The Handoff Mechanism Based on Mobile Location Prediction

Benefiting from convenient cycling and flexible parking locations, the Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. However, redundant and low-utility stations waste public urban space and maintenance costs of DL-PBS vendors. In this article, we propose a Bicycle Station Dynamic Planning (BSDP) system to dynamically provide the optimal bicycle station layout for the DL-PBS network. The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation. In the bicycle drop-off location clustering module, candidate bicycle stations are clustered from each spatio-temporal subset of the large-scale cycling trajectory records. In the bicycle-station graph modeling module, a weighted digraph model is built based on the clustering results and inferior stations with low station revenue and utility are filtered. Then, graph models across time periods are combined to create a graph sequence model. In the bicycle-station location prediction module, the GGNN model is used to train the graph sequence data and dynamically predict bicycle stations in the next period. In the bicycle-station layout recommendation module, the predicted bicycle stations are fine-tuned according to the government urban management plan, which ensures that the recommended station layout is conducive to city management, vendor revenue, and user convenience. Experiments on actual DL-PBS networks verify the effectiveness, accuracy, and feasibility of the proposed BSDP system.

Download Full-text

Japan’s voluntary lockdown: further evidence based on age-specific mobile location data

Japanese Economic Review ◽

10.1007/s42973-021-00077-9 ◽

2021 ◽

Author(s):

Tsutomu Watanabe ◽

Tomoyoshi Yabu

Keyword(s):

Age Groups ◽

Evidence Based ◽

Intervention Effect ◽

Information Effect ◽

Mobile Location ◽

Serious Illness ◽

Location Data ◽

Voluntary Response ◽

Younger Age ◽

Age Profile

AbstractChanges in people’s behavior during the COVID-19 pandemic can be regarded as the result of two types of effects: the “intervention effect” (changes resulting from government orders for people to change their behavior) and the “information effect” (voluntary changes in people’s behavior based on information about the pandemic). Using age-specific mobile location data, we examine how the intervention and information effects differ across age groups. Our main findings are as follows. First, the age profile of the intervention effect shows that the degree to which people refrained from going out was smaller for older age groups, who are at a higher risk of serious illness and death, than for younger age groups. Second, the age profile of the information effect shows that the degree to which people stayed at home tended to increase with age for weekends and holidays. Thus, while Acemoglu et al. (2020) proposed targeted lockdowns requiring stricter lockdown policies for the oldest group in order to protect those at a high risk of serious illness and death, our findings suggest that Japan’s government intervention had a very different effect in that it primarily reduced outings by the young, and what led to the quarantining of older groups at higher risk instead was people’s voluntary response to information about the pandemic. Third, the information effect has been on a downward trend since the summer of 2020. It is relatively more pronounced among the young, so that the age profile of the information effect remains upward sloping.

Download Full-text

Home location prediction with telecom data: benchmarking heuristics with a predictive modelling approach.

Expert Systems with Applications ◽

10.1016/j.eswa.2020.114507 ◽

2020 ◽

pp. 114507

Author(s):

Dieter Oosterlinck ◽

Philippe Baecke ◽

Dries F. Benoit

Keyword(s):

Predictive Modelling ◽

Location Prediction ◽

Home Location ◽

Modelling Approach

Download Full-text

Behavior Modeling for a Beacon-Based Indoor Location System

Sensors ◽

10.3390/s21144839 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4839

Author(s):

Aritz Bilbao-Jayo ◽

Aitor Almeida ◽

Ilaria Sergi ◽

Teodoro Montanaro ◽

Luca Fasano ◽

...

Keyword(s):

Behavior Modeling ◽

Percentage Error ◽

Location Prediction ◽

Indoor Environments ◽

Prediction System ◽

Indoor Location ◽

The Mean ◽

Locating System ◽

Monitoring Devices ◽

Do So

In this work we performed a comparison between two different approaches to track a person in indoor environments using a locating system based on BLE technology with a smartphone and a smartwatch as monitoring devices. To do so, we provide the system architecture we designed and describe how the different elements of the proposed system interact with each other. Moreover, we have evaluated the system’s performance by computing the mean percentage error in the detection of the indoor position. Finally, we present a novel location prediction system based on neural embeddings, and a soft-attention mechanism, which is able to predict user’s next location with 67% accuracy.

Download Full-text