Map++: A Crowd-sensing System for Automatic Map Semantics Identification

Digital maps have become a part of our daily life with a number of commercial and free map services. These services have still a huge potential for enhancement with rich semantic information to support a large class of mapping applications. In this paper, we present Map++, a system that leverages standard cell-phone sensors in a crowdsensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others. Our analysis shows that cell-phones sensors with humans in vehicles or walking get affected by the different road features, which can be mined to extend the features of both free and commercial mapping services. We present the design and implementation of Map++ and evaluate it in a large city. Our evaluation shows that we can detect the different semantics accurately with at most 3% false positive rate and 6% false negative rate for both vehicle and pedestrian-based features. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.Comment: Published in the Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (IEEE SECON 2014

Traffic Control Recognition with AN Attention Mechanism Using Speed-Profile and Satellite Imagery Data

Traffic regulators at intersections act as an essential factor that influences traffic flow and, subsequently, the route choices of commuters. A digital map that provides up-to-date traffic control information is beneficial not only for facilitating the commuters’ trips, but also for energy-saving and environmental protection. In this paper, instead of using expensive surveying methods, we propose an automatic way based on a Conditional Variational Autoencoder (CVAE) to recognize traffic regulators, i. e., arm rules at intersections, by leveraging the GPS data collected from vehicles and the satellite imagery retrieved from digital maps, i. e., Google Maps. We apply a Long Short-Term Memory to extract the motion dynamics over a GPS sequence traversed through the intersection. Simultaneously, we build a Convolutional Neural Network (CNN) to extract the grid-based local imagery information associated with each step of the GPS positions. Moreover, a self-attention mechanism is adopted to extract the spatial and temporal features over both the GPS and grid sequences. The extracted temporal and spatial features are then combined for detecting the traffic arm rules. To analyze the performance of our method, we tested it on a GPS dataset collected by driving vehicles in Hannover, a medium-sized German city. Compared to a Random Forest model and an Encoder-Decoder model, our proposed model achieved better results with both accuracy and F1-score of 0.90 for the three-class (arm rules of uncontrolled, traffic light, and priority sign) task. We also carried out ablation studies to further investigate the effectiveness of the GPS input branch, the image input branch, and the self-attention mechanism in our model

Traffic Regulator Detection Using GPS Trajectories

This paper explores the idea of enriching maps with features predicted from GPS trajectories. More specifically, it proposes a method of classifying street intersections according to traffic regulators (traffic light, yield/priority-sign and right-of-way rule). Intersections are regulated locations and the observable movement of vehicles is affected by the underlying traffic rules. Movement patterns such as stop events or start-and-stop sequences are commonly observed at those locations due to traffic regulations. In this work, we test the idea of detecting traffic regulators by learning them in a supervised way from features derived from GPS trajectories. We explore and assess different settings of the feature vector being used to train a classifier that categorizes the intersections based on traffic regulators; also, we test several experimental setups. The results show that a Random Forest classifier with oversampling and Bagging booster enabled can predict the intersection regulators with 90.4% accuracy. We discuss future research directions and recommend next steps for improving the results of this research. © 2020, The Author(s)

Participatory sensing fuel-efficient navigation system GreenGPS

The proliferation of smartphones has led to increased interest in mobile participatory sensing. This paradigm enables low cost establishment of a wide range of applications in variety of domains, including environmental monitoring, transportation, safety, healthcare, social networks, urban sensing, etc. This thesis proposes, designs and develops a novel application in this genre, called GreenGPS, which owes its practicality to the widespread usage of smart mobile devices. GreenGPS is a navigation service that finds fuel optimal routes, customized to individual drivers and vehicles, between arbitrary end-points. This thesis studies research challenges revealed in development of GreenGPS on how to build an easy-to-deploy and inexpensive participatory sensing system to support data collection, how to generalize sparse samples of high- dimensional spaces to develop models of complex nonlinear phenomena, how to build a general but personalizable fuel-saving navigation system, how to infer the information on location and type of traffic regulators with low effort and expense, and how to insure reliability of the modeling throughout the lifetime of the service, especially the early deployment stage through which service adoption is sparse and proper modeling facilitates getting the participatory sensing based system off the ground and surviving conditions of sparse deployment. GreenGPS navigation service is offered in both web-based and smartphone application forms. To launch GreenGPS, we deployed a medium scaled vehicular participatory sensing system, consisting of 46 user subjects, collecting over 6700 miles of GPS driving data. To provide a testbed for future transportation fuel saving research, we started to deploy GreenGPS on over 100 vehicles of UIUC Facilities and Services fleet. To give the reader a sense of how effective are route choices provisioned by GreenGPS, it was assessed that compared to alternative fastest and shortest routes provided by traditional navigation tools, green routes are respectively 21.5% and 11.2% more fuel economic. The GreenGPS fuel optimal routes were further compared to Garmin ecoRoutes, a well-known commercial GPS product, and presented 8.4% more fuel savings

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda

Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning

International audienceThe increasing availability of large-scale Global Positioning System (GPS) data stemming from in-vehicle embedded terminal devices enables the design of methods deriving road network cartographic information from drivers' recorded traces. Some machine learning approaches have been proposed in the past to train automatic road network map inference, and recently this approach has been successfully extended to infer road attributes as well, such as speed limitation or number of lanes. In this paper, we address the problem of detecting traffic signals from a set of vehicle speed profiles, under a classification perspective. Each data instance is a speed versus distance plot depicting over a hundred profiles on a 100-meter-long road span. We proposed three different ways of deriving features: the first one relies on the raw speed measurements; the second one uses image recognition techniques; and the third one is based on functional data analysis. We input them into most commonly used classification algorithms and a comparative analysis demonstrated that a functional description of speed profiles with wavelet transforms seems to outperform the other approaches with most of the tested classifiers. It also highlighted that Random Forests yield an accurate detection of traffic signals, regardless of the chosen feature extraction method, while keeping a remarkably low confusion rate with stop signs

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI