# Energy Consumption of the Urban Transport Fleet in UNESCO World Heritage Sites: A Case Study of Ávila (Spain)

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

_{2}-eq, having increased by almost 50% since 1990, due to the increase in the demand for passenger and freight mobility [19,20,21]. However, since 2007 there has been a decrease in emissions, because of the economic crisis and mitigation measures implemented in this sector [21]. The transport sector accounts for 25% of total greenhouse gas emissions in Spain. Recently, in 2020, the national government approved the Long Term Decarbonization Strategy [21,22].

^{2}[24], is the largest region in Spain, even larger than several states which are members of the EU, including Belgium and Portugal.

_{2}emissions and environmental pollution, must be the criteria to be considered when opting for regenerative technology. The feasibility of this technology would allow the preservation of the monumental wealth of the old town of Ávila, thanks to the reduction of CO

_{2}emissions from its UT. In the same way, once the viability of this technology is validated, it could be replicated by other fleets of UT buses, favoring the achievement of the desired goal of climate neutrality by the year 2050.

## 2. Materials and Methods

#### 2.1. Phase I: Route Data Collection

#### 2.2. Phase II: Data Processing

^{®}. Using cartographic techniques [39,40], the complete itinerary is defined, with all the route and stop data, for each of the transport lines included in the study. This information is exported to a file “*.kmz”. The application GPSVisualizer

^{®}is used to extract all the information contained in the file “*.kmz”. The obtained information will be exported in a file “*.txt” containing the hundreds of points that make up the studied route. In addition to the information of the latitude and longitude of each point, the elevation data of each of the points is also available. Once all the information of the coordinates that make up the route is in text, the different parameters of the route are determined (Figure 2).

#### 2.2.1. Kinematic Characteristics

- The bus must be stopped at all bus stops (velocity v = 0 km/h).
- The bus shall not exceed 50 km/h (maximum velocity allowed within the urban area).
- The bus increases its velocity with constant acceleration a corresponding to going from 0 to 50 km/h in 10 s (a = 1.39 m/s
^{2}). The same acceleration value will be used for braking.

_{x}) [45], indicated in Table 1.

_{x}= 0.65 has been taken, a typical value for passenger buses [47].

#### 2.2.2. Dynamic Characteristics

_{roll}) (Phase II, Figure 2), it is proportional to mass and gravity, affected by the rolling drag coefficient and then it is expressed in the following form:

_{wind}) (Phase II, Figure 2) and is developed in the following expression:

#### 2.2.3. Energetic Characteristics

_{roll}) (Phase III, Figure 2) is equal to the following:

_{roll}is the rolling drag, and v is the velocity.

_{grad}) (Phase III, Figure 2) coincides with the product of the grading drag times the velocity:

_{grad}is the grading drag, and v is the velocity.

_{C}), whose expression is well-known:

_{wind}(Phase III, Figure 2). Once the powers corresponding to the four phenomena considered in the study have been computed for each second (Phase III, Figure 2), the sum of the four power values is calculated, so that a time series of the vehicle’s power input or “recoverable” power as a function of time will be obtained. The results shown below are based on the analysis of the energy variations of this time series.

## 3. Results

_{grad}and $\mathsf{\Delta}Ec$ (Phase III, Figure 2) may be positive and oppose the movement of the vehicle [the vehicle is ascending an slope (${P}_{\mathrm{grad}}>0$) or accelerating ($\mathsf{\Delta}Ec>0$)] or negative, favoring the movement [in the case of descending a slope (${P}_{\mathrm{grad}}<0$) or slowing down ($\mathsf{\Delta}Ec<0$)]. In this way, the powers transmitted to the vehicle from the engine, or from the vehicle to the brakes (in case the sum of Equation (11) has a negative value), are known and are plotted in Figure 4.

^{sup}) (Phase III, Figure 2).

_{c}required to accelerate (Equation (11)). Table 3 shows, as a summary, the results obtained in terms of the Maximum Recoverable Power (kW) and the Average Recoverable Power (kW) in each of the lines under study.

## 4. Discussion

^{sup}(in red) corresponds to the so-called Supplied energy (${E}^{\mathrm{sup}}$) (Phase III, Figure 2), which is the energy that has been supplied to the vehicle between two specific time instants.

^{rec}(Phase III, Figure 2) on each of the UT lines in the city of Ávila (WHS) are computed. However, when evaluating the incorporation of regenerative braking systems, the information provided in Table 4 is not sufficient to study in which lines ${E}^{\mathrm{rec}}$ (Phase III, Figure 2) is greater. In Table 5, the different terms used in this study according to the unit of travel considered (km) is obtained. To compare E

^{sup}and ${E}^{\mathrm{rec}}$ (Phase III, Figure 2), regardless of the length of the route, it is necessary to consider relative terms. From the data of Table 5 it is concluded that the amounts of energy required to cover one kilometer of each of the lines are much less than would be expected based on the amounts of energy provided in a complete route. The ${E}^{\mathrm{sup}}$/km is 12% on the most demanding route (line 2), which is higher than that required to travel one km on line 6. Similarly, it can be observed that the ${E}^{\mathrm{rec}}$ per km on line 2 is 18.5% higher than on line 6.

^{rec}versus E

^{sup}(right column, Table 5). Based on the ratio between ${E}^{\mathrm{rec}}$ and ${E}^{\mathrm{sup}}$, it can be concluded that the line with the highest percentage of energy that can be recovered is line 2, with 70.87% of ${E}^{\mathrm{sup}}$ recovered. It is important to observe that the scope of this study is to compute the theoretical gross energy available for recovery and that it does not consider either the quality/availability of the energy or the yields that apply to each of the energy transformations. This is therefore a preliminary study which aims to compare the size of the recoverable energy niches, but it has nevertheless made it possible to classify the lines according to the percentage of recoverable energy. So far, it can be observed that for the same distance traveled, there are significant changes in both E

^{sup}and ${E}^{\mathrm{rec}}$, due to two variables: (i) Number of Bus Stops per km (X

_{1}) and (ii) Cumulative elevation gain per km $\left({X}_{2}\right)$. To estimate the contribution of the accumulated difference in elevation and the number of bus stops per kilometer in the overall computation of both ${E}^{\mathrm{sup}}$ and ${E}^{\mathrm{rec}}$, the following multiple linear regression model is proposed:

_{1}and X

_{2}values (Table 5). This linear regression model yields an R = 0.9995 and an R

^{2}= 0.9990. Equation (18) is obtained by means of a multiple linear regression, taking as dependent variable the values of column ${E}^{\mathrm{rec}}$, and as independent variable the values X

_{1}and X

_{2}(Table 5). This linear regression model yields an R = 0.9999 and an R

^{2}= 0.9998.

_{1}: number of bus stops per km, and X

_{2}: accumulated difference in altitude per km (m/km). These operations are listed in Table 6 and can be easily computed for any route that can be evaluated for any distance d in km.

_{1}and X

_{2}in Table 5. The values ${E}^{\mathrm{sup}}$ computed according to Equation (15), are far from the estimated values ${E}_{\mathrm{est}}^{\mathrm{sup}}$ (decreasing) as the altitude decreases. This is because in the intervals where the accumulated elevation gain is less than the value ${X}_{2}\xb7d$, ${E}^{\mathrm{sup}}$ decreases (less energy needs to be supplied since the vehicle is moving on a negative slope). By the same reasoning, it follows that ${E}^{\mathrm{rec}}$ is greater than the expected ${E}_{\mathrm{est}}^{\mathrm{rec}}$for that interval. Therefore, real values of local accumulated vertical drop that are below the total accumulated vertical drop for a given stretch cause E

^{sup}to decrease compared to ${E}_{\mathrm{est}}^{\mathrm{sup}}$ and E

^{rec}increases compared to ${E}_{\mathrm{est}}^{\mathrm{rec}}$. This behavior can be observed in interval III of Figure 5. The lower the altitude, the more the computed values are farther away from the estimated values. In interval IV (Figure 5) it can be observed that as the altitude increases, the computed values get closer to the estimated values until the maximum relative altitude value, when they move away again. In the final values of the graph, the accumulated elevation gains up to this point coincides with the total accumulated elevation gain of the stretch, and the estimated and calculated values coincide.

^{rec}, which again rise with respect to their estimated equivalents of ${E}_{\mathrm{est}}^{\mathrm{rec}}$. In the same way that occurs with the “accumulated slope” variable, at the end of the graph (Figure 5 and Figure 6a), the accumulated values coincide with the value of the interval, obtaining computed values very close to those of the estimated model.

^{sup}and E

^{rec}move away from each other and symmetrically to the bisector of the lines ${E}_{\mathrm{est}}^{r}$ and ${E}_{\mathrm{est}}^{\mathrm{rec}}$. From km 4 to km 7.5 the line profile is descending and the values of ${E}^{\mathrm{sup}}$ and ${E}^{\mathrm{rec}}$ approach each other and symmetrically to the bisector of the ${E}_{\mathrm{est}}^{\mathrm{sup}}$ and ${E}_{\mathrm{est}}^{\mathrm{sup}}$ lines. The same effects are repeated for the upward interval from 7.5 km to 9 km and for the downward interval from 9 km to 13 km.

^{sup}versus ${E}_{\mathrm{est}}^{\mathrm{sup}}$ without a similar displacement of ${E}^{\mathrm{rec}}$ versus ${E}_{\mathrm{est}}^{\mathrm{rec}}$. This is because in that 0–9 km interval there is a conjunction of two factors. On the one hand, there is a very steep decline and, on the other hand, very little concentration of bus stops, 2.7 bus stops/km versus 3.27 bus stops/km on the full line 3. This occurs because the vehicle moving at a constant v and down a negative gradient, does not need much ${E}^{\mathrm{sup}}$ to move. However, since there are fewer bus stops in that interval, there is less braking (the event where more E

^{rec}can be recovered), so there is no significant increase in ${E}^{\mathrm{rec}}$ versus ${E}_{\mathrm{est}}^{\mathrm{rec}}.$ In the interval from 9 km to 13 km the opposite occurs, many bus stops with a positive slope, which causes a large amount of ${E}^{\mathrm{sup}}$ to be needed so that, in this interval ${E}^{\mathrm{sup}}$ again approaches the values of ${E}_{\mathrm{est}}^{\mathrm{sup}}$.

## 5. Conclusions

_{1}) and the steeper the landscape (accumulated slope per km; X

_{2}). Therefore, if the driving is done including many of these variables, it will be necessary to implement a regenerative braking system, while, if the driving is done smoothly and on the flat, the benefit of regenerative braking is of little importance.

_{1}and X

_{2}.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Conflicts of Interest

## Abbreviations

Symbol | Variable Description |

${P}_{\mathrm{wind}}$ | Aerodynamic power |

${F}_{\mathrm{wind}}$ | Aerodynamic drag |

C_{x} | Aerodynamic drag coefficient |

ρ | Air density |

X_{2} | Cumulative elevation gains per km |

d | Distance |

g | Earth gravity acceleration |

r | Earth radius |

${E}_{\mathrm{est}}^{\mathrm{sup}}$ | Estimated supplied energy |

${E}_{\mathrm{est}}^{\mathrm{rec}}$ | Estimated recoverable energy |

A_{f} | Frontal area |

${P}_{\mathrm{grad}}$ | Grading power |

${F}_{\mathrm{grad}}$ | Grading drags |

H | Height |

$\Delta {E}_{C}$ | Increased kinetic energy |

IP | Instantaneous power |

${\phi}_{1},{\phi}_{2}$ | Latitude of point 1 and latitude of point 2 |

${\lambda}_{1},{\lambda}_{2}$ | Longitude of point 1 and longitude of point 2 |

MRO | Mass in running order |

MAM | Maximum authorized mass |

pax | Number of passengers |

X_{1} | Number of bus stops per km |

${P}^{\mathrm{sup}}$ | Supplied power |

${P}^{\mathrm{rec}}$ | Recoverable power |

${P}_{\mathrm{roll}}$ | Rolling power |

${F}_{\mathrm{roll}}$ | Rolling drag |

$fr$ | Rolling drag coefficient |

S | Separation between two points |

$\alpha $ | Slope angle |

${E}^{\mathrm{sup}}$ | Supplied energy |

m | Vehicle mass |

v | Velocity |

W | Width |

v_{w} | Wind velocity |

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**Figure 3.**Profile of the different UT lines in the city of Ávila (WHS): (

**a**) Line 1; (

**b**) Line 2; (

**c**) Line 3; (

**d**) Line 4; (

**e**) Line 5; and (

**f**) Line 6.

**Figure 4.**Power distribution of the different UT lines in the city of Ávila (WHS): (

**a**) Line 1; (

**b**) Line 2; (

**c**) Line 3; (

**d**) Line 4; (

**e**) Line 5; and (

**f**) Line 6.

**Figure 6.**Distribution of accumulated energy in the different UT lines in the city of Ávila (WHS): (

**a**) Line 1; (

**b**) Line 2; (

**c**) Line 3; (

**d**) Line 4; (

**e**) Line 5; and (

**f**) Line 6.

H (m) | W (m) | MRO (kg) | MAM (kg) | pax | C_{x} |
---|---|---|---|---|---|

3.10 | 2.55 | 10,000 | 18,000 | 86 | 0.65 |

UT Line | Total Distance in Each Line (km) | Number of Bus Stops | Cumulative Elevation Gain (m) |
---|---|---|---|

1 | 13.286 | 45 | 231.96 |

2 | 13.030 | 48 | 189.09 |

3 | 14.390 | 47 | 190.85 |

4 | 16.641 | 50 | 234.53 |

5 | 14.683 | 46 | 192.26 |

6 | 20.729 | 65 | 200.38 |

**Table 3.**Maximum Recoverable Power and Average Recoverable Power (kW) in the different UT lines of Ávila (WHS).

UT Line | $\mathbf{Maximum}{\mathit{P}}^{\mathit{r}\mathit{e}\mathit{c}}\left(\mathbf{kW}\right)$ | $\mathbf{Average}{\mathit{P}}^{\mathit{r}\mathit{e}\mathit{c}}\left(\mathbf{kW}\right)$ |
---|---|---|

1 | 264.55 | 73.29 |

2 | 305.37 | 71.59 |

3 | 321.84 | 67.81 |

4 | 278.28 | 68.74 |

5 | 263.50 | 71.59 |

6 | 254.93 | 75.29 |

UT Line | $\mathbf{Total}{\mathit{E}}^{\mathit{s}\mathit{u}\mathit{p}}\mathbf{in}\mathbf{Each}\mathbf{Line}\left(\mathbf{MJ}\right)$ | $\mathbf{Total}{\mathit{E}}^{\mathit{r}\mathit{e}\mathit{c}}\mathbf{in}\mathbf{Each}\mathbf{Line}\left(\mathbf{MJ}\right)$ |
---|---|---|

1 | 63.50 | 44.48 |

2 | 63.43 | 44.95 |

3 | 65.87 | 45.23 |

4 | 73.19 | 49.08 |

5 | 66.65 | 45.46 |

6 | 90.14 | 60.24 |

**Table 5.**Number of bus stops, cumulative elevation gain, ${E}^{\mathrm{sup}}$ and ${E}^{\mathrm{rec}}$ (per km traveled) in the different UT lines of Ávila (WHS).

UT Line | Total Distance in Each Line (km) | Relative Results | Relative Energy Results | Percentage of Recoverable Energy (%) | ||
---|---|---|---|---|---|---|

Number of Stop per km (n°/km) X _{1} | Cumulative Elevation Gains per km (m/km) X _{2} | ${\mathit{E}}^{\mathit{s}\mathit{u}\mathit{p}}\text{}\mathbf{per}\text{}\mathbf{km}\text{}(\mathbf{MJ}/\mathbf{km})$ | E^{rec} per km(MJ/km) | |||

2 | 13.0305 | 3.68 | 14.51 | 4.87 | 3.45 | 70.84 |

1 | 13.2861 | 3.39 | 17.46 | 4.78 | 3.35 | 70.08 |

6 | 20.7292 | 3.14 | 9.67 | 4.35 | 2.91 | 66.90 |

3 | 14.3902 | 3.27 | 13.26 | 4.58 | 3.14 | 68.56 |

5 | 14.6833 | 3.13 | 13.09 | 4.54 | 3.10 | 68.28 |

4 | 16.6416 | 3.00 | 14.09 | 4.40 | 2.95 | 67.05 |

**Table 6.**Results obtained for ${E}_{\mathrm{est}}^{\mathrm{sup}}$ and ${E}_{\mathrm{est}}^{\mathrm{rec}}$ with the regression model.

UT Line | X_{1} | X_{2} | ${\mathit{E}}_{\mathrm{est}}^{\mathrm{sup}}\text{}\left(\mathbf{MJ}\right)$ | ${\mathit{E}}_{\mathrm{est}}^{\mathrm{rec}}\text{}\left(\mathbf{MJ}\right)$ |
---|---|---|---|---|

1 | 3.39 | 17.46 | 4.93 d | 3.36 d |

2 | 3.68 | 14.51 | 5.22 d | 3.50 d |

3 | 3.27 | 13.26 | 4.65 d | 3.13 d |

4 | 3.00 | 14.09 | 4.32 d | 2.93 d |

5 | 3.13 | 13.09 | 4.46 d | 3.00 d |

6 | 3.14 | 9.67 | 4.37 d | 2.90 d |

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Santos-Iglesia, C.; Fernández-Arias, P.; Antón-Sancho, Á.; Vergara, D.
Energy Consumption of the Urban Transport Fleet in UNESCO World Heritage Sites: A Case Study of Ávila (Spain). *Sustainability* **2022**, *14*, 5641.
https://doi.org/10.3390/su14095641

**AMA Style**

Santos-Iglesia C, Fernández-Arias P, Antón-Sancho Á, Vergara D.
Energy Consumption of the Urban Transport Fleet in UNESCO World Heritage Sites: A Case Study of Ávila (Spain). *Sustainability*. 2022; 14(9):5641.
https://doi.org/10.3390/su14095641

**Chicago/Turabian Style**

Santos-Iglesia, Carlos, Pablo Fernández-Arias, Álvaro Antón-Sancho, and Diego Vergara.
2022. "Energy Consumption of the Urban Transport Fleet in UNESCO World Heritage Sites: A Case Study of Ávila (Spain)" *Sustainability* 14, no. 9: 5641.
https://doi.org/10.3390/su14095641