Spatial-temporal Constraints Research Articles

Temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing Spatial-Temporal Constraints for Heterogeneous Grasslands

Continuous satellite-derived leaf area index (LAI) time series are critical for modeling land surface process. In this study, we present an interpolation algorithm to predict the missing data in LAI time series for ecosystems with high within-ecosystem heterogeneity, particularly heterogeneous grasslands. The algorithm is based on spatial-temporal constraints, i.e., the missing data in the LAI time series of a pixel are predicted by the phenological links with other pixels. To address the uncertainties in the construction and selection of reference curves in a heterogeneous landscape, the algorithm constructs a reference dataset for each missing data in the LAI time series from all pixels showing very strong linear phenological links with the target pixel within a region. We also use an iterative process to update the available spatial-temporal constraints. We tested the algorithm with an eight-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product in the Songnen grasslands, Northeast China in 2010 and 2011. The validation dataset was generated based on high quality time series by artificially adding data gaps. The algorithm achieved high overall interpolation accuracies with high coefficient of determination R2 (>0.9) and low root mean square error (RMSE) (<0.2) in both dry (2010) and wet (2011) years. The algorithm showed advantages in predicting missing data for different seasons and proportions of missing data versus the algorithm that uses regional average LAI curve as a reference. These results suggest that the proposed algorithm could more effectively characterize spatial-temporal constraint information in heterogeneous grasslands for temporal interpolation.

Spatial–Temporal Analysis of the Heat and Electricity Demand of the Swiss Building Stock

In 2015, space heating and domestic hot water production accounted for around 40% of the Swiss final energy consumption. Reaching the goals of the 2050 energy strategy will require significantly reducing this share despite the growing building stock. Renewables are numerous but subject to spatial-temporal constraints. Territorial planning of energy distribution systems enabling the integration of renewables requires having a spatial-temporal characterization of the energy demand. This paper presents two bottom-up statistical extrapolation models for the estimation of the geo-dependent heat and electricity demand of the Swiss building stock. The heat demand is estimated by means of a statistical bottom-up model applied at the building level. At the municipality level, the electricity load curve is estimated by combining socio-economic indicators with average consumption per activity and/or electric device. This approach also allows to break down the estimated electricity demand according to activity type (e.g. households, various industry & service activities) and appliance type (e.g. lighting, motor force, fridges). The total estimated aggregated demand is 94 TWh for heat and 58 TWh for electricity, which represent a deviation of 2.9% and 0.5% respectively compared to the national energy consumption statistics. In addition, comparisons between estimated and measured electric load curves are done to validate the proposed approach. Finally these models are used to build a geo-referred database of heat and electricity demand for the entire Swiss territory. As an application of the heat demand model, a realistic saving potential is estimated for the existing building stock; this potential could be achieved through by a deep retrofit program. One advantage of the statistical bottom-up model approach is that it allows to simulate a building stock that replicates the diversity of building demand. This point is important in order to correctly account for the mismatch between gross and net energy saving potential, often called performance gap. The impact of this performance gap is substantial since the estimated net saving potential is only half of the gross one.

Service-Oriented Cooperation Models and Mechanisms for Heterogeneous Driverless Vehicles at Continuous Static Critical Sections

As driverless vehicles are increasingly becoming possible, so does the use of such vehicles as intelligent carriers in different domains. Intelligent transportation systems (ITSs) show increasingly heterogeneous, cyber-physical, cooperative, and service-oriented features and are beginning to be merged with the emerging cyber-physical-social systems. Given this new trend, how to make these intelligent vehicles cooperate more safely and efficiently with one another according to novel constraints, such as mission type and quality-of-service (QoS), has become a vital aspect of cooperative ITS (C-ITS). With these emerging characteristics, the classical passing-through-intersection problem has gained new connotations, worth further exploring. After analyzing the essences of this new problem, service-oriented cooperation models and mechanisms for whole autonomous vehicles approaching intersections are investigated in this paper. First, related traffic objects and possible vehicular behaviors are abstracted and modeled with the cyber-physical cooperative features and QoS constraints. A new reservation-based scheduling procedure is then conducted by employing the concepts of vehicle-to-infrastructure communication, and typical vehicular passing-through behaviors and several spatial-temporal constraints are designed to coordinate vehicles passing through an intersection divided as a series of continuous static critical sections. Given these considerations, a priority-based centralized scheduling algorithm, named cs PriorFIFO, which adopts a novel priority inheritance mechanism to promote the traffic QoS of emergent vehicles, is proposed. Finally, all these designs are implemented in a traffic simulator named QoS-CITS, and the functions and the performance of these studied methods are verified and compared.

The Study of Subtitle Translation Based on Multi-Hierarchy Semantic Segmentation and Extraction in Digital Video

As a set of visual, auditory and text information in one integrated media information, video plays an increasingly prominent role in people's lives. This paper established a reasonable and effective multi-hierarchy semantic information descriptive model based on video segmentation and extraction technology to realize the mapping of video semantic information from the low-level feature to high-level and meanwhile carried out the subtitle translation studies based on the technical level. The study shows that the model can well describe the service practice in subtitle translation in the premise of integration of video multi-modal information, based on the right segmentation and extraction of video object, on account of the consideration of each video object synchronization as well as temporal-spatial constraints related issues.

Part-aware trajectories association across non-overlapping uncalibrated cameras

This paper focuses on the problem of multi-person tracking across non-overlapping uncalibrated cameras using data association method. The problem is extremely difficult as we have very limited cues to associate persons between cameras. To tackle the problem, our system consists of firstly building multiple trajectories from each camera independently, and then finding associations of trajectories between every two cameras of interest, where the later is the most challenging process. Our contributions are mainly two folds: First, we introduce a method to explore the human part configurations on every trajectory to describe the inter-camera spatial-temporal constraints for trajectories association. Second, we formulate trajectories association across non-overlapping cameras as a multi-class classification problem via the Markov Random Field (MRF) to effectively utilize domain priors such as group activity between persons. With the proposed part-aware correspondences and pair-wise group activity constraints of trajectories, we can achieve robust multi-person tracking. Experimental results on a benchmark dataset validates the effectiveness of our proposed approach.

Efficient Lane Boundary Detection with Spatial-Temporal Knowledge Filtering.

Lane boundary detection technology has progressed rapidly over the past few decades. However, many challenges that often lead to lane detection unavailability remain to be solved. In this paper, we propose a spatial-temporal knowledge filtering model to detect lane boundaries in videos. To address the challenges of structure variation, large noise and complex illumination, this model incorporates prior spatial-temporal knowledge with lane appearance features to jointly identify lane boundaries. The model first extracts line segments in video frames. Two novel filters—the Crossing Point Filter (CPF) and the Structure Triangle Filter (STF)—are proposed to filter out the noisy line segments. The two filters introduce spatial structure constraints and temporal location constraints into lane detection, which represent the spatial-temporal knowledge about lanes. A straight line or curve model determined by a state machine is used to fit the line segments to finally output the lane boundaries. We collected a challenging realistic traffic scene dataset. The experimental results on this dataset and other standard dataset demonstrate the strength of our method. The proposed method has been successfully applied to our autonomous experimental vehicle.

Inverse vehicle routing for activity-based urban freight forecast modeling and city logistics

ABSTRACTGoods movement is one of the fastest growing transportation sectors, affecting both economic and environmental sustainability, particularly in dense urban areas with traffic congestion and air pollution. To meet this challenge, urban public agencies have paid attention to policies and systems to facilitate efficient and sustainable city logistics. This paper proposes a modeling framework to consider both spatial–temporal constraints and a means to calibrate the model from observable data, based on an adaptation of an activity-based passenger model called the household activity pattern problem. Conceptual comparisons with a state-of-the-art freight forecasting methodology are made using an example. Application of the model is illustrated through formulating and implementing a Sequential Selective Vehicle Routing Problem associated with drayage truck activities at the San Pedro Bay Ports in Southern California.

Impact of Improved Accessibility on Shopping Activity: Person-Based Measure

AbstractAccessibility has long been considered as an important factor that influences activity generation, especially nonwork activities. However, the conclusions on whether greater accessibility leads to more travel have been mixed. One reason for the conflicting results is that individual heterogeneity is not taken into account in the previous location-based accessibility measures. This paper fills this gap by operationalizing a person-based space-time accessibility (STA) measure based on the individual’s spatial-temporal constraints. A discrete-continuous model of shopping activity participation and duration is developed incorporating this STA measure in order to investigate the influences of accessibility on activity generation. Using data from a global positioning system (GPS)–based activity travel survey from the Shangdi area of Beijing, China, this paper confirms that accessibility remains a significant positive effect on shopping activity generation. This implies that an individual’s spatial-tempo...

A Case-Based Reasoning Approach for Task-Driven Remote Sensing Image Discovery Under Spatial–Temporal Constraints

Recent advances in earth observation technology have led to the generation of large numbers of remote sensing (RS) observations. As RS applications continue to increase in magnitude, major problems include how to discover and make quick and robust use of such data in spatial data infrastructures (SDIs). In this paper, a new RS image discovery method is proposed for discovering observational data based on task, location, and time. This method approaches location and time not only as filters but also as spatial–temporal constraints in the discovery process, and exploits the relationships between tasks and RS data sources under spatial–temporal constraints through case-based reasoning (CBR). In this method, cases are past experiences which comprise task, time, location, and image parameters, describing what images were used to satisfy a particular task, and at what time and place each discovery was made. CBR, given its similarity to assessment and result reasoning models, finds past cases that satisfy a user’s query and generates image parameters for specific RS data needed to satisfy that request. A prototype called iGeoportal was developed to evaluate the effectiveness of the proposed method. Experiments show that it performs efficiently when discovering RS images and can be easily integrated into current SDIs through a service-oriented architecture.

Querying and integrating spatial–temporal information on the Web of Data via time geography

The Web of Data is a rapidly growing collection of datasets from a wide range of domains, many of which have spatial–temporal aspects. Hägerstrand’s time geography has proven useful for thinking about and understanding the movements and spatial–temporal constraints of humans. In this paper, we explore time geography as a means of querying and integrating multiple spatial–temporal data sources. We formalize the concept of the space–time prism as an ontology design pattern to use as a framework for understanding and representing constraints and interactions between entities in space and time. We build on a formalization of space–time prisms and apply it in the context of the Web of Data, making it usable across multiple domains and topics. We demonstrate the utility of this approach through two use cases from the domains of environmental monitoring and cultural heritage, showing how space–time prisms enable spatial–temporal and semantic reasoning directly on distributed data sources.

From interventions to mechanistic explanations

An important strategy in the discovery of biological mechanisms involves the piecing together of experimental results from interventions. However, if mechanisms are investigated by means of ideal interventions, as defined by James Woodward and others, then the kind of information revealed is insufficient to discriminate between modular and non-modular causal contributions. Ideal interventions suffice for constructing webs of causal dependencies that can be used to make some predictions about experimental outcomes, but tell us little about how causally relevant factors are organized together and how they interact with each other in order to produce a phenomenon. I argue that lab research relies on more elaborated types of interventions targeting in a controlled fashion multiple variables at the same time in order to probe the temporal organization of causally-relevant factors along distinct causal pathways and to test for non-modular interaction effects, thus providing crucial spatial-temporal constraints guiding the formulation of more detailed mechanistic explanations.

Querying and Integrating SpatiallTemporal Information on the Web of Data Via Time Geography

The Web of Data is a rapidly growing collection of datasets from a wide range of domains, many of which have spatial–temporal aspects. Hagerstrand’s time geography has proven useful for thinking about and understanding the movements and spatial–temporal constraints of humans. In this paper, we explore time geography as a means of querying and integrating multiple spatial–temporal data sources. We formalize the concept of the space–time prism as an ontology design pattern to use as a framework for understanding and representing constraints and interactions between entities in space and time. We build on a formalization of space–time prisms and apply it in the context of the Web of Data, making it usable across multiple domains and topics. We demonstrate the utility of this approach through two use cases from the domains of environmental monitoring and cultural heritage, showing how space–time prisms enable spatial–temporal and semantic reasoning directly on distributed data sources.

Recommending travel packages based on mobile crowdsourced data

Mobile crowdsoured data from location based social network services (LBSNs) provide information on individual's preferences for locations. In this article, we propose a travel package recommendation system to help users make travel plans by leveraging mobile crowdsourced data. We extract user preferences, discover points of interest (POIs), and determine location correlations from check-in records. We then generate personalized travel packages by considering user preferences, POI characteristics, and temporalspatial constraints such as travel time and starting location. A prototype system is built and evaluated on real-world crowdsourced data from Jie Pang, one of the most popular LBSNs in China.

A spatial–temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images

The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-resolution mapping (SRM) can produce land cover maps with a finer spatial resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse-resolution image as input. Uncertainty always exists in resultant fine-resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of fine spatial resolution remotely sensed images. These data can provide fine-resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial–temporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-resolution image and a previous fine-resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Landsat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the optimal STHNN weight parameters.

Analysis of train movement dynamics under various temporal–spatial constraints in fixed-block railway network using extended cellular automaton model

In the fixed-block railway traffic, the trains adjust their speeds in view of their preceding allowable spaces caused by their respective front adjacent trains or specified by scheduling commands. The railway lines have the line-type speed limits within some block sections and the point-type ones at the terminals of block sections. Those speed limits originate from line conditions, scheduling commands and indications of signal equipment. This paper attempts to in detail reveal the train movement mechanism synthetically considering those temporal–spatial constraints. The proposed train movement model defines four kinds of target points and utilizes them to successively engender the instantaneous target points with their corresponding target speeds. It adopts the rule-based description mechanism in cellular automata (CA) but with continuous spaces to replicate restrictive, autonomous and synergistic behaviors of and among trains. The selections of accelerations and decelerations are based upon the data models of practical acceleration and deceleration processes; thereupon, the model is data-driven. The analysis of train movement dynamics through case studies demonstrates that the extended CA model can reproduce the train movement mechanism of grading speed control to satisfy the aforementioned temporal–spatial constraints. The model is applicable to represent the as-is or should-be states of train movements when adjustable parameters are properly configured.

Online decentralized information gathering with spatial–temporal constraints

We are interested in coordinating a team of autonomous mobile sensor agents in performing a cooperative information gathering task while satisfying mission-critical spatial–temporal constraints. In particular, we present a novel set of constraint formulations that address inter-agent collisions, collisions with static obstacles, network connectivity maintenance, and temporal-coverage in a resource-efficient manner. These constraints are considered in the context of the target search problem, where the team plans trajectories that maximize the probability of target detection. We model constraints continuously along the agents’ trajectories and integrate these constraint models into decentralized team planning using a computationally efficient solution method based on the Lagrangian formulation and decentralized optimization. We validate our approach in simulation with five UAVs performing search, and through hardware experiments with four indoor mobile robots. Our results demonstrate team planning with spatial–temporal constraints that preserves the performance of unconstrained information gathering and is feasible to implement with reasonable computational and communication resources.

Incorporating Intrahousehold Interactions into a Tour-Based Model of Public Transport Use in Car-Negotiating Households

Intrahousehold interactions in travel are fundamental to an understanding of activity travel behavior, as reflected by the substantial percentage of regional travel that is made jointly. The development of travel demand models that incorporate intrahousehold interactions is crucial to a credible analysis of traveler response to policies. A tour-based modeling framework is used to examine intrahousehold interactions in travel mode choice with a focus on public transport use in households having differences in car availability. An important distinction is made between car-sufficient households (in which there are at least as many cars in the household as license holders) and car-negotiating households (households that have fewer cars than license holders). Intrahousehold interactions and temporal–spatial constraints are explicitly represented by different patterns of joint household tours, with home-based tours as the unit of analysis. The empirical analysis is based on a nested logit model that was developed to integrate intrahousehold interactions with tour-based mode choices; Sydney Household Travel Survey data are used. The results show that joint household travel accounts for more than half of weekday home-based tours in Sydney, Australia. The arrangement of joint household tours is shown to depend on household context, situational factors, and social constraints. Mode choice associated with different joint tour patterns is influenced by household and individual characteristics, tour attributes, and transport-related fringe benefits.

A Structured and Flexible Language for Physical Activity Assessment and Characterization

Developing more accurate assessments of physical activity (PA) and sedentary behavior (SB) is an important public health research priority. Assessing PA and SB is challenging in all segments of the population, but it is especially difficult in children due to cognitive limitations and more sporadic and intermittent activity patterns. Moreover, they are influenced by several factors including temporal-spatial constraints and social conditions. To accurately assess PA and SB, it is essential to clearly define methods for describing all these factors. The goal of this paper is to potentiate advances in the field by proposing a base ontology for characterizing physical activity, sedentary behavior, and the context in which it occurs. The ontology would establish a flexible base language to facilitate standardized descriptions of these behaviors for researchers and public health professionals.

Spatial-temporal constraints on decision-making during shooting performance in the team sport of futsal

In this paper we examined the influence of opposing players constraining the decision-making of an attacker during shooting performance in futsal. Performance during 10 competitive matches was recorded and examined from the moment a shot was taken until the ball was intercepted or entered the goal in sequences of play: ending in a goal, a goalkeeper's save, or an interception by the nearest defender. The variables under scrutiny in this study were (i) the distance of each player to the ball's trajectory, (ii) the time for the ball to arrive at that same point (i.e. the interception point), and (iii), the required movement velocity of the nearest defender and the goalkeeper to intercept the ball. Results showed that values of distance from a defender and goalkeeper to the interception points were significantly lower when they intercepted the ball. The time of ball arrival at the interception point of the defender was also lower when the ball was intercepted. The required velocities of the nearest outfield defender and the goalkeeper to intercept the ball were significantly lower during plays in which they intercepted the ball, than in plays in which the ball was not intercepted. Our results suggest that researchers and practitioners should consider simultaneously both space and time in analysis of interceptive actions in team sports. The required movement velocities of the opponents to intercept the ball are reliable spatial-temporal variables constraining decision-making during shooting performance in team sports like futsal.

Recognizing Interactive Group Activities Using Temporal Interaction Matrices and Their Riemannian Statistics

While video-based activity analysis and recognition has received much attention, a large body of existing work deals with activities of a single subject. Modeling and recognition of coordinated multi-subject activities, or group activities, present in a variety of applications such as surveillance, sports, and biological monitoring records, etc., is the main objective of this paper. Unlike earlier attempts which model the complex spatial temporal constraints among multiple subjects with a parametric Bayesian network, we propose a compact and discriminative descriptor referred to as the Temporal Interaction Matrix for representing a coordinated group motion pattern. Moreover, we characterize the space of the Temporal Interaction Matrices using the Discriminative Temporal Interaction Manifold (DTIM), and use it as a framework within which we develop a data-driven strategy to characterize the group motion pattern without employing specific domain knowledge. In particular, we establish probability densities on the DTIM for compactly describing the statistical properties of the coordinations and interactions among multiple subjects in a group activity. For each class of group activity, we learn a multi-modal density function on the DTIM. A Maximum a Posteriori (MAP) classifier on the manifold is then designed for recognizing new activities. In addition, we have extended this model to one with which we can explicitly distinguish the participants from non-participants. We demonstrate how the framework can be applied to motions represented by point trajectories as well as articulated human actions represented by images. Experiments on both cases show the effectiveness of the proposed approach.