# A Dynamic Urban Mobility Index from Clustering of Vehicle Speeds in a Tourist-Heavy City
## 4 Discussion  
In this work, we proposed the Zone-based Speed Index, a time-varying index for transport mobility in an urban area based on GPS data (speeds) collected from probe  
Figure 4: Intraday variation in the Zone-based Speed Index (ZSI) for ten months spanning October 2022 to July 2023. Higher values of the ZSI indicate better mobility. The solid blue line corresponds to the mean, while the top and bottom dashed lines are one standard deviation on either side of the mean. Both average and standard deviation are calculated across the days of the given month. Figure 5: Hourly Zone-based Speed Index for the month of July 2023. The decrease in ZSI successfully captures the decrease in vehicle mobility when supertyphoon _Doksuri_ passed through Baguio city on 26–28 July 2023 (red vertical lines). vehicles. This section examines several aspects of the data collected and the calculated ZSI across time. First is the observed bimodality of the speed distribution, which holds across zones and time. The bimodality implies the existence of two speed regimes for the probe vehicles: a low-speed regime and a high-speed regime, something which we have observed during several visits to Baguio city and had previously been reported from GPS data in other cities as well [40]. The wide variations in the characteristics of each zone (such as different topographic, building, and land-use patterns) make the choice of using zone-specific speed thresholds natural, as is the fact that the ZSI is a city-level index, not a zone-level one. In contrast to existing indices for mobility, which rely on a city-level aggregation or averaging of data, e.g., of travel times, the ZSI encodes the _spatial_ extent of a desirable condition (high-speed zones) and thus complements existing indices in providing a fuller picture of mobility in a smart city. Throughout the data-gathering period, the ZSI captured extraordinary events that caused mobility patterns to deviate from the typical behavior in our study area. Mobility patterns in the city during the closing stages of the COVID-19 pandemic regime (October and November 2022) were different from the mobility during the transition into the post-pandemic era (December 2022 onwards). Furthermore, our mobility index demonstrated its effectiveness in capturing the fluctuations in vehicle mobility during various events, such as typhoons and holidays, thus enabling a comprehensive understanding of the impact of external factors on urban mobility patterns. The high-resolution (hourly) characterization of urban vehicle mobility provided by the ZSI holds significant promise in helping local government authorities develop and optimize transportation systems, especially in tourism hotspots, where efficient mobility is pivotal for both residents and visitors, as well as to monitor mobility conditions to avoid or mitigate the negative impact on public perceptions by traffic congestion. Despite limitations, such as the absence of devices like induction loop detectors or CCTV cameras installed at as many sections of the city's road network as possible (to measure other traffic quantities such as vehicle flux and density) and the possibility of low speeds having causes beyond traffic congestion, our proposed speed clustering index represents an important first step in measuring a resource-constrained city's vehicle mobility index, which only requires the measurement of the speeds of probe vehicles among its public transport fleet via GPS. Our work and proposed mobility index is a contribution towards the literature and discourse on smart city indicators in a developing country context, something that is important, as a substantial fraction of the global population is expected to reside in such cities in the medium to long term.