Activity participation, episode duration and stop-making behavior of pilgrims in a religious event: An exploratory analysis

Verma, Ashish; Verma, Meghna; Sarangi, Punyabeet; Yadav, Vivek; Manoj, M.

doi:10.1016/j.jocm.2021.100267

Cited by 10 publications

(5 citation statements)

References 61 publications

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“…Previous studies have discussed the variables contributing to the likelihood of stop making within a tour [11] [14] [15] [16] [18] and some of them such as [21] have discussed how these variables are associated with stops with different purposes across a tour. Some of the variables contributing to stop making on tours include personal and household demographic variables [9] [15] [16] [17] [22] [23] [24]; environmental or land use related variables [9] [10] [15] [22] [25] [26]; activity related variables such as arrival/departure time; activity duration [11] [16] [22]; transportation related attributes such as mode choice [10] [11] [12] [16] [22], and travel time or distance [10] [11] [12] [25] [26].…”

Section: Stop Frequency Literaturementioning

confidence: 99%

Accounting for Heterogeneity in Stop Frequency Models of Work Tours Using Latent Class Poisson Models

Mirzazadeh¹

2023

JTTs

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Stop frequency models, as one of the elements of activity based models, represent an important part of travel behavior. Unobserved heterogeneity across the travelers should be taken into consideration to prevent biasedness and inconsistency in the estimated parameters in the stop frequency models. Additionally, previous studies on the stop frequency have mostly been done in larger metropolitan areas and less attention has been paid to the areas with less population. This study addresses these gaps by using 2012 travel data from a medium sized U.S. urban area using the work tour for the case study. Stop in the work tour were classified into three groups of outbound leg, work based subtour, and inbound leg of the commutes. Latent Class Poisson Regression Models were used to analyze the data. The results indicate the presence of heterogeneity across the commuters. Using latent class models significantly improves the predictive power of the models compared to regular one class Poisson regression models. In contrast to one class Poisson models, gender becomes insignificant in predicting the number of tours when unobserved heterogeneity is accounted for. The commuters are associated with increased stops on their work based subtour when the employment density of service-related occupations increases in their work zone, but employment density of retail employment does not significantly contribute to the stop making likelihood of the commuters. Additionally, an increase in the number of work tours was associated with fewer stops on the inbound leg of the commute. The results of this study suggest the consideration of unobserved heterogeneity in the stop frequency models and help transportation agencies and policy makers make better inferences from such models.

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Section: Stop Frequency Literaturementioning

confidence: 99%

Accounting for Heterogeneity in Stop Frequency Models of Work Tours Using Latent Class Poisson Models

Mirzazadeh¹

2023

JTTs

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“…This section tries to break down CO 2 emissions as effects of multiple spatiotemporal variables. Multiple spatiotemporal variables can explain the variability of day-to-day travel distance and travel time (Hägerstrand, 1970;Van Acker et al, 2010;Ellegård and Svedin, 2012;Dharmowijoyo et al, 2016Dharmowijoyo et al, , 2018Verma et al, 2021). which correspond with individuals' daily CO 2 production.…”

Section: The Variability Effect Of Socio-demographic Activity-travel ...mentioning

confidence: 99%

“…This finding corroborates the existing body of literature that highlighted higher traveling intensity among households with larger members (Kang andScott, 2010, Liu et al, 2018;Dharmowijoyo et al, 2021a). In developing country case, women and senior citizens are often associated with non-workers with much lower travel production (Dharmowijoyo et al, 2017(Dharmowijoyo et al, , 2018Manoj and Verma, 2016;Liu et al, 2018;Verma et al, 2021). Figure 6 shows that travelers taking more than 80% of private vehicle use per day tend to emit the highest CO 2 emissions per person per day.…”

Section: The Variability Effect Of Socio-demographic Activity-travel ...mentioning

confidence: 99%

The spatiotemporal variable effects of individuals’ CO2 emission in Bandung Metropolitan Areas

Dharmowijoyo

Maharani²

2022

Journal of Infrastructure &Amp; Facility Asset Management

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Many investigations have been using disaggregated measurements to estimate individuals' CO 2 from road passenger transport, especially in developed countries. Nevertheless, similar investigation in developing countries emphasized their focus on aggregate measurements and system dynamics. There is a lack of disaggregated measurement in developing countries, particularly in Indonesia, and how the effects of spatiotemporal variables such as socio-demographic, travel parameters, and activity pattern variables correspond with CO 2 estimations. Since Indonesia is dominated by motorcycle users, this study will show motorcycles' contribution to CO 2 emissions. The results of 2.23 kg-CO 2 per day of individuals' CO 2 emissions in Indonesia is quite low in comparison to results in some developed countries such as the Netherlands and Sweden. It is presumably because around 86.05% of individuals in the observations used private motorcycles to travel and a low number of the undertaken trip in the BMA (Bandung Metropolitan Area). Around 91.39% of CO 2 emissions were emitted by individuals who take 80% of using private motorcycles and cars. This study shows that the disaggregated modeling on estimating CO 2 emissions might be able to reveal which individuals can be targeted to reduce their CO 2 emissions and what can be done to help government policy in reducing CO2. Female part-timer workers, female non-workers, and senior citizens are those who show the lowest contributions to CO 2 , and providing acknowledgments might make them keep such achievements. Whose daily travel time is below 106 and 125 minutes might be clustered as the targeted group of individuals that can reduce their CO 2 production. Incentive schemes such as providing internet vouchers or vouchers to use ride-hailing might help to change their habits to shift some of their trips by taking non-motorized mode, public transport, and/or ride-hailing services. Keeping public amenities at a farther distance might reduce people increase trips and travels by using motorized mode. But increasing the distance might make effects social exclusion, in turn, social health.

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“…In the previous studies, most of the explanatory variables used in tour complexity or stop frequency modeling are classified into personal and household demographic variables (Chu, 2003(Chu, , 2005Bhat & Singh, 2000;Chowdhury & Scott, 2018;Schneider et al, 2021;Verma, Verma, Sarangi, Yadav, & M, 2021); environmental or land use related variables (Chu, 2003(Chu, , 2005(Chu, , 2022Chowdhury & Scott, 2018;Zhu & Guo, 2022); activity related variables such as ar-rival/departure time; activity duration (Chu, 2003(Chu, , 2005Bhat & Singh, 2000;Chowdhury & Scott, 2018;Xianyu, 2013); transportation related attributes such as mode choice (Chu, 2003;Chowdhury & Scott, 2018;Xianyu, 2013;Liu, 2013), and travel time or distance (Bhat & Singh, 2000;Xianyu, 2013;Liu, 2013;Zhu & Guo, 2022;Chu, 2022).…”

Section: Introductionmentioning

confidence: 99%

The Impact of Socio-Economic, Land Use, and Travel Related Variables on Escort and Non-Escort Intermediate Stops on Work Tours

Mirzazadeh¹

2023

CUS

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Stop frequency prediction model is one of the components of the activitybased travel demand models. Most of the previous studies have considered stops during commutes regardless of their purposes. This approach does not yield the contribution of the explanatory variables to the likelihood of making stops of different purposes. Besides, most of the former studies have been conducted in larger metropolitan areas. This study attempts to cover these gaps by using 2012 travel data of Fargo-Moorhead medium-sized US metropolitan area and classifying stops on work tours into escort, non-escort, and a combination of all stops. The results of logit models indicate that personal characteristics of the commuters do not contribute to the escort stop participation likelihood. In addition, household size variables have a large impact on the likelihood of participating in escort stops and participating in the combined stops on the outbound leg of the commutes. Contrary to several previous studies, the significance and sign of the coefficient of income level vary for different stop purposes. Commuters seemed to be more likely to make more than one non-escort stop close to their workplace on the outbound legs of their commutes. The general results suggest separating the stop purposes yields more illustrative results rather than using one model for the combined stops.

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Activity participation, episode duration and stop-making behavior of pilgrims in a religious event: An exploratory analysis

Cited by 10 publications

References 61 publications

Accounting for Heterogeneity in Stop Frequency Models of Work Tours Using Latent Class Poisson Models

Accounting for Heterogeneity in Stop Frequency Models of Work Tours Using Latent Class Poisson Models

The spatiotemporal variable effects of individuals’ CO2 emission in Bandung Metropolitan Areas

The Impact of Socio-Economic, Land Use, and Travel Related Variables on Escort and Non-Escort Intermediate Stops on Work Tours

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