scenarios for future. • Growth is never a straight line going up. What goes up comes down. • Poor Quality Data • The entire basis for forecasts is a statistical model based on data. • Garbage in Garbage out. • Statistically poor travel models due to bad data, lack of expertise and limited QA/QC. • Module has a checklist of items that need to be reviewed to ensure a sound model

9. Current Deficiencies and Solutions • No consistent baseline for planning studies • Problem: Consultants develop their own population/employment databases and forecasts using vastly different assumptions with no oversight. • Impact: Very easy to justify new developments if growth assumptions are high. Conflicting results from different studies using different assumptions. Result in lack of trust of the process. • Solution: One region, one approved demographic database.

10. Current Deficiencies and Solutions • Lack of sharing of Data. • Problem: Planning agencies do not take possession of their own models and data from consultants. • Impact: Every study results in new data collection, new model development and with differences in budget, time and expertise, results are very different. Gross wastage of money and time in redoing what has already been done. • Solution: You pay, you own (the model and data) and you share.

11. Current Deficiencies and Solutions • No QA/QC of the modeling process • Problem: Most cities do not have the capacity to review technical models • Impact: “The mice will play when the cat is away”. The best work is done only when there is a fear that somebody will check it. In the absence of a structured QA/QC process, the quality of work is highly questionable. • Solution: Review what you can or hire an independent expert

12. Current Deficiencies and Solutions • No single approved model for all studies • Problem: Most cities have several models built for various projects, each at various levels of detail, budget, data collection and expertise. • Impact: Impossible to evaluate projects if each of them uses different travel models and datasets. • Solution: One Region, One Database, One Model

13. What Type of Modeling Tool do you Need?

14. What Type of Model do you Need?

15. What Type of Model do you Need?

16. What Type of Model do you Need?

17. Tools Based on Time, Budget and Type of Project

18. Key Steps in Demand Assessment Planning • Definition of plans (mode, alignment, service etc) • Collect Primary and Secondary Data • Prepare a Baseline scenario depicting current ground conditions • Estimate travel models and validate them • Prepare forecasts • Evaluate several land use and transportation scenarios to prepare a final shortlist

19. 1 Data for Demand Assessment

20. Why Do We Need Data? • Understand the socioeconomic and demographic profile of the population • Understand the existing transportation infrastructure • Understand travel patterns in the city • Understand the current situation with respect to traffic congestion, usage of public transport, adequacy of public transport (coverage, crowding, etc.)

21. What Data do we need?

22. What Do We Do with Data? • Create a baseline report to give current status • Create market segments • Identify the most "transit competitive" corridors • Develop Travel Demand Forecasting Models

23. Required Land Use and Employment Data • Employment by type (retail, service, etc.) for each traffic analysis zone • Land use showing acreage of unused land, parks, agriculture, waste land, etc.

24. Sources of Land use and Employment Data • City Development Plans • Statistical Departments • National Information Center – Repository of aggregate data at all levels. • Industrial data/ workforce data • Data from specific organizations for employment of a particular kind like the Medical Association, Retail association, etc.

25. Vehicle Registration Data and Sources • Number of vehicles in the study area by types of vehicles including cycles, motor cycles, auto rickshaws, cars, taxis, medium and heavy duty trucks • Information can be obtained from the RTO. Some places such as Karnataka have this information online (Create a table as an example from Bangalore data from the internet) • Data for the last 5 years gives a good idea about the rate of growth in vehicle ownership

26. Physical Infrastructure • Inventory of all roads, bridges, flyovers, etc. and their capacity in the study area. • Inventory of Public transport infrastructure such as bus stops, depots, terminals, etc. • Inventory of all pedestrian and bike paths • Inventory of utilities, green spaces, etc.

27. Public Transport Data • All information on regulated public transport buses and trains in the study area including routes, frequency, fares, rolling stock, ridership, etc. • Information on informal public transport such as minibuses, jeepneys, etc. • Information on paratransit and demand response transit systems

28. Existing Studies • Review existing master plans, comprehensive mobility plans, and comprehensive traffic and transportation study reports

29. Types of Surveys • Household Interview Surveys • Origin-Destination Surveys • Traffic Volume Count Surveys • External Gateway Surveys • Commercial Vehicle Surveys • Public Transport On-Board Surveys • Stated Preference Surveys

30. Good Data Leads to Good Forecasts • Data collection should be qualitative and quantitative • Sample size based on the size of city and population • Data for all modes of travel including walking and biking should be collected • Data should be collected for all economic and social classes of population • Survey data should be weighted to avoid bias of any kind

31. Household Surveys • Household Activity Diaries – To get information on type of household and their travel activities. • Sample size should be based on study population. • Collect data that includes information from people of all household types, income groups, genders, and age groups • A geographically stratified random sampling scheme should be employed to ensure adequate geographic coverage by the overall respondent sample.

32. Methods of Sampling Data • Simple Random Sampling: • Each unit in the population is assigned an identification number. • These numbers are sampled at random to obtain the sample. • Each number is chosen entirely by chance and each has the same probability of being chosen. • Mostly done in small populations such as a community or a small town. Interactive: Simple Random Sampling: Pick out random ppl from within the class

33. Methods of Sampling Data Why do you think that random selection of people for your sample is so important?

34. Methods of Sampling Data In order to make the sample representative of the rest of the community.

35. Methods of Sampling Data • Stratified Sampling: • Used to ensure an adequate representation of key subgroups of population/ geographic areas. • Stratification may be done by city, planning district, or any other appropriate geographic jurisdiction. • The main goal is to divide the study area into relatively homogenous groups. • Once the surveying is complete, weights are developed for each group so that the data for all groups may be homogenized. Interactive: Divide the groups into representatives from government and other organizations respectively. Then conduct random sampling from within these groups.

36. Methods of Sampling Data • Systematic Sampling: • This is the process of selecting every ‘I’th unit occurring after a randomly selected unit. • An appropriately structured list can result in a systematic sampling procedure which automatically performs stratification as well. • Cluster/ Area Sampling: • In this method, the total population is first divided into clusters of sampling units, usually on a geographic basis. • These clusters are then sampled randomly, and the units within the cluster are either selected in total or else sampled. Selecting the last participant from every row

37. Methods of Sampling Data • Multi-Stage Sampling: • A random sampling technique which is based on the process of selecting a sample in two or more successive stages (may be used when sampling large populations, such as within a large region). • Divide the region into districts and sample from total population of these districts. • Divide the sampled districts into cities and towns and sample these. • This process would go on until sampling has been done at the household level. Random Sampling Random Sampling

38. Sampling Error • Error caused by using only a portion of the population rather than the entire population • The magnitude of the sampling error can be controlled by the sample size (it decreases as the sample size increases), the sample design, and the method of estimation. • Surveys are subject to non-sampling errors. • Examples of non-sampling errors are measurement errors and processing errors.

39. Sample Size Calculation • Factors affecting sample size determination: • Response rate • Margin of Error • Confidence level • Response percentage • Operational constraints, such as time available to conduct the survey, etc.

40. Weighting and Expansion of Data • Need for weighting data: • In most surveys, it will be the case that some groups are over-represented in the raw data and others under-represented. • These misrepresentations are usually dealt with by weighting the data. • Weighting involves assigning a survey weight to each case in the data file (value of weight is <1 for over-represented groups and >1 for under-represented groups). • It is used to make statistics computed from the data more representative of the population.

41. Expansion of Data • Expansion is the process of assigning weights to different groups of the population • Weighting and expansion are often combined into a single factor or weight, which reflects both the relative representativeness of each observation in the sample and the number of similar cases each observation in the sample represents in the population. • Separate weights are usually assigned to households, persons, and trips. • These weights sum to the number of households, persons, and trips in the population, respectively. Observed value Expanded Sample Expansion Factor

42. Quality of Data • Because of time constraints and lack of knowledgeable surveyors, data quality is often very poor. • This leads to poor forecasts. • As a result, the project is not successful. • Agencies should require their consultants to give a report showing all the QA/QC checks that they conducted to ensure quality.

43. 2 Baselining

44. Introduction • This is the process of documenting the current demographic, socio-economic, and transportation situation in the study area. • It gives a good idea of the present scenario in the region in terms of identifying demand and supply trends and revealing deficiencies in the system. • Developing a baseline report is an absolute necessity in developing short- and long-term transportation plans. • This section lists types of data that are necessary to build a good baseline, the sources of said data, and gives examples of maps, tables, and graphs for easy understanding.

45. Data Required for Baselining • Secondary data: • Historic Information • Demographic and Land Use Analysis • Socio-economic Analysis • Transportation Supply • Future Planned Improvements • Primary data • Travel Patterns Analysis • State of existing road traffic • Public Transport Analysis

46. Baseline – Historic Information • Historic analysis will also help understand the reasons behind both excellent and inefficient practices. For example: • A historic analysis of BEST in Mumbai shows that bus services started in early nineteenth century and how institutional setup led to a high number of employees and low fares • BMTC, on the other hand, is a new agency with low employees per bus ratio and has a rule in place to periodically raise fares

47. Demographic Analysis • Population and employment are the main drivers of trip activity • Trips are primarily generated at residences and attracted to jobs • The relative location of homes, jobs, and recreational facilities dictates the number and pattern of trips • Current population and employment can be mapped to highlight residential and commercial areas

48. Example: Mumbai Metropolitan Region • The graph below shows the population growth rate per census decade for the Mumbai area. • The population growth rate peaked first in the core, then in Inner Mumbai, and later Outer Mumbai before falling substantially. Growth Rate by District (1901-2011) for Mumbai Larger Metropolitan Region

49. Socio-Economic Analysis • It is important to summarize socio-economic data within a given region, as travel behavior of a household depends on factors such as: • Number of people in a household: More people results in more trips. • Number of workers in a household: More workers means more work trips and fewer recreational trips. • Age group of household members: Children and the elderly have different trip patterns. Most trips made by children are to schools by walking, bike, or school bus. Elderly people make very few motorized trips. • Income category: Higher income people have a higher number of discretionary trips, such as shopping, social, and recreational trips. Also, these trips tend to be by personal vehicle. • Vehicle Ownership: People with personal vehicles make more trips than people who take public transport.

50. Example: Surat, Gujarat Percentage Distribution of Households by Family Size in Surat Distribution Members by Age in Surat Average Family Income in Surat