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The development of a conflict hazardous assessment model for evaluating urban intersection safety/Transporto priemoniu susidurimo pavojaus vertinimo modelio raida, analizuojant susikirtimo tasku sauga miesto sankryzose.

1. Introduction

Compared with other urban road locations, urban intersections generate more traffic crashes because of considerable conflicts in motorized and non-motorized traffic, conflicts between motorized traffic and non-motorized traffic, motorized traffic and pedestrians and non-motorized traffic and pedestrians. According to past statistics, about 55% of total traffic crashes and 23% of total fatal crashes in urban areas in the US occur at intersections (Antonucci et al. 2004). In China, about 30% of urban traffic crashes take place at intersections (Annual Bulletin ... 2008). These statistical data indicate that intersections are the places of significant safety concerns. There is a need to establish a feasible model for evaluating intersection safety levels, specifically in China.

The traffic safety evaluation model currently used in the country is based mainly on historical traffic crash data or the traffic conflict technique (TCT). Although these models can provide an objective evaluation of safety levels, the specific circumstances of China present a number of challenges:

* The safety level evaluation model based on crash data is a post-mortem analysis method intended for use after accidents. Obtaining the accident characteristics of small samples, a long collection cycle and stochastic processes necessitate a long period in determining safety improvement outputs; the length of time consumed translates to increased safety risks (Chin, Quek 1997; De Leur, Sayed 2002).

* The safety level evaluation model based on TCT focuses on conflicts arising in motorized traffic. Studies on conflicts among mixed-traffic modes are scarce (Lu et al. 2008), although mixed-traffic modes are a typical characteristic of urban road traffic in China.

With the limited capabilities of the basic model in analyzing traffic crash data and TCT, some researchers (Lu et al. 2008) have taken the weighted sum of the crossing point numbers of ideal movement trajectories as the basic conflict model for the safety level evaluation of highway intersections. In this model, the physical conditions of intersections are used for assessing safety levels without need for crash data. This method is a preanalysis procedure of safety level; however, it is still hindered by some limitations:

* The model depends on the crossing point numbers of different movement trajectories. In reality, however, vehicles or other participants do not encounter one another at these points to become traffic conflict events (TCEs) for signal controls or channel designs. TCEs are highly related to traffic safety (Kaub 2000), whereas the crossing points of ideal movement trajectories influence only the factors of TCE.

* This model is mainly used for un-signalized highway intersections. Therefore, identifying the factors influencing safety, such as signal controls in urban intersections, is difficult to carry out. Its applications have limitations in terms of urban intersection design and operation stage, even though it presents advantages at the planning stage.

Using the present studies on TCT as bases, we propose a conflict hazardous assessment model (CHAM) for the evaluation of urban intersection safety. The following objectives are targeted:

* CHAM is established, incorporating factors such as conflict types, conflict angles, velocity, weight and TCE in different traffic modes. Therefore, the model can be used for the safety assessment of specific schemes in both urban signalized and un-signalized intersections.

* The method of determining conflict hazardous level (CHL) of different conflict types among mixed-traffic modes is proposed through the integration of the accident collision theory and HIC index for head hazard assessment.

* The calibration and validation of CHL are carried out using 341 intersection crash reports in Beijing within the period from 2006 to 2008.

The remainder of the paper is organized as follows. Section 2 reviews previous research on the validity and severity of TCT. Section 3 explains the approach to urban intersection CHAM. Section 4 illustrates the CHL determination procedure of CHAM. The applications are stated in Section 5. Conclusions are drawn and recommendations for future studies are presented in Section 6.

2. Research Review

For the purpose of this study, traffic conflict is defined as an observable situation, in which two or more road users approach each other in time and space to the extent that the risk of collision presents itself if their movements remain unchanged.

TCT has a long history of development covering research on its validity (Kaub 2000; Glauz et al. 1985; Migletz et al. 1985; Hauer, Garder 1986) and severity measures (Williams 1981; Sayed, Zein 1999; Minderhoud, Bovy 2001; Gettman, Head 2003; Kiefer et al. 2005; Svensson, Hyden 2006; Gettman et al. 2008).

TCT validity is often judged by adequacy in the correlation between observed conflict counts and accident records. Glauz (1985) established relationships between traffic conflicts and accidents and found that traffic conflicts of certain types were good surrogates for accidents, in which the estimates of the average accident rates were produced nearly as accurately as those produced from historical accident data. Based on this perspective and using the statistical analysis of historical accident data, Glauz (1985), Migletz et al. (1985), Hauer and Garder (1986) and Kaub (2000) reported that traffic crashes were highly related to severe traffic conflicts. The aforementioned authors attempted to build some models considering traffic crashes and severe conflicts. All these studies reflect the validity of TCT.

Because traffic crashes are strongly correlated with severe traffic conflicts, many studies focus on how to express conflict severity; some severity measures such as traffic conflict frequency (Williams et al. 1981; Sayed et al. 1999), time-to-collision (TTC) (Minderhoud, Bovy 2001; Gettman, Head 2003; Kiefer et al. 2005), post-encroachment time (PET) (Gettman, Head 2003), speed (Gettman, Head 2003), time-to-accident /conflicting speed value (Svensson, Hyden 2006), etc. have been proposed. The primary proposed conflict severity measure is TTC. Williams (1981) suggested that a hierarchy of TCE ranging in severity from minor conflicts to fatal accidents existed. Sayed and Zein (1999) established traffic conflict frequency and severity standards of motorized traffic for signalized and un-signalized intersections using data collected from 94 conflict surveys. To obtain critical TTC values, Minderhoud and Bovy (2001) promoted the basic idea of sampling TTC values over time to examine how well a driver understood the given lower safety limit. Gettman and Head (2003) proposed the best indices such as TTC, PET, deceleration rate, maximum speed and speed differential to measure the severity of conflicts in motorized traffic. They also presented definitions of possible conflict events and algorithms for calculating surrogate indices for conflict points and lines. Kiefer et al. (2005) developed an inverse TTC model to implement motorized traffic crash alerts when thresholds were surpassed. Svensson and Hyden (2006) constructed severity hierarchies based on a uniform severity dimension (time-to-accident/conflicting speed value) to acquire a comprehensive understanding of a connection between behaviour and safety. Gettman et al. (2008) established the Surrogate Safety Assessment Model (SSAM) and developed corresponding software for calculating surrogate indices according to the principles of the aforementioned five surrogate indices (Gettman, Head 2003).

As previously discussed, TTC is the primary conflict severity measure which is mainly focused on conflicts in motorized traffic. Based on these studies, CHAM is put forward to carry out the pre-analysis of safety levels by incorporating comprehensive conflict types such as conflicts among motorized traffic, non-motorized traffic and pedestrians as well as comprehensive influencing factors such as TTC, velocity and weight. CHAM depends on factual TCE and can assess safety influence levels of specific schemes at planning, design or operation stages.

3. Approach

3.1. Basic Model

In accordance with the traffic conflict mechanism analysis performed by Gettman et al. (2008), Lu (2008), etc, we establish CHAM to assess safety levels by considering TTC, weight, velocity, conflict types and conflict angles.

CHAM is a model used for assessing safety levels from two aspects: severe conflict numbers and CHL when TCE between mixed-traffic modes occur. The higher CHAM is, the higher hazard level of the intersection is. The relationship can be expressed by the basic model of Equation (1):

CHAM = [N.summation over (i=1)] [CT.sub.i] [CHL.sub.i]. (1)

In the equation, CHAM reflects the entire intersection CHL; i is traffic conflict type; [CT.sub.i] represents the severe conflict number of ith conflict type singled out according to TTC index (Gettman et al. 2008; Lu 2008); CHLi is the CHL of ith conflict type influenced by conflict angles, velocity and the weight of different traffic modes.

Traffic conflict types are classified using numerous methods having different rules (Sayed, Zein 1999; Gettman et al. 2008). However, these conflicts can be expressed through conflict angles and conflict participants after cluster analysis. Therefore, conflict types are grouped according to conflict angles and conflict participants taken as primary indices and conflict angles as secondary indices. The two hierarchical grouping results of conflict types are presented in Table 1.

According to Equation (1) and Table 1, CHAM can be transformed into the following form:

CHAM = [5.summation over (i=1)] [3.summation over (j=1)] [n.summation over (k=0)] [CT.sub.ijk] CHL, (2)

where: i is the primary index; j represents the secondary index; n is the severe conflict number of conflict types i and j; the total severe conflict number is 5x3xn. The rest of the symbols are defined similarly as in Equation (1).

In Equation (2), CT is a severe conflict number. To identify a severe conflict, some researchers (Minderhoud, Bovy 2001; Gettman, Head 2003; Kiefer et al. 2005; Svensson, Hyden 2006; Gettman et al. 2008) adopted TTC index. Based on TTC, Lu (2008) proposed an 85 percentile severe conflict determination method and used it for identifying severe conflicts in the mixed-traffic modes through the field survey. These methods are employed to identify CT. This paper, on the other hand, focuses mainly on CHL.

3.2. Methodology

The studies by Williams (1981) and Kaub (2000) showed a certain linear relationship ([beta]) between a severe conflict and traffic crash. The hazard level of traffic crash (HLOTC) can be derived through the accident collision theory (Mizuno, Kajzer 1999) and HIC index for head hazard assessment (Hutchinson et al. 1998; Yoganandan et al. 2010). The functional relationship of CHL can be expressed as Equation (3):

CHL = [beta] HLOTC. (3)

Equation (3) is used for determining CHL in Equation (2). The procedure is described as follows:

* Step 1. Determination of HLOTC: HLOTC is derived through combining the accident collision theory and HIC index used for head hazard assessment (Section 4.1).

* Step 2. Establishment of CHL models: CHL models are developed in line with the linear relationship between a severe conflict and traffic crash (Section 4.2).

* Step 3. Calibration and validation of CHL models: A total of 341 intersection crash reports in Beijing from 2006 to 2008 are used for the calibration and validation of CHL models (Section 4.3).

4. Core Technique--A CHL Determination Procedure for CHAM

CHAM evaluates safety levels based on CT number and CHL. In CT identification, we adopt the current 85 percentile severe conflict determination method (Lu 2008). This paper focuses mainly on CHL, and this section specifically illustrates a procedure for CHL determination.

4.1. Determination of HLOTC

HLOTC (marked y in the deduction process) is primarily affected by traffic modes ([x.sub.1]), collision types ([x.sub.2]), velocity ([x.sub.3]) and weight ([x.sub.4]) (Abdel- Aty, Abdelwahab 2004; Conroy et al. 2008). It can be expressed as Equation (4):

y = f ([x.sub.1], [x.sub.2] > [x.sub.3], [x.sub.4]). (4)

For conflicts in specific traffic modes and collision types, Equation (4) can be transformed into Equation (5), and thus:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (5)

According to grouping conflict types in Table 1, collision types are categorized via a similar classification method (Abdel-Aty, Keller 2005). Therefore, Equation (5) can be expressed as Equation (6):

[y.sub.if] = [f.sub.if] ([x.sub.3], [x.sub.4]). (6)

As to functional relationship f in Equation (6), related research (Hutchinson et al. 1998; Yoganandan et al. 2010) adopted the head hazard level as the equivalence foundation. Versace proposed HIC index in 1971 adopted as the hazard level criterion of passenger protection system FMV SS208 by the US National Highway Traffic Safety Administration. Currently, HIC value is employed as one of the vehicle safety criteria by nearly all the countries in the world. It is expressed as Equation (7), and thus:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (7)

In the equation, a is head C.G. acceleration and its value is the multiple of gravity acceleration; [t.sub.1] represents time on acceleration wave; [t.sub.2] denotes the maximum time of HIC corresponding to t1 with an interval time of less than 36 ms. As difference in interval time [absolute value of [t.sub.2] - [t.sub.1]] is nonsignificant in different collisions, it can be taken as a constant. Thus, Equation (7) can be expressed as Equation (8):

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (8)

In the equation, K is a constant and all other symbols are defined similarly to those in the previous equations.

HLOTC y can be stated as Equation (9) by combining Equations (6) and (8):

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (9)

In the equation, i and j are collision types. They have similar classification methods as those of the conflict types in Table 1. All other symbols are similar to those in the previous equations. Symbol a is an independent variable.

Regarding the determination of head C.G. acceleration a in Equation (9), the principle of the conservation of momentum in the crash collision theory can be employed to compute this index. The HLOTC of different collision types can then be deduced. The following section presents the deduction process of head-on collisions in motorized traffic.

Collisions can be taken as completely inelastic collisions, i.e. two vehicles stick to each other having the same velocity v after the collision (Abdel-Aty, Abdelwahab 2004; Conroy et al. 2008; Teresinski, Madro 2001; Fricke 1990). When the velocity of the two vehicles is marked [v.sup.'.sub.1] and [v.sup.'.sub.2] after the collision, relationship v = [v.sup.'.sub.1] = [v.sup.'.sub.2] is obtained. The weights of the two vehicles are assumed as [m.sub.1] and [m.sub.2], and velocities before the collision are [v.sub.1] and [v.sub.2]. The principle of the conservation of momentum can then be applied as Equation (10):

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (10)

Equation (10) can be transformed into Equation (11), and thus:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (11)

The head C.G. acceleration a of the two vehicles can be deduced as Equations (12) and (13) respectively.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (12)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (13)

When Equations (12) and (13) are substituted into Equation (9), the hazard levels of the two vehicles can be acquired. They are added together to obtain the HLOTC of the collision (Equation (14)). In the deduction process, [absolute value of [t.sub.2]/[t.sub.1]] is nonsignificant in different collisions and taken as a constant.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (14)

In the equation, [K'.sub.ij] is a constant and the other symbols are similar to those in the previous equations.

4.2. Establishment of CHL Models

Head-on conflicts in motorized traffic are also taken as examples. When the relationship between CHL and HLOTC is marked [[beta].sub.ij], CHL can be expressed as Equation (15) by combining Equations (3) and (14):

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (15)

In the equation, the multiple of constant [[beta].sub.ij] and [K'.sub.ij] is marked [[beta]'.sub.ij]. Other symbols are similar to those in the equations above. Regarding k-th severe conflict among conflict types i and j, the CHL model is expressed as Equation (16):

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (16)

A similar method can be used for deriving the CHL of other conflict types. The results are provided in Table 2.

4.3. Calibration and Validation of CHL Models

392 urban intersection crash reports in Beijing from 2006 to 2008 are collected for calibration and validation. In these reports, incomplete records are filtered and yielding 341 valid samples. Their spatial distribution is indicated in Figure.

[TABLE 2 OMITTED

341 samples are divided into three groups according to the year, i.e. 102 samples in 2006, 128 samples in 2007 and 111 samples in 2008. They are used for calibrating parameter [[beta]'.sub.ij] of CHL model. Three groups of [[beta]'.sub.ij] are acquired. The Friedman test is used for determining the differences among the three groups of [[beta]'.sub.ij]. The status 'difference is not significant' and reflects the validity of CHL model.

For the basic CHL models in Table 2, hazard level CHL is transformed into a comparable level using the same criteria published by the Public Security Ministry of P.R.C. (Rules of Urban Road ... 2009) presented in Table 3.

The weight and velocity of the basic CHL models in Table 2 are the corresponding weight and velocity in each crash report. The linear regression process in SPSS18.0 is utilized to compute parameter [[beta]'.sub.ij]. The results are shown in Table 4. All the [R.sup.2] of the models are greater than 0.8, which reflects good correlation.

The Friedman test (Garcia et al. 2010) is then used to determine validity. This test is a nonparametric analogue of two-way ANOVA. The objective of this test is to determine whether there is the difference among treatment effects. The null hypothesis is that there is no difference among treatment effects. The alternative hypothesis is that there is the difference among treatment effects. Test statistics is stated as Equation (17). The decision rule of validity is that the null hypothesis is accepted if the statistical value of the test is less than the critical value at a significant level of 5%.

[[chi square].sub.F] = 12/ls(s + 1) [summation over (0)] [RK.sup.2.sub.0] - 3l(s + 1). (17)

[FIGURE OMITTED]

In the equation, l refers to data sets (l = 5); s is the number of groups (s = 3); [RK.sub.0] represents the average ranks of the algorithm (Garcia et al. 2010).

The calculations are presented in Table 5.

From the results in Table 5, the critical value of test statistics for l = 5 and s = 3 at a significant level of 5% is 6.40. Test statistics is [[chi square].sub.F] = 4.80 < [[chi square].sub.[alpha]] = 6.40, which reflects the acceptance of the null hypothesis and the rejection of the alternative hypothesis. Thus, the difference in [[beta]'.sub.ij] among three groups is non-significant. The test index illustrates the good validity of CHL models. The average values of [[beta]'.sub.ij], as stated in the last column of Table 4, are used for computing the CHAM of the entire intersections.

5. Applications

5.1. Software Development

VISSIM, developed by PTV Corporation, is microscopic traffic simulation software based on time step and driving behaviour. SSAM is an identification model of severe conflicts in motorized traffic (Gettman et al. 2008). We have developed a platform by integrating VISSIM software (VISSIM 5.20 User Manual 2009) and SSAM model to achieve severe conflict identification in mixed traffic through Vb.net programming (Zhou et al. 2009, 2010). In this research, CHAM is embedded into the platform to form an auxiliary software analysis tool, which enables the safety level pre-analysis of mixed-traffic design or operation schemes.

5.2. Practical Application

CHAM can evaluate the safety levels of planning, design and operation schemes. The basic application characteristics are as follows:

* CHAM cannot be directly applied because of the deficiencies of specific design schemes at the planning stage. However, as in Lu et al. (2008), the idea of taking the crossing points of ideal movement trajectories as reference can be adopted to evaluate safety performance at the planning level.

* At design stage, the developed auxiliary software analysis tool can be employed to carry out the pre-analysis of intersection safety levels.

* At the operation stage, the field video survey can be conducted to acquire factual conditions for mixed traffic. Conflict types, angles, weight and the velocity of severe conflicts can be surveyed. These factors are used to directly compute CHAM. The auxiliary software analysis tool can also be utilized through simulation analysis.

Since 2005, CHAM has been used in a research program of the Ministry of Construction. Thirty-four urban intersections in Sichuan Province are taken as demonstration projects of safety improvement measures. For the existing traffic safety problems, safety improvement actions are implemented and CHAMs are used for evaluating the safety levels of improvement schemes. With the implementation of safety improvement actions, the results of CHAM and factual traffic crash reports have high consistency.

6. Conclusions

1. A novel CHAM procedure for urban intersection safety evaluation based on the current TCT research is proposed. The CHAM procedure can assess the safety levels of intersection schemes at planning, design and operation stages. It is also suitable for mixed-traffic safety evaluation.

2. CHAM evaluates safety levels using conflict numbers and CHL as bases. This paper mainly illustrates the establishment, calibration and validation of CHL model. The CHL of different conflict types are first built by integrating the crash collision theory and HIC index for head hazard assessment. The calibration and validation of CHL model are then carried out using 341 intersection crash reports in Beijing from 2006 to 2008. The auxiliary software analysis tool is developed to enable the safety level pre-analysis of design or operation schemes.

3. We study CHAM as an import component of a management tool for traffic safety quality. A recommendation for the future study is encouraging the gradual acceptance of CHAM applications by traffic engineers and practitioners, and acquiring feedback in different cities in China. CHAM can be perfected so that it can be adopted as the index at national traffic safety levels. Another recommendation is to determine the safety level criterion of CHAM, which is the decision core of the six-sigma traffic safety quality management of 'Define-Measure-Analysis-Design-Verify' model used for improving the level of traffic safety from the viewpoint of total quality management.

doi: 10.3846/16484142.2011.589494

Acknowledgement

The authors would like to thank National Science and Technology Supporting Program during the Eleventh Five-year Plan Period (2006BAJ18B07) for supporting this research.

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Sien Zhou (1), Jian Sun (2), Xiao An (3), Keping Li (4)

(1, 2, 3) Dept of Traffic Engineering, Tongji University, No 4800, Caoan road, 201804 Shanghai, China (4) Sino-German Centre for Transportation Research, Tongji University, No4800, Caoan road, 201804 Shanghai, China

E-mails: (1) josyln.zh@gmail.com; (2) sunjian@tongji.edu.cn (corresponding author); (3) popotooth@126.com; (4) keping_li@vip.163.com

Received 12 September 2010; accepted 20 April 2011
Table 1. Hierarchical grouping results of conflict types

                                                    Motorized and
                                        Motorized   non-motorized
Primary indices Secondary indices        traffic       traffic

Head-on (135[degrees] / 180[degrees])      11            21
Crossing (45[degrees] / 135[degrees])      12            22
Rear end (0[degrees] / 45[degrees])        13            23

                                         Motorized
                                        traffic and   Non-motorized
Primary indices Secondary indices       pedestrians      traffic

Head-on (135[degrees] / 180[degrees])       31             41
Crossing (45[degrees] / 135[degrees])       32             42
Rear end (0[degrees] / 45[degrees])         33             43

                                        Non-motorized
                                         traffic and
Primary indices Secondary indices        pedestrians

Head-on (135[degrees] / 180[degrees])        51
Crossing (45[degrees] / 135[degrees])        52
Rear end (0[degrees] / 45[degrees])          53

Table 3. Different accident levels

Seriousness   Definition                                       Score

Minor         Slight injury to only 1 to 2 persons, not        0 / 30
accident      more than 1000 Chinese Yuan worth of property
              lost in a vehicle accident, or not more than
              200 Chinese Yuan worth of property lost in
              one cycle accident

Moderate      Serious injury to 1 to 2 persons, more than 3   30 / 60
accident      persons slightly injured, or not more than
              30000 Chinese Yuan worth of property lost in
              one accident

Serious       Death of 1 to 2 persons, 3 to 10 persons        60 / 80
accident      seriously injured, or 30000 to 60000 Chinese
              Yuan worth of property lost in one accident

Extra         Death of more than 3 persons, more than 11      80 / 100
serious       persons seriously injured, 1 death with more
accident      than 8 persons seriously injured, 2 deaths
              with more than 5 persons seriously injured,
              or more than 60000 Chinese Yuan worth of
              property lost in one accident

Table 4. Parameters [[beta]'.sub.ij] of the CHL model

                                             [[beta]'.sub.ij]
Conflict/
collision types                      2006     2007     2008    Average

Conflicts in motorized   Head-on    0.0055   0.0059   0.0057   0.0057
traffic                  Crossing   0.0215   0.0208   0.0224   0.0216
                         Rear-end   0.0811   0.0828   0.0835   0.0825

Conflicts between motorized         0.0046   0.0049   0.0047   0.0047
and non-motorized traffic

Conflicts between motorized         0.005    0.0056   0.0052   0.0053
traffic and pedestrians

Note: The valid conflict samples of non-motorized traffic and
pedestrians are limited. They are not calibrated and validated
in this research, although similar methods can be utilized.

Table 5. Results of the Friedman test

Conflict/collision types                        Ranks

                                         2006   2007   2008

Conflicts in                  Head-on     1      3      2
motorized traffic             Crossing    2      1      3
                              Rear-end    1      2      3
Conflicts between motorized               1      3      2
and non-motorized traffic
Conflicts between motorized               1      3      2
traffic and pedestrians
Values of [summation] [RK.sub.0]          6      12     12
Values of [summation] [RK.sub.0.sup.2]    36    144    144
[summation] [RK.sub.0.sup.2]                    324
12/ls (s + 1)                                   0.20
3l (s + 1)                                       60
Test statistics [[chi square].sub.F]            4.80
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Author:Zhou, Sien; Sun, Jian; An, Xiao; Li, Keping
Publication:Transport
Article Type:Report
Geographic Code:9CHIN
Date:Jun 1, 2011
Words:5119
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