The registration fee includes a two and a half day conference program with refreshments, lunch, proceedings and social events in the evening. Accommodation is not included within the registration fee and must be pre-booked separately. Register here.
+The registration fee includes a two and a half day conference program with refreshments, lunch, proceedings and social events in the evening. Accommodation is not included within the registration fee and must be pre-booked separately.
The conference language is English.
Our ability to virtually recreate infrastructure and traffic flow at many different scales, from the very detailed simulation of systems used to drive and control vehicles to the depiction of congestion in country-wide highway networks, has improved drastically over the last decades. The data needed to develop, calibrate and validate simulation models is increasingly at our fingertips. An active and growing community of academics and industry personnel dedicate time and expert knowledge to improving and optimizing traffic simulation tools and workflows. At the same time, issues such as air pollution, traffic injuries and fatalities, noise and disconnected urban space persist. In this keynote, I want to explore how tools for creating virtual, simulated worlds are helping to solve actual problems in our real world: what is done today, and what opportunities are there for tomorrow?
The agent-based demand model TAPAS is an open source development of DLR’s Institute for Transport Research. TAPAS simulated the mobility patterns of a virtual population of a modelled regions. Both the population as well as the activity places are represented individually. TAPAS distinguishes a large set of person groups, and different modes of transport. The mobility behavior of the population is determined using empirical daily mobility plans. The result is a list of daily activities for each person within the modeled area, including the starting location, the destination location, the begin of the ride and the ride’s duration as well as the chosen mode, among others. The result can be used as an input for the microscopic traffic flow simulation SUMO.
diff --git a/lychee.toml b/lychee.toml new file mode 100644 index 0000000..2ed2899 --- /dev/null +++ b/lychee.toml @@ -0,0 +1,14 @@ +# Maximum number of allowed redirects. +max_redirects = 20 + +# Proceed for server connections considered insecure (invalid TLS). +insecure = true + +# Remap URI matching pattern to different URI. +#remap = ['%7B%7BVersion%7D%7D 1.19.0'] + +# Exclude URLs and mail addresses from checking (supports regex). +exclude = ['dlr\.de', 'linkedin\.com', 'twitter\.com'] + + + diff --git a/src/content/2019.md b/src/content/2019.md index d2dd7c6..cd60467 100644 --- a/src/content/2019.md +++ b/src/content/2019.md @@ -102,7 +102,7 @@ Instructions for authors and a template can be found here: