Forecasting Adverse Weather Situations in the Road Network

Tomás, Vicente R.; Pla‐Castells, Marta; Martínez, Juan José; Martinez, J.

doi:10.1109/tits.2016.2519103

Cited by 17 publications

(9 citation statements)

References 18 publications

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“…Existing personalized routing solution is based on user's preference like traffic condition [12], shortest [13], fastest [13][14][15], the least expensive path, lower crime [16][17][18] and lower accident-prone path [19] [20] but not on the air pollution exposure level. Nowadays, one has to be more concerned about adverse air pollution [21] and weather [22] condition impact on the road network for safer travel. Personalized routing solution, part of personalized pollution management, is a critical aspect of air pollution management.…”

Section: Literature Surveymentioning

confidence: 99%

Predicting the least air polluted path using the neural network approach

Samal

Babu

Das

2021

ICST Transactions on Scalable Information Systems

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Air pollution exposure during daily transportation is becoming a critical issue worldwide due to its adverse effect on human health. Predicting the least air polluted healthier path is the best alternative way to mitigate personal air pollution exposure risk. Computing the least polluted path for the current time might not be helpful for real-time applications. Therefore, we develop a routing algorithm based on a neural network-based CNN-LSTM-EBK (CLE), a temporal-spatial interpolation model. The proposed model predicts pollution levels at high temporal granularity. This paper introduces a weight function to compute air pollution concentration at the road network. It also predicts the least air polluted path among all possible paths from a source to a destination at different time granularity. The results show that the predicted path may be longer than the shortest route but minimize pollution exposure risk all the time, which proves its effectiveness during daily transportation.

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Section: Literature Surveymentioning

confidence: 99%

Predicting the least air polluted path using the neural network approach

Samal

Babu

Das

2021

ICST Transactions on Scalable Information Systems

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“…These approaches take the advantages of regular and structural information gathering especially alongside the daily service work of vehicles (like Uber, taxi, etc.) (Dey et al , ; Tomas et al , ). Multidisciplinarity: Meteorological data collection is not limited to meteorological and atmospheric science, but many related fields affected by meteorology can provide data support for social weather through crowdsourcing collection in their industry. In other words, multidisciplinarity enables problem solving assisted by knowledge from other domains.…”

Section: Review Of Previous Work and Analysismentioning

confidence: 99%

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Zhu

Zhang

et al. 2019

Geoscience Data Journal

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Crowdsourcing has significantly motivated the development of meteorological services. Starting from the beginning of 2010s and highly motivating after 2014, crowdsourcing-driven meteorological services have evolved from a single collection and observation of data to the systematic acquisition, analysis and application of these data. In this review, by focusing on papers and databases that have combined crowdsourcing methods to promote or implement meteorological knowledge services, we analysed the relevant literature in three dimensions: data collection, information analysis and meteorological knowledge applications. First, we selected the potential data sources for crowdsourcing and discussed the characteristics of the collected data in four dimensions: consciousness, objectiveness, mobility and multidisciplinary.Second, based on the purpose of these studies and the extent of utilizing data as well as knowledge, we categorize the crowdsourcing-based meteorological analysis into three levels: relationship discovery, knowledge generalization and systemized service. Third, according to the application scenario, we discussed the applications that have already been put into use, and we suggest current challenges and future research directions. These previous studies show that the use of crowdsourcing in social space can expand the coverage as well as enhance the performance of meteorological 62 | ZHU et al.

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“…Lee et al [23] made full use of the weather data along the freeway from Seoul to Gyeongpodae to assess levels of traffic congestion using multiple linear regressions. Tomas et al [24] explored a scheme and system architecture to deploy forecasting models assisted by adverse weather in a real road network and discussed an efficient way to provide early warnings. Stamos et al [25] explored the impact of adverse weather on traffic from a data-driven perspective.…”

Section: Traffic Situation Awareness Models In Itssmentioning

confidence: 99%

Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting

et al. 2018

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Traffic situation awareness and alerting assisted by adverse weather conditions contributes to improve traffic safety, disaster coping mechanisms, and route planning for government agencies, business sectors, and individual travelers. However, at the city level, the physical sensor-generated data are partly held by different transportation and meteorological departments, which causes problems of “isolated information” for data fusion. Furthermore, it makes traffic situation awareness and estimation challenging and ineffective. In this paper, we leverage the power of crowdsourcing knowledge in social media and propose a novel way to forecast and generate alerts for city-level traffic incidents based on a social approach rather than traditional physical approaches. Specifically, we first collect adverse weather topics and reports of traffic incidents from social media. Then, we extract temporal, spatial, and meteorological features as well as labeled traffic reaction values corresponding to the social media “heat” for each city. Afterwards, the regression and alerting model is proposed to estimate the city-level traffic situation and give the suggestion of warning levels. The experiments show that the proposed model equipped with gcForest achieves the best root mean square error (RMSE) and mean absolute percentage error (MAPE) score on the social traffic incidents test dataset. Moreover, we consider the news report as an objective measurement to flexibly validate the feasibility of proposed model from social cyberspace to physical space. Finally, a prototype system was deployed and applied to government agencies to provide an intuitive visualization solution as well as decision support assistance.

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Forecasting Adverse Weather Situations in the Road Network

Cited by 17 publications

References 18 publications

Predicting the least air polluted path using the neural network approach

Predicting the least air polluted path using the neural network approach

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting

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