Browsing by Author "Sipetas, Charalampos (Haris)"
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- Evaluation Framework for Multi-Modal Public Transport Systems Based on Connectivity and Transfers at Stop Level
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2024-10) Sipetas, Charalampos (Haris); Huang, Zhiren; Espinosa Mireles de Villafranca, AlonsoMulti-modal public transport (PT) networks within metropolitan areas are often characterized by complexity resulting mainly from their infrastructure, design, operations, and demand. This complexity leads to a significant amount of effort on behalf of the transit agencies to properly evaluate their performance at certain locations and proceed with improvements. This study proposes a methodology based on clustering techniques that facilitates the evaluation of PT networks. The evaluation framework refers to the comparison between the levels of supply and demand at a certain stop. Service supply is quantified through an existing connectivity index, whereas demand is considered through the number of transfers that are performed at each stop. Transfers are critical within multi-modal mobility and often serve as a hindrance for choosing PT. The case study here is the Helsinki PT network in Finland. General Transit Feed Specification (GTFS) data are used for quantifying connectivity and a dataset deriving from smartphone ticketing application for quantifying transfers. Results include the evaluation for each PT mode and for the overall multi-modal PT network. Focusing on the evaluation of the overall multi-modal PT network, connectivity and transfers levels for 75.60% of stops are found to be well aligned. Therefore, these stops could be eliminated from the list of candidate stops for performing improvements. Of the remaining stops, 19.73% belongs to the case of higher connectivity than transfers and 4.67% to the case of lower connectivity than transfers. Stops included in these two cases require further attention and prioritization during planning processes. - Using temporal public transport demand profiles to reveal urban spatial patterns
A4 Artikkeli konferenssijulkaisussa(2024) Espinosa Mireles de Villafranca, Alonso; Huang, Zhiren; Sipetas, Charalampos (Haris)We present a versatile method, inspired by computational neuroscience, for reconstructing smooth demand profiles from sparse timestamp data for public transport boardings. We show how areas can be clustered based on the similarity of their temporal demand profiles to reveal urban spatial patterns. We use the Helsinki metropolitan region to showcase the method using data on boarding events from the TravelSense data from HSL (the Helsinki region transport authority) collected through their mobile ticketing app. Our results show the show the dependence of travel demand on available public transit and modes and supply volume. Furthermore, the clusters align with extremely well with the types of urban areas in the region. Due to the high supply and even frequency of transit options, the differences in demand profiles are due to mode availability and land-use features rather than frequency patterns.