INTRODUCTION:

Travel demand refers to the amount and type of mobility that people would choose in a particular situation (Litman, 2011). Travel demand is a derived demand from the need or desire to participate in activities spread over space and time.

According to Hurst (1974), there are three movement spaces:

a core is frequently travelled space,

a medianis occasionally travelled space, and

extensive movement space is the learned or cosmological space.

A core movement constitutes personal travel like movement to work, for education, etcThe study examines shopping for grocery, which constitutes core movement spaces and is frequently undertaken by households . It is the most polluting of all travel.

OBJECTIVES OF THE STUDY:

1. The study examines the Demographic Economic and Transport Profile of the City-Pune, India. Pune is the eight largest city in India and second largest city in Maharashtra after Mumbai.

2. To examine the shopping aspect of the household with specific reference to grocery shopping

3. The study examines the socio economic parameters which can have an impact on the frequency of shopping

Research Question:

Do people who use NMT for shopping tend to undertake more shopping trips than people who use other modes of transport.

Dimensions Influencing Travel of Households:

The unit of study is household. A 'household' is usually a group of persons who normally live together and take their meals from a common kitchen unless the exigencies of work prevent any of them from doing so. Persons in a household may be related or unrelated or a mix of both (Census, 2001).

Income:

Income has a significant influence on the residence location and the household location with respect to Central Business District (CBD). The individuals categorized on the basis of their income reveal differences in travel behaviour. The study also looks into gender categorization.

Occupation:

Individuals are classified on the basis of their occupation such as service, business, professional, student, others. Others constitute housewives, retired, unemployed, and farmer. The study examines the occupations and the mode of transport used in the purpose of journey. In the study business would refer to a person who would undertake an activity for the purpose of generating revenue for self. Occupational pattern of user’s is correlated with the pattern of use of the mode. The study further analyzes job opportunities under taken by women and their modal choice for traveling to work.

Personal Vehicle Ownership:

The study looks into the vehicle ownership by the individuals. The vehicle ownership is further studied in terms of ownership by men and women.

Expenditure:

The percentage of income spent on traveling is attempted to be examined and understood.

REVIEW OF LITERATURE:

Activities are undertaken to fulfill our needs-either personal or professional. According to Wen and Koppelman (2000), activities are classified into three categories, namely, Subsistence (work or work related business), Maintenance (shopping, household maintenance, pick-up/drop-off passengers) and Leisure (social and recreational purpose). Household subsistence needs would depend upon the employment of household members, place of stay, place of work, etc. Mobility decisions refer to travel plans of individuals which will depend upon place of work, residential location, and ownership of mode. Maintenance activity fulfils the need of family members and is not person specific. Researchers have constructed a two-stage structure to model the system. In stage one, the model predicts the number of maintenance stops and allocation of stops and autos to household members. In the second stage, the model includes individual decisions, number of tours and assignment of stops to tours. It was clearly shown that children have an impact on maintenance stop frequency and there is increased participation of women in maintenance.While the model included generation and allocation of maintenance stops and automobiles to household members, it excluded mode and destination choice.Further, the study focused on entirely inter-individual interactions in activity decisions but was unable to examine individual activity.

Bhat and Singh (2000) attempted a description of the activity-travel pattern of workers with the help of an econometric model which aimed at capturing Comprehensive Activity-Travel Generation for Workers (CATGW) based on the entire diary activity-travel pattern of individuals within the context of a continuous time domain. The results indicated strong effects of socio-economic variables, residential and workplace location characteristics and work schedule characteristics on evening commute mode choice, number of evening commute stops, and number of stops after arriving home from work.It was also found that there is an increased number of out of home activity after arriving home from work in evening. Also that more people use car (drive alone) in case a lot of work has to be done after reaching home after work. Essentially, the study aimed to examine and to understand the problem of traffic congestion and did not offer more insights on activity based patterns, in particular.

Bowman and Ben-Akiva (2001) used integrated choice model system of an individual’s activity and travel schedule for forecasting urban passenger travel demand based on 1991 travel surveys and transportation system levels of service data for the Boston Metropolitan area in the USA. Gliebe and Koppelman (2002) developed a utility-based model of household decision making regarding joint activity participation. Joint participation is defined as “activities and travel involving multiple persons, particularly of the same household, result from a collective decision process that requires its participants to fit periods of joint activity engagement into individual schedules while considering their own needs along with those of other persons” (p.49). The study found that when both partners work, there is presence of children and multiple automobile possessions, there is reduced possibility of joint activity between adults in household. When both partners are working, it was observed that there is task specialization among parents i.e. one partner is involved in subsistence activity and the other with upbringing of children. Also, households with multiple automobiles pursue activities independently and joint participation is greater in one car household especially for maintenance activities. Families with children have lesser joint adult only participation, increased adult-female participation in out of home maintenance activities and decreased female work time relative to males.Using data for the Dallas-Fort Worth metropolitan area in the USA, Bhat and Steed (2002) developed a continuous time hazard model to analyse choice of departure time for shopping trips. They found that older people liked to pursue shopping later in the day as compared to younger individuals.

People who are externally employed and with lower income undertook shopping during off-peak and evening time reflecting tighter work schedule constraints. Comparatively, student and unemployed shopped during mid-day. It is obvious that basic economic conditions can induce temporal shifts in daily routine travel.

Ettema et.al. (2007) investigated interpersonal linkages in activity participation with the help of a Cox regression model on the data set collected in Amsterdam-Utrecht region in the Netherlands. In regard to intra-household activity, it was observed that higher density areas enabled greater involvement in out-of-home activities, out-of-home recreation both for men and women and independent participation of spouses. Also, there seemed to be even sharing in maintenance and subsistence task among spouses. But it must be noted that the regression model applied in the study was not been able to represent the full interdependency of activity scheduling, as some endogenous variables were are treated as exogenous variable in the context of locational differences.

Lu and Pas (1999) analyzed the direct impact of socio demographic factors (such as age, gender and employment status, number of children) on travel behavior (measured in terms of number of trips, chains, travel time and percentage of trips made by auto mode for each individual over a two day period). Also, the indirect impact of socio demographic factors on travel behaviour through activity participation (measured in terms of the amount of time in hours spent over a two-day period on subsistence, maintenance, recreation and other activities) was explored. Thus, the study was able to capture the relationship among socio-demographics, activity participation and travel behaviour through a structural equations model. But it can be suggested that aspects of travel behaviour could have been explained better by including activity participation in an endogenous manner in the model instead of considering socio demographic factors alone.

Cao et.al. (2007) used the structural equation modeling approach to determine whether self-selection of neighborhood leads to changes in travel behavior. The study explored the causal relationship by using quasi-longitudinal data collected from four traditional neighborhoods and four suburban neighborhood of Northern California. It was observed that people intending to minimize their daily travel tend to move to neighborhood with high accessibility and those who value ‘safety nature of cars” moves to lower accessibility neighborhood.

As seen in other studies, it shows that changes in income and changes in number of driving-age members in household have positive association with changes in auto ownership. Also, spacious environment encourages driving and also greater auto ownership. The study conclude that land policies that are designed to keep residences near to destinations and alternative transportation driving options will lead to less driving and more walking. Despite the improvement in the methodology, there is a certain limitation. In the study, attitude is measured retrospectively for “movers”. Since information only on current attitude was available, the model could can only assess the current attitude rather than the change in attitude.

Demographic Economic and Transport Profile of the City-Pune, India:

As per the Census 2011, the population of the city was 3.11 million. The reported average population density as per the 2011 census was 603 persons per sq. km.Pune is known by varied names namely “Oxford of India”, “Pensioners Paradise”, Cultural Capital of Maharashtra”, “Cyber City”, “Queen of Deccan”, “Cycle City” now “Motorcycle city” All the numerous names of the city of Pune highlights different aspects and dimensions of the city.

Figure 1 gives the Strength, Weakness, Opportunities and Challenges (SWOC) faced by Pune.

Figure 1: SWOC of Pune

TRANSPORT SCENARIO IN THE CITY:

Pune is connected by roadways and railways. The modal split of Pune shows that 12 percent use public transport, private transport is 54 percent and non-motorised transport use (walking and Bicycle) is 33 percent. Pune has 600-800 vehicles added on to roads every day. Total vehicle population of Pune city has increased by more than twice since the last eight years. Pune city vehicle population was 8.30 lakh in 2000 and it increased to 16.38 lakh in 2008.

Personalized Transport:

Pune has a very high usage of personalized transport. Personalized transport constitutes of two-wheeler and car.

Public Transport:

Pune Mahanagar Parivahan Mahamandal Ltd. (PMPML) is the public bus transport service provider in thePune Metropolitan Region. The combined operations of Pune Municipal Transport and Pune Chinchwad Municipal Corporation, known as PMPML commenced on 19^th Oct 2007. PMPML has a daily ridership of 8,07,511. It plies 1000 buses in a given day, on 282 routes. PMPML has 10 depots (Swargate, NerveerTanajiWadi,Kothrud, Katraj, Hadapsar, Marketyard, Pune Station, Bhakti Shakti (Nigdi), Sant Tukaram Nagar (Pimpri), Sadguru Nagar (Bhosari) and 66 bus stands for the operation of buses.PMPML plies on 282 routes and makes 21,998 trips per day (http://www.pmpml.org/).CIRT recommends anideal ratio of 40 buses per lakh of population. For PMC with apopulation of 30 lakhs, this implies having 1,200 buses. Currently the PMT has about 850 buses, ofwhich only about 700 are roadworthy. PMT thus needs at least an additional 500 buses. (Patankar, 2006)

This shortage has an impact on travel choices by people. The average trip length is 6.1km. The trip length is an indication that there is an urban sprawl. The urban sprawl will have a tremendous impact on PMPML, in the years to come since it would move people over longer distances to meet the needs of commuters.

Railways:

There are local (suburban) trains that ply between Pune Station and Lonavala. The Electric Multiple Unit (EMU)-local train service is being provided by Central Railways since 1978.It runs on broad gauge and there are 17 stations between Pune and Lonavala. It covers a distance of 63km in about an hour and a half. The daily ridership is about one lakh. The local train has twelve compartments.

Intermediate Public Transport:

In 2007, auto-rickshaw constitutes 3% of total vehicle population and constitutes 11% of modal share (in terms of trips) among the motorized road transport modes. Auto-rickshaw handles about 5% of work trips, 17% of education trips and 23% of shopping, and recreational trips. Other than the auto-rickshaw, six seaters play an important role in moving people especially in the peripheral areas of the city. They are also called as “tum-tum”, and are a shared means of public transport. Due to high pollution emission, these are not allowed to ply in the core areas of the city. For example, On the Eastern part of the city, the six-seaters ply only till Yerwada (before Bund Garden).

Company Bus:

In order to enable people to reach their work place companies ply busses. The availability of company bus means that people don’t have to rely on personalised transport, but the negative side is the bus availability at fixed time intervals. Fixed time means limited flexibility in movement which causes some people to move to personalised transport. Companies also provide its employees the facility of sharing company cars.

Non- Motorized Transport (NMT):

The non-motorized transport constitutes of walking and cycling.

A study JnNURM (2006) have indicated the following reasons for increasingcongestion in Pune. The most important reasons for congestion are listed below:

· Existence of poor public transport i.e. lack of sufficient buses, routes, poor frequency of services

· Growing Economic activity in the City

· Increase in student population

· Increase in the population of the city

· Indiscriminate parking of vehicles

· Hawkers occupying roads for trade

· Encroachment in footpath resulting in people walking on roads

· Insufficiency of flyovers

Pune also faces the problem of unorganised growth, especially in the peripheral areas. Developers have purchased agricultural land and have developed them as residential areas. The new residential and commercial areas are being developed without planning of the transportation system. There is a need to connect these new pockets of development. This imposes severe strain on resources. This has a severe impact on the transport. Proper planning can help these centres to be self-sufficient.

The success of Pune as an educational hub and an IT hub has changed the features of Pune. Despite the lack of physical infrastructure and transport linkages, Pune evolved as an IT hub since the telecommunication network has been good. The prosperity of IT sector meant greater pressure on the existing infrastructure. Traffic congestion has increased due to greater ownership of personalized modes, housing demand has escalated in quantity and quality leading to rapid increase in land and housing prices and greater demand for power supply systems. (Mohan, 2008). The edge that Pune has over other cities will be wiped away if necessary infrastructure investment is not undertaken.

Table 1 gives the transport performance indices of Pune. This gives an over view of the transport scenario in Pune.

Table 1: Transport Performance Indices of Pune

Index	Formulation	Target	Existing
Network Speed	Average Running Speed for all vehicles	30	18
PT ModeShare (Motorized)	Public Transport Trips / Total Motorized Trips	80%	18%
NMT Mode Share	NMT Trips / Total Trips	50%	33%
Volume Capacity Ratio	Road Traffic Volume / Road Capacity	0.8	1.4
Accessibility	Work trips with Travel Time less than 15 min /Total Trips	60%	33%
Bus Supply	Bus Fleet / Lakh of Population	55	30
IPT	Registered IPT vehicles / Lakh of Population	1000	1841
Walkability	Footpath Length / Road Length	100%	53%
Cyclability	Cycle Track Length / Road Length	100%	0%
Fatality	No. of Fatalities / Lakh of Population	0	11

Source: WSA, 2008 and PMC, 2013

4. DESCRIPTIVE ANALYSIS OF HOUSEHOLD SAMPLE:

The data for the study is collected from 450 households in the Pune Municipal Corporation area, located in Pune, State of Maharashtra, India. In this sample there are total of 1854 members. This indicates that most of the families are nuclear in composition. The study aims to look in to the frequency with which shopping trips are undertaken by individuals and the means of transport used and the mean shopping trip distance i.e. mean distance covered for shopping in Pune city. In the study, shopping refers to grocery shopping

Figure 2: Mode of Transport Owned by Respondents

Source: Primary survey conducted by the author

Shopping is only considered with regard to grocery shopping. This type of shopping is more consistent in terms of the number of times one does shopping, the place for shopping and the time for shopping. Analysis of shopping show that 514 individual respondents undertakeprovision shopping. Female (54.28 percent) respondent undertake more shopping than malerespondent (45.72 percent). Majority (90 percent) shop at a distance below 5km and the preferred shopping time is between 3pm to 6pm. For very short distance (below 1km) the respondents prefer walking. For a distance upto 5km two-wheeler is preferred mode of transport

Model of Travel Demand for Shopping:

The Poisson regression analysis is done to study the shopping frequency in a month for household. Poisson regression is similar to regular multiple regressions except that the dependent (Y) variable is an observed count. In Poisson regression, we suppose that the Poisson incidence rate is μ determined by a set of k regress or variables (the X’s). The expression relating these quantities is

The regression coefficients β_1,β_2,…. β_kare unknown parameters that are estimated from a set of data. Their estimates are labeled b₁ b₂…….b_k

The fundamental Poisson regression model for an observation iis written as

Where

This analysis explore its relationship with the covariates such as, number of male shopper, number of female shopper, number of workers, distance for shopping, number of family member who use NMT for shopping, number of family members who use personalized transport(car, two-wheeler) for shopping, number of family member who use private transport(private bus/van train, six-seaters, auto-rickshaw for shopping, number of family member who use public transport for shopping and factors such as business, Early Morning Shopping Time (before 9 am), Morning Shopping Time (9am-12 pm), Afternoon Shopping Time(12pm - 3 pm), Late Afternoon Shopping Time(3pm – 6pm), Evening Shopping Time(after 6pm). All factors are binary variables which are taking values 0 or 1.

The Poisson regression model is used to study the travel demand for shopping on the basis of information such as sex, socio-economic characters and travel behavior of respondents. The Poisson regression analysis is done to study the shopping frequency in the month for household.

Table 2: Goodness of Fit for Travel Demand for Shopping

	Value	Df	Value/df
Deviance	185.264	434	.427
Scaled Deviance	185.264	434
Pearson Chi-Square	191.205	434	.441
Scaled Pearson Chi-Square	191.205	434
Log Likelihood^b	-676.643

Goodness of Fit table (Table 2) gives statistics indicating model fit. This table reveals that the model fits reasonably well.

Table 3: Omnibus Test for Travel Demand for Shopping

Likelihood Ratio Chi-Square	df	Sig.
106.782	15	.000

In the Omnibus Test (Table 3) all of the estimated coefficients are equal to zero. This test shows that model is statistically significant.

Table 4 reveals business is significant at 1% level of significance. Main effect of covariates such as income, number of workers, number of family members who use NMT for shopping, number of family member who use personalized transport for shopping and number of family member who used private transport are significant in the model. The number of workers is significant at 5% level of significance and remaining variables are significant at 1% level of significance.

Table4: Parameter Estimates for Shopping

Parameter	Coefficient	P value
Business	0.265	0.000
Early Morning	-0.156	0.678
Morning	-0.036	0.83
Afternoon	-80	0.674
Late Afternoon	0.38	0.827
Evening	0.06	0.732
Income	-2.602	0.01
Male Shopper	-0.124	0.448
Female Shopper	-0.2	0.217
Worker	0.094	0.023
Shopping Distance	-0.013	0.2
NMT	0.875	0.000
Personalized	0.648	0.001
Private	0.539	0.006
Public	0.465	0.143

Table 4 shows that as income increases the shopping frequency declines. With the increase in the number of working members the shopping frequency increases. Also, people who use NMT for shopping tend to undertake more shopping trips than people who use other modes of transport. Also, people undertaking businesses have higher frequency of shopping.

The Poisson regression analysis is done to study the shopping frequency in a month for household. The analysis explore its relationship with the covariates such as, number of male/female shopper, distance of shop from home, mode used for shopping by family member. The study found that as income increases the shopping frequency declines. With the increase in the number of working members the shopping frequency increases. Also, people who use NMT for shopping tend to undertake more shopping trips than people who use other modes of transport.

Concluding Remarks:

Travel for shopping is an essential part of a family life. A vast majority of time and energy is spent on this process. The study has found that it is the women who undertake greater trips for grocery shopping than men. Also the study found that the economically well-off tend to use personalized transport rather than to walk or use cycle. Often the travel arrangements are meant to fulfill the desire of the well-off rather than of the poor. The poor tend to travel shorter distance for grocery shopping, since they travel by cycle or walking. Also the poor tend to purchase smaller quantity of grocery rather than to purchase in bulk.

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Received on 13.05.2019 Modified on 28.05.2019

Int. J. Rev. and Res. Social Sci. 2019; 7(3):641-647.

DOI: 10.5958/2454-2687.2019.00041.8