Sao Paulo, a megacity of almost 11 million inhabitants in its city center, is facing environmental, societal and infrastructural challenges due to its rate of rapid urbanization. The rivers in the Tiete basin reflect the painful truth of unmanageable urban planning and the unawareness of water as a valuable source. The rivers, once the blood vessels of the city, now function as a barrier in the development of the city by their stench and filthy appearance. An integrated design approach, covering multiple scales and disciplines, aims at tackling the issues by emphasizing the value of water. The Pinheiros should function as a bridge to improve social accessibility, environmental quality, and the connection between people and water. In a natural environment nothing is straight, everything is connected and balanced in an organic form. In Sao Paulo, as the city has expanded, the urban form has become linear and rigid. The city has modified the natural shape of the river. Blue arches envisions a resemblance of the original meandering shape of the Pinheiros. By connecting blue-green infrastructure on both sides with a green bridge for commuting and leisure, allowing for the meandering shape of the ecological places to cross the river.

Time-of-day (TOD) choice can be considered as a fifth stage in the modelling of transport behaviour, additional to the conventional four stages. Twenty years ago in The Netherlands, a stated preference (SP) study was designed for investigating the choice of time-of-day (departure time) and transport mode. A nested logit time period and mode choice model, largely based on this SP data set, was included as one of the components of The Netherlands national transport model (LMS). A new TOD SP survey has now been developed to obtain up-to-date information for the next re-estimation round of the LMS. The fieldwork was carried out in in 2019, followed by the re-estimation of the nested logit model of period and mode choice on the new SP data. The context for the SP is that of a tour (round trip) carried out by the respondent as car driver or by train, also distinguishing by travel purpose (commuting, business, education and other). This means that we are asking questions both about the outward leg of the tour and the inward leg. Both car drivers and train users are asked to participate in two SP experiments on TOD and mode choice: the first focussing on the trade-off between congestion or crowding and the departure/arrival times; the second also with differentiation in costs between peak and off-peak. Our tentative conclusion is that TOD choice seems to have become (relatively to mode choice) more flexible in the past two decades, in line with the trends towards more flexibility in scheduling activities over the day and a 24 hours economy. Moreover, we now estimate nest coefficients for both car drivers and train users (until now the assumption that had to be made in the LMS was that the nest coefficients for train followed those for car).@en

Tour formation is a distinguishing feature of freight transportation. An increasing amount of research is dedicated to the consideration of tour formation in freight simulation models, but these tour formation models lack statistical calibration on empirical data, do not represent shipments explicitly, or focus on a narrow segment of freight transportation. This paper presents a novel shipment‐based approach to model tour formation behavior. We developed an algorithm that constructs tours through iterative allocation of an additional shipment. Two choice models provide an empirical foundation to this algorithm. Parameters of these choice models are estimated on a dataset with information regarding over two million shipments. This information is gathered automatically from the planning systems of carriers transporting goods in the Netherlands. With this model we are able to reproduce observed tour patterns excellently for a given set of shipments. In addition, the model considers many objectives and constraints that determine the tour formation process and acknowledges differences between goods, vehicle, and location types. For example, shipments at ports tend to be transported with direct tours, while tours starting at a distribution center have more stops. This model can be applied in a shipment‐based freight simulation framework to construct tours for large third‐party carriers in the Netherlands.