Towards a Sustainable City for Cyclists: Promoting Safety through a Mobile Sensing Application

Boronat, Pablo; Pérez-Francisco, Miguel; Calafate, Carlos T.; Cano, Juan-Carlos

doi:10.3390/s21062116

Cited by 8 publications

(11 citation statements)

References 20 publications

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“…Nevertheless the controller deals with this possibility in the following way: if the communications link towards a specific UAV is temporarily broken, the server estimates its current position based on the applicable future element in the positions array that was included in the last received message. It is also worth pointing out that, according to [25], tests on the 4G phone system were conducted, and the stability and reliability tests showed that such networks suit well the proposed real‐time reactive system. A similar treatment is given if a UAV loses contact with the GPS system.…”

Section: Resultsmentioning

confidence: 99%

“…Regarding the communications time, in [25] we measured the mean communication round trip time in real mobile scenarios, finding results close to 0.1 s. In these tests, messages were sent by vehicles to a server on the Internet through the 4G wireless infrastructure, and the server then sends back a confirmation message (round‐trip time).…”

Section: Resultsmentioning

confidence: 99%

“…It must be noted that, if a conflict is detected in the i component of the arrays, it will take place in about

(i \cdot t_{step}) - t_{com} - t_{proc}

seconds, where

t_{com}

is the communication time, and

t_{proc}

is the computation time. In the tests conducted in [25] under real conditions, the communication time through the 4G infrastructure was close to 50 ms, while the processing time was mostly load dependent, and different approaches to reduce this time are presented further in this paper. Also, the conflict distance depends on the maximum speed and on the

t_{step}

.…”

Section: Detection Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Assessing the limits of centralized unmanned aerial vehicle conflict management in U‐Space

Boronat,

Pérez‐Francisco,

Wubben

et al. 2023

IET Intelligent Trans Sys

Self Cite

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There is an important growth of unmanned aerial vehicles (UAVs) performing planned missions in urban environments, which poses significant challenges to the research community. The possibility of collisions represents a critical challenge. UAVs can suffer collisions due to different causes external or internal to their flight plans. In this context, dynamic geo‐fencing is a useful approach, whereby each UAV is able to provide a prediction of its future positions within a limited time. These predictions could be used to detect conflicts, allowing to dynamically modify the flight plans so as to avoid imminent collisions. In this work, a conflict detection algorithm/method is proposed, implemented and tested on a central server performing real‐time conflict analysis for a large number of UAVs flying in the aerial space of a city (U‐Space). The architecture assumes that UAVs send their future locations to a traffic controller. This controller compares the predicted positions of nearby vehicles to detect possible conflicts. The results of this work demonstrate the feasibility of the proposed conflict detection algorithm and its interest to improve the security and efficiency in U‐Space environments. The server is able to track thousands of UAVs in real time with a conflict anticipation around 11 s.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…It must be noted that, if a conflict is detected in the i component of the arrays, it will take place in about

(i \cdot t_{step}) - t_{com} - t_{proc}

seconds, where

t_{com}

is the communication time, and

t_{proc}

t_{step}

.…”

Section: Detection Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Assessing the limits of centralized unmanned aerial vehicle conflict management in U‐Space

Boronat,

Pérez‐Francisco,

Wubben

et al. 2023

IET Intelligent Trans Sys

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, enhancements in the information quality provided by the underlying sensor system, including aspects such as accuracy, reliability, sampling rate, aggregation level, latency, and privacy, could substantially improve the efficacy of our analyses and estimation tools. Investigating the potential integration of advanced sensor technologies or protocols within bicycle network [ 4 , 22 , 50 ] may yield richer and more granular data, thereby enhancing the overall reliability and precision of insights derived from the sensor system.…”

Section: Discussionmentioning

confidence: 99%

Bicycle Data-Driven Application Framework: A Dutch Case Study on Machine Learning-Based Bicycle Delay Estimation at Signalized Intersections Using Nationwide Sparse GPS Data

Yuan,

Wang,

Duives

et al. 2023

Sensors

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Data-driven approaches are helpful for quantitative justification and performance evaluation. The Netherlands has made notable strides in establishing a national protocol for bicycle traffic counting and collecting GPS cycling data through initiatives such as the Talking Bikes program. This article addresses the need for a generic framework to harness cycling data and extract relevant insights. Specifically, it focuses on the application of estimating average bicycle delays at signalized intersections, as this is an essential variable in assessing the performance of the transportation system. This study evaluates machine learning (ML)-based approaches using GPS cycling data. The dataset provides comprehensive yet incomplete information regarding one million bicycle rides annually across The Netherlands. These ML models, including random forest, k-nearest neighbor, support vector regression, extreme gradient boosting, and neural networks, are developed to estimate bicycle delays. The study demonstrates the feasibility of estimating bicycle delays using sparse GPS cycling data combined with publicly accessible information, such as weather information and intersection complexity, leveraging the burden of understanding local traffic conditions. It emphasizes the potential of data-driven approaches to inform traffic management, bicycle policy, and infrastructure development.

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“…Environmental sensing is not limited to the observation of changing weather conditions. Other applications include, but are not limited to, automatic temperature and humidity control with anomaly detection in smart buildings [ 84 ], early detection of collisions between pedestrians, cyclists and drivers to generate timely alerts on mobile devices [ 97 ], data collection from in-car sensors to predict generated noise, travel time and fuel consumption [ 98 ], and overseeing the food production process in the agri-food sector to reduce

emission levels and energy consumption levels [ 99 ]. The applications discussed are summarised in Table 1 .…”

Section: Applications In Ubiquitous Sensingmentioning

confidence: 99%

Semantic Data Mining in Ubiquitous Sensing: A Survey

Nalepa

Bobek

Kutt

et al. 2021

Sensors

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Mining ubiquitous sensing data is important but also challenging, due to many factors, such as heterogeneous large-scale data that is often at various levels of abstraction. This also relates particularly to the important aspects of the explainability and interpretability of the applied models and their results, and thus ultimately to the outcome of the data mining process. With this, in general, the inclusion of domain knowledge leading towards semantic data mining approaches is an emerging and important research direction. This article aims to survey relevant works in these areas, focusing on semantic data mining approaches and methods, but also on selected applications of ubiquitous sensing in some of the most prominent current application areas. Here, we consider in particular: (1) environmental sensing; (2) ubiquitous sensing in industrial applications of artificial intelligence; and (3) social sensing relating to human interactions and the respective individual and collective behaviors. We discuss these in detail and conclude with a summary of this emerging field of research. In addition, we provide an outlook on future directions for semantic data mining in ubiquitous sensing contexts.

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Towards a Sustainable City for Cyclists: Promoting Safety through a Mobile Sensing Application

Cited by 8 publications

References 20 publications

Assessing the limits of centralized unmanned aerial vehicle conflict management in U‐Space

Assessing the limits of centralized unmanned aerial vehicle conflict management in U‐Space

Bicycle Data-Driven Application Framework: A Dutch Case Study on Machine Learning-Based Bicycle Delay Estimation at Signalized Intersections Using Nationwide Sparse GPS Data

Semantic Data Mining in Ubiquitous Sensing: A Survey

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