Identifying Damage-Sensitive Spatial Vibration Characteristics of Bridges from Widespread Smartphone Data

The knowledge gap in the expected and actual conditions of bridges has created worldwide deficits in infrastructure service and funding challenges. Despite rapid advances over the past four decades, sensing technology is still not a part of bridge inspection protocols. Every time a vehicle with a mobile device passes over a bridge, there is an opportunity to capture potentially important structural response information at a very low cost. Prior work has shown how bridge modal frequencies can be accurately determined with crowdsourced smartphone-vehicle trip (SVT) data in real-world settings. However, modal frequencies provide very limited insight on the structural health conditions of the bridge. Here, we present a novel method to extract spatial vibration characteristics of real bridges, namely, absolute mode shapes, from crowdsourced SVT data. These characteristics have a demonstrable sensitivity to structural damage and provide superior, yet complementary, indicators of bridge condition. Furthermore, they are useful in the development of accurate mathematical models of the structural system and help reconcile the differences between models and real systems. We demonstrate successful applications on four very different bridges, with span lengths ranging from about 30 to 1300 meters, collectively representing about one quarter of bridges in the US. Supplementary work applies this computational approach to accurately detect simulated bridge damage entirely from crowdsourced SVT data in an unprecedentedly timely fashion. The results presented in this article open the way towards large-scale crowdsourced monitoring of bridge infrastructure

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Inferring transportation mode from smartphone sensors:Evaluating the potential of Wi-Fi and Bluetooth

Understanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic information to improve transportation detection on smartphones. Wi-Fi information also improves the identification of transportation mode and helps conserve battery since it is already collected by most mobile phones. Our approach uses a machine learning approach to determine the mode from pre-prepocessed data. This approach yields an overall accuracy of 89% and average F1 score of 83% for inferring the three grouped modes of self-powered, car-based, and public transportation. When broken out by individual modes, Wi-Fi features improve detection accuracy of bus trips, train travel, and driving compared to GPS features alone and can substitute for GIS features without decreasing performance. Our results suggest that Wi-Fi and Bluetooth can be useful in urban transportation research, for example by improving mobile travel surveys and urban sensing applications

Crowdsourcing Real-Time Traveler Information Systems

In the last decade, the concept of collecting traffic data using location aware and data enabled smartphones in place of traditional sensor networks has received much attention. With a steady market growth for smartphones enabled with GPS chipsets, the potential of this technology is enormous. This combined with the pervasion of participatory paradigms such as crowdsourcing wherein individuals with portable sensors instead of physical networks serve as sensors providing information. Crowd sensed data overcome a number of issues with traditional physical sensor networks by providing wider coverage, real-time data, data redundancy and cost effectiveness to name a few. While there has been a lot of work on actual implementations of crowd sensed traffic monitoring programs, there is limited work on assessing the quality, and validity of crowd sensed data. A systematic analysis of quality and validity is needed before this paradigm can be more commonly adopted for traffic monitoring applications. To this end, research is underway to deploy a crowdsourced platform for monitoring and providing real-time transit information for shuttles that serve the University of Connecticut. The thesis develops a framework and an open-source prototype system that is able to produce real-time traveler information based on crowdsourced data. In order to build the prototype, first it implements a robust Hidden Markov Model based map-matching algorithm to position the crowdsourced data on the underlying road network and retrieve the likely path. The accuracy of the map-matching algorithm has been found satisfactory for the current usage even when the GPS points are sampled at low frequency. Next, to predict the travel condition across the network from the crowdsourced data, a travel time prediction algorithm, based on Regularized Least Square Regression, has been implemented as well. This travel time prediction algorithm, together with the map-matching algorithm, has been applied in a simulated crowdsourcing environment. The travel time prediction results of the simulation show that the prototype system is quite capable of predicting travel time even when the crowdsourced real-time data is sparse. The simulation tests the performance of the travel time prediction algorithm in different scenarios. From the demonstrated predictive performance of the implemented prototype system, this approach to providing real-time traveler information is found promising. It is also possible to apply the prototype to all regions and all modes of transportation, exploiting its generalized approach of providing real-time traveler information from crowdsourced data

Transport Mode Detection and Classification from Smartphone Sensor Data Using Convolutional Neural Networks

Transportation is a significant component of human lives and understanding how individuals travel is an essential task in many fields. Understanding the modes of transport individuals use can lead to improvements in urban planning, traffic control, human health, and environmental sciences. The goal of transport mode detection and classification is to use smartphone devices as human behavioural sensors, to detect and classify individuals movement continuously. Smartphone devices are suitable for transport mode detection, as they are proliferated in modern societies and contain sensors that are suitable for transport mode detection. These sensors include GPS, accelerometers, gyroscopes, magnetometers, barometers, or microphones. The research in this thesis will focus on transport mode detection and classification using data from motions sensors; accelerometers, gyroscopes, magnetometers, and barometers as they do not contain the sensitive private data that is collected when using GPS or microphones. Currently, there are two approaches in state of the art in transport mode detection. In the first approach, time and frequency domain features are extracted from the signals of the motion sensors and used as input to decision tree or neural network machine learning models. In the second approach, Convolutional Neural Networks extract features by finding spatial relations in the signal data and using these for classification. This thesis investigates the use of Convolutional Neural Networks, as they have shown to outperform models trained using time and frequency domain features extracted from the data in the state of the art research. This research studies the effect of different model architectures on the accuracy of Convolutional Neural Network models when using multiple different sensors as input, as well as focusing on which combinations of sensors produce optimal results. Furthermore, the focus will be evaluating the models on real-world data in order to evaluate the feasibility of deploying applications utilizing transport mode detection. This research compares an optimized model architecture along with preprocessing techniques to state of the art Convolutional Neural Network architectures on real- world data. The best baseline algorithm achieved an overall F1 score of 0.57, while the final optimized achieved an overall F1 score of 0.72 on the testing dataset. The optimal combination of motion sensors is with the accelerometer, gyroscope, and barometer