An Empirical Study on Low-cost, Portable Vehicle’s Weight

Estimation Solution using Smartphone’s Acceleration Data

for Developing Countries

Saima Mohan and Prashant Kumar

Hitachi India Private Limited, Research and Development Centre, Bangalore, India

Keywords: Accelerometer, Speed, Smartphone Sensor, Vehicle Weight Estimation, Intelligent Transportation System.

Abstract: One in every three trucks in developing countries are overloaded, causing damage to roads and susceptible to

accidents. Conventionally, vehicle’s weight is measured at fixed weigh stations and result in high traffic

congestions at toll booths. To improve highway traffic and enhance regulation, we propose a low-cost,

portable sensor-based system viable for continuous real-time assessment of vehicle’s weight. A smartphone-

based sensing device is installed in vehicle and weight is estimated by applying multiple linear regression

model on acceleration data. In this paper, we include statistical features having relationship with target

variable. A consistent model performance of vehicle’s weight estimated at all speed ranges is established; we

also evaluate the improvised model under engine idling state. An increased accuracy is obtained with error of

2% in engine idling state and overall system error of 6% with vehicle in motion. A heterogenous data source

(such as vehicle class, load condition, goods, sensor locations, etc.,) of vehicle operating on Indian highway

segment are collected to evaluate model robustness. With exploitation of big data and advanced analytics;

advent of this solution will leverage contribution in Intelligent Transport System, focused towards smart and

sustainable transportation for ASEAN region.

1 INTRODUCTION

Logistics is one of the important sectors for a

country’s economy. Efficiency improvement in this

area can boost economic growth, increasing export

through global supply chains and helps in generating

employment. The robust growth in manufacturing

envisioned in developing countries like India is

through Government initiatives like “Make in India”,

which demands high level of logistic efficiency. It has

been reported, due to poor logistics, management has

led to unsafe practices such as overloading of trucks,

compromising road safety both for truck drivers and

other road users. In reports from developing countries

such as India, it is stated that National highways

connecting the major corridors to metro cities like

Delhi, Kolkata, Chennai, Kochi, Mumbai account for

less than 0.5% of the road network capacity but still

carry more than 40% of the freight movement by road

(NITI Aayog, 2018). Trucks spend just 40% of their

time moving on the road. The rest of the time is taken

up at checkpoints and tollgates. India Government in

July 2018 announced the increase in axle load limit to

25%. While experts feel overloading will continue,

and industry players expect the “life of roads” to

decrease. With the revised permissible weight for the

transport vehicles, the state enforcement authorities

are requested to rigorously enforce the regulations

and take strict action against overloading by goods

vehicles on roads. Similar issue exists in other

developing countries too, for example according to a

case study of Indonesia as provided by (APEC

Vietnam, 2017), 22% of trucks exceed the legal 10

tonne single axle dual tyre limit. In Central Java, 38%

of trucks exceed pavement design limit. 6.5% of the

axle loadings that exceeded the 10-tonne limit caused

90% of pavement damage.

Overloaded trucks also add to air pollution in the

city as well, as emissions from such vehicles is

significantly higher than trucks weighing within the

prescribed limits.

Challenges associated with Weigh-in-Motion

(WIM) system is installing sensors in the roadway

pavement. They require temporary roadway closure,

pavement cuts for placing the sensors. Pavement at

the site must be sufficiently smooth for a minimum

Mohan, S. and Kumar, P.

An Empirical Study on Low-cost, Portable Vehicle’s Weight Estimation Solution using Smartphone’s Acceleration Data for Developing Countries.

DOI: 10.5220/0010402600440055

In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2021), pages 44-55

ISBN: 978-989-758-513-5

distance before and after the location of the weight

sensor to minimize the influence of vehicle dynamics

on the weight measurements as mentioned in (Weigh-

in-motion, Pocket Guide, 2018). A huge maintenance

and rehabilitation cost of WIM is required to increase

lifespan of WIM installation.

As a potential use case of ITS solution for Traffic

Safety on highways serving central and state

requirement to curb overloaded vehicle on national

and state highways, we propose a smart IoT sensor-

based technology. This system is easy to handle by its

users, portable, and requires minimum cost to

maintenance, difficult to defeat and provide reliable

information to the concerned regulators by

continuously monitoring and estimate weight from

moving vehicles on road.

Vehicle’s weight and its relationship to road

management is been identified as an important topic

of research in Transportation Engineering. In this

paper, we brief accelerometer-based sensing

techniques that are being used to determine

behaviours of vehicle dynamics in real-world

scenario of heavy-duty vehicles. The state-of-the art

Vehicle’s Weight Estimation by (Phong X. Nguyen et

al., 2018) uses smartphone’s acceleration data with

statistical features to predict the weight. However, the

method requires the vehicle to be at certain speed

range of 20-22 kmph to estimate vehicle’s weight

accurately and inconsistent for other speed ranges. In

real-time scenarios the vehicle’s weight requires to be

monitored continuously for violations, which limits

above method for such deployment.

In this paper, we aim to extend the method to

overcome the mentioned challenges, (a) evaluate

model performance consistency at different vehicle

speed profiles while vehicle is in motion on road, also

including vehicle in static state i.e., engine idling

condition; and (b) assess the optimal sensor location

for deployment on vehicle by evaluating with

heterogenous vehicle data.

Initially, a feasibility study was conducted to

estimate the vehicle’s weight method on data

obtained from different sources. The method failed to

provide a stable result due to variability of data source

as identified in Table 1. We then study in detail

different speed profiles of vehicle undergone during

the journey and propose an improved multiple linear

regression model by extracting more statistical

features from linear acceleration data which shows

high significance with load factor. The improved

method estimates vehicle’s weight with an average

error of 1800 kg, which accounts for 5.5% of true

average vehicle’s weight with vehicle in idling

condition; and 1932 kg, which accounts for 5.7% with

vehicle under constant speed range. We also compare

the model performance with other speed profile. We

extend the validation of our improvised method on

Indian road segment considering variability of data

with combinations of vehicle factors such as vehicle

class type, manufacture make, load, and goods. The

results obtained confirm the validity of applying the

improved method for determining the weight of

vehicle across vehicle class types and for all speed

ranges.

As a pilot project we likewise mount sensors on

three different locations in each vehicle to evaluate

the performance of our proposed solution and assess

which sensor location mount is ideal for our solution

considering as a system.

The rest of the paper is organized as follows.

Section 2 discusses related work on vehicle’s weight

estimation. Section 3 presents our proposed method.

In Sections 4 and 5 we present our experimental setup

and evaluation of the experimental results. In Section

6, we discuss the strengths and weaknesses of our

method. We conclude in Section 7 with a summary

and details on future work.

2 RELATED WORK

We identify that Weigh-in-Motion (WIM)

technology (Magdalena et al., 2020) is the closest

competitor to the technology under development,

some of which have accuracy more than 95%.

However, the technology is based on static sensors

over which the vehicle moves at low speed (LTBP,

2016). This solution however is limited to toll way

deployment. (Kadlecek et al., 2005) describes a

weight estimation method that measures the energy

output from engine of a given vehicle and measures

the acceleration derived of it. Here, we take the

inspiration of including energy as one of the

explanatory features to our model. (Jyotishman

Ghosh et al., 2017 and Nan Lin et al., 2019) describes

a real-time vehicle mass estimation from CAN data

and drivetrain torque observation. This technique

considers different driving forces acting on

longitudinal motion, where majority of contribution

is due to traction and braking forces. (Viengnam

Douangphachanh et al, 2014), describes collecting

sensing data from android smartphone. They find a

relationship of acceleration data with road roughness

condition and its significance partially dependent on

speed. This work is being investigated in frequency

domain to analyse the behaviour of road roughness on

an average speed. (Joshua E. Siegel et al., 2015),

explore a novel application of fault detection in

An Empirical Study on Low-cost, Portable Vehicle’s Weight Estimation Solution using Smartphone’s Acceleration Data for Developing

Countries

wheels tires and related suspension components in

vehicles. The smartphone is mounted vertical on

dashboard of a vehicle and validation is performed on

at least two different vehicle model. The approach

mentioned in this research, is referred for further

analysis in our proposed development. (Phong X.

Nguyen et al., 2018), uses smartphone-based sensor

to estimate vehicle overloading, and claims to achieve

an average 5.89% error on true vehicle's weight,

however the model requires the vehicle to run at

certain speed range only. For other speed ranges, the

model error is high, and smartphone is placed on

truck chassis within an encapsulated box. These two

factors inspire us to derive at a more robust solution

experimenting on different vehicle model and roads

of Indian highways.

The benefit with the proposed method is, we will

be able to assess at what speed zone the model

performance is high; and which placement of mobile

sensor on vehicle gives least accuracy error against its

actual weight. For vehicle overload detection as a

system, one can further make use of the classification

method, provided, payload weights are known prior;

and included as an exploratory variable to model as

described by the author, which is currently not in

scope of this work.

3 IMPROVISED VEHICLE’S

WEIGHT ESTIMATION

METHOD

Figure 1: Vehicle Overloading Control System (Phong,

2018).

Described in Figure 1, is the proposed ubiquitous

system for detecting suspicious overloaded vehicle

running on highway and send notification to

concerned authorities in real-time. A smartphone

mounted on the vehicle is enabled for capturing the

vehicle registration, driver information and transmit

along with data collected by sensors via cellular

network to the centralized server. The capture of data

is enabled batch-wise, which is received at analytics

data server where our proposed model is deployed for

prediction of weight as part of backend process. The

estimated weight is compared with legal permissible

weight to determine if vehicle is overloaded in the

system. If vehicle is determined to be overloaded, the

vehicle and driver information are notified to the road

authorities, traffic police, and logistic company.

Based on the event information shared to the

authorities a penalty will also be processed and sent

to the driver of vehicle. All these processes are

automated, and remotely monitored with high

efficiency.

Table 1: Comparison of data specifications from two

different sources.

To improve the accuracy of Vehicle’s Weight

Estimation (VWE), our methodology is based on the

significant correlation of vehicle’s payload on

vertical acceleration got from smartphone’s

acceleration sensor data. Heavier the payload lesser

dispersed are the vertical acceleration and vice versa

for vehicle with no payload or partial payload. The

payload material also significantly plays a role which

can be considered as a future work considering the

amount of data availability. Along with the statistical

features being considered from the baseline method,

we propose more features considering the higher

order analysis and frequency analysis of vertical

acceleration data. We continue to improve the

multiple linear regression model to improve accuracy

of VWE solution. The model is evaluated on 75-25 %

ratio of dataset. In our observation, the vehicle at

static i.e., idling condition and vehicle with constant

speed profile shows stable response of vehicle’s

vertical acceleration. Due to minimum impact from

road surface condition; and engine response on

acceleration/deceleration event.

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

Figure 2: Depiction of random pair of velocity profile segments ‘Flat’, ‘Accelerating’, and ‘Decelerating’ for ‘Empty’ and

‘Loaded’ vehicle.

Therefore, it is essential to consider the influence

of such statistical factors including the vehicular

velocity while considering a generic estimation

model to predict weight.

In this section, a comparison of data specification

got from two different sources; observations of data

analysis with vehicle under ‘Empty’ and ‘Loaded’

conditions; identification of vehicular speed

category; and construction of estimation model are

described below. Here, we do not consider the

different load category within the class of vehicle

model, since it is already proved in baseline model

(Phong X. Nguyen et al., 2018). The supervised

classification model result for identifying different

vehicle load category, can be included as an encoded

exploratory variable to improve estimation accuracy

of multiple linear regression model.

As indicated in Table 1 the accelerometer data

received from vehicle source ‘A’ is compared with

accelerometer data got from vehicle source ‘B’. It is

observed, the sensor placement, mounting, insulation

and vehicle trip parameters as mentioned are

different; and thus, sets the challenge to existing

baseline model to predict accuracy with high

precision. Here, we compare the results and propose

features which can be utilized for prediction of

vehicle weight at different speed categories and

smartphones positioned at different locations.

3.1 Velocity Profile Analysis

We observe in total the trip information has had

varied range of speed pattern, which may be due to

(a) terrain, (b) traffic conditions, and (c) road

conditions. To evaluate the obtained data for

feasibility study, we investigate the response of

baseline VWE model with different patterns of

velocity profiles. The different velocity profile is as

presented in Figure 2.

From the velocity profile, we consider sub-portion(s)

of raw data as indicated in different colours in Fig. 2

comprising off and classified to categories such as:

i. Flat response (same range of speed)

ii. Accelerating response (velocity ramp up)

iii. Decelerating response (velocity ramp down)

3.2 Feature Extraction

The single trip vertical acceleration sensor data is

investigated both in time and frequency domain. In

time domain, we explore the relevance of statistical

features derived from ‘Empty’ and ‘Loaded’ dataset

to estimate vehicle weight. The details of time-

domain analysis are as mentioned in 3.2.1. In

frequency domain, we analyse the spectral

information of measured raw acceleration data,

details are as mentioned in 3.2.2.

3.2.1 Time Domain Analysis

Considering z-axis of tri-axial accelerometer i.e.,

linear accelerometer; the vertical acceleration

captures effect of longitudinal movement of a body

i.e., vibration from road with effect of mass. From

each dataset ‘Empty’ and ‘Loaded’, a defined length

of window sample (here we consider 5 second), non-

overlap in nature; from which ‘N’ number of

segmented outputs are generated. These segmented

raw data are further used for feature extraction.

To analyse in detail, we consider the vertical

acceleration (z-axis) data, with an average response

of 10 segmented data for a defined window size (5

seconds) is as shown in Figure 2.

The features derived are further described below.

i. Average of Upper and Lower Acceleration.

The upper and lower acceleration threshold value is

set +/- 1 of median value of vertical acceleration for

each window sample computed. The mean of data

points satisfying the condition is calculated.

Acceleration value along z-axis of source ’B’ data is

different from z-axis acceleration value of source ’A’

An Empirical Study on Low-cost, Portable Vehicle’s Weight Estimation Solution using Smartphone’s Acceleration Data for Developing

Countries

dataset; this is due to change in sensor placement,

orientation and vehicle model, refer Table 1. In case

of such variations, a possible method to rectify can be

by applying Euler transformation. To negate the

effect of different orientation with placement of

sensor, one possible solution is to consider the

transformation in android application interface. In

this experiment, we have considered the

transformation matrix to be included for sensor axis

correction in our second phase of evaluation where

we consider different sensor position.

Figure 3: Average response of z-axis vertical acceleration

profile for ‘Empty’ and ‘Loaded’ dataset.

ii. Proportion of Upper and Lower Acceleration.

Based on the upper and lower acceleration threshold

value set, we consider the ratio of data points in upper

& lower proportion range to total number of samples

in the window (refer Figure 3, red highlighted

portion).

iii. Standard Deviation.

Is measured to quantify the amount of variation in

signal. Consider the random time-series signal 



Number of variables available in the data distribution

and denoted by .

The standard deviation is calculated as shown in

Equation 1.











 









(1)

iv. Mean Absolute Difference.

Is measured to compute the average absolute

difference of discrete values within a window sample.

From Figure 3, it is observed the number of data

points considered in Proportion of upper and lower

acceleration (within highlighted red box), is lower in

‘Loaded’ set when compared to ‘Empty’ set. This

constitutes the relationship between mass and

acceleration. The magnitude information got from

continuous time-series data alone is in-sufficient for

a model to predict the vehicle’s weight.

Henceforth, in this feasibility study, in addition to

already considered baseline features, we explore the

influence of statistical features derived from higher

order moment of probability distribution of each

dataset.

The primary hypothesis of this research is that the

accelerometer data parameters both unique and

derived has significant impact on the weight

estimation of a vehicle. To validate the hypothesis,

we consider the probability distribution of sample of

data from each ‘Empty’ and ‘Loaded’ cases,

respectively. The statistical hypothesis considered to

evaluate the relation of histogram to weight is;

















The formulation of test statistic is to compute the

measure of significance of feature set and weight. We

use p-value to weigh the strength of evidence against

the null hypothesis. One-sample T-test in R is

considered for the features mentioned here after,

whose p-value <2.2e-16, indicates strong evidence

against the null hypothesis.

Probability distribution function (PDF) is a

statistical function that describes all the possible

values and likelihoods that a random variable can take

within a given range. Here, we normalize the relative

probabilities by a number (number of elements in

the input data) as shown in Equation 2:













(2)

where, 



is the bin value





is the number of elements in the bin

From PDF of 2 consecutive segmented data of

‘Empty’ and ‘Loaded’ datasets (refer Figure 4), the

corresponding ‘Median and ‘Standard Deviation’ are

plotted. The green dotted line plotted in Figure 4,

marks the median for (a) Empty (8.25 g) and (b)

Loaded (8.65 g). The red dotted line indicates the

Standard deviation (Sd) computed for (a) Empty

(1.31) and (b) Loaded (0.52).

The Standard deviation (Sd) has reduced for

‘Loaded’ case, but not sufficient to validate





Henceforth, we consider alternative indicators of

distribution, i.e., third and fourth order moment of

distribution Skewness and Kurtosis.

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

Figure 4: An example of Probability density with quartiles,

Sd, Median, and Mean for (a) ‘Empty’ and (b) ‘Loaded’

case.

v. Skewness and Kurtosis.

Skewness, measures the degree of distortion from the

symmetrical bell curve or normal distribution. It is

measured as the third moment of probability

distribution as indicated in Equation 3.









 









 



 



(3)

In general terms, a normal distribution will have a

skew of zero under a bell curve; in our

experimentation for Flat Response of velocity profile,

on an average shows positive skewness; ‘Empty’ (on

an average = 0.13) case being more skewed than

‘Loaded’ (on an average = 0.037). This response is

subject to vary case to case.

Kurtosis is measured as the fourth moment of

probability distribution as indicated in Equation 4.

They measure extreme values in either tail, here

in Figure 4(b), it is observed persistence of tail

behaviour highlighted in red as an example; and

narrow Peakiness of distribution for ‘Loaded’ (-0.02),

when compared to ‘Empty’ (0.02) as seen in Fig. 4(a).









 









 



 



(4)

vi. Quantiles.

Is statistical measure with cut points dividing the

range of a probability distribution into continuous

intervals with equal probabilities. In our experiment,

it was observed the Standard deviation (σ) for normal

distribution is not sufficient; hence Quantiles (2σ, 3σ)

i.e., Q1, Q3 are considered.

vii. Energy.

From Figure 5, it is observed there is high

significance of vehicle weight with amount of energy

dissipated from vehicle. Heavier the load, higher the

range of energy. Hence, in consideration to

improvement of baseline model we include ‘energy’

variable in final regression equation.

Figure 5: Relationship of Energy with weight of vehicle.

Thus, in this experiment, with measure of

Kurtosis, Skewness, Quantiles, and Energy derived

from sample windows; we can validate the rejection

of 



3.2.2 Frequency Domain Analysis

In this research, the frequency response of

acceleration measured under two conditions with

vehicle Engine in ON state and with payload (kg) are

(a) vehicle idling and (b) vehicle in motion. For data

measurement, the assumption is as the vehicle’s

engine is ignited (ON), the sensor recording begins,

by which the accelerometer measurement starts

recording the vibration due to throttling of engine and

chassis; and with payload added to empty vehicle,

there is expected longitudinal force acting against the

mass lowering the vibration amplitude when

compared to information gathered from empty

vehicle alone. Further to this, our proposition is,

overload of vehicle occurs with payload added when

vehicle is brought to a halt and idling (stationary with

engine ON). Henceforth, we analyse for both vehicle

in idling state along with vehicle in motion. We

consider the Welch’s power spectral density (PSD)

method, also called the periodogram method for

estimating power of a signal at different frequencies.

The PSD is computed for (a) Vehicle at Rest/Idle, and

(b) Vehicle in motion with signals sampling

frequency at 50 Hz. From power spectrum in Figure

6, it is observed for (b) Vehicle in motion, there

appears presence of prominent signal strengths as

highlighted in red arrow in frequency range [5 to 20]

Hz infused due to certain external factors unknown to

trial. Whereas, in (a) Vehicle at Rest/Idle, the signal

has a smoother roll-off when compared to PSD in (b);

which indicates the signal is free from influence of

other external factors except that of vehicle’s engine;

and suitable for extracting information from vehicle’s

linear accelerometer sensor data and its relationship

with mass.

An Empirical Study on Low-cost, Portable Vehicle’s Weight Estimation Solution using Smartphone’s Acceleration Data for Developing

Countries

Figure 6: Power spectral density response of (a) Vehicle at

Rest/Idle and (b) Vehicle in motion with sampling

frequency of 50 Hz.

viii. FFT Function.

In addition, since the vibration component in this test

experiment is unknown, we limit ourselves to

compute amplitude and phase response of the signal

only from each sample window; and add to the feature

set list.

The additional indicator features of accelerometer

data derived from time-frequency analysis as detailed

in 3.2 are listed in Table 2. This table contains

features considered apart from time domain features

considered in baseline solution.

3.3 Estimation of Vehicle’s Weight

During the evaluation process, stepwise bi-directional

Akaike information criterion (AIC) regression

algorithm is used to derive the best features from the

accumulated feature set list (Total of 20 in number)

as mentioned in 3.2. The final model may contain

smaller number of features to reduce the complexity

but maintain same level of accuracy or better. For

evaluation purpose, 75% of data sample created are

used for Training and remaining 25% for Testing.

AIC regression algorithm was implemented over a

batch of data samples considering the new set of

feature list; a set of best features were provided as

output and referred as final model.

Table 2: Summary list of additional time-frequency

statistical indicators considered.

Results of evaluation on improvised method and its

comparison with baseline model is presented in

section 4 for further discussion.

4 FEASIBILITY STUDY AND

PRELIMINARY RESULTS

Based on the input shared as ground truth, we identify

the trip begin and end time-stamp in seconds for

‘Empty’ and ‘Loaded’ vehicle trip respectively; here

a continuous 2 hour sensor inputs of vehicle running

with and without load condition are extracted from

raw data for experiment evaluation. The sensor inputs

extracted are GPS, 3-axis accelerometer, and

gyroscope data information. The data is processed at

a sampling rate of 50 Hz. As ground truth, for

example, Empty refers to vehicle (truck) curb weight

of 16000 kg; and Loaded refers to vehicle (truck)

gross weight of 33900 kg after loaded with goods

weighing 17900 kg. The reference to time of vehicle

running with and without load are also captured. The

afore mentioned analysis is conducted offline. We are

currently in final phase on development of system

that can automatically capture the above-mentioned

sensor information in real-time.

4.1 Baseline Evaluation

We evaluate the vertical acceleration with baseline

model considering the null hypothesis that velocity

has no relationship with weight of vehicle. In Table

3, in 2nd and 3rd column we project results of VWE’s

mean absolute percentage error (MAPE) for baseline

solution with and without velocity condition

respectively; in 4th column VWE response with

improved model without any velocity conditions, and

in 5th column i.e., last column we represent the

results obtained of improved VWE model

considering a flat response pattern of vehicle velocity.

Note: In this vehicle data, the highest speed range

observed is 15.0 to 20.0 m/s.

Table 3: Baseline vs Proposed model with and without

velocity condition.

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

Table 4: Error rate (MAPE) of different velocity profile vs

engine idling state.

4.2 Evaluation on Velocity Profiles

Considering the improved model to efficiently predict

vehicle’s weight we continue to evaluate data for

possible different scenarios.

(a)

(b)

Figure 7: Error rate measure of flat response trials vs Engine

idle state (a) with ‘mean.vel’; (b) without ‘mean.vel’

variable.

As described in Figure 2, section 3.1, different

velocity profile of vertical acceleration is considered,

and their respective MAPE (error rate) are tabulated

in Table 4. We also compare results against the

Engine Idle state as explained in frequency domain

analysis in 3.2.2. For this experiment, a 5 sec window

size for each of the responses are considered; the

prediction performance is gathered individually for

‘Empty’ and ‘Loaded’ case and an overall error rate

of model against each of the response is tabulated in

last row in Table 4.

Additionally, we evaluate the VWE model and its

dependency with velocity for various random pair of

flat response segments as shown in bar graph in

FigureFigure 7. The error rate results are projected

where (a) refers to VWE response considering its

dependency with velocity within the segment, and (b)

without considering velocity variable in improvised

model.

5 RESULTS OF ACTUAL DATA

After the feasibility study of improved VWE method,

we evaluate this model on actual data collected from

Indian road segment. Here, the smartphones are

mounted at three different locations on to the chassis

of each vehicle as shown in Figure 8. The smart-

phones in Placement 2 &3 are put in a case to restrict

any free movement of smartphone within the case

during the journey as shown in Figure 9. The

smartphone in Placement 1 is placed over the

dashboard of the vehicle and tapped to avoid any

lateral shifts or any accidental fall-off. We do not

make use of mobile holders here, as it may lead to

additional vertical vibration infused to the

acceleration data.

The process flow considered for VWE model is as

shown in Figure 10. Each of the steps are explained

in detail in below sub-section.

Figure 8: Illustration on placement positions of mobile

sensor on vehicle planned for data collection.

Figure 9: Examples of smartphone placements on vehicle.

5.1 Data Collection

The data collection method is carried out based on the

below mentioned conditions and were collected from

Indian roads.

▪ To confer two or more reference vehicles from

target vehicle group with repeated runs (trips).

▪ The reference vehicles will be driven on its

regular routes, with different load condition, thus

An Empirical Study on Low-cost, Portable Vehicle’s Weight Estimation Solution using Smartphone’s Acceleration Data for Developing

Countries

study is conducted under dynamic vehicle

environment unlike in a controlled setup.

▪ Details of each vehicle regarding their

manufacture make, age, suspension types,

number of axles, goods being carried, route(s)

considered, running start and end time, and goods

loading factor are gathered in a checklist and

referred as ground truth information per trip.

▪ The actual gross weight (GVW) of vehicle are

also recorded for each vehicle trip as ground truth

to evaluate the model.

Figure 10: Process flow of VWE project.

5.2 Smartphone Orientation

Calculation

Knowledge of smartphone’s placement on vehicle at

three different position is important to assess the

reliability of collected data. On vehicle’s placement-

1 position, a mobile phone is installed on dashboard

secured with double tape. In placement-2 and

placement-3 the smartphones are packed inside an

insulated case and secured at the bottom of the vehicle

chassis at around centre of gravity (CoG) and near

rear axle suspension. The two smartphones are

restricted of free movement inside the box. However,

it is observed, with different vehicle make, the

provision for tying the insulated case with

smartphone would need to be different (as shown in

Figure 11); which in turn results in rotation of sensor

axis with reference to vehicle’s reference frame (x-

axis, y-axis, and z-axis).

Figure 11: Smartphone installation under vehicle chassis

and their different orientations.

In order to acknowledge the relation between

smartphone-to-vehicle orientation as referenced by

(Johan et al., 2019), in their study on “Smartphone

placement within vehicle” and the smartphone’s

placement in the vehicle, we compute a 3-axis Euler

rotation in Equation 5, as a pre-liminary step to

correction.



































(5)

where, 









refers to counter clockwise rotation

around x-axis;











refers to counter clockwise rotation around y-

axis and





 refers to counter clockwise rotation around z-

axis.

5.3 Data Calibration or Conditions

It is observed the model performance alters for

varying input sets such as (i) speed zone (very low,

low, medium, high to very high); and (ii) sample

window size (1 sec to 5 sec of epoch size).

For automating the process of computation, we

consider the following sequence combination of

parameters of input vertical acceleration data i.e.,

sensor location, load condition (vehicle is loaded or

empty), window size and speed zone.

Initially, we considered the model performance

evaluation, with varying non-overlapping sampling

window ranging from 1sec to 5sec, at a sampling rate

of 50 Hz. However, it was observed, as the sampling

window increases the performance of model drops

due to lack of continuous data; hence, for analysis

purpose we resort to minimum 1sec sampling window

with an assumption that each segment holds

continuous datapoints.

Additionally, we consider the average speed of

each segmented dataset as an independent feature to

regression model considered.

5.4 Feature Extraction

To evaluate the model, we make use of all the time

and frequency domain features mentioned in section

3.2.

5.5 Model Evaluation

We confer to two or more reference vehicles

availability for this experiment. From 21 vehicle trips

data available, four of the trip information had to be

discarded due to loss of data. For model evaluation,

from 17 trips, we identify three vehicle class dataset

and their use cases defined are as indicated in Table

5. The results of considered cases 1, 2 & 3 as

mentioned are evaluated considering the statistical

features derived from vertical acceleration data,

including the average of speed considered per

segment. Evaluation is represented in three different

conditions of data sample.

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

Table 5: Test cases with varying vehicle and load

conditions.

Test

Cases

Vehicle

Criterion

Load

Capacity

Load

Condition

Goods

Route

Case1

Single

Different

Case2

Different

Single

Different

Case3

Different

Single

Different

5.5.1 Vehicle Running State

We fit a multiple linear regression model which

minimize sum of the squared residuals using

accelerometer features. In this section, each of the

smartphone placement results are presented for

running condition of vehicle. We compare estimated

results with actual weight as ground truth to evaluate

the model. For error determination, we consider

MAPE as before. This defines on an average what is

the error of the model trained.

Case 1: Light Duty Vehicle – Evaluation of single

vehicle (12T capacity), with varying load condition

(<50%, 75%, 100%), carrying different goods

material. Consider all four loaded datasets; a k-fold

cross-validation based evaluation method is carried

out as the given number of data sample to split is

limited with varying load condition.

Case 2: Heavy Duty Vehicle – Evaluation of

different vehicles, carrying different goods and load

condition.

For training we consider trips all loaded with same

material, for example, cement on vehicle having

tonnage capacity of 49T, 43T and 35T respectively.

For testing, we consider unseen trips loaded with

capacity of 49T, 31T, 36T and 43T carrying

similar/same material.

Case 3: Over Dimensional Cargo – Evaluation of

Trailer type truck. Dataset comprises of different

vehicle, same manufacture makes, with full load

condition (100%), carrying same goods material, but

comprises of only 3 instances.

Considering the above cases, on an average the

accuracy of different vehicle classes considered are as

listed in Table 6.

5.5.2 Vehicle Idling State

Additionally, we also analyse the data for idling

condition of vehicle. For comparison, we consider the

vehicle running and idling state of Case1: Light Duty

vehicle, since a single vehicle is being used for

multiple trips. The accuracy measure of vehicle in

idling state is as indicated in Table 7; however, the

number of datapoints available are less.

Table 6: Summary of VWE for different use cases.

We then consider evaluating VWE with different

speed condition of vehicle. The speed calculated from

smartphone’s GPS feeds are divided into 5 different

speed zones such as, speed zone1 range 0 to 3 m/s (0

– 10 km/h); speed zone2 range 3 to 6 m/s (10 – 20

km/h); speed zone3 range 7 to 10 m/s (20 – 40 km/h);

speed zone4 range 11 to 14 m/s (40 – 55 km/h); and

speed zone5 range 15 to 20 m/s (55 – 80 km/h).

Table 7: Summary of VWE for Case1 with Vehicle

Running and Idling State.

Since, speed is considered as a feature, and to

assess robustness of model, we further examine if

model’s accuracy is consistent across different

driving speed zones identified. Here, we consider all

loaded data Placement1-Dashboard position of Light

Duty vehicle only, to verify the variability or

relationship of VWE with vehicle speed information

as referenced in Figure 12.

Figure 12: Graph of speed zone vs model error for all loaded

conditions of Light Duty vehicle case.

An Empirical Study on Low-cost, Portable Vehicle’s Weight Estimation Solution using Smartphone’s Acceleration Data for Developing

Countries

6 DISCUSSION

VWE technology based on acceleration and GPS data

collected by smartphone in logistics vehicle is

validated using multiple linear regression model. We

identified additional statistical features derived from

vertical acceleration whose response shows

significant importance pertaining to vehicle vibration

information.

• Considering the improvised multiple linear

regression model, we evaluate real data with

different velocity profile. From table 4, it

indicates that (i) ‘Flat response’ shows better

performance i.e., reduced error rate when

compared to (ii) Accelerating response and (iii)

Decelerating response.

• Flat response shows reduced error rate of 6%.

• To test further, we conducted few more trials by

selecting random pairs of flat response segment.

It is observed, vehicle speed responses with flatter

(constant) velocity profile shows reduced error

rate in prediction of VWE, when compared to

trials which shows some amount of variation in

vehicle speed. These variations can be attributed

due to road surface condition and/or traffic

conditions.

• In this experiment, we confirm that in moving

vehicle scenarios the “velocity of vehicle” has

high influence in accurate prediction of vehicle’s

weight.

• Case 1: Light Duty Vehicle placement1-

Dashborad, sensor location shows on an average

accuracy error of 6.87% for vehicle data

comprising of 12T and 10T.

• Since, single vehicle was used for to-and-from

trips, we validate with assumption that vehicle is

being driven by a single driver; hence, assuming

driving pattern influence on vibration data to be

unchanged. Stats from Figure 12, shows

consistent model performance across varying

speed zone, whereas, in (Phong et.al., 2018)

research work it is observed model performs best

under speed zone of 20 – 22 km/h.

• We also validate results with vehicle in idling

state, under the assumption, external factors such

as vehicle dynamics, road condition, and other

environmental parameters will not affect vibration

data captured from sensors. We observe the idling

values of data got from Light Duty vehicle class,

shows improved accuracy by at least 5 % from its

running state, i.e., with overall highest accuracy

of 98% when compared to state of vehicle in

motion as referenced from Table 7.

• Case 2: Heavy Duty Vehicle from Table 6,

considering model accuracy error on an average

across all trips, shows that placement1 –

Dashboard has an MAPE value of 5% lower error

rate when compared to the other two sensor

location.

• When vehicle trip carrying different goods

material was tested against Heavy Duty vehicle

class model, the performance drops. This

indicates the carrying goods material also has an

inference on feature engineering during model

learning process. However, it requires to be

confirmed with exploratory data approach.

• Case 3: Over dimensional cargo the configuration

of vehicle regarding its design, axle distribution,

number of wheels are different and require further

studies.

7 CONCLUSIONS

A machine learning model considering statistical

parameters of vertical acceleration applied for

evaluation of overloaded vehicle using Indian vehicle

dataset is introduced in this paper. The developed

VWE model shows improved and consistent accuracy

considering vehicle engine idle state and flat response

of velocity for both ‘Empty’ and ‘Loaded’ dataset

scenario. It is observed from our studies moving

vehicle’s acceleration response is highly influenced

by velocity measure of vehicle. For validation on

installation location, three sensor placements on the

vehicle are considered to determine the feasible

sensor position for system integration. For which, the

developed model was evaluated considering different

vehicle class type based on their tonnage. It is

observed, on an average, model performance gives

low MAPE error on dashboard, for vehicle carrying

same goods to full capacity. The model accuracy is

observed to be consistent at all speed range of vehicle

motion, which makes our technology reliable for real-

time assessment of vehicle’s weight.

As a continued research we are investigating deep

learning-based regressor model on time series data

considering different rate of load filling on vehicle.

Other candidates for research improvements are:

• To study the impact of different axle

configuration and vehicle’s suspension type

on accelerometer data.

• The impact of road grade condition, driving

behaviour pattern, which in-turn influences

vibration on vehicle.

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

As a system, we propose integration of VWE

technology to on-board unit alike AIS-140 vehicle

system which abides to the law enforcement

regulation mandated by the government.

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An Empirical Study on Low-cost, Portable Vehicle’s Weight Estimation Solution using Smartphone’s Acceleration Data for Developing

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