Automated evaluation of urban traffic congestion using bus as a probe

Carli, Raffaele; Dotoli, Mariagrazia; Angelico, Biagio; Vinciullo, Antonio

doi:10.1109/coase.2015.7294224

Cited by 22 publications

(16 citation statements)

References 20 publications

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“…Consistently with existing scientific definitions, Logistics 4.0 represents the logistical system that enables the sustainable satisfaction of individualized customer demands without increasing costs [5]. Logistics 4.0 changes the existing solutions already adopted in traditional logistics, and introduces new enabling technologies, such as the cyber-physical systems (CPSs), which allow us to realize the networking and automation of storage system transportation, and decentralized software control [2,3,5,6]. In other cases, the support of the Internet of Things (IoT) technology allows linking physical objects to enable real-time data visualization in order to automate the logistics flows [7-9] under either an uncertain or a given demand [10], and when considering different kind of materials [11].…”

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confidence: 91%

“…The term "Logistics 4.0" is focused on the specific applications of Industry 4.0 in the area of logistics. Consistently with existing scientific definitions, Logistics 4.0 represents the logistical system that enables the sustainable satisfaction of individualized customer demands without increasing costs [5]. Logistics 4.0 changes the existing solutions already adopted in traditional logistics, and introduces new enabling technologies, such as the cyber-physical systems (CPSs), which allow us to realize the networking and automation of storage system transportation, and decentralized software control [2,3,5,6].…”

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confidence: 98%

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A Maturity Model for Logistics 4.0: An Empirical Analysis and a Roadmap for Future Research

et al. 2019

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The adoption of Industry 4.0 technologies has become particularly important nowadays for companies in order to optimize their production processes and organizational structures. However, companies sometimes find it difficult to develop a strategic plan that innovates their current business model and develops an Industry 4.0 vision. To overcome the growing uncertainty and dissatisfaction in implementing Industry 4.0, new methods and tools that specifically address dedicated companies' areas, such as logistics, supply chain management, and manufacturing processes, were developed to provide guidance and support to align companies' business strategies and operations. In particular, this paper develops and presents the application of a maturity model for Logistics 4.0, focusing on the specific applications of Industry 4.0 in the area of logistics. To do so, extant maturity models, linked to the context of Industry 4.0 implementation in logistics processes, were examined in the main scientific research. Afterward, two companies have been investigated through a survey, built around three fundamental macro-aspects, named (i) the propensity of the company towards Industry 4.0 and Logistics 4.0, (ii) the current use of technologies in the logistics process, and (iii) the investments' level towards Industry 4.0 technologies for a Logistics 4.0 transition. By doing so, a maturity model for Logistics 4.0 emerged as the main result of our research, able to identify the level of maturity of companies in implementing the Industry 4.0 technologies in their logistics processes. Moreover, the model highlighted the strengths and weaknesses of the two investigated companies with respect to the transition towards Logistics 4.0. On the basis of the obtained results, a roadmap for enhancing the digitalization of logistics processes, according to the principles of the fourth industrial revolution, was finally proposed.Sustainability 2020, 12, 86 2 of 18 At the intersection of these three pillars lie organizational activities that have a positive effect on the environment and result both in long-term benefits and in an increased competitive ability. According to Strandhagen et al. [1], the sustainability development can be considered a crosscutting capability for individual companies, since it requires the entire company to change its way of thinking. Accordingly, sustainability is becoming an important element of business models that radically changes the way companies operate. Currently, there is an increasing attention toward the management of social and environmental impacts in business, which makes the integration with environmental, economic, and social issues an obvious choice to ensure the company's long-term stability and development.In addition, at a more operational level, it can be very important for companies to redesign their logistics processes in order to achieve specific sustainability targets [2]. On this point, Industry 4.0 technologies can effectively help to achieve the desired sustainability targets in the logistics ...

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A Maturity Model for Logistics 4.0: An Empirical Analysis and a Roadmap for Future Research

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“…On the highway, long-term cross-lane driving is extremely dangerous. However, in practice, it is found that, if two vehicles are driving in parallel when the driving distance is too close, the sampling points will gather and cover each other [35]. At this time, it is usually judged that this is a car driving on the lane line.…”

Section: Radar Signal Preprocessingmentioning

confidence: 99%

A Novel Density Peak Fuzzy Clustering Algorithm for Moving Vehicles Using Traffic Radar

et al. 2019

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The detection of adjacent vehicles in highway scenes has the problem of inaccurate clustering results. In order to solve this problem, this paper proposes a new clustering algorithm, namely Spindle-based Density Peak Fuzzy Clustering (SDPFC) algorithm. Its main feature is to use the density peak clustering algorithm to perform initial clustering to obtain the number of clusters and the cluster center of each cluster. The final clustering result is obtained by a fuzzy clustering algorithm based on the spindle update. The experimental data are the radar echo signal collected in the real highway scenes. Compared with the DBSCAN, FCM, and K-Means algorithms, the algorithm has higher clustering accuracy in certain scenes. The average clustering accuracy of SDPFC can reach more than 95%. It is also proved that the proposed algorithm has strong robustness in certain highway scenes.

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“…We also remark that our approach can be used as a pattern recognition tool (to compare current flow patterns with expected ones based on historical data or theoretical considerations) and as a time‐series algorithm (to provide short‐term traffic forecasts based on the traffic flow in previous time intervals). Finally, we remark that this paper is a revised version of the baseline methodology proposed in Reference , where the approach was preliminary tested. With respect to Reference , the traffic monitoring is here performed on the entire urban area and over a much larger time period and time slots.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, we remark that this paper is a revised version of the baseline methodology proposed in Reference , where the approach was preliminary tested. With respect to Reference , the traffic monitoring is here performed on the entire urban area and over a much larger time period and time slots. In addition, more significant traffic indices are now evaluated for suitably monitoring and analyzing the traffic congestion.…”

Section: Introductionmentioning

confidence: 99%

Monitoring traffic congestion in urban areas through probe vehicles: A case study analysis

Carli

Dotoli

2017

Internet Technology Letters

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We present a probe vehicles approach to evaluate traffic congestion in urban areas. The method is illustrated by a real case study in Bari, Italy. Global Positioning System (GPS) data generated by buses are transmitted via General Packet Radio Service (GPRS) to a control station, where they are used to analyze traffic conditions. Traffic indices are evaluated to provide an exhaustive view of roads congestion and implement suitable control strategies. Thus, traffic administrators can efficiently manage sustainable mobility in cities and people can acquire awareness on traffic and public transport performance.

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Automated evaluation of urban traffic congestion using bus as a probe

Cited by 22 publications

References 20 publications

A Maturity Model for Logistics 4.0: An Empirical Analysis and a Roadmap for Future Research

A Maturity Model for Logistics 4.0: An Empirical Analysis and a Roadmap for Future Research

A Novel Density Peak Fuzzy Clustering Algorithm for Moving Vehicles Using Traffic Radar

Monitoring traffic congestion in urban areas through probe vehicles: A case study analysis

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