Lessonofmobilitymanagement

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This document provides lessons on mobility management and public transport. It stresses the importance of understanding demand rather than just supply, considering the total range of activities, and being aware of existing arrangements and how money flows. It also recommends doubling planning efforts, knowing the target group, timing initiatives well, and finding support beyond just the director.

Forget supply, what’s the demand ,[object Object]

The public transport world offers concessions ,[object Object]

Concessions come to an end ,[object Object]

Be aware of existing arrangements and how money flows ,[object Object]

Find more support than the director ,[object Object]

The large amount of data coming from the numerous lawsuits in progress or already judged by the STF (Brazilian Supreme Court) consists of non-structured data, which leads to a large number of hidden or unknown information, as some relationships between lawsuits that are not explicit in the available data. These conditions also contribute to generate non-intuitive influences between variables and to increase the degree of uncertainty. However, many lawsuits can be decided based in certain patterns like: (i) the comparison to lawsuits with similar features (area of the Law, parties involved, nature of the claim, rapporteur, etc), (ii) the comparison to outcomes taken by a judge with a history of judging similar lawsuits, and (iii) the comparison to laws considered in past cases. All these parameters and some other patterns observed in past lawsuits provide a framework of non-structured data that can be transformed in useful data to predict new outcomes. This work proposes an approach to identify possible judgement outcomes that considers aspects beyond the analytical techniques. Through the use of similarity calculations and clustering mechanisms, the proposed solution was built in order to find the most similar lawsuits for a new instance that is being tested. By analysing some meta-data, it is possible to find a similar case already judged, since the amount of data provided by the judicial system are quite large. By the developing of a program that detects clusters and compiles past votes, the results shown that is possible to verify the most likely outcome and to detect its degree of uncertainty.

The Netherlands is known for its high cycling shares and great cycling infrastructure. What many people less realize is the challenge to increase those shares even more to enhance cycling in the sustainability smart cities objectives. To push cycling to the next level a national collaboration has been set up to join forces in the field of cycling data collection, storage, visualisation and analyses. Next, cycling knowledge sharing between a variety of stakeholders, ranging from academic to highly applied level, is essential. This paper not only covers an overview of challenges of the national organisation and the development of a cycling data platform but also shows how European award winning best practises add to the smart city objectives in the Netherlands. Combining mobile phone data, GPS/GNSS data, traffic light data, national survey data, traffic forecast model data and virtual and augmented reality results in a joint approach to smart cycling cities and ITS cycling developments. These developments can lead to a greener mobility system, but if done wrongly eradicate the strengths of cycling in an autonomous-vehicle dominated city.

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