Identification Determinant Variables of the Injury Severity Crashes at Road-Railway Level Crossing in Indonesia

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Transportation Research Procedia 37 (2019) 211–218 www.elsevier.com/locate/procedia st 21 EURO Group onon Transportation Meeting, EWGT 2018, 17th 17-19 – 19th September 21st EUROWorking Working Group Transportation Meeting, EWGT 2018, September2018, 2018, 21st EURO Working Group on Transportation Meeting, EWGT 2018, 17-19 September 2018, Braunschweig, Germany Braunschweig, Germany Braunschweig, Germany

Identification Determinant Variables of the Injury Severity Crashes Identification Determinant Variables of the Injury Severity Crashes at Road-Railway Level Crossing in Indonesia at Road-Railway Level Crossing in Indonesia Tri Tjahjonoaa*, Andyka Kusumaaa, Yodya Yola Pratiwibb, Robby Yudo Purnomoaa Tri Tjahjono *, Andyka Kusuma , Yodya Yola Pratiwi , Robby Yudo Purnomo Universitas Indonesia, Kampus Baru UI Depok - 16424, Indonesia Indonesia, Kampus Baru UI Depok - 16424, Indonesia Tokyo Institute of Technology, Meguro Tokyo 152-8550, Japan b Tokyo Institute of Technology, Meguro Tokyo 152-8550, Japan

a

a b Universitas

Abstract Abstract Accident between road and railway traffic at the railway level crossings (RLCs) is a significant issue in developing countries Accident between road and traffic at the traffic railwayaccident level crossings (RLCs)recorded is a significant issue in900 developing particularly in Indonesia. Therailway Indonesia national data (IRSMS) approximately accidents countries at RLCs particularly in and Indonesia. national accident data (IRSMS) recorded approximately 900number accidents at RLCs between 2013 2016. InThe thisIndonesia study, accessed fortraffic 154 RLCs detail accident records can be completed. The of accidents between and 2016. this study,high accessed 154of RLCs detail and accident records can be completed. The number ofRLCs accidents at RLCs 2013 in Indonesia areInsubstantial with for a ratio accidents fatality accidents were 40.47 accidents/1,000 and at RLCs in Indonesia RLCs are substantial highbywith a ratio oftoaccidents and fatality accidents RLCs and 14.96 fatalities/1,000 respectively comparison the international standard. This were paper40.47 adoptsaccidents/1,000 the ordered probit model 14.96 fatalities/1,000 RLCs respectively by comparison to the international standard. This paper adopts the ordered probit model for identifying the determinant variables of the injury severity crashes at various RLCs types (i.e. active and passive) and locations for identifying determinant variables the injury severity crashesthe at various RLCs types (i.e.Two active and passive) locations (i.e. urban and the rural). The findings are asoffollows: rain will increase risk of fatality, Power Wheelers have aand high risk of (i.e. urbaninand rural). findings are passive as follows: rainFatal will accident increase crossing the risk of Power Twolow Wheelers have a high of fatalities urban and The less risk in rural RLCs; at fatality, RLCs occur in the traffic condition andrisk dawn fatalities in urban and drivers less riskare in more rural passive RLCs; Fatalinaccident crossing ataccidents RLCs occur in the low The traffic condition and dawn period. Finally, Male likely being killed the vehicles-train in the RLCs. recommendations are: period. drivers are more likely being the vehicles-train accidents the RLCs. recommendations First, toFinally, suggestMale the train authority to provide full killed lengthinboom gate at both sides at theinactive RLCThe to reduce the chancesare: for First, to suggest the train authority provide full length at both at the level activecrossing. RLC to Second, reduce the for motorcyclists’ violate the boom gatetofrom the other side ofboom roadsgate when train sides passing forchances all passive motorcyclists’ violate the boom gate from other side roads when trainand passing level crossing. Second, all passive RLCs must be equipped with a warning siren,the flashing lightof prior train passing, streetthe light for improving alertnessfor drivers/riders RLCs be equipped with as a warning flashinghours. light prior train passing, and street light for improving alertness drivers/riders duringmust day and night as well in peak siren, or non-peak during day and night as well as in peak or non-peak hours. © 2019 The Authors. Published by Elsevier Ltd. © 2018 The Authors. by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The under Authors. Published by Elsevier B.V. Peer-review responsibility of the scientific of the 21stof EURO Group on Transportation Meeting. st Selection and peer-review under responsibility of thecommittee scientific committee the 21Working EURO Working Group on Transportation Meeting, Peer-review the scientific committee of the 21st EURO Working Group on Transportation Meeting. EWGT 2018,under 17th –responsibility 19th Septemberof2018, Braunschweig, Germany. Keywords: Road-Railway Level Crossing (RLC); Accident; Fatality Keywords: Road-Railway Level Crossing (RLC); Accident; Fatality

* Corresponding author. Tel.: +62-21786-2962; Fax: +62-21786-2962. * Corresponding Tel.: +62-21786-2962; Fax: +62-21786-2962. E-mail address:author. [email protected]. E-mail address: [email protected]. 2352-1465 © 2018 The Authors. Published by Elsevier B.V. 2352-1465 2018responsibility The Authors. of Published by Elsevier B.V.of the 21st EURO Working Group on Transportation Meeting. Peer-review©under the scientific committee Peer-review under responsibility of the scientific committee of the 21st EURO Working Group on Transportation Meeting. 2352-1465  2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 21st EURO Working Group on Transportation Meeting, EWGT 2018, 17th – 19th September 2018, Braunschweig, Germany. 10.1016/j.trpro.2018.12.185

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1. Introduction 1.1. Background Railway-Road level crossings (RLCs) are the important locations for both spatial development and transportation system point of view. It provides a connection between two sides of the railways right-of-way and on the other hand, it strengthens the highways and roads network systems (Lu and Tolliver, 2016). However, with increasing the number of motorized vehicles in particular in the built-up areas and major interurban highways, RLCs will result in increasing catastrophic road vehicles-train crashes (Lu and Tolliver, 2016). Fig. 1 shows the number of RLCs in Indonesia divided by legal and illegal (RLCs built by community without any concern from government or rail operators) and by active and passive controlled. Active RLCs have a boom gate (usually only a half of the length of road carriageway width), warning light and siren. The system is controlled by nearby station automatically and/or by RLCs official guards on site. Passive RLCs have only St. Andrew’s type of traffic signs on both sides of the roads. A few number of passive RLCs have a flashing amber warning light to improve awareness to the motorists. The Indonesia Railway law 2007 dictum is stated that in the first instance all the railway-road crossing should be a grade separation crossing. However, mostly RLCs are still RLCs with the current situation 66% are still passive controlled and active control RLCs are only 34%. Replacement to grade separations are very expensive and concentration are given to location with the severe traffic congestion. This condition is also the case in the developed countries. For example, in the UK, RLCs is still exist, the average number of RLCs between ‘1946 and 1950’ and ‘2006 and 2009’ were 27,050 and 6,617 respectively (Evans, 2011). It fell due partly to rail route closures and partly to individual closures or replacement by a grade separation crossing.

Fig. 1. (a) Status of RLCs in Indonesia; (b) Types of RLCs in Indonesia (Source: Official website kai.id, 2016)

The number of accidents at RLCs in Indonesia are substantial high with a ratio of accidents and fatality accidents over 1,000 RLCs were 40.47 and 14.96 respectively in 2016 by comparison to international standard. From this perspective, it suggests if no RLCs safety intervention in Indonesia, train-vehicle crashes will be the main contributor to both the rail and road accidents in the following years. Fig. 2 shows road accident ratios at RLCs between 2013 and 2016 in Indonesia. It should be noted that the number of RLCs includes illegal level crossing. The ratio of fatalities/1,000 RLC in Indonesia in 2016 was 14.96 compared to the UK between 2005 and 2009 was only 1.46 (Evans, 2011).

Fig. 2. Train Accidents Ratio at RLCs in Indonesia over 1,000 RLCs



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The objectives of this paper are: First, to investigate determinant variables to the level of severity of vehicle-train crashes at RLCs in Indonesia and; Second, to recommend a practical solution to improve RLC’s safety by best practices from the literature reviews. Two dichotomy conditions were addressed. First, the comparison between active and passive RLCs, and; Second, the comparison between urban and rural areas. 1.2. Literature Review Active RLCs (Protected) is RLC that have warning of the approach of a train through closure of gates or barriers, or by warning lights and sound. While Passive RLCs (Unprotected) do not have any given warning that a train is approaching. Even though passive RLCs do not have any warning, specific design issues must be met and instructions for safe use with appropriate signage. Indonesia has many illegal passive RLCs in urban areas as the pressure of settlement development along the railway corridors. Active RLCs is safer than passive RLCs. For instance, in India, 61% of RLCs fatality crashes happened at unmanned (or passive) RLCs (Sharad et al., 2016). Passive RLCs are usually associated with low volume roads in which will give less exposure to the risk than active RLCs. It is not arguably that replacing passive to active RLCs are helping to reduce the observation failure and hence improve safety significantly (Laapotti, 2016). Accidents at RLCs are multi-factors and rarely caused by a single factor (Salmon et al., 2013). Driver behaviour characterized by human error and intentional risk-seeking behaviour. Drivers’ age and gender are overwhelming studied as a risk factor for the accident and the level of severity at RLCs. (Hao et al., 2016; Hao et al., 2015). Others determinants to the level of severity accident at passive RLCs are including environmental and weather characteristics and visibility (Hao et al., 2016; Yu and Abdel-Aty, 2014). Indonesia is one of the country with the highest power two-wheeler (PTW) ownership globally. In 2013, 81% of the motorized vehicles were PTWs, and the proportion of fatalities associated with PTWs accident was around 70% (Tjahjono, 2016). Another issue, which faced by the road safety programs in Indonesia, is the high number of unlicensed riders and under-aged unlicensed riders. Increasing the proportion of PTWs was also significant to increase the accident rates of particular road segment (Tjahjono, 2009). Therefore, underage drivers/riders and unlicensed drivers included as the determinant factors of the accident’s level of severity at RLCs. PTWs proportion is very high and PTWs riders tend to behave improperly such as blocked the entire road including the opposite lane and crossed the railroad dangerously even when the gate is already closed and train approaching the level crossing. 2. Illustrations 2.1. Ordered Probit Model This data involved an application of ordered discrete data. Ordered probability models are employed to establish ranked outcomes of injury severity, i.e. categorical frequency data: fatal, injured and property damage only (PDO) accidents. It is based on the assumption of normal distributed error. The ordered probit model is given by Eq. (1): z = βX + ε

(1)

Where  is a vector of variables determining the discrete ordering observation n, measuring the attributes of accident victim of n,  is a vector of estimate parameter, and  is a random error term. Using this equation, the observed ordinal data in this study, Y defined by Eq. (2): 1ifμ0 < z ≤ μ0

2ifμ0 < z ≤ μ1−1 Y =  3ifμ < z ≤ μ 2−1 1 … { Jifμ0 < z ≥ μJ−1

(2)

Where  are parameters that define Y, which corresponds to integer ordering, and J is the highest integer ordered

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response represent injury levels (in this case J = 3). It should be noted that non-numerical orderings of severity level are converted to integers i.e. PDO = 0, Injured = 1 and Fatal = 2. The  are parameters that are estimated jointly with the model parameters (). The estimation becomes one of determining the probability of J specific ordered response for each observation (n). With the assumption that the distribution of  is normally distributed across the observation with mean = 0 and variance = 1, then the ordered probit model results with ordered selection probabilities are as follows by Eq. (3): P(y = 1) = Φ(−βX) − Φ(μ1 − βX) − Φ(X) P(y = 2) = Φ(μ1 − βX) − Φ(βX) − Φ(βX) P(y = 3) = Φ(μ2 − βX) + Φ(μ1 − βX) − Φ … P(y = J) = 1 − Φ(μJ−2 − βX) − Φ(μ1 − βX)

(3)

Where  (..) is the cumulative normal distribution, (μ) =

1

√2π

μ

1

 ∫−∞ EXP [− w 2 ] dw 2

(4)

The parameters of the ordered probit model are estimated using a maximum likelihood estimation. In this study, yi is the injury severity, which divided into three categories, i.e. PDO=0, injury=1 and fatal accident=2. This equation leads to a log-likelihood by Eq. (5) as follows: 3 LL = ∑N n=1 ∑j=1 log⌈(μj − βXn ) − (μj−1 − βXn )⌉

(5)

P(Y=i)

(6)

Marginal effects are estimated in ordered probit model to obtain impacts of variables on the probability of each injury severity, i.e. PDO, Injured and killed. For continuous variables, it can be obtained by Eq. (6) as follows: ∂x

= [∅(μi−j − βX) − ∅(μi − βX)]β

For binary variable cases, the marginal effect of a variable for injury severity can be obtained by Eq. (6) by comparing the outcome when the variable takes one value with that when variable takes zero value, while all other variables are remaining constant, which it follows by Eq. (7): Δ(Y = j|xn ) = P(Y = j|xn = 1) − P(Y = j|xn = 0)

(7)

2.2. Likelihood Ratio Test

To determine significant differences between parameter estimate for urban and types of RLCs group, likelihood ration test was employed. According to Hao et al. (2015) and based on Islam and Mannering (2006), LL() estimates a model for all data and then 𝛴𝛴𝐺𝐺 𝐿𝐿𝐿𝐿(𝛽𝛽𝐺𝐺 ) estimates separate models for each individual locations/types of RLCs group. 3. Data Source and Preparation

Basic Data gathered from the traffic police accident database of IRSMS (Integrated Road Safety Management System). RLCs accidents in IRSMS can be easily distinguished by sorting train as a type of vehicle involved in the accidents. The analysis will be based on 154 accidents on legal RLCs in Java between 2013 and 2016 in Java. It should be noted that in Indonesia, RLCs accidents data with detail information is relatively difficult to obtain. These data then carefully check for both location definition and types of RLCs for obtaining reliable data. Fig. 3 shows the



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distribution of accidents at RLCs by location (urban and rural), types of RLC (active and passive), and injury severity levels (property damage only, injured, and fatal).

Fig. 3. Distribution of Injury Levels by: (a) RLC’s Location; (b) Types of RLC Table 1. Description of collision of a roads-rail collision at a level crossing (n=154) Urban Active Description RLCs No % Dependent Variable 0 = PDO (Permanent damage only) Accident 6 10% 1 = Injury Accident 23 37% Driver 2 = Fatal Accident 34 54% Independent Variable 1 = Trucks 3 5% 2 = Buses 2 3% 3 = Passenger Cars 11 17% 4 = PTW (Power two-wheeler) 41 65% Types of vehicle 5 = NMV (non-motorised vehicle) 6 10% 0 = Peak Hours 24 38% Temporal Types 1 = Off Peak 39 62% 1 = Dawn 1 2% 2 = Dust 3 5% 3 = Dark 27 43% Periods of Acc 4 = Day 32 51% 0 = Rain 1 2% Weather 1 = No Rain 62 98% 0 = Commuter 39 62% Train Type 1 = Otherwise 24 38% 0 = Underage (< 18 years old) 2 3% 1 = Young (18-55 years old) 52 83% Age 2 = Old (>55 years old) 9 14% 0 = Unlicensed 37 59% Driving Licensed 1 = Licensed 26 41% 0 = Female 3 5% Gender 1 = Male 60 95%

Urban Passive RLCs No %

Rural Active RLCs No %

Rural Passive RLCs No %

0 6 7

0% 46% 54%

9 5 11

36% 20% 44%

6 10 37

11% 19% 70%

0 0 3 7 3 4 10 0 0 2 11 0 13 3 10 0 8 5 11 2 1 12

0% 0% 23% 54% 23% 29% 71% 0% 0% 15% 85% 0% 100% 23% 77% 0% 62% 38% 85% 15% 8% 92%

4 0 6 12 3 8 16 1 11 2 11 1 24 1 24 3 18 4 18 7 0 25

16% 0% 24% 48% 12% 33% 67% 4% 44% 8% 44% 4% 96% 4% 96% 12% 72% 16% 72% 28% 0% 100%

2 0 12 35 4 11 42 0 0 15 38 2 51 0 53 4 46 3 39 14 8 45

4% 0% 23% 66% 8% 21% 79% 0% 0% 28% 72% 4% 96% 0% 100% 8% 87% 6% 74% 26% 15% 85%

Table 1 shows the frequency and percentage distribution of both dependent and independent variables. Four ordered probit models be developed, namely: active urban RLCs, urban passive RLCs, rural active RLCs, and rural passive RLCs.

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4. Analysis The data were divided into four ordered probit models, namely: rural active RLCs, rural passive RLCs, active urban RLCs, and urban passive RLCs, these models were analysed through open-source software, R statistical software. The first three models were analysed using Ordered Generalized Linear Models. Meanwhile, because there is no PDO accident happened in urban passive RLCs (mostly injured or passed away), the analysis can do through binomial probit and logit model. Ordered discrete dependent variable models such as ordered probit and ordered logit are frequently used across the social sciences (Caroll, 2017), the negative parameter leads into less likely to have severe driver injury and it was applied vice versa. The p-value is defined as the probability, under the null hypothesis, of obtaining a result equal to or more extreme than what was observed. The smaller the p-value, the larger the significance. Several variables showed that its p-value was quite high (almost 1). It happened because of the low range of data sample. In other side, types of vehicle are one of a variable which has stable p-value because it's diverse data. Table 2. Model Estimation and Marginal Effect Result for Urban Active, Rural Active, and Rural Passive RLCs Parameter Estimate

p-value

PDO

Injured

Fatal

UA

RA

RP

UA

RA

RP

UA

RA

RP

UA

RA

RP

UA

RA

RP

Temporal type

-0.02

0.85

1.63

0.97

0.29

0.10

0.00

-0.27

-0.13

0.01

-0.06

-0.30

-0.01

0.33

0.44

Period of Acc

-0.08

0.76

0.82

0.77

0.11

0.07

0.00

-0.25

-0.07

0.03

-0.05

-0.15

-0.03

0.30

0.22

Weather

-1.97

-4.91

-5.82

1.00

0.99

0.99

0.09

1.58

0.48

0.70

0.33

1.09

-0.79

-1.91

-1.56

Types of Vehicle

1.37

0.77

0.47

0.00

0.03

0.04

-0.06

-0.25

-0.04

-0.49

-0.05

-0.09

0.55

0.30

0.13

Age

0.47

-0.74

-0.63

0.32

0.44

0.35

-0.02

0.24

0.05

-0.17

0.05

0.12

0.19

-0.29

-0.17

Gender

-0.79

0.66

-0.42

0.42

1.00

0.53

0.03

-0.21

0.03

0.28

-0.04

0.08

-0.32

0.26

-0.11

Driving licence

0.27

0.23

-0.73

0.65

0.80

0.23

-0.01

-0.07

0.06

-0.09

-0.02

0.14

0.11

0.09

-0.20

Note: UA = Urban Active; RA = Rural Active; RP = Rural Passive

In this paper, fatal accident will be focused in the result as the Indonesia Road Safety target is reducing the number of fatalities (Prihartono et al., 2014). Table 2 shows the probability of likelihood of PDO, injured, and fatal accident by different variables on different type of location. The negative and positive value explains the direction of the ordered values of the variables (i.e. positive sign on type of vehicle indicates that the higher mode dimension has higher likelihood). From all the 7 (seven) variables, weather and types of vehicle have the high level of likelihood to be a fatal accident. Drivers are likely to be killed by accident when the weather is raining based on the marginal effect of -79%, -191%, and -156% for urban active, rural active, and rural passive RLCs respectively. This means that rain will increase the risk of fatality in the train-vehicle accidents. Types of vehicle had the highest marginal effect for fatal accident occurrence in urban active RLCs by +55% while they are only +30% and +13% in rural active and passive RLCs respectively. This means that PTWs have a high risk of fatalities in urban RLCs and less substantial in rural passive RLCs. This can be explained by the fact motorist in rural areas mostly local people and already noticed the pattern of the train crossing at particularly passive RLCs. Another interesting fact is in rural area both active and passive RLCs experienced substantial risk of fatal accident when off peak period with probability of +33% and 44% respectively. The data shows that the highest accidents occurred during the dawn and PTWs that maybe they are not obey the warning given by the active RLCs and not stop first before crossing the passive RLCs caused by very low traffic. Based on the accident data, in an urban area with passive rail-crossings found very few PDO accidents. Mostly, the victim would be injured or passed away. Therefore, in this particular part, the data was analysed through binomial probit and logit model. Additionally, the p-values in Table 3 are very high (almost 1) because the diversity of the data is quite low. Below is the result of the analysis.



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Table 3 shows that in urban off peak, traffic accidents are +57.7% more likely to have severe injury rather than in peak period. The highest marginal effects are for age and gender variables. Underage drivers are more likely to have higher fatal accidents by 64%. This can be explained that underage (mostly PTW riders) are vulnerable. Furthermore, male drivers are more likely for being killed by 66% than female drivers. Male is likely more aggressive than female. Interesting that licensed drivers/riders are having a risk involved fatal accidents in RLCs, while unlicensed drivers/riders are otherwise in urban passive RLCs. Table 3. Model Estimation and Marginal Effect Result for Urban Passive RLCs Logit

Probit

Coefficient

Coefficient

Temporal type

20.573

5.909

Periods of Accident

1.099

0.675

NA

Variable Description

Weather

p-value

Marginal Effect Logit

Probit

0.999

1.19

0.58

1

0.06

0.07

NA

NA

NA

NA

Types of Vehicle

21.266

6.029

0.999

1.23

0.59

Age

-21.579

-6.566

0.999

-1.25

-0.64

Gender

22.665

6.758

0.999

1.31

0.66

Driving licence

20.467

5.409

0.999

1.18

0.53

Table 3 shows that in urban off peak, traffic accidents are +57.7% more likely to have severe injury rather than in peak period. The highest marginal effects are for age and gender variables. Underage drivers are more likely to have higher fatal accidents by 64%. This can be explained that underage (mostly PTW riders) are vulnerable. Furthermore, male drivers are more likely for being killed by 66% than female drivers. Male is likely more aggressive than female. Interesting that licensed drivers/riders are having a risk involved fatal accidents in RLCs, while unlicensed drivers/riders are otherwise in urban passive RLCs. 5. Conclusion This research is conducted the determinant variables of the injury severity crashes at RLCs in Indonesia by considering the differences between active and passive RLCs and urban and rural locations. Through the ordered probit model, it found that: First, rain will increase the risk of fatality in the train-vehicle accidents; Second, PTWs have a high risk of fatalities in urban RLCs and less risk in rural passive RLCs; Third, Fatal accident crossing at RLCs in the low traffic condition in particular in the dawn period (between 4:00 -06:00); Finally, Male drivers are more likely being killed in the vehicles-train accidents in the RLCs. The issue of the active RLCs is the design of the half boom gate instead of the full boom gate to protect the entirely road carriageway. A number of vehicles (mainly PTWs) takes a risk by using incoming lane to cross the RLCs when the boom gates already closed. Lastly, at passive RLCs, instead of unlicensed drivers, the licensed drivers and riders were the most victims who involved in severe accident. Unlicensed drivers/riders usually associated with local community and short distance travelers that they already know the pattern of trains passing the RLCs. It shows how dangerous passive RLCs because for the non-local community travelers, they were not aware of the approaching train as mostly the facility is only by showing the RLC St. Andrew sign. Acknowledgements This research is funded by PITTA funds from Universitas Indonesia with contract number 847/UN2.R3.1/HKP05.00/2017 and acknowledgement extends to the Indonesia Traffic Corps for providing access to the IRSMS accident database.

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