## 11 Focus on Route Selection  
The focused search for MCDA used in route selection returned 64 articles covering a wide variety of applications. Only 2 of the papers overlapped with the broad civil engineering search. Although it would be expected with route selection studies, very few articles integrated formal network analysis algorithms with the multi-criteria decision process. An exception is [112] who combined TOPSIS with [PERSON]'s algorithm for finding the shortest path through a network. Other papers used artificial neural network (ANN) optimization methods (or a fuzzy number version, ANFIS [113]) for this, combined with AHP for determining the multi-criteria weights, see for instance [114]. It was interesting to note that in humanitarian relief and disaster management, the choice of access route(s) is an important factor in the planning of humanitarian logistics and was included in the articles found [115].  
An example of selecting the best rapid transfer bus routes used 6 criteria (population density, existing routes, locations of bus stops, proximity to homes, proximity to non-residential destinations and land use) and the AHP method [116]. In contrast, for selecting optimal cycling routes, [117] described a general procedure for determining Pareto optimal solution sets. In a later development, the best cycling routes in Imotske city (Croatia) were selected using fuzzy AHP and 5 criteria (length of route, type of road, slope, distance from emergency services, distance from refreshment facilities) [118]. Drivers' preferences were not explored in many articles, but [119] describes a method for finding and using these in route selection using a fuzzy neural network approach, ANFIS [113]. A method for selecting the route for a new monorail system for Ankara used ANP and TOPSIS and had fifteen criteria under four main headings (economic, social impact, performance, and environmental impact). To select routes for cruise ships in Venice, the SAW and MAVT methods were used with 30 criteria under three main headings (environmental, economic and social) [120]. Airlines use MCDM for selection of new routes. One example in Turkey used a fuzzy TOPSIS method with twelve criteria to select a new destination city for an expanding airline [121]. A GIS based method for oil and gas pipeline route selection was described by [122] and used Landsat satellite imagery in a GIS system and the AHP multi-criteria method. Below the sea surface, the selection of routes for sub-sea hydrocarbon pipes lines was addressed by [123] using fuzzy AHP and seven criteria (distance to environmentally sensitive features [there were 6 sub categories of this criterion], distance to existing platforms, proximity to shipping activities, distance to exclusion zones, proximity to favorable seabed conditions, and distance to existing pipelines). Table 10 summarizes the more recent papers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Citation** & **Method** & **No. Criteria** & **Notes** \\ \hline
[122] & AHP & - & Oil and gas pipeline routes. Uses \\  & & Landsat imagery and GIS \\ \hline
[116] & AHP & 6 & \\ \hline
[118] & AHP, fuzzy & 5 & Bicycle routes (Imotske, Croatia) \\ \hline
[120] & SAW with MAVT & 30 & Cruise ships in Venice (Italy). \\  & & & Did a sensitivity analysis of the \\  & & & results to changes in weights \\ \hline
[124] & ANP, TOPSIS & 15 & Monorail system \\  & & & (Ankara, Turkey) \\ \hline
[123] & AHP, Fuzzy & 7 & Subsea hydrocarbon pipelines \\ \hline
[121] & TOPSIS, fuzzy & 12 & Selection of new destination city \\  & & & in the USA for Turkish airlines. \\ \hline \hline \end{tabular}
\end{table}
Table 10: Examples of applications of MCDA to route selection decisions.