Quality of Private Sector Travel-Time Data on Arterials

Hu, Jia; Fontaine, Michael D.

doi:10.1061/(asce)te.1943-5436.0000815

Cited by 21 publications

(19 citation statements)

References 3 publications

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“…Most of these studies compare the level of similarity between FCD and a ground truth data source, typically stationary detector data, in terms of the relevant traffic variables, e.g., speed and travel time (Jurewicz et al 2018; de Boer and Krootjes 2012; Clergue and Buttignol 2014; Clergue and Buttignol 2015; Hrubes and Blümelová 2015;Diependaele et al 2015;Ambros et al 2017). Some also look at other aspects such as the coverage of the road network (Jurewicz et al 2018;Aarts et al 2015) or timeliness to recognize jams (Hu et al 2016;Kessler et al 2018;Wang et al 2014). It has been suggested that theoretically mean point speed from sensors would often be greater than mean link speeds from FCD (Jurewicz et al 2018) and this has turned out to be the case in some empirical observations (Jurewicz et al 2018;Clergue and Buttignol 2015;Hrubes and Blümelová 2015).…”

Section: Traditional Sources and Online Real-time Traffic Datamentioning

confidence: 99%

“…Others, however, found FCD speeds higher than fixed-point measurements (Diependaele et al 2015;Ambros et al 2017). Studies have also suggested poor agreements between private and ground truth data, and concluded that private sector data are not suitable for realtime measurements as they tended to show less variability though they could still be suitable for a longer-term trend analysis (Hu et al 2016).…”

Section: Traditional Sources and Online Real-time Traffic Datamentioning

confidence: 99%

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Measuring Traffic in Cities Through a Large-Scale Online Platform

Verendel

Yeh

2019

J. Big Data Anal. Transp.

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Online real-time traffic data services could effectively deliver traffic information to people all over the world and provide large benefits to the society and research about cities. Yet, city-wide road network traffic data are often hard to come by on a large scale over a longer period of time. We collect, describe, and analyze traffic data for 45 cities from HERE, a major online real-time traffic information provider. We sampled the online platform for city traffic data every 5 min during 1 year, in total more than 5 million samples covering more than 300 thousand road segments. Our aim is to describe some of the practical issues surrounding the data that we experienced in working with this type of data source, as well as to explore the data patterns and see how this data source provides information to study traffic in cities. We focus on data availability to characterize how traffic information is available for different cities; it measures the share of road segments with real-time traffic information at a given time for a given city. We describe the patterns of real-time data availability, and evaluate methods to handle filling in missing speed data for road segments when real-time information was not available. We conduct a validation case study based on Swedish traffic sensor data and point out challenges for future validation. Our findings include (i) a case study of validating the HERE data against ground truth available for roads and lanes in a Swedish city, showing that real-time traffic data tends to follow dips in travel speed but miss instantaneous higher speed measured in some sensors, typically at times when there are fewer vehicles on the road; (ii) using time series clustering, we identify four clusters of cities with different types of measurement patterns; and (iii) a k-nearest neighbor-based method consistently outperforms other methods to fill in missing real-time traffic speeds. We illustrate how to work with this kind of traffic data source that is increasingly available to researchers, travellers, and city planners. Future work is needed to broaden the scope of validation, and to apply these methods to use online data for improving our knowledge of traffic in cities.

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Section: Traditional Sources and Online Real-time Traffic Datamentioning

confidence: 99%

Section: Traditional Sources and Online Real-time Traffic Datamentioning

confidence: 99%

Measuring Traffic in Cities Through a Large-Scale Online Platform

Verendel

Yeh

2019

J. Big Data Anal. Transp.

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“…This can be measured by a number of techniques, such as GPS travel time runs (1) or vehicle re-identification (2,3), and estimated by the analysis of segment speed data sampled from connected vehicles from private-sector data providers (4,5,6,7). Several researchers have recently explored the viability of private sector speed data for analysis of arterial travel times (4,5,6). While results have varied, the growing consensus is that such data is viable on corridors with higher traffic volumes.…”

Section: Introductionmentioning

confidence: 99%

Outcome Assessment Using Connected Vehicle Data to Justify Signal Investments to Decision Makers

Krohn¹,

Rymarcsuk²,

Mathew³

et al. 2020

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Elected officials and decision makers are increasingly seeking outcome assessment of capital projects, such as corridor signal modernization and adaptive control projects. This paper describes the use of connected vehicle data to perform corridor travel time outcome assessment along five corridors in the greater Philadelphia, Pennsylvania area. These corridors are comprised of a total of 2,184 signals and are considered five of the most critical corridors in this region, experiencing a high volume of traffic, with AADT greater than 30,000 vehicles. These corridors were evaluated for six weeks before and after the adaptive installation through the use of private-sector segment speed data. Medians and interquartile ranges of travel times were used to assess the impact on arterial progression. Various graphs, charts, and figures produced through web tools and traditional metrics provide a user-interactive component to the dashboards. In addition, user cost reductions and CO2 emission impacts were also determined. Four out of the five corridors had substantial reductions of arterial travel times that amounted to approximately $36.6 million in annualized user benefits.

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“…From this, a total user cost reduction of about $18,000 per day can be estimated, which scales up to about $350,000 over a 4-week period, considering only the weekdays. The quality of probe data on arterials is currently well suited for those with moderate to high levels of traffic (Hu, Fontaine, & Ma, 2016;Sharifi et al, 2016;Young et al, 2015). However, the quality of the data has improved to reach this point, and with additional improvement it is likely that probe data will become invaluable to support active management of traffic signal systems.…”

Section: Signalized Arterial Performancementioning

confidence: 99%