Measuring Traffic in Cities Through a Large-Scale Online Platform
Artikel i vetenskaplig tidskrift, 2019

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.

urban traffic

big data,

time series clustering

data availability

travel delays

Författare

Vilhelm Verendel

Chalmers, Data- och informationsteknik, CSE Verksamhetsstöd, Data Science Research Engineers

Sonia Yeh

Chalmers, Rymd-, geo- och miljövetenskap, Fysisk resursteori

Journal of Big Data Analytics in Transportation

2523-3556 (ISSN) 2523-3564 (eISSN)

Vol. In Press 1-13

HÅLLBARA STÄDER: ANVÄNDANDE AV STORA DATAMÄNGDER FÖR ATT FÖRSTÅ OCH HANTERA RÖRELSEMÖNSTER OCH TRAFIKSTOCKNINGAR

Formas, 2017-01-01 -- 2019-12-31.

Ämneskategorier

Annan data- och informationsvetenskap

Transportteknik och logistik

Infrastrukturteknik

Styrkeområden

Informations- och kommunikationsteknik

Transport

Energi

Drivkrafter

Hållbar utveckling

DOI

10.1007/s42421-019-00007-7

Mer information

Senast uppdaterat

2019-11-15