Predicting link travel times from floating car data

Jones, Michael; Geng, Yanfeng; Nikovski, Daniel; Hirata, Takahisa

doi:10.1109/itsc.2013.6728483

Cited by 13 publications

(12 citation statements)

References 16 publications

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“…There are a wide range of related work regarding probe data as well as vehicle platooning, and only a few recent work are mentioned here. Studies using probe data have been conducted for map-matching algorithms [5]- [7], path inference [8], [9], and travel time estimations and predictions [10]- [12]. Many aspects in platooning has been studied, such as string stability [13]- [15], vehicle control [16], vehicle-to-vehicle (V2V) communication [17], and fuel savings [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Fuel-saving potentials of platooning evaluated through sparse heavy-duty vehicle position data

Liang

Mårtensson

Johansson

2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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Section: Introductionmentioning

confidence: 99%

Fuel-saving potentials of platooning evaluated through sparse heavy-duty vehicle position data

Liang

Mårtensson

Johansson

2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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“…Several studies, including [4], [8], [22], explored temporal and spatial dependencies in traffic. The integration of temporal and spatial relationships of traffic information into traffic models could enhance their estimation capabilities [4].…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, the moving observers can collect travel time data at irregular, less frequent intervals and in limited duration of time, which means that, at some times of a day there might be no travel time data available for a particular road segment. Also, the moving observers enable collection of travel time information across the entire urban road network [6], [8].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data

Passow

Paluszczyszyn

et al. 2021

IEEE Intell. Transport. Syst. Mag.

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Travel time is a basic measure based on which intelligent transportation systems such as traveller information systems, traffic management systems, public transportation systems are developed. Although many methodologies have been proposed, they have not yet adequately solved many challenges associated with travel time, in particular, travel time estimation for all links in a large and dynamic urban traffic network is still an open problem that needs addressing. Typically focus is placed on major roads such as motorways and main city arteries but there is an increasing need to know accurate travel times for minor urban roads. Such information is crucial for tackling air quality problems, accommodate the growing number of cars and provide accurate information for routing. This study aims to address the aforementioned challenges by introducing a methodology, namely Similar Model Searching (SMS), to estimate travel times by using historical sparse travel time data. The SMS learns the temporal and spatial relationship between the travel time of adjacent links and utilise labelled data of similar models in order to improve its overall performance. The effectiveness of the proposed method is evaluated on a section of Leicestershire traffic network in the UK. The obtained results show that SMS efficiently estimates travel time of target links using models of adjacent traffic links.

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“…All these approaches presuppose large databases in order to yield accurate predictions for all possible routes in a given city and for a given time of the day (see e.g. [11] and [18]). …”

Section: Introductionmentioning

confidence: 99%

Transferring urban traveling speed model fits across cities

Heinze

Leodolter

Koller

et al. 2016

Eur. Transp. Res. Rev.

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Purpose Link traveling time models form the basis for route planning methods used in navigation devices as well as for logistic applications. These models are provided based on extensive real world data sets which are available to a differing degree in different cities as well as for different links within a given city. For smaller cities, where typically fewer data is available or less frequently measured links, it might be beneficial to transfer models from close-by cities or links from the same city with sufficient data basis. In this paper, the potential for transferring link traveling time model fits, that is, the estimated models, between cities and within a city is investigated. Methods that combine information typically contained in street maps with empirically derived features that are easily transferred are developed and tested with substantial real world data sets. This provides the basis for developing route planning methods in cities with insufficient real world data coverage to base accurate route traveling time predictions on. Methods Link traveling time models are derived on the basis of an extensive floating taxi data set in Vienna, Austria. The models incorporate typical map information such as speed limits and functional road classification (frc). Estimation is performed using penalized least squares methods to control for overfitting. The expected accuracy for the model transfer is investigated both in terms of intracity transfer (from

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Predicting link travel times from floating car data

Cited by 13 publications

References 16 publications

Fuel-saving potentials of platooning evaluated through sparse heavy-duty vehicle position data

Fuel-saving potentials of platooning evaluated through sparse heavy-duty vehicle position data

Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data

Transferring urban traveling speed model fits across cities

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