In this paper, we solve the problem of predicting the next locations of the
moving objects with a historical dataset of trajectories. We present a Next
Location Predictor with Markov Modeling (NLPMM) which has the following
advantages: (1) it considers both individual and collective movement patterns
in making prediction, (2) it is effective even when the trajectory data is
sparse, (3) it considers the time factor and builds models that are suited to
different time periods. We have conducted extensive experiments in a real
dataset, and the results demonstrate the superiority of NLPMM over existing
methods.

本文提出了一种基于马尔可夫建模的下一位置预测算法（NLPMM），能够考虑个人和集体的移动模式，适用于不同时间段的数据，通过实验验证了该算法在预测移动对象位置方面的优越性。

基于马尔科夫建模的下一位置预测器 (NLPMM)

NLPMM: a Next Location Predictor with Markov Modeling

Estimating the travel time of a path is of great importance to smart urban
mobility. Existing approaches are either based on estimating the time cost of
each road segment which are not able to capture many cross-segment complex
factors, or designed heuristically in a non-learning-based way which fail to
utilize the existing abundant temporal labels of the data, i.e., the time stamp
of each trajectory point. In this paper, we leverage on new development of deep
neural networks and propose a novel auxiliary supervision model, namely
DeepTravel, that can automatically and effectively extract different features,
as well as make full use of the temporal labels of the trajectory data. We have
conducted comprehensive experiments on real datasets to demonstrate the
out-performance of DeepTravel over existing approaches.

本文提出一种基于深度神经网络的辅助监督模型 DeepTravel，能够自动提取不同特征并充分利用轨迹数据的时间戳，通过在真实数据集上的全面实验，证明了 DeepTravel 相对于现有方法的卓越性能。

DeepTravel：一种基于神经网络的旅行时间估计模型，配有辅助监督

DeepTravel: a Neural Network Based Travel Time Estimation Model with  Auxiliary Supervision

The explosion in the availability of GPS-enabled devices has resulted in an
abundance of trajectory data. In reality, however, majority of these
trajectories are collected at a low sampling rate and only provide partial
observations on their actually traversed routes. Consequently, they are mired
with uncertainty. In this paper, we develop a technique called InferTra to
infer uncertain trajectories from network-constrained partial observations.
Rather than predicting the most likely route, the inferred uncertain trajectory
takes the form of an edge-weighted graph and summarizes all probable routes in
a holistic manner. For trajectory inference, InferTra employs Gibbs sampling by
learning a Network Mobility Model (NMM) from a database of historical
trajectories. Extensive experiments on real trajectory databases show that the
graph-based approach of InferTra is up to 50% more accurate, 20 times faster,
and immensely more versatile than state-of-the-art techniques.

本研究介绍了一种名为 InferTra 的技术，可以从网络约束的部分观测中推断出不确定的轨迹，该技术采用了基于图形的方法，展示了所有可能的轨迹，并在实验中证明比现有技术更准确，快速且功能更强大。

从部分观测推断不确定轨迹

Inferring Uncertain Trajectories from Partial Observations

The increased availability of large-scale trajectory data around the world
provides rich information for the study of urban dynamics. For example, New
York City Taxi Limousine Commission regularly releases source-destination
information about trips in the taxis they regulate. Taxi data provide
information about traffic patterns, and thus enable the study of urban flow --
what will traffic between two locations look like at a certain date and time in
the future? Existing big data methods try to outdo each other in terms of
complexity and algorithmic sophistication. In the spirit of "big data beats
algorithms", we present a very simple baseline which outperforms
state-of-the-art approaches, including Bing Maps and Baidu Maps (whose APIs
permit large scale experimentation). Such a travel time estimation baseline has
several important uses, such as navigation (fast travel time estimates can
serve as approximate heuristics for A search variants for path finding) and
trip planning (which uses operating hours for popular destinations along with
travel time estimates to create an itinerary).

利用出租车轨迹数据，提出一种简单的行程预估基准方法，可以用于导航和行程规划等多个领域，其效果优于现有的大规模实验平台（如 Bing Maps 和百度地图）的状态 - of-the-art 方法。