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Journal Articles | 2018

Geographic mobility of recent immigrants and urban transit demand in the U.S.: New evidence and planning implications

Sandip Chakrabarti and Gary Painter

Transportation Research Part A: Policy and Practice

Residential mobility rates in the U.S. have been in steady decline. Most notably, between 2005 and 2013, one-year intercity migration rate for immigrants has decreased by 0.7 percentage-points, compared to a 0.2 percentage-point decline for the U.S.-born population. Literature on urban implications of geographic mobility suggests that consideration of migration trends, or population flows, can improve urban planning, including transportation. Our research focuses on recent immigrants, a group that significantly contributes to public transit ridership in the U.S. In this study, we analyze the influence of the annual average in-migration rate of recent immigrants to various urban areas from within the country on transit ridership changes across the urban areas between 2008 and 2013. We also compare this effect with the effect of annual average in-migration rate of new immigrants to various urban areas from foreign countries. While the average effect of inflow of new foreign migrants on transit demand is suggested in the literature, distinguishing the transit demand of immigrants that are not movers and those that are movers from various locations remains unexplored.

We derive migration flows from the American Community Survey microdata, and transit ridership from the U.S. National Transit Database. We perform geospatial analysis to overcome several constraints that make exploration of the migration-transportation connection difficult, particularly the lack of uniformity in geographic boundaries used for data presentation across and within government agencies, and over time.

Our results indicate that consideration of domestic in-migration rates of recent immigrants can improve transit demand forecasting. As past literature has found, recent immigrants are highly likely to use transit. Recent immigrant migrants that have arrived directly from another country are even more likely to use transit. Interestingly, recent immigrants that move to a metropolitan area from another location in the U.S. are relatively less likely to use transit. Among domestic migrants, however, those that move to cities undergoing large increase in transit service relative to population are more likely to use transit. High population and transit stop density of both previous and current cities seem to positively affect transit mode choice for commute trips of recent immigrant movers. Declining inter-urban mobility among recent immigrants can indeed alter future transit demand trends. Transit agencies should not treat recent immigrants as a monolithic group. Consideration of the migration patterns of various types of recent immigrants, and factors that determine those patterns, can improve demand forecasting and planning.

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Journal Articles | 2018

The effect of parenthood on travel behavior: Evidence from the California Household Travel Survey

Sandip Chakrabarti and Kenneth Joh

Transportation Research Part A: Policy and Practice

Literature suggests that young children have a significant influence on activity patterns and time-use of adult men and women in dual-earner households. The resultant impact on travel behavior, however, remains largely unexplored. In this study, we use the 2012–13 California Household Travel Survey to compare daily (weekday) travel behavior of adult men and women belonging to dual-earner heterosexual couple households without children, with their adult counterparts in dual-earner heterosexual nuclear households with one or more young (aged 15 years or less) children living in urban California. We find that the presence of young children is, on average, associated with relatively higher auto use, and lower levels of physically active travel (i.e., walking and bicycling) and public transit use. Specifically, parents of school-age (6–15 years) children, without other small (5 years or less) children, are found to engage in significantly more auto use than childless couples. The likelihood of engaging in 20 min or more of active travel per day falls as couples transition to parenthood, and drops further as small children turn school-age. The likelihood of making at least one transit trip per day follows a similar pattern. We also find that the negative impact of young children on average, and school-age children in particular, on adults’ active travel is significantly greater for men than women. Additionally, we identify factors that can help reduce gender inequality in auto use and active travel within households with one or more young children. This study enhances our understanding of travel behavior variations across household types in cities, and over the life courses of individuals. Planning and policy implications are discussed.

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Journal Articles | 2018

'I show off, so I am well off': Subjective economic well-being and conspicuous consumption in an emerging economy

Saravana Jaikumar, Ramendra Singh, and Ankur Sarin

Journal of Business Research

Conspicuous consumption may be explained by the need to signal higher social status in a society. However, whether this consumption actually translates to improved perception of well-being remains unexamined. In the emerging economy context, we argue that conspicuous consumption may play the role of elevating one's own perception of economic well-being. Further we hypothesize the effect to be higher for the households in the ‘bottom of the pyramid’ (BOP). Using data from a panel of 34,621 households from India Human Development Surveys (2004 and 2011), we examine the relationship between conspicuous consumption and subjective economic well-being (SEWB) using several empirical strategies. Results support our hypotheses that higher conspicuous consumption may result in improved SEWB and that the effect is higher for households in the BOP. Our findings contribute to the domain of conspicuous consumption and BOP in emerging markets. Further, our results have significant marketing and policy implications.

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Journal Articles | 2018

India in 2 C and well below 2 C Worlds: Opportunities and challenges

Saritha S. Vishwanathan, Amit Garg, Vineet Tiwari, and P.R. Shukla

Carbon Management

India's contributions to meeting global mean temperature increases of 2 °C or well below 2 °C would require transformational changes in its energy systems. A bottom-up model analyzes alternate futures (reference, intended nationally determined contributions and low-carbon scenarios) assuming equal per-capita cumulative emissions rights from 2011 through 2050. The cumulative CO2 budget for India for low-carbon scenarios during this period is estimated to be around 115 Bt-CO2, as against 165 Bt-CO2 for the reference scenario. To achieve such emission reductions, while maintaining high economic growth and meeting sustainable development goals, the paper projects that a range of endemic transformations are required such as shifting toward cleaner fuels, resource efficient technologies, widespread use of Information and communication technology (ICTs) to balance demand and supply (e.g. smart grids), substituting demand in transport (e.g. work from home), aggressive promotion of renewables, lifestyle changes, and CO2 capture, storage and use. Modeling decarbonization to meet the needs of the increasing population and urbanization faces myriad challenges due to the distributed nature of technologies used to provide various services, involving risks and uncertainties. The paper finally outlines specific opportunities and challenges faced to meet the increased mitigation ambition to limit the warming to 2 °C and below.

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Journal Articles | 2018

Energy system transitions and macroeconomic assessment of the Indian building sector

Saritha S. Vishwanathan, Panagiotis Fragkos, Kostas Fragkiadakis, Leonidas Paroussos, and Amit Garg

Building Research & Information

India’s energy sector has grown rapidly in recent years with buildings playing a major role as they constitute about 40% of India’s final energy demand. This paper provides a quantitative model-based assessment of the evolution of India’s building sector in terms of both energy systems transition and its macroeconomic implications. The coupling of a bottom-up technology-rich energy system model with a macroeconomic computable general equilibrium (CGE) model provides an innovative approach for the in-depth robust analysis of the energy transition in India’s building stock and the induced macroeconomic and employment impacts on the Indian economy. Two main scenarios are explored, namely: the business-as-usual (BAU) and the advanced nationally determined contribution (Adv. NDC) scenarios. The investigation shows that efficiency improvements are vital to counteract the upward pressure on energy demand in the building sector. Energy demand in the building sector results in an increase of CO2 emissions by 27% between 2015 and 2030 due to the technology transition from inefficient solid fuels (traditional biomass) to cleaner energy (liquefied petroleum gas (LPG), piped natural gas (PNG)) before shifting to electricity. The Adv. NDC scenario also leads to a shift in employment from agriculture and towards sectors that benefit from the implementation of Adv. NDC, especially in the construction sectors, electricity and manufacturing sectors.

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Journal Articles | 2018

Proactive vs. reactive order-fulfillment resource allocation for sea-based logistics

Seyed Shahab Mofidi, Jennifer A. Pazour, and Debjit Roy

Transportation Research Part E: Logistics and Transportation Review

We study proactive and reactive sea-based order-fulfillment decisions for a set of SKUs. In such systems, a proactive strategy may be more costly than a reactive strategy and variable marginal costs change with respect to an activity profile. We derive the optimal sets of SKUs and their quantities to handle prior (proactive strategy) or after (reactive strategy) demand materializes. Counterintuitive results show the proactive set may not necessarily include the high-demanded SKUs. This work extends the newsvendor model by analyzing negative marginal shortage costs. The model is illustrated with historical data from a sea-based logistics military application.

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Journal Articles | 2018

Qasab: Kutch Craftswomen's Producer Co. Ltd.

Shweta Mittal, Vishal Gupta, and Manoj Motiani

Asian Case Research Journal

This case was prepared by Assistant Professor Shweta Mittal of Institute of Management & Research, Ghaziabad, India, Associate Professor Vishal Gupta of Indian Institute of Management Ahmedabad, India and Assistant Professor Manoj Motiani of Indian Institute of Management Indore, India, as a basis for classroom discussion rather than to illustrate either effective or ineffective handling of an administrative or business situation.

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Journal Articles | 2018

Informed trading around earnings announcements Spot, futures, or options?

Sobhesh Kumar Agarwalla, Jayanth R. Varma, and Ajay Pandey

Journal of Futures Markets

Recent literature reports higher single stock options (SSO) volume before earnings announcements (EA). There are no studies that explore single stock futures (SSF) in this context because of illiquid SSF markets in developed countries. Similar to SSO, SSF provide embedded leverage and facilitate short selling although at a lower cost, but do not provide downside-risk protection. India’s liquid SSO and SSF provide a unique setting to study the preference of informed traders. We observe an increase in both SSO and SSF volume before EA. Further, SSF dominate SSO possibly due to SSO becoming expensive before EA and higher information leakage in India.

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Journal Articles | 2018

Efficient mining of high utility itemsets with multiple minimum utility thresholds

Srikumar Krishnamoorthy

Engineering Applications of Artificial Intelligence

Mining high utility itemsets is considered to be one of the important and challenging problems in the data mining literature. The problem offers greater flexibility to a decision maker in using item utilities such as profits and margins to mine interesting and actionable patterns from databases. Most of the current works in the literature, however, apply a single minimum utility threshold value and fail to consider disparities in item characteristics. This paper proposes an efficient method (MHUI) to mine high utility itemsets with multiple minimum utility threshold values. The presented method generates high utility itemsets in a single phase without an expensive intermediate candidate generation process. It introduces the concept of suffix minimum utility and presents generalized pruning strategies for efficiently mining high utility itemsets. The performance of the algorithm is evaluated against the state-of-the-art methods (HUI-MMU-TE and HIMU-EUCP) on eight benchmark datasets. The experimental results show that the proposed method delivers two to three orders of magnitude execution time improvement over the HUI-MMU-TE method. In addition, MHUI delivers one to two orders of magnitude execution time improvement over the HIMU-EUCP method, especially on moderately long and dense benchmark datasets. The memory requirements of the proposed algorithm was also found to be significantly lower.

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Journal Articles | 2018

Efficiently mining high utility itemsets with negative unit profits

Srikumar Krishnamoorthy

Knowledge-Based Systems

A High Utility Itemset (HUI) mining is an important problem in the data mining literature that considers utilities of items (such as profits and margins) to discover interesting patterns from transactional databases. Several data structures, pruning strategies and algorithms have been proposed in the literature to efficiently mine high utility itemsets. Most of these works, however, do not consider itemsets with negative unit profits that provide greater flexibility to a decision maker to determine profitable itemsets. This paper aims to advance the state-of-the-art and presents a generalized high utility mining (GHUM) method that considers both positive and negative unit profits. The proposed method uses a simplified utility-list data structure for storing itemset information during the mining process. The paper also introduces a novel utility based anti-monotonic property to improve the performance of HUI mining. Furthermore, GHUM adapts key pruning strategies from the basic HUI mining literature and presents new pruning strategies to significantly improve the performance of mining. The proposed method is evaluated on a set of benchmark sparse and dense datasets and compared against a state-of-the-art method. Rigorous experimental evaluation is performed and implications of the key findings are also presented. In general, GHUM was found to deliver more than an order of magnitude improvement at a fraction of the memory over the state-of-the-art FHN method.

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