# Multimodal Dynamic Journey-Planning +
## 4 Experimental Evaluation
### Input Data and Parameters  
The input data for creating the multimodal transport networks are (a) timetable data sets in the General Transit Feed Specification (GTFS) format, containing various means of public transport and (b) road and pedestrian network data sets in the Open Street Map (OSM) format. The integrated networks concern the metropolitan areas of Berlin and London. The source of the timetable data for London is [20] and for Berlin is [21]. The source of the road and pedestrian network data is at [22]. Tables 2 and 3 provide detailed information about the input timetables. In Table 2, we report, for each timetable, the number of stations \(|\Phi|\) and the number of elementary connections \(|\mathcal{C}|\) between stops (a proxy for size), as well as the number of nodes \(|\mathrm{V}|\) and arcs \(|\mathrm{E}|\) of the corresponding graph, for each model. In the same table we include the sizes of the realistic time-expanded (TE-real) and the reduced time-expanded models (TE-red) [2; 10] for comparison. The timetable time period of the connection sets is Monday for Berlin, and Monday to Wednesday for London. In Table 3, we report, for each timetable, the average transfer time for changing vehicles, the average number of adjacent stops or stations, and the percentage of the existing transportation means to the total number of the elementary connections. For experimenting with MDTM, we additionally added non-restricted departure traveling paths, using the following two approaches:  
* Limited walking and driving travel time paths on transitively closed pedestrian and road networks. Via the pedestrian networks, we added single foot-paths for enabling walking between nearby \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Map**} & \multirow{2}{*}{\(|\Phi|\)} & \multirow{2}{*}{\(|\mathcal{C}|\)} & \multicolumn{2}{c}{**TE-Real**} & \multicolumn{2}{c}{**TE-Red**} & \multicolumn{2}{c}{**DTM**} & \multicolumn{2}{c}{**MDTM**} \\ \cline{3-10}  & & & \(|\mathrm{V}|\) & \(|\mathrm{E}|\) & \(|\mathrm{V}|\) & \(|\mathrm{E}|\) & \(|\mathrm{V}|\) & \(|\mathrm{E}|\) & \(|\mathrm{V}|\) & \(|\mathrm{E}|\) \\ \hline Berlin & 12,838 & 4,322,549 & 12,967,647 & 21,612,745 & 8,645,098 & 17,024,138 & 4,335,387 & 12,701,695 & 4,335,387 & 12,708,568 \\ London & 20,843 & 14,064,967 & 42,194,901 & 70,324,835 & 28,129,934 & 55,758,468 & 14,085,810 & 41,837,355 & 14,085,810 & 41,856,048 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Tested timetables and sizes of the corresponding graphs. \begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathcal{T}\) & **Berlin** & **London** \\ \hline transfer time & 0.7 & 0.8 \\ adjacent stops & 2.7 & 1.2 \\ bus & 76\% & 98\% \\ train & 15\% & 2\% \\ tram & 9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Timetable characteristics: average transfer time (mins), average degree of adjacent stops/stations, and percentage of transportation means (% of total). stops. The foot-paths that were selected and added had the shortest travel time of 10 mins at most, with walking speed being 1m/s. Also, via the road networks, we added free-flow speed driving paths to enable driving between stops with EVs. For this scenario, we considered 10 random EV stations providing public communal EVs with the shortest travel time of 1 h at most. In addition, the driving paths connected only EV stations. In the Berlin instance, the switch-switch arcs representing foot-paths are 2381 and the driving paths are 39. In the London instance, the switch-switch arcs representing foot-paths are 412,614 and driving-paths are 60. * Unlimited walking travel time paths on the full pedestrian network. For this purpose, we connected each switch node in the public transit network with the nearest node in the pedestrian network by an access edge. This approach was inspired by that in [19]. In the Berlin instance, the embedded pedestrian network had 932,108 nodes and 1,059,556 edges. In the London instance, the embedded pedestrian network had 1,520,056 nodes and 1,653,052 edges.