Determining the contributing factors to traffic accident in Ho Chi Minh city using binary logit model

The results might be helpful for effective measures suggestion to improve traffic safety at signalized intersections. A case study is conducted in Ho Chi Minh City (HCMC), Vietnam. Historical traffic accident data in the city are collected during five years (2011-2015). Binary logit models have been used to identify contributing factors to serious traffic accident. The results show that the involvement of intersection type, land use and road type are contributing factors to the accident severity. Based on the findings, strategies and measures for safety improvement are formulated and discussed.

TẠP CHÍ KHOA HỌC CƠNG NGHỆ GIAO THƠNG VẬN TẢI, SỐ 21-11/2016 21 DETERMINING THE CONTRIBUTING FACTORS TO TRAFFIC ACCIDENT IN HO CHI MINH CITY USING BINARY LOGIT MODEL ỨNG DỤNG MÔ HÌNH HỒI QUY LOGIT NHỊ THỨC ĐỂ XÁC ĐỊNH CÁC YẾU TỐ ẢNH HƯỞNG ĐẾN TAI NẠN GIAO THÔNG Ở THÀNH PHỐ HỒ CHÍ MINH Tran Quang Vuong University of Transport and Communications Campus in Ho Chi Minh City Abstract: Traffic accident patterns, the severity level and the factors determination to the accidents have been investigated in this research The results might be helpful for effective measures suggestion to improve traffic safety at signalized intersections A case study is conducted in Ho Chi Minh City (HCMC), Vietnam Historical traffic accident data in the city are collected during five years (2011-2015) Binary logit models have been used to identify contributing factors to serious traffic accident The results show that the involvement of intersection type, land use and road type are contributing factors to the accident severity Based on the findings, strategies and measures for safety improvement are formulated and discussed Keywords: Road traffic accident, signalized intersection, logit model, traffic safety measures, factor analysis Tóm tắt: Nghiên cứu này tập trung phân tích đặc điểm tai nạn, mức độ nghiêm trọng và các yếu tố ảnh hưởng đến tai nạn Kết quả nghiên cứu sẽ là cứ rất hữu ích để đề xuất các giải pháp hiệu quả nhằm nâng cao an toàn giao thông tại các nút giao thông có đèn tín hiệu Nghiên cứu này được thực hiện cho trường hợp ở Thành phố Hồ Chí Minh, Việt Nam, dựa dữ liệu thống kê về tai nạn giao thông năm (2011-2015) Mô hình hồi quy logit nhị thức được sử dụng để xác định các yếu tố ảnh hưởng đến tai nạn giao thông nghiêm trọng Kết quả phân tích cho thấy loại nút giao thông, vị trí nút giao và loại đường là những yếu tố ảnh hưởng đến mức độ nghiêm trọng của tai nạn Dựa vào kết quả nghiên cứu này để xuất các chính sách, giải pháp nhằm nâng cao an toàn giao thông Từ khóa: Tai nạn giao thông đường bộ, nút giao thông có đèn tín hiệu, mô hình logit, giải pháp an toàn giao thông, phân tích yếu tố Introduction Nearly 25 percent of all fatal crashes occur at intersections and about 30 percent of those are at intersections controlled by signals In 2015, the number of traffic accident, fatalities, injuries which occurrence in HCMC, have been slightly decreased accounted for 3,694 (accidents); 693 (fatalities) and 3,301 (injuries) Although, this showed that comparison with 2014, the number of traffic accident, fatalities and injuries in 2015 have slightly reduced, accounted for 14.51%, 4.15% and 18.07%, respectively, these increase at signalized intersections in HCMC accounted for 41% of total accident occurrence at intersections (9.7% of total traffic accident in HCMC) Until now, there is lack of empirical research about traffic safety for signalized intersections under mixed traffic conditions since most of previous research on this topic focusing on vehicle dominance To address the road traffic accident problems, it is necessary to deeply understand contributing factors to traffic accident The objectives of this research are to investigate traffic accident patterns, the severity levels and contributing factors to traffic accidents This study aims, however, at exploring not all contributing factors, since substantial limitations in data obtained from accident reports Logistic regression was used in this study to estimate the effect of the significant contributing factors to accident severity This paper are divided into five parts, introductof accide nt 1.000 -.042 1.000 412 670** 061 1.000 000 239 188** -.013 179** 1.000 000 803 001 679** -.029 391** 181** 000 577 000 1.000 000 282** 166** 252** 203** 000 001 000 000 159** 1.000 002 4.3 Development of logistic model The entry method of logistic regression was followed using SPSS version 21 The Omnibus tests of traffic accident severity 24 Journal of Transportation Science and Technology, Vol 21, Nov 2016 model coefficients is analyzed to assess (percentage correct 95%) while only 18.2% is whether data fit the model or not as percentage correct for fatal accident illustration in Table prediction From Wald - value test at Table 4, it appears that the variables loc, Uroad, Table Omnibus Tests of Model Coefficients Proad, Croad and Zone 2, show some Chi-square df Sig Step Step 47.081 000 significant effect (loc, Uroad, Proad, Croad Block 47.081 000 are about significant) Model 47.081 000 The specified model is significant (Sig < 0.05), hence it is recommended that the independent variables improve on the predictive power of the null model Table contains the two pseudo R2 measures that are Cox - Snell and Nagelkerke Cox and Snell’s R-square attempts to imitate multiple R - square based on ‘likelihood’, but its maximum can be (and usually is) less than 1.0, making it difficult to interpret Here it is indicating that 11.8% of the variation is explained by the logistic model The Nagelkerke modification that does range from to is a more reliable measure of the relationship Nagelkerke’s R2 will normally be higher than the Cox and Snell measure In this case it is 0.263 indicating the relationship of 26.3% between the predictors and the prediction In addition, in Table Hosmer - Lemeshow (H - L) test illustrate the significance of the developed logistic regression models (sig >0.05) Table Goodness of fit (Pseudo R2 and H-L Test) Pse udo R2 Te st -2 Log likelihood Cox & Snell R Nagelkerke R Square Square 118 263 176.333a a Estimation terminated at iteration number because parameter estimates changed by less than 001 Ste p Ste p Hosme r and Le me show Te st Chi-square df 5.739 Sig .220 Our H - L statistic has a significance of 0.22 which means that it is not statistically significant and therefore our model is quite good fit Rather than using a goodness – of fit statistic, we often want to look at the proportion of cases we have managed to classify correctly In a perfect model, the overall percent correct will be 100% for all cases In our study overall 88.3% were correctly classified Nevertheless, it trends skew prediction for non - fatal accident Table The result of Wald test B S.E Wald df Sig Exp(B) loc 770 426 3.258 049 2.159 Uroad 1.008 522 3.727 049 2.740 Proad 929 415 5.020 025 2.533 Croad 1.188 563 4.451 035 3.280 Zone 792 541 2.143 043 2.207 Consta -4.422 558 62.782 000 012 nt a Variable(s) entered on step 1: loc, Uroad, Proad, Croad, Zone2 Step 1a According to the previous analysis, the logit model with the significant variables is as follows: g(x) = - 4.422 + 0.77loc + 1.008Uroad + 0.929Proad + 1.188Croad + 0.792zone (9) Hence the logistic regression model developed in this study is (x) = eg(x)/ (1+eg(x)), where g(x) in Eqs.(9) 4.4 Model interpretation Interpretation of any models means the ability to explain practical inferences from the estimated coefficients The estimated coefficients for the independent variables represent the trend or rate of change of the dependent variables per unit of change in the independent variable The interpretation of the model developed in this study are presented in detailed, as follows 4.4.1 Impact of location on accident severity It should pay attention that due to ‘loc’ has two levels: loc = (fatal accident occurrence at junction and the others) loc = (fatal accident occurrence at intersections) According to this coding, our model shows loc in the logit model with the coefficient of 0.77 To interpret this parameter, the logit difference should be computed as follows: Logit (fatal accident/ junction & other) =  Logit (fatal accident/ Intersection) TẠP CHÍ KHOA HỌC CƠNG NGHỆ GIAO THƠNG VẬN TẢI, SỐ 21-11/2016 =  Logit difference =  Hence the odds ratio is e1 =e0.77 = 2.16 This value shows that the odds of being in a fatal accident at a junction and the others location are 2.16 higher than those at an intersection By using the same method, we can explain the zone factor to impact on accident severity easily, the odds of being in a fatal accident happening in zone are 2.2 (e0.792) higher than those occurrence related to zone and zone 4.4.2 Impact of Uroad on accident severity 2(1.008) measures the differential effect on the logit of two cases, whether fatal accident occurrence on urban road or not To interpret this parameter, the logit difference is computed first: Logit (Fatal/Uroad)  For any other type of road: Logit (Fatal/not Uroad) = Logit difference =   Hence the odds ratio is e(-1.109) = 0.33 Thus, the odds that accident will be fatal, in case it occurrences on urban road is 0.33 times its being fatal related to the other type of road The similar method was used to compute the odds for Proad and Croad, which account for 0.28 and 0.47, respectively Conclusions Logit model was developed in this study in order to determine significant contributing factors to accident severity in HCMC basing on response variable which is binary nature (i.e has two categories – fatal or non-fatal) with three variables namely, type of road, location and land use This model is reasonable statistic fit with 88.3% overall 25 percentage, although it trend skew prediction for non - fatal accident case (18.2%) The findings might help the authorities in HCMC should focus on improvement safety at junctions in zone where involve commune road for their strategies It also help the authorities that should be pay attention to make own safety policies for each zone instead of for whole HCMC as they have made before This may make safety policies more cost - effectively The odds presented in this paper can be used to help establish priorities solutions to reduce serious accident Such as the odds of being involved in a fatal accident at junctions and other on commune road in zone 2, where there is few policeman to control the traffic, lack of traffic signs and drivers with low safety awareness, are relatively higher than those for other cases It is important should pay attention that, some significant variables such as road surface, traffic signal pattern, light condition, collision type, license status and so on which are not available or difficult to obtain in HCMC condition So they are not including in this research Nevertheless, the findings of this study can be considered as guidance methods for future study when these variables are available References [1] Yau, K.K.W (2004), Risk factors affecting the severity of single vehicle traffic accidents in Hong Kong Accident Analysis & Prevention [2] Abdel-Aty et al., (2005), Exploring the overall and specific crash severity levels at signalized intersections Accident Analysis & Prevention [3] Yan, X et al., (2005), Characteristics of rear-end accidents at signalized intersections using multiple logistic regression model Accident Analysis & Prevention [4] Huang, H et al., (2008), Severity of driver injury and vehicle damage in traffic crashes at intersections: A Bayesian hierarchical analysis Accident Analysis & Prevention [5] Jin, Y., X Wang, and X Chen (2010) Right-angle crash injury severity analysis using ordered probability models Intelligent Computation Technology and Automation (ICICTA), IEEE Ngày nhận bài: 26/9/2016 Ngày chuyển phản biện: 30/9/2016 Ngày hoàn thành sửa bài: 21/10/2016 Ngày chấp nhận đăng: 28/10/2016 ... the others) loc = (fatal accident occurrence at intersections) According to this coding, our model shows loc in the logit model with the coefficient of 0.77 To interpret this parameter, the logit. .. higher than those at an intersection By using the same method, we can explain the zone factor to impact on accident severity easily, the odds of being in a fatal accident happening in zone are... measure In this case it is 0.263 indicating the relationship of 26.3% between the predictors and the prediction In addition, in Table Hosmer - Lemeshow (H - L) test illustrate the significance of the