Making our cities smarter by changing the daily commute. It works if you do it at the right time.
Getting people to change their habits is just plain hard. But, there are in fact ‘moments in life’ when people may have or want to change what they’ve pretty much always been doing since their needs, constraints, or priorities may change. These moments include things like having kids, moving house and changing jobs. So what if not just individuals, but employers took advantage of such change in the life of their personnel to influence how they commute to work? After all, who doesn’t tell their HR department when they have a child or a new address?
At the Xerox Research Centre Europe we’ve been exploring the potential of this idea within the framework of making our cities smarter. More precisely we’re interested in finding ways of helping people changing their daily commute in a sustainable fashion to reduce carbon emissions and even better, make us all a little bit fitter.
As part of this work our team conducted qualitative studies with interviews of groups of commuters in local businesses to get a good understanding of how, why, and when people make decisions on how they are going to travel. After all, it is only by understanding the issue at stake that you’re going to reasonably be able to suggest a solution – with or without technology.
It may come as no surprise that we observed that the things that may influence our choices are: how flexible we are, the constraints we have, the time or want to spend travelling as well as, of course, our personal preferences and how important the environment is to us. Once we’ve got into our daily or weekly routines we’re predictably reluctant to change – quite simply because there could be a potentially ‘high cost associated with change’. That means that you would have to put in a bit of effort to explore other possibilities and then try them out. Something most of us can never find the time or self-motivation to do when there’s no big reason staring us in the face.
So what if we could seize these moments when people are going through a period of change and when they are that bit more open to rethinking the daily commute?
Another element of reflection for us is what cities have been doing to make transportation more sustainable. Many cities have been doing an awful lot over the last few years to make it harder to get away with using your car in major urban areas (i.e. with congestion charges), and easier to get around without one. The number of park and rides and dedicated bus lanes have increased and transport authorities have been enthusiastically behind the creation of some pretty good, free, trip planning apps that make it easy to check all available travel options, instantaneously, with arrival and departure times and places. These apps have revolutionized public transport information systems making it easy to know how best get around town even for a complete stranger. Yet some European countries are going one step further. In the same study, we looked at some emerging initiatives of public authorities in the UK and France to work with businesses to accompany a change in how people travel. They believe that businesses and organizations can be an important facilitator and communication channel between them and citizens. They’re even considering how organizations can provide employees with additional and more personalized incentives at those times propitious to change. This is encourage them to make sustainable choices in how they commute.
For example, in France the government requires that companies implement and publically report on what are called ‘Work Travel Plans’. These plans contain financial incentives for employees to choose a means of transport to get to work that doesn’t entail driving there alone and especially one that includes the use of some kind of public transport. The researchers discovered that the incentives provided actually work much better if they become an integrated part of the company’s culture instead of being just one offs as a result of some legal obligation. And, despite having a financial cost, these incentives can be easily outweighed by the positive impact they have on the company brand making it more valuable and attractive to all stakeholders including current and potential employees.
So the next time you go for a job interview that’s not at Google CA, you might want to ask what the company has to offer in helping get you to and from where they want you to be most days of the week.
Watch the video on Smart Sustainable Commuting.
More information on the study can be found in the paper Understanding commuting to accompany work organisations and employees behaviour change by Stefania Castellani, Tommaso Colombino, Antonietta Grasso, and Matthieu Mazzega.
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To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimization problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimization to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments. More details on our research can be found in the Explore section below.
For a robot to be useful it must be able to represent its knowledge of the world, share what it learns and interact with other agents, in particular humans. Our research combines expertise in human-robot interaction, natural language processing, speech, information retrieval, data management and low code/no code programming to build AI components that will help next-generation robots perform complex real-world tasks. These components will help robots interact safely with humans and their physical environment, other robots and systems, represent and update their world knowledge and share it with the rest of the fleet. More details on our research can be found in the Explore section below.
Visual perception is a necessary part of any intelligent system that is meant to interact with the world. Robots need to perceive the structure, the objects, and people in their environment to better understand the world and perform the tasks they are assigned. Our research combines expertise in visual representation learning, self-supervised learning and human behaviour understanding to build AI components that help robots understand and navigate in their 3D environment, detect and interact with surrounding objects and people and continuously adapt themselves when deployed in new environments. More details on our research can be found in the Explore section below.
Details on the gender equality index score 2024 (related to year 2023) for NAVER France of 87/100.
The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and Indicator n°4: +5 points in 2025) have been achieved.
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2024 au titre des données 2023 : 87/100
Détail des indicateurs :
Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2 points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.
Details on the gender equality index score 2024 (related to year 2023) for NAVER France of 87/100.
1. Difference in female/male salary: 34/40 points
2. Difference in salary increases female/male: 35/35 points
3. Salary increases upon return from maternity leave: Non calculable
4. Number of employees in under-represented gender in 10 highest salaries: 5/10 points
The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and Indicator n°4: +5 points in 2025) have been achieved.
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2024 au titre des données 2023 : 87/100
Détail des indicateurs :
1. Les écarts de salaire entre les femmes et les hommes: 34 sur 40 points
2. Les écarts des augmentations individuelles entre les femmes et les hommes : 35 sur 35 points
3. Toutes les salariées augmentées revenant de congé maternité : Incalculable
4. Le nombre de salarié du sexe sous-représenté parmi les 10 plus hautes rémunérations : 5 sur 10 points
Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2 points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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