PRESS: A Novel Framework of Trajectory Compression in Road Networks

Location data becomes more and more important. In this paper, we focus on the trajectory data, and propose a new framework, namely PRESS (Paralleled Road-Network-Based Trajectory Compression), to effectively compress trajectory data under road network constraints. Different from existing work, PRESS proposes a novel representation for trajectories to separate the spatial representation of a trajectory from the temporal representation, and proposes a Hybrid Spatial Compression (HSC) algorithm and error Bounded Temporal Compression (BTC) algorithm to compress the spatial and temporal information of trajectories respectively. PRESS also supports common spatial-temporal queries without fully decompressing the data. Through an extensive experimental study on real trajectory dataset, PRESS significantly outperforms existing approaches in terms of saving storage cost of trajectory data with bounded errors.Comment: 27 pages, 17 figure

Extract human mobility patterns powered by City Semantic Diagram

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Shape predominant effect in pattern recognition of geometric figures of rhesus monkey

AbstractThree monkeys were trained successively with discrimination, concurrent matching to sample, and sameness–difference judgment tasks in which learning curves were compared. Then, the display duration for the stimuli was shortened to 100, 50, and 30 ms respectively to test the changes in accuracy and reaction time. All results in three experimental paradigms suggested consistently that the geometric shape (triangle, circle, and square) plays a more predominant role than topological features (the hole inside of a figure and the hole numbers) in monkey figure recognition. The results are different from the experiment by human subjects who presented hole predominant in figure recognition. Therefore, the precedence in perception depends on subject species, stimulus set, and ecological significance of the perceiving process

Suggested versus Extended Gifts: How Alternative Market Institutions Mitigate Moral Hazard

Gift exchange can partially mitigate supply-side moral hazard, even in anonymous market interactions. In a market where quality is not fully contractable, the amount that a price exceeds the market-clearing price for the lowest quality is a gift from the buyer. We show that the gift formation process, inextricably linked with a market institution’s price formation process, greatly influences the size and effectiveness of the gift. When the market institution dictates that prices are formed by bids posted by buyers, the gift is extended to the seller. When the market institution dictates that prices are formed by offers posted by sellers, the gift is suggested by the seller. We conjecture that extended gifts do not instill as strong a concern for the material welfare of the other party as suggested gifts. We show in experiments that this effect is quite profound in both monopsonist and thick markets. Posted offer markets generate higher prices, in turn larger gifts, and higher levels of product quality than posted bid ones. In addition, the posted offer institution generates a higher quality given the price, rather than simply generating higher prices. Both sides of the market obtain higher payoffs under posted offer institutions

Water pollutant fingerprinting tracks recent industrial transfer from coastal to inland China: a case study

In recent years, China’s developed regions have transferred industries to undeveloped regions. Large numbers of unlicensed or unregistered enterprises are widespread in these undeveloped regions and they are subject to minimal regulation. Current methods for tracing industrial transfers in these areas, based on enterprise registration information or economic surveys, do not work. The authors have developed an analytical framework combining water fingerprinting and evolutionary analysis to trace the pollution transfer features between water sources. We collected samples in Eastern China (industrial export) and Central China (industrial acceptance) separately from two water systems. Based on the water pollutant fingerprints and evolutionary trees, we traced the pollution transfer associated with industrial transfer between the two areas. The results are consistent with four episodes of industrial transfers over the past decade. The results also show likely types of the transferred industries - electronics, plastics, and biomedicines - that contribute to the water pollution transfer

RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer

GPS trajectories are the essential foundations for many trajectory-based applications, such as travel time estimation, traffic prediction and trajectory similarity measurement. Most applications require a large amount of high sample rate trajectories to achieve a good performance. However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints.We study the task of trajectory recovery in this paper as a means for increasing the sample rate of low sample trajectories. Currently, most existing works on trajectory recovery follow a sequence-to-sequence diagram, with an encoder to encode a trajectory and a decoder to recover real GPS points in the trajectory. However, these works ignore the topology of road network and only use grid information or raw GPS points as input. Therefore, the encoder model is not able to capture rich spatial information of the GPS points along the trajectory, making the prediction less accurate and lack spatial consistency. In this paper, we propose a road network enhanced transformer-based framework, namely RNTrajRec, for trajectory recovery. RNTrajRec first uses a graph model, namely GridGNN, to learn the embedding features of each road segment. It next develops a spatial-temporal transformer model, namely GPSFormer, to learn rich spatial and temporal features along with a Sub-Graph Generation module to capture the spatial features for each GPS point in the trajectory. It finally forwards the outputs of encoder model into a multi-task decoder model to recover the missing GPS points. Extensive experiments based on three large-scale real-life trajectory datasets confirm the effectiveness of our approach