Annis Whitlow and Demian Raspall Millennium Database Annis Whitlow and Demian Raspall

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

Mind Map and Relationships

Hypothesis The relationship between the network speed, the road length, thenumber of vehicles on the road, and the distance traveled—this 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 The relationship between Gross Domestic Product, mobility, anddensity The relationship between urban density and the productivity ofbus lines The relationship between externalities as pollution and accidentsand elements of supply, demand, and density The relationship between the amount of subsidy, the cost,ridership, and urban density

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

Amount of subsidy, the cost, ridership, and urban density (US)

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

Urban Density and Productivity (US)

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

Urban Density and Productivity (World)

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