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How gig driving makes congestion both better and worse

Applying proprietary machine learning algorithms, the company can measure diverse travel patterns across cities

Gig driving had different impacts in Miami depending on traffic, time and location
Gig driving had different impacts in Miami depending on traffic, time and location

StreetLight Data has conducted a congestion study in Miami, Florida, in a bid to discover whether “gig driving” (ride-sharing services for consumers and delivery such as Uber, Lyft and Postmates) is leading to an increase in traffic congestion.


The mobility analytics firm contends it is a major issue that cities and local transportation agencies, planners and governments are struggling with now that gig driving has become “mainstream”.


For better and worse


Basing the analysis on discrete big mobility data, the findings show that it makes traffic both better and worse. According to StreetLight Data, gig driving has different impacts depending on a range of factors such as existing traffic, location, time of day, and transit and bike availability.


In the Miami-Dade region of Florida, and depending on context, the company found a road with a higher “gig mode share” can have a positive, negative, or neutral correlation with congestion.


The contexts explored in the analysis included time of day, proximity to a major transit centre, road class (highway/non-highway), land use and density.


Some of the key findings of the gig driving analysis in the Miami-Dade region are:

  • Gig mode share is higher in certain parts of town, notably tourist- and hotel-heavy areas like Miami Beach, downtown Miami, and at TNC ramps into Miami-Dade Airport;
  • In general, gig driving patterns follow overall temporal patterns. Morning and evening peaks occur on weekdays. However, gig share goes up on evenings, late night, and weekends. These are times with low congestion, and a few extra gigs won’t make a big difference. As a result, the impact of gig share on congestion does not really vary by time of day or day of week;
  • Gig driving is a fairly consistent share of highway driving. Highways with higher gig shares have a very slight increase in congestion (especially on weekdays). On non-highway roads, it is 6x that impact. In general, gig mode share, therefore, has more variability, and more of an impact, on congestion, on non-highway roads;
  • In commercial areas (as opposed to residential areas) the findings are revealing. In Miami-Dade, in extremely dense neighborhoods (top 10 per cent of POI density, see map) a high share of gig is correlated with a lower congestion. However, in more standard commercial areas, gig is correlated with higher congestion. StreetLight Data said it interpret this to mean that in Miami there’s a threshold of activity density above which gig associated with ‘less’ congestion, and below which Gig is associated with ‘more’ congestion;
  • Transit hub proximity – StreetLight Data examined the subset of roads that were within 400 metres (walkable range) of the various transit stations in the region. Overall, these roads are more congested than the typical Miami-Dade road. This doesn’t mean transit causes congestion, the company pointed out, it means the tendency is to put transit in busy places. In these zones, the higher the gig mode share, the lower the congestion. This finding may also mean that many of people are taking gig TO transit stations in the further-flung, less congested parts of town.

StreetLight Data applies proprietary machine learning algorithms to more than four million location data points which enables it to measure diverse travel patterns. The information is available on-demand through its mobility platform, StreetLight Insight.


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