Human Mobility Prediction with Region-based Flows and Road Traffic Data

Predicting human mobility is a key element in the development of intelligent transport systems. Current digital technologies enable capturing a wealth of data on mobility flows between geographic areas, which are then used to train machine learning models to predict these flows. However, most works have only considered a single data source for building these models or different sources but covering the same spatial area. In this paper we propose to augment a macro open-data mobility study based on cellular phones with data from a road traffic sensor located within a specific motorway of one of the mobility areas in the study. The results show that models trained with the fusion of both types of data, especially long short-term memory (LSTM) and Gated Recurrent Unit (GRU) neural networks, provide a more reliable prediction than models based only on the open data source. These results show that it is possible to predict the traffic entering a particular city in the next 30 minutes with an absolute error less than 10%. Thus, this work is a further step towards improving the prediction of human mobility in interurban areas by fusing open data with data from IoT systems

An analysis of twitter as a relevant human mobility proxy A comparative approach in spain during the COVID-19 pandemic

During the last years, the analysis of spatio-temporal data extracted from Online Social Networks (OSNs) has become a prominent course of action within the human-mobility mining discipline. Due to the noisy and sparse nature of these data, an important effort has been done on validating these platforms as suitable mobility proxies. However, such a validation has been usually based on the computation of certain features from the raw spatio-temporal trajectories extracted from OSN documents. Hence, there is a scarcity of validation studies that evaluate whether geo-tagged OSN data are able to measure the evolution of the mobility in a region at multiple spatial scales. For that reason, this work proposes a comprehensive comparison of a nation-scale Twitter (TWT) dataset and an official mobility survey from the Spanish National Institute of Statistics. The target time period covers a three-month interval during which Spain was heavily affected by the COVID-19 pandemic. Both feeds have been compared in this context by considering different mobility-related features and spatial scales. The results show that TWT could capture only a limited number features of the latent mobility behaviour of Spain during the study period

QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis

[EN] Road traffic pollution is one of the key factors affecting urban air quality. There is a consensus in the community that the efficient use of public transport is the most effective solution. In that sense, much effort has been made in the data mining discipline to come up with solutions able to anticipate taxi demands in a city. This helps to optimize the trips made by such an important urban means of transport. However, most of the existing solutions in the literature define the taxi demand prediction as a regression problem based on historical taxi records. This causes serious limitations with respect to the required data to operate and the interpretability of the prediction outcome. In this paper, we introduce QUADRIVEN (QUalitative tAxi Demand pRediction based on tIme-Variant onlinE social Network data analysis), a novel approach to deal with the taxi demand prediction problem based on human-generated data widely available on online social networks. The result of the prediction is defined on the basis of categorical labels that allow obtaining a semantically-enriched output. Finally, this proposal was tested with different models in a large urban area, showing quite promising results with an F1 score above 0.8.This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Projects 20813/PI/18 and 20530/PDC/18 and by the Spanish Ministry of Science, Innovation and Universities under Grants TIN2016-78799-P (AEI/FEDER, UE) and RTC-2017-6389-5.Terroso-Saenz, F.; Muñoz-Ortega, A.; Cecilia-Canales, JM. (2019). QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis. Sensors. 19(22):1-22. https://doi.org/10.3390/s19224882S1221922Di, Q., Wang, Y., Zanobetti, A., Wang, Y., Koutrakis, P., Choirat, C., … Schwartz, J. D. (2017). Air Pollution and Mortality in the Medicare Population. New England Journal of Medicine, 376(26), 2513-2522. doi:10.1056/nejmoa1702747Li, B., Cai, Z., Jiang, L., Su, S., & Huang, X. (2019). Exploring urban taxi ridership and local associated factors using GPS data and geographically weighted regression. Cities, 87, 68-86. doi:10.1016/j.cities.2018.12.033Yang, Y., Yuan, Z., Fu, X., Wang, Y., & Sun, D. (2019). Optimization Model of Taxi Fleet Size Based on GPS Tracking Data. Sustainability, 11(3), 731. doi:10.3390/su11030731Smith, A. W., Kun, A. L., & Krumm, J. (2017). Predicting taxi pickups in cities. Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. doi:10.1145/3123024.3124416Liu, L., Qiu, Z., Li, G., Wang, Q., Ouyang, W., & Lin, L. (2019). Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction. IEEE Transactions on Intelligent Transportation Systems, 20(10), 3875-3887. doi:10.1109/tits.2019.2915525Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., & Ratti, C. (2014). Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science, 41(3), 260-271. doi:10.1080/15230406.2014.890072James, N. A., Kejariwal, A., & Matteson, D. S. (2016). Leveraging cloud data to mitigate user experience from ‘breaking bad’. 2016 IEEE International Conference on Big Data (Big Data). doi:10.1109/bigdata.2016.7841013Kuang, L., Yan, X., Tan, X., Li, S., & Yang, X. (2019). Predicting Taxi Demand Based on 3D Convolutional Neural Network and Multi-task Learning. Remote Sensing, 11(11), 1265. doi:10.3390/rs11111265Thomee, B., Shamma, D. A., Friedland, G., Elizalde, B., Ni, K., Poland, D., … Li, L.-J. (2016). YFCC100M. Communications of the ACM, 59(2), 64-73. doi:10.1145/2812802Cho, E., Myers, S. A., & Leskovec, J. (2011). Friendship and mobility. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’11. doi:10.1145/2020408.2020579Estevez, P. A., Tesmer, M., Perez, C. A., & Zurada, J. M. (2009). Normalized Mutual Information Feature Selection. IEEE Transactions on Neural Networks, 20(2), 189-201. doi:10.1109/tnn.2008.2005601Zheng, X., Han, J., & Sun, A. (2018). A Survey of Location Prediction on Twitter. IEEE Transactions on Knowledge and Data Engineering, 30(9), 1652-1671. doi:10.1109/tkde.2018.2807840Assam, R., & Seidl, T. (2014). Context-based location clustering and prediction using conditional random fields. Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia - MUM ’14. doi:10.1145/2677972.2677989Genuer, R., Poggi, J.-M., Tuleau-Malot, C., & Villa-Vialaneix, N. (2017). Random Forests for Big Data. Big Data Research, 9, 28-46. doi:10.1016/j.bdr.2017.07.003Tong, Y., Chen, Y., Zhou, Z., Chen, L., Wang, J., Yang, Q., … Lv, W. (2017). The Simpler The Better. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. doi:10.1145/3097983.3098018Markou, I., Rodrigues, F., & Pereira, F. C. (2018). Real-Time Taxi Demand Prediction using data from the web. 2018 21st International Conference on Intelligent Transportation Systems (ITSC). doi:10.1109/itsc.2018.8569015Zhou, Y., Wu, Y., Wu, J., Chen, L., & Li, J. (2018). Refined Taxi Demand Prediction with ST-Vec. 2018 26th International Conference on Geoinformatics. doi:10.1109/geoinformatics.2018.8557158Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., & Damas, L. (2013). Predicting Taxi–Passenger Demand Using Streaming Data. IEEE Transactions on Intelligent Transportation Systems, 14(3), 1393-1402. doi:10.1109/tits.2013.2262376Jiang, S., Chen, W., Li, Z., & Yu, H. (2019). Short-Term Demand Prediction Method for Online Car-Hailing Services Based on a Least Squares Support Vector Machine. IEEE Access, 7, 11882-11891. doi:10.1109/access.2019.289182

A Collaborative Application for Assisting the Management of Household Plastic Waste through Smart Bins: A Case of Study in the Philippines

The management and collection of household waste often represents a demanding task for elderly or impaired people. In particular, the increasing generation of plastic waste at home may pose a problem for these groups, as this type of waste accumulates very rapidly and occupies a considerable amount of space. This paper proposes a collaborative infrastructure to monitor household plastic waste. It consists of simple smart bins using a weight scale and a smart application that forecasts the amount of plastic generated for each bin at different time horizons out of the data provided by the smart bins. The application generates optimal routes for the waste-pickers collaborating in the system through a route-planning algorithm. This algorithm takes into account the predicted amount of plastic of each bin and the waste-picker’s location and means of transport. This proposal has been evaluated by means of a simulated scenario in Quezon City, Philippines, where severe problems with plastic waste have been identified. A set of 176 experiments have been performed to collect data that allow representing different user behaviors when generating plastic waste. The results show that our proposal enables waste-pickers to collect more than the 80% of the household plastic-waste bins before they are completely full

Fuzzy Modelling for Human Dynamics Based on Online Social Networks

Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities

A Novel Learning Algorithm Based on Bayesian Statistics: Modelling Thermostat Adjustments for Heating and Cooling in Buildings

The temperature of indoor spaces is at the core of highly relevant topics such as comfort, productivity and health. In conditioned spaces, this temperature is determined by thermostat preferences, but there is a lack of understanding of this phenomenon as a time-dependent magnitude. In addition to this, there is scientific evidence that the mental models of how users understand the operation of the billions of air-conditioning machines around the world are incorrect, which causes systems to ‘compensate’ for temperatures outside by adjusting the thermostat, which leads to erratic changes on set-points over the day. This paper presents the first model of set-point temperature as a time-dependent variable. Additionally, a new mathematical algorithm was developed to complement these models and make possible their identification on the go, called the life Bayesian inference of transition matrices. Data from a total of 75 + 35 real thermostats in two buildings for more than a year were used to validate the model. The method was shown to be highly accurate, fast, and computationally trivial in terms of time and memory, representing a change in the paradigm for smart thermostats

A Cooperative Approach to Traffic Congestion Detection With Complex Event Processing and VANET

Currently, distributed traffic information systems have come up as one of the most important approaches for detecting traffic flow problems on a road. For that purpose, they usually make use of the location information that vehicles share among them through periodical messages that are transmitted across a vehicular ad hoc network (VANET). This paper puts forward an event-driven architecture (EDA) as a novel mechanism to get insight into VANET messages to detect different levels of traffic jams; furthermore, it also takes into account environmental data that come from external data sources, such as weather conditions. The proposed EDA has been developed through the complex-event-processing technology. Simulation tests show that the proposed mechanism can detect traffic congestions, which involve different numbers of lanes and lengths with short delay

Vehicle Maneuver Detection with Accelerometer-Based Classification

In the mobile computing era, smartphones have become instrumental tools to develop innovative mobile context-aware systems. In that sense, their usage in the vehicular domain eases the development of novel and personal transportation solutions. In this frame, the present work introduces an innovative mechanism to perceive the current kinematic state of a vehicle on the basis of the accelerometer data from a smartphone mounted in the vehicle. Unlike previous proposals, the introduced architecture targets the computational limitations of such devices to carry out the detection process following an incremental approach. For its realization, we have evaluated different classification algorithms to act as agents within the architecture. Finally, our approach has been tested with a real-world dataset collected by means of the ad hoc mobile application developed

Applicability of Big Data Techniques to Smart Cities Deployments

This paper presents the main foundations of big data applied to smart cities. A general Internet of Things based architecture is proposed to be applied to different smart cities applications. We describe two scenarios of big data analysis. One of them illustrates some services implemented in the smart campus of the University of Murcia. The second one is focused on a tram service scenario, where thousands of transit-card transactions should be processed. Results obtained from both scenarios show the potential of the applicability of this kind of techniques to provide profitable services of smart cities, such as the management of the energy consumption and comfort in smart buildings, and the detection of travel profiles in smart transport.</p