An Experiment of A Streetlamp Classifying
and A Vertical Illuminance Assessing
Utilizing Smartphones’ Light Sensors
Yuki Matsuda

Ismail Arai

Advanced Course of Mechanical andElectronic System Engineering,
Akashi National College of Technology

Department of Electrical and Computer Engineering,
Akashi National College of Technology

Abstract—There is a growing demand for public managing
security information such as the brightness of night sidewalks inthe field of pedestrian navigation systems. Such security information is collected and visualized by some Japanese municipalities,
but the coverage of the survey is not sufficient due to measurement equipment costs and labor costs. Therefore, in this study,
we address these cost problems by collecting the illuminanceof streetlamps utilizing the light sensors of smartphones andapplying collective intelligence. However, there are differencesof streetlamps’ illuminance and light transition because manyvarious streetlamps (e.g. fluorescent lights and LED lights) areinstalled. In this paper, we propose and evaluate the streetlamp
classifying method based on the illuminance characteristics inthe vertical direction of each streetlamp and light sensors forassessing safety level of night sidewalk. As a result of theevaluation, in case of setting allowable error to 2 lx, the precision
ratio was 79 %, and the recall ratio was 48 %. Because therecall ratio of LED lights or inverter-type fluorescent lights (Theseilluminance transitions tend to become wide dispersion) became
low, the precision ratio of them became low. But, the precision
ratio of starter-type fluorescent lights became higher than otherkind of streetlamps.

I.

I NTRODUCTION

There is a growing demand for applications utilizing location information with a diffusion of smartphones installed various sensors, such as GPS, accelerometer, light sensor, and soon. In the background, public managing security informationsuch as the brightness of night sidewalks becomes importantfor pedestrian navigation systems.
An example of such security information is a safety level ofstreetlamps installed in the road. The conventional assessmentof a safety level follows the illuminance standard [1] defined by
SSAJ (Japan Security Systems Association); the coverage ofthe survey is not sufficient due to the measurement equipment
costs and labor costs. Therefore, in this study, we address thesecost problems by collecting the illuminance of streetlamps withsmartphone’s light sensors and applying collective intelligence.
For assessing a safety level, we follow the horizontal andvertical illuminance standard. However, there are characteristic
differences of streetlamps’ illuminance and light transition
among various streetlamps such as fluorescent lights and LED

lights. And, these differences affect assessment safety level.
In this paper, we propose and evaluate a modeling method ofilluminance transition in the vertical direction and a classifyingmethod of streetlamps based on the characteristics of eachstreetlamp and each sensor.
As a result of the field study, in case of setting allowableerror to 2 lx, the precision ratio was 79 %, and the recall ratio
was 48 %. Because the recall ratio of LED lights or invertertype fluorescent lights (These illuminance transitions tend tobecome wide dispersion) became low, the precision ratio ofthem became low. But, the precision ratio of starter-type
fluorescent lights became higher than other kind of streetlamps.
II.

R ELATED W ORK

In this study, we focus on brightness of night sidewalk asindicator for implementing pedestrian navigation system whichtakes account of safety information. Then, we consider theassessment method of safety level of night sidewalks based on
the illuminance standard [1].
Such security information is collected and visualized byYuma K. at el in cooperation with municipalities and localorganizations [2]. It describes safety assessment of night sidewalk based on streetlamps’ horizontal illuminance measured
with illuminometer and tabulated manually. But, in this study,
the coverage of the survey was not sufficient due to themeasurement equipment costs and the labor costs. Furthermore, streetlamps’ owners are separated into municipalities,
prefectures, block clubs, house owners and so on. In manycase, not public but private owners are not managing streetlamps sufficiently. Because of needs to take account of aged
deterioration of them and above-mentioned managing problem,
we should survey streetlamps’ illuminance periodically.
This paper describes a trial for addressing above-mentioned
problems by participatory sensing and collective intelligenceusing smartphone’s light sensors. Incidentally, a participatory
sensing and a collective intelligence using mobile gadget was
proposed by J.Burke et al [3]. In order to infer streetlamps’
illuminance and assessing safety level of streetlamps, we need
to apply the illuminance standard to both an average valueof horizontal illuminance and a minimum value of vertical

TABLE I.

T HE I LLUMINATION S TANDARDS FOR S ECURITY L IGHTSClassAverage Value ofHorizontal Illuminance
Minimum Value ofVertical Illuminance
Illumination EffectABover 5 lx

over 3 lx

over 1 lx

over 0.5 lx

Recognizable the Outlineof the Face

Recognizable the Postureand the Behavior

StreetlampsBermRoad

Fig. 2.

Measurement
IntervalA Sample of Illuminance Transition

100Scatter diagrams of illuminometer

Measurement Point of{Horizontal IlluminanceVertical Illuminance

Illuminance (normalized) [%]Scatter diagrams of light sensor

Center Lineof Road

806040200-10010203040

Distance from under the streetlamp [m]Fig. 1.

A Measurement Method of Horizontal and Vertical IlluminanceFig. 3.

illuminance according to the illuminance measurement method[4] defined by JIS (Japanese Industrial Standards). we hadalready done the experiments for the horizontal illuminance[5]. So, in this paper, we approach the issue of the vertical
illuminance.
However, there are characteristic differences of streetlamps’ illuminance and light transition among various streetlamps such as fluorescent lights and LED lights. These differences affect minimum value of vertical illuminance. Minimum value of vertical illuminance is fundamental parameterof assessment safety level. Consequently these differenceswill affect assessment. So, we consider streetlamp classifying
method using the collective intelligence of measured data withsmartphone’s light sensors. In this paper, we try to classify thethree type streetlamps (starter-type fluorescent lights, invertertype fluorescent lights, LED lights) by applying the streetlamp
classifying method, because these kinds of streetlamps are themajority in Japan.
III.

P ROPOSED M ETHOD BASED ON T HE P RELIMINARYS URVAY

For assessing safety level of streetlamps, we need tomeasure an average value of horizontal illuminance, anda minimum value of vertical illuminance according to theilluminance measurement method defined by JIS (JapaneseIndustrial Standards). Then, we compare them according to
the illuminance standard defined by JSSA (Table I). Fig. 1
shows an outline of the illuminance measurement method.
The horizontal illuminance is measured by a horizontally held
illuminometer against to the road surface, and the vertical
illuminance is measured by a vertically held illuminometer

Results of Vertical Illuminance Transition Research (LED Lamps)

against a road direction. Then, these illuminance transitions areclassified by the kinds of streetlamps. Therefore, the quality of
the classifying streetlamps by using smarphones’ light sensors
is important for assessing safety level of night sidewalk. In
this section, we report a result of a fundamental survey aboutcharacteristics of vertical illuminance transitions and smartphone’s light sensors. According to these characteristics, we
propose a modeling method of vertical illuminance transitions
and classifying method of streetlamps.
A. Survey of Characteristics of Streetlights and Light SensorsIlluminance is the total amount of luminous flux incident
per unit area, so it has an cosine-like attenuation characteristic
depending on the incident angle, and has characteristic of
inversely proportional to the square of the distance from thelight source. According to these characteristics, illuminance issusceptible to the distance and angle from the light source.
Fig. 2 shows an illuminance transition while walking andholding smartphone at θs degrees. According to the characteristics described above, the illuminance transition is formed
corresponding to each kind of streetlamps. In this study, wesurveyed vertical illuminance transitions of three kinds ofstreetlamps (starter-type fluorescent lights, inverter-type fluorescent lights, and LED lights). We used the illuminometer(LX-1108) and the smartphone (Galaxy Nexus) to measurevertical illuminances. We set the angle of the illuminometer’sacceptance unit to 0 degree (the vertical angle against a road direction), and set angle of the smartphone (θs ) to 30 degrees (theangle to hold the smartphone). Then we measured illuminanceof streetlamps. Fig. 3 shows the scatter diagram of vertical

100806040200Fig. 4.

Illuminance Transition Normalized Models (All Lamps)

illuminance value of LED lights normalized by [0,1] interval.
Black circles mean a value measured by the illuminometer;
green squares mean a value measured by the smartphone’s
light sensor. Also, we surveyed vertical illuminance of otherkinds of streetlamps by the same way.
We assume that forms of scatter diagrams depends on kindsof streetlamps. So, we tried to model illuminance transitionsof all kinds of streetlamps by using the LOWESS smoothing
method which is one of the scatter plot smoothing methods. Incidentally, LOWESS smoothing method is a topically weighted
scatterplot smoothing method, utilizing one order polynomial
and linear least squares approximation.
Fig. 4 shows the results of the modeling of each scatter diagrams of streetlamps’ illuminance transition. Each line shows
the modeling results from an illuminometer and a smartphone’s
light sensors respectively. Because starter-type and invertertype fluorescent lights have same characteristic in transition
direction, these illuminance transitions become similar shape.
And, illuminance of LED lights increases characteristically.
The maximum illuminance are difference between startertype and inverter-type fluorescent lights despite their similar
shape of illuminance transition. Fig. 5 shows the vertical
illuminance transition model for each kinds of streetlamps(hereinafter referred to as the illuminance transition model).
This transition model is calculated by multiplying the verticalaxis (illuminance) of Fig. 4 and the the average value of eachmeasured maximum illuminance. We confirm that the vertical
illuminance of each kinds of streetlamps have large difference.
So, kinds of streetlamps will affect the safety assessment.
B. Proposal of the Streetlamp Classifying Method
The illuminance transition models as shown in the lastsection have differences of waveform, illuminance transitions,max values of illuminance. So, we considered it is possible toclassify streetlamps utilizing waveform comparison of illuminance transition model and illuminance value measured withsmartphones’ light sensors. The streetlamp classifying method
is described as follows. At community roads, it is conceivablethat an illuminance data measured with smartphones’ lightsensors indicate zero lx, excepting a place where is close tounder streetlamps. In other words, a measured illuminance data

Fig. 5.

Illuminance Transition Models (All Lamps)

forms like a pulse near under streetlamps. So, we can divide illuminance data of every streetlamps. Also, the illuminance datais temporal data, whereas the illuminance transition models
are spatial data. So, illuminance data is converted into spatial
data by using distance calculated from two points of GPSinformation. And, we calculate errors of mean square between
the converted illuminance data and illuminance transition models, compare each errors, and select an illuminance data thathas minimum error as the classifying result. But, we have toconsider effects of measurement conditions such as angle ofsmartphones and walking speed. we select an illuminance dataas the classifying result, in the case of fulfilling conditions ofweighted allowable error.
IV.

E VALUATION OF THE S TREETLAMP C LASSIFYINGM ETHODTo evaluate proposal method, we experimented on thecommunity roads (Fig. 6) in Uozumi-cho Akashi-city, Japan(this field has an area of one km2 ). Fig. 7 shows threetypes of streetlamps (starter-type fluorescent lights, invertertype fluorescent lights, LED lights) installed in the evaluation
field. Incidentally, visual aspect of starter-type and invertertype fluorescent lights are about the same. Three testers wereusing a smartphone (Galaxy Nexus) for a month at somenights without concerns for holding angle of smartphones andwalking speed.
As a result of the experiment, we collected 4,435 samples
for 261 streetlamps in the field, and used 2,288 samples thoseare able to convert into spatial data for the evaluation. Table IIshows the result of the streetlamp classifying method. thisresults include precision (ratio of correct answer against streetlamps that was recognized), recall (a ratio of correct answer
against all streetlamps), and F-measure value (a harmonicmean of precision and recall) for each allowable error value.
A precision is over 75% in all case of allowable error.
And, if allowable error become small, a precision will increase.
This result suggests that we can classify general three typesof streetlamps. In this experiment, we could collect only 10samples per a streetlamp for the evaluation. So, we couldn’t
realize the advantage of collective intelligence such as highnoise immunity and high recall. Therefore, we should evaluatewith more samples, and improve the precision of illuminanceTABLE II.

E VALUATION OF THE S TREETLAMP C LASSIFYING M ETHOD

Allowable Error [lx2 ]12491625Precision Ratio [%]

84.6

79.1

79.4

72.9

76.0

77.4

Recall Ratio [%]

4.9

23.7

48.2

67.4

73.7

76.3

F-measure

0.09

0.36

0.60

0.70

0.75

0.77

TABLE III.

P RECISION AND R ECALL R ATIO OF E ACH S TREETLAMPS

Allowable Error [lx2 ]PrecisionRatio

Starter-type
fluorescent lights
Inverter-type
fluorescent lights
LED lights

RecallRatio

Starter-type
fluorescent lights
Inverter-type
fluorescent lights
LED lights

200 m
Judged Point as
400 ft{Starter-type Fluorescent Lights
Inverter-type Fluorescent Lights

Fig. 7.

491625

91.7

87.7

89.1

87.8

85.6

85.1

-75.0

75.0

63.0

71.0

71.9

0.0

0.0

7.1

12.5

36.4

48.6

7.1

32.5

63.6

84.4

85.1

85.1

0.0

9.1

27.3

51.5

66.7

69.7

0.0

0.0

2.7

10.8

32.4

45.9

LED Lights

The Map for Field for Evaluationa. Fluorescent Lights(Inverter-type and Starter-type)2

is independent of a kinds of streetlamps, and investigate
optimal number of samples about each kinds of streetlamps.
Furthermore, we should consider the classifying method using
collective intelligence that reduce the rejection ratio.
V.

Fig. 6.

1b. LED Lights

Samples of Streetlamps

transition models. Also, we should find out the best optimum
allowable error by considering a trade-off between precisionand recall. Finally, we should increase a number of streetlamp
samples.
Table III shows the results from the viewpoint of thekinds of streetlamps. A precision is over 85% in startertype streetlamps, over 70% in inverter-type streetlamps. The
measured illuminance value of LED lights tend to have wide
dispersion, because LED lights have high illuminance andstrong directivity; it is difficult to get ideal illuminance data.
So, the precision and the recall decrease more than thoseof starter-type fluorescent lights or inverter-type fluorescentlights. From this result, it is tend to exclude excessively
illuminance value by allowable error. We should consider anilluminance refinement method for reducing dispersion which

C ONCLUSION

In this paper, we surveyed effects that kinds of streetlamps give to assess the vertical illuminance as one of thestandards to assess safety level of night sidewalks. Based on
the fundamental survey, we modeled the vertical illuminancetransitions for each kind of streetlamps, and proposed thestreetlamp classifying method utilizing waveform comparisonof illuminance transition model and illuminance measured withsmartphones’ light sensors.
As a result of the evaluation, we found that we should improve the recall to reduce the allowable error and improve theprecision. We will consider the illuminance refinement method
against LED lights or inverter-type fluorescent lights which
tend to become wide dispersion, and improve the methodof waveform comparison, then, propose inferring method ofvertical illuminance. Additionally, we will survey similarly
about the other standard. Eventually, we will consider methodof assessing safety level of night sidewalk.
R EFERENCES[1] Japan Security Systems Association. Standard for Levels of Illumination
on Crime Prevention Lighting. SES E 1901-3 , 2012.
[2] (All in Japanese) 神田祐磨, 高橋伸元, 三浦昌生. 農地と住宅地が共存する地区における防犯対策を目的とした住民主体の街灯照度改善活動の支援. 日本建築学会大会学術講演梗概集 (東海), 2012.
[3] J. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy,and M. B. Srivastava. Participatory sensing. ACM Sensys 2006, 2006.
[4] Japanese Industrial Standards Committee. Illuminance measurements forlighting installations. JIS C 7612, 1985.
[5] Y. Matsuda and I. Arai. Safety level assessment system for sidewalk
utilizing collective light sensor of smartphones. IPSJ SIG TechnicalReport Vol.2013-UBI-37 No.52, 2013.