# Multimodal Dynamic Journey-Planning +
## 4 Experimental Evaluation
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Map**} & \multicolumn{3}{c}{**Algorithm**} & \multicolumn{3}{c}{**Travel Modes**} & \multicolumn{3}{c}{**Query [ms]**} \\ \cline{2-10}  & **ALT (\(\times\))** & **ALT (\(\check{\vee}\))** & **Object** & **Public** & **Transport** & **Walk** & **EV/Car** & **Cyde** & **ALT (\(\times\))** & **ALT (\(\check{\vee}\))** \\ \hline \multirow{4}{*}{**Algorithm**} & TE-QH [10] & TE-QH-ALT [10] & EA & \(\bullet\) & & & 17.50 & 6.28 \\  & DTM-QH [10] & DTM-QH-ALT [10] & EA & \(\bullet\) & & & & 27.12 & 11.66 \\  & MDTM-QH [10] & MDTM-QH-ALT [10] & EA & \(\bullet\) & & & & 14.24 & 5.71 \\  & MDTM-QH & MDTM-QH-ALT [10] & EA & \(\bullet\) & \(\bullet\) & \(\bullet\) & 15.32 & 8.15 \\ \hline \multirow{4}{*}{**Algorithm**} & TE-QH [10] & TE-QH-ALT [10] & EA & \(\bullet\) & & & 14.85 & 5.09 \\  & DTM-QH [10] & DTM-QH-ALT [10] & EA & \(\bullet\) & & & 29.87 & 9.81 \\  & MDTM-QH & MDTM-QH-ALT & EA & \(\bullet\) & & & 6.48 & 4.01 \\  & MDTM-QH & MDTMTM-QH-ALT & EA & \(\bullet\) & \(\bullet\) & \(\bullet\) & 9.83 & 6.02 \\  & McMDTM-QH-1.0 & McMMDTM-QH-ALT-1.0 & MC & \(\bullet\) & \(\bullet\) & \(\bullet\) & 10.13 & 6.22 \\  & McMDTM-QH-1.2 & McMMDTM-QH-ALT-1.2 & MC & \(\bullet\) & \(\bullet\) & \(\bullet\) & 18.97 & 15.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between query algorithms. Symbol \(\times\) (\(\check{\vee}\)) denotes a query algorithm without (or with ALT. McMMDTM-QH-ALT-11.0 (McMDTM-QH-ALT-11.2) denotes McMMDTM-QH-ALT) with \(r_{mc}=1\) (\(r_{mc}=1.2\)). Bullets (\(\bullet\)) indicate the options taken into account. EA denotes an earliest arrival journey. MC denotes a multicriteria journey on arrival time and number of transfers. at the next station. Therefore, DTM has an advantage of finding the optimal paths between the stops and a disadvantage of finding the valid paths between the stops. In comparison to DTM, MDTM has the departure nodes ordered by arrival time within a set of groups (\(\Gamma_{1}\) and \(\Gamma_{2}\)), along with the earliest arrival index tables. In that way, MDTM gains an advantage on blocking non-selected transportation modes as well as non-valid paths between the stops. In all cases, the combination of the optimized [PERSON]'s query algorithm with the ALT goal-directed speedup technique leads to a significant decrease in query time, by at least 39%. With ALT, the query algorithms need less iterations for finding the target stop. Our multicriteria algorithms with \(r_{mc}=1.0\) are faster than those with \(r_{mc}=1.2\), since they compute less journeys. In the case of profile queries with a departure time interval of 2 h, the computation cost is higher. Such queries involve the execution of several earliest arrival queries on different departure times, considering any optimal reachable boarding via public transport that provides the minimum travel time at the chosen destination. Algorithms HLCSA [29] and HLRaptor[29] are extensions of CSA[30] and Raptor[31], respectively, requiring substantial prepossessing time for producing the hub labels. In particular, the hub labeling walking graph data of both algorithms are obtained from [32] and their computation on a dual 10-core Intel Xeon E5-2670-v2 CPU [29] (a server much more powerful than ours) requires about 4 h. Both HLCSA and HLRaptor are designed to efficiently compute journeys with unrestricted walking. Comparing HLCSA, HLRaptor, and PrMDTM-QH-ALT, we see a trade-off, but we need to stress that the implementations were applied on different London instances. The hub labeling algorithms provide (by design) faster query times at the expense of heavy preprocessing, but they were applied on a much smaller London instance (consisting of 19,746 stops and 4,695,285 elementary connections). PrMDTM-QH-ALT has an extremely fast preprocessing time at the expense of a (not so much) slower query time, but it was applied on an instance three times larger (consisting of 20,843 stops and 14,064,967 elementary connections) than that used by HLCSA and HLRaptor.