88 research outputs found
Modeling Taxi Drivers' Behaviour for the Next Destination Prediction
In this paper, we study how to model taxi drivers' behaviour and geographical
information for an interesting and challenging task: the next destination
prediction in a taxi journey. Predicting the next location is a well studied
problem in human mobility, which finds several applications in real-world
scenarios, from optimizing the efficiency of electronic dispatching systems to
predicting and reducing the traffic jam. This task is normally modeled as a
multiclass classification problem, where the goal is to select, among a set of
already known locations, the next taxi destination. We present a Recurrent
Neural Network (RNN) approach that models the taxi drivers' behaviour and
encodes the semantics of visited locations by using geographical information
from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to
predict the exact coordinates of the next destination, overcoming the problem
of producing, in output, a limited set of locations, seen during the training
phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge
2015 dataset - based on the city of Porto -, obtaining better results with
respect to the competition winner, whilst using less information, and on
Manhattan and San Francisco datasets.Comment: preprint version of a paper submitted to IEEE Transactions on
Intelligent Transportation System
Exploring universal patterns in human home-work commuting from mobile phone data
Home-work commuting has always attracted significant research attention
because of its impact on human mobility. One of the key assumptions in this
domain of study is the universal uniformity of commute times. However, a true
comparison of commute patterns has often been hindered by the intrinsic
differences in data collection methods, which make observation from different
countries potentially biased and unreliable. In the present work, we approach
this problem through the use of mobile phone call detail records (CDRs), which
offers a consistent method for investigating mobility patterns in wholly
different parts of the world. We apply our analysis to a broad range of
datasets, at both the country and city scale. Additionally, we compare these
results with those obtained from vehicle GPS traces in Milan. While different
regions have some unique commute time characteristics, we show that the
home-work time distributions and average values within a single region are
indeed largely independent of commute distance or country (Portugal, Ivory
Coast, and Boston)--despite substantial spatial and infrastructural
differences. Furthermore, a comparative analysis demonstrates that such
distance-independence holds true only if we consider multimodal commute
behaviors--as consistent with previous studies. In car-only (Milan GPS traces)
and car-heavy (Saudi Arabia) commute datasets, we see that commute time is
indeed influenced by commute distance
CT-Mapper: Mapping Sparse Multimodal Cellular Trajectories using a Multilayer Transportation Network
Mobile phone data have recently become an attractive source of information
about mobility behavior. Since cell phone data can be captured in a passive way
for a large user population, they can be harnessed to collect well-sampled
mobility information. In this paper, we propose CT-Mapper, an unsupervised
algorithm that enables the mapping of mobile phone traces over a multimodal
transport network. One of the main strengths of CT-Mapper is its capability to
map noisy sparse cellular multimodal trajectories over a multilayer
transportation network where the layers have different physical properties and
not only to map trajectories associated with a single layer. Such a network is
modeled by a large multilayer graph in which the nodes correspond to
metro/train stations or road intersections and edges correspond to connections
between them. The mapping problem is modeled by an unsupervised HMM where the
observations correspond to sparse user mobile trajectories and the hidden
states to the multilayer graph nodes. The HMM is unsupervised as the transition
and emission probabilities are inferred using respectively the physical
transportation properties and the information on the spatial coverage of
antenna base stations. To evaluate CT-Mapper we collected cellular traces with
their corresponding GPS trajectories for a group of volunteer users in Paris
and vicinity (France). We show that CT-Mapper is able to accurately retrieve
the real cell phone user paths despite the sparsity of the observed trace
trajectories. Furthermore our transition probability model is up to 20% more
accurate than other naive models.Comment: Under revision in Computer Communication Journa
Inferring accurate bus trajectories from noisy estimated arrival time records
National Research Foundation (NRF) Singapore under its International Research Centres in Singapore Funding Initiativ
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