Typical Mobility Patterns Research Articles

L2MM: Learning to Map Matching with Deep Models for Low-Quality GPS Trajectory Data

Map matching is a fundamental research topic with the objective of aligning GPS trajectories to paths on the road network. However, existing models fail to achieve satisfactory performance for low-quality (i.e., noisy, low-frequency, and non-uniform) trajectory data. To this end, we propose a general and robust deep learning-based model, L2MM , to tackle these issues at all. First, high-quality representations of low-quality trajectories are learned by two representation enhancement methods, i.e., enhancement with high-frequency trajectories and enhancement with the data distribution . The former employs high-frequency trajectories to enhance the expressive capability of representations, while the latter regularizes the representation distribution over the latent space to improve the generalization ability of representations. Secondly, to embrace more heuristic clues, typical mobility patterns are recognized in the latent space and further incorporated into the map matching task. Finally, based on the available representations and patterns, a mapping from trajectories to corresponding paths is constructed through a joint optimization method. Extensive experiments are conducted based on a range of datasets, which demonstrate the superiority of L2MM and validate the significance of high-quality representations as well as mobility patterns.

Goal-oriented possibilistic fuzzy C-Medoid clustering of human mobility patterns-Illustrative application for the Taxicab trips-based enrichment of public transport services.

The discovery of human mobility patterns of cities provides invaluable information for decision-makers who are responsible for redesign of community spaces, traffic, and public transportation systems and building more sustainable cities. The present article proposes a possibilistic fuzzy c-medoid clustering algorithm to study human mobility. The proposed medoid-based clustering approach groups the typical mobility patterns within walking distance to the stations of the public transportation system. The departure times of the clustered trips are also taken into account to obtain recommendations for the scheduling of the designed public transportation lines. The effectiveness of the proposed methodology is revealed in an illustrative case study based on the analysis of the GPS data of Taxicabs recorded during nights over a one-year-long period in Budapest.

Recognizing and Analyzing Private Car Commuters Using Big Data of Electronic Registration Identification of Vehicles

Private cars’ travel has been one of the main factors causing urban traffic congestion. Especially during morning and evening rush hours, private car commuters bring a significant burden to traffic. However, there is very little literature on them due to the lack of access to relevant data. A real-world dataset containing vehicle passing records of Electronic Registration Identification (ERI) of vehicles provides us with an opportunity to research private car commuters. We propose a regular behavior-based model to recognize private car commuters. In the model, a regular behavior-based definition of private car commuters is firstly proposed. Then, TDSP(Time dependent shortest path)-based distance measurement and a hierarchical clustering method are designed to extract regular behaviors. Furthermore, we utilize a regular threshold <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p $ </tex-math></inline-formula> to help determine regular behaviors. The experiment, which is conducted on a real-world dataset containing one-week vehicle passing records in Chongqing of China, validates the effectiveness and accuracy of the proposed model. Moreover, we analyze the mobility pattern of private car commuters, and some typical mobility patterns of them are successfully found.

Family figurations in displacement: entangled mobilities of refugees towards Germany and beyond

ABSTRACT Refugees rarely flee in isolation. Instead, their everyday lives and mobilities are fundamentally shaped by the broader set of social relations in which they are embedded, particularly by their families. Drawing on interviews with sixty displaced people living in Germany and in-depth case studies of the trajectories of refugees from Eritrea and Syria, we reconstruct the role that families and other social relations transgressing national borders have played in their mobility to Germany and how their lives have been rescaled since initial displacement. Based on central ideas from figurational sociology, which we link with scholarship on forced migration, transnationalism and family relations, our paper identifies four specific family figurations in displacement that represent typical mobility patterns and constellations of ‘doing family’ in transnational spaces: the lone yet connected traveller; the reunited nuclear family; the transnationally separated family; and the transnationally extended family. We argue that family figurations in displacement are, on the one hand, decisively shaped by specific family relations and distinct displacement trajectories with various phases of family separation and reunion. On the other hand, they are fundamentally configured by migration regimes and asylum systems that substantially constrain the opportunities to live dignified local or transnational family lives.

Simulating spatial crime patterns: What do we learn in standard ecological studies of crime?

ObjectivesGiven the spatial nature of offender and target behavior, what do standard ecological studies of crime aggregating measures to different geographic units actually tell us? MethodsThis study used a simple stylized simulation model of crime patterns based on offenders, an exponential distance decay function based on Euclidean distance to capture their typical mobility patterns when committing offenses, and immobile targets. ResultsThere were four key results. First, although a measure of targets can explain much of the variance in micro-level models, knowing where offenders live, and their typical distances traveled to offending, greatly improved the model performance. Second, accounting for the typical spatial movement of offenders before aggregating to larger units produces better results based on explanatory power. Third, the explanatory power of targets alone was much weaker when aggregating to larger units despite the fact that the simulated model of crime events was entirely based on micro processes, highlighting that variance explained is distinct from causal processes. Fourth, knowing how offenders behave in target-rich versus target-poor environment impacts the results considerably. ConclusionsThe findings demonstrated the consequences of a spatially explicit model of offender and target behavior for ecological studies of crime that aggregate measures to geographic units that are either at the micro-, meso-, or macro-level.

SmartHO: mobility pattern recognition assisted intelligent handoff in wireless overlay networks

Handoff management providing continuous mobility services is important for end-users in wireless overlay networks. The existing studies focus on improving the handoff performance with little attention to determine the current mobility pattern to provide different advanced services for end-users, and the handoff process is not intelligent as well. Due to the randomness and fuzziness of human mobility, predicting the mobility is still an open issue. In this paper, a mobility pattern recognition assisted intelligent handoff (SmartHO) is proposed. By exploring the regularity and rationality in the seemingly random daily itinerary and analyzing the typical mobility patterns, a spatial---temporal mobility pattern model is firstly constructed, and the mobility pattern recognition algorithm based on the joint space---time correlation then is designed. In different handoff cases, the specific handoff optimization strategies corresponding to the mobility patterns are employed to make the handoff process more intelligent. With the configurations from the authors real life, the simulations conducted on the NS-2 platform show that SmartHO outperforms FHMIPv6 in the critical handoff performance metrics.