Modeling Courier Vehicles’ Travel Behavior

Kim, Si-Jin; Park, Dongjoo; Kim, Seheon; Park, Hyeongjun

doi:10.3141/2410-08

Cited by 7 publications

(6 citation statements)

References 15 publications

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“…There are also significant security challenges. Customers often do not know the delivery executors, so the persons who presented themselves as executors committed many criminal acts (Kim et al, 2014).…”

Section: Fig 8 Reception Methods In Home Deliverymentioning

confidence: 99%

Home Delivery: A Framework for Structuring

2021

IJTTE

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Home delivery, as a flow of goods between the company and the consumer (B2C) at the end of the supply chain ("last mile"), can be realized by the manufacturer, retailer or third party (logistics provider, crowd-worker). The growth of home delivery services in recent decades has been predominantly stimulated by the development of online ordering, i.e. e-commerce. Delivery characteristics depend on the characteristics of the environment and a number of factors related to stakeholders (customers, companies, governments). In order to improve the quality of service and customer satisfaction, company profitability and (or) reduce the negative effects of deliveries on the environment, area and community functionality, various delivery models are designed and implemented in terms of need for ordering, frequency, ordering and payment system, starting and end points, executors, reception method, security, delivery area, speed and time of realization, return flows, etc. This paper proposes a framework for a comprehensive structuring and classification of home delivery models according to the stated characteristics/criteria. Also, the advantages, disadvantages, interdependence and applicability of different delivery models in changing circumstances are described. In this way, a comprehensive review of home delivery flows was performed, the literature that deals with this area in a similar way was supplemented, but the basis for future research was also created.

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Section: Fig 8 Reception Methods In Home Deliverymentioning

confidence: 99%

Home Delivery: A Framework for Structuring

2021

IJTTE

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“…The truck movements in urban areas have been previously modeled by traditional four-step, commodity-based, and tour-based approaches [1]. Recently, many studies have adopted the tour-based approach due to its ability to explicitly represent trips and trip chaining [28][29][30][31]. The present study utilizes the tour-based approach in modeling truck movements.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed framework incorporates three models to capture collaboration and non-collaboration logistics behaviors: (i) p-regions problem [24], which is applied for modeling the major company's regional allocation. This allocates the delivery regions before assigning trucks, (ii) the vehicle routing problem (VRP) [25][26][27], that utilizes the truck routings within allocated regions, (iii) next destination-choice and trip-purpose models [28], that capture the minor company's truck routing without regional allocations. The first two models utilize the major logistics company model that represents logistics collaboration, and the third model explains the minor logistics company model under non-collaboration logistics.…”

Section: Introductionmentioning

confidence: 99%

An Effects Analysis of Logistics Collaboration: The Case of Pharmaceutical Supplies in Seoul

Park

Chung

et al. 2019

Sustainability

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This paper estimates the environmental, social and financial effects of logistics collaboration of the existing logistics companies in Seoul, Korea. The truck routing models for collaborative and non-collaborative deliveries are proposed to estimate the collaboration effects. Findings show that both major and minor companies can benefit from logistics collaboration by saving delivery costs and time through economies of scale. The results from the study further indicate that logistics collaboration can mitigate negative environmental impacts resulting from urban logistics by reducing the number of delivery trucks, and shortening delivery times and travel distances. Discussion of related challenges that must be addressed during the implementation of logistic collaboration is included as well.

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“…For instance, Agarwal [27] suggests that there is a decrease in vehicle trips on weekends compared to weekdays at the household level. Kim et al [28] find different weekend and weekday bike-sharing patterns. Their results point out that there is an increase in bike-sharing traffic volume on the weekends at the stations near parks and schools, which can be due to the rise in leisure and school activities on the weekends.…”

Section: Introductionmentioning

confidence: 99%

“…In general, weekday travel is primarily made up of commuting to and from work, whereas weekend travel behavior is motivated by recreational activities. Differences in activity types can lead to different travel patterns that can be hypothesized on weekends and holidays, compared to weekdays [22,27,28]. For instance, Agarwal [27] suggests that there is a decrease in vehicle trips on weekends compared to weekdays at the household level.…”

Section: Introductionmentioning

confidence: 99%

When crisis hits: Bike-Sharing platforms amid the Covid-19 pandemic

et al. 2023

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In this work, we examine the changes in demand for bike-sharing platforms with the onset of the Covid-19 pandemic. Using the fixed-effects regression formulation of difference-in-differences, we evaluate how the demand for bike-sharing platforms changed after the first cases of Covid were discovered and after the first executive orders were implemented. Accounting for weather conditions, socio-economic characteristics, time trends, and fixed effects across cities, our findings indicate that there is an increase in daily bike-sharing trips by 22% on average after the first Covid-19 case diagnosis, and a decrease of 30% after the first executive order implementation in each municipality, using the data up to August 2020. Moreover, we observe a 22% increase in weekday-specific trip frequency after the first Covid-19 case diagnosis and a 28% decrease in weekend-specific trip frequency after the first executive order implementation. Finally, we find that there is an increase in the frequency of trips on bike-sharing platforms in more bike-friendly, transit-friendly, and pedestrian-friendly cities upon both the first Covid-19 case diagnosis and the first executive order implementation.

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Modeling Courier Vehicles’ Travel Behavior

Cited by 7 publications

References 15 publications

Home Delivery: A Framework for Structuring

Home Delivery: A Framework for Structuring

An Effects Analysis of Logistics Collaboration: The Case of Pharmaceutical Supplies in Seoul

When crisis hits: Bike-Sharing platforms amid the Covid-19 pandemic

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