Improve service quality for bike sharing

Bike sharing has become commonplace in many places, and you've certainly seen them in various Swiss cities: Electric bikes or e-scooters in bright colors that can be used at train stations and other public places - usually via app - to get from A to B quickly. But often enough, these vehicles are parked somewhere after use, for example on sidewalks, in building entrances or elsewhere in public space. There they often become a nuisance for other road users or for residents. And they have to be collected again by the city bike providers at great expense.

Users' expectations of bike sharing are diverse: They want a vehicle that is quickly available and functional. And they want it where there is a need for it, i.e. not only at train stations but also, for example, at concert halls, sports stadiums or restaurants, so that they can get home quickly and safely from there. A study by the Norwegian University of Technology and Natural Sciences (NTNU) in Trondheim has therefore looked at how cities and bike-sharing providers can improve the service and also traffic management.

How to shoot at a moving target

Providing bicycles or e-scooters where and when people need them is a challenge. The problem is described as dynamic because it is constantly changing, and stochastic because it changes in random and often unpredictable ways. Steffen Bakker, a researcher at NTNU's Department of Industrial Economics and Technology Management, explains this as follows: "Users of the bike-sharing system pick up their bikes at one location and then take them to another. Then the state of the system changes because the bikes are suddenly no longer where they were originally, that's the dynamic part," he said. "On top of that, you don't know when the customers will pick up the bikes and where they will leave them. That's the stochastic part. So if you want to plan at the start of the day, you don't know what's going to happen." It's like shooting at a moving target. In other words, the aim is to develop a system that allows more accurate predictions to be made about where and when there will be an increased demand for bicycles and e-scooters. Bakker and his fellow researchers have therefore developed an optimization model that provides bike-sharing operators with recommendations on how they should schedule their bikes and scooters as well as their service vehicles. The aim is to improve the process of "rebalancing", i.e. collecting and transporting bikes from one parking station to another.

Assemble the parts correctly

The Norwegian researchers carried out a pilot test in Trondheim for this purpose. "With this, we want to use existing city bike systems as a test base and increase the efficiency of the rebalancing teams by 30 % and the lifetime of the bikes by 20 % by developing new decision-making tools," says Jasmina Vele, project manager at Urban Sharing, the bike sharing company involved in the research project. "This can be achieved through better decisions in terms of rebalancing and preventive maintenance, which will lead to a major cost reduction in the existing urban bike systems." With the help of the optimization model, which is still in the development phase, a new plan can be sent to the drivers of the service vehicles every time they arrive at a bike station.

That's the tricky part. It's important not to be too short-sighted and only focus on the current state of the system, says Bakker, especially if certain stations are expected to have more demand in the next hour or so. "It's very complex because it's a big system," he says. "Maybe there will be a lot of demand at the station in an hour. So you already want to get some bikes there. At the same time, there may also be stations that are almost empty now and need bikes. So you have to find a compromise."

Bike sharing modeling with digital twin

Bakker and his colleagues are working with NTNU's Department of Computer Science to create a "digital twin" or computer simulation of the systems. This allows them to test different models and try out different approaches without having to test them in the real world. Initial tests have shown that the model created by the group can reduce the number of problems (i.e. either too few bikes in the place where the user wants one, or too many bikes so that the user cannot park their bike) by 41 % compared to not rebalancing.

Steffen Bakker's team also worked on a component of the optimization model called the criticality score. A criticality score is basically a score assigned to different bike sharing parking spaces based on the number of bikes they currently contain or require. These scores are relatively easy to calculate and can be provided to riders as they travel around the city to balance the number of bikes at each station. "It's a score that tells the service rider which station they should definitely visit," says Bakker. "It allows us to offer something that, while not the best, is probably good and much better than what bike sharing companies currently have available." Jasmina Vele from Urban Sharing also confirms that the use of this type of optimization model can help to make bike sharing an important part of urban transport. "Urban Sharing's vision for the mobility of the future is a transportation system that is responsive and adaptable. By using data and machine learning/optimization algorithms, we can combine the best of traditional and modern transport systems and create a resource-efficient system that responds to demand and adapts to the individual needs of users," says Vele.

The research paper was published in the European Journal of Operational Research. Source: Techexplore.com

This article originally appeared on m-q.ch - https://www.m-q.ch/de/servicequalitaet-bei-bike-sharing-verbessern/