## 5 Conclusions  
Considering that many accessibility differences are unavoidable and irremediable by transport policies [1], and that _STA_ measures tend to incorporate a broad variety of factors, this study has introduced a transport performance measure isolating the role of the transport system in individual accessibility differences. As suggested by the test, the results obtained by _STTP_ may be sensibly different from those of _STA_. On the one hand, individual differences tend to be smaller, according to the more narrowed and transport-specific approach of _STTP_. On the other hand, the individuals registering the highest _STA_ do not necessarily correspond to those with the highest _STTP_ and vice versa. Indeed, an individual might register some transport difficulties in running their daily schedule but at the same time have enough time to engage in several discretionary activities (like Individual D in Section 4). Even the opposite may apply: a person could easily reach their usual daily destinations but have little free time and few surrounding amenities so as to register a low space-time accessibility (like Individual E in Section 4). These results highlight the complementary value of _STTP_ for _STA_. Indeed, researchers and policymakers might gain relevant benefits from the combined analysis of these two measures since they may evaluate both the overall and transport-specific impacts of various transport policies on individual accessibility differences (e.g., refs. [57; 58; 59]). This would be particularly relevant to assess transport policies that are expected to trigger controversial impacts on individual accessibility differences. For instance, in the case of high-speed railways, autonomous vehicle applications and transport sharing services, which are often found to increase accessibility in general, but also the accessibility differences across (groups of) people (e.g., refs. [57; 59; 60]). At the same time, _STTP_ might be used to discuss the individual accessibility impacts of growing mobility trends, such as the usage of individual slow mobility solutions (such as e-scooters), which are progressively replacing walking travels, especially for the first- and last-mile. To prove the suitability of the combination of _STA_ and _STTP_, it is necessary to refine the estimation of user transport costs on the one hand and extend the application of this methodology to a broader case study on the other. The former challenge is mainly related to reliable estimation of the value of travel time (_VTT_). This study relied on the wage rate method, given its popularity and ease of application. However, more articulated estimation approaches could be used (such as stated and revealed preferences) to differentiate better _VTT_ values depending on, e.g., the travel purposes, length of the travel and transport modes [61; 62]. The latter challenge implies collecting _STPA_ information from a substantial sample of individuals and applying sound statistical distribution analyses to the results. The first aspect is problematic for many studies using space-time measures, and it is one of the main practical reasons for the low usage of this kind of measure compared with, e.g., gravity-based measurements [15]. To overcome this limit, recent studies have deployed multi-stage stratified random sampling approaches, reducing the survey sampling rates while maintaining high accuracy [12]. The usage of statistical distribution analyses is problematic for a conceptual reason, since they imply the focus on one or more equity typologies [63]. For instance, calculating the coefficient of variation (CV) over the whole sample is closely linked to an egalitarian point of view, since the CV measures how spread the distribution of a good over a population is. Conversely, calculating the percentile ratio (PR) by comparing the accessibility values scored at the median (50%) with those scored at e.g., 10% is linked to the vertical-equity point of view, since it focuses on the individuals with the lowest accessibility values.