Scaling GPS trajectories to match point traffic counts: A convex programming approach and Utah case study

Miller, Seth; Laan, Zachary Vander; Marković, Nikola

doi:10.1016/j.tre.2020.102105

Cited by 10 publications

(4 citation statements)

References 29 publications

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“…This study used a case study research design, which is defined by McCombes (2021) as a methodology to obtain specific, contextual, in-depth knowledge about a specific real-world subject through a thorough analysis of a specific issue, such as a person, group, place, or phenomenon. A case study research design enabled the researchers to collect data using a wide range of data collection methods such as in-depth interviews and focus group discussions (FGD) which improved our understanding of the problem (Miller, Vander Laan, & Marković, 2020).…”

Section: Methodsmentioning

confidence: 99%

The Influence of Culture on Child Rights Violations in Zimbabwe: A Case Study

Tshugulu,

Simbine,

Charamba

et al. 2023

SWORK

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The article explores child rights violations emanating from harmful cultural practices in Zimbabwe. A qualitative case study research design was used. The targeted population included people considered custodians of the Ndebele culture in the Nkayi district of Zimbabwe. Social workers familiar with Ndebele culture and working in the child protection field within the Nkayi district also participated in the study. In-depth interviews were used to collect data. The data were analysed using Creswell’s thematic analysis. Whilst culture undoubtedly provides some ecological resources for the realisation of child rights, findings highlight harmful cultural practices which violate children’s rights, and these are the focus of this article. Keywords: Child rights, culture, rights violations, social work, Ndebele, Zimbabwe

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Section: Methodsmentioning

confidence: 99%

The Influence of Culture on Child Rights Violations in Zimbabwe: A Case Study

Tshugulu,

Simbine,

Charamba

et al. 2023

SWORK

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“…Different methods have been introduced to infer population traffic flows from the observed data that capture a proportion of the population traffic. In our study, to facilitate the implementation of the IRL-F model, the controlled Least Absolute Deviation (cLAD) method [33] ). Note that in this paper, it is assumed that the difference in local capture rate is small so that the local capture rates do not deviate much from the median.…”

Section: Solution Processmentioning

confidence: 99%

Estimating Link Flows in Road Networks With Synthetic Trajectory Data Generation: Inverse Reinforcement Learning Approach

Zhong

Kim

Zheng

2023

IEEE Open J. Intell. Transp. Syst.

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While traffic volume data from loop detectors have been the common data source for link flow estimation, the detectors only cover a subset of links. These days, other data sources such as vehicle trajectory data collected from vehicle tracking sensors are also incorporated. However, trajectory data are often sparse in that the observed trajectories only represent a small subset of the whole population, where the exact route sampling rate is unknown and may vary over space and time. In this paper, we develop a method that leverage these two limited data sources to enhance link flow estimation. This study proposes a novel generative modelling framework, where we formulate a vehicle's link-to-link movements as a sequential decision-making problem using the Markov Decision Process framework. We propose an Inverse Reinforcement Learning-based method, based on which synthetic population vehicle trajectories can be generated to estimate link flows across the whole network. The proposed method ensures the generated population vehicle trajectories are consistent with the observed traffic volume and trajectory data. The proposed generative modelling framework is compared to two existing methods in a synthetic road network and validated in a real road network.

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“…Furthermore, the combination of smart supply chain management, smart logistics (Fazi et al. , 2020; Miller et al. , 2020; Yavas and Ozkan-Ozen, 2020) and blockchain technology (Yang et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Furthermore, the combination of smart supply chain management, smart logistics (Fazi et al, 2020;Miller et al, 2020;Yavas and Ozkan-Ozen, 2020) and blockchain technology (Yang et al, 2021;Agrawal et al, 2021;Cao et al, 2020;Wang et al, 2020;Wamba et al, 2020), have been proposed to have a synergy in the best possible operation of logistics and supply chain companies (Issaouia et al, 2019), addressing logistical issues, for instance, improving the precast delivery and identifying bottlenecks timely (Mora et al, 2021). In this context, Internet-based online platforms can play an important role, since they can provide flexible and elastic responses (Choi, 2020a, b;Xu et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

The dynamic relationship of cryptocurrencies with supply chain and logistics stocks – the impact of COVID-19

Daglis

2022

JES

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PurposeThe COVID-19 pandemic is known to have affected the logistics and supply chains; however, there is no adequate empirical evidence to prove in which way it has affected the relationship between the stocks related to this field with the corresponding cryptocurrencies. This paper aims to test the dynamic relationship of cryptocurrencies with supply chain and logistics stocks.Design/methodology/approachIn this paper, the author tests the causal and long-run relationship between logistics and supply chain stocks with the corresponding cryptocurrencies related to these fields, or those that are known to exhibit characteristics that can be utilized by these fields, testing also whether the COVID-19 pandemic affected this relationship. To do so, the author performs the variable-lag causality to test the causal relationship, and examines if this relationship changed due to COVID-19. The author then implements the multifractal detrended cross-correlation analysis to investigate the characteristics of a possible long-run relationship, testing also whether they changed due to COVID-19.FindingsThe results indicate that there is a positive long-run relationship between each logistics and supply chain stocks and the corresponding cryptocurrencies, before and also during COVID-19, but during COVID-19 this relationship becomes weaker, in most cases. Moreover, before COVID-19, the majority of the cases indicate a causal direction from cryptocurrencies to the stocks, while during COVID-19, the causal relationships decrease in multitude, and most cases unveil a causal direction from the stocks to cryptocurrencies.Originality/valueThe causal pattern changed during COVID-19, and the long-run relationship became weaker, showing a change in the dynamics in the relationship between logistics and supply chain stocks with cryptocurrencies.

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Scaling GPS trajectories to match point traffic counts: A convex programming approach and Utah case study

Cited by 10 publications

References 29 publications

The Influence of Culture on Child Rights Violations in Zimbabwe: A Case Study

The Influence of Culture on Child Rights Violations in Zimbabwe: A Case Study

Estimating Link Flows in Road Networks With Synthetic Trajectory Data Generation: Inverse Reinforcement Learning Approach

The dynamic relationship of cryptocurrencies with supply chain and logistics stocks – the impact of COVID-19

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