The role of real-time in times surreal

The role of real-time in times surreal

Big data is often perceived as ‘the end of theory’ because it provides the opportunity to derive insight from empirical evidence rather than building it on theoretical constructs. Never is this need greater than during an emergency situation when an amalgam of unprecedented conditions take place.

Real-time mobility data is having major impacts on the transport planning field in terms of how we are planning for future mobility. The ongoing pandemic situation has shown us how big data input can play a major role in tracing and redirecting travel behavior during different phases of the pandemic response cycle: from complete mobility lockdown to a variety of in-between scenarios.

Not restricted to the pandemic, the availability of real-time data during any emergent situation opens up the possibility to design reactive intervention measures long before the causal links to the emerging situation are fully understood. In the U.S. as in around the world, various policy measures have been enacted in attempts to curb the pandemic and reduce the virus spread, in part guided by real-time analysis of mobility patterns.

City governments and transit agencies are on the front lines of the pandemic response, relying on the support of real-time data to efficiently allocate resources in rapidly changing times. Beyond contact tracing, travel data is also used to guide street reclamation efforts via vehicular flow restrictions in areas with low pedestrian access to public space. Such programs include “Slow Street,” “Safe Street,” “Stay Healthy Street,” “Keep Moving Street” and a variety of other versions deployed across the country. Likewise, measures to reduce contact on public transport have varied, relying, in part, on data monitoring methods to prevent heavy crowding during rush hour movements.

The 57 Freeway in Orange County, California nearly empty at rush hour on March 23rd, 2020, with people staying inside in response to the COVID-19 outbreak.
Image credits: Matt Gush

Analysis of trip trends before the disruption is important. It provides a detailed look into the direction of mobility trends before the transient, extrinsic factor prompted large-scale structural changes and modified people’s mobility behaviors. In times of disruption, often the best lense to use to anticipate trends in the future is not based on what is happening now as a result of multiple confounding factors, but in the moment before that when people and governments were not reacting to an abnormal threat and mobility decisions were based on long-term overarching conditions. To give one example from this book, it helps to see that telecommuting trends were already on the rise before the pandemic reinforced them. This shows us that a significant portion of the American workforce was already willing to give up the office, virus spread risk aside. We can infer then, that the potential for this trend to continue – even if in different ways than anticipated – is strong.

What real-time information shows us is a snapshot of how mobility patterns developed under ‘normal’ circumstances. For the ‘new normal’ or the ‘next normal’, it will take continuous monitoring, dissecting and analyzing of large-scale datasets to determine what that might look like on the long run, beyond the current transitory period.

Using Big Data to read travel trends

As popularized by MetLife VP Oscar Herencia, the ability to harness the benefits of big data depends on how we manage its five defining V’s: volume, velocity, variety, veracity and value. The same applies to applications in the transport planning field, where information potentials diverge from traditional data gathering methods.


Continuous monitoring offers possibilities to produce high-level resolution information on travel behavior, especially in terms of temporal aspects, which are systemically weak in traditional datasets. It also allows for long-term tracking, which had previously been very difficult to quantify. Moreover, the fact of automated data collection eliminates data inaccuracy associated with travel survey responses due to human error.


Since data collected through passive big data sources was originally designed for other purposes, essential attributes typically collected through traditional methods (such as trip purpose) are often missing. Proxies designed to infer these lacking attributes can only partially compensate for these qualitative losses. Moreover, sampling bias tends to be a concern when it comes to GPS, cellphone and Bluetooth data, which tend to overrepresent movements of financially active, technologically apt and younger members of society.


Big data provides the opportunity to trace unexpected trends and changes at a fine scale of granulation as they come up, without the need to extrapolate or deduce events post-hoc. The rawness of the data form allows for direct manipulation at any point in time. Moreover, the sheer overall volume of data allows for countering sampling bias with targeted analysis for otherwise underrepresented groups, who make up a small share but sizeable sample in the overall dataset.


‘Future-proofing’ the analytical methods associated with big data is difficult given the uncertain availability of privately owned non-purpose-oriented data. The tradeoff between data accuracy and privacy is another long-discussed challenge. Filtering systems intended to protect user confidentiality ultimately tamper with the extent to which the data can become useful.

Project attribution and image credits: Simon Weckert
Text by Moritz Alhert – The Power of Virtual Maps

“99 second-hand smartphones are transported in a handcart to generate a virtual traffic jam in Google Maps. Through this activity, it is possible to turn a green street [into] red which has an impact in the physical world by navigating cars on another route to avoid being stuck in traffic.”


When things took a sharp turn: a final reflection

By using a diversified data approach to observe growing mobility trends, understand where they prevail (topographic tendencies), when they prevail (chronological tendencies) and who their main proponents are (demographic structure), we can begin to compose a robust reading of mobility trends that transcends beyond mono-dimensional particularities to detect overarching patterns. While the trends may be forever altered after the current moment of disruption has passed, the methodology remains intact. It will take continued observation of location-based data over the next several years to patternize the country’s new mobility landscape.

Only time (and data) will tell.