# **3 Empirical Framework**

## **3.1 Data**

There are three main types of data that we use in this paper. First, to study road traffic crashes (RTCs), we use data from the National Police Service that we digitized from paper situation reports. Police officers generate these reports for each crash that they record where there was an injury, fatality, or significant property damage. Second, we use data from Flare, the largest emergency response dispatch platform in Kenya with over 800 ambulances available on call, 200 of which are in Nairobi. Finally, we use several big data sources to study mobility and congestion including video-generated traffic counts, Waze, Uber and Google Maps.

Our main source of data on road traffic crashes comes from the Kenya National Police Service (NPS). We construct a unique, detailed dataset on the time and location of RTCs which previously did not exist for Kenya by working with NPS to digitize all the Situation Reports for the 14 traffic police stations in Nairobi. These are paper records that are produced for each crash that involves an injury or notable property damage. We use 5,075 crash reports from January 1, 2017-December 31, 2020 that we have manually digitized. While this data does not include minor crashes reported to the police or crashes that were not reported to the police, it is the most comprehensive set of data on the location, casualties and time of crashes for the city of Nairobi. We aggregate total crashes occurring per hour for Nairobi.

We cross-reference this dataset with data on road traffic crashes from Flare, an emergency response dispatch platform that aggregates emergency response fleets into a single platform and runs a 24/7 dispatch center much like 911. The majority of emergency calls made to Flare are for RTCs. Of the calls coming in, almost half come from good Samaritans who witness a situation requiring an ambulance. The other half of the calls are evenly split between calls coming from the police who show up on the scene of an emergency and calls coming from those who are part of the membership service that Flare offers.<sup>1</sup> Similarly to the data from the police, we aggregate data on the number of RTCs recorded by Flare per hour for January 1, 2019 to December 31, 2020.

We rely on four data sources to measure changes in vehicle density and speed. We use traffic jam information from the Waze Connected Cities Program (Waze, 2021). Waze provides information on the location, speed, and delay time of traffic jams; data is updated at 2 minute intervals. We compute the total traffic delay time due to traffic jams in Nairobi hourly. We use this delay time of traffic jams as a proxy for congestion on the roads.

We use data on number of vehicles on the road measured from from road sensors installed in six major intersections in Nairobi by the Kenya Urban Roads Authority. Data for four of the intersections is available for January and April 2020, for one of the intersections data is available for February and April in 2019 and 2020 and July 2020, and for one intersection data is available for January-April 2020. We use the relationship between vehicles on the road and

> 1Businesses can sign up through Flare’s membership service, rescue.co, to have their employees or customers receive access to Flare services free of charge. This service has been especially popular with ride sharing companies.

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