ITARDA 23th Work Shop Report

2020
23rd Presentation Session for Traffic Accident Investigations, Analysis, and Research

Research on Methods of Assessing Traffic Accident Risk in Residential Road Block Areas
Surrounded by Arterial Roads

Satoshi Hyodo
Visiting Researcher(Assistant Professor, College of Science and Technology, Nihon University)

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ITARDA 23th Work Shop Report

1.

Introduction
Generally, when comparing numbers of traffic accidents by road type, it is well known that residential roads

such as municipal roads account for a higher proportion of accidents than arterial roads. For example, a breakdownof 2018 statistics from the Chiba Prefectural Police1) shows that 60.3% of traffic accidents and 45.4% of fatal
accidents occurred on such municipal roads1). Furthermore, the fact that a high proportion of these accidents involve
pedestrians is one of their known features. Given such circumstances, it goes without saying that ensuring safe
passage on residential roads is important. However, in recent years, probe data showing individual vehicle behavior
has been collected and accumulated from sources such as vehicle navigation systems, making it possible to collectinformation on the traffic conditions of residential roads. By utilizing this information to perform “bird’s eye view”
macro analysis of accident situations together with conventional "worm's eye view" micro investigations andanalysis, it is expected that more effective and efficient safety measures can be developed. When planning suchmeasures, it is extremely important to grasp the relationships between factors that determine the features of an area
and the likelihood of an accident occurring (hereinafter, “accident risk”). Despite much research having been
performed on these relationships over the years, most of that research has analyzed the road characteristics androadside characteristics of a given area, with no analytical research being performed by focusing on macro trafficcharacteristics such as the congestion statuses of peripheral arterial roads, at least as far as we are aware. Against
this background, the purpose of this research is to clarify relationships between macro-traffic characteristics ofresidential roads and accident risks in the same area, while also examining accident risk assessment methods thatcomprehensively investigate these relationships.

2.

Analysis methods

2-1. Data overview
The six types of data used for the analysis performed in this research are described in detail below. The trafficaccident data used in this analysis is data provided by the Chiba Prefectural Police. This data can be confirmed on
GIS using information for the location of accident occurrence (latitude/longitude). Furthermore, approximately 22
data items are recorded for each location, including the date and time of the occurrence and the type of accident.
The period of the data spans over four years from January 1, 2015 to December 31, 2018, with the 34,751 traffic
accidents that occurred within that period being subjected to analysis. The traffic accidents extracted and subjected
to analysis were accidents that occurred on municipal roads within the skeletal trunk road block defined in thisresearch as roads with a width of 3.5 m or more and less than 13.0 m. Other data used included road network datain the form of 2019 DRM data and road network data from ArcGIS Geo Suite (ESRI Japan). Also, the probe data
used included such data obtained from mobile GPS employing mobile vehicle navigation services provided byNavitime® Japan. 41 items of data are recorded in these data, including average travel speed, number of uniqueusers, and number of passes per link for each aggregation period. In this research, we used data on the number ofprobe passes, link length, and average travel speed per link collected over a four-year period from January 1, 2015
to December 31, 2018 in Chiba Prefecture. As road traffic census data, it is based on the general traffic volume2

ITARDA 23th Work Shop Reportsurvey data from the 2015 road traffic census, andreferences congestion level data and census road link
position data. As peripheral environment data, it uses
information such as the area of 30 km/h zones set up by
the Chiba Prefectural Police2), positional information on
ブロックエリア
Block area j ｊschools, stations, and medical institutions obtained fromdigital national land information, DID area data, andpositional information on large shopping malls andconvenience stores obtained from Navitime and Zenrin.
Furthermore, actual measurement surveys were conductedType of road
道路の種類

Roadwidth
道路幅員

Traffictype
通行法

during this research to obtain observation traffic volumeCensus
road link
センサスリンク

1313m以上
m or more

Two-way双方

data for residential roads. These surveys were conductedMaincommunity road
主要生活道路5.5-13.0m

Two-way
双方

Neighborhood
区画道路roadLess than
5.5m未満5.5 m

Two-way/One-way
双方/一方

on weekdays from April to July under clear weather andfor approximately one-hour observation periods between

Fig. 1 - Concept diagram of block areas

10 AM and 11 AM. Also, 50 locations on residential roads with differing features were used as the locations of thesesurveys, with results for the number of observed passing vehicles being used.

2-2. Methods of analyzing accident risk models(1) Definition of “residential road block area”
The block area, which is the aggregate unit
used in this research, is set based on skeletal arterial
roads in order to be consistent with the area
bordered by the arterial roads, which is the area inwhich safety measures have been implemented.
Residential roads consist of main community roads
and neighborhood roads, which are bordered by thecensus links (road links shown in blue in the figure)as shown in the concept diagram (Fig. 1). Also, acertain standard will be set for the size of the area.
For this reason, subject block areas are extracted
based on the conditions of (1) block area size: 1.0 to
10.0 [km2] and (2) residential road density: 1.0[km/km2] or more. As a result, 287 areas out of a
total of 507 areas were extracted for analysis.

(2) Block area accident risk
Block area accident risk

λmj

as specified inTable 1 - List of explanatory variables
Characteristic

Variable

Average degree of congestion ofsurrounding trunk roads
Average travel speed (maincommunity roads) [km/h]
Traffic
Average travel speed
characteristics (neighborhood roads) [km/h]
Variance of average travel speed(main community roads) [km2/h2]
Variance of average travel speed(neighborhood roads) [km2/h2]
Signalized intersection density(medium × medium) [location/km]
Signalized intersection density(medium × small) [location/km]
Signalized intersection density(small × small) [location/km]
Unsignalized intersection densityRoad(medium × medium) [location/km]
characteristics Unsignalized intersection density(medium × small) [location/km]
Unsignalized intersection density(small × small) [location/km]
Road density [km/km2]
Ratio of major community roads
Connection density with arterial
roads [location/km]
Ratio of areas in which 30 km/h
zones have been set up
School density [location/km]
Station density [location/km]
Surrounding
Large shopping mall density
environment[location/km]
characteristics
Medical institution density[location/km]
Convenience store density[location/km]

Average
value

Min.
value

Max.
value

0.90

0.21

1.90

32.0

0.0

54.4

22.99.6

53.0

173.4

02947

239.4

47.4

5145

0.16

0

1.23

0.12

0

0.71

0.002

0

0.10

0.36

0

4.88

2.60

0.22

7.03

5.30

0.49

11.7

11.26
0.23
0.45

1.24
0
0.19

27.8
0.89
0.96

0.01

0

0.33

0.06
0.01
0.008

00
0

0.88
0.43
0.19

0.26

0

2.00

0.09

00.75

this research is calculated using the formula (1) shown below. Specifically, block area accident risk is expressed as
the number of traffic accidents per hundred million vehicle-kilometers, or in other words, the number of accidents3

ITARDA 23th Work Shop Report

per unit total traffic volume (vehicles) × distance (km) in a certain block area. Note that the number of accidents
used for counting here is only for accidents that occurred on residential roads, in other words, major community
roads and neighborhood roads.
Nmjλm
j =Lj

(1)

Here, j is the block area ID (j = 1,2,3 ..., 287), while Njm is the total number of accidents (no. of accidents/year)of accident type m that occurred at each of the links that constitute block area j in a certain target period. Also, Lj is
the total traffic volume (vehicles) × distance (km) [hundred million vehicle-kilometers] of each link that constitutes
the block area j for a certain target period.

(3) Accident risk model
In this research, the relationships between each feature quantity of the block area shown in the previous section
and the block area accident risk were analyzed using the Poisson regression model. Specifically, the objective
variable is the number of traffic accidents in the block area, and the explanatory variables include various factors
such as traffic characteristics and their offsets are formulated as the total traffic volume (vehicles) × distance (km)in a block area. For this reason, the model formula is described as shown by the formulas (2) and (3) shown below.

Here, Yjm

𝑌𝑗𝑚 ~Poisson (𝜇jm )(2)

mln(𝜇jm )=am + ∑ βjk xk + ln(L𝑗)(3)

is the annual number of accidents of accident type m (m = vehicle-vehicle accident, pedestrian-vehicle accident) that occurred in block area j, μj m is the number of accidents (accidents/year) of accident type m
expected to occur in area j, xk is a group of variables indicating the features of the block area (k = 1,2,3, ..., 20), a is
a constant, β is an unknown parameter, and Lj is the total traffic volume (vehicles) × distance (km) [hundred
million vehicle-kilometers] for each link of which block area j is constituted in a certain target year. Also, thefeatures of the areas inside the block are varied and include road characteristics and roadside conditions, in addition
to traffic characteristics. For this reason, these factors are also considered so that factors that impact the block area
accident risk can be comprehensively analyzed. The explanatory variables used in this research are shown in Table
1. Here, factors are roughly classified into three types of characteristics including traffic characteristics, roadcharacteristics, and surrounding environmental characteristics, with a total of 20 explanatory variables
corresponding to each characteristic being taken into account.

3.

Model estimation results
The results of estimations performed using the Poisson regression model are shown in Table 2. In those analysis

results, the likelihood ratio ρ2 (goodness of fit) of both models was high (0.36 and 0.40), indicating that they aregood models. The impact of each type of variable is as follows. In the case of vehicle-vehicle accidents, threevariables of (1) traffic characteristics were extracted as significant parameters, including average degree ofcongestion (census links), average travel speed on major community roads, and average travel speed onneighborhood roads. It has been shown that accident risk increases in block areas with multiple links in which theaverage travel speeds on major community roads and neighborhood road is decreasing. Of the variables considered

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ITARDA 23th Work Shop Reportas (2) road characteristics, all variables except unsignalized intersection density (medium × small) have a significant
impact. For example, while road density shows a positive value, the ratio of major community roads shows anegative value, meaning the higher the ratio of main community roads, the lower the accident risk tended to be. It
has been shown that the density of schools and other facilities such as large shopping malls and convenience storeshas a significant positive impact on the factors of the (3) surrounding environment, causing accident risk to increase.
In the case of pedestrian-vehicle accidents, impact from average degree of congestion (census links), average
travel speed on major community roads, and average travel speed on neighborhood roads were added as (1) trafficcharacteristics, and it has been shown that the variance of average travel speeds on neighborhood roads has asignificant positive impact on accident risk. As (2) road characteristics, signalized intersection density (medium ×small) and unsignalized intersection density (small × small) show a positive impact, with the impact of intersection
density shown to differ depending on the type of roads that intersect. Furthermore, although accident risk increases
in areas with higher road density, accident risk tends to decrease in block areas with a high share of major community
roads. As for (3) surrounding environment characteristics, accident risk increases in areas with dense concentrationsof amusement facilities such as retail stores, restaurants, and movie theaters. Conversely, accident risk tends todecrease in areas with high ratios of 30 km/h zones, as is the case with vehicle-vehicle accidents.

Table 2. Model estimation results

Variable

Vehicle-vehicle accidents
Coefficient
Z-valuevalue
2.58
18.2***

Pedestrian-vehicle accidents
Coefficient
Z-valuevalue
0.76
2.67**

Traffic characteristics
Average degree of congestion (census links)
0.34
8.19***
0.48
Average travel speed (main community roads)-0.03-11.29***-0.03
Average travel speed (neighborhood roads)-0.02-4.68***-0.03
Variance of average travel speed (main community-1.55*10-4-1.57roads)
Variance of average travel speed (neighborhood roads)-9.55*10-5-1.70*
2.54*10-4
Road characteristics
Signalized intersection density (medium × medium)
Signalized intersection density (medium × small)
1.36
9.12***
0.86
Signalized intersection density (small × small)-3.51-2.59***
Unsignalized intersection density (medium × medium)-0.20-6.47***-0.14
Unsignalized intersection density (medium × small)
0.03
1.48
Unsignalized intersection density (small × small)
0.08
10.44***
0.10
Road density
0.02
9.69***
0.03
Ratio of major community roads-0.90-6.57***-1.34
Connection density with arterial roads-0.42-5.61***
Surrounding environment characteristics
Ratio of 30 km/h zones-0.69-3.03***-1.21
School density
0.77
3.64***
0.83
Station density-5.08-8.05***
Large shopping mall density
3.77
7.33***
Medical institution density-0.15-3.23***
0.41
Convenience store density1.04
8.22***
0.80
Sample size N10261026
AIC5763
3196
Likelihood ratio ρ20.40
0.36
Significant level: ‘***’: p<0.01, ‘**’: p<0.05, ‘*’: p<0.1
Note: Signalized intersection density (small = width less than 5.5 m, medium = width 5.5 m or more and less than 13 m)

4.

Conclusion

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5.93***-5.10***-4.20***

3.48***

3.19***-2.67***
6.82***
5.67***-4.88***-3.04***
2.09**

5.88***
3.60***

ITARDA 23th Work Shop Report

In this research, we focused on macro traffic characteristics of residential roads in Chiba Prefecture andcomprehensively analyzed the impact of area-determining factors on traffic accident risk. Specifically, a residential
road block area surrounded by arterial roads was selected based on the coverage of areas in which safety measures
have been implemented, with relationships between determining factors, including traffic characteristic indicators,
and the traffic accident risk of each area being analyzed using the Poisson regression model. As a result, we foundthat accident risk for both vehicle-vehicle accidents and pedestrian-vehicle accidents was higher in areas with a highdensity of arterial roads around the area’s periphery which also had decreased average travel speeds on its maincommunity roads and neighborhood roads than in the case of areas without such features. Our results also showedthat the greater the variance of the average travel speed on neighborhood roads, the higher an area’s accident riskfor pedestrian-vehicle accidents would be. Furthermore, it was found that accident risk for pedestrian-vehicleaccidents is higher in areas where facilities in which people gather are concentrated, while accident risk is lower inareas with high ratios of 30 km/h zones. As an issue for the future, we believe other efforts will be necessary
including the use of information based on individual vehicle history such as trip length and driving speed to consider
differences due to the traffic characteristics of each link.

<References/Sources>(1) 千葉県警察本部 HP：交通事故統計資料集(https://www.police.pref.chiba.jp/kotsusomuka/traffic-safety_hakusyo.html).
(2) 千葉県警察本部 HP：「ゾーン 30」の概要(https://www.police.pref.chiba.jp/kotsukiseika/traffic-safety_defend-08.html）．
(End)

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