Ali Modarres
 Urban Studies, University of Washington Tacoma, 1900 Commerce St., Tacoma, WA 98402, United States
 
 
 Highlights

•Estimation of commute-related energy consumption patterns in Southern California.
•Energy consumption patterns by socioeconomic and geographic location of commuters.
•Relationship between energy consumption, urban form, and social geography.
•Importance of social geography in developing energy-related public policies.


Keywords
Fuel consumption; Urban transportation; Density; Commuting; Minorities; Immigrants


Abstract
Like other major metropolitan areas, the urban complex that extends from Los Angeles to Orange County faces numerous transportation challenges. Daily traffic congestion, reduced productivity and loss of income, air pollution, environmental degradation and significant energy consumption are only a few outcomes of the millions of miles travelled every day on the region’s highways and streets. An important response to this significant urban challenge has been the desire for further expansion of an efficient public transportation network and increasing densities in particular areas within the larger metropolitan region. In this paper, we estimate the current energy consumption patterns in various communities, arguing that policy attempts to achieve higher density and better jobs-housing balance should fully consider the social geography of our metropolitan areas and their close relationship with energy consumption patterns.



Article Outline
1. Introduction1
2. Literature review
3. Data and analysis
3.1. Southern California urban transportation context
3.2. Average vehicle ridership and travel time
3.3. Energy consumption and data mapping
3.4. Commuter energy usage by social cohort

4. Spatially-focused planning for equitable reduction in energy consumption
5. Conclusion
References


Figures
   

Fig. 1.

Distribution of travel time to work.


Fig. 2.

Income, gender and average travel time to work.


Fig. 3.

Average commuter BTU usage.


Fig. 4.

Comparison of average BTUs in each PUMA with overall average BTUs for all PUMAs in Los Angeles and Orange Counties.


Tables

Table 1. Relationship between annual personal income and travel time to work.

Table 2. Travel time to work by gender.

Table 3. Native and foreign born comparison on use of public transportation.

Table 4. Influence of the decade of entry on immigrant transportation habits.

Table 5. Travel time and average vehicle ridership.

Table 6. Influence of income on average vehicle ridership.

Table 7. Energy consumption conversion table.

Table 8. Energy consumption by selected social and demographic indicators.

Table 9. Energy consumption by various socioeconomic indicators.

Table 10. Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions. Variables ordered by absolute size of correlation within function.