Scholarly article on topic 'Modeling the Choice of Tele-work and its Effects on Travel Behaviour in Indian context'

Modeling the Choice of Tele-work and its Effects on Travel Behaviour in Indian context Academic research paper on "Social and economic geography"

CC BY-NC-ND
0
0
Share paper
OECD Field of science
Keywords
{Tele-work / ICT / IT/ITES}

Abstract of research paper on Social and economic geography, author of scientific article — P.C. Lila, M.V.L.R. Anjaneyulu

Abstract Information and communication technology (ICT) empowers people with virtual accessibility to a wide range of activities reducing physical travel and traffic congestion. Since 1990, accessibility to ICT technologies, along with a drop in internet tariffs and prices of various devices like computers and mobile phones, has brought about a transformation of the activity travel behaviour of Indian employees. This paper analyses the choice of tele-work and its effects on the travel behaviour in Indian context through the disaggregated data collected from Bangalore, considering all potentially influencing variables on tele-work. The study covered employees who work from home either for a few days in a week or occasionally. The analysis was carried out in two stages. In the first stage, a preliminary exploration of the data was carried out to assess the presence of the tele-work scenario in the Indian context with respect to the worker's individual-and household-related characteristics. Secondly, the frequency of tele-work was modelled with the conventional variables of socio-demographic and transport- related characteristics. This research will be continued to quantify the benefits of tele-work in terms of vehicle miles travelled, savings in time and fuel consumption. The ultimate aim would be to convey to the policy-makers the strategic advantage of adopting tele-work as an alternative traffic management scheme for Indian cities.

Academic research paper on topic "Modeling the Choice of Tele-work and its Effects on Travel Behaviour in Indian context"

Available online at www.sciencedirect.com

ScienceDirect

Procedia - Social and Behavioral Sciences 104 (2013) 553 - 562

2nd Conference of Transportation Research Group of India (2nd CTRG)

Modeling the Choice of Tele-work and its Effects on Travel

Behaviour in Indian context

Lila P Ca , Dr. Anjaneyulu, M.V.L.Rbl

aDiscipline Leader, Transport Modeling, CDMSmith,#8, Second Floor, 80 Feet Road, R. T Nagar, Bangalore,560032,India

bProfessor ,Department of Civil Engineering ,National Institute of Technology, Calicut ,673 601, India_

Abstract

Information and communication technology (ICT) empowers people with virtual accessibility to a wide range of activities reducing physical travel and traffic congestion. Since 1990, accessibility to ICT technologies, along with a drop in internet tariffs and prices of various devices like computers and mobile phones, has brought about a transformation of the activity travel behaviour of Indian employees. This paper analyses the choice of tele-work and its effects on the travel behaviour in Indian context through the disaggregated data collected from Bangalore, considering all potentially influencing variables on tele-work. The study covered employees who work from home either for a few days in a week or occasionally. The analysis was carried out in two stages. In the first stage, a preliminary exploration of the data was carried out to assess the presence of the tele-work scenario in the Indian context with respect to the worker's individual-and household-related characteristics. Secondly, the frequency of tele-work was modelled with the conventional variables of socio-demographic and transport-related characteristics. This research will be continued to quantify the benefits of tele-work in terms of vehicle miles travelled, savings in time and fuel consumption. The ultimate aim would be to convey to the policy-makers the strategic advantage of adopting tele-work as an alternative traffic management scheme for Indian cities.

© 2013 The Authors. Published by Elsevier Ltd.

Selectionandpeer-reviewunderresponsibilityoflnternationalScientificCommittee. Keywords: Tele-work; ICT, IT/ITES

1. Introduction

Demand for travel arises from the need of people to participate in different activities like work, shopping, education, entertainment etc. in different places at different times of the day. Since 1990, the traditional notion about accessibility to activities has changed considerably with the emergence of Information and Communication Technology (ICT), which encourages people to perform activities virtually. According to Golob (2000), accessibility can no longer be measured in terms of travel time, distance or generalised cost alone. Technology -cell phones, personal computers, and the internet-facilitates performance of all sorts of activities viz. e-shopping,

* Corresponding author. Tel.: +91 80 3918-7559, Mob: 9343596256 Fax: +91 80 2363-4097

E-mail address: lilapc@cdmsmith.com

1877-0428 © 2013 The Authors. Published by Elsevier Ltd.

Selection and peer-review under responsibility of International Scientific Committee. doi: 10.1016/j.sbspro.2013.11.149

e-leisure, online reading, online services like travel booking, transactions, bill payments, etc. without physical travel. Future generations will evolve new technologies in the telecommunication sector allowing people to perform activities faster and more efficiently. Golob & Regan (2001) reviewed the technological developments that are likely to influence personal travel and activity behaviour. The interaction of ICT with transportation can substitute, modify, generate and add activities (Mokhtarian & R Meenakshisundaram, 1999; Salmon, 1986). Adoption of ICT and its impact on personal travel has been in the limelight from 1970's (Albertson, 1977; Viswanathan K & K G Goulias, 2001; Hjorthol, 2002).

The adoption of tele-work and its impact on the traffic scenario have been in the focus of research for more than two decades (Mokhtarian, Kitamura & Pendyala, 1991; Nilles, 1994; Sangho Choo, L Mokhtarian & I Salomon, 2005). Telecommuting (Tele-work) can be defined as working at home or at a location close to home instead of commuting to conventional working locations (Mannering and Mokhtarian, 1995). Tele-work has been considered as a strategy for reducing traffic congestion, energy consumption and air pollution (Niles, 1991). Niles has quantified the impact of tele-work in terms of reduction of fuel consumption and respective emissions of CO, NO, HC and particulate matter. Most studies claim significant reductions in emission values with the adoption of telecommuting (Henderson et al, 1996; Mokhtarian et al, 1996; Mokhtarian & Varma, 1998; Erasmia Kitou & Arpad Horvath, 2006). Erasmia Kitou & Arpad Horvath (2006) have analysed the effects of mode of transportation and the benefits of tele-work. Morten Falch (2012) has categorized the environmental impact of tele-work on transport behaviour under three broad effects i.e. first order effect, second order or rebound effect and third order effect. The study outcomes indicated that a substantial part of transport savings are nullified by increased transport for other purposes such as shopping and increased transport by other members of the household. Telecommuting opportunities might also influence choice of residential and employment locations, which in turn will affect travel demand (Nilles & Pendyala et al, 1991; Collantes, G.O. & Mokhtarian, P.L, 2003).

There exist many ambiguities in the overall impact of tele-work. The empirical information available does not convincingly indicate that peak hour trips will be reduced by tele-working (De Graaff, 2004). In contrast to the general outlook on the factors that influence tele-work, it is surprisingly individual needs, compulsions or temperaments that influence the decision rather than the availability of ICT or considerations of reduced commuting time (Thomas De Graff & Piet Rietveld, 2006). Pratt (2002) indicated that telecommuting has proven to be less effective in trip reduction than anticipated. Wells et al (2001) and Pratt (2002) suggested that the actual impact of telecommuting may vary with the frequency of telecommuting the individual is engaged in. In an effort to understand the relationship between virtual and physical mobility, Hjorthol, R., & Gripsrud, M (2009) have found that the relation between virtual and physical mobility varies depending on the type of activity and the social group, but overall that is not very strong.

Indian cities are witnessing a huge migration from rural areas because of the employment, education and other opportunities that they offer. This results in enormous population pressure on the cities. The infrastructure improvements are not commensurate with the increased population density, resulting in congestion, increased travel time, high vehicle operating costs, pollution emissions and psychological stress. They also create many health problems. According to a study conducted by IISC, Bangalore, the daily congestion cost in Bangalore is Rs 208 million, computed on the basis of the approximate cost incurred by a traveller for a one-hour journey which amounts to Rs 91.35 per hour. The loss due to congestion, calculated in terms of the number of hours lost, is nearly a tenth of the average Bangalorean's daily wage. Statistics compiled by IISC has found that if one out of every 100 persons who commute by public and private transport shifts to bicycles or walks, Bangalore will save Rs 2.5 lakh every day. Findings of a survey made by IBM Smarter Cities Forum in New Delhi revealed that commuter stress in Bangalore is about twice as high as in Mumbai. However, the commuters' opinion on their choice of transport showed that they still preferred to travel by private modes.

Since India is a low-cost production center for various jobs with an abundance of high-skilled labour, developed countries are utilising the mobile workforce in the country on a larger scale. Indian companies are encouraging employees who are working away from office and can access corporate information remotely. The recent slowdown in the economy has forced many companies to adopt tele-work to cut costs and to improve efficiency and productivity. The complexities of maintaining office space for the growing staff, in view of rising real estate prices and loss of man hours in traffic congestion, have made many firms choose tele-work as a strategy to achieve higher profitability and to improve employee morale.

Morten Falch (2012) has defined tele-work in many ways like 1) telecommuting i.e. working from home and thereby avoiding personal transport to and from the work place, 2) tele-working centers, 3) teleconferencing, 4) mobile tele-working, 5) self-employed tele-workers and 6) offshore tele-workers. Among these, Indian employees adopt all the modes except the second option on a minuscule level. Exact statistics of tele-work in India is not readily available. Some experts estimate that the percentage of tele-workers in India is slightly higher than in North America. A report published by AVTAR I-WIN, a career service for Indian women, has documented that tele-work and flexible work arrangements are being considered most useful for woman employees.

In an Indian context, it is pertinent to examine the effects of tele-working on travel behaviour, considering the traffic congestion and related issues in Bangalore. The purpose of this paper is to present a conceptual exploration of the telecommuting pattern of dwellers in Bangalore (IT capital of India) and its impact on their travel behaviour. This study attempts to develop a relationship between the characteristics of individuals and frequency of tele-work. The paper is organised in the following way: Section 2 covers a brief description of the survey diary instrument, data collection methodology and sample characteristics of the data used in the analysis. Section 3 describes the development of a model for the frequency of tele-work. The effect of variables that influence tele-work is described in section 4. Section 5 offers some concluding remarks on the derived model and the way forward.

2. Data Description

2.1 Data Source

The primary data source for this analysis is the activity travel diary survey from persons working in different organizations in Bangalore city such as IBM, TCS, HCL, WIPRO, Accenture and Cognizant, where tele-work is being practised. The survey was conducted during the period of February 2013 to April 2013. The four-section activity diary instrument, six pages long was collected through a web-based survey as well as personnel interviews. The first two sections covered the demographic and economic characteristics of individuals and households and the travel information on a typical working day. The second section collected information about the intensity of use of various ICT instruments including land line phones, mobile phone and personal computers. The utilization of the landline and mobile phones was recorded in terms of the number of calls made in a working day. The usage of computers and internet was measured in terms of the number of hours used. The activities performed using ICT instruments and the frequency of engaging in those activities were collected in the same section. The third section included stated preferences of non-teleworkers on issues that are related to tele-work and their preferred frequency of telecommuting in the future. The issues listed were associated with travel concerns in the peak hour, commuting time, transport system characteristics and time for other activities including extending more valuable time for family. The fourth section was exclusively for tele-workers specific to telecommuting frequency and participation in activities other than work while working from home.

Respondents were contacted through e-mail and survey materials were sent to them. They were asked to participate in the survey and describe their trip-making behaviour on a typical working day and a telecommuting day. The follow-up mails were sent to those who did not respond. This helped to increase the response rates. Of

the 432 surveys mailed, around 144 were returned. As the response rate was very low, more samples were added through personal interviews at workplaces/residences. After cleaning the data for validity and missing data on variables pertaining to the study objectives, a total of 201 samples were retained for the study analysis.

2.2 Survey Participants Characteristics

2.2.1 Demographic characteristics

Out of the samples, the respondents' age ranged from 23 to 69, with 32 % in the group of 23- 30. Around 10% is aged more than 50. In the gender distribution, the percentage of women respondents is very less, compared to that of men. One reason for this might be the low work participation ratio of woman in India. (According to U.N. statistics, India's rate of female participation in the labour force is the lowest of any of the BRIC countries at 34.2%. NSSO data further reiterate that woman labour participation ratio in India is only 22.9% in 2011-12. Most of the samples (88%) are well educated with graduate degrees and 42.8% are professional degree holders. The share of technical diploma holders is 10%.

In terms of employment characteristics, the share of samples involved in IT/ITES is high at 57.7 % followed by other technical jobs at 18.4%. With respect to the reported personal income, the respondents can be tagged as middle and upper income class with 28.4% falling in the middle income bracket of Rs 3,000- Rs 50,000 per month. The higher income is due to dominance of IT/ITES jobs in the overall samples. With respect to vehicle ownership, more than two-thirds of the samples owned a vehicle, which constitutes an average vehicle ownership of 1.12. Nine per cent possess more than 2 vehicles per household.

The distribution of the type of house reported that 61.7% respondents live in rented houses, 32.8% in their own houses and the rest in government quarters or on lease. The distribution of the size of residence was assessed to find any relation between availability of space at home and the decision to tele-work. The size of the house is less than 1,000 sq ft for almost 50% of the respondents.

2.2.2 Transport Related Characteristics

A large percentage of the respondents (76.1%), travel during the morning peak hours i.e. between 8.00 AM and 11.00 AM for their work. An equal share (71.1 %), travel during the evening peak hours for their return journey from work. Of this, 58% travel in both morning and evening peak congestion hours. This constitutes an average commuting time of about three hours per day, equivalent to over 20% of the hours of daylight. The average commuting distance to work is 25 km (both ways). Most of the employees (51%) commute more than 20 km with a significant share (10%) travelling more than 50 km. Private mode (2-wheelers and cars) users dominated the sample with 60.7%. Public transport is used by 34.4%. Public transport included buses, taxis, cabs, company buses and metro. Carpool arrangements were found to be very negligible. The share of cabs used is substantial, which is typical of all cities where IT/ITES jobs are predominant. The majority of the respondents hold driving licences which reinforce the share of use of private modes for work. Nearly two-thirds of the samples have vehicles available for exclusive use, and this gives them the freedom to commute as per personal wish.

2.2.3 Telecommunication characteristics

The access to the ICT facility, its intensity of utilization and its role in virtual activity participation are assessed by designing the questionnaire related to their participation in listed activities and its frequency. Hundred per cent respondents have access to the mobile phone. On an average, ten calls are made by respondents on a typical working day. More than 20 calls are made per day by 16.4 %. Most of the respondents have access to computers and internet. The share of non-users is very negligible (6%). The respondents can be categorized as medium and highly connected with an average usage time of more than 3 hours. Around 25% use the internet for more than 6

hours per day and 19.4% use it for more than one hour per day. ICT facility was being provided completely by company for 34% of the samples and partially by the company for 35.3%.

3. Model Development

The purpose of the study is to understand the tele-work scenario in an Indian context and to establish the relation of tele-work to the employees' socio-economic and travel behaviour parameters. For the analysis, multinomial logit (MNL) modelling technique was adopted for predicting the probability of choosing a certain frequency of tele-work. The respondents were asked to report the frequency of tele-work in terms of never, tele-work for one day per week, two days/week, three days/week, four days /week, five days/week, everyday and occasionally. Since the sample size for the higher frequency categories were not enough to create independent models, the samples were merged to form never, low, medium and high frequency categories. Occasional tele-workers are parked in the low category. A frequency of one day per week was considered as medium. The samples reported two days and above were grouped as high frequent tele-workers. Table 1 presents the distribution of samples by the frequency of tele-work.

Table 1 Distribution of Samples by Frequency of Tele-work

Tele work Samples Samples

Frequency (No) (%)

Never 79 40%

Low 39 19%

Medium 50 25%

High 33 16%

Total 201 100%

The explanatory variables considered in the model development are grouped into household characteristics, individual characteristics, transport characteristics, ICT use and its intensity, opinion on travel related issues, and participation in nonwork-related activities. The variables under each class are listed in Table 2.

Table 2 List of Variables

Group of Variables Dependent Variables

Frequency of Tele-work Never

Medium

Explanatory Variables

Household Characteristics Household size

House Ownership

Type of Dwelling

Floor Space in Sqft

Adults in Household

Children in Household

Individual Characteristics Age of Respondent

Education Qualification Level

Job Profile

Marital Status

Personal Monthly Income

Transport Characteristics Vehicle available exclusively

Possession of Driving License

Mode of Travel to Work

Travel Distance in Kilometers

Travel Time in Hours

ICT Use and Intensity Mobile technology usage

Computer usage

Internet usage

Opinion on Travel Related Issues

Opinion on Time Management

Participation in Non- work related activities

Avoid Peak hour Travel High Commute Distance Crowded Public Transport Parking Scarcity Commute Stress Time Savings More Time with Family Take Care of Children Take Care of Elders Time for Health Activities Time for Leisure Activities Shopping

Drop/pickup(Spouse & Children)

Religious

Medical

Social meetings

Eating Outside Bank

Post Office Payment of Bills

A preliminary exploration of the data was carried out to examine the explanatory power of variables considered for the model development. Among the household variables, the effect of household size is (Figure 1) that as the number of members in the family increases, the share of tele-workers reduces. This is in agreement with the models developed on the choices of work hour arrangement, location, and frequency of telecommuting (Vana et al, 2006). The reasons attributed for the lower frequency of tele-work for those from households with a high number of members are the presence of small children, the need to take children to play schools/ schools and pick them up from there, drop and pick up spouses and attending to the medical issues of elders. However the statistical significance of household size does not demonstrate enough to explain the tele-work frequency completely.

Telecommute Freq. Vs Age of Respondents

Low Medium High

■ 23-30

■ 30-39 40-49

■ >50

Telecommute Freq.Vs Household size 30 -

Uril Low Medium High - B1.0 ■2.0 3.0 1_ B4.0 ■5.0

Figure 1 Tele-work frequency w.r. to (a) Household Size (b) Age of Respondent

The more preferred age group for tele-work is the 30-40 group. This seems to be very true in the Indian scenario where the responsibility towards the family, including the setting up of a house, giving education to children and doing other household tasks, is at the peak during this period of life. The results of MNL model is presented in Table 3. Separate models were developed for low, medium and high frequent tele-workers.

4. Results and Discussions

The likelihood ratio test evaluates the overall relationship between an independent variable and the dependent variable. The -2 Log Likelihood (-2LL) is a likelihood ratio which represents the unexplained variance in the outcome variable. A likelihood ratio comparison between the full model and the null model reported a difference

of 256.90 with 33 degrees of freedom. The -2LL is lower for the full model which includes all the predictors and the intercept than it is for the null model (only intercept) indicating better fit. The likelihood ratio chi-square test is an alternative test of goodness-of-fit. Here the model fit is significant with p value less than 0.05(0.001), which implies that the model predicts significantly better or more accurately than the null model. Pseudo R-Square value is more than 0.6, showing that the predictor variables represent a significant amount of variance in the outcome variable. Higher values reiterate better fit. The Wald test values are indicated against each parameter estimate for different models. The Wald test evaluates whether or not the independent variable is statistically significant in differentiating between the two groups in each of the embedded binary logistic comparisons. These values along with the associated p value indicate that the logistic coefficients are different from zero and they predict the frequency of tele-work based on the predictor variables related to individual and household characteristics.

Table 3 presents the explanatory variables, parameters estimates and the Wald statistics which indicates that variables related to household characteristics i.e. the type of dwelling, house ownership and floor space greatly influence the decision to tele-work. The dwelling type considered two options - independent houses and apartments. Employees prefer to tele-work more from an independent dwelling than from apartments. The reason may be that there are more distractions in apartments than in independent houses, with the presence of non-working people, children and routine maintenance activities during working hours. Tenants have emerged as more frequent tele-workers than those who own houses. The availability of working space at the residence is very vital in deciding whether to work from home or not and it is measured in terms of the floor area in sq ft. The more the floor space, the more likely employees are to work from home as it is indicated by a positive coefficient in the model. This implies that the availability of space at home is crucial for the decision to tele-work even for a day. The influence of the marital status of employees on the choice of tele-work is very complex and estimates indicate that there is great variability across tele-work frequency. Married people tele-work more frequently compared to unmarried people. This is again in agreement with the general idea about the relative responsibilities of people of different age groups. Married people in the Indian scenario fall in the age category of 30-40 and as per the observations, this group tele-work frequently.

Regarding the correlation between ICT use and the tele-work decisions, the mobile technology usage has a stronger relation than other ICT instruments. The ICT use was measured across three parameters including use by mobile phones, personal computers and the internet. The models indicate a positive relation between use of mobile technology and the travel behaviour towards tele-work. In an effort to explore the effect of the ICT facility provided by company to choose tele-work, no significant relation was observed as 70% of the overall respondents have the facility provided either completely or partially by company. This is true as ICT facilities, especially mobile phone, have become a common man's device in developing countries. ICT figures and facts from the ITU telecom world illustrate that mobile cellular prices in developing countries have dropped down significantly in the last decade.

ICT facts and figures reveal that mobile broad band is the only access method available to people in developed countries. The figures observed in the present study regarding the fixed landline phone use in Bangalore support these ICT figures as only less than 40% use the fixed facility. This, along with the innovations in mobile technology, has contributed to the strong relation between work and mobile technology use. The job profiles considered for the study were IT/ITES, other technical jobs and those from the areas of finance, sales, marketing, human resources etc. Employees from the IT/ITES appeared to be more likely to choose tele-work than those from other professions. Those from other professions are reluctant to perform tele-work as these jobs require physical presence and interactions with their colleagues and the public. Other than IT/ITES & other technical jobs, employees prefer to tele-work only occasionally.

Table 3- Estimated Parameters of MNL Model_

_Low_Medium_High

Variables Parameter Wald Statistics Parameter Wald Parameter Wald

Estimate Estimate Statistics Estimate Statistics

Intercept -0.09 0.001 -8.64 8.678 -6.26 3.484

Type of Dwelling -3.13 10.02 -2.11 6.434 -1.76 3.763

House Ownership 0.353 0.26 1.496 8.441 1.111 2.972

Floor space in Sqft 1.569 10.6 1.273 9.493 1.644 12.57

Intensity of ICT (Mobile Phone) 0.058 0.028 0.723 7.251 0.755 6.576

Marital Status -0.55 0.229 0.864 0.984 2.383 3.906

Job Profile -0.39 3.924 -0.29 3.514 -0.43 6.012

Personal Monthly Income (Rs) -0.63 5.687 -0.32 2.561 -0.92 14.41

Vehicle Available Exclusively -0.65 4.78 -0.37 2.541 -0.995 11.78

Time Savings 2.563 4.621 2.159 4.084 3.196 7.704

Travel Time in Hours 0.764 4.297 1.302 16.75 0.926 6.908

Participation in Non- work Related Activities 8.64 28.55 6.337 17.91 8.589 29.94

High personal monthly income levels have a negative effect on tele-work. When the income, position and job responsibilities are high, employees do not completely stay away from the office for a long time. The tele-work as per this study ignored partial working from home involving a few hours a day. As income increases, vehicle ownership increases and the availability of vehicles for exclusive use for various activities is more. The negative sign for the vehicle available exclusively supports the same. According to Ory, D.T. & Mokhtarian, P.L (2005) the typical predictors of tele-work includes gender, household income, and the mobility constraints. This is in agreement with the model developed in this study. The travel time to work in hours has a strong positive effect on the frequency of tele-work. When the travel time to work is more, people prefer to work from home very frequently. The reason for the increased travel time is the heavy congestion on roads during peak hours.

Opinions on travel related issues related to tele-work decisions were collected to find out the most frustrating element in the travel experience of people. These are represented in Figure 2. Employees opting for high frequency of tele-work are more upset with commuting stress. However the saving in commute time has been a motive for employees to choose tele-work across the frequency, which is illustrated with a positive sign in the model. This supports the study carried out by Haddad, H., Lyons, G., & Chatterjee, K. (2009) on "An examination of determinants, influencing the desire for and frequency of part-day and whole-day home working". It was reported that four elements are relevant in the desire to do part-day and whole-day homework: avoiding interruptions at work; avoiding wasted time in traffic; other household members appreciating the employee home working; and working longer hours. Regarding the time allocation for other activities, employees would like to spend more valuable time with their families by working from home occasionally. The two other top activities are taking care of children followed by leisure activities. This is very obvious as jobs requiring extended working hours and increased travel time to work place reduce the time spent with family.

High Teleworkers Medium Teleworker. L™ Meworlters

■ Avoid Peakhour Travel BHigh Commute Distance ■Crowded Public Transport

■ Parking Scarcity ■CommuteStress ■ Commute Time Saving More Time with Family Take Care of Children BTake Care of Elders Time for Health Activities Time for Leisure Activities

Our findings are commensurate with the observation that "the presence of small children in the household (irrespective of respondent gender) was one of the most important variables in explaining the choice of frequency of telecommuting from home" (Mannering, J.S & Mokhtarian, P.L, 1995).

An analysis of other activities undertaken during work from home has revealed that respondents wish to do shopping and religious duties, make payment of bills and drop and pick up children and spouses. The influence of these parameters is decided on the basis of the participation of the respondent in a given list of activities. The positive sign of the estimate indicates that respondents take part in non-work related activities during work from home. Shopping activities are observed more for the medium frequent tele-workers.

5. Conclusion and Future Research Questions

This paper focuses on the relation between the decision to tele-work and socio-demographic characteristics & transport-related variables of respondents. Travel data shows that a large per cent of employees (76.1%) travel during the morning peak hours i.e. between 8.00 AM and 11.00 AM for their work. An equal share (71.1%) of people travels during the evening peak hours (between 5.00 PM to 8.00 PM) from their work. The model results indicate that as the commuting time increases, respondents prefer to work from home more often. Generally employees do other non-work activities like shopping, religious duties, payment of bills and dropping and picking up of children and spouses during tele-work. Medium frequent tele-workers participate more in shopping activities when they work from home. An analysis on the trip length of these non-work related activities shows that the trip length is much less than their commuting distance and that most of these activities are not being performed during peak hours. This would eventually lead to reduction in overall traffic during peak hours and hence to reduced emission levels. The savings in the overall vehicle distance travelled and the reduction in fuel consumption would be the main focus of the research in the future. Further research can focus on employer's willingness to offer the provision of tele-work to the employees and the related issues in Indian context.

References

1. Golob, T.F. (2000). Travel Behaviour.Com: Activity approaches to modeling the effects of information technology on personal travel behaviour. In D.A. Hensher (Ed.), Travel Behaviour Research: The Leading Edge: 145-183. Oxford: Pergamon.

2. Golob, T.F. and A.C. Regan (2001). Impacts of information technology on personal travel and commercial vehicle operations: Research challenges and opportunities. Transportation Research - Part C: Emerging Technologies, 9: 87-121.

3. Mokhtarian, P. and R. Meenakshisundaram (1999). Beyond tele-substitution: disaggregate longitudinal structural equations modeling of communication impacts. Transportation Research C, 7, 33-52.

4. Salomon. I(1986), Telecommunications and Travel Relationships, A Review, Transportation Research A,20(3):p(223-238).

5. Albertson, L. A. (1977) "Telecommunications as a travel substitute: Some psycho-logical, organizational, and social aspects". Journal of Communication 27(2), 32-43.

6. Viswanathan K and K G Goulias (2001), Travel behavior implications of information technology in Puget sound region, Transportation Research Record, 1752:p,157-165.

7. Hjorthol. R.J (2002), The relation between daily travel and use of home computer; Transportation Research A 36:437:452.

8. P L Mokhtarian, R.Kitamura & R M Pendyala (1991), Evaluation of Telecommuting as a trip reduction measure, working paper No:5.

9. Niles, J., Beyond Telecommuting (1994), A New Paradigm for the Effect of Telecommunications on Travel, Report DOE/ER-0626, U.S. Department of Energy, Offices of Energy Research and Scientific Computing .

10. Choo, S., Mokhtarian, P.L. and I. Salomon (2005). Does telecommuting reduce vehicle-miles traveled? An aggregate time series analysis for the US. Transportation, 32, 37-64.

11. Mannering, J.S., and Mokhtarian, P.L. (1995): Modeling the choice of telecommuting frequency in California: an exploratory analysis, Technological Forecasting and Social Change, 49.

12. Nilles, J.M. (1991), Telecommuting and urban sprawl: mitigator or inciter? Transportation, 18: 411-432.

13. Henderson, D.K., and Mokhtarian, P.L (1996), Impacts of center-based telecommuting on travel and emissions: analysis of the Puget Sound Demonstration Project, Transportation Research, part D, 1

20. 21. 22.

26. 27.

Mokhtarian, P.L ,Henderson, D.K., and Koenig, B.E. (1996),Using travel diary data to estimate the emissions impacts of transportation strategies: the Puget Sound Telecommuting Demonstration Project, Journal of the Air and Waste Management Association, 46

Varma, K.V., and Mokhtarian, P.L. (1998),The tradeoff between trips and distance traveled in analyzing the emissions impacts of center-based telecommuting, Transportation Research, part D, 3

Kitou, E. and Horvath, A. (2006). "Transportation Choices and Air Pollution Effects of Tele-work." J. Infrastruct. Syst., 12(2), 121-134.

Morten Falc,( 2012),Environmental impact of ICT on transport sector

Collantes, G.O., and Mokhtarian, P.L. (2003), Telecommuting and residential location relationships with commute distance traveled for State of California workers, Research Report UCD-ITS-RR-03-16. Available at www.its.ucdavis.edu/publications/2003/RR-03-16.

De Graaff T, (2004), On the substitution between tele-work and travel: a review and application, Research memorandum ,16,free university Amsterdam

Thomas De Graff & Piet Rietveld ,(2007), Substitution between working at home and out-of-home: The role of ICT and commuting costs, Transportation research Part A 41;142-160

Pratt, J., Tele-workers (2002),Trips and Telecommunications: Technology Drives Telework - But Does It Reduce Trips? Transportation Research Record, 1817: pp. 58 - 66

Wells, K., et al.(2001), Telecommuting Implications for Travel Behavior: Case Studies from Minnesota. Transportation Research Record, 1752: p. 148-156.

Hjorthol, R., and Gripsrud, M.(2009), Home as a communication hub: the domestic use of ICT, Journal of Transport Geography 17 , 115-123

Prasad Vana, Chadra Bhat and P L Mokhtarian (2006), On modeling the choices Work hour arrangement, location and frequency of telecommuting, Institution of Transport studies, Research Report, UCD-ITS-RR-08-48.

Ory, D.T., and Mokhtarian, P.L. (2005), Don't work, work at home, or commute? Discrete choice models of the decision for San Francisco Bay Area residents, Research Report UCD-ITS-RR-05-05. Available at http://pubs.its.ucdavis.edu/publication_detail Haddad, H., Lyons, G., and Chatterjee, K.(2009), An examination of determinants influencing the desire for and frequency of part-day and whole-day homeworking, Journal of Transport Geography 17 , 124-133

Mannering, J.S., and Mokhtarian, P.L.(1995), Modeling the choice of telecommuting frequency in California: an exploratory analysis;. Technological Forecasting and Social Change, 49, 49-73