Route Reconstruction from Traffic Flow via Representative Trajectories

@article{Custers2020RouteRF,
  title={Route Reconstruction from Traffic Flow via Representative Trajectories},
  author={Bram Custers and Wouter Meulemans and Bettina Speckmann and Kevin Verbeek},
  journal={Proceedings of the 29th International Conference on Advances in Geographic Information Systems},
  year={2020}
}
Understanding human mobility patterns is an important aspect of traffic analysis and urban planning. Trajectory data provide detailed views on specific routes, but typically do not capture all traffic. On the other hand, loop detectors built into the road network capture all traffic flow at specific locations, but provide no information on the individual routes. Given a set of loop-detector measurements as well as a (small) set of representative trajectories, our goal is to investigate how one… 

Figures from this paper

References

SHOWING 1-10 OF 24 REFERENCES

An algorithm for map matching given incomplete road data

A map-matching algorithm is extended that is based on a hidden Markov model and a discrete set of candidate matches for each point of the trajectory to solve the problem of matching a GPS trajectory with a road data set in which some roads are missing.

Bilevel Generalized Least Squares Estimation of Dynamic Origin–Destination Matrix for Urban Network with Probe Vehicle Data

Methods of estimating dynamic origin–destination (O-D) matrices for urban networks from probe vehicle data are explored and a dynamic traffic assignment–based bilevel generalized least squares (GLS) estimator is formulated.

Online Map-Matching of Noisy and Sparse Location Data With Hidden Markov and Route Choice Models

This paper presents a novel map-matching solution that combines the widely used approach based on a hidden Markov model (HMM) with the concept of drivers’ route choice, which uses an HMM tailored for noisy and sparse data to generate partial map-matched paths in an online manner.

Fast map matching, an algorithm integrating hidden Markov model with precomputation

Investigation on the running time of different steps in FMM reveals that after precomputation is employed, the new bottleneck is located in candidate search, and more specifically, the projection of a GPS point to the polyline of a road edge.

Addressing the Need for Map-Matching Speed: Localizing Global Curve-Matching Algorithms

Output-sensitiveness paired with error-aware pruning makes adaptive clipping the first map- matching algorithm that provably solves a well-defined map-matching task.

Modeling Checkpoint-Based Movement with the Earth Mover's Distance

This paper proposes to use the Earth Mover’s Distance as a versatile tool to reconstruct individual movements or flow based on checkpoint counts at different times, and analyses the modeling possibilities and provides experiments that validate model predictions, based on coarse-grained aggregations of data about actual movements of couriers in London, UK.

Hidden Markov map matching through noise and sparseness

A novel, principled map matching algorithm that uses a Hidden Markov Model (HMM) to find the most likely road route represented by a time-stamped sequence of latitude/longitude pairs, which elegantly accounts for measurement noise and the layout of the road network.

The Observability Problem in Traffic Network Models

The results show that the proposed methods provide useful information on which OD‐pair or link flows are informative on other OD‐ Pair and link flows, and that the methods are applicable to large networks.