Measuring Traffic in Cities Through a Large-Scale Online Platform

Abstract

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.

Disparities in travel times between car and transit: Spatiotemporal patterns in cities

“…We calculate the hourly average speed for each segment over weekdays. More details of the preprocessing method can be found in 21 .…”

“…Given the same travel distance, these cities have different travel times by car. São Paulo has the longest travel time by car for the same distance due to the high congestion levels 21 . Within the range of 0-40 km, the three other cities show similar levels of travel time by car, with Amsterdam having the lowest.…”

“…While there is strong evidence that car-based travel is often faster than public transit, the spatial and temporal patterns of this time discrepancy are crucial to better inform urban planning and policy efforts to encourage travel mode shifts. Recent studies show how transit travel times can vary greatly by route and time of day 17,18 , while a growing body of research using GPS and mobile phone data to record travel speed shows that overall transport performance varies significantly by time of day due to congestion in cities [19][20][21] . Traditional static views on travel time calculation have largely relied on simplistic assumptions, using constant vehicle speeds 22 and overlooking how travel speeds vary over the course of the day 23 .…”

“…New data sources, such as HERE Traffic 30 with extensive coverage of cities in 83 countries to date 29 , can collect and provide information about real-time road speed, incidents, and accidents. The amount of available data and the level of spatial and temporal resolution allow more realistic estimates of travel time 21 . The rise of open data also supports more realistic travel time calculations.…”

The Right Tools for the Job: The Case for Spatial Science Tool-Building

“…Other researchers have used OSMnx for automated indicator calculation and visualization. Brandily and Rauch Etten, Lindenbaum, and Bacastow (2019) and Verendel and Yeh (2019) visualize street network characteristics across their respective studies' sites. Boeing (2020a) models the street networks of every US urbanized area, city/ town, and Zillow-defined neighborhood to calculate dozens of indicators across tens of thousands of study sites at multiple spatial scales, then shares these models and indicators in a public repository.…”