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Real distance vs. perception – implementing the IAPI in GOAT

by Santiago Linares & Ulrike Jehle, TUM

Comfort in streets

Have you ever felt how the time waiting for your food in front of the microwave feels that each second lasts an eternity? Or when you take the bus, the time you spend at the stop feels way longer than riding the bus? And if you do not have anywhere to sit the situation is even more dramatic. In a study analyzing comfort at bus stations, Fan et al., (2016) found that uncomfortable stations, a lack of benches or shelter, can increase the perception of time by up to 1.3 times. And how does this relate to streets, rather than just bus stops? Well, the same effect applies to the urban environment, where if a route goes through an unpleasant area, it might feel longer than it actually is (Jehle et al., 2022; Ralph et al., 2020; Bahn Ville-Konsortium, 2010). Notice how the shape of a tree can completely change the perception of a street?

Comfort can determine the routing, the distance, and even the possibility of a trip from a certain point. The challenge is considering the comfort-effect within a numerical methodology to analyze and later promote walking, cycling and wheelchair trips. Politecnico di Milano (Polimi) conceived an accessibility indicator to measure access levels to urban opportunities and basic services through active mobility. It is called Inclusive Accessibility by Proximity Index (IAPI). For the accessibility calculation, the IAPI considers a series of urban characteristics that are the parameters to determine the network available to three profile users, pedestrians, cyclists, and wheelchair users. These parameters are road type, road traffic, sidewalk width, obstacles, and a group of urban elements that can influence positively the comfort perception, such as trees and urban furniture. In addition, the IAPI sets a basket of services. These are the points of interest (POI) to which accessibility is calculated. These POIs include amenities such as commercial points, health and social facilities, cultural area, sports, education, and public transport. The IAPI has been tested in Crescenzago, a district in the northeast of Milan. It was chosen because it is a peripheral area with heterogeneous morpho-functional conditions, an imbalance in the availability of services, and the local administration has plans to support the 15-minute-city there. Also, as specified in an earlier blog post about the IAPI, a further purpose of the experimentation is to evaluate the transferability of the approach to other contexts and cities. Here’s when the Geo Open Accessibility Tool (GOAT) developed by TUM enters the stage. As defined by its authors (Pajares et al., 2021), GOAT is an open-source web tool capable of modelling walking and cycling accessibility. It has special features that allow it to be interactive and flexible for accessibility planning. GOAT is highly transferable and has been implemented in multiple study areas, such as Munich, Fürstenfeldbruck, Freising, Freiburg, San Pedro Garza García and Bogota for accessibility analysis in research and practice.

IAPI at a city scale

To automatize the calculations and improve the transferability features, the IAPI was implemented in GOAT. For the implementation, the POI and network data had to be included within GOAT’s logic. First, for the POI, most of them were obtained from OpenStreetMap. In case of incomplete or inexistent information, they were complemented by the official data portal of the Comune di Milano. At this point, the data is ready, but how can we translate the effect of comfort into a numerical methodology? The solution, we make our calculations based on a perceived distance. In comparison with the normal distance between two points, the perceived distance increases according to the presence of uncomfortable elements such as obstacles, or bad smoothness of a street. Equally, the distance decreases with the presence of comfortable elements such as street lights or vegetation. Mathematically, the presence or absence of street elements and characteristics are called impedance factors. As a result, when the urban conditions are comfortable, users can reach farther distances because the perceived distance is shorter. In contrast, when the environment is not comfortable, the catchment area is smaller. To visualize the results, isochrones represent the maximum distance reached from a point, according to parameters for a certain network. For example, the figure below shows two isochrones. Isochrones reveal that depending on the characteristics of the streets, perceived time can change and allow farther distances, where the urban conditions are perceived as more comfortable, to be reached. On the map, trees represented by green dots indicate more comfortable areas. “Square A” shows how the perception-based isochrone shrinks next to the river; this is caused by the high impedance value of the bridge. On the contrary, “Square B” shows how streets with a higher density of trees influence the comfort of the area, reducing the perceived travel time.

Figure 1 Comparison of perceived vs distance Isochrones

The second indicator produced to visualize accessibility is heatmaps. With this indicator, accessibility is calculated to multiple POIs by defining a grid; each cell is assigned the value of accessibility for the point of interest being analyzed. For example, Figure 2 shows side by side the “perceived accessibility” and the accessibility estimation for all supermarkets using a calculation based only on distance. In comparison, perceived accessibility shows lower levels of accessibility than distance-based accessibility across the city. At the city scale, the reduction of accessibility shows the effect of including impedance values within the calculation, and that the streets are not as comfortable, and this effect has major consequences.

Figure 2 Comparison of perceived vs distance accessibility heatmaps

As mentioned earlier, the IAPI considers different categories of POI. The perceived accessibility by walking was calculated for each of the categories, this analysis enables the visualisation of zones of the city that lack access to some of the services. In contrast, the heatmaps show high accessibility to all the categories of services analyzed in the historic centre and the nearby surroundings. In addition, the analysis includes a comparison of the accessibility calculation with the population distribution in the city. The Gini Index was used to calculate the social component of this analysis. This indicator is very popular measuring the distribution of income over the population of a country (Weymark, 1981). In this case, the resource was accessibility to services, and the closer to zero, the more equally distributed the accessibility is, and vice versa.

Figure 3. Perceived accessibility by walking to the POI

Next steps

The IAPI is originally conceived as a quantitative tool that expresses the actual levels of accessibility to selected destinations at the neighbourhood scale. To implement it at a city scale, the requirements on the network data and implementation are suggested to change. The IAPI is now incorporating parameters and indicators that facilitate both the transferability and scalability of the Index, as well as trying to base more on open data. For the implementation of comfort within the gravity-based accessibility calculation, the impedance factors were estimated following methodologies and analysis from the literature review, however, Jehle et al., (2022) show how comfort factors heavily depend on the person. Therefore, making local surveys can help to better estimate the impedance factors and calibrate the accessibility. To conclude, these results reflect the importance of including people’s perception of the urban environment and how the comfort calculation is key to getting more realistic results. For example, there are platforms such as Commonplace that allow online engagement to connect with communities to hear their perceptions and help planners in shaping the livable cities of tomorrow.


Bahn Ville-Konsortium. (2010). Die Bahn als Rückgrat einer nachhaltigen Siedlungs-und Verkehrsentwicklung, Synthesebericht zum Projekt Bahn. Ville 2. Technische Universität München München. Fan, Y., Guthrie, A., & Levinson, D. (2016). Waiting time perceptions at transit stops and stations: Effects of basic amenities, gender, and security. Transportation Research Part A: Policy and Practice, 88, 251–264. Jehle, U., Coetzee, C., Büttner, B., Pajares, E., & Wulfhorst, G. (2022). Connecting people and places: Analysis of perceived pedestrian accessibility to railway stations by Bavarian case studies. Journal of Urban Mobility, 2, 100025. Pajares, E., Büttner, B., Jehle, U., Nichols, A., & Wulfhorst, G. (2021). Accessibility by proximity: Addressing the lack of interactive accessibility instruments for active mobility. Journal of Transport Geography, 93, 103080. 1. Ralph, K. M., Smart, M. J., Noland, R. B., Wang, S., & Cintron, L. (2020). Is it really too far? Overestimating walk time and distance reduces walking. Transportation Research Part F: Traffic Psychology and Behaviour, 74, 522–535. Weymark, J. A. (1981). Generalized gini inequality indices. Mathematical Social Sciences, 1(4), 409–430.

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This article was originally published by Verkeerskunde on 13 December. To read the original article (in Dutch) please visit


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