Setting up a cloud of virtual stops to find the balance in terms of maximum walking distance-- taking into consideration distance, time and even comfort--is a routine task for DRT planners. Understanding the best metrics, or mix of metrics, to determine the density of virtual stops requires experience, local knowledge and a touch of empathy with the people (passengers) that the DRT solution will serve. At the end of the day, having to walk a few yards or an entire kilometer can make a big difference to the passenger experience.
Having an immense amount of variables--population density, weather patterns, the existence of sidewalks, architectural barriers, security, space for stopping, accessibility demand and many more--in play, local knowledge is the key to success in DRT service implementation. Many failures can be explained by a lack of knowledge about the real needs of the population served. This is where close partnerships are crucial and why Shotl works with its clients as partners during a process of continuous assessment and data exchange.
One thing is paramount: the passenger experience must be at the center, and overall convenience must include walking distance as a key factor. With that in mind, what, then, is a sensible approach to take? What are the rules to help decide the number and location of stops?
The environmental goal of having communities switch from car ownership to public transport will only be attained if mobility solutions match the convenience of the private car. Measuring walking distances and times seem a fair choice and a quick, smart way of achieving conformity regarding a standardized decision about virtual-stop density. But, does it really hit that sweet spot of passenger convenience?
Letting passengers rate the service, and allowing them to go into detail, is part of a constant learning process at Shotl. With this feedback, combined with support from collected data, our engineers propose, discuss and implement improvements on the design of each of our operations.
Whether it’s the addition of stops, a relocation, the door-to-door requirements of passengers with special needs (or all passengers), or dealing with public space regulation limits and proposing dynamic space management at curbsides, at Shotl we tailor each operation by partnering not only with operators, corporations and authorities but also with communities.
Aiming, as we do, to create the most relevant and accurate proposal for each operation, we take a multidisciplinary approach to combine insights from all parties and factors, involving not only traffic and demand data but also mobility expertise algorithms, marketing, apps, etc.
Yes, big numbers and data can lead to faster setups but a deeper approach will cater more for the needs of passengers, communities and the operation as a whole. Anyone combining both perspectives will obtain better results and configure a more useful platform to tackle the social, environmental and economic challenges we wish to overcome.
Machine Learning (ML) is a field of Artificial Intelligence (AI) which focuses on the development of techniques that allow computers to learn how to perform a specific task.
Public transport systems worldwide have suffered weeks or months of plummeting demand, up to 80% in some cases. This is due both to the disappearance of commuting to school or work and the drastic change in habits we are all experiencing.
The capital of Portugal, with a population of more than half a million inhabitants, is the chosen location for the deployment of a new Shotl operation in a completely urban setting.