Urban well-being (UWB) is increasingly recognized as a complex, multidimensional construct influenced by sociocultural and geographic contexts. Yet, existing approaches to assessing local perceptions of UWB often depend on standardized indicators, limiting sensitivity to context-specific priorities. This paper presents the Urban Well-Being Deck (UWBdeck), a participatory, card-based method designed to explore citizens’ situated perception of UWB. We conducted eight workshops across multiple cities, engaging 140 participants and collecting 256 participant-generated proposals. A thematic analysis of collected data revealed 22 distinct themes of UWB, highlighting both cross-city variation and underrepresented dimensions in existing indexes. This work contributes a participatory method for context-sensitive user research and offers empirical insights that advance ongoing discussion on subjective and objective dimensions of UWB.

Safe motion planning is essential for autonomous vessel operations, especially in challenging spaces such as narrow inland waterways. However, conventional motion planning approaches are often computationally intensive or overly conservative. This paper proposes a safe motion planning strategy combining Model Predictive Control (MPC) and Control Barrier Functions (CBFs). We introduce a time-varying inflated ellipse obstacle representation, where the inflation radius is adjusted depending on the relative position and attitude between the vessel and the obstacle. The proposed adaptive inflation reduces the conservativeness of the controller compared to traditional fixed-ellipsoid obstacle formulations. The MPC solution provides an approximate motion plan, and high-order CBFs ensure the vessel’s safety using the varying inflation radius. Simulation and real-world experiments demonstrate that the proposed strategy enables the fully-actuated autonomous robot vessel to navigate through narrow spaces in real time and resolve potential deadlocks, all while ensuring safety.

In autonomous robot navigation, the trajectories from path planners are considered to be safe regions, and deviations could endanger vessels. Model Predictive Control (MPC) stands as a popular choice for trajectory tracking problems as it naturally addresses operational constraints, such as dynamics and control constraints. Nevertheless, achieving robustness in changing environments like oceans and rivers, which are constantly subject to significant external disturbances, remains an ongoing challenge for MPC. It must consistently keep the system within a predefined safe region (such as a reference trajectory) even in the presence of model inaccuracies and perturbations. To address this challenge, we present a robust model predictive control strategy utilizing Control Barrier Functions (CBFs), which increases the disturbance-rejection abilities. We verify our method on an autonomous surface vessel in simulation and natural waters, both with external disturbances. Specifically, compared with the traditional MPC method, our proposed MPC-CBF strategy reduces tracking errors by 17.82% and 40.26% in simulations and field experiments, respectively. Although the control effort slightly increases by 7.78% and 4.20%, respectively, these results clearly demonstrate the enhanced resilience of MPC-CBF to disturbances.

Recent booming successes of electric vehicles (EVs) motivate emerging exploration of spatio-temporal EV charging demand forecasting to inform policy making. Recent studies have contributed to remarkable accuracy improvement by developing deep learning methods. However, when they access massive amounts of data and frequently exchange data through the Internet of Things (IoT), data silos and inefficient training emerge as main challenges. To tackle these challenges, this study proposes an integrated approach for regional EV charging demand forecasting, named FMGCN, which consists of two modules, namely 1) Spatio-temporal Learning module, which introduces spatial and temporal attentions to capture the underlying charging patterns between different regions and cities effectively; and 2) Distributed Pretraining module, which incorporates Federated Learning and Meta-Learning to enhance the adaptivity and generalisability of the forecasting model. A comprehensive evaluation based on a real-world dataset of 25,246 public EV charging piles shows that the proposed model outperforms other representative models with 1) an average improvement of 29.9% in forecasting errors; 2) an acceleration of 65% in convergence speed; and 3) a sound adaptability to support varying charging demand.

Accurate control of autonomous marine robots still poses challenges due to the complex dynamics of the environment. In this paper, we propose a Deep Reinforcement Learning (DRL) approach to train a controller for autonomous surface vessel (ASV) trajectory tracking and compare its performance with an advanced nonlinear model predictive controller (NMPC) in real environments. Taking into account environmental disturbances (e.g., wind, waves, and currents), noisy measurements, and non-ideal actuators presented in the physical ASV, several effective reward functions for DRL tracking control policies are carefully designed. The control policies were trained in a simulation environment with diverse tracking trajectories and disturbances. The performance of the DRL controller has been verified and compared with the NMPC in both simulations with model-based environmental disturbances and in natural waters. Simulations show that the DRL controller has 53.33% lower tracking error than that of NMPC. Experimental results further show that, compared to NMPC, the DRL controller has 35.51% lower tracking error, indicating that DRL controllers offer better disturbance rejection in river environments than NMPC.

Within the next several years, there will be a high level of autonomous technology that will be available for widespread use, which will reduce labor costs, increase safety, save energy, enable difficult unmanned tasks in harsh environments, and eliminate human error. Compared to software development for other autonomous vehicles, maritime software development, especially in aging but still functional fleets, is described as being in a very early and emerging phase. This presents great challenges and opportunities for researchers and engineers to develop maritime autonomous systems. Recent progress in sensor and communication technology has introduced the use of autonomous surface vehicles (ASVs) in applications such as coastline surveillance, oceanographic observation, multi-vehicle cooperation, and search and rescue missions. Advanced artificial intelligence technology, especially deep learning (DL) methods that conduct nonlinear mapping with self-learning representations, has brought the concept of full autonomy one step closer to reality. This article reviews existing work on the implementation of DL methods in fields related to ASV. First, the scope of this work is described after reviewing surveys on ASV developments and technologies, which draws attention to the research gap between DL and maritime operations. Then, DL-based navigation, guidance, control (NGC) systems and cooperative operations are presented. Finally, this survey is completed by highlighting current challenges and future research directions.

Autonomous latching is essential for autonomous surface vessels (ASV) to reach full independence from human intervention. As part of the ASV Roboat project, a new solution for self-latching maneuvers has been developed and is presented here. We propose a system that has the key requirements of full integration with the navigation control system and zero-gap connection with the dock, the latter being essential for wireless charging of the ASV. Dedicated markers are used to identify docking targets, relying on computer vision algorithms to determine distance and bearing to the target. In its idle state, the locking solution uses mechanical power-off brakes, minimizing energy consumption while ensuring the boat stays in position indefinitely once docked. A prototype of the proposed mechanism has been built and installed in Roboat. Experimental tests showing the mechanism performance and capability to autonomously approach the docking station are discussed in this work.

The metagenome embedded in urban sewage is an attractive new data source to understand urban ecology and assess human health status at scales beyond a single host. Analyzing the viral fraction of wastewater in the ongoing COVID-19 pandemic has shown the potential of wastewater as aggregated samples for early detection, prevalence monitoring, and variant identification of human diseases in large populations. However, using census-based population size instead of real-time population estimates can mislead the interpretation of data acquired from sewage, hindering assessment of representativeness, inference of prevalence, or comparisons of taxa across sites. Here, we show that taxon abundance and sub-species diversisty in gut-associated microbiomes are new feature space to utilize for human population estimation. Using a population-scale human gut microbiome sample of over 1,100 people, we found that taxon-abundance distributions of gut-associated multi-person microbiomes exhibited generalizable relationships with respect to human population size. Here and throughout this paper, the human population size is essentially the sample size from the wastewater sample. We present a new algorithm, MicrobiomeCensus, for estimating human population size from sewage samples. MicrobiomeCensus harnesses the inter-individual variability in human gut microbiomes and performs maximum likelihood estimation based on simultaneous deviation of multiple taxa's relative abundances from their population means. MicrobiomeCensus outperformed generic algorithms in data-driven simulation benchmarks and detected population size differences in field data. New theorems are provided to justify our approach. This research provides a mathematical framework for inferring population sizes in real time from sewage samples, paving the way for more accurate ecological and public health studies utilizing the sewage metagenome.

Recent technological developments have shown significant potential for transforming urban mobility. Considering first- and last-mile travel and short trips, the rapid adoption of dockless bike-share systems showed the possibility of disruptive change, while simultaneously presenting new challenges, such as fleet management or the use of public spaces. In this paper, we evaluate the operational characteristics of a new class of shared vehicles that are being actively developed in the industry: scooters with self-repositioning capabilities. We do this by adapting the methodology of shareability networks to a large-scale dataset of dockless bike-share usage, giving us estimates of ideal fleet size under varying assumptions of fleet operations. We show that the availability of self-repositioning capabilities can help achieve up to 10 times higher utilization of vehicles than possible in current bike-share systems. We show that actual benefits will highly depend on the availability of dedicated infrastructure, a key issue for scooter and bicycle use. Based on our results, we envision that technological advances can present an opportunity to rethink urban infrastructures and how transportation can be effectively organized in cities.

Today, and probably for a long time to come, humans will remain an integral part of vehicles for driving tasks. Therefore, it is essential to understand how vehicles and drivers interact with each other and how drivers’ behavior and physical and mental states affect vehicle performance and traffic safety. This article explores the relationship between driver and vehicle in real-world driving conditions by analyzing large-scale naturalistic data collected from cars and drivers. Specifically, more than 800 hours of driving data from 16 drivers were collected using three different data sources (telematics data, video frames, and physiological data) and two types of analysis were done. The first one analyzes different types of driver-vehicle interactions during driving. The second one investigates the effect of different driving conditions on drivers’ stress and explores the relationship between driver and vehicle in different driving conditions. Our experimental results show that drivers’ physiological signals are correlated with some variables from vehicle kinematics and influenced by drivers’ behavior inside the vehicle. These findings could be used to help manage comfort-related in-vehicle intervention systems and could provide a continuous measure of how different external conditions (traffic, road, weather, etc.) affect drivers.

Autonomous surface vessels (ASVs) always carry payloads such as passengers and cargoes. The change in the payload can sometimes be several times the weight of the vessel. The payload can cause significant changes in the dynamics of the vessel, thereby degrading the performance of the controller. This paper proposes an adaptive nonlinear model predictive control (A-NMPC) strategy for ASV trajectory tracking, which allows real-time changes in dynamics caused by severe payload variation. First, a nonlinear dynamic model that updates with the vessel’s payload is established. Then a pressure sensing method is proposed to estimate the payload of the vessel. Further, a parametric cost function that considers changing dynamics, as well as input and state constraints, is formulated in the NMPC algorithm. The tracking ability of A-NMPC is systematically studied on three different sizes of vessels in the simulation where the payload of these vessels changes eight times their inherent weight. Numerical results show that when the payload changes greatly the vessels with A-NMPC can accurately track the reference trajectory while the vessels with conventional NMPC cannot. Finally, the tracking experiments with a quarter-scale vessel in a swimming pool further verify the effectiveness of the proposed A-NMPC strategy.

We propose a methodology for robust, real-time place recognition using an imaging lidar, which yields image-quality high-resolution 3D point clouds. Utilizing the intensity readings of an imaging lidar, we project the point cloud and obtain an intensity image. ORB feature descriptors are extracted from the image and encoded into a bag-of-words vector. The vector, used to identify the point cloud, is inserted into a database that is maintained by DBoW for fast place recognition queries. The returned candidate is further validated by matching visual feature descriptors. To reject matching outliers, we apply PnP, which minimizes the reprojection error of visual features’ positions in Euclidean space with their correspondences in 2D image space, using RANSAC. Combining the advantages from both camera and lidar-based place recognition approaches, our method is truly rotation-invariant, and can tackle reverse revisiting and upside down revisiting. The proposed method is evaluated on datasets gathered from a variety of platforms over different scales and environments. Our implementation and datasets are available at https://git.io/image-lidar.

We propose a framework for tightly-coupled lidar-visual-inertial odometry via smoothing and mapping, LVI-SAM, that achieves real-time state estimation and map-building with high accuracy and robustness. LVI-SAM is built atop a factor graph and is composed of two sub-systems: a visual-inertial system (VIS) and a lidar-inertial system (LIS). The two sub-systems are designed in a tightly-coupled manner, in which the VIS leverages LIS estimation to facilitate initialization. The accuracy of the VIS is improved by extracting depth information for visual features using lidar measurements. In turn, the LIS utilizes VIS estimation for initial guesses to support scan-matching. Loop closures are first identified by the VIS and further refined by the LIS. LVI-SAM can also function when one of the two sub-systems fails, which increases its robustness in both texture-less and feature-less environments. LVI-SAM is extensively evaluated on datasets gathered from several platforms over a variety of scales and environments. Our implementation is available at https://git.io/lvi-sam.

In this article, we propose a trajectory planning algorithm that enables autonomous surface vessels to perform socially compliant navigation in a city's canal. The key idea behind the proposed algorithm is to adopt an optimal control formulation in which the deviation of movements of the autonomous vessel from nominal movements of human-operated vessels is penalized. Consequently, given a pair of origin and destination points, it finds vessel trajectories that resemble those of human-operated vessels. To formulate this, we adopt kernel density estimation (KDE) to build a nominal movement model of human-operated vessels from a prerecorded trajectory dataset, and use a Kullback-Leibler control cost to measure the deviation of the autonomous vessel's movements from the model. We establish an analogy between our trajectory planning approach and the maximum entropy inverse reinforcement learning (MaxEntIRL) approach to explain how our approach can learn the navigation behavior of human-operated vessels. On the other hand, we distinguish our approach from the MaxEntIRL approach in that it does not require well-defined bases, often referred to as features, to construct its cost function as required in many of inverse reinforcement learning approaches in the trajectory planning context. Through experiments using a dataset of vessel trajectories collected from the automatic identification system, we demonstrate that the trajectories generated by our approach resemble those of human-operated vessels and that using them for canal navigation is beneficial in reducing head-on encounters between vessels and improving navigation safety.

We propose a scalable cooperative control approach which coordinates a group of rigidly connected autonomous surface vessels to track desired trajectories in a planar water environment as a single floating modular structure. Our approach leverages the implicit information of the structure's motion for force and torque allocation without explicit communication among the robots. In our system, a leader robot steers the entire group by adjusting its force and torque according to the structure's deviation from the desired trajectory, while follower robots run distributed consensus-based controllers to match their inputs to amplify the leader's intent using only onboard sensors as feedback. To cope with the nonlinear system dynamics in the water, the leader robot employs a nonlinear model predictive controller (NMPC), where we experimentally estimated the dynamics model of the floating modular structure in order to achieve superior performance for leader-following control. Our method has a wide range of potential applications in transporting humans and goods in many of today's existing waterways. We conducted trajectory and orientation tracking experiments in hardware with three custom-built autonomous modular robotic boats, called Roboat, which are capable of holonomic motions and onboard state estimation. Simulation results with up to 65 robots also prove the scalability of our proposed approach.

This paper presents a novel autonomous surface vessel (ASV), called Roboat II for urban transportation. Roboat II is capable of accurate simultaneous localization and mapping (SLAM), receding horizon tracking control and estimation, and path planning. Roboat II is designed to maximize the internal space for transport, and can carry payloads several times of its own weight. Moreover, it is capable of holonomic motions to facilitate transporting, docking, and inter-connectivity between boats. The proposed SLAM system receives sensor data from a 3D LiDAR, an IMU, and a GPS, and utilizes a factor graph to tackle the multi-sensor fusion problem. To cope with the complex dynamics in the water, Roboat II employs an online nonlinear model predictive controller (NMPC), where we experimentally estimated the dynamical model of the vessel in order to achieve superior performance for tracking control. The states of Roboat II are simultaneously estimated using a nonlinear moving horizon estimation (NMHE) algorithm. Experiments demonstrate that Roboat II is able to successfully perform online mapping and localization, plan its path and robustly track the planned trajectory in the confined river, implying that this autonomous vessel holds the promise on potential applications in transporting humans and goods in many of the waterways nowadays.

We propose a framework for tightly-coupled lidar inertial odometry via smoothing and mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory estimation and map-building. LIO-SAM formulates lidar-inertial odometry atop a factor graph, allowing a multitude of relative and absolute measurements, including loop closures, to be incorporated from different sources as factors into the system. The estimated motion from inertial measurement unit (IMU) pre-integration de-skews point clouds and produces an initial guess for lidar odometry optimization. The obtained lidar odometry solution is used to estimate the bias of the IMU. To ensure high performance in real-time, we marginalize old lidar scans for pose optimization, rather than matching lidar scans to a global map. Scan-matching at a local scale instead of a global scale significantly improves the real-time performance of the system, as does the selective introduction of keyframes, and an efficient sliding window approach that registers a new keyframe to a fixed-size set of prior "sub-keyframes." The proposed method is extensively evaluated on datasets gathered from three platforms over various scales and environments.

Direct sampling of building wastewater has the potential to enable "precision public health" observations and interventions. Temporal sampling offers additional dynamic information that can be used to increase the informational content of individual metabolic "features", but few studies have focused on high-resolution sampling. Here, we sampled three spatially close buildings, revealing individual metabolomics features, retention time (rt) and mass-to-charge ratio (mz) pairs, that often possess similar stationary statistical properties, as expected from aggregate sampling. However, the temporal profiles of features-providing orthogonal information to physicochemical properties-illustrate that many possess different feature temporal dynamics (fTDs) across buildings, with large and unpredictable single day deviations from the mean. Internal to a building, numerous and seemingly unrelated features, with mz and rt differences up to hundreds of Daltons and seconds, display highly correlated fTDs, suggesting non-obvious feature relationships. Data-driven building classification achieves high sensitivity and specificity, and extracts building-identifying features found to possess unique dynamics. Analysis of fTDs from many short-duration samples allows for tailored community monitoring with applicability in public health studies.

The problem of unicity and reidentifiability of records in large-scale databases has been studied in different contexts and approaches, with focus on preserving privacy or matching records from different data sources. With an increasing number of service providers nowadays routinely collecting location traces of their users on unprecedented scales, there is a pronounced interest in the possibility of matching records and datasets based on spatial trajectories. Extending previous work on reidentifiability of spatial data and trajectory matching, we present the first large-scale analysis of user matchability in real mobility datasets on realistic scales, i.e. among two datasets that consist of several million people's mobility traces, coming from a mobile network operator and transportation smart card usage. We extract the relevant statistical properties which influence the matching process and analyze their impact on the matchability of users. We show that for individuals with typical activity in the transportation system (those making 3-4 trips per day on average), a matching algorithm based on the co-occurrence of their activities is expected to achieve a 16.8 percent success only after a one-week long observation of their mobility traces, and over 55 percent after four weeks. We show that the main determinant of matchability is the expected number of co-occurring records in the two datasets. Finally, we discuss different scenarios in terms of data collection frequency and give estimates of matchability over time. We show that with higher frequency data collection becoming more common, we can expect much higher success rates in even shorter intervals.

Autonomous robotic boats are devised to transport people and goods similar to self-driving cars. One of the attractive features specially applied in water environment is to dynamically link and join multiple boats into one unit in order to form floating infrastructure such as bridges, markets or concert stages, as well as autonomously self-detach to perform individual tasks.In this paper we present a novel latching system that enables robotic boats to create dynamic united floating infrastructure while overcoming water disturbances. The proposed latching mechanism is based on the spherical joint (ball and socket) that allows rotation and free movements in two planes at the same time. In this configuration, the latching system is capable to securely and efficiently assemble/disassemble floating structures. The vision-based robot controller guides the self-driving robotic boats to latch with high accuracy in the millimeter range. Moreover, in case the robotic boat fails to latch due to harsh weather, the autonomous latching system is capable to recompute and reposition to latch successfully. We present experimental results from latching and docking in indoor environments. Also, we present results in outdoor environments from latching a couple of robotic boats in open water with calm and turbulent currents.

We propose a feedback control system for a reconfigurable multi-vessel platform. The platform consists of N propeller-driven vessels each of which is capable of latching to another vessel to form a rigid body of connected vessels. The main technical challenges are that i) depending on configurations of the platform the dynamic model would be different, and ii) the number of control variables in control system design increases as does the total number of vessels in the platform. To address these challenges, we develop a coordinated robust control scheme. Through experiments we assess trajectory tracking and disturbance attenuation performance of the control scheme in various configurations of the platform. Experiment results yield that average position and orientation tracking error are approximately 0.09m and 3°, and the maximum tracking error-to-disturbance ratio is 1.12.

This paper investigates an online system identification problem of estimating unknown parameters in nonlinear system dynamics in the absence of persistently excitation. To estimate parameters, we develop an algorithm that updates parameter estimates using sensor data and a basis that is built on a finite number of recorded sensor data. Based on our proposed approach we show that the algorithm achieves exponential convergence in both state and parameter estimation errors without the persistent excitation condition. We demonstrate the effectiveness of the proposed approach using both simulations and experiments on a reconfiguration autonomous multi-vessel platform: Simulation results illustrate that the parameter estimated by the developed algorithm converge to their ground truths. Experiment results validate the performance of the developed algorithm in estimating platform's system parameters across different multi-vessel configurations.

This paper presents a Learning-based Nonlinear Model Predictive Control (LB-NMPC) algorithm for reconfigurable autonomous vessels to facilitate high-accurate path tracking. Each vessel is designed to latch to a pre-defined point of another vessel that allows the vessels to form a rigid body. The number of possible configurations of such vessels exponentially grows as the total number of vessels increases, which imposes a technical challenge in modeling and identification. In this work, we propose a framework consisting of a real-time parameter estimator and a feedback control strategy, which is capable of ensuring high-accurate path tracking for any feasible configuration of vessels. Novelty of our method is in that the parameter is estimated on-line and adjusts control parameters (e.g., cost function and dynamic model) simultaneously to improve path-tracking performance. Through experiments on different configurations of connected-vessels, we demonstrate stability of our proposed approach and its effectiveness in high-accuracy in path tracking.

Unmanned surface vehicles (USVs) are typically designed for open area marine applications. In this paper, we present a new autonomy system (Roboat) for urban waterways which requires robust localization, perception, planning, and control. A novel localization system, based on the extended Kalman filter (EKF), is proposed for USVs, which utilizes LiDAR, camera, and IMU to provide a decimeter-level precision in dynamic GPS-attenuated urban waterways. Area and shape filters are proposed to crop water reflections and street obstacles from a pointcloud. Euclidean clustering and multi-object contour tracking are then introduced to detect and track the static and moving objects reliably in urban waters. An efficient path planner is tailored to calculate optimal trajectories to avoid these static and dynamic obstacles. Lastly, a nonlinear model predictive control (NMPC) scheme with full state integration is formulated for the four-control-input robot to accurately track the trajectory from the planner in rough water. Extensive experiments show that the robot is able to autonomously navigate in both the indoor waterway and the cluttered outdoor waterway in the presence of static and dynamic obstacles, implying that Roboat could have a great impact on the future of transportation in many coastal and riverside cities.

Parking spaces are costly to build, parking payments are difficult to enforce, and drivers waste an excessive amount of time searching for empty lots. Accurate quantification would inform developers and municipalities in space allocation and design, while real-time measurements would provide drivers and parking enforcement with information that saves time and resources. In this paper, we propose an accurate and real-time video system for future Internet of Things (IoT) and smart cities applications. Using recent developments in deep convolutional neural networks (DCNNs) and a novel vehicle tracking filter, we combine information across multiple image frames in a video sequence to remove noise introduced by occlusions and detection failures. We demonstrate that our system achieves higher accuracy than pure image-based instance segmentation, and is comparable in performance to industry benchmark systems that utilize more expensive sensors, such as radar. Furthermore, our system shows significant potential in its scalability to a city-wide scale and also in the richness of its output that goes beyond traditional binary occupancy statistics.

Cars can nowadays record several thousands of signals through the controller area network (CAN) bus technology and potentially provide real-time information on the car, the driver, and the surrounding environment. This paper proposes a new methodology for near-real-time analysis and classification of driver behavior using a selected subset of CAN bus signals, specifically gas pedal position, brake pedal pressure, steering wheel angle, steering wheel momentum, velocity, RPM, longitudinal and lateral acceleration. Data have been collected in a completely uncontrolled experiment involving 54 people, where over 2000 trips have been recorded without any type of predetermined driving instruction on a wide variety of road scenarios. While only few works have analyzed the driving behavior of more than 50 drivers using CAN bus data, we propose an unsupervised learning technique that clusters drivers in different groups, and offers a validation method to test the robustness of clustering in a wide range of experimental settings. The minimal amount of data needed to preserve robust driver clustering is also computed, showing that by properly choosing a subsampling strategy it is possible to reduce the size of the database as much as 99% without impairing the clustering performance.

The increasing availability and adoption of shared vehicles as an alternative to personally owned cars presents ample opportunities for achieving more efficient transportation in cities. With private cars spending on the average over 95% of the time parked, one of the possible benefits of shared mobility is the reduced need for parking space. While widely discussed, a systematic quantification of these benefits as a function of mobility demand and sharing models is still mostly lacking in the literature. As a first step in this direction, this paper focuses on a type of private mobility which, although specific, is a major contributor to traffic congestion and parking needs, namely, home-work commuting. We develop a data-driven methodology for estimating commuter parking needs in different shared mobility models, including a model where self-driving vehicles are used to partially compensate flow imbalance typical of commuting, and further reduce parking infrastructure at the expense of the increased traveled kilometers. We consider the city of Singapore as a case study and produce very encouraging results showing that the gradual transition to shared mobility models will bring tangible reductions in parking infrastructure. In the future-looking, self-driving vehicle scenario, our analysis suggests that up to 50% reduction in parking needs can be achieved at the expense of the increasing total traveled kilometers of less than 2%.

In this paper we present two programs for underrepresented Spanish minorities: Model Institute for Excellence (MIE) and Broader Participation in Computing-Alliance (BPC-A). MIE was a National Science Foundation funded program ran in Puerto Rico, whereas BPC-A was a project designed to impact Hispanic American institutions in Puerto Rico and the African Americans in the US Virgin Islands. Both programs enabled pre-college students to participate in early research, during which they learned from mentors how to conduct research using university facilities - going through the whole research cycle starting from documentation, formulation of a research question up to giving oral presentations or making posters, which were then presented in a research symposium. The programs also affected undergraduates through an intensive ten-week summer research program at partner institutions in both the US and internationally. Mentoring by faculty and peer mentoring produced very positive outcomes in transferring these undergraduates to graduate schools: over 300 Bachelor of Science degrees in Science, Engineering, and Mathematics and 130 undergraduates went to graduate schools (70 Master of Science and 60 PhD).

In this paper, we present the design, modeling, and real-time nonlinear model predictive control (NMPC) of an autonomous robotic boat. The robot is easy to manufacture, highly maneuverable, and capable of accurate trajectory tracking in both indoor and outdoor environments. In particular, a cross type four-thruster configuration is proposed for the robotic boat to produce efficient holonomic motions. The robot prototype is rapidly 3D-printed and then sealed by adhering several layers of fiberglass. To achieve accurate tracking control, we formulate an NMPC strategy for the four-control-input boat with control input constraints, where the nonlinear dynamic model includes a Coriolis and centripetal matrix, the hydrodynamic added mass, and damping. By integrating “GPS” modules and an inertial measurement unit (IMU) into the robot, we demonstrate accurate trajectory tracking of the robotic boat along preplanned paths in both a swimming pool and a natural river. Furthermore, the code generation strategy employed in our paper yields a two order of magnitude improvement in the run time of the NMPC algorithm compared to similar systems. The robot is designed to form the basis for surface swarm robotics testbeds, on which collective algorithms for surface transportation and self-assembly of dynamic floating infrastructures can be assessed.

A large number of vehicles routinely navigate through city streets; with on-board sensors, they can be transformed into a dynamic network that monitors the urban environment comprehensively and efficiently. In this paper, drive-by approaches are discussed as a form of mobile sensing, that offer a number of advantages over more traditional sensing approaches. It is shown that the physical properties of the urban environment that can be captured using drive-by sensing include ambient fluid, electromagnetic, urban envelope, photonic, and acoustic properties, which comprise the FEELS classification. In addition, the spatiotemporal variations of these phenomena are discussed as well as their implications on discrete-time sampling. The mobility patterns of sensor-hosting vehicles play a major role in drive-by sensing. Vehicles with scheduled trajectories, e.g., buses, and those with less predictable mobility patterns, e.g., taxis, are investigated for sensing efficacy in terms of spatial and temporal coverage. City Scanner is a drive-by approach with a modular sensing architecture, which enables cost-effective mass data acquisition on a multitude of city features. The City Scanner framework follows a centralized IoT regime to generate a near real-time visualization of sensed data. The sensing platform was mounted on top of garbage trucks and collected drive-by data for eight months in Cambridge, MA, USA. Acquired data were streamed to the cloud for processing and subsequent analyses. Based on a real-world application, we discuss and show the potential of using drive-by approaches to collect environmental data in urban areas using a variety of nondedicated land vehicles to optimize data collection in terms of spatiotemporal coverage.

Cities are encountering extensive deficits in infrastructure service while they are experiencing rapid technological advancements and overhauls in transportation systems. Standard bridge evaluation methods rely on visual inspections, which are infrequent and subjective, ultimately affecting the structural assessments on which maintenance plans are based. The operational behavior of a bridge must be observed more regularly and over an extended period in order to sufficiently track its condition and avoid unexpected rehabilitation. Mobile sensor networks are conducive to monitoring bridges vibrations routinely, with benefits that have been demonstrated in recent structural health monitoring (SHM) research. Though smartphone accelerometers are imperfect sensors, they can contribute valuable information to SHM, especially when aggregated, e.g., via crowdsourcing. In an application on the Harvard Bridge (Boston, MA), it is shown that acceleration data collected using smartphones in moving vehicles contained consistent and significant indicators of the first three modal frequencies of the bridge. In particular, the results became more precise when informatics from several smartphone datasets were combined. This evidence is the first to support the hypothesis that smartphone data, collected within vehicles passing over a bridge, can be used to detect several modal frequencies of the bridge. The result defines an opportunity for local governments to make partnerships that encourage the collection of low-cost bridge vibration data, which can contribute to more effective management and informed decision-making.

For centuries quality of life was a subject of studies across different disciplines. However, only with the emergence of a digital era, it became possible to investigate this topic on a larger scale. Over time it became clear that quality of life not only depends on one, but on three relatively different parameters: social, economic and well-being measures. In this study we focus only on the first two, since the last one is often very subjective and consequently hard to measure. Using a complete set of bank card transactions recorded by Banco Bilbao Vizcaya Argentaria (BBVA) during 2011 in Spain, we first create a feature space by defining various meaningful characteristics of a particular area performance through activity of its businesses, residents and visitors. We then evaluate those quantities by considering available official statistics for Spanish provinces (e.g., housing prices, unemployment rate, life expectancy) and investigate whether they can be predicted based on our feature space. For the purpose of prediction, our study proposes a supervised machine learning approach. Our finding is that there is a clear correlation between individual spending behavior and official socioeconomic indexes denoting quality of life. Moreover, we believe that this modus operandi is useful to understand, predict and analyze the impact of human activity on the wellness of our society on scales for which there is no consistent official statistics available (e.g., cities and towns, districts or smaller neighborhoods).

Recent availability of geo-localized data capturing individual human activity together with the statistical data on international migration opened up unprecedented opportunities for a study on global mobility. In this paper, we consider it from the perspective of a multi-layer complex network, built using a combination of three datasets: Twitter, Flickr and official migration data. Those datasets provide different, but equally important insights on the global mobility - while the first two highlight short-term visits of people from one country to another, the last one - migration - shows the long-term mobility perspective, when people relocate for good. The main purpose of the paper is to emphasize importance of this multi-layer approach capturing both aspects of human mobility at the same time. On the one hand, we show that although the general properties of different layers of the global mobility network are similar, there are important quantitative differences among them. On the other hand, we demonstrate that consideration of mobility from a multi-layer perspective can reveal important global spatial patterns in a way more consistent with those observed in other available relevant sources of international connections, in comparison to the spatial structure inferred from each network layer taken separately.

In recent years, cars have evolved from purely mechanical to veritable cyberphysical systems that generate large amounts of real-time data. These data are instrumental to the proper working of the vehicle itself, but make them amenable to a multitude of other uses. For instance, GPS information has recently been used for a large number of mobility studies in the academic community[1],[5], as well as to feed traffic apps such as Google Traffic and Waze. This use of vehicle data is already having a profound impact in science, industry, economy, and society at large. Now, imagine that instead of accessing one single source of vehicle-generated data (GPS), one can access the entire wealth of data exchanged on the controller area network (CAN) bus in near real timeamounting to over 4000 signals sampled at high frequency, corresponding to a few gigabytes of data per hour. What would be the implications, opportunities, and challenges sparked by this transition?

Cities are, by definition, plural, public, and productive. They are created by society itself (barring exceptional cases like master-planned Brasilia or Chandigarh) and they function as culture's petri dish for progress. Living in space and creating space can go hand in hand. We propose to employ design in a systematic exploration and germination of possible futures, exploring how ubiquitous computing -- i.e. the increasing deployment of sensors and hand-held electronics in recent years, what we call Senseable City -- is opening up a new approach to the study of the built environment. Design can investigate and intervene at the interface between people, technologies and the city -- developing research and applications that empower citizens to make choices that result in a more livable urban condition.

Traffic incidents such as road accidents and vehicle breakdowns are a major source of travel uncertainty and delay, but the mechanism by which they cause heavy traffic is not fully understood. Traffic management controllers are tasked with routing repair and clean up crews to clear the incident and often have to do so under time pressure and with imperfect information. To aid their decision making and help them understand how past incidents affected traffic, we propose Traffic Origins, a simple method to visualize the impact road incidents have on congestion. Just before a traffic incident occurs, we mark the incident location with an expanding circle to uncover the underlying traffic flow map and when it ends, the circle recedes. This not only directs attention to upcoming events, but also allows us to observe the impact traffic incidents have on vehicle flow in the immediate vicinity and the cascading effect multiple incidents can have on a road network. We illustrate this technique using road incident and traffic flow data from Singapore.

Due to recent technical developments, urban systems generate large and complex data sets. While visualizations have been used to make these accessible, often they are tailored to one specific group of users, typically the public or expert users. We present Touching Transport, an application that allows a diverse group of users to visually explore public transit data on a multi-touch tabletop. It provides multiple perspectives of the data and consists of three visualization modes conveying tempo-spatial patterns as map, time-series, and arc view. We exhibited our system publicly, and evaluated it in a lab study with three distinct user groups: citizens with knowledge of the local environment, experts in the domain of public transport, and non-experts with neither local nor domain knowledge. Our observations and evaluation results show we achieved our goals of both attracting visitors to explore the data while enabling gathering insights for both citizens and experts. We discuss the design considerations in developing our system, and describe our lessons learned in designing engaging tabletop visualizations.

Land-use classification is essential for urban planning. Urban land-use types can be differentiated either by their physical characteristics (such as reflectivity and texture) or social functions. Remote sensing techniques have been recognized as a vital method for urban land-use classification because of their ability to capture the physical characteristics of land use. Although significant progress has been achieved in remote sensing methods designed for urban land-use classification, most techniques focus on physical characteristics, whereas knowledge of social functions is not adequately used. Owing to the wide usage of mobile phones, the activities of residents, which can be retrieved from the mobile phone data, can be determined in order to indicate the social function of land use. This could bring about the opportunity to derive land-use information from mobile phone data. To verify the application of this new data source to urban land-use classification, we first construct a vector of aggregated mobile phone data to characterize land-use types. This vector is composed of two aspects: the normalized hourly call volume and the total call volume. A semi-supervised fuzzy c-means clustering approach is then applied to infer the land-use types. The method is validated using mobile phone data collected in Singapore. Land use is determined with a detection rate of 58.03%. An analysis of the land-use classification results shows that the detection rate decreases as the heterogeneity of land use increases, and increases as the density of cell phone towers increases.

In this paper, we introduce a simple and accurate indoor navigation system that can track and re-direct flying objects modeled from digital pixel designs. Using Pseudolite idea, this system works accurately indoors in spite of a high noise and multipath, to track and control helicopters with mounted LED lights mimicking the digital designs. The system was built using Simulink and Universal Software Radio Peripheral (USRP) hardware. It can also be used for many indoor applications, for example, tracking persons or things in a hospital with high accuracy.

Nowadays, the huge worldwide mobile-phone penetration is increasingly turning the mobile network into a gigantic ubiquitous sensing platform, enabling large-scale analysis and applications. In recent years, mobile data-based research reaches important conclusions about various aspects of human mobility patterns and trajectories. But how accurately do these conclusions reflect the reality? In order to evaluate the difference between the reality and the approximation methods, we study in this paper the error between real human trajectory and the one obtained through mobile phone data using different interpolation methods (linear, cubic, nearest and spline interpolations) while taking into account some mobility parameters. From extensive evaluations based on real cellular network activity data of the Boston metropolitan area, we show that the linear interpolation offers the best estimation for sedentary people and the cubic one for commuters. Moreover, the nearest interpolation appears as the best one for “ordinary people” doing regular stops and standard displacements. Another important experimental finding described in this paper is that trajectory estimation methods show different error regimes whether used within or outside the “territory” of the user defined by the radius of gyration.

This paper describes a new real-time urban monitoring system. The system uses the Localizing and Handling Network Event Systems (LocHNESs) platform developed by Telecom Italia for the real-time evaluation of urban dynamics based on the anonymous monitoring of mobile cellular networks. In addition, data are supplemented based on the instantaneous positioning of buses and taxis to provide information about urban mobility in real time, ranging from traffic conditions to the movements of pedestrians throughout the city. This system was exhibited at the Tenth International Architecture Exhibition of the Venice Biennale. It marks the unprecedented monitoring of a large urban area, which covered most of the city of Rome, in real time using a variety of sensing systems and will hopefully open the way to a new paradigm of understanding and optimizing urban dynamics.

In this paper, we propose a tool to explore human movement dynamics in a Metropolitan Area. By analyzing a mass of individual cell phone traces, we build a Human-City Interaction System for understanding urban mobility patterns at different user-controlled temporal and geographic scales. We solve the problems that are found in available tools for spatio-temporal analysis, by allowing seamless manipulability and introducing a simultaneous\multi-scale visualization of individual and aggregate flows. Our tool is built to support the exploration and discovery of urban mobility patterns and the daily interactions of millions of people. Moreover, we implement an intelligent algorithm to evaluate the level of mobility homophily of people moving from place to place.

Over the past decade there has been an explosion in the deployment of pervasive systems like cell phone networks and content aggregators on the Internet that produce massive amounts of data as by-products of their interaction with users. This data is related to the actions and opinions of people and thereby to the overall dynamics of cities, how they function and evolve over time.

The traditional relationship between the car, driver, and city can be described as waypoint navigation with additional traffic and maintenance information. The car can receive and store waypoint information, find the shortest route to these waypoints, integrate traffic information, find points-of-interest, and alert the driver of a pre-programmed set of maintenance issues related to the car. Here, we propose a new route system that is multi-goal-centric rather than waypoint-centric. Instead of focusing on determining the route to a specified waypoint, as done in commercially available navigation systems, the system will analyze the driver's behavior in order to extract the potential set(s) of goals that the driver would like to achieve. The system must also understand the city on a number of levels: physical, social, and commercial. This provides the foundation for a social and intelligent driving assistant, that helps the driver achieve his goals and helps the city perform better through interaction between both entities.

Recent developments in the technology industry and academia show that users are increasingly becoming the focus in technology development. Not only new applications capable of performing certain tasks are brought to potential users, but also technical innovations that are built around people in order to best fit their needs. Beyond standard web and desktop applications, an ideal use of this principle would be in a digital representation of a real-world city which allows city inhabitants to access, add, and modify information and thus use the city in a more informed way. The term WikiCity represents this concept. The development of a WikiCity requires answers to questions such as: how can a city be sensed, modeled, and actuated in realtime? How can inhabitants interact with information in the city? The paper presents this idea through a use-case scenario in which users can inform themselves about certain events in the city. They can retrieve real-time information related to the events such as nearby happenings, availability of public transportation, and traffic conditions, or create a personalized event route. Users can also feed information back into the WikiCity system before, during, and after attending events. This requires new developments in modeling of human behavior, flexible back-end infrastructures, and semantic-web. The architecture of such system including the relations between data sources and user services, and data flow procedures are explained in the paper. A first implementation of the WikiCity concept was presented at the White Night event in Rome, Italy on September 8, 2007.

Three-dimensional datasets (voxel datasets), generated by different types of sensing or computer simulations, are quickly becoming crucial to various disciplines - from biomedicine to geophysics. Phoxel-Space is an interface that enables the exploration of these datasets through physical materials. It aims at overcoming the limitations of traditional planar displays by allowing users to intuitively navigate and understand complex 3-dimensional datasets. The system works by allowing the user to manipulate a freeform geometry whose surface intersects a voxel dataset. The intersected voxel values are projected back onto the surface of the physical material to reveal a non-planar section of the dataset. The paper describes how the interface can be used as a representational aid in several example application domains, overcoming many limitations of conventional planar displays.

This paper describes a novel system for the real-time computational analysis of landscape models. Users of the system - called Illuminating Clay - alter the topography of a clay landscape model while the changing geometry is captured in real-time by a ceiling-mounted laser scanner. A depth image of the model serves as an input to a library of landscape analysis functions. The results of this analysis are projected back into the workspace and registered with the surfaces of the model.We describe a scenario for which this kind of tool has been developed and we review past work that has taken a similar approach. We describe our system architecture and highlight specific technical issues in its implementation.We conclude with a discussion of the benefits of the system in combining the tangible immediacy of physical models with the dynamic capabilities of computational simulations.