The name is absent



3.  Summary

The results presented in this paper show that there are spatial as well as individual differences
in commuting behaviour. In the spatial context the employment density as a chosen indicator
was only partly suitable to measure the impact of spatial structure on commuting behaviour.
While it was possible to show the influence of spatial structure on commuting rate - the higher
the density of employees in a region the lower was the commuting rate - the impact on
average commuting distance was weak. While average commuting distance increases with
decreasing employee density in urban fringe and low density regions this connection is not
valid for agglomeration centers and peripheral regions. In these regions the average
commuting distance increases with density of employees. It is assumed that different
commuting distances seem not to be strongly determined by spatial structure as understood in
this paper. Rather it seems that beyond spatial structure there must be other reasons

responsible for the differences in commuting distance. For further investigations the
examination of special regional circumstances like polycentric or monocentric spatial
characteristics could be a promising approach.

In contrast to spatial level the impact of individual characteristics like age, education and
working hours on commuting rate and average commuting distance is much stronger. The
higher the educational degree the higher is the commuting rate and the covered distance.
Further it was shown that the life phases of employees influence the commuting rate as well
as the average commuting distances. After the beginning of working life the commuting rates
and average commuting distances are low. Average commuting distances reach at the middle
age a temporal maximum and then decrease for a short period and increase again at the end of
working life. With increased age commuting rates decrease rapidly after the maximum at
middle age. Further investigations have to test the impact of individual characteristics in
relation to different spatial categories. How does the probability of a specific individual to
commute change in regard to spatial category of place of residence? Moreover it is necessary
to prove if spatial structure or individual characteristics are the deciding factors in commuting
behaviour.

13



More intriguing information

1. Evaluation of the Development Potential of Russian Cities
2. CREDIT SCORING, LOAN PRICING, AND FARM BUSINESS PERFORMANCE
3. The name is absent
4. MANAGEMENT PRACTICES ON VIRGINIA DAIRY FARMS
5. MICROWORLDS BASED ON LINEAR EQUATION SYSTEMS: A NEW APPROACH TO COMPLEX PROBLEM SOLVING AND EXPERIMENTAL RESULTS
6. Income Growth and Mobility of Rural Households in Kenya: Role of Education and Historical Patterns in Poverty Reduction
7. The name is absent
8. CAN CREDIT DEFAULT SWAPS PREDICT FINANCIAL CRISES? EMPIRICAL STUDY ON EMERGING MARKETS
9. The name is absent
10. The Making of Cultural Policy: A European Perspective
11. Conflict and Uncertainty: A Dynamic Approach
12. Spousal Labor Market Effects from Government Health Insurance: Evidence from a Veterans Affairs Expansion
13. Human Development and Regional Disparities in Iran:A Policy Model
14. An Economic Analysis of Fresh Fruit and Vegetable Consumption: Implications for Overweight and Obesity among Higher- and Lower-Income Consumers
15. The name is absent
16. Iconic memory or icon?
17. The Context of Sense and Sensibility
18. An Intertemporal Benchmark Model for Turkey’s Current Account
19. Social Cohesion as a Real-life Phenomenon: Exploring the Validity of the Universalist and Particularist Perspectives
20. Spatial agglomeration and business groups: new evidence from Italian industrial districts