Crowdsourcing-based traffic simulation for smart freight mobility

Chandra, Shailesh; Naik, Ramakanta; Jimenez, Jose

doi:10.1016/j.simpat.2019.04.004

Cited by 17 publications

(11 citation statements)

References 15 publications

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“…Chandra et al [3] pointed out that the resources of the crowd, including people, objects, and entities, provided more possibilities to solve delivery problems arising from the surge of individual packages and delivery vehicles. In this context, cyclists and pedestrians become temporary deliverers due to their own delivery needs [6], and most of them can switch between the roles of the deliverer and recipient.…”

Section: Related Workmentioning

confidence: 99%

“…Giret et al [6] used multiagent system techniques and complex network-based algorithms to design an intelligent crowd-based approach to sustainable last mile delivery, and the test showed that the approach significantly reduced CO 2 emissions and the use of trucks. Similarly, Chandra et al [3] developed a simulation framework by leveraging crowdsourced big data to improve truck mobility and realize a "smart freight" solution by avoiding downstream congestion in the delivery route.…”

Section: Related Workmentioning

confidence: 99%

“…Crowdsourcing delivery, as a successful combination of traditional logistics activities and innovative crowd resources sharing, is becoming a prevalent tool in practice. More possibilities for solving delivery problems have been provided by using the resources of the crowds [3] including maximizing crowds' utility [4], reducing infrastructure expenses, enhancing efficiency in parcel turnaround time and failure rate [5], and optimizing loading capacity [6], as well as reducing traffic congestions and pollution using spare capacity [7,8].…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian Best-Worst Method-Based Multicriteria Competence Analysis of Crowdsourcing Delivery Personnel

Wang

Rezaei

2020

Complexity

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Crowdsourcing delivery is becoming a prevalent tool for tackling delivery problems by building a large labor-intensive service network. In this network, the delivery personnel consist of a large number of people with a complex composition and high level of mobility, creating enormous challenges for the quality of service and the management of a crowdsourcing platform. Hence, we attempt to conduct a competence analysis to determine whether they can provide promised services with high quality, i.e., they are competent for their job. To this end, the competence theory is introduced, and a multicriteria competence analysis (MCCA) approach is developed. To illustrate the MCCA approach, a real-world case study is conducted involving a Chinese takeaway delivery platform, where the Bayesian best-worst method is used to determine the weights of the criteria based on the data collected from managers of the platform company. Also, the competence scores of the personnel involved are collected through surveys and data sources of the company. Given the weights and the competence scores, we use additive value function to identify the overall competence scores of them, which reflects the level of competence for their job. The results show that Skills is the most important competence, while Knowledge is the least important of the four competence dimensions. In subcriteria, four core elements are identified such as punctuality, customer service awareness, responsible, and goods intact. In addition to the importance of criteria, a ranking of a sample of personnel is provided, and almost half of the crowdsourcing delivery personnel’s competence is below the average and vary significantly, while the relationship between the competence level and some other variables is also discussed. Moreover, the developed MCCA approach in this paper can be applied to analyze the competence of personnel in many other industries as well.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Bayesian Best-Worst Method-Based Multicriteria Competence Analysis of Crowdsourcing Delivery Personnel

Wang

Rezaei

2020

Complexity

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“…Since the implementation of the smart city policy, it has penetrated all aspects of human production and life, such as smart medical care [28] and intelligent transportation [29]. In the process of building a smart city, various types of smart sensors have been embedded in public water, electricity, oil, gas, buildings, transportation and other public service resources to form the Internet of Things.…”

Section: Theoretical Foundationmentioning

confidence: 99%

Effects of Smart City Policies on Green Total Factor Productivity: Evidence from a Quasi-Natural Experiment in China

Xin

2019

IJERPH

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When cities develop rapidly, there are negative effects such as population expansion, traffic congestion, resource shortages, and pollution. It has become essential to explore new types of urban development patterns, and thus, the concept of the “smart city” has emerged. The purpose of this paper is to investigate the links between smart city policies and urban green total factor productivity (GTFP) in the context of China. Based on panel data of 200 cities in China from 2007–2016 and treating smart city policy as a quasi-natural experiment, the paper uses a difference-in-differences propensity score matching (PSM-DID) approach to prevent selection bias. The results show: (a) Smart city policies can significantly increase urban GTFP by 16% to 18%; (b) the larger the city, the stronger and more significant this promotion.

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“…This research was conducted with the average values, which constitute a limitation for this research. In the future, mobility data could be enhanced using crowdsourcing, including social media and mobile applications [19,20]. Most data were obtained in shapefile format in the shape of polygon or line, which includes attributes labeled in different fields (Table 2).…”

Section: Data Collectionmentioning

confidence: 99%

Impact of the Mobility Restrictions in the Palestinian Territory on the Population and the Environment

Aburas

Shahrour

2021

Sustainability

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This paper analyzes the mobility restrictions in the Palestinian territory on the population and the environment. The literature review shows a scientific concern for this issue, with an emphasis on describing mobility barriers and the severe conditions experienced by the population due to these barriers as well as the impact of mobility restrictions on employment opportunities. On the other hand, the literature review also shows a deficit in quantitative analysis of the effects of mobility restrictions on the environment, particularly on energy consumption and greenhouse gas emissions. This paper aims to fill this gap through a quantitative analysis by including data collection about mobility restrictions, using network analysis to determine the impact of these restrictions on inter-urban mobility, and analysis of the resulting energy consumption and CO2 emissions. The results show that mobility restrictions induce a general increase in energy consumption and CO2 emissions. The average value of this increase is about 358% for diesel vehicles and 275% for gasoline vehicles.

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Crowdsourcing-based traffic simulation for smart freight mobility

Cited by 17 publications

References 15 publications

A Bayesian Best-Worst Method-Based Multicriteria Competence Analysis of Crowdsourcing Delivery Personnel

A Bayesian Best-Worst Method-Based Multicriteria Competence Analysis of Crowdsourcing Delivery Personnel

Effects of Smart City Policies on Green Total Factor Productivity: Evidence from a Quasi-Natural Experiment in China

Impact of the Mobility Restrictions in the Palestinian Territory on the Population and the Environment

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