Multi-Modal Assessment of Highway Performance

Multi-Modal Assessment of Highway Performance

Markus Mailer (Universität Innsbruck, Austria)
DOI: 10.4018/978-1-5225-2116-7.ch013


This chapter presents a multi-modal method for the assessment of highway performance. It is derived by extending a traditional assessment concept step by step taking into account the capacity and quality of different modes on the road as well as in the corridor. It defines an appropriate performance target and explains why a multi-modal concept has to consider transport demand in persons and goods rather than traffic volumes in vehicle units. It is shown that the concept allows for different options and measures to improve traffic quality and so supports the efficient use of existing infrastructure and the effective allocation of limited funds.
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The basis of the multi-modal concept described in the following chapter was laid when the Austrian Standard for the Assessment of Road Design RVS 3.7 (FSV, 1994) had to be revised in 2001. Austria, like many other countries, had to deal with growing road traffic volumes, the increasing financial problems of rail, and limited budgets. Moreover, there was evidence that the traditional approach of road standards assessing highway performance might not be useful to solve these problems but that tools which basically adjust road capacity to traffic demand might even be part of the problem.

In 1994, Knoflacher published considerations based on feedback loops that the level-of-service concept introduced by the HCM has repercussions on traffic growth (Knoflacher, 1994). The short-term satisfaction of transport demand, in conjunction with the assessment procedures and criteria used, forms a reinforcing feedback loop that generates ever greater demand for transport in the long term (see Figure 1).

Figure 1.

Reinforcing feedback loop between the level-of-service approach and traffic volume

Mailer, 2004.

The left side of the figure shows the logic of the HCM and of similar standards focused on road sections. When traffic volumes grow, service quality decreases. This inverse interaction is marked with a minus in the figure. Based on the standards, measures to increase road capacity have to been taken when service quality falls below a benchmark in order to raise it above the desired level again. The relation between travel quality and capacity seems to form a negative and therefore self-stabilizing feedback loop. But, in the long term, service quality, especially if it is directly related to travel speed, affects land use. This relationship is shown on the right side of the figure. Increasing travel speeds resulting in longer travel distances have caused developments such as urban sprawl and economic concentration in the last few decades. Longer travel distances led to more mileage hence to more road traffic in the road network which finally results in a higher traffic volume on the road section being the starting point of the loop. These proportional relations are marked with a plus. The inverse interaction between traffic volume and service quality again forms a negative, self-stabilizing feedback loop at the top. In combination, the two loops, however, form a positive and therefore reinforcing feedback circle. So, responding to traffic growth by increasing road capacity is generating even more traffic in the long term. Since there is a substantial time lag in the relations between increased travel speeds resulting from infrastructure improvement, land use changes, and traffic growth, this vicious circle is difficult to realize. However, the growth of road infrastructure and traffic has reached several limits already, not at least a financing limit for infrastructure construction, operation, and maintenance (Mailer, 2004).

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