Improvements needed in planning transport projects under JNNURM

The key goal of transport projects under the National Urban Transport Policy (NUTP) is “moving people not vehicles”. As transport projects receiving assistance under JNNURM (Jawaharlal Nehru Urban Renewal Mission) are required to confirm to NUTP, they need to be people-centric. This implies investment priorities favouring public transport and non-motorised transport (NMT).

To plan transport infrastructure, robust forecasts of the travel demand and responses of travel demand to changes in the transport system characteristics and parameters are needed.

Such robust forecasts in turn require reliable, consistent, timely and appropriately disaggregated data which can then be analysed using various transport planning and modelling techniques.

In general, data gaps could arise due to lack of collection efforts from the agencies, or from insufficient appreciation of the importance of the available data for planning process and decisions.

Even if there is existing data, the inability to maintain or utilise this in the best way in policymaking constitutes a gap. Thus, a lot of existing data which should be used for making policy decisions is either not utilised or is utilised in inappropriate ways.

For transport planning, data concerning travel characteristics, preferences, and behaviour (eg. responsiveness to price and income of a particular mode of transport such as personal transport or public transport) are essential.

For Indian cities, planning for transport projects under JNNURM is severely constrained by various data gaps. These gaps include spatial distribution of job opportunities, disaggregated by gender, occupation, age etc; home-workplace travel behaviour and preferences; shopping and leisure travel patterns; freight related data; and a good quality mapping of existing infrastructure.

Another major constraint concerns the approach used for planning transport projects. The most common approach used is the trip-based approach, which uses trips between two points to project travel demand, and treats separate trips independently. This approach gives insufficient weight to individual’s behaviour over space and time, and therefore to interdependence of trips.

A trip from home to office including a movie at the theatre can be completed either by coming home first, and then a separate trip to the theatre, or by going to the theatre directly from office, which of these modes is used may depend on several variables, including options for public transit, parking charges, etc.

Recognising such interdependence is essential when the goal of transport policy is to move individuals. Complex behavioural responses of individuals to demand management policies cannot be properly modelled by focusing on individual trips.

The main limitations of the current methods (and models) to project transport demand and JNNURM can be summarised as follows:

• The models do not reflect people recalling some trips they undertook and many short trips that people forget. The models also don’t capture adequate information on the purpose and interdependence of trips and the factors impacting the demand and preference for travel.
• The calibration of these models to fit the community or region is done by existing data such as population density and growth, occupational structure and household incomes. While trip-related and individual level data such as vehicle ownership, cost of travel and trip lengths are obtained by household surveys, most socio economic and demographic data comes from existing sources.
• Occupational structure and population growth are based on decennial census which is not truly reflective of the recent changes in socio-economic and demographic profile of a region. As a result, significant data gaps arise. The dynamic nature of the Indian economic and social change, shifting geographical location of economic activities and consequent internal migration and rapid urbanisation and constraints in transport planning due to lack of recent data add  these gaps.
• Data inadequacy is the lack of enough disaggregation in current household travel data for modelling separate market segments.
• Origin-destination data on freight movement and private sector passenger transport is hardly available.
• Household surveys do not sufficiently permit analysis of the impact of improvements in non-motorised transport on travel conditions.
• For better transport planning, under the JNNURM, there are several measures designed to address data gaps and other limitations that merit consideration.
• Questionnaire surveys and diaries, which are widely used internationally for collecting data required for transport planning, need to be used more widely. The frequency and choice of days for maintaining diaries should conform to standards that ensure minimal underreporting of trips and less respondent burden. Transport studies and data collection efforts should be used for generating socio-economic data which are recent and policy relevant.
• Available technologies could be used more strategically and extensively for collecting travel flow and behaviour data on the roads for better accuracy and timeliness. GIS (geographic information system) techniques could be considered for collecting information on institutional and location characteristics such as opening hours and features of transport modes which impact on the choice of activities and travel.
• Automated pedestrian counters can be a useful way for collecting pedestrian-related information for continuous and heterogeneous traffic. These counters will allow planners to collect accurate pedestrian flow data and pedestrian characteristics (age, gender mix, etc). Such data on pedestrian volumes and their behaviour can help in evaluating the impacts of investments in pedestrian infrastructure and modelling pedestrian flows.
• The conventional modelling uses macro level aggregates of area locations for calculating trip generation and distribution. Use of GPS (global positioning system) technology to collect disaggregated data with spatial and temporal components could be useful for understanding travel behaviour and therefore transport planning.
• l The Ministry of Statistics and Programme Implementation (MSPI) has suggested that to improve freight data collection more coordinated approach to log books of the truck operators and to various check-posts are to be considered. Regular updating of databases on transport infrastructure/facilities in urban areas is also needed
• Urgency needs to be exhibited in establishing and in the smooth functioning of Unified Metropolitan Transport Authorities. It can help integrate transport service planning and provision at city level, and serve as a single agency overseeing all the modes of transport. It can help minimise multiplicity of departments/ agencies involved in transport planning, provision and regulation.
• As India experiences rapid urbanisation, and as transport needs grow, better transport planning, including under JNNURM, has become essential. In addition to the measures suggested above, others such as flexible work timings, telecommuting, congestion pricing, better integration of urban amenities and services to minimise motorised and public transport merit consideration.
   

Mukul Asher is professor at Lee Kuan Yew School of Public Policy,
National University of Singapore (sppasher@nus.edu.sg) and Tarun Sharma is an independent urban policy consultant