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Article

Route Choice of Spanish Adolescent Walking Commuters: A Comparison of Actual and Shortest Routes to School

by
Iris Díaz-Carrasco
1,
Palma Chillón
2,
Pablo Campos-Garzón
3,4,
Javier Molina-García
5,6 and
Sergio Campos-Sánchez
1,*
1
Department of Urban and Spatial Planning, School of Architecture, University of Granada, 18009 Granada, Spain
2
Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, 18011 Granada, Spain
3
Faculty of Health Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
4
Department of Global Public Health, Karolinsk Institutet, 171 77 Stockholm, Sweden
5
AFIPS Research Group, Department of Teaching of Physical Education, Arts and Music, University of Valencia, Avda. dels Tarongers, 4, 46022 Valencia, Spain
6
Epidemiology and Environmental Health Joint Research Unit, The Foundation for the Promotion of Health and Biomedical Research of the Valencian Community (FISABIO), the University of Jaume I (UJI) and the University of Valencia (UV), Avda FISABIO-UJI-UV, de Catalunya, 21, 46020 Valencia, Spain
*
Author to whom correspondence should be addressed.
Land 2025, 14(9), 1821; https://doi.org/10.3390/land14091821
Submission received: 15 July 2025 / Revised: 27 August 2025 / Accepted: 4 September 2025 / Published: 6 September 2025
(This article belongs to the Special Issue Reimagining Public Spaces in Europe: Towards Inclusive, Livable, and Resilient Human-Scale Urban Environments)
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Abstract

A growing body of scientific literature emphasizes the role of the built environment in shaping commuting behavior among adolescents. However, the comparison of the built environment on adolescents’ route choice remains underexplored. A total of 317 Spanish adolescents participated in the study, of whom 67 adolescents provided a valid GPS-identified walking route between home and school (54.5% girls; mean age = 14.4 ± 0.7 years). Built environment variables—including residential density, number of intersections, land use mix, number of services, number of visible services from the route, street width, walkability, park area, elevation gain, elevation loss, and topographic cost—were measured using 3.28.8 QGIS software. A paired-sample analysis was performed using the Wilcoxon signed-rank test and the sign test to compare the actual route with the shortest available route. The results showed a deviation of 63.96%. Comparisons between the actual routes and the shortest ones revealed a statistically significant difference in the number of intersections (p = 0.009) and topography cost (p = 0.050). Likewise, a significant trend was found with the residential density (p = 0.080). These findings suggest that in this case study, the built environment plays an important role in adolescents’ decision-making when choosing routes for commuting to school. Some urban planning and design recommendations were given to address the results from a school built-environment-oriented approach.

1. Introduction

Active commuting to school (ACS) has been widely examined in the scientific literature due to its affordability, consistency, and health benefits [1]. On the one hand, ACS has been associated with psychological factors such as self-efficacy and the companionship of friends during active journeys [2], and it may also promote additional activities, such as playing or jumping [3]. On the other hand, a growing body of the scientific literature emphasizes the role of the built environment in shaping commuting behavior among children and adolescents [4,5,6,7]. Furthermore, ACS is influenced by the built environment, which is defined as “aspects of a person’s surroundings which are human-made or modified” [8], such as urban design and public transportation [9]. Previous scientific research primarily examined several associations between the built environment and ACS. For instance, a cross-continental systematic review among children suggested that walking to school was positively associated with walkability, density, and accessibility [6]. Another systematic review [10] highlighted a general consensus regarding the positive impact of traffic calming measures [11,12] and speed limit signage [13], with both being widely associated with ACS.

ACS, Built Environment, and Route Choice

Analyzing built environment characteristics along both the actual routes chosen by students and the shortest ones could provide valuable insights into which built environmental features may influence route choice during ACS [14]. The shortest route is the most efficient path in terms of distance. This methodological approach involving comparing the built environment features of participants’ actual routes against the shortest possible ones has been applied across diverse populations and transportation modes. First, walking routes among Korean adults were assessed in [15], comparing healthy versus shortest route choices, with 56.7% of adults preferring a healthier walking route over the shortest option. Second, cycling routes using bike-share systems among Canadian adults were investigated in [16], suggesting that cyclists are willing to take longer routes if they offer favorable conditions, such as bike infrastructure and reduced traffic levels. Third, cycling routes among Australian university students were studied by [17], where it was found that distance and direction are likely to explain 53% of route choice decisions. Fourth, an analysis of cycling routes among Austrian adults confirmed that bicyclists often used bicycle lanes and pathways, as well as flat and green areas [18]. Fifth, a study of pedestrian and cycling routes among Finnish commuters revealed significant associations between route choice and intersection density, institutional land use, slope, and the age of buildings [19]. And finally, most relevant to our study was a study among Dutch children suggesting that children mainly travelled through residential areas on their way to school [14].
As explained above, there are several articles that have compared actual routes with the shortest ones. However, this comparison has not been made using actual routes as geolocated routes tracked by GPS. It is common to model routes based on home coordinates or postcodes [20,21]. Nevertheless, such modelled routes do not accurately reflect the actual streets that adolescents walk along or, consequently, the GPS-measured exposure to obesogenic environments [22]. To sum up, evidence is still lacking regarding which built environment features lead adolescents to prefer their actual GPS-identified routes over the shortest possible ones. To help address this gap, two objectives were addressed. Firstly, the route choice preferences among 67 adolescents across four Spanish cities were analyzed. Secondly, differences in built environment features between the shortest modelled routes and the actual GPS-identified walking routes taken by Spanish adolescents were compared. The scope of the study was to identify the attributes of the built environment that lead adolescents to deviate from their shortest origin–destination routes that theoretically minimize physical effort and travel time, reduce topographic costs (i.e., elevation changes), and are typically chosen by adolescents familiar with the neighborhood built environment, as in the case of routine home–school commutes.

2. Materials and Methods

2.1. Study Population and Design

The CiudActiva (Active City) research project was a cross-sectional study which aimed to analyze the associations between the built environment and the physical activity levels of the previous PACO study sample [23]. The CiudActiva research project was conducted in four Spanish capital cities (Almería, Granada, Jaén, and Valencia). Briefly, secondary schools were randomly selected in each city. A total of 317 adolescents aged 14–15 (enrolled in the third grade of secondary education years) took part in the study. However, the sample for the first objective consisted of 67 adolescents (Figure 1) as it was restricted to participants who met the following inclusion criteria: (1) sociodemographic data completed; (2) at least one valid walking route to school identified, as determined by identified GPS data; and (3) accurate geographical data from the home-to-school route attained. For the second objective, the sample consisted of 47 adolescents who met the additional criterion of having a discrepancy between their actual route and the shortest possible one (Figure 1).
The CiudActiva research project received ethical approval from the University of Granada’s Review Committee for Research Involving Human Subjects (Reference No. 2359/CEIH/2020). Additionally, a protocol study of this project is currently undergoing review for publication in a scientific journal.

2.2. Measures

2.2.1. Procedure and Definition of the Actual Home–School Route and Its Shortest Counterpart

The following section outlines the methodology used to define both the actual walking routes taken by participants and the shortest walking routes possible between home and school, providing a basis for comparison and analysis. It should be noted that this study focused exclusively on home-to-school walking trips, excluding return journeys and routes involving other modes of transport such as public transport or bicycles. The analysis was conducted during the morning school commute in the case study. Focusing on this timeframe allowed for a clearer assessment of how built environment characteristics influence adolescents’ walking route choices. The actual home–school route was identified using a GPS (QStarz BT-Q1000XT, Taipei, Taiwan) device that participants carried, attached on their hip, for seven days. However, due to COVID-19 restrictions in Spain, the GPS protocol was changed from seven consecutive days to two consecutive days. The data were collected over the period from January 2019 to June 2021. Since participants may record multiple home–school routes, only the first route recorded by GPS for each participant was considered for analysis, discarding any subsequent routes. The actual route was defined by starting with the initial GPS coordinate within the home location and by finishing with the end GPS coordinate within the school location during the school term when, depending on the school, entrance in Spain takes place between 8:00 and 8:30 a.m. The software Qtravel (v1.55.003) was used to initialize the GPS with a 15 s epoch and to download the recorded data.
The identification of walking routes to school was conducted in two stages. First, the Human Activity Behavior Identification Tool and data Unification System (HABITUS) software designed by the University of Southern Denmark software was used to process and clean the GPS data. Invalid GPS points were identified as those showing extreme values, such as speeds exceeding 130 km/h, distance changes greater than 1000 m, or elevation changes over 100 m between consecutive points [24]. In these cases, invalid points were replaced with the last valid recorded point for a period of up to 10 min. Next, a route was defined as any continuous movement lasting more than 120 s and covering a distance of at least 100 m. Short pauses of up to 120 s, such as those occurring at traffic lights, were considered part of the route [25]. The route was deemed to have ended when there was a pause of over 180 s. Routes with an average speed of less than 10 km/h were categorized as walking routes.
Subsequently, as every home–school route was reported within the city, the GPS tracks were partially adjusted to the street network obtained from spatial databases such as the Unified Andalusian Digital Street Map (CDAU in Spanish) for Almería, Granada, and Jaén, and the CartoCiudad from the National Geographic Institute (IGN in Spanish) for Valencia. Specifically, the adjustments of the GPS tracks to the street network were carried out using automatic and semi-automatic procedures with GIS. Finally, this adjustment procedure was also manually reviewed to obtain the final home–school routes. After geolocating the participants’ actual routes as described above, the shortest routes were generated using the QGIS (Quantum Geographic Information System) plugin “Open Route Service Tool (ORS Tool)”. Once both the actual and the shortest routes were identified, 94 non-overlapping routes were generated comprising 47 actual routes and their corresponding 47 shortest ones (Figure 1). The non-overlapping section of each route were the street segments analyzed to extract the BE variables of both the actual route and the shortest one and to compare them.

2.2.2. Built Environment Variables

Once the actual and shortest route pair was defined and processed, a street network buffer of 25 m on each side of both types of route was applied to measure the BE variables. While it is more common to use a buffer area of 200 m around the home–school route [26], a buffer of 50 m was ultimately chosen as the route catchment area to capture data at the street level, which better aligns with the range of vision in the city [27].
The macro-scale analysis was conducted using the latest version of QGIS software and complemented by Excel. The BE variables are described in Table 1, along with their respective units of measurement.

2.3. Statistical Analysis

To achieve the first objective, descriptive data from the 67 adolescents were analyzed in order to examine their route preferences, using IBM SPSS Statistics 22 (Table 2). Regarding the second objective, the scope of the pair-based analysis was to identify which characteristics of the built environment could explain why adolescents chose actual routes over the shortest ones. This approach is particularly relevant because the shortest routes represent the most efficient origin–destination connections in terms of distance and effort and are typically those chosen by individuals familiar with their daily environment, such as those undertaking the home-to-school commute.
To operationalize this, descriptive statistics were first computed to summarize the built environment variables (Figure 2), presented as minimum, maximum, mean, and standard deviation values (Table 3). The normality of the data was then assessed using the Kolmogorov–Smirnov test, which confirmed a non-normal distribution (p < 0.05). Accordingly, non-parametric paired-sample analyses were applied. The symmetry of the paired data was evaluated in RStudio (v.4.3.2) using the “lawstat” package (Supplementary Materials). When the data pairs were symmetrical, the Wilcoxon signed-rank test was performed (Table 4), which considered both the direction and magnitude of differences between paired observations. The test produced a standardized statistic (Z) and a p-value to assess statistical significance. In cases where the paired samples were not symmetrical, the sign test was applied, following the same procedure.
Finally, the magnitude of the observed effects was quantified by calculating the effect size r, which is defined as the absolute value of the standardized test statistic divided by the square root of the sample size r = Z N for non-parametric tests. This statistic provides a standardized measure of the strength of the difference observed, allowing for interpretation independent of the sample size [38]. According to Cohen’s guidelines, effect sizes can be interpreted as low (0.10 ≤ r < 0.30), medium (0.30 ≤ r < 0.50), or high (r ≥ 0.50).

3. Results

Descriptive data of the adolescent sample are presented in Table 2. The sample included 67 adolescents, 54.4% of whom were girls, and for whom the median age was 14.40 years (SD = 0.719) and the average GIS-measured distance to school was 856.27 m (SD = 672.56). Of the 67 adolescents in total (54.4% girls), just 20 adolescents (29.85%) followed the shortest route completely. Moreover, for the remaining 47 adolescents, route deviation percentage was quantified as the length of the non-overlapping part of the actual route over the shortest route pair, with regards to the total length of the actual route. Moreover, the overall route deviation—calculated as the difference between the length of the actual route and the shortest possible route, expressed as a percentage of the actual route length—was 12.12%
Figure 2 presents the GPS-recorded home-to-school route of an adolescent from Granada city alongside the corresponding paired shortest route and the percentage of route deviation.
Land 14 01821 g002
Figure 2. An example of adolescent walking to school (shortest vs. actual routes) in Granada, Spain. Acronym: m = meters. The percentage deviation of the actual adolescent route from the shortest route was 57.62. The route deviation percentage is the length of the non-overlapping part of the actual route over the shortest route pair, with regards to the total length of the actual route.
Figure 2. An example of adolescent walking to school (shortest vs. actual routes) in Granada, Spain. Acronym: m = meters. The percentage deviation of the actual adolescent route from the shortest route was 57.62. The route deviation percentage is the length of the non-overlapping part of the actual route over the shortest route pair, with regards to the total length of the actual route.
Land 14 01821 g002
Descriptive data of the built environment variables are presented in Table 3. It compares the actual (as geolocated) routes chosen by adolescents with their paired shortest possible ones. On average, the actual routes passed through more densely populated areas (1974.98 residents vs. 1445.17) than the shortest routes. Participants’ chosen routes also had a higher mean number of intersections (18.17 vs. 14.91), a slightly lower land use mix (0.22 vs. 0.24), and a marginally higher number of services (6.74 vs. 6.53), as well as a slightly lower number of visible services (164.11 vs. 170.02). The actual routes also presented slightly higher street width barriers (0.76 vs. 0.74) than the shortest ones. Moreover, adolescents actively commuted through streets with a higher walkability index (0.10 vs. −0.02) and larger park areas (2631.07 m2 vs. 1592.32 m2). Additionally, the actual routes exhibited slightly greater elevation changes with higher mean values for elevation gain (6.93 m vs. 6.62 m), elevation loss (19.07 m vs. 18.40 m), and topographic cost (0.67 vs. 0.61) than the shortest routes.
Table 3. Descriptive characteristics of the built environment along both the actual routes and the shortest ones. N = 47.
Table 3. Descriptive characteristics of the built environment along both the actual routes and the shortest ones. N = 47.
Actual RouteShortest Route
MinimumMaximumMeanStandard DeviationMinimumMaximumMeanStandard Deviation
Number of
residents		317.006083.001974.981393.86222.003820.001445.17984.16
Number of
intersections		1.00116.0018.1717.302.0064.0014.9110.79
Land use mix (units)0.000.500.220.160.000.790.240.20
Number of
services (u)		0.0031.006.748.020.0030.006.538.59
Number of
visible services (u)		26.00780.00164.11189.0931.00843.00170.02220.57
Street width (m)
(Barrier effect)		0.311.000.760.130.170.950.740.17
Walkability (u)−3.022.160.101.06−3.474.42−0.021.45
Park area (m2)0.0014,136.722631.073315.990.007601.091592.311664.39
Elevation gain (m)0.0426.826.937.840.0027.406.628.02
Elevation loss (m)0.1967.7319.0717.720.3569.7918.4017.02
Topographic cost (m/lm)0.012.620.660.740.003.630.600.78
N = 47 adolescents; m = meters; lm = linear meters; m2 = square meters; km = kilometers; u = unit.
After applying the different related-pairs analyses (Wilcoxon signed-rank test and paired-sample t-test, as appropriate), a statistical trend towards significance and two significant results were identified. Regarding the number of intersections, a low-to-medium effect size was observed, and a trend toward significance was found (p < 0.01), where the residential density along the shortest route was lower than along the actual route. For the number of intersections, with a medium effect size, a statistically significant difference was observed (p < 0.05), where the number of intersections along the shortest route was lower than along the actual route. A final significant difference was found in topographic cost, which was lower along the shortest route than along the actual one (p = 0.05) and yielded a low-to-medium effect size. No significative differences or trends were found in land use mix, number of services, number of visible services, street width, park area, walkability, elevation loss or elevation gain (Table 4).
Table 4. Paired-sample test results (Wilcoxon signed-rank and sign tests). N = 47.
Table 4. Paired-sample test results (Wilcoxon signed-rank and sign tests). N = 47.
Built Environment VariableZSig. Asin. (Bilateral)Size Effect |r|Ranks: Shortest Trip vs. Real TripSample Size (N)Average RangeSum of Ranks
Number of residents−1.75* 0.080 b0.255Negative ranks30NANA
Positive ranks17NANA
Ties0NANA
Number of
intersections		−2.624** 0.009 *a0.383Negative ranks2723.43632.50
Positive ranks1416.32228.50
Ties6NANA
Land use mix−0.8250.409 a0.120Negative ranks2123.14486.00
Positive ranks2624.69642.00
Ties0NANA
Number of services−0.9170.359 a0.134Negative ranks2617.52455.50
Positive ranks1324.96324.50
Ties8NANA
Number of visible services−0.8730.382 a0.127Negative ranks2723.94646.50
Positive ranks2024.08481.50
Ties0NANA
Street width (m)
(Barrier effect)		−0.0110.992 a0.002Negative ranks2820.18565
Positive ranks1929.63563
Ties0NANA
Walkability (u)−0.5830.560 b0.085Negative ranks21NANA
Positive ranks26NANA
Ties0NANA
Park area (m2)−0.6250.532 b0.091Negative ranks2324.87572.00
Positive ranks1816.06289.00
Ties6NANA
Elevation gain (m)−1.3120.189 a0.191Negative ranks2824.57688.00
Positive ranks1923.16440.00
Ties0NANA
Elevation loss (m)−1.5560.120 a0.227Negative ranks3023.70711.00
Positive ranks1724.53417.00
Ties0NANA
Topographic cost (m/lm)−1.9580.050 ** a0.286Negative ranks3223.41749.00
Positive ranks1525.27379.00
Ties0NANA
N = 47 adolescents; m = meters; lm = linear meters; m2 = square meters; km = kilometers; paired-sample analysis for symmetric data: ** p < 0.05, * p < 0.1, NA = not applicable, Wilcoxon signed-rank a; paired-sample analysis for asymmetric data: sign test b; Z = standardized test statistic (z-score) from the Wilcoxon signed-rank test and the sign test; according to Cohen’s guidelines, effect sizes |r| can be interpreted as low (0.10 ≤ r < 0.30), medium (0.30 ≤ r < 0.50), or high (r ≥ 0.50).

4. Discussion

This cross-sectional study had two objectives: (1) to analyze route preferences among 67 adolescents across four Spanish cities, and (2) to compare pairs of actual and shortest routes (94 non-overlapping sections of the 47 participants’ routes) by identifying built environment variables (including residential density, number of intersections, land use mix, number of services, park area, elevation gain, elevation loss, street width, number of visible services, topographic cost, and walkability) that may explain adolescents’ choice to take the actual route instead of the shortest one. Regarding the first objective, our findings indicated that taking the entire shortest route was not the adolescents’ priority. Regarding the second objective, the findings suggested that in Spain, some characteristics of the built environment—particularly the residential density, the number of intersections, and the topographic cost—played an important role in adolescents’ decision-making when choosing routes for walking to school.

4.1. Built Environment Impacts Walking More than Distance

To address the first objective, the following section presents a discussion analysis of route preference among the 67 adolescents. Firstly, the average distance that adolescents walked from home to school was 856.27 m, which is below the 1350 m threshold previously identified as the maximum distance Spanish adolescents are generally willing to walk to school [39]. Figure 3 illustrated the actual routes followed by the adolescents in Almería, Spain.
Secondly, it was noteworthy that just 29.85% of the sample followed the entire shortest route and, of these remaining 70.16%, the route deviation percentage was 63.96%, indicating that while the origin–destination distance played a role, especially in the commuting mode choice [40,41], built environment variables may had a greater influence on adolescent’s route choice [42]. These findings further suggested that while familiarity with the built environment—such as in adolescents who commute daily to school—may encourage the shortest route choice [43], some specific features of the built environment along the actual route may play an even more decisive role in the decision to deviate somewhat from the shortest one.

4.2. More Walking with High Residential Density Despite High Topographic Cost

Regarding the second objective, the following section discusses the differences in the built environment variables measured along the pairs of actual and shortest routes. When comparing paired actual and shortest routes, an interesting observation emerged: the non-overlapping part of the actual routes tended to have a higher residential density along them than that of the shortest ones, showing a trend toward statistical significance when compared to each other (Figure 4). This finding aligns with a study conducted in England among adolescents commuting to school that also involved a comparison between the shortest route and the route chosen by the children [42]. Moreover, this result is linked to social norms and Jane Jacobs’ theory of the importance of ‘eyes on the street’ [44]. This theory illustrates the concept that ‘people attract people’, whereby the presence of others improves a sense of safety through mutual surveillance. In particular, it was interesting to highlight in our study that adolescents preferentially selected routes characterized by higher residential density (Figure 4), which was safer, despite showing a higher topographic cost. This finding evidenced that higher residential density may contribute to increased street activity, fostering a safer social environment along home-to-school routes [4], an aspect that seems to be more influential on adolescents’ route choice than topographic barriers. Positive associations of actual route with topographic cost were also observed among Dutch adults, where the presence of slopes, stairs, or both was linked to a higher perceived attractiveness of walking [45]. However, this same association has also been found to increase the perceived resistance to walking [46]. Therefore, more research should be carried out to better clarify these differences.

4.3. More Intersections Facilitates Cognitive-Effort-Saving Navigation

Another significant finding had to do with the fact that the number of intersections was statistically higher along the actual routes compared to the shortest ones. A higher number of intersections is directly connected with fewer direction changes along the actual route, as can be seen in Figure 2 and Figure 5. This behavior may be explained by the tendency of people to prefer routes with fewer directional changes, turns, or lower angular deviation when navigating to a destination such as a school, as these routes require fewer mental instructions to reach their destination [47]. This behavior may be cognitively advantageous, considering that the number of mental instructions a person can retain in working memory while commuting is limited, and potentially even more so for adolescents, whose spatial cognitive abilities are still developing [48]. Routes with fewer direction changes tend to be longer and therefore accumulate a greater number of intersections than the shortest ones.

4.4. Unexpected Results in the Case Study

Although land use mix is widely recognized as a factor that promotes pedestrian- and bike-friendly environments [49]—and was used in the calculation of the walkability index [30]—our study did not find significant results. Similarly, a study conducted in schools across the United States found no association between land use mix and route choice during ACS [50]. Likewise, while this study did not reveal statistically significant differences in park area, evidence from a study among Finnish adults suggested a preference for routes with greater park area even when they are not the shortest ones during active commuting. Moreover, no significance or trends toward significance were found in the case study regarding the total number of visible services. Nevertheless, the importance of visibility in urban design evaluation [51], and particularly visible street level features, was suggested to explain approximately 18% of the variation in perceptions of safety, liveliness, and beauty perceptions [52]. Lastly, it is important to note that no statistically significant differences were found for the street width variable as a barrier. In contrast, Appleyard‘s (1981) [53] theory highlighted how street width can work as a barrier to social interaction and community cohesion, though this was evidenced for a larger city than the ones in the case study. These unexpected results—namely, the lack of significant differences in several built environment variables (e.g., land use mix, number of services, number of visible services, street width, walkability, and park area) when comparing the actual and the shortest routes—may be attributed to the limitations imposed by obtaining general findings on heterogeneous urban contexts, as well as to other unmeasured contextual or individual factors. Further research with a representative stratified sample would be necessary to confirm these suggestions.

4.5. Strengths and Limitations

One of the principal strengths of this study was the use of objective, device-based methodologies to assess the built environment along the routes, as recommended by a recent systematic review [54]. Adolescents’ travel patterns were geocoded using GPS, and the built environment was analyzed through QGIS software, mitigating bias from self-reported home–school routes. Nevertheless, this study had several limitations. Firstly, the cross-sectional design meant that adolescents’ decisions were not compared over time (longitudinal study). Secondly, only a single route per adolescent was estimated for the home–school route. The analysis assumed that adolescents chose the same route each day. However, this may not account for variations in daily routines. For example, some adolescents may split time between multiple friends’ homes, leading to different starting or ending points of the route, which finally leads us to recommendations for future studies to analyze the psychosocial factors of the adolescent’s route choice.

4.6. Future Studies

Concerning future research, it would be valuable to examine connectivity variables other than the number of intersections, such as Pedestrian Route Directness [55], in order to explore how direct the actual routes are compared to the shortest ones. Likewise, the use of configurational variables based on the space syntax of the street network could help to objectively measure the cumulative directional changes or angular deviation of both actual and shortest routes. Furthermore, conducting studies in other countries is recommended to enable cross-national comparisons and to assess the generalizability of findings. Similarly, future analyses could also stratify by city and other factors, given that the associations between walkability and the built environment can vary across cities, emphasizing the need to account for local context [56] alongside personal, family, and social factors that may influence adolescents’ route choices [2,57]. For instance, a higher likelihood of active commuting was found among students who reported having higher self-efficacy to walk/cycle to school, as well as among adolescents who reported being encouraged by their parents and having the partnership of friends in active journeys. Finally, due to the complexity of urban built environments, reflected in the large number of variables that could influence adolescents’ ACS [54], it is advisable for future research to examine different categories of services and to consider factors such shading from trees or buildings, which, in the context of climate change, may influence adolescents’ route choices, particularly during the return trip from school to home.

5. Conclusions

This research contributes to the field from two different perspectives. With respect to the first objective, the results showed that it may not be fully assumed that the shortest route represents the actual route, given that a large proportion of students did not entirely follow the shortest route. Also, the deviations in length of the actual route over the shortest one were substantial. In other words, the built environment may impact walking more than distance. In relation to the second objective, when comparing paired homologous routes, the findings suggested that characteristics of the built environment may be decisive factors in choosing actual routes over the shortest ones. Specifically, an increase in the number of intersections appeared to be a decisive factor in adolescents’ choice of longer routes over the shortest ones, which may reduce cognitive effort during commuting. Similarly, a higher number of residents on the actual route compared to the shortest one appears to be indicative of choosing the former. Finally, topographical features do not seem to hinder adolescents in choosing their actual routes. To conclude, in Spain, the built environment plays an important role in adolescents’ decision-making when choosing routes for commuting to school. Therefore, some urban planning and design recommendations based on some characteristics of the school neighborhood built environment are proposed below to be addressed through urban planning.

Final Considerations: Urban Planning Recommendations

The results of this study provide valuable built environment insights for a range of stakeholders, including researchers, urban planners, public health professionals, policymakers, educators, and parents. Collaborative efforts between stakeholders are essential in order to help governments to create youth-friendly environments [58]. Based on the results previously discussed, the following specific recommendations are proposed.
Firstly, it is recommended that the residential density of the urban areas surrounding schools be increased—within a 1350 m catchment radius, which has been identified as the threshold distance that Spanish adolescents are generally willing to actively commute to school [39]—and with particular attention to the 856.23 m from the school along the street network that corresponds to the average walking distance observed in our study. Increasing the number of residents in the school neighborhood may be achieved by raising building density, which in turn can be achieved by increasing building height and introducing or adapting the urban area to include collective housing typologies. A similar approach is the subject of the well-known Transit-Oriented Development (TOD) strategy, focused on transport stations. In addition, as a criterion for prioritizing building densification, it has been shown that the most central streets—measured by indicators such as closeness and betweenness centrality—tend to have a greater diversity and concentration of land uses [59,60], which in turn is associated with higher pedestrian volumes. Thus, these streets should be prioritized for investments in safety and comfort from an urban planning perspective, as the presence of people can enhance the perception of safety through mutual surveillance [44]. Also, given that school commuting usually takes place within a defined time window, increasing the residential population may contribute to a higher number of adolescent commuters, as previously mentioned in a study examining adolescent school neighborhood built environments in North America [4].
Secondly, the number of intersections depends on block length. If urban planning encourages shorter blocks or incorporates passageways that allow shortcuts through longer blocks (which could also be considered intersections), the number of intersections—i.e., connectivity—along participants’ actual routes would increase. As a result, participants’ actual routes would become more direct [55], minimizing deviations from the shortest routes and thereby reducing origin–destination travel times.
Lastly, urban planning should incorporate measures to facilitate walking under high topographic cost, promoting more inclusive mobility. Examples of such interventions include streets designed with accessible gradients, ramps, escalators, elevators, or even funiculars. Both national and international cases illustrate this approach—for instance, the historic center of Vitoria-Gasteiz with the intervention of Roberto Ercilla [61], or the rehabilitation of Lisbon’s Chiado neighborhood by Álvaro Siza, and particularly, the neo-gothic iron structure, Santa Justa Elevator, designed by Raoul Mesnier du Ponsard. In the latter case, the intervention has not only become a key element for vertical mobility but also a symbol of the city—a sensory and artistic feature [62], as well as a point of interest for tourists—highlighting the need for measures to ensure that increasing tourist flows do not compromise the neighborhood’s accessibility and everyday functionality for local residents.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land14091821/s1. Statistical analysis: symmetry, paired-sample analysis (Wilcoxon signed-rank test and sign test), size effect and desviation.

Author Contributions

Conceptualization, I.D.-C., P.C. and S.C.-S.; methodology, I.D.-C. and S.C.-S.; software and S.C.-S. and I.D.-C.; formal analysis, I.D.-C. and S.C.-S.; investigation, I.D.-C.; data curation, I.D.-C. and S.C.-S.; writing—original draft preparation, I.D.-C.; writing—review and editing, I.D.-C., P.C., S.C.-S., P.C.-G. and J.M.-G.; visualization, I.D.-C.; supervision, S.C.-S.; funding acquisition, I.D.-C., P.C. and S.C.-S. All authors have read and agreed to the published version of the manuscript.

Funding

EU FEDER/Regional Council for Economic Transformation, Industry, Knowledge and Universities of Andalucía, Spain: B-CTS-160-UGR20; MICIU/AEI/10.13039/501100011033: Ministry of Science, Innovation and Universities of Spain. Co-funded by the European Union. State Research Agency: PTA2023-023892-I.

Data Availability Statement

The statistical analyses of this study are provided in the Supplementary Materials. The database can be requested with the consent of the CiudActiva project directors.

Acknowledgments

Our gratitude extends to the entire CiudActiva research team.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ACSActive commuting to and from school
CDAUUnified Andalusian Digital Street Map
DERAReference Spatial Data of Andalusia
DTMDigital Terrain Model
GISGeographic Information System
GPSGlobal Positioning System
LiDARLight Detection and Ranging
QGISQuantum Geographic Information System
IGNNational Geographic Institute
INEStatistic National Institute
OSMOpen Street Map

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Figure 1. Flow chart of the participants.
Figure 1. Flow chart of the participants.
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Figure 3. Illustration of the adolescents’ actual routes lengths in Almería, Spain. Acronym: m = meters.
Figure 3. Illustration of the adolescents’ actual routes lengths in Almería, Spain. Acronym: m = meters.
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Figure 4. Actual route vs. the shortest one for an adolescent in Jaén, Spain. Variable: residential density. The percentage deviation of the actual adolescent route from the shortest one was 100%. Acronym: m = meters. The route deviation percentage is the length of the non-overlapping part of the actual route over the shortest route pair, with regards to the total length of the actual route.
Figure 4. Actual route vs. the shortest one for an adolescent in Jaén, Spain. Variable: residential density. The percentage deviation of the actual adolescent route from the shortest one was 100%. Acronym: m = meters. The route deviation percentage is the length of the non-overlapping part of the actual route over the shortest route pair, with regards to the total length of the actual route.
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Figure 5. Actual route vs. shortest route chosen by adolescents in Valencia, Spain. Variable: number of intersections. The percentage deviation of the actual adolescent route from the shortest one was 37.85%. Acronym: m = meters.
Figure 5. Actual route vs. shortest route chosen by adolescents in Valencia, Spain. Variable: number of intersections. The percentage deviation of the actual adolescent route from the shortest one was 37.85%. Acronym: m = meters.
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Table 1. Built environment variables and units: category, description and references, spatial database, and methodology.
Table 1. Built environment variables and units: category, description and references, spatial database, and methodology.
CategoryVariable (Unit)Definition and ReferencesSpatial DatabaseMethodology
ProximityHome–school distance (shortest route) (m)The shortest route from the home address to the main school entrance. The home address was obtained by identifying its location point using GPSOSMCalculating the origin–destination shortest route using the OpenRouteService (ORS) plugin based on OpenStreetMap (OSM) data
Home–school distance (actual route) (m)The route layout was geolocated using GPSCartoCiudad (IGN), DERACleaning and joining walking route sections, with the latter applied when they were found divided; adjusting GPS routes to the road spatial layer using an automatic and semi-automatic process first, followed by manual correction
Active commutingNumber of residentsRatio of the number of residents to the route catchment area [28]Cadastral information from ATOM Inspire, census section from DERA and CartoCiudad (IGN). Population data from INEFirst, obtaining population data and georeferencing by census section; then applying both data aggregation and disaggregation processes considering cadastral information and the catchment area
Number of street intersectionsThe number of street intersections in the route catchment area [29]Street sections from CDAU (Almería, Granada, and Jaén) and CartoCiudad (IGN) (Valencia)Obtaining a points layer as intersections, cleaning it, and associating data to the catchment area
Land use mix index (u)Distribution of different land use areas within the route catchment area. A total of four land use areas were considered: residential, commercial, office, and service [30]Cadastral information from ATOM Inspire, spatial data from DERA, CartoCiudad (IGN) and OSM Applying the formula proposed [30], based on ln(x) and areas, to quantify how evenly the square footage of urban land uses is distributed within the built environment (range 0–1)
Walkability index (u)Sum of the z-scores of data from the above three BE variables: No. of residents, No. of intersections, and land use mix index [31]The same as the three variables considered (above)The three-variable-based Z-Score statistical value of each route catchment area was calculated to allow comparison between catchment areas
Service levelNumber of servicesRatio of the number of services and facilities to the route catchment area. A total of 37 services and facilities distributed in seven categories were considered: food, education, engine, leisure and culture, health, public services, and transport [32]Urban services information from OSMObtaining a points layer of urban services through hierarchical classification by categories, and associating data to the catchment area
Visibility (only for home–school route)Visible urban area (m2)Visual catchment area of the visible built environment from the route [33,34]LiDAR images from IGN, topographic information from a DTM, cadastral information from ATOM InspireFirst, developing a Digital Terrain Model (DTM); second, obtaining building heights from cadastral information as raster layer, and merging it with the DTM; then performing visibility analysis by generating viewpoints and calculating the visual basin along the route
Number of visible servicesNumber of services visible from the route [33,34]LIDAR images from IGN, topographic information from a DTM, cadastral information from ATOM Inspire, urban services information from OSMOverlapping the visual basin from the route with the layer of the number of services for data association
ComfortabilityPark area (m2)Area of parks in the route catchment area [35]Open space information from DERA and OSMObtaining a park polygons layer, calculating and adding areas, and associating data to the catchment area
TopographyElevation gain (m)Total meters climbed along the route [36]Topographic information from a DTMExtracting topographic information from a DTM using QGIS plugins and applying aggregation processes along the route
Elevation loss (m)Total meters dropped along the route [36]Topographic information from a DTMExtracting topographic information from a DTM using QGIS plugins and applying aggregation processes along the route
Topographic cost (m/lm)Ratio of the accumulative positive difference in altitude along the home–school route to the length (linear meters) of the route [26]Topographic information from a DTMExtracting topographic information from a DTM using QGIS plugins and applying aggregation processes along the route
SafetyStreet width (m) (Barrier effect)Average width of the roads that are crossed along the route. The wider the road, the stronger the barrier effect on pedestrian commuting [37]Cadastral information from ATOM Inspire, street sections from CDAU (Almería, Granada, and Jaén) and CartoCiudad (IGN) (Valencia)Using a script to calculate centroids by road section and to generate automatic one-meter buffers reaching the building layer and returning road widths; obtaining the average width of the crossing roads per route
Acronyms: Reference Spatial Data of Andalusia (DERA); Statistic National Institute (INE); Unified Digital Street Map of Andalusia (CDAU); National Geographic Institute (IGN); Open Street Map (OSM); Light Detection and Ranging (LiDAR); Digital Terrain Model (DTM); meters (m); kilometers (km); linear meters (lm); natural logarithm (Ln).
Table 2. Descriptive characteristics of the participants.
Table 2. Descriptive characteristics of the participants.
Participants
(Number of Adolescents = 67)
VariableMean (±SD)Frequency
Gender (girls) 54.4%
Age (years)14.4 ± 0.7
FAS (score)
(0 = low income,
4 = high income)		3.0 ± 0.9
Home–school distance (m)856.3 ± 672.56
Choice to adhere to the shortest route 29.85%
FAS = family affluence scale; m = meter; SD = standard deviation; % = percentage.
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Díaz-Carrasco, I.; Chillón, P.; Campos-Garzón, P.; Molina-García, J.; Campos-Sánchez, S. Route Choice of Spanish Adolescent Walking Commuters: A Comparison of Actual and Shortest Routes to School. Land 2025, 14, 1821. https://doi.org/10.3390/land14091821

AMA Style

Díaz-Carrasco I, Chillón P, Campos-Garzón P, Molina-García J, Campos-Sánchez S. Route Choice of Spanish Adolescent Walking Commuters: A Comparison of Actual and Shortest Routes to School. Land. 2025; 14(9):1821. https://doi.org/10.3390/land14091821

Chicago/Turabian Style

Díaz-Carrasco, Iris, Palma Chillón, Pablo Campos-Garzón, Javier Molina-García, and Sergio Campos-Sánchez. 2025. "Route Choice of Spanish Adolescent Walking Commuters: A Comparison of Actual and Shortest Routes to School" Land 14, no. 9: 1821. https://doi.org/10.3390/land14091821

APA Style

Díaz-Carrasco, I., Chillón, P., Campos-Garzón, P., Molina-García, J., & Campos-Sánchez, S. (2025). Route Choice of Spanish Adolescent Walking Commuters: A Comparison of Actual and Shortest Routes to School. Land, 14(9), 1821. https://doi.org/10.3390/land14091821

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