A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data

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).Comment: 12 page

Trajectory Representation in Location-Based Services: Problems and Solution

Recently, much work has been done in feasibility studies on services offered to moving objects in an environment equipped with mobile telephony, network technology and GIS. However, despite of all work on GIS and databases, the situations in which the whereabouts of objects are constantly monitored and stored for future analysis are an important class of problems that present-day database/GIS has difficulty to handle. Considering the fact that data about whereabouts of moving objects are acquired in a discrete way, providing the data when no observation is available is a must. Therefore, obtaining a "faithful representation" of trajectories with a sufficient number of discrete (though possibly erroneous) data points is the objective of this research

Dynamic-parinet (D-parinet) : indexing present and future trajectories in networks

While indexing historical trajectories is a hot topic in the field of moving objects (MO) databases for many years, only a few of them consider that the objects movements are constrained. DYNAMIC-PARINET (D-PATINET) is designed for capturing of trajectory data flow in multiple discrete small time interval efficiently and to predict a MO’s movement or the underlying network state at a future time. The cornerstone of D-PARINET is PARINET, an efficient index for historical trajectory data. The structure of PARINET is based on a combination of graph partitioning and a set of composite B+-tree local indexes tuned for a given query load and a given data distribution in the network space. D-PARINET studies continuous update of trajectory data and use interpolation to predict future MO movement in the network. PARINET and D-PARINET can easily be integrated into any RDBMS, which is an essential asset particularly for industrial or commercial applications. The experimental evaluation under an off-the-shelf DBMS using simulated traffic data shows that DPARINET is robust and significantly outperforms the R-tree based access methods

Review & Perspective for Distance Based Clustering of Vehicle Trajectories

International audience—In this paper we tackle the issue of clustering trajectories of geolocalized observations based on distance between trajectories. We first provide a comprehensive review of the different distances used in the literature to compare trajectories. Then based on the limitations of these methods, we introduce a new distance: Symmetrized Segment-Path Distance (SSPD). We compare this new distance to the others according to their corresponding clustering results obtained using both the hierarchical clustering and affinity propagation methods. We finally present a python package : trajectory distance, which contains the methods for calculating the SSPD distance and the other distances reviewed in this paper