Data processing – spatial matching
GWR/GVZ Housing units Construction year Value
Soil coverage zones (AV) Surface information Building footprints
Parcel Size FAR covered
Land use zone Planning constraints
specific firms. This means that the relocation of firms, which is normally due to growth, will be accounted for, as well as factors affecting the firms such as economic environment,
policy environment residential location factors.
Econometrics UrbanSim relies heavily on sub-models to predict long term urban development. They are used to predict land value, location of households, location of jobs and location of
and
are based on more modern grid plans. This usually makes the city centre an attractive location, due to historical attributes and the presence of amenities, and means that lower income households are more likely to be located in peripheral zones. These differences will be considered in several of UrbanSim’s example,
sub models. For the Household Location Choice
model and Land Price model account for the attractiveness probably
of give the less city centre importance to
and the
“European cities are also concentric, having usually developed from medieval cities, compared to US cities which are based on more modern grid plans”
new real estate developments. Land price is particularly difficult to model due to the fact that the price of a housing unit is not only affected by its attributes but also by the attributes
of its surroundings, and so
UrbanSim-E incorporates one of two new models depending on the available data for each case study.
Descriptive and geographical data European cities are more compact and dense than US cities, meaning that people tend to take shorter trips and land value is higher, with more apartments available due to the lack of
space. European cities are also
concentric, having usually developed from medieval cities, compared to US cities which
42
presence of private green spaces (gardens or patios) and attach more relevance to the presence of public green areas and amenities.
Transport microsimulation Transport microsimulation models mimic the traffic behaviour of real travellers, but do not fully represent the travel behaviour of individuals, which consists of a connected sequence of trips between locations which can alter due to traffic congestion and socio-economic status of the individual. For example, a person might leave at a different time than expected in order to avoid the rush hour. This time of departure dimension has been used in both the transport models used by SustainCity.
Insight Publishers | Projects
Andre de Palma Prof. Dr. Andre de Palma teaches economics (industrial organization and transport economics) at École Discrete choice models at Paris 1, Sorbonne and microeconomics He is an expert in discrete choice, transport economics, experimental economics and Industrial organization. He published several books on transportation economics and has a few dozen international papers in transportation journals
MAIN CONTACT
Balz R. Bodenmann Tel: +41 71 351 10 89 Email:
bodenmann@ivt.baug.ethz.ch Web:
www.sustaincity.org
Kay W. Axhausen Prof. Dr. Kay W. Axhausen is Professor of Transport Planning at the ETH Zürich. He has been involved in the measurement and modelling of travel behaviour for the last 30 years contributing especially to the literature on stated preferences, micro- simulation of travel behaviour, valuation of travel time, activity scheduling and travel diary data collection. Current work focuses on the agent-based micro-simulation toolkit MATSim and on the land- use/transport interaction
AT A GLANCE
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68