Last week while I was cycling through downtown Amsterdam an incident occurred, which has become a common practice on my daily trips: a Deliveroo courier cut me off. With the rise of food delivery services, having become indispensable to the lives of consumers, one starts to wonder if there is a negative side to the rapid increase of food couriers. Most food couriers I encounter are always in a hurry, reaching dangerous speeds, often skipping red lights in the process. Because of this overall contempt for traffic regulations, I consequently witness dangerous situations. It made me wonder what the reasons could be for this roadrunner-culture and in my investigations, I found some interesting conclusions. One of the biggest food delivery services, Deliveroo, pays their couriers per delivery. This means that more deliveries equal more money, which I believe leads to unsafe road behavior and should be reconsidered.

Deliveroo is part of the sharing economy, which enables peer to peer sharing of resources via the internet (Hamari, Sjöklint, Ukkonen, 2015). The sharing economy gave rise to online labor platforms, which act as matchmakers between supply and demand. In the past decade we have seen a rapid increase of these new platforms, Uber probably being the most well-known. A dive into news articles and reports points out that these new platforms are not free from criticism on the way they manage their workers. A testimony of 83 Uber drivers in the UK showed that wages of their drivers are often not enough to make a living (Field&Forsey, 2016). The reason why labor platforms are allowed to pay per performance instead of paying an hourly rate, is because most platform, including Deliveroo, classify their workers as independent contractors. Independent contractors are by Dutch law not entitled to a minimum wage (Witteman, 2017).

The three-major food delivery services in the Netherlands are Thuisbezorgd, Deliveroo and Uber Eats. Out of these three platforms only Thuisbezorgd pays their couriers an hourly rate. That is why I would like to make the case that the other food couriers platforms, including the ones not mentioned, should pay their couriers on an hourly basis. This would take away the financial incentive for rapid completion of deliveries, making the practice of skipping lights and cutting people a thing of the past. What do you guys think? Let me know in the comments below.

References:

Field, F., & Forsey, A. (2016). Sweated Labour, Uber and the ‘gig economy’.

Hamari, J., Sjöklint, M., & Ukkonen, A. (2015). The Sharing Economy: Why People Participate in Collaborative Consumption.

Witteman, J. (2017, November 17). Verplicht door als zzp’er? Deze 19-jarige fietskoerier sleept Deliveroo voor de rechter.

Four days ago, on October the first 2019, a news article came out about the Dutch police force introducing smart cameras to counter smartphone use by drivers. The camera and its software are able to identify smartphones, tablets and other electronics devices and whether they are held by drivers (RTLNieuws, 2019). After reading the article, I shared the news with my housemates. Being both frequent drivers, who often witness smartphone use by other drivers, they shared my positive attitude towards the introduction of this camera.

A study conducted by an American institute, which investigates road safety, monitored 3,500 drivers by using dash cameras and other surveillance equipment. In case of an accident, the researchers looked into what the driver was doing before the moment occurred. This investigation concluded that using a smartphone increased the risk of an accident by six times (Dingus, et al, 2016). These figures help explain why the Dutch government is so extensively campaigning to discourage distracted driving. Despite these efforts, the Dutch police force handed out over 80,000 fines of 240 euro last year for handheld calls (Politie.nl, 2019).

Electronic device distraction causing accidents on the road is the reason the police invested in this machine learning based technology (Politie.nl). Machine learning, a subset of artificial intelligence, allows computer systems to perform a task without explicit instructions (Bishop, 2006). By the input of training data, machine learning algorithms build a mathematical model relying on patterns and inference to identify, in this case, electronic device use on the road. These models learn via training date, which is the input data and the expected outputs (Gonfalonieri, 2019). In the past months, the police have trained the model with input data of pictures displaying drivers holding different kind of objects (Oostvogels, 2019). By telling the computer when an electronic device is used, they gave expected outputs for the model. If the police camera identifies an electronic device held by a drive, the camera makes a picture and send it to the police officer on duty. The officer still has to verify if indeed an electronic device is used before a fine is send to the driver (RTLnieuws, 2019).

I strongly agree with the introduction of the smart camera. Like my housemates, I still witness fellow road users using their smartphone while they should be paying attention to the road. Because campaigning against distracted driving does not have a satisfying effect, a higher likelihood of receiving a fine will hopefully help. I am curious about your thoughts on this topic and whether this post made you think of other emerging technologies the police could adopt, let me know in the comments!

References:

Bishop, C. M. (2006). Pattern recognition and machine learning.

Dingus, T. A., Guo, F., Lee, S., Antin, J. F., Perez, M., Buchanan-King, M., & Hankey, J. (2016). Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proceedings of the National Academy of Sciences, 113(10), 2636-2641

Gonfalonieri, A. (2019). How to Build A Data Set For Your Machine Learning Project. Retrieved from https://towardsdatascience.com/how-to-build-a-data-set-for-your-machine-learning-project-5b3b871881ac

Politie Nederland. Inzet slimme camera´s tegen afleiding in het verkeer. Retrieved from https://www.politie.nl/nieuws/2019/september/30/00-inzet-slimme-cameras-tegen-afleiding-in-het-verkeer.html.

Oostvogels, B. (2019, October 1). Zo werken de slimme smartphone-‘flitspalen’ van de politie. Retrieved from https://autorai.nl/zo-werken-de-slimme-smartphone-flitspalen-van-de-politi.

RTL Nieuws. Politie zet vanaf vandaag camera’s in tegen appende automobilisten. (2019, September 30). Retrieved from https://www.rtlnieuws.nl/nieuws/nederland/artikel/4867361/camera-controle-appen-achter-stuur-auto-politie-boete-240-euro.