It has repeatedly been noted that there is a clear logic behind the geographical expansion of transport networks. That implies that one can model how those transport networks expand geographically; and how the final network would change if the network is constructed with different objectives.
In the last years I have developed the so-called Transport Link Scanner model, which is a GeoDMS based tool that allows the exploration of the effects of economic context and policy preferences on transport network expansion. The model combines a conditional logit model, some heuristics and techniques from the literature on corridor location problems and transport modelling methods to simulate the most likely network after the introduction of a new transport innovation.
The most recent version of Transport Link Scanner can be downloaded through this page. The scripts are downloadable here. The data is available here. Instructions to get you started are available here.
The recent years have seen a rise of websites that serve easily available public transport information for travellers; a great example is 9292 in the Netherlands. There is a substantial collection of data behind such websites, with many useful analytical and commercial possibilities. Those data are gradually becoming publicly available; see initiatives such as Transport for London’s.
For a scientific test case I recently developed a prototype method to generate network data and travel time matrices for the Netherlands using GeoDMS and a large raw database dump from the 9292 website. The generated networks have been combined with street data from OpenStreetMap to get a complete picture of travel time, including the necessary fore and after transport.
Some exciting things are possible with this sort of data. One application is generating accessibility maps and exploring how much public-transport enabled accessibility changes through the day. The top map show potential accessibility values by public transport for the Netherlands when arriving at five or six in the morning. The maps below display traveltimes to Amsterdam when leaving at different hours of the morning. These maps clearly show the variability of access throughout the day – from some areas in The Netherlands it’s not even possible to reach Amsterdam early in the morning! Quite a shock for a country that claims to strive for a 24/7 economy!
One of my goals for 2016 is to build a program to generate spatiotemporally informed public transport networks, traveltime matrices and assorted network information from generic GTFS formatted files. When ready the methods will be made available through this website.
In the 1960s Jane Jacobs shook urban planning with her famous work ‘The Death and Life of Great American Cities’. If you haven’t read it, give it a try, it’s a must-read! One of the things Jane Jacobs claimed after observing streetlife in her own neighbourhood was that city streets need to be occupied constantly to warrant safety and a pleasant atmosphere in public spaces. She claimed that mixed land-use patterns are necessary to provide constant occupation, and thus that there is a direct link between a city’s physical composition and its social sustainability. Unfortunately, apart from anecdotical evidence, proof from practice for Jacobs’s hypotheses has long been unavailable.
To verify whether land-use mixing indeed affects activity patterns I have recently used a massive dataset of mobile phone usage per antenna in Amsterdam, together with Piet Rietveld, Eric Koomen and Emmanouil Tranos. The used data are recorded by Dutch mobile phone provider KPN and have kindly been provided by the Dutch ministry of Transportation.
That research proved beyond doubt that, yes, in Amsterdam mixed land-use patterns have more diverse and longer during activity patterns; and that in fact neighbourhoods that have more mixed land uses coincide with neighbourhoods that are more attractive. Thus these findings are, as far as I know, the first ever city-wide proof of Jane Jacobs’s assumptions. A full description of the methods and the results of this analysis have been published in Environment and Planning A. A summary of the research has been published in Rooilijn, a Dutch-language magazine for planning professionals.
For the Environment and Planning A article: click here.
Congestion due to traffic has received a lot of political attention in the last decades. Costs are often mentioned as a reason why congestion should not be acceptable; while economists often uphold that, to some degree, congestion is a sign of economic success rather than a sign that a system is unsuccessful. In fact there is very little evidence that congestion on roads really hinders a city’s economic growth and that congestion only becomes a problem if traffic is severe throughout the day. If congestion is only a peak-time problem, spreading activities over the day becomes a feasible strategy to reduce travel delays.
Together with Paul Koster (VU University Amsterdam) I have edited data and made the video I share here. It shows real average working day travel times to Schiphol airport, the Netherlands for every quarter hour of the day. For Paul’s research and this video, the Dutch ministry of transportation kindly shared data on measured travel times for all of the motorways in the Netherlands.
The video shows that congestion is, from a time-of-day point of view, actually a very limited problem: before 7:00, between 9:00 and 16:00 and after 19:00 one can use the Dutch motorways presumably without any hindrance.
The construction of overland transport infrastructure has had a major social and economic impacts by reducing travel costs and travel times. One of the key questions I am interested in is how travel time improvements have changed the level of accessibility and interaction opportunity within territories. I have made this movie to demonstrate the extent in which historical railroad construction in the Netherlands has affected travel times to the country’s capital, Amsterdam. Greater travel time improvements are demonstrated by lighter hues. The actual year is shown in the upper left corner.
To me, the movie and underlying data show two important things: first, that railroad construction has substantially altered travel times; second, that the largest improvements in travel times were realized in the first stages of railroad network development. The latest stages, when numerous local lines were added to the network, have much less effect on travel time savings.