Overseas trade vis-a-vis overseas shipping: Growth and performance in India (1999–2009)

Maritime trade has been and even continues to account for about a lion's share of India's total cargo volumes. Despite the growth of multimodal transport (by land, water and air), shipping still continues to be the major mode of transport in the bulk carriage of country's overseas trade. In view of this vital role of shipping, in the first four decades of independence, under the initiative of planned development and active government support, India's shipping and port sector saw dramatic growth in their performance to build adequate national fleet, in keeping up with the transport of overseas cargo. However, the onset of economic liberalization in 1991 has given rise to many new dimensions in the development of the shipping and port sector of the country with a significant redefinition of shipping and port services, in response to the new global trend patterns. For instance, it has also established the new era of containerization in the mode of cargo delivery from the dominance of the era of bulk and break-bulk trade during the decade of sixties and seventies. Moreover, as global competition increases, in response to this emerging trade patterns within this country, India's volume of traffic growth also increases manifold. So, India's shipping and port sectors need, significantly, to build up and furnish their capacity by increasing the frequency of this mode of transport i.e. the growth of the national overseas fleet to meet this surging demand. This paper, therefore, have focused on this role of shipping in such rising overseas trade, with a view to examine the shipping performance (the growth of overseas fleet) in response to the growing overseas trade at all ports of India during the period (1999–2000 to 2008–2009), in terms of both a mathematical model and a graphical representation. Finally, it concludes that the absolute overseas trade, being highly import dependent, have led to a more or less stagnant performance in overseas shipping, owing to the lack of the adequate growth of absolute overseas exports during this period.     

Correcting for endogeneity in behavioral choice models with social influence variables

While psychologists and behavioral economists emphasize the importance of social influences, an outstanding issue is how to capture such influences in behavioral models used to inform urban planning and policy. In this paper we focus on operational models that do not require explicit knowledge of the individual networks of decision makers. We employ a field effect variable to capture social influences, which is calculated as the percent of population in the peer group that has chosen the specific alternative. We define the peer group based on socio-economic status and spatial proximity of residential location. As in behavioral economics and psychology, the concept is that one is influenced by the choices made by one’s peers. However, using such a social influence variable in a behavioral model causes complications because it is likely endogenous; unobserved factors that impact the peer group also influence the decision maker, yielding correlation between the field effect variable and the error. The contribution of this paper is the use of the Berry, Levinsohn, and Pakes (BLP) method to correct the endogeneity in a choice model. The two-stage BLP introduces constants for each peer group to remove the endogeneity from the choice model (where it is difficult to deal with) and insert it into a linear regression model (where endogeneity is relatively easier to deal with). We test the method using a mode choice data set from the Netherlands and readily available software and find there is an upward bias of the field effect parameter when endogeneity is not corrected. The procedure outlined presents a practical and tractable method for incorporating social influences in choice models.     

Grey-Fuzzy Taguchi approach for multi-objective optimization of performance and emission parameters of a single cylinder crdi engine coupled with EGR

The present study attempts to address the challenges of the multiobjective optimization problem of the BSFC-NOx-PM trade-off paradox of an existing diesel engine by harnessing the synergetic benefit of PM and BSFC reduction through CRDI operation and simultaneous NOx reduction by EGR application. Load, FIP and EGR were chosen as the input parameters while NOx, PM and BSFC were the response variables. In order to reduce the experimental effort, the Taguchi L₁₆ orthogonal array technique was employed to obtain the corresponding values of the response variables. The grey relational analysis coupled with fuzzy logic has been employed as the optimization routine. The optimal combination of the input parameters corresponding to the calibrated values of the response variables were obtained by employing the Grey-Fuzzy Grade and S-N ratio strategy as a performance index. The computed optimal combination so obtained were further validated through actual experimentation. EGR was found to be the most influencing factor in the present optimization endeavour. The study also established that the Grey-Fuzzy-Taguchi method was not only comparable but superior to the Grey-Taguchi method usually employed for such optimization studies.     

Understanding Travel Time Expenditures Around the World: Exploring the Notion of a Travel Time Frontier

Travel behavior researchers have been intrigued by the amount of time that people allocate to travel in a day, i.e., the daily travel time expenditure, commonly referred to as a “travel time budget”. Explorations into the notion of a travel time budget have once again resurfaced in the context of activity-based and time use research in travel behavior modeling. This paper revisits the issue by developing the notion of a travel time frontier (TTF) that is distinct from the actual travel time expenditure or budget of an individual. The TTF is defined in this paper as an intrinsic maximum amount of time that people are willing to allocate for travel. It is treated as an unobserved frontier that influences the actual travel time expenditure measured in travel surveys. Using travel survey datasets from around the world (i.e., US, Switzerland and India), this paper sheds new light on daily travel time expenditures by modeling the unobserved TTF and comparing these frontiers across international contexts. The stochastic frontier modeling methodology is employed to model the unobserved TTF as a production frontier. Separate models are estimated for commuter and non-commuter samples to recognize the differing constraints between these market segments. Comparisons across the international contexts show considerable differences in average unobserved TTF values.