# Multimodal Dynamic Journey-Planning +
## 4 Experimental Evaluation
### Experimental Results  
In the experimental evaluation we included the EA query algorithms TE-QH and TE-QH-ALT for TE-red [10], DTM-QH and DTM-QH-ALT for DTM [10], and the new algorithms PrMDTM-QH-ALT, MDTM-QH and MDTM-QH-ALT for MDTM. For the latter, we have also included the multicriteria (EA,MNT) query algorithms McMMDTM-QH and McMMDTM-QH-ALT with threshold \(r_{mc}\) on EA 100% and 120% (denoted with extension 1.0 and 1.2, respectively). Note that QH denotes an optimized version of [PERSON]'s algorithm (with the corresponding node blocking and extended arc relaxation methods [10]) and QH-ALT denotes its ALT extension. For each input instance, we generated 10,000 random queries consisting of source and target stop pairs, along with a random departure time, with respect to the time period, at each source stop both for the earliest arrival queries and multicriteria queries. Also, we generated 1000 random profile queries consisting of source and target stop pairs, and a random 2 h departure time interval. Table 6 shows the performance of the algorithms with (\(\check{\vee}\)) and without (\(\times\)) the ALT heuristic for the case of limited walking (the unlimited case exhibited a similar difference in performance). It is clear from Table 6 that the ALT-based algorithms have much better performance. For this reason, in the rest of our experimental study we considered only the ALT-based algorithms (marked with the ALT suffix). The experimental results regarding the practical performance of the algorithms for answering multimodal queries are reported in Table 7. We added in Table 7 the query times of the best previous (_RAPTOR_-based) approaches MCR-ht and MR-co-t10 in [7,8] (MCR-ht weakens the domination rules by trading off walking and arrival time; in MR-co-t10 the walking duration was not used as a criterion and it was limited to 10 minutes). We stress that the former computes multicriteria (on arrival time, number of transfers, and walking duration) multimodal journeys, while the latter computes multicriteria (on arrival time and number of transfers) multimodal journeys. The times are scaled versions of those reported in [7,8] using the benchmark for scaling factors in [28]. Note that since the MCR-ht, MR-co-t10, McMMDTM-QH-ALT-1.0, and McMMDTM-QH-ALT-1.2 report multicriteria multimodal queries, it is natural that they take more time than regular multimodal EA (unicriterion) query algorithms. This is also true for the case of unlimited walking, due to the much larger search space explored by the algorithms. Nevertheless, McMMDTM-QH-ALT-1.0 and McMMDTM-QH-ALT-1.2 are competitive to MCR-ht and MR-co-t10. From Tables 6 and 7, we observe that MDTM-QH achieves a smaller query time than TE-QH and DTM-QH. This is due to the grouping and ordering of nodes within each station. In the reduced model TE-red, within each stop, the arrival time events are not merged and the departure nodes are ordered by departure time. Therefore, TE-red has a disadvantage on finding optimal paths between stops and an advantage on finding valid paths between the stops. In DTM, within each stop, the arrival time events are merged into a switch node and the departure time events are ordered by their arrival time