Millennium Database

1
Millennium Database

• Annis Whitlow and Demian Raspall

2
Approach

• Mind Mapping
• Relationship Building
• Hypothesis
• Visual Analysis
• North America
• Rest of The World

• Toronto
• Chicago
• Houston
• Los Angeles
• New York
• Phoenix
• San Francisco
• Paris
• Rome
• Oslo
• Zurich
• Budapest
• Cairo
• Manila
• Rio de Janeiro

3
Mind Map and Relationships
4
Hypothesis

1. The relationship between the network speed, the
   road length, thenumber of vehicles on the road,
   and the distance traveledthis is significant
   because of its potential implications for
   alternatives to public transportation and because
   most buses are also subject tothe road conditions
   of private cars
2. The relationship between Gross Domestic Product,
   mobility, anddensity
3. The relationship between urban density and the
   productivity ofbus lines
4. The relationship between externalities as
   pollution and accidentsand elements of supply,
   demand, and density
5. The relationship between the amount of subsidy,
   the cost,ridership, and urban density

5
Amount of subsidy, the cost, ridership, and urban
density (US)
Income Riders Fare Cost Vehicle-km
Unitary Cost
Riders Fare Vehicle-km Operating Cost
Density -
GDP

• From the analysis, we conclude that higher
  density and Gross Domestic Product have a
  positive effect on the income side of the
  equilibrium while the effect is not clear on the
  cost side, since higher densities seem to reduce
  unitary costs but increase the output

6
Amount of subsidy, the cost, ridership, and urban
density (US)
7
Urban Density and Productivity (US)

• There is a fairly strong direct
  relationshipbetween every measure of
  publictransportation productivity and
  urbandensity
• higher fare-box revenue per boarding and
  perpassenger kilometer
• higher vehicle occupancy and seat occupancy
• higher rates of public transport operating cost
  recovery

8
Urban Density and Productivity (US)
9
Urban Density and Productivity (World)

• Cities in Europe generally have a higher rate
  ofoperating cost recovery
• tend to follow a pattern where denser cities
  havehigher rates of recovery
• Cities in developing countries have asurprisingly
  high rate of operating cost recovery
• farebox revenue and cost indicators were less
  useful in looking at world cities
• public transportation will have better
  productivityin denser cities, but that other
  factors can affectthe productivity as well

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Urban Density and Productivity (World)
11
Conclusions

• Transportation systems are very complex and even
  commonly assumed relations such as density and
  trip generation seem to be influenced by other
  factors
• Potential factors are also very difficult to
  quantify and did not appear in the database
• Data analysis and interpretation (and the ability
  to question the meaning of the data) is critical
  to using that data to inform transportation
  decisions