Journal of Robotics, Networking and Artificial Life, Vol. 3, No. 1 (June 2016), 5-8

Evaluation of a safety map generated from a collection of difference of Individuals
Masao Kubo

Department of Computer Science, National Defense Academy of Japan, hasirimizu 1-10-20
yokosuka, kanagawa 239-8686, Japan
Viet Chau Dan

Department of Computer Science, National Defense Academy of Japan, hasirimizu 1-10-20
yokosuka, kanagawa 239-8686, Japan
Hiroshi Sato

Department of Computer Science, National Defense Academy of Japan, hasirimizu 1-10-20
yokosuka, kanagawa 239-8686, Japan
Akira Namatame

Department of Computer Science, National Defense Academy of Japan, hasirimizu 1-10-20
yokosuka, kanagawa 239-8686, Japan
E-mail: dangvietchau@gmain.com, {masaok,hsato,nama}@nda.ac.jp
www.nda.ac.jp/cs/

Abstract
In this paper, a method to generate traffic safety maps automatically from a collection of GPS logs of a drivers'smartphone. So far methods that residents can update their traffic environment better by their opinion are requiredfor. The proposed method which uses a collection of individual summary not raw data achieves a consistent safety
map while their privacy is protected moderately. In this paper, the summarization method is explained.
Keywords: Traffic Safety Map; Risk Estimation; Smartphone Sensing; Collective Intelligence; Reality mining;

1. Introduction
In this paper, we propose a method for generating traffic
safety maps based on differences in individual
recognition of the road environment by using
smartphone data from various users.
Safety map is a kind of risk analysis result of trafficenvironment. There are some works related to risk
analysis. For example, Google Live Traffic is a service
that utilizes GPS data from Android smartphones toestimate traffic jams based on average GPS speed.
Honda initiated a project for traffic safety map in 2013using data recorded by their Internavi in-vehicle unit,
along with police reports and user contributions to theirsafety map website[1]. The locations where sudden
brake occurs as risky spots are plotted automatically inthe map.

Fig.1. Process for generating traffic safety map

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Masao Kubo, Viet Chau Dang, Hiroshi Sato, Akira Namatame

accident. The relation between the current speed and themoving distance is described in the following equation:xdx= v 0 (1 − ) nXsdt

Fig.2. IMAC: it supposes that each grid forms a two state
Markov chain with two states of being: free and occupied.

In this paper, we propose a method for integrating acollection of hazard locations based on differences indrivers’ recognition of the road environment. The
method is inspired by STEP technique of risk analysis intraffic safety field. We suppose that if every drivers havethe same recognition of a crossroad, the level of dangerwill depend on one’s driving skill. By contrast, where
one driver recognizes a crossroad as passable and whereother drivers recognize it as impassable, accidents are
more likely to occur.
2. The Proposed Method
The process for generating the proposed traffic safety
maps is depicted in Fig.1. We refer to the proposed
traffic safety map as a TimeDiff map. The map
integrates a collection of traffic-incident maps that aregenerated from users’ smartphone log data (proposed in[2][3]). In this section, we explain the method forconstructing a user’s traffic-incident map which is theleft side of Fig.1. Finally, we describe the proposedsummarization method for integrating these incident
maps into a global hazard map which is shown by theright of Fig.1.

(1)where Xs is the initial distance to the incident, x iscurrent distance to the incident (x=0 when the vehicle
begins to slow down), n>0, and v0 is the initial speed.
By fitting by eq(1), it can tell the distance Xs to anincident.
2.2. Generating a traffic-incident map with IMAC
We explain the method for generating a user’s trafficincident map [2]. Saarinen et al. proposed IMAC[4], amodel for describing dynamic environments with anoccupancy grid map, and this model is utilized torepresent traffic incidents. With the IMAC model, anenvironment is evenly divided into grids. Each grid ismodeled as a two-state Markov chain with two states ofbeing: free and occupied (see Fig.2). The IMAC model
is suitable for representing dynamic objects, such as
traffic signals or traffic incidents, with a grid map.
Furthermore, IMAC is used to estimate the transitionprobability parameters ( λ exit , λ entry ) by observe) byobserving the occurrence of the state occupied or free
and the transitions between them.
To estimate the grid states from the observed events,Saarinen proposed a method for observing two
processes in each grid in a dynamic environment with
Eq. (2):


λexit =# freetooccupied + 1# occupied to free + 1 , λentry =# occupied# free2.1. Simple Braking Model(SBM)
We explain a method for generating a traffic incident
map that can be used to interpret how a driverrecognizes the road environment. The Simple Braking
Model (SBM) is used for estimation of location of ahazard spot from a log stream of a smartphone.
We assume that whenever a vehicle encounters anincident, it always decreases its speed to avoid an

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6Fig. 3. Input: a log stream of smartphone(2)Evaluation of a safety

Fig. 4. Output; the user’s traffic-incident map

Fig.3 shows an example of a GPS log of a vehicle whichis the input to generate a traffic incident map. Fig.4 and5 illustrate the traffic incident map, Fig.4 shows theIMAC map of λ exit and Fig.5 depicts the map of λ entry .
Small circles indicate incidents of this driver. As thesefigures show the proposed method can detect incidents
at each corner adequately. In the next section we explain
a method to merge multiple users’ traffic incident maps.
2.3. TimeDiff method[3]This section proposes an integration method forgenerating a global map from a collection of IMAC
users’ grid maps for ( λ exit , λ entry ). This method is basedon the average time that users hold a different
recognition of the same road environment. We call thisthe TimeDiff method. The global map (i.e., the TimeDiffmap) shows the total time for road-recognitiondifferences between users in a given area.

occurrence of an accident depends on the recognition ofboth drivers. If two vehicles change their status at thesame time, they both have the same recognition of roadenvironment. Thus, an accident will not occur unless adriver makes a mistake in controlling the vehicle. By
contrast, the more difference in time between when therecognition changes - that is, between when one vehicle
recognizes the road as Free and other recognizes it asOccupied - the higher the likelihood of a collision. Thus,
the global TimeDiff map is based on the roadrecognition level of the users.
Let D be the set of users. We assume that all users
generate their own traffic incident map ( λ exit , λ entry ) byusing the update method in Eq. (2). We define thehazard level of each grid g in the global map with Eq.
(3). Suppose two users, i and j in D, initially report anOccupied status. Suppose further that user i changes thisstatus to Free before user j does. Alternatively, supposethat i and j initially report a Free status, and that thestatus from user i switches to Occupied before that ofuser j. The total difference in time between such statuschanges (whether Occupied or Free) from all pairs ofusers i and j in D is calculated as follows:∑ c ij (E(Pdiff (i, j)) + E(Pdifo (i, j)))

TimeDiff g = ∑i∈D j ≠i∈Dwhere E(Pdiff (i, j)) is the difference in time between
switches when each pair of users initially reports a Freeostatus, and E(Pdif(i, j)) is the difference in time between
switches when each pair of users initially reports anOccupied status. c ij is a probability that user i and j
meet.
E(Pdiff (i, j)) =Fig. 5. Time differences in the judgment of a road environment

Consider a scenario where two vehicles are driving
along a road. At this time, the road upon which thevehicles are traveling is recognized as Free (see Fig. 5).
As the two vehicles approach a red traffic signal, theyencounter an incident, and the road at this location isrecognized as Occupied. Consequently, both vehicles
come to a stop. At this time, both vehicles must change
their recognition of the road environment from Free toOccupied. TimeDiff method focuses on the time thateach vehicle changes its status. It is clear that the(3)∫Bf0t ⋅ Pr ob[ X i = occ ≤ t](1− Pr ob[ X j = occ > t])dt(4)oE(Pdif(i, j)) =∫ t ⋅ Pr ob[ X = free ≤ t](1 − Pr ob[ X = free > t])dt
Bo0ij(5)We suppose that this follows a Poisson process so thatthese 2 terms can be calculated briefly. For example,
when λ i,exit =0.01, λ j,exit =0.1, Bo=10, c ij =1, theno(i, j)) =1.5505.
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Masao Kubo, Viet Chau Dang, Hiroshi Sato, Akira Namatame

3. Experiment
The global hazard map (i.e., the proposed TimeDiffmap) is shown in Fig.8. This map is generated by thepersonal incident map of a vehicle shown by Fig.6 andthe incident map of a motor cycle rider in Fig.7. In theFig.8 the spots where accidents occurred in the last 5years are also illustrated. This proposed map agrees
with the accident data.

Fig. 9. Spot F

4. Conclusion
Fig. 6. The Incident map of λexit of the car driver.

In this paper, a method for generating traffic safety
maps based on differences in individual recognition of
the road environment by using smartphone data fromvarious users is proposed.
ReferencesFig. 7. The Incident map of λexit of the motor cycle rider.

Fig. 8. Process for generating traffic safety map

1. http://www.honda.co.jp/safetymap/
2. V. C. Dang, M. Kubo, H. Sato, T. Shirakawa, A.
Namatame,Anomaly Stop Detection by Smartphone,
Journal of Robotics, Networking and Artificial Life, Vol.
1, No. 1 (June 2014), 2-6.
3. V. C. Dang, H. Sato, M. Kubo and A. Namatame,
Building Safety Road Maps Based on Difference ofJudgment of Road Users by their Smartphone,International Journal of Advanced Computer Science andApplications(IJACSA), 6(9)(2015) 15-23.
4. J. Saarinen, A. Henrik, and J. L. Achim, Independent
Markov chain occupancy grid maps for representation ofdynamic environment., in Proceedings of IEEE/RSJ
International Conference on Intelligent Robots andSystems (IROS)( 2012)3489-3495.

Also spots where a field survey is conducted are shownin Fig.7 For example spot J has many pedestrians
because it is in front of a gate of a train station. Spot H
is an ext of a parking area of restaurants. Fig.9 shows
spot F. Spot F is on a straight road, but some accidents
have been occurring. As shown by Fig.9 spot F is alonga park and has a zebra crossing. The proposed safety
map can detect a zebra crossing without a signal.

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