Robust road link speed estimates for sparse or missing probe vehicle data

Widhalm, Peter; Piff, Markus; Brändle, Norbert; Koller, Hannes; Martin, Randall V.

doi:10.1109/itsc.2012.6338906

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…For time dimension, Chrobok et al employed historical data with similar traffic behavior to aggregate missing data, which is based on road classification schemes such as Tele Atlas Functional Road Classes [4,17] and day categories [18]. Without pre-classification, Widhalm et al [19] presented a GMM based method to learn information about typical shapes of the diurnal speed time series. Considering both of time and space dimension, Shan et al [6] modified the multiple linear regression models for applying the information from adjacent times and roads with highest correlation coefficients.…”

Section: Review Of Related Workmentioning

confidence: 99%

Camera location for real-time traffic state estimation in urban road network using big GPS data

Shan

Zhu

2015

Neurocomputing

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Section: Review Of Related Workmentioning

confidence: 99%

Camera location for real-time traffic state estimation in urban road network using big GPS data

Shan

Zhu

2015

Neurocomputing

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“…This ability helps them to outperform the traditional methods since they exploit more temporal correlations than traditional methods. Widhalm et al [ 12 ] proposed a method based on Gaussian-mixture model to estimate road link speed from sparse or missing probe vehicle data. The traffic speed is estimated only from sparse (only a few available observations) historical data of all links in the road network.…”

Section: Introductionmentioning

confidence: 99%

Traffic Speed Data Imputation Method Based on Tensor Completion

Tan

Feng

Liu

et al. 2015

Computational Intelligence and Neuroscience

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Traffic speed data plays a key role in Intelligent Transportation Systems (ITS); however, missing traffic data would affect the performance of ITS as well as Advanced Traveler Information Systems (ATIS). In this paper, we handle this issue by a novel tensor-based imputation approach. Specifically, tensor pattern is adopted for modeling traffic speed data and then High accurate Low Rank Tensor Completion (HaLRTC), an efficient tensor completion method, is employed to estimate the missing traffic speed data. This proposed method is able to recover missing entries from given entries, which may be noisy, considering severe fluctuation of traffic speed data compared with traffic volume. The proposed method is evaluated on Performance Measurement System (PeMS) database, and the experimental results show the superiority of the proposed approach over state-of-the-art baseline approaches.

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“…Different data may have different suitable granularity. For example, [33,34] apply a granularity of 15 minutes. [35,36] apply 10 minutes.…”

Section: Speed Profile Collectionmentioning

confidence: 99%

A Study on Time-Dependent Reachability and Route Scheduling in Road Network

Li¹

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For thousands of years, humans have been innovating new technologies to plan their journeys: from looking up the starry sky, to depending on the magnetic compass; from referring to precious ancient maps, to interacting with locals for nearby information. However, these approaches are either inaccurate or hard to grasp by ordinary people. Thanks to the recent rapid development of online map services and GPS devices, we are able to identify where we are on earth, find any place we want to go, and retrieve a route to it. Although it is convenient and fast enough for basic uses, it is still far from optimal. For starters, most systems just provide a shortest path without considering the traffic condition. Secondly, some systems consider the current traffic condition to provide an estimated travel time. However, the lack of estimation for future traffic condition cannot help us plan the travel ahead of time. To make the things worse, the computation that takes traffic condition into consideration grows slower as the planning time interval and the distance grow longer. Therefore, we study how to plan a travel that considers traffic information from the following aspects. The first one is the reachability problem. A road network, or a map, is essentially a graph with nodes representing intersections and edges representing roads. For a well-maintained map, the nodes are reachable to each other. However, this is not always the case when we obtain our map data. For example, the nodes along the boundary might not reach the other nodes on the map. We propose a High-Dimensional Graph Dominance Drawing approach to answer if one node can reach another quickly on large graphs. In fact, it takes only constant time to answer reachability query in road network. We run our algorithm on various of graph structures with different configurations to fully test its performance. The results help us have a deeper understanding on the reachability problem. The second one is the speed profile generation. A speed profile is a set of functions that return the travel time of any road by providing any departure time. Many existing works just assume such a speed profile exists, or generate one synthetically. Other real-life applications tend to use real-time data from sensors monitoring major roads, which is expensive to deploy and unable to cover a large area. In this work, we use historical trajectories of taxis to generate a speed profile. It involves map-matching, speed data collecting, missing value estimation and compression. By using different speed profile for different types of day, we can provide route scheduling that satisfying user's need. Extensive experiments show that our speed profile is accurate and space efficient. The third one is the minimal on-road travel time route scheduling (MORT). This is a general form of all the single criteria path problems. All the existing path finding problem does not allow iii waiting on some vertices along the route, nor can they benefit from it. We extend this problem by allowing waiting. In this...

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Robust road link speed estimates for sparse or missing probe vehicle data

Cited by 7 publications

References 5 publications

Camera location for real-time traffic state estimation in urban road network using big GPS data

Camera location for real-time traffic state estimation in urban road network using big GPS data

Traffic Speed Data Imputation Method Based on Tensor Completion

A Study on Time-Dependent Reachability and Route Scheduling in Road Network

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