Modeling of speed distribution for mixed bicycle traffic flow

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1 Special Issue Article Modeling of speed distribution for mixed bicycle traffic flow Advances in Mechanical Engineering 2015, Vol. 7(11) 1 9 Ó The Author(s) 2015 DOI: / aime.sagepub.com Cheng Xu 1,2, Qiangwei Li 2, Zhaowei Qu 1 and Pengfei Tao 1 Abstract Speed is a fundamental measure of traffic performance for highway systems. There were lots of results for the speed characteristics of motorized vehicles. In this article, we studied the speed distribution for mixed bicycle traffic which was ignored in the past. Field speed data were collected from Hangzhou, China, under different survey sites, traffic conditions, and percentages of electric bicycle. The statistics results of field data show that the total mean speed of electric bicycles is km/h, 3.63 km/h faster and 27.0% higher than that of regular bicycles. Normal, log-normal, gamma, and Weibull distribution models were used for testing speed data. The results of goodness-of-fit hypothesis tests imply that the log-normal and Weibull model can fit the field data very well. Then, the relationships between mean speed and electric bicycle proportions were proposed using linear regression models, and the mean speed for purely electric bicycles or regular bicycles can be obtained. The findings of this article will provide effective help for the safety and traffic management of mixed bicycle traffic. Keywords Mixed bicycle traffic, electric bicycle, regular bicycle, speed distribution, linear regression Date received: 16 July 2015; accepted: 6 October 2015 Academic Editor: Geert Wets Introduction Because of characteristics including low cost, convenience, mobility, long trip distance, and environmental friendliness of electric bicycles (E-bikes), they have been one of the most important travel modes in some developing countries such as China, Vietnam, Indonesia, and Malaysia. 1 Due to the inadequate and low-efficiency public transportation facilities in some Chinese cities, E-bikes have been a very popular transportation choice for low-income people in China, especially in many southern Chinese cities (due to not very cold weather). E-bike ownership in China reached approximately 200 million in The rapid increase in the E-bikes has led to a mixture of bikes in operation in many cities: E-bikes and regular bicycles (R-bikes). Previous studies have proposed that the speed differences between E-bikes and R-bikes will engender some efficiency and safety issues. 3 6 Speed is one of the most significant traffic flow parameters for capacity analysis and level of service. The speed characteristics of mixed bicycle traffic flow are more complicated than those of purely R-bikes traffic flow. The speed-related traffic crashes caused by E-bikes are more severe than those caused by R-bikes. Therefore, the speed of mixed bicycle traffic is important to understand and will provide in-depth knowledge for traffic flow theory and traffic safety more generally. 1 College of Transportation, Jilin University, Changchun, China 2 Department of Traffic Management Engineering, Zhejiang Police College, Hangzhou, China Corresponding author: Pengfei Tao, College of Transportation, Jilin University, 5988 Renmingdajie, Changchun , China. Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License ( which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( open-access-at-sage).

2 2 Advances in Mechanical Engineering Mathematical models or distributions used to describe speed characteristics of mixed bicycle traffic flow are very useful, especially since they can be utilized for simulation, safety analysis, and determining bicycle speed limits. Therefore, the purpose of this article is to analyze the characteristics of field speed data for mixed bicycle traffic conditions and to develop a speed distribution model considering the effect of E-bike proportions. The mixed bicycle traffic consists of two different types of bicycles (E-bikes and R-bikes). The rest of the article is organized into five sections. Section Literature review reviews related literatures for bicycle speed characteristics and distributions. Section Data collection presents the collected field data of bicycle speeds and related analyses. Section Speed distribution model presents distribution models and fitting results of the proposed models. Section Effect of E-bike proportions on speed develops speed models incorporating E-bike proportion factors. Section Conclusion concludes the article with a summary of our findings. Literature review Speed, as one of the three parameters (flow, speed, density) for traffic flow, is a fundamental measure of traffic performance of highway systems. Lots of research has been conducted to examine the speed characteristics of motorized vehicles, effect on speed, speed dispersion, safety-based speed characteristics, variable speed limits, and speed density relationship models Similarly, there is a lot of research that has examined distribution models for motorized vehicle speed data, such as normal distribution, skewed distribution, and composite distribution. In regard to normal distribution, Leong 11 and McLean 12 found that, for lightly trafficked two-lane roads where most vehicles are traveling freely, car speeds measured in time are approximately normally distributed with a coefficient of variation ranging from about Sahoo et al. 13 found that the vehicular speeds conform to the normal distribution with a mean of km/h and a standard deviation of 9 13 km/h on different intercity roads in India. Kumar and Rao 14 proposed that the spot speed data conform to the normal distribution. They considered the 85th percentile speed as the average free speed, and the average free speeds of cars, buses, and trucks were found to be approximately 65, 56, and 52 km/h, respectively. Hossain and Iqbal 15 also found that the speed of commonly available vehicles in Dhaka, India, follow a normal distribution. A linear regression analysis was carried out to explore the relationship of drivers free speed with the pavement and shoulder widths. For skewed distribution, Haight and Mosher 16 considered that the speed data could be well represented by either a gamma or a log-normal distribution. These distributions offer the advantage that the same functional form is retained when the time speed distribution is transformed into a space-speed distribution and avoid the theoretical difficulty of the negative speeds given by the infinite tails of the normal distribution. Gerlough and Huber 17 proposed the use of the log-normal distribution. This resembles the normal distribution, but is skewed with a larger tail to the right. For composite distribution, Ko and Guensler 18 stated that speed distribution could be characterized as two different components, congested and non-congested speeds, depending on location-specific congestion definitions. The change between unimodal (expected) speed distribution and bimodal (unexpected) speed distribution will indicate the pattern of traffic variations of specified interstate freeway systems. Jun 19 evaluated the traffic congestion patterns during the Thanksgiving holiday period in 2006 using a Gaussian mixture distribution estimated by the Expectation Maximization (EM) algorithm for modeling speed data. Previous research focuses on motorized vehicle speed distributions. Recently, however, there has been some research on bicycles or mixed bicycle traffic speed distributions. Katti and Raghavachari 20 reported that the spot speeds of cars, heavy commercial vehicles, light commercial vehicles, scooters, and cycle rickshaws (pedal tricycles) follow a normal distribution, whereas for bicycles, they follow a log-normal distribution. Dey et al. 21 proposed speed distribution curve models under mixed traffic conditions which included fast moving vehicles such as car/jeep, truck/bus, two-wheeler, and three-wheeler and slow moving vehicles such as bicycle and tractor. Lin et al. 22 surveyed the operating speeds of both E-bikes and R-bikes on the non-motorized lanes in Kunming, China, and presented the comparison results of speeds and its distributions between E- bikes and R-bikes. Statistical analysis indicated that the mean speed of E-bikes was km/h, which is 7.05 km/h faster, or 47.6% higher, than that of R-bikes. The normal distribution and logarithmic normal distribution for E-bikes and R-bikes were tested. Wang et al. 23 analyzed the impact of various factors on the speed of non-motorized vehicles and established a normal speed distribution for mixed bicycle traffic flow. In previous research, many researchers focus on motorized vehicle speed distributions, and the speed distributions of E-bikes and R-bikes were analyzed separately. To the best of our knowledge, the speed distributions of mixed bicycle traffic flow have rarely been reported, until now. This study examined the statistical significance of differences in the distribution of speeds between E-bikes and R-bikes under different conditions. This study also examined the relationship between the speed parameters of mixed bicycle traffic and E-bike proportions.

3 Xu et al. 3 Table 1. Basic statistical description of four survey sites. No. Survey site Path width (m) Sample size Bicycle type Gender Age 1 Jiaogong Road RB 32.3% M 60.2% Y 67.3% EB 67.7% F 39.8% M 27.4% E 5.3% 2 Wensan Road RB 43.7% M 68.4% Y 75.6% EB 56.3% F 31.6% M 18.5% E 5.9% 3 Xueyuan Road RB 35.6% M 64.3% Y 58.6% EB 64.4% F 35.7% M 34.3% E 7.1% 4 Wener Road RB 42.5% M 68.2% Y 72.4% EB 57.5% F 31.8% M 23.3% E 4.3% Total 11,829 RB 38.2% M 64.4% Y 67.6% EB 61.8% F 35.6% M 27.2% E 5.2% RB: regular bicycles; EB: electric bicycles; M: male; F: female; Y: young; M: middle-aged; E: elderly. Data collection Survey sites and time Field bicycle speed data mixed with E-bikes and R- bikes are of significance for modeling of speed distributions. Four survey sites in the Chinese city of Hangzhou, where bicycle exclusive paths are separated from motorized vehicle lanes, were selected for data collection. There are several basic requirements for the selection of survey sites and times. First, the bicycle path sections were located in the downtown area of Hangzhou, where more E-bikes and R-bikes and different traffic conditions can be observed. Therefore, the amount of heterogeneous bicycle traffic should be sufficiently large to provide enough data samples. Second, the survey sites are located at least 100 m away from an intersection so as to minimize the effects of traffic signals. Third, the selected survey path should be straight and of low gradient. Fourth, the survey days were weekdays, and the weather was sunny. Fifth, the time of data collection covered both peak hours and off-peak hours. Therefore, the speed data under these different traffic conditions could be collected. Video data collection Video investigation technology is popular for bicycle traffic data survey. In this study, a camera was set up on the roadside of the bicycle path to record the operation of bicycle traffic. 24,25 In order to ensure that the riders would be unaware they were being observed, the camera was covertly and carefully placed. When a bicycle crosses the marked two fixed positions, the video surveillance application could record the time and the speed can be calculated easily. The other parameters such as bicycle types, gender, and riders age were recorded and coded manually. For the results reported in this article, bicycle type consists of two categories, E-bikes and R-bikes. Gender is easily recorded by the investigator. In regard to age, the riders were divided into three groups: (1) under 30 years, (2) between 30 and 50 years, and (3) over 50 years. Survey results Altogether, 11,829 speed samples, including 61.8% E- bike data, have been collected from four survey sites. Each site generated nearly 3000 samples for analysis. Table 1 presents the survey sites name, bicycle path width, sample size, bicycle type proportion, riders gender proportion, and riders age proportion, where RB denotes regular bicycles and EB denotes electric bicycles; M denotes male riders and F female riders; and Y denotes young (\30 years), M middle-aged (30 50 years), and E elderly (.50 years) riders. It can be seen that the EB was the largest proportion (average up to 61.8%) and that male and young riders were the major riders for non-motorized vehicles (average up to 64.4% and 67.6%). This result is likely due to users of E-bikes and R-bikes being mainly high school students and low-income persons who use bicycles as the travel mode for commuting. Basic statistics relating to speed samples from the four field survey sites are shown in Table 2, where Min, Max, Median, Mean, and SD refer to minimum speed value, maximum speed value, median speed value, average speed value, and standard deviation, respectively. Skewness and kurtosis are two important parameters in probability theory and statistics. Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive or negative. Kurtosis is any

4 4 Advances in Mechanical Engineering Table 2. Results of field speed data (km/h). Survey site Min Max Mean SD Skewness Kurtosis Jiaogong Road RB EB Total Wensan Road RB EB Total Xueyuan Road RB EB Total Wener Road RB EB Total Total RB EB Total RB: regular bicycles; EB: electric bicycles; SD: standard deviation. measure of the peakedness of the probability distribution of a real-valued random variable. In a similar way to the concept of skewness, kurtosis is a descriptor of the shape of a probability distribution. Positive kurtosis indicates a peaked distribution and negative kurtosis indicates a flat distribution. Skewness and kurtosis can be calculated by g = b = P n i = 1 ðv i v Þ 3 ðn 1 P n i = 1 Þs 3 v ðv i v Þ 4 ðn 1 Þs 4 v ð1þ ð2þ where g and b are skewness and kurtosis of bicycle speed samples, respectively. v i is the speed of bicycle i and n is the number of speed sample. v and s v are the mean and standard deviation of speed samples, respectively. Table 2 shows that the means of R-bike speed at the different survey sites range from to km/h, and the means of E-bike speed at the different survey sites range from to km/h. The total means of R-bike and E-bike are and km/h, respectively. The mean speed of E-bikes is 3.63 km/h faster and 27.0% higher than that of R-bikes. The maximum speed of E-bike is km/h, the same as a motorized vehicle s speed limit. Meanwhile, the values of skewness and kurtosis show that the speed data do not obey normal distribution. Speed distribution model Speed distribution is very important for traffic flow. Figure 1 shows the field speed data for R-bikes, E-bikes, and total samples from two sites. It can be seen that the distributions of R-bikes and E-bikes have different shapes and lead to the complicated distributions of total samples composed of R-bikes and E- bikes. However, a single normal distribution would not fit the data very well because of the high values of skewness and kurtosis for mixed bicycle speed data. In order to examine the characteristics of bicycle speed distribution under mixed bicycle traffic conditions, four distribution models (normal, log-normal, gamma, and Weibull) were proposed for modeling bicycle speed samples. The probability density functions of four distributions are shown as follows f n (v)= ffiffiffiffiffiffi 1 (v m)2 p exp 2p s 2s 2 8! >< 1 pffiffiffiffiffiffiffiffiffi exp ð ln v m Þ2 f 1 ðþ= v 2ps 2s 2 v.0 >: 0 v 0 8 < v k 1 e v u f g (v)= u k v.0 : G(k) 0 v 0 ( k v k 1e (v=l) k f w (v)= v 0 l l 0 v\0 ð3þ ð4þ ð5þ ð6þ where f n (v), f l (v), f g (v), and f w (v) are probability density functions of normal, log-normal, gamma, and Weibull distributions, respectively. m, s, u, k, and l are the model parameters, respectively. Using the field data from four survey sites, the four distribution parameters could be easily estimated using MATLAB 7.0. Maximum Likelihood (ML) estimation was used for parameter estimates and confidence intervals. The field data density function and the estimated

5 Xu et al. 5 Figure 1. Field speed data distributions of R-bikes, E-bikes, and total, respectively: (a) Jiaogong Road and (b) Xueyuan Road. density functions are shown in Figure 2, respectively. As can be roughly seen from the figure, the log-normal and Weibull distributions have better fitting results than the other two distributions. In order to test the fitting results quantitatively, a single sample Kolmogorov Smirnov (K-S) goodnessof-fit hypothesis test was used. 26,27 H indicates the result of the hypothesis test, where H = 0 means that do not reject the null hypothesis at significance level alpha = 0.05, and H = 1 means reject the null hypothesis at significance level alpha = The K-S test statistics KSSTAT and critical value of the test (CV) were also calculated and are shown in Table 3. If KSSTAT is larger than CV, it means accepting the null hypothesis; the field data obey the proposed distribution model. The larger the difference between KSSTAT and CV, the better the results of goodness-of-fit. From Table 3, it can be seen that the field data from four survey sites can all accord with the log-normal and Weibull distributions and pass the K-S test. However, only the field data from Xueyuan road and Wener road can accord with the normal and gamma distributions. This may be due to the field data from Jiaogong road and Wensan road having included more congested speed data and due to the traffic flow being more complicated and mixed. Therefore, normal and gamma distributions would not have the ability to fit the data very well. The results confirm that log-normal and Weibull distributions can be used for modeling the distributions of mixed bicycle speed data.

6 6 Advances in Mechanical Engineering Figure 2. Speed distribution results of each survey site: (a) Jiaogong Road, (b) Wensan Road, (c) Xueyuan Road, and (d) Wener Road. Table 3. Summary statistics of K-S goodness-of-fit hypothesis test. Survey site K-S test result Normal Log-normal Gamma Weibull Jiaogong Road H KSSTAT CV Wensan Road H KSSTAT CV Xueyuan Road H KSSTAT CV Wener Road H KSSTAT CV K-S: Kolmogorov Smirnov; CV: critical value of the test. Effect of E-bike proportions on speed Because of the higher speed of E-bikes, it can be easily seen that with the increase in E-bike proportions, the average speed of mixed bicycle traffic will increase. In order to analyze the effect of E-bike proportions on bicycle traffic speed, this section proposed a linear

7 Xu et al. 7 Figure 3. Linear regression results between E-bike proportions and speed means for each survey site:. (a) Jiaogong Road, (b) Wensan Road, (c) Xueyuan Road, and (d) Wener Road. regression model for modeling the effect of E-bike proportions. The regression model is as follows v m = f v ðp e Þ= a + bp e ð7þ where v m is estimated speed means of mixed bicycle traffic flow using log-normal distribution, p e is the proportions of E-bikes in mixed bicycle traffic flow, and a and b are parameters of regression function. The R 2

8 8 Advances in Mechanical Engineering coefficient of determination is a statistical measure reflecting how well the regression line approximates the field data. The coefficient of determination can be defined as R 2 = 1 P N n = 1 P N n = 1 v s n 2 v0 n ð8þ 2 v where v s n, v0 n, v, and N are estimated mean using regression model, field mean, average value of filed mean, and sample number, respectively. In order to analyze the effect of E-bikes, there were four steps for modeling the regression model. First, the field data from four sites can be classified to several intervals. Each interval has some speed samples. Second, a log-normal distribution model was used to estimate the speed mean of each interval, and the E- bike proportions can also be calculated. Third, the relationship between speed means and E-bike proportions for each interval would be plotted. Finally, the regression model (equation (7)) was used for estimating the model parameters. Figure 3 shows the regression results from four survey sites. It can be seen that there are strong linear relationships between E-bike proportions and mean speed. The values of R 2 are all above 0.5, which means good fitting results. Using the regression function, the speed mean under pure R-bikes or E-bikes can be easily obtained by v r = f v ðp e = 0Þ= a ð9þ v e = f v ðp e = 1Þ= a + b where v r and v e are the mean speed for pure R-bikes and mean speed for pure E-bikes, respectively. In order to verify the effect of the regression model, the speed means of purely R-bikes and E-bikes calculated using equation (9) would be compared with those from field data. We employed two commonly used performance indices to evaluate the proposed regression model. The first was the mean absolute percentage error (MAPE), and the second was the root mean square error (RMSE). Table 4 shows the estimated results and performance indices of the proposed regression model. These results indicate that the MAPE value is 2.3% and the RMSE value is This shows that the regression model can accurately estimate the speed mean for all four datasets. Therefore, this method can be applied for calculating the speed mean for different E-bike proportions under mixed bicycle traffic conditions. v 0 n Table 4. Results of estimated speed mean (km/h). Survey site Field mean Estimated mean Jiaogong road RB EB Wensan road RB EB Xueyuan road RB EB Wener road RB EB MAPE 2.30% RMSE 0.45 RB: regular bicycles; EB: electric bicycles; MAPE: mean absolute percentage error; RMSE: root mean square error. Conclusion Speed distribution modeling for mixed bicycle traffic has largely been ignored in previous research. Based on the field survey of four roads from the downtown area of Hangzhou, China, the bicycle speed samples using different bicycle types, genders, and rider ages were collected. Several interesting results were obtained using a modeling approach to the data. First, the total speed mean of E-bikes was km/h, 3.63 km/h faster and 27.0% higher than that of R-bikes. The maximum speed of E-bikes is up to km/h, approaching the speed of motorized vehicles. Second, speed samples of R-bikes, E-bikes, and total bicycles have different distributions. The total field speed samples follow the lognormal and Weibull distributions very well. Third, a linear regression model between speed means and E- bike proportions was proposed, and the parameters were estimated using field data. Fourth, the speed means for purely R-bike or E-bike traffic were estimated and verified using field data. The findings of the article can be used for bicycle safety analysis and bicycle traffic management. Future research will focus on the effect of the bicycle riders characteristics (such as gender or age) on bicycle traffic speed. Acknowledgements Many thanks to Prof. Wang, Dr Jin, and Mr Zhou in Zhejiang University for providing parts of the field bicycle data. We thank Dr Judy Fleiter from the Centre for Accident Research and Road Safety, Queensland, for assistance with proof-reading. Declaration of conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

9 Xu et al. 9 Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Science Foundation of China (No , , ), the China Postdoctoral Science Foundation (20141M551178), and the Key Science and Technology Innovation Team of Zhejiang Province (2013TD09). References 1. Weinert J, Ma Z and Cherry C. The transition to electric bikes in China: history and key reasons for rapid growth. Transportation 2007; 34: Xinhua News. Electric bike ownership in China was over 200 million, 20/c_ htm (2013, accessed January 2015). 3. Liu X, Shen L-D and Ren F. Operational analysis of bicycle interchanges in Beijing, China. Transp Res Record 1993; 1396: Wei H, Huang J and Wang J. Models for estimating traffic capacity on urban bicycle lanes. Presented at 76th annual meeting of the transportation research board, Washington, DC, January, Allen DP, Rouphall N, Hummer JE, et al. Operational analysis of uninterrupted bicycle facilities. Transp Res Record 1998; 1636: Parkin J and Rotheram J. Design speeds and acceleration characteristics of bicycle traffic for use in planning, design and appraisal. Transp Policy 2010; 17: Qu X, Yang Y, Liu Z, et al. Potential crash risks of expressway on-ramps and off-ramps: a case study in Beijing, China. Safety Sci 2014; 70: Jin S, Wang D and Yang X. Non-lane-based car following model using visual angle information. Transp Res Record 2011; 2249: Jin S, Qu X, Xu C, et al. Dynamic characteristics of traffic flow with consideration of pedestrians road-crossing behavior. Physica A 2013; 392: Jin S, Wang D, Huang Z, et al. Visual angle model for car following theory. Physica A 2011; 390: Leong HJW. The distribution and trend of free speeds on two-lane two-way rural highways in New South Wales. In: Proceedings of the 4th Australian road research board conference, part 1, Melbourne, VIC, Australia, October 1968, pp Vermont South, VIC, Australia: Australian Road Research Board. 12. McLean JR. Observed speed distributions and rural road traffic operations. In: Proceedings of the 9th Australian road research board conference part 5, Brisbane, QLD, Australia, August 1978, pp Vermont South, VIC, Australia: Australian Road Research Board. 13. Sahoo PK, Rao SK and Kumar VM. A study of traffic flow characteristics on two stretches of national highways-5. Indian Highw 1996; 24: Kumar VM and Rao SK. Headway and speed studies on two-lane highways. Indian Highw 1998; 26: Hossain M and Iqbal GA. Vehicular headway distribution and free speed characteristics on two-lane two-way highways of Bangladesh. J Inst Eng AG 1999; 80: Haight FA and Mosher WW. A practical method for improving the accuracy of vehicular speed distribution measurements. Washington, DC: Highway Research Board, 1962, pp Gerlough DL and Huber MJ. Traffic flow theory: a monograph. Special report 165. Washington, DC: Transportation Research Board, National Research Council, June, Ko J and Guensler R. Characterization of congestion based on speed distribution: a statistical approach using Gaussian mixture model. Presented at 82nd annual meeting of the transportation research board, Washington, DC, January, Jun J. Understanding the variability of speed distributions under mixed traffic conditions caused by holiday traffic. Transport Res C: Emer 2010; 18: Katti BK and Raghavachari S. Modelling of mixed traffic speed data as inputs for the traffic simulation models. Highway Res Bull (Indian Roads Congress, New Delhi, India) 1986; 28: Dey PP, Chandra S and Gangopadhaya S. Speed distribution curves under mixed traffic conditions. J Transp Eng: ASCE 2006; 132: Lin S, He M, Tan Y, et al. Comparison study on operating speeds of electric bicycles and bicycles: experience from field investigation in Kunming, China. Transp Res Record 2008; 2048: Wang D, Zhou D, Jin S, et al. Characteristics of mixed bicycle traffic flow on conventional bicycle path. Presented at 94th annual meeting of the transportation research board, Washington, DC, January Jin S, Qu X, Zhou D, et al. Estimating cycleway capacity and bicycle equivalent unit for electric bicycles. Transport Res A: Pol 2015; 77: Zhou D, Jin S, Ma D, et al. Modeling mixed bicycle traffic flow: a comparative study on the cellular automata approach. Discrete Dyn Nat Soc 2015; 2015: Qu X, Wang S and Zhang J. On the fundamental diagram for freeway traffic: a novel calibration approach for single-regime models. Transport Res B: Meth 2015; 73: Jin S, Wang D, Xu C, et al. Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter. J Zhejiang Univ Sci A 2013; 14:

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