Changes
On February 10, 2015 at 1:50:21 AM +1100, unknown:
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f | 1 | { | f | 1 | { |
2 | "author": "", | 2 | "author": "", | ||
3 | "author_email": "", | 3 | "author_email": "", | ||
4 | "creator_user_id": "aa05e915-b682-4fc9-aa19-5baf0671fe23", | 4 | "creator_user_id": "aa05e915-b682-4fc9-aa19-5baf0671fe23", | ||
5 | "id": "ba0ce484-fdd6-4b78-b92f-115809f356c9", | 5 | "id": "ba0ce484-fdd6-4b78-b92f-115809f356c9", | ||
6 | "license_id": "cc-by", | 6 | "license_id": "cc-by", | ||
7 | "maintainer": "", | 7 | "maintainer": "", | ||
8 | "maintainer_email": "", | 8 | "maintainer_email": "", | ||
t | 9 | "metadata_modified": "2015-02-10T05:49:37.608531", | t | 9 | "metadata_modified": "2015-02-10T05:50:21.237232", |
10 | "name": "travel-forecasts", | 10 | "name": "travel-forecasts", | ||
11 | "notes": "The BTS produces travel forecasts using the Strategic | 11 | "notes": "The BTS produces travel forecasts using the Strategic | ||
12 | Travel Model (STM). This model is a world class tool that projects | 12 | Travel Model (STM). This model is a world class tool that projects | ||
13 | travel patterns in the Sydney Greater Metropolitan Area under | 13 | travel patterns in the Sydney Greater Metropolitan Area under | ||
14 | different land use, transport and pricing scenarios. It can be used to | 14 | different land use, transport and pricing scenarios. It can be used to | ||
15 | test alternative settlement, employment and transport policies, to | 15 | test alternative settlement, employment and transport policies, to | ||
16 | identify likely future capacity constraints, or to determine potential | 16 | identify likely future capacity constraints, or to determine potential | ||
17 | usage levels of proposed new transport infrastructure or | 17 | usage levels of proposed new transport infrastructure or | ||
18 | services.\r\n\r\nThe STM is built largely in the EMME transport | 18 | services.\r\n\r\nThe STM is built largely in the EMME transport | ||
19 | modelling software. It is comprised of a series of models and | 19 | modelling software. It is comprised of a series of models and | ||
20 | processes that attempt to replicate, in a simplified manner, | 20 | processes that attempt to replicate, in a simplified manner, | ||
21 | people\u2019s travel choices and behaviour under a given scenario. The | 21 | people\u2019s travel choices and behaviour under a given scenario. The | ||
22 | STM combines our understanding of travel behaviour with likely | 22 | STM combines our understanding of travel behaviour with likely | ||
23 | population and employment size and distribution, and likely road and | 23 | population and employment size and distribution, and likely road and | ||
24 | public transport networks and services to estimate future travel under | 24 | public transport networks and services to estimate future travel under | ||
25 | different strategic land use and transport scenarios.\r\n\r\nThe STM | 25 | different strategic land use and transport scenarios.\r\n\r\nThe STM | ||
26 | produces travel forecasts by origin (2,690) and destination (2,690) | 26 | produces travel forecasts by origin (2,690) and destination (2,690) | ||
27 | STM zones for:\r\n\r\n* The Sydney Greater Metropolitan Area which | 27 | STM zones for:\r\n\r\n* The Sydney Greater Metropolitan Area which | ||
28 | includes the Sydney Statistical Division, Newcastle Statistical | 28 | includes the Sydney Statistical Division, Newcastle Statistical | ||
29 | Subdivision and Illawarra Statistical Division.\r\n\r\n* 5 yearly | 29 | Subdivision and Illawarra Statistical Division.\r\n\r\n* 5 yearly | ||
30 | intervals from the latest Census year up to a 35-year horizon\r\n\r\n* | 30 | intervals from the latest Census year up to a 35-year horizon\r\n\r\n* | ||
31 | 9 travel modes: Car driver, Car passenger, Rail, Bus, Light rail, | 31 | 9 travel modes: Car driver, Car passenger, Rail, Bus, Light rail, | ||
32 | Ferry, Bike, Walk and Taxi\r\n\r\n* 7 purposes: Work, Business, | 32 | Ferry, Bike, Walk and Taxi\r\n\r\n* 7 purposes: Work, Business, | ||
33 | Primary/Secondary/Tertiary education, Shopping, Other\r\n\r\n* 24 | 33 | Primary/Secondary/Tertiary education, Shopping, Other\r\n\r\n* 24 | ||
34 | hour, average workday (Monday to Friday excluding public | 34 | hour, average workday (Monday to Friday excluding public | ||
35 | holidays)\r\n\r\n* am/pm peak, interpeak and evening travel", | 35 | holidays)\r\n\r\n* am/pm peak, interpeak and evening travel", | ||
36 | "owner_org": "c96c25b8-bbff-4296-9f19-637ebfb50f59", | 36 | "owner_org": "c96c25b8-bbff-4296-9f19-637ebfb50f59", | ||
37 | "private": false, | 37 | "private": false, | ||
38 | "revision_id": "5e8a271d-b333-420e-8f4f-75f7cd9c7cac", | 38 | "revision_id": "5e8a271d-b333-420e-8f4f-75f7cd9c7cac", | ||
39 | "state": "active", | 39 | "state": "active", | ||
40 | "title": "Travel Forecasts", | 40 | "title": "Travel Forecasts", | ||
41 | "type": "dataset", | 41 | "type": "dataset", | ||
42 | "url": "", | 42 | "url": "", | ||
43 | "version": "" | 43 | "version": "" | ||
44 | } | 44 | } |