The influence of passenger load, driving cycle, fuel price and different types of buses on the cost of transport service in the BRT system in Curitiba, Brazil

The energy use of the buses was estimated using the software tool Advanced Vehicle Simulator (ADVISOR). The latest free and open-source version of ADVISOR was used (Wipke et al. 1999; Markel et al. 2002). ADVISOR allows the user to model a road vehicle and its driving on a route to analyse the vehicle’s performance. To run the simulations, the buses were modelled by inserting technical data (see Tables 1 and 2 and the electronic supplementary material). Then, the driving on the BRT route was modelled by using two inputs: (1) the elevation profile data set that represents the topology of the BRT route, loaded as road gradient versus distance in ADVISOR, and (2) the driving cycle data set that represents the driving of a bus, loaded as speed versus time in ADVISOR. After this, the simulations were run and energy use results were obtained. The estimations of the energy use and fuel cost are presented in two functional units: ‘distance’ in kilometres (km) and ‘passenger-distance’ in passenger-kilometres (pkm). The term ‘pkm’ refers to the accumulated distance travelled by all passengers carried in a bus when driving a distance of one kilometre.

Concerning the uncertainty and validation of ADVISOR, the software tool uses a deterministic modelling approach of vehicles including an open-source code written in MATLAB/Simulink (The MathWorks Inc. 2015) and open input data. Thus, open-source code and open data make the functional principle and assumptions transparent and address endogenous and exogenous uncertainties, respectively. Furthermore, (Wipke and Cuddy 1996) carried out a sensitivity analysis of key parameters to quantify endogenous uncertainty of ADVISOR, e.g. for conventional and hybrid-electric vehicles. Their results suggest, for instance, a fairly linear relationship of mass changes on the fuel economy. These insights are relevant, since ADVISOR has got a scaling function that dimensions the components of default vehicle models according to the specific adjustments made through inserting of new input data for parameters. Thereby, ADVISOR possess the flexibility to analyse a wide range of different types of vehicles. As a result of the open-source code and flexibility, ADVISOR has been used in many scientific studies, e.g. for conventional, hybrid-electric, plug-in hybrid-electric, battery-electric and fuel cell buses, as summarised for the studies by (Khanipour et al. 2007; Lajunen 2012a, b, 2014a, b; He et al. 2014; Melo et al. 2014; Mirmohammadi and Rashtbarzadeh 2014; Ribau et al. 2014; Correa et al. 2017; Wang et al. 2017). In regards to the types of buses analysed in this study, the ADVISOR models of the four buses were already used in a previous study (Dreier et al. 2018) and showed representative energy use estimations when compared to real-world fuel consumption data from the bus fleet in Curitiba, Brazil. Furthermore, the energy use estimation of this present study are also validated against real-world data from Curitiba as later shown in the results and discussion section. Thus, the bus models used in ADVISOR are empirically validated against real-world data. Besides, a few relative recent studies exist that explicitly measured the accuracy of ADVISOR and found a discrepancy of 3–8% for a wide range of different vehicles (Ma et al. 2011, 2012).

The currency US Dollar (USD) is used as monetary unit in this study. The local currency Brazilian Real (BRL) was converted to USD using the average currency exchange rate of 0.2833 USD/BRL over the period from 1st Jan 2014 to 31st Dec 2017 (X-rates 2018). The average value was used to have a constant factor between BRL and USD. This allowed a systematic conversion without introducing random uncertainty that could have been potentially caused by the fluctuation of the exchange rate. The fuel prices were retrieved from Ref. (ANEEL 2018; ANP 2018). The historical trends from 1st Jan 2014 to 31st Dec 2017 are shown in Fig. 4 and the corresponding descriptive statistics are provided in Table 4. Obviously, the fuel prices of both the biodiesel blend and electrical energy gradually increased over this period in Brazil. The mean fuel prices of the biodiesel blend and electrical energy amount to (0.913 ± 0.052) USD/L and (0.160 ± 0.029) USD/kWh, respectively. A brief remark on the values of skewness and kurtosis: although both values are within a range of ± 2 indicating normality of the distributions, an additional observation of histograms in Past 3.x (Hammer et al. 2001) showed that both fuel prices are not normally distributed. Therefore, Chebyshev’s inequality was used to interpret the findings in the results and discussion section to derive more general conclusions concerning the uncertainty and probability distribution. The difference between a normal distribution and Chebyshev’s inequality concerns the spread of data. Normally distributed data follows the empirical rule that 68%, 95% and 99.7% of the data is within the width of one, two and three standard deviations from the mean, respectively. In contrast, Chebyshev’s inequality is more conservative in stating the coverage of expected values following the rule \(1 - 1/k^{2}\), where \(k\) is the number of standard deviations. Chebyshev’s inequality does not state any useful insight for one standard deviation \(k = 1\), but for example for \(k\) values of \(k = \sqrt 2 : 1 - 1/\sqrt 2^{2} = 1 - 0.5 = 50\%\), i.e. 50% of the expected values are covered by \(\sqrt 2\) standard deviations from the mean; or \(k = 2: 1 - 1/2^{2} = 1 - 0.25 = 75\%\); or \(k = 3: 1 - 1/3^{2} = 1 - 0.111 = 88.9\%\).

The fuel cost \(FuelCost\) (in USD/km) was calculated based on the energy use estimation and fuel price/s. In the case of the buses ConvBi, HybTw and HybAr, their fuel costs could be estimated straightforward as they only consumed the biodiesel blend (i.e. \(E_{elec} = 0)\). In the case of PlugTw, both the biodiesel blend and net electrical energy use had to be taken into account:where \(TE_{BB}\) (in MJ) and \(TE_{elec}\) (in MJ) are the mean values for the total energy use of biodiesel blend and electrical energy, respectively, considering all driving cycles \(j\) from the set of driving cycles \(\left( {N = 11} \right)\):

Since small differences were recorded between the driven distances in the set of driving cycles due to minor measurement deviations, a mean distance \(D\) (in km) was calculated:

And lastly, \(P_{BB}\) (in USD/MJ) and \(P_{elec}\) (in USD/MJ) are the mean values of the fuel prices of the biodiesel blend and electrical energy, respectively, considering all months \(i\) from the set of months \(M\) over the period 1st Jan 2014 to 31st Dec 2017 \(\left( {M = 48} \right)\):

The provision and operation of a bus transport systems comes along with a variety of different cost components that eventually must pay off through the revenues generated from selling of tickets to the passengers. The public transport authority URBS in Curitiba published an extensive amount of information online (URBS 2017b) together with a description of their applied methodology (URBS 2018b) how they determine the fare for the passengers based on the cost of the transport service (\(CTS\)) related to the driven distance by the buses (Table 5). Thus, \(CTS\) represents the cost to operate a bus in a profitable manner. Cost data was available for the two currently operated buses, i.e. the conventional bi-articulated bus (ConvBi) in the BRT system and hybrid-electric two-axle bus (HybTw) on regular bus routes. In contrast, since no reliable data has been published for the cost components of the hybrid-electric articulated bus (HybAr) and the plug-in hybrid-electric two-axle bus (PlugTw), some assumption had to be made (see footnotes of Table 5 for more information). Overall, the \(CTS\) of a bus transport system consists of the operating cost of the bus fleet, personnel cost of the bus transport system, administration cost of the bus transport system, amortisation of buses and facilities, profitability requirements for a fair return on the investments made by bus operators, taxes and another small cost addition. The cost information for the \(CTS\) (in USD/km) in Table 5 were used as input data in the scenario analysis as presented next.

The operation of the BRT route is in accordance with a time table in Curitiba at present. The number of buses that leave the bus stop ‘Tubo Praça Carlos Gomes’ is shown in an aggregated form as sum of buses by hourly time slices in Fig. 5. The figure shows the current situation, in which conventional bi-articulated buses are exclusively operated (baseline scenario). As the aggregation is made by hourly time slices and taking into account that the time for one roundtrip amounts to approximately 1 h (URBS 2016), then the actual number of buses that simultaneously drive on the BRT route are the same as the number of buses shown for each time slice in Fig. 5. Furthermore, the figure indicates that more buses are operated during peak hours such as in the morning and evening from Monday to Friday (URBS 2018c). These are the times when the residents of Curitiba commute between home and work and vice versa and thus, more buses are needed to transport the larger ridership. On the weekend, the distribution of buses is rather constant, while more buses operate on Saturday than on Sunday. A commonly applied measure to evaluate the transport service is the headway, i.e. the inverse of the frequency of buses. The headway states the time between buses (in minutes/bus) leaving a bus stop or in other word the waiting time for passengers at a bus stop. Therefore, a short headway is desirable as it indicates more convenience for the passengers. The headway \(Headway_{d,ts}\) on day \(d\) during time slice \(ts\) (in minutes/bus) is therefore calculated by:where \(t_{d,ts}\) is the duration on day \(d\) of time slice \(ts\) and \(n_{d,ts,b}\) is the number of buses operating simultaneously on day \(d\) during time slice \(ts\) of bus type \(b\) (in buses/hour). As all times slices were chosen in 1-h intervals, \(t_{d,ts}\) always amounts to 60 min in this study. Then, the average headway \(AverageHeadway\) over several days and time slices of operation (e.g. 1 week) is calculated as follows:

A couple of different scenarios were developed in this study to evaluate how the use of buses other than the conventional bi-articulated bus as in the baseline scenario would differ concerning the cost of transport service evaluated by the weekly cost of transport service \(WeeklyCTS\) (in USD/week) in Eq. (25) and service quality quantified by the \(AverageHeadway\) (in minutes/bus) in Eq. (23). The compilation of the new bus fleet in each scenario was determined by using a techno-economic optimisation model formulated in Eqs. (23)–(29). Variables and parameters are listed in Table 6. This optimisation model ensured an objective and data-driven decision making concerning how many buses of which bus type \(b\) should be operated on day \(d\) during time slice \(ts\). The objective function aims at minimising the \(WeeklyCTS\):where \(WeeklyCTS\) was calculated by:where \(n_{d,ts,b}\) is again the number of buses operated on day \(d\) during time slice \(ts\) of bus type \(b\) (in buses/hour); \(RD\) is the roundtrip distance of the BRT route \((2 \cdot 10\,{\text{km}})\); and \(CTS_{b}\) (in USD/km) is the cost of transport service of bus type \(b\) (Table 5). The optimisation was subject to two constraints. The first constraint ensured the provision of sufficient transport service considering the current ridership. This prevented an undersizing of the new bus fleet consisting of buses other than ConvBi, because a one-by-one substitution of ConvBi by any of the other three options (HybTw, HybAr or PlugTw) would result in a lower aggregated passenger carrying capacity, since their passenger carrying capacities are smaller. The underlying assumption of this constraint is that the current ridership does not exceed the current aggregated passenger carrying capacity in the baseline scenario. This seems to be reasonable, because otherwise this would imply that already an undersizing of the bus fleet exists at present. Thus, the first constraint required to achieve at least the same aggregated passenger carrying capacity \(AggPCC_{d,ts}\) (in passengers/hour) as the one in the baseline scenario \(BL\_AggPCC_{d,ts}\) (in passengers/hour):where \(AggPCC_{d,ts}\) is the sum of the number of buses operated on day \(d\) during time slice \(ts\) times the respective passenger carrying capacity of bus type \(b\)\(PCC_{b}\) (in passengers/bus):

The second constraint set minimum \(MinTarget_{d,ts,b}\) (dimensionless) and maximum targets \(MaxTarget_{d,ts,b}\) (dimensionless) for minimum and maximum shares of a particular bus type \(b\) that shall be operated on day \(d\) during time slice \(ts\):

Note: This optimisation model focuses exclusively on the choice of buses considering technology and cost, and does not include any management for the charging schedule for buses of the type PlugTw.

Table 7 provides an overview of all scenarios. Each scenario considers a technology change aiming at an introduction of hybrid-electric and/or plug-in hybrid-electric buses on the BRT route. The scenarios were run using the values for \(CTS\) as input data from Table 5.

The descriptive statistics of the energy use estimations for the buses are provided in Table 8 and visualised in Fig. 6. Assessment for normality using the Shapiro–Wilk test in Past 3.x (Hammer et al. 2001) as well as considering the observation that the values for both skewness and kurtosis are within a range of ± 2 indicate together that the energy use estimations are normally distributed for all buses at all passenger loads. This, by implication, means a normal distribution for bus driver behaviour in the sample of the driving cycles from Curitiba.

The energy use per distance increases gradually with increasing passenger load for all buses as shown in Fig. 6a. The following ranges represent the energy use per distance estimations from 0 to 100% passenger load. The highest values are estimated for the conventional bi-articulated bus (ConvBi) amounting to (24.89–36.50) MJ/km, which is not surprising as it is the heaviest bus in the comparison as well as uses only the energy-inefficient internal combustion engine. A lower energy use is found for the two hybrid-electric buses amounting to (11.51–14.20) MJ/km for HybTw and (13.45–16.91) MJ/km for HybAr. The least energy is used by PlugTw amounting to a value range of (6.24–10.33) MJ/km. The simulations show that PlugTw uses 63% less energy in CD mode than in CS mode. This difference indicates a significant energy efficiency improvement when using all-electric drive (i.e. only the electric motor is used for propulsion) compared to parallel operation of internal combustion engine and electric motor. Based on this, it is desirable that a bus driver avoids aggressive driving to allow a longer operation in CD mode to benefit the most from this energy efficiency advantage. However, the simulations also show that differences exist between the driving cycles as well as sensitivities of the buses to this uncertainty. In this respect, ConvBi has the smallest coefficient of variance (\(C_{v}\)) out of all buses, i.e. the smallest value dispersion around the mean. The \(C_{v}\) of ConvBi’s energy use amounts to 4–5%. This means that ConvBi is the least influenced by varying bus driver behaviour. The influence of bus driver behaviour gains in importance as the degree of electrification increases in the powertrain, e.g. hybrid-electric buses (HybTw, HybAr) have a \(C_{v}\) of 8–9% and the plug-in hybrid-electric bus (PlugTw) has a \(C_{v}\) of 13–16%. Therefore, it is crucial to pay attention to the bus driver behaviour for buses with advanced powertrain technologies. In this respect, PlugTw is the most sensitive bus as indicated by having the highest \(C_{v}\). Another observation for PlugTw is a decreasing \(C_{v}\) with increasing passenger load, i.e. from a \(C_{v} = 16\%\) at a passenger load of 0% to a \(C_{v} = 13\%\) at a passenger load of 100%. This opposite trend is explained by the fact that more energy is needed at higher passenger load. As a result, the CD mode becomes shorter, while the CS mode becomes longer. Thus, the share of CS mode to CD mode increases and hence, the energy use increases due to more combustion of the biodiesel blend in the less energy-efficient internal combustion engine rather than using electrical energy in the more energy-efficient electric motor. As a result, PlugTw’s operation approximates to the operation of a hybrid-electric two-axle bus such as HybTw, leading to a similar uncertainty concerning energy use variations due to bus driver behaviour.

The estimations for energy use per passenger-distance follow strong regressive trends for all buses, because the denominator (i.e. passenger-distance in pkm) influences more the ratio MJ/pkm than the nominator (i.e. energy use in MJ), see Fig. 6b. On one hand, this means, the energy use per passenger-distance increases drastically when approaching zero passengers, which corresponds to the operation of an empty bus. On the other hand, this means that energy use per passenger-distance decreases as the passenger load increases, which confirms the importance of utilising the passenger carrying capacity in a bus. For instance, if ConvBi is operated, many passengers should be transported to benefit effectively from its large passenger carrying capacity in terms of energy use per passenger-distance.

The fuel cost and uncertainty estimations are provided in Table 10. The trends of the cost of transport service (\(CTS\)) and uncertainties are shown in Fig. 7 (after adding federal and municipal taxes to the \(CTS\) such as in Table 5). The error bars in this figure indicate the probability distribution corresponding to Chebyshev’s inequality for \(k = \sqrt 2\), \(k = 2\), \(k = 3\) that cover 50, 75 and 88.9% of the expected fuel cost values around the mean, respectively. As the generated amount of data could be used to discuss various different cases, the discussions had to be limited and are only presented for the case of 80% passenger load in the following. This passenger load was chosen as an example as it represents the case that buses are quite occupied, but yet, some passenger variation is possible, e.g. 60% passenger load at operation start, then reaching a peak of 100% passenger load in the middle of the BRT route and eventually, arriving at the end station again with 60% passenger load and thus, having 80% passenger load on average.

The contribution of the fuel cost to \(CTS\) decreases with increasing degree of electrification in the powertrain in a bus. While the fuel cost contribute 19.7% to the \(CTS\) for the conventional bus ConvBi, it is less for the two hybrid-electric buses (10.9% for HybTw, 9.7% for HybAr) and the least for the plug-in hybrid-electric bus PlugTw (8.1%). The reason for this decreasing trend is the result of the much lower energy use of both hybrid-electric and plug-in hybrid-electric buses compared to the conventional bus. This observation becomes particularly important concerning the combined standard uncertainty of varying bus driver behaviour that influence directly the energy use as well as fluctuating fuel prices of the biodiesel blend and electrical energy. As a result, the coefficient of variation (\(C_{v}\)) is consistently higher for all buses by approx. 2–3%-points when accounting both varying bus driver behaviour and fluctuating fuel prices compared to only accounting varying bus driver behaviour. For instance, while the \(C_{v}\) value at 80% passenger load for the energy use of ConvBi amounted to 4.8%, it increases for the fuel cost to 7.9%. Similarly, the \(C_{v}\) values increase for the hybrid-electric buses HybTw and HybAr from 8.9% to 8.5% for energy use to 11.0% and 10.6% for fuel cost as well as for the plug-in hybrid-electric bus (PlugTw) from 13.7% to 15.6%. The largest increase in uncertainty is found for ConvBi (+ 3.1%-points for \(C_{v}\)) due to its much higher energy use compared to the other buses. Noteworthy, although PlugTw uses two different energy sources, namely the biodiesel blend and electrical energy, this bus is only slightly more influenced by accounting the additional uncertainty from fuel price fluctuations. The reason for this rather small increase is that PlugTw consumes considerable less energy than any other bus in this study and hence, its fuel cost is less impacted by fuel price uncertainties than the other buses. So, the energy-efficient operation in charge-depleting (CD) mode does not only save energy, but also mitigates the effect of fuel price fluctuations on the actual fuel cost for PlugTw.

Next, Chebyshev’s inequality is used to make statements about the probability distribution and to quantify the extent of deviation for expected fuel cost values from the estimated mean at 80% passenger load. For example, in the case of \(k = 3\), three times the combined standard uncertainty (\(3 \cdot u_{c}\)) must be considered to cover 88.9% of the expected values. Similarly, three times the coefficient of variation (\(3 \cdot C_{v}\)) can be used to express the deviation in percentage. For instance, ConvBi has got a value for \(3 \cdot C_{v}\) of 24%, which is much lower than for HybTw (33%), HybAr (32%) and PlugTw (47%). While these numbers show that a rather large dispersion of expected values around the mean must be considered for a coverage of 88.9%, they also show that the estimated combined standard uncertainty increases with increasing degree of electrification in the powertrain, i.e. the relative dispersion is the smallest for ConvBi, whereas it is the largest PlugTw. Hence, fuel cost and uncertainty pose opposite trends, because the fuel cost is the highest for ConvBi, whereas it is the lowest for PlugTw, and the uncertainty is the lowest for ConvBi, whereas it is the highest for PlugTw. Additionally, the estimation of the two hybrid-electric buses HybTw and HybAr are situated in-between the values for ConvBi and PlugTw and therefore, they represent trade-off options concerning fuel cost and uncertainty.

This scenario analysis assesses potential replacement scenarios, in which hybrid-electric and plug-in hybrid-electric buses are introduced and replace the currently operated conventional bi-articulated buses (i.e. the baseline scenario ScBaseline), considering the actual time table of the BRT route in Curitiba. The evaluation is done based on the weekly cost of transport service (\(WeeklyCTS\)), while taking into account the service quality in terms of average headway (\(AverageHeadway\)). The \(WeeklyCTS\) are rounded to thousands of USD. The complete output from the optimisation model is provided in the electronic supplementary material. A summary of the key findings is provided in Table 11. The average bus fleet composition states the share between the four types of buses on average, i.e. the average share of the buses of type \(b\) over all days \(d\) and time slices \(ts\).

The \(WeeklyCTS\) of the baseline scenario ScBaseline amounts to 61,000 USD/week, while achieving an \(AverageHeadway\) of 11.0 min/bus. In contrast, if 50% of the conventional bi-articulated buses (ConvBi) are replaced, then the \(WeeklyCTS\) increases by 29%, while, however, also achieving an improved transport service with an \(AverageHeadway\) of 8.1 min/bus, i.e. 26% less waiting time for passengers at a bus stop until the next bus leaves. Although, most buses are still of the conventional type (46.4%), there is a considerable share of 40.0% hybrid-electric articulated buses (HybAr) that is complemented by 12.0% hybrid-electric two-axle buses (HybTw). The remainder (1.6%) are plug-in hybrid-electric two-axle buses (PlugTw), which is an interesting finding as it show that this bus can compete with HybTw, despite higher cost of transport service \(CTS\) per distance (PlugTw: 3.716 USD/km; HybTw: 3.049 USD/km). This highlights that the replacement of buses is not only a question of \(CTS\), but also of the actual passenger carrying capacity of a bus. In this respect, PlugTw’s passenger carrying capacity amounts to 96 passengers, which is more than for HybTw (PCC: 79). As a result, fewer buses of type PlugTw can provide the necessary transport service on the BRT route in a more cost-effective way than using more buses of the type HybTw. If all ConvBi are replaced such as in the scenario ScConv0, then the \(WeeklyCTS\) amounts to 100,000 USD/week (64% more than in ScBaseline), while having an \(AverageHeadway\) of 6.4% (42% less than in ScBaseline). The bus fleet in this scenario consists of mostly HybAr (88.2%), followed by similar shares of HybTw (6.5%) and PlugTw (5.3%). Since the shares of HybTw and PlugTw are quite similar, this finding again confirms that PlugTw represents a competitive techno-economic option to HybTw when aiming at a minimisation of the \(WeeklyCTS\).

The next scenario ScHybrid100 considers only hybrid-electric buses for the replacement to illustrate how buses of this type would influence the \(WeeklyCTS\) and service quality on the BRT route. In this scenario, the ConvBi fleet is almost completely replaced by a share of 93.5% HybAr, while being complemented by a small share of 6.5% HybTw. This gives the same \(WeeklyCTS\) as well as same \(AverageHeadway\) as previously found in the scenario ScConv0. The difference between the ScConv0 and ScHybrid100 scenario is though that the share of 5.3% PlugTw in the ScConv0 is completely replaced by HybAr, while the share of 6.5% HybTw remains the same.

The next scenario promotes more electrification of the BRT route by requiring a minimum share of 25% plug-in hybrid-electric two-axle buses (PlugTw), i.e. the scenario ScPlug25. Comparing the findings of ScPlug25 to the previous two scenarios ScConv0 and ScHybrid100 shows that the set target leads to a further increase by 10,000 USD/week amounting to \(WeeklyCTS\) of 110,000 USD/week (80% more than in ScBaseline), while the \(AverageHeadway\) decreases from 6.4 min/bus to 5.7 min/bus (48% less than in ScBaseline). In regards to the bus fleet composition, the target of 25% PlugTw is actually exceeded and reaches a share of 40.9%. This finding reinforces the previous observation that PlugTw can compete with HybTw, despite its higher \(CTS\). The remainder (59.1%) buses in the bus fleet are HybAr buses. Since HybAr did not reach a share of 75%, PlugTw represents also a viable techno-economic option compared to HybAr buses under the given circumstances. A further increase of the PlugTw buses as in the next two scenarios ScPlug50 (at least 50% PlugTw) and ScPlug100 (all buses PlugTW) shows that the \(WeeklyCTS\) increase gradually to 113,000 USD/week (85% more than in ScBaseline) and 147,000 USD/week (139% more than in ScBaseline), respectively. Besides, the \(AverageHeadway\) is reduced to 5.5 min/bus in ScPlug50 (50% less than in ScBaseline) and 4.0 min/bus in ScPlug100 (64% less than in ScBaseline). The 50% of PlugTw buses are complemented by the same share of HybAr buses in the scenario ScPlug50.

Based on the scenario analysis, there exists obviously a trade-off between cost (\(WeeklyCTS\)) and service quality (\(AverageHeadway\)) when replacing large conventional bi-articulated buses (ConvBi) by smaller but technologically more advanced buses (HybTw, HybAr and PlugTw). The \(WeeklyCTS\) increases between 64% and 139% in the case of a complete replacement of all conventional bi-articulated buses in the bus fleet by advanced buses. The optimisation model demonstrated that HybAr is the most preferred bus to replace ConvBi.

In addition to changes in cost and service quality, other side effects should be mentioned. For example, the findings by (Kim et al. 2011) for the case of Seoul in South Korea showed that advanced buses can positively influence the perception of the city’s residents on public buses. And thus, the operation of more advanced buses such as hybrid-electric and plug-in hybrid-electric buses could also potentially contribute to a more positive perception of Curitiba’s residents on the public bus transport service. Furthermore, as it was shown in the analysis, the choice of a bus is influenced by both \(CTS\) and passenger carrying capacity. The applied optimisation model demonstrated that the plug-in hybrid-electric bus PlugTw represents a viable option that can compete with the two hybrid-electric buses HybTw and HybAr. Additionally, the scenario analysis showed that the \(AverageHeadway\) could be reduced by up to 64% compared to the baseline scenario. In this regards, a comprehensive study by (Hensher and Li 2012a, b) analysed 46 BRT systems globally (including Curitiba) and found statistical significance that the shorter the headway is, the more passenger trips are made. Thus, in addition to the commonly discussed aspect of cost, also convenience of the transport service must be considered to attract more paying passengers that can potentially increase the revenues.