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Register a new userSelectivity correction in discrete-continuous models for the willingness to work as crowd-shippers and travel time tolerance
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The objective of this study is to understand the different behavioral considerations that govern the choice of people to engage in a crowd-shipping market. Using novel data collected by the researchers in the US, we develop discrete-continuous models. A binary logit model has been used to estimate crowd-shippers willingness to work, and an ordinary least-square regression model has been employed to calculate crowd-shippers maximum tolerance for shipping and delivery times. A selectivity-bias term has been included in the model to correct for the conditional relationships of the crowd-shippers willingness to work and their maximum travel time tolerance. The results show socio-demographic characteristics (e.g. age, gender, race, income, and education level), transporting freight experience, and number of social media usages significant influence the decision to participate in the crowd-shipping market. In addition, crowd-shippers pay expectations were found to be reasonable and concurrent with the literature on value-of-time. Findings from this research are helpful for crowd-shipping companies to identify and attract potential shippers. In addition, an understanding of crowd-shippers - their behaviors, perceptions, demographics, pay expectations, and in which contexts they are willing to divert from their route - are valuable to the development of business strategies such as matching criteria and compensation schemes for driver-partners.
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Public transit disruption is becoming more common across different transit services, which can have a destructive influence on the resiliency and reliability of the transportation system. Utilizing a recently collected data of transit users in the Chicago Metropolitan Area, the current study aims to analyze how transit users respond to unplanned service disruption and disclose the factors that affect their behavior.
A majority portion of the slum people is involved in service sectors. The city dwellers are somehow dependent on the services of those people. Pure drinking water and hygiene is a significant concern in the slums. Because of the lack of these two items, the slum people are getting sick, which causes the interruption to their services. In addition, they can transmit the diseases they suffer from to the service receiver. With these aims, this study endeavors to explore the willingness to pay of the households who receive the services of the slum people using the mixed-method techniques. Under this technique, 265 households were surveyed through face-to-face interviews, and 10 KIIs were conducted with slum people. The studys findings suggest that the households showed their willingness to pay for the improvement of the water and sanitation facilities in the slums. However, the KIIs findings show that the slum people are not willing to pay for the improvement as they claim that government should finance the project of improving water and sanitation facilities in the slums.
Flextime is one of the efficient approaches in travel demand management to reduce peak hour congestion and encourage social distancing in epidemic prevention. Previous literature has developed bi-level models of the work starting time choice considering both labor output and urban mobility. Yet, most analytical studies assume the single trip purpose in peak hours (to work) only and do not consider the household travels (daycare drop-off/pick-up). In fact, as one of the main reasons to adopt flextime, household travel plays an influential role in travelers decision making on work schedule selection. On this account, we incorporate household travels into the work starting time choice model in this study. Both short-run travel behaviours and long-run work start time selection of heterogenous commuters are examined under agglomeration economies. If flextime is not flexible enough, commuters tend to agglomerate in work schedule choice at long-run equilibrium. Further, we analyze optimal schedule choices with two system performance indicators. For total commuting cost, it is found that the rigid school schedule for households may impede the benefits of flextime in commuting cost saving. In terms of total net benefit, while work schedule agglomeration of all commuters leads to the maximum in some cases, the polarized agglomeration of the two heterogenous groups can never achieve the optimum.
The explosive nature of Covid-19 transmission drastically altered the rhythm of daily life by forcing billions of people to stay at their homes. A critical challenge facing transportation planners is to identify the type and the extent of changes in peoples activity-travel behavior in the post-pandemic world. In this study, we investigated the travel behavior evolution by analyzing a longitudinal two-wave panel survey data conducted in the United States from April 2020 to October 2020 (wave 1) and from November 2020 to May 2021(wave 2). Encompassing nearly 3,000 respondents across different states, we explored pandemic-induced changes and underlying reasons in four major categories of telecommute/telemedicine, commute mode choice, online shopping, and air travel. Upon concrete evidence, our findings substantiate significantly observed and expected changes in habits and preferences. According to results, nearly half of employees anticipate having the alternative to telecommute and among which 71% expect to work from home at least twice a week after the pandemic. In the post-pandemic period, auto and transit commuters are expected to be 9% and 31% less than pre-pandemic, respectively. A considerable rise in hybrid work and grocery/non-grocery online shopping is expected. Moreover, 41% of pre-covid business travelers expect to have fewer flights (after the pandemic) while only 8% anticipate more, compared to the pre-pandemic. Upon our analyses, we discuss a spectrum of policy implications in all mentioned areas.
Due to the unavailability of nationally representative data on time use, a systematic analysis of the gender gap in unpaid household and care work has not been undertaken in the context of India. The present paper, using the recent Time Use Survey (2019) data, examines the socioeconomic and demographic factors associated with variation in time spent on unpaid household and care work among men and women. It analyses how much of the gender gap in the time allocated to unpaid work can be explained by differences in these factors. The findings show that women spend much higher time compared to men in unpaid household and care work. The decomposition results reveal that differences in socioeconomic and demographic factors between men and women do not explain most of the gender gap in unpaid household work. Our results indicate that unobserved gender norms and practices most crucially govern the allocation of unpaid work within Indian households.
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