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

Abstract

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.

Representing Data Visualization Goals and Tasks through Meta-Modeling to Tailor Information Dashboards

“…However, the great quantity of available data can make the analysis process a resource-and time-consuming task. For this reason, many tools have emerged to support and ease the discovery of insights through data from different domains [2][3][4][5][6].…”

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

“…As free communication platforms, social networks accommodate different angles and attitudes expressed by people of different backgrounds and roles. Thus, this makes the data collection of cross‐domain opinion and knowledge possible and convenient (Lu et al , ). Sensors provided by weather‐sensitive departments: Weather and climate influence many industrial sectors. Conversely, using these weather‐sensitive industry data in meteorology also has an advantageous influence.…”

“…smoothness) on freeways with a linear regression model (Lin et al , ). A city‐level traffic awareness alerting model was also proposed by Lu et al, (), and this model extracted opinions about severe weather and traffic from Weibo and validated the prediction and alert by referring to news reports, thereby saving large costs on deploying physical sensors and integrating data from different sources and departments. In hydrological and agricultural research, Ravazzani et al, () proposed a prediction model to estimate soil moisture and crop water requirements by combining ground observed data from space and reporting information from cyberspace with a crowdsourcing approach.…”

“…Adjusted by the feedback of traffic administrators, their case in Madrid reaches a premising predicting accuracy and similar results are also reported by Lu et al . on cases in Beijing (Lu et al , ) and Qingdao (Lu et al , ). From the view of individualized services, the knowledge service is usually located in personalized advising and recommendations.…”

Sensing Urban Transportation Events from Multi-Channel Social Signals with the Word2vec Fusion Model

“…TrafficWatch demonstrated the potential to report traffic incidents earlier than other data sources when deployed in the traffic management center of Australia. Hao et al [31], mined the correlation between adverse weather topic heat and traffic incidents in social media, and further proposed traffic situation awareness and alerting model assisted by adverse weather data to provide information on city-level traffic situations.…”