By Chris Langdon and Bryan Tüscher
How do you fix a problem like the coronavirus crisis, ultimately? With the right solution… the right medicine and vaccine. How do you find the right medication? Testing! How do you speed up the process? Simulation! It still has to be done right, however, with the right science and by following a rigorous experimentation process. That is what we have done here to identify solutions to the traffic problem in dense urban areas such as Berlin.
Digital twins have become valuable in manufacturing, and other domains are experimenting with them too – such as the healthcare sector (this author is involved in exploring human or consumer digital twins, link). Now we are exploring how this concept can help with mobility. Specifically, we have devised a multi-step approach:
In our case, however, we did not select at random, but instead based on the topology of the city of Berlin. For example, we picked real parking spots instead of drawing random sites, which could have been in the river Spree or some other ridiculous location.
Our results were focused on a narrow problem, and therefore, limited, yet they still deliver insights – loud and clear. Our simulation using a Berlin digital twin …
Figure 1: BVG’s Jelbi app for multimodal mobility in Berlin
According to the Statistical Office for Berlin-Brandenburg [Amt für Statistik Berlin-Brandenburg], there are 3.6 million citizens living in Germany’s capital, Berlin (Statistik Berlin Brandenburg, 2016, link [in German]) … along with 1.2 million registered passenger cars (Kraftfahrt Bundesamt, Federal Motor Transport Authority 2019). This is a lot of people and cars. As a result, it may not come as a surprise that there is nowhere else in Germany where drivers are wasting more time in traffic than in Berlin. Last year, they spent an average of 154 hours in heavy traffic and congestion, the equivalent of more than six days. In second and third place follow Munich, with 140 hours, and Hamburg, with 139 hours (Inrix 2019). There is little improvement in sight. Cars last longer, the industry needs to sell them, and more every year at that – even though space remains limited. Consequently, traffic jams are getting longer and longer. By the end of this decade, passenger transport in Germany is forecasted to grow by more than 12 percent (BMVI 2016).
Solutions are seen in micro-, multimodal and intermodal mobility (Bitkom 2019). Micromobility has received widespread attention in particular. No wonder, it seems electric scooters are everywhere on the streets of Berlin: white, green, red; Lime, Tier, Voi (Tier was “born” at Hubraum, Deutsche Telekom’s digital business incubator in Berlin). Ever since a federal law was enacted in 2019 (eKFV, link), every electric scooter provider seems to have expanded into Berlin. For the first time, there is also a multimodal offering in the form of Jelbi by Berliner Verkehrsbetriebe (BVG) with its partner Trafi, a Lithuanian service provider. Jelbi comes with an app that provides various mobility offers through one registration. Offers include bus, train, electric scooter, bicycle, car, ride sharing, and taxi. Each individual offer is displayed on a map and it is possible to check the costs and availability of the service in real time. In addition, the app offers a routing function for the respective service and it is possible to pay via the app with PayPal.
A multimodal offer would be a steppingstone toward intermodal transport. Merriam-Webster defines intermodal transportation as “being or involving transportation by more than one form of carrier during a single journey” (Merriam-Webster 2020-04-11). While multimodal is like a supermarket where you can buy ingredients for dinner, intermodal is dinner; it is the finished dish, where someone else figured out the ingredients and the recipe. Intermodal transport is explicitly about linking up or orchestrating different vehicle and mobility options as an end-to-end chain of transport. The more seamless – planning, booking, execution – the better for the consumer.
Figure 2: A simple intermodal model and “smart” scenarios
Key to success with simulation is simplicity in process and model (for example, see Schlueter Langdon 2014) coupled with scientific rigor in model implementation and the experimental strategy (for example, see Schlueter Langdon 2005). Our model is based on a 3-segment design (Schlueter Langdon 2020, Figure 1, link). An important feature is its recognition of “near B”, which splits a point A to point B journey into three segments: (1) the first leg originating in A, (2) a “near B” element, and (3) a last leg terminating in B. Based on insights from MaaS analytics (Schlueter Langdon 2017, link) and exploratory studies, three scenarios have been selected for simulation: A baseline scenario and two intermodal derivatives. The baseline scenario is our default. It provides our critical starting observation and data used for comparison and control: Someone drives from A in their car to near B, parks the car, and walks to destination B. A could be the driver’s garage and B could be their office, a restaurant, or shopping venue. This is a very common, and therefore, highly useful baseline scenario: No one can drive all the way to B … through the front door, the reception area, up the stairs into the office on the 3rd floor. Cars need to be parked near B.
The first intermodal scenario (the same as S2 in “Intermodal Mobility,” link) introduces a smart parking solution, such as Deutsche Telekom’s “Park and Joy,” to guide our user to an empty parking spot. Using parking probability data provided by Park and Joy, the driver will be navigated to a road section with a high parking probability located near B, high meaning greater than or equal to 80 percent. This generates a new location for the transfer point (called Near B), from which the walking distance to end point B is calculated. The travel time for this scenario is calculated from the start point A to the transfer point Near B (first mile) and from Near B to the end point B (last mile).
In the second intermodal scenario, the last mile section of the previous scenario is upgraded from walking to an electric scooter solution. It is based on S3 in our story “What’s holding back intermodal mobility?” (link) – albeit with an e-scooter for the last leg instead of Hamburg’s Moia van shuttle (link), which is not available in Berlin. It is a very smart scenario: Near B parking will be matched with the closest e-scooter offer, and last mile travel duration will be calculated based on this match. For this, e-scooter offers that are located near the determined parking section are calculated and the nearest e-scooter is used to calculate the last mile (Tüscher 2019, p. 36).
Figure 3: Where Berliners …
The 1999 Hollywood blockbuster pioneered the notion of human digital twins living in an artificial world, “The Matrix.” Our digital world is an extremely limited one. It is limited to fit our mobility scenarios; our Berlin digital twin is limited to fit the needs of running realistic experiments with our three scenarios. Specifically, our digital twin is limited in terms of time, space, and user behavior – namely rush hours, using only very specific routes, and aggregate traffic flows – for privacy protection reasons. In order for it to be a Berlin digital twin, a relevant, digital artifact of the city, we had to select the right destination zone, travel routes to it, and time windows.
Figure 4: From traffic flows into Berlin and within Berlin to four focal routes
Having created the data pipeline for our Berlin digital twin, our next step shifts to analytics, creating the algorithms for the “smart” elements of our hypothesis.
Figure 5: Last mile matching
Our “Matrix” lives in the “Testbed”, a virtual sandbox of the Deutsche Telekom Data Intelligence Hub (link). Its organs are cloud-based Jupyter notebooks, Python libraries, and other modules, such as routing engines, visualization tools, and additional dockerized third-party components (go check it out for free, or contact us for your own sandbox). It provides us with a cloud-based environment for open source data analytics as a default (more expensive and proprietary tools, such as from Cloudera are also available). Testbed solves a number of problems for us. It is our:
Furthermore, it comes with a “roles & rights” management feature to allow for multi-tenancy, which enables us to co-create with third parties, such as other researchers or institutions, despite the use of proprietary data, such as Deutsche Telekom’s Park and Joy and anonymized signaling data from mobile networks.
Figure 6: More results to confirm our “the smarter, the faster” hypothesis (see Figure 5 in “Intermodal Mobility,” link)
In an earlier paper, we had already demonstrated that “smart”, the clever linking of transportation modes, can reduce travel times for end-users, albeit based on some abstract location with average values. Now the model has been re-run for the city of Berlin using times of day and routes that are key to the traffic in Berlin and these results confirm the trend quantitatively. Furthermore, our experiments suggest that there could time savings in excess of 10 percent. This result is real and not a random fluctuation. More than 10 percent is a signal not noise. Particularly since we have run the model in a very specific context, the city of Berlin. We even call it our “Berlin digital twin” because we literally use values that represent the reality of Berlin, as if we were running a real experiment and not a simulation. In other words, you would get similar values if you were to run it on the streets of Berlin (pick your stopwatch and go try …). Yes, “Berlin digital twin” is also catchy and captures your attention. It also makes it clear that at every turn, we took great care to avoid cutting corners or resorting to a convenience sample. Just the opposite: We complemented government statistics with real life traffic data to pick real destinations. We selected routes from the major traffic arteries and did not just select one route, but four, from very different direction. When selecting times, we did not simply use an average time of day but a time when traffic is at its worst in Berlin, and we did so for three different time frames. If it works then and there, it will work anytime and anywhere. Figure 7 illustrates the variety of simulation runs and summarizes our quantitative findings.
Figure: 7: Reduced travel time in percentages (adapted from Tüscher 2019)
What are we waiting for? Well, consumer benefits are only a first-order condition. It would be important for any business to understand the extent to which this value can be monetized and how. This would require another, different model as discussed in our previous paper (see “Intermodal Mobility,” link). Tied into this second-order issue is the role of data. “Smart” requires data. Think Uber: Being able to match a rider with a driver depends on data, specifically rider location, driver location, availability, and traffic conditions… all at the same time. Without it, Uber would not be able to orchestrate a transportation offering. Uberization needs all parties – riders and drivers – to share data, in near real time. It is the same with intermodal mobility: data sharing is key. The problem is obvious: some transport options are in competition with one other (public transport, ride hailing, electric scooters, etc.). Some service providers are therefore competitors who do not necessarily trust one other. Everybody likes to “own” the customer or customer connection and keep their customer data close. Data privacy regulations, such as the GDPR in Europe, can serve as an excuse not to share important data. The trick will be to create technology that does not require trust between parties. It would be sufficient if parties were to trust a particular data transaction for mutual gain. This is where our work with the IDS standard comes in. IDS is a DIN Spec standard to facilitate data sharing so that data sovereignty can be maintained (Otto et al. 2019 and IDSA Blog, link). Or in my words: IDS allows parties that do not trust each other to trust a particular data transaction.
Stay tuned for additional experimentation on “killer” mobility apps, such as intermodal transport, as well as on our work concerning the underlying data infrastructure that can make these killer apps possible.
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