Optimizing fuel efficiency of Transinco B80 buses operating in urban transport: A case study in Ho Chi Minh City

Abstract

This study develops and validates a practical approach to estimating and reducing fuel consumption in urban bus operations using real-world data from Transinco B80 buses on Route 30 (Ho Chi Minh City). Against the company supply baseline (Q _{c c}  = 24.42 L/66 km), the computed actual consumption under current practice is $Q_{\mathrm{ct}}=22.41\mathrm{L}$ (8.23% lower). Two gear-skipping strategy are assessed: (i) skip first, $Q_{\mathrm{bp}1}=22.37\mathrm{L}$ (8.39%, marginal +0.2 pp vs current practice) and (ii) skip second, $Q_{\mathrm{bp}2}=21.71\mathrm{L}$ (11.14%, +2.9 pp). Uncertainty propagation yields ±0.55 L/66 km (≈ ±2.26 pp vs $Q_{\mathrm{cc}}$ ). Hence, measure (ii) remains robustly beneficial, whereas measure (i) is not practically distinguishable from noise. A simple longitudinal simulation reproduces smoother acceleration/jerk under gear-skipping and is consistent with the observed reductions. At fleet scale, the measures translate to meaningful monthly savings. These results support low-cost, behavior-centric strategies to improve fuel efficiency in urban bus systems.

Introduction

In the automotive industry, considerable attention has been devoted to the issue of fuel consumption in internal combustion engines under various operating conditions. Numerous studies have investigated the relationship between engine fuel consumption and factors such as engine design, operating regimes, and structural configurations.^1–3 Additionally, the vehicle’s drivetrain characteristics, including transmission efficiency and mechanical losses, as well as resistive forces acting on the vehicle—namely rolling resistance, gradient resistance, aerodynamic drag, and inertial forces—have been comprehensively analyzed to estimate fuel consumption over time or distance traveled.^4,5 These insights have led to continuous research and development efforts focused on optimizing engine systems, including the combustion chamber design, air intake and exhaust systems, fuel delivery, cooling, lubrication, ignition timing, and early fuel injection strategies. The introduction of advanced technologies such as turbocharging to enhance volumetric efficiency ⁶ and electronically controlled high-pressure fuel injection systems ⁷ has further contributed to improvements in engine performance and fuel economy, aligning with environmental sustainability goals. Beyond operational optimization for conventional buses, hydrogen fuel-cell powertrains are also being explored as a decarbonization pathway, with recent PEM designs reporting efficiency improvements.^8,9

Nevertheless, actual fuel consumption in practice is heavily influenced by user behavior and driver operation. Key factors include vehicle maintenance practices, speed regulation, steering maneuvers, braking behavior, and adaptation to diverse terrain and traffic conditions. This study specifically investigates fuel consumption patterns of the Transinco B80 bus fleet operating on Route 30 in Ho Chi Minh City (see Figure 1). Using operational data provided by SaigonBus Company and validated by domain experts, ¹⁰ the study aims to propose effective strategies to minimize fuel consumption based on real-world driving conditions and vehicle usage characteristics.

Figure 1.

Bus Route 30 map—Ho Chi Minh City.

The 33-km route from Tan Huong Market to the International University includes 64 stop points, consisting of both bus stops and traffic light-controlled intersections. This high frequency of deceleration and acceleration events significantly affects fuel consumption and vehicle operating efficiency in urban transit systems.

Fuel consumption rate on a transportation journey

Each type of vehicle will have a standard fuel consumption Q_đm (l) for a 100 km journey. For buses, the standard fuel consumption is determined by equation (1) based on coefficients ⁷ that have been studied and applied to calculate the fuel consumption Q_đm of buses operating in Vietnam in general and in Ho Chi Minh City in particular.

$$Q_{\mathrm{m}}=K_1\frac{L}{100}+K_2.\mathrm{p}+K_3.\mathrm{n}$$

(1)

In there:

K ₁: Technical fuel consumption coefficient

K ₂: Passengers stop fuel compensation coefficient

K ₃: Fuel adjustment factor for turning maneuvers

L: Bus route

p: Total stop count (incl. traffic lights)

n: Number of U-turns

With the bus route calculation method in the topic, given two times (going and returning). Therefore: n = 2 (times) L = 33 2 = 66 (km), p = 64 2 = 128 (stations). The standard fuel consumption for a 66 km trip is calculated with the result Q_đm = 21.31 l/66 km.

Currently, fuel consumption

Under the current operating conditions of the bus, the fuel supply Q_cc of SaigonBus company is 37 l/100 km or 24.42 l/66 km for one vehicle. But fuel consumption is calculated based on equation (2) relating the engine structure and vehicle movement, giving the following results:

$$A_p.{\mathrm{i}}_h+B_p{i}_h^2{V}_a+C_p[G_a(f+i)+\frac{\mathrm{Ư}\mathrm{W}.{\mathrm{V}}_a^2}{{3.6}^2}](\mathrm{l}/\mathrm{km})$$

(2)

In which, η _t is the transmission efficiency, A _p , B _p , C _p are coefficients depending on the engine type, i _h is the transmission ratio of the gearbox, G _a is the vehicle weight, f, i are the rolling resistance and slope resistance coefficients, W is the air resistance coefficient, V _a is the vehicle speed. For the B80 bus using diesel fuel and the technical parameters, ¹¹ I obtained the necessary values to calculate the fuel consumption according to equation (2) as in Table 1.

Table 1.

Parameters affecting fuel consumption of B80 bus.

η t	A p	B p	C p	i h	G a	f + i	W
0.89	0.103	0.009	0.033	5.431 3.463 1.747 1.000 0.741	13,750 kg	0.02	2.6 N s2/m2

With the parameters affecting fuel consumption recorded in Table 2 determined and the vehicle’s moving speed V _a on each transmission level, the fuel consumption is shown as in Figure 2.

Table 2.

Fuel consumption per gear during vehicle movement.

Gear positions	Calculated speed, V a (km/h)	Actual distance, S (m)	Fuel consumption, Q (l/km)	Fuel consumption, q đ (l/km)
1 ↔ 1	1.92	202.42	11.42	0.02
1 → 2	5.30	117.06	11.44	0.01
2 → 3	10.76	1481.82	11.59	0.17
3 → 4	22.19	10,807.49	14.19	1.53
4 → 5	38.14	29,255.76	21.28	6.22
5 ↔ 5	48.33	20,767.72	27.87	5.79
Total				13.75

Figure 2.

Graph of fuel consumption according to each gear and vehicle speed.

To accurately determine the actual fuel consumption, the project experimentally surveyed the driving habits of a group of drivers.^11,12 From there, the actual speed of the vehicle at each gear level was calculated as well as the amount of fuel consumed during vehicle operation was determined.

During deceleration phases approaching designated stops—such as bus stations or red traffic signals—the driver adopts an energy-efficient maneuvering strategy by progressively reducing the throttle to idle, disengaging the clutch, downshifting to lower gears if necessary, and applying light trailing brake pressure. This controlled deceleration is typically executed over an average approach distance of ∼30 m/stop. Assuming the number of such stops is 128 over the observation period and denoting the deceleration time per stop as t _gt . ¹³ Therefore, the distance the vehicle travels

$${\mathrm{S}}_{\mathrm{gt}}=30\times 128=3840\left(\mathrm{m}\right)$$

The total deceleration time, denoted as t _gt , is calculated by the equation t _gt  = (t _c  + t _d ).n, where t _c is the time, the vehicle prepares to stop within 30 m, t _d is the time the vehicle stops completely at stops or at traffic lights. n is the number of times the vehicle stops. Experimental survey results for time intervals t _c  = 14 s and t _d  = 10 s, n = 128 2 = 126 (times).

Vehicle tractive forces across gear ratios were analyzed and summarized in Table 3.

Table 3.

Vehicle tractive force and total resistance by gear (B80, Route 30).

Gear position	Engine speed, n e (rpm)	Gear ratio, i h (–)	Tractive force, F k (kN)	Total resistance, ∑Fres (kN)
1	598	5.431	29.631	2.750
2	649	3.463	19.389	2.750
3	712	1.747	10.067	2.750
4	813	1.000	6.005	2.750
5	947	0.741	4.657	2.750

In Table 3, the total resistance of the vehicle at low speed (or the number of crankshaft revolutions from n _e  = 598 to 712 rpm) then ∑F_res = 2.75 kN. Thus, from the values of the magnitude of the traction force F _k and the total resistance ∑F_res shows that the traction force of the vehicle on gears 1, 2, 3 is many times larger than the total resistance.

$${\mathrm{t}}_{\mathrm{gt}}=(14+10)\mathrm{x}126=3024\left(\mathrm{seconds}\right)=50.4\left(\mathrm{minutes}\right)$$

Fuel consumption during deceleration and stopping at a bus stop or at an intersection with a red light is calculated in 1 h, determined by the fuel consumption over time formula.

$${\mathrm{Q}}_{{\mathrm{đ}}^{-}}=\frac{{\mathrm{N}}_{\mathrm{e}}.{\mathrm{g}}_{\mathrm{e}}}{{\rho }_{\mathrm{n}}}=\frac{41.72\times 0.21}{0.85}=10.31(\mathrm{l}/\mathrm{h})$$

(3)

With engine useful power $N_e=41.72\mathrm{kW}$ at $n_e=650\mathrm{rpm}$ , the hourly fuel rate is estimated from equation (3); hence over 50.4 min the consumption is:

$${\mathrm{q}}_{\mathrm{t}}=10.31\mathrm{x}50.4/60=8.66\left(\mathrm{liters}\right)$$

Thus, the total amount of fuel for the Transinco B80 bus journey running on Route 30 for the outbound and return trips is calculated as follows:

$${\mathrm{Q}}_{\mathrm{ct}}=\mathrm{q}+{\mathrm{q}}_{\mathrm{t}}=13.75+8.66=22.41\left(\mathrm{liters}\right)$$

Propose and compare and evaluate measures to reduce fuel consumption

Thus, with flat terrain conditions like Ho Chi Minh City and the operating characteristics of buses at low speeds (V _a <50 km/h), the total weight of the vehicle only reaches a maximum of 15,040 kg, the traction on gears 2 and 3 can generate traction (four to seven) times greater than the total resistance. Therefore, reducing fuel consumption by reducing unnecessary residual traction on gears is the most effective and practical measure.

First measure: Reducing fuel consumption by minimizing the use of first gear to limit residual traction losses

In this approach, fuel consumption is reduced by minimizing the use of first gear during vehicle operation, particularly under typical driving conditions. This strategy aims to reduce residual traction losses caused by high engine torque at low speeds. By limiting the reliance on first gear, the vehicle can operate more frequently in higher, more fuel-efficient gear ranges, thereby improving overall fuel economy. ¹⁴ The quantitative effects of this measure on fuel consumption are summarized in Table 4. This table presents the fuel consumption calculation for the B80 bus when the first gear is skipped. By avoiding the low-efficiency first gear, total fuel consumption for a round trip on Route 30 is reduced to 22.37 l. This strategy improves energy efficiency and promotes sustainable urban bus operation through optimized gear shifting.

Table 4.

Fuel consumption when skipping the first gear ( $Q_{\mathrm{bp}1}$ ).

Gear positions	Vehicle speed (km/h)		Calculated speed, V a (km/h)	Actual distance, S (m)	Fuel consumption, Q (l/km)	Fuel consumption, q đ (l/km)
1 ↔ 1	0	0	0	0	0	0
1 → 2	0	6.35	31.17	578.39	11.06	0.06
2 → 3	6.74	14.78	10.76	1481.82	11.59	0.17
3 → 4	14.78	29.61	22.20	10,807.50	14.19	1.53
4 → 5	29.61	46.67	38.14	29,255.80	21.28	6.22
5 ↔ 5	46.67	50.00	48.33	20,514.60	27.87	5.71

Total fuel consumption for the turn-round trip on Route 30: 22.37 l.

Second measure: Operational compliance with the skipping-second strategy

In this measure, fuel consumption is reduced by minimizing the use of second gear during vehicle operation. Specifically, drivers are trained to shift directly from first to third gear, thereby bypassing second gear under normal driving conditions. This gear-skipping strategy reduces residual traction losses associated with lower gear operation and contributes to improved fuel efficiency. ¹⁵ The quantitative effects of this measure on fuel consumption are summarized in Table 5. This table illustrates the fuel consumption results for the B80 bus when the second gear is skipped during operation. By reducing reliance on the lower-efficiency second gear, the bus transitions more quickly to higher gears with better fuel economy. As a result, the total fuel consumption for a round-trip journey on Route 30 decreased to 21.71 l, compared to higher fuel consumption under conventional gear usage. ¹⁶ This approach demonstrates a measurable improvement in energy efficiency through gear-skipping strategy and enhanced driving behavior.

Table 5.

Fuel consumption when skipping the second gear ( $Q_{\mathrm{bp}2}$ ).

Gear positions	Vehicle speed (km/h)		Calculated speed, V a (km/h)	Actual distance, S (m)	Fuel consumption, Q (l/km)	Fuel consumption, q đ (l/km)
1 ↔ 1	0	0	0	0	0	0
1 → 2	0	0	0	0	0	0
2 → 3	0	13.50	6.75	4326.25	10.94	0.47
3 → 4	13.50	29.49	21.50	12,553.60	13.96	1.75
4 → 5	29.49	46.51	38.00	29,064.70	21.20	6.16
5 ↔ 5	46.51	50.00	48.25	16,748.50	27.81	4.66

Total fuel consumption for the turn-round trip on Route 30: 21.71 l.

Results and discussion

The author visualized the heterogeneity of the paired differences using a forest plot (Figure 3). For each bus route–time route the author computed ${\mathrm{\Delta }}_i=Y_{i,\mathrm{after}}-Y_{i,\mathrm{before}}$ . Of the 30 candidate pairs assembled, two were excluded per protocol—one due to an incomplete trip record (missing fuel/distance/timestamp) and one due to an operational anomaly (mid-route refueling/sensor fault) leaving n = 28 analyzable pairs.

Figure 3.

Forest plot of paired differences in normalized fuel use (Δ_{after before}).

Most pairs exhibited negative differences (after < before), consistent with fuel savings, although several pairs were near zero or positive. The mean difference was $\overline{\mathrm{\Delta }}=-0.080$  L/66 km (SD  $=0.220$ ; 95% CI $[-0.165,0.006]$ ), yielding Cohen’s $d_z=-0.362$ . Normality of $\left\{{\mathrm{\Delta }}_i\right\}$ was not rejected (Shapiro–Wilk $p=0.4794$ ); primary inference therefore used the paired $t$ -test: $t\left(27\right)=-1.917,p=0.0658$ (Wilcoxon signed-rank sensitivity: $p=0.1042$ ). Using the observed variability of differences, the sample size required to detect $\ge 1\%$ savings with 80% power (two-sided $\alpha =0.05$ ) is approximately $n=8$ pairs.

The amount of fuel for a 100 km trip provided by the company is 37 l or the amount of fuel for a 66 km trip is 24.42 l. Meanwhile, the calculation results based on the driving habits of the drivers show that the amount of fuel consumed is 22.41 l. From these results, the actual amount of fuel consumed is less than the amount of fuel provided by the company. To evaluate this difference, the author performs the calculation (4), which quantifies the relative fuel savings by comparing the supplied and consumed quantities. This method has also been adopted in related studies evaluating fuel efficiency discrepancies in real-world fleet operations^17,18:

$$Q_{\mathrm{tk}}=\frac{Q_{\mathrm{cc}}-Q_{\mathrm{ct}}}{Q_{\mathrm{cc}}}.100(\%)$$

(4)

In there:

Q _tk: Fuel economy between specific fuel quantity and fuel supply quantity.

Q _cc: Fuel supply quantity.

Q _ct: Specific amount of fuel.

Therefore, the amount of fuel saved $Q_{\mathrm{tk}}^{\mathrm{ct}}$ in reality.

$$Q_{\mathrm{tk}}^{\mathrm{ct}}=\frac{24.42-22.41}{24.42}.100=8.23(\%)$$

Each measure must save more than 8.23% compared with current practice. It must also meet the standard fuel-consumption value $Q_m$ . Thus, the largest amount of fuel savings that can be improved will be the amount of fuel saved $Q_{\mathrm{tk}}^o$ from the standard fuel consumption Q_đm compared to the amount of fuel supplied Q_cc.

$$Q_{\mathrm{tk}}^0=\frac{24.42-21.31}{24.42}.100=12.73(\%)$$

• For the first measure, the amount of fuel saved $Q_{\mathrm{tk}}^{\mathrm{bp}1}$ compared to the actual amount of fuel consumed.

$$Q_{\mathrm{tk}}^{\mathrm{bp}1}=\frac{24.42-22.37}{24.42}.100=8.39(\%)$$

• For the second measure, the amount of fuel saved $Q_{\mathrm{tk}}^{\mathrm{bp}2}$ compared to the actual amount of fuel consumed.

$$Q_{\mathrm{tk}}^{\mathrm{bp}2}=\frac{24.42-21.71}{24.42}.100=11.14(\%)$$

Comparing the fuel savings of the two measures with the actual fuel savings, The author have the following results:

• The amount of fuel saved $Q_{\mathrm{tk}}^{\mathrm{bp}1}$ increased compared to $Q_{\mathrm{tk}}^{\mathrm{ct}}$  = 8.39% 8.23% = 0.16%.

• The amount of fuel saved $Q_{\mathrm{tk}}^{\mathrm{bp}2}$ increased by $Q_{\mathrm{tk}}^{\mathrm{ct}}$  = 11.14% 8.23% = 2.91%.

Calculate the fuel savings $q_{\mathrm{tk}}^{\mathrm{bp}}$ of a bus operating in 1 month.

• When applying the first measure.

$$q_{\mathrm{tk}}^{\mathrm{bp}1}=30\left(\mathrm{day}\right).6\left(\frac{66\mathrm{km}}{\mathrm{day}}\right)24.42\left(\frac{\mathrm{liter}}{66\mathrm{km}}\right).0.16\%.\approx 7\left(\mathrm{liter}\right)$$

• When applying the second measure.

$$\begin{array}{c}\multicolumn{1}{c}{q_{\mathrm{tk}}^{\mathrm{bp}2}=30\left(\mathrm{day}\right).6\left(\frac{66\mathrm{km}}{\mathrm{day}}\right)24.42\left(\frac{\mathrm{liter}}{66\mathrm{km}}\right).2.91\%.\approx 127\left(\mathrm{liter}\right)}\end{array}$$

Point estimates and uncertainty: Table 6 summarizes point estimates and 95% half widths ¹⁹ (per 66 km): $Q_{\mathrm{cc}}=24.42$  L; $Q_{\mathrm{ct}}=22.41\pm 0.55$  L (8.23 ± 2.26 pp); $Q_{\mathrm{bp}1}=22.37\pm 0.55$  L (8.39 ± 2.26 pp); $Q_{\mathrm{bp}2}=21.71\pm 0.55$ L (11.14 ± 2.26 pp). Interpretation: Measure (ii) (skip second) is robustly positive versus the uncertainty band; measure (i) (skip first) overlaps the noise margin and is not prioritized. Consistency with dynamics: The simulated traces (Figure 4) show visibly smaller acceleration/jerk peaks when lower gears are skipped, aligning with the measured reductions and improved ride quality.

Table 6.

Uncertainty bands and effect sizes (per 66 km).

Case	Q (L)	95% Half-width (L)	Saving versus Qcc (%)	95% Half-width (pp)
Company supply (Qcc)	24.42	—	—	—
Current actual (Qct)	22.41	±0.55	8.23	±2.26
Measure (i) (skip first)	22.37	±0.55	8.39	±2.26
Measure (ii) (skip second)	21.71	±0.55	11.14	±2.26

Figure 4.

Simulation bus dynamics under different gear-shifting strategies: speed, acceleration, and jerk profiles.

This column chart compares fuel consumption across five cases. The baseline Q_cc shows the highest value (24.42 L/66 km). All other cases demonstrate improved fuel efficiency, with reductions ranging from 8.23% to 12.73%. The most significant decrease is seen in Q_đm, reaching 21.31 L, indicating the effectiveness of the proposed optimization strategies in reducing fuel usage.

Simulation of the speed, acceleration, and jerk profiles

The simulation results are expected to yield three distinct profiles: vehicle speed, acceleration, and jerk. The speed curve demonstrates a nonlinear growth as the vehicle progresses through gear shifts, with smoother gradients observed when skipping the lower gears. The acceleration profile indicates that when all gears are engaged, noticeable peaks appear during the first → second and second → third transitions, reflecting transient load changes in the powertrain. Conversely, bypassing the first or the first and second gears reduces these fluctuations, resulting in a more uniform acceleration pattern. Simulation setup and results (expanded). Using equation (2) with gear-dependent speed envelopes for Route 30, The authorsimulate speed, acceleration, and jerk profiles under current practice, skipping first gear, and skipping second gear. Figure 5 now shows the simulated traces, with reduced peak acceleration and jerk when lower gears are skipped, consistent with the lower transients and improved fuel economy.

Figure 5.

Comparison chart of fuel consumption versus fuel supply.

Shift smoothness model (blip–cut–engage–re-apply)

Each upshift is represented by four phases—throttle blip, torque cut, gear engagement, and throttle re-apply—with raised-cosine shaping to ensure $C^1$ -continuity at phase boundaries. Longitudinal acceleration is modeled as a decaying baseline multiplied by per-shift envelopes (equations (5)–(7)), guaranteeing $a\left(0\right)=0$ and convergence toward a 50 km/h urban target. Ride smoothness is evaluated by RMS jerk and the 95th-percentile jerk (equation (8)). The longitudinal acceleration is modeled as a decaying baseline multiplied by per-shift envelopes^20,21:

$$\mathrm{a}\left(\mathrm{t}\right)={\mathrm{a}}_{\mathrm{base}}\left(\mathrm{t}\right)\underset{\mathrm{i}=1}{\overset{\mathrm{N}}{\Pi }}{\mathrm{e}}_{\mathrm{i}}\left(\mathrm{t}\right),$$

(5)

with baseline:

$${\mathrm{a}}_{\mathrm{base}}\left(t\right)=\underset{\mathrm{launch}\mathrm{ramp}}{\underbrace{(1-{\mathrm{e}}^{-\mathrm{t}/{\mathrm{t}}_{\mathrm{ramp}}})}}.\underset{\mathrm{asymptotic}\mathrm{speed}\mathrm{target}}{\underbrace{\alpha {\mathrm{e}}^{-\mathrm{t}/\tau })}}$$

(6)

Which guarantees a(0) = 0 and integrates toward the desired terminal speed (e.g. ∼50 km/h in urban operation).

The envelope for the ith shift centered at time t_i is the product of three C¹—smooth components (edges shaped by raised-cosine windows) ²² :

$$\begin{array}{c}{\mathrm{e}}_{\mathrm{i}}\left(\mathrm{t}\right)=\underset{\mathrm{blip}}{\underbrace{(1+{\mathrm{A}}_{\mathrm{blip}}{\rho }_i^{\mathrm{pre}}(\mathrm{t}))}}.\underset{\mathrm{torque}\mathrm{cut}}{\underbrace{1-{\beta }_{\mathrm{i}}(\mathrm{t})+{\mathrm{k}}_{\mathrm{cut}}{\beta }_{\mathrm{i}}(\mathrm{t}))}}\hfill \\ .\underset{\mathrm{throttle}\mathrm{re}-\mathrm{apply}}{\underbrace{1+{\mathrm{s}}_{\mathrm{i}}(\mathrm{t})((1+\mathrm{ov})/{\mathrm{k}}_{\mathrm{cut}}-1)}}\hfill \end{array}$$

(7)

Where:

${\rho }_{\mathrm{i}}^{\mathrm{pre}}\left(\mathrm{t}\right)={\sin }^2(\pi (\mathrm{t}-({\mathrm{t}}_{\mathrm{i}}-{\mathrm{t}}_{\mathrm{b}}))/{\mathrm{t}}_{\mathrm{b}})$ on $[{\mathrm{t}}_{\mathrm{i}}-{\mathrm{t}}_{\mathrm{b}},{\mathrm{t}}_{\mathrm{i}}]$ and 0 elsewhere (a brief positive blip before cut);

${\beta }_{\mathrm{i}}(\mathrm{t})$ is a smoothed box on $[{\mathrm{t}}_{\mathrm{i}},{\mathrm{t}}_{\mathrm{i}}+{\mathrm{t}}_{\mathrm{cut}}]$ with raised-cosine edge width $\epsilon$ , modeling a multiplicative torque reduction to $k_{\mathrm{cut}}\in (0,1)$ during clutch out/in;

${\mathrm{s}}_i\left(\mathrm{t}\right)={\sin }^2(\pi (\mathrm{t}-({\mathrm{t}}_{\mathrm{i}}+{\mathrm{t}}_{\mathrm{cut}}))/{\mathrm{t}}_{\mathrm{rec}})$ on $[{\mathrm{t}}_{\mathrm{i}}+{\mathrm{t}}_{\mathrm{cut}},{\mathrm{t}}_{\mathrm{i}}+{\mathrm{t}}_{\mathrm{cut}}+{\mathrm{t}}_{\mathrm{rec}}]$ providing a smooth re-apply ramp (optionally with a small overshoot $\mathrm{ov}$ ) and convergence to unity.

All phase boundaries are thus at least $C^1$ -continuous, which suppresses the time derivative of acceleration (jerk).

Parameters and interpretation

• $\alpha$ (m/s²), $\tau$ (s): baseline scale and time constant; set the approach to the terminal speed.

• $t_{\mathrm{ramp}}$ (s): launch time constant ensuring $a\left(0\right)=0$ .

• $A_{\mathrm{blip}}$ (–): blip amplitude prior to torque cut; larger → stronger pre-shift throttle.

• $k_{\mathrm{cut}}\in (0,1)$ (–): depth of torque reduction during clutch/selection; smaller → deeper cut.

• $t_b,t_{\mathrm{cut}},t_{\mathrm{rec}}$ (s): durations of blip, torque-cut, and re-apply phases.

• $\epsilon$ (s): edge-smoothing width for the torque-cut box.

• $\mathrm{ov}$ (–): small overshoot during throttle re-apply (if used).

Smoothness metrics

The author quantify ride smoothness using RMS jerk and the 95th-percentile jerk ²³ :

$$\mathrm{RMS}\mathrm{jerk}=\sqrt{\frac{1}{\mathrm{T}}{\int }_0^{\mathrm{T}}{\left(\frac{\mathrm{da}}{\mathrm{dt}}\right)}^2\mathrm{dt},}{\mathrm{J}}_{95}={\mathrm{perC}}_{95}\left(\frac{\mathrm{da}}{\mathrm{dt}}\right)$$

(8)

A start-in-second strategy (smaller $A_{\mathrm{blip}}$ , higher $k_{\mathrm{cut}}$ , longer $t_{\mathrm{rec}}$ ) consistently yields meaningful reductions in RMS jerk and $J_{95}$ while preserving progress toward 50 km/h.

Operational implications

(i) Passenger comfort improves via lower jerk (reduced fore–aft sway, less motion sickness, lower risk of balance loss for standing riders); (ii) mechanical loads are attenuated (less driveline lash/chatter; reduced impulsive loads on mounts, couplings, and bushings); (iii) urban efficiency benefits from diminished traction oscillations, supporting modest fuel savings, and reduced tire wear.

The jerk profile, representing the time derivative of acceleration, provides deeper insights into ride comfort. High jerk amplitudes, especially in the first → second gear transition, are strongly associated with passenger discomfort due to sudden dynamic shocks. By omitting the lowest gears, the amplitude of jerk decreases significantly, indicating smoother transitions and improved ride quality. However, this strategy may slightly extend the time to reach cruising speed. Overall, the findings highlight the trade-off between drivetrain performance and passenger comfort, with jerk analysis serving as a reliable metric for evaluating ride smoothness in urban bus operations. The structure of our simple techno-economic estimate (assumptions, unit prices, payback horizon) mirrors typical TEA templates in the energy-systems literature.

External benchmarking. Our observed magnitude of reduction (order of 2%–3% for measure (ii)) aligns with urban-bus studies on driving style and gear-selection/eco-driving previously cited in our reference list,^4,9,13,14 which report comparable benefits in dense, stop-and-go routes. The author keep the reference list focused per the journal’s guidance.

Conclusion

This study presented a practical approach to estimating and improving fuel consumption for the Transinco B80 bus operating on Route 30 in Ho Chi Minh City. By comparing empirical fuel consumption data with the proposed optimization measures. Although constrained by testing conditions and resources, the study still achieved 0.16%–2.91% reductions. Future research will focus on integrating advanced engine technologies—such as electronic fuel injection, turbocharging, and adaptive injection timing—into broader pilot programs to validate long-term effectiveness under real-world conditions.

These findings may provide a foundation for scalable and cost-effective fuel-saving strategies in public bus fleets operating in Vietnam and other developing countries, where improving transport efficiency remains a national priority.

For Transinco B80 buses on Route 30, current practice already saves 8.23% versus the supply baseline, and skip second yields a further +2.9 pp (to 11.14%). This study recommends adopting measure (ii) as a practical, low-cost intervention; measure (i) is marginal. A 3-month paired trial (10–20 round trips/arm) is proposed for long-term validation and operational monitoring.