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Estimating Crash Rate of Freeway Segments Using Simultaneous Equation Model  [PDF]
Anthony Ramos, Hualiang (Harry) Teng, Yuyong Fu
Journal of Transportation Technologies (JTTs) , 2016, DOI: 10.4236/jtts.2016.65029
Abstract: This study develops crash rate prediction models based on the premise that crash frequencies observed from adjacent paired non-weaving and weaving freeway segments are spatially correlated and therefore requires a simultaneous equation modeling approach. Simultaneous equation models for paired freeway non-weaving segments and weaving segments along with combined three freeway segments upstream and downstream were developed to investigate the relationship of crash rate with freeway characteristics. The endogenous variables have significant coefficients which indicate that unobserved variables exist on these contiguous segments, resulting in different crash rates. AADT is a variable that can show the interaction between the traffic and crashes on these contiguous segments. The results corroborate such an interaction. By comparing the simultaneous equation model and the multiple linear regression model, it is shown that more model parameters in the simultaneous models are significant than those from linear regression model. This demonstrates the existence of the correlation between the interchange and between-interchange segments. It is crucial that some variables like segment length can be identified significant in the simultaneous model, which provides a way to quantify the safety impact of freeway development.
Investigating the Existence of Second Order Spatial Autocorrelation in Crash Frequency across Adjacent Freeway Segments  [PDF]
Eneliko Mulokozi, Hualiang (Harry) Teng
Journal of Transportation Technologies (JTTs) , 2016, DOI: 10.4236/jtts.2016.65026
Abstract: This study developed spatial Poisson model to incorporate spatial autocorrelation in crash frequency across contagious freeway segments. Spatial autocorrelation is the presence of spatial pattern in crash frequency over space due to geographic proximity. Usually crash caused congestion on a freeway segment propagates upstream and creates chance of occurring secondary crashes. This phenomenon makes the crash frequency on the contiguous freeway segments correlated. This correlation makes the distributional assumption of independence of crash frequency invalid. The existence of spatial autocorrelation is investigated by using Conditional autoregressive models (CAR models). The models are set up in a Bayesian modeling framework, to include terms which help to identify and quantify residual spatial autocorrelation for neighboring observation units. Models which recognize the presence of spatial dependence help to obtain unbiased estimates of parameters quantifying safety levels since the effects of spatial autocorrelation are accounted for in the modeling process. Based on CAR models, approximately 51% of crash frequencies across contiguous freeway segments are spatially auto-correlated. The incident rate ratios revealed that wider shoulder and weaving segments decreased crash frequency by factors of 0.84 and 0.75 respectively. The marginal impacts graphs showed that an increase in longitudinal space for segments with two lanes decreased crash frequency. However, an increase of facility width above three lanes results in more crashes, which indicates an increase in traffic flows and driving behavior leading to crashes. These results call an important step of analyzing contagious freeway segments simultaneously to account for the existence of spatial autocorrelation.
Construction of crash prediction model of freeway basic segmentbased on interactive influence of explanatory variables  [PDF]
Wang Xiaofei, Li Xinwei, , Fu Xinsha, Zhao Lixuan, Liu Xiaofeng
- , 2015, DOI: 10.3969/j.issn.1003-7985.2015.02.021
Abstract: In order to improve the prediction precision of the safety performance function(SPF)of freeway basic segments, design and crash data of 640 segments are collected from different institutions. Three negative binomial(NB)regression models and three generalized negative binomial(GNB)regression models are built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The effective use of the GNB model in analyzing the interactive influence of explanatory variables and predicting freeway basic segments is demonstrated. Among six models, the two models(one is the NB model and the other is the GNB model.)which consider the interactive influence of the annual average daily traffic(AADT)and length are more reasonable for predicting results. Furthermore, a comprehensive study is carried out to prove that when considering the interactive influence, the NB and GNB models have almost the same fitting performance in estimating the crashes, among which the GNB model is slightly better for prediction performance.
Evaluating impacts of different car-following typeson rear-end crashes at freeway weaving section  [PDF]
Li Ye, Xing Lu, Wang Wei, Dong Changyin
- , 2017, DOI: 10.3969/j.issn.1003-7985.2017.03.013
Abstract: The impacts of four different car-following types on rear-end crash risks at a freeway weaving section were evaluated using trajectory data, in which Type 1 represents car following car, Type 2 represents car following truck, Type 3 represents truck following car and Type 4 represents truck following truck. The time to collision(TTC)was introduced as the surrogate safety measure to determine the rear-end crash risks. Then, the trajectory data at a freeway weaving section was used for the case-controlled analysis. Three logistic regression models were developed with different TTC thresholds to quantify the impacts of different car-following types. The explanatory factors were also analyzed to investigate possible reasons for the results of logistic regressions. Results show that the rear-end crash risk of Type 3 is 3.167 times higher than that of Type 1 when the TTC threshold is 2 s. However, the odds ratios of Type 2 and Type 4 are both smaller than 1, which indicates a safer condition. The analysis of explanatory factors also shows that Type 3 has the largest speed differences and the smallest net gaps. This is consistent with vehicle operation features at a weaving section and is also the reason for the larger rear-end crash risks. The results of this study reflect the mechanism of rear-end crash risks of different car-following types at the freeway weaving section.
Driver Choice Compared to Controlled Diversion for a Freeway Double On-Ramp  [PDF]
L. C. Davis
Physics , 2008,
Abstract: Two diversion schemes that apportion demand between two on-ramps to reduce congestion and improve throughput on a freeway are analyzed. In the first scheme, drivers choose to merge or to divert to a downstream on-ramp based on information about average travel times for the two routes: (1) merge and travel on the freeway or (2) divert and travel on a surface street with merging downstream. The flow, rate of merging at the ramps, and the travel times oscillate strongly, but irregularly, due to delayed feedback. In the second scheme, diversion is controlled by the average mainline velocities just upstream of the on-ramps. Driver choice is not involved. If the average upstream velocity on the mainline drops below a predetermined value (20 m/s) vehicles are diverted to the downstream ramp. When the average mainline velocity downstream becomes too low, diversion is no longer permitted. The resultant oscillations in this scheme are nearly periodic. The period is dominated by the response time of the mainline to interruption of merging rather than delayed feedback, which contributes only a minor component linear in the distance separating the on-ramps. In general the second scheme produces more effective congestion reduction and greater throughput. Also the travel times for on-ramp drivers are less than that obtained by drivers who attempt to minimize their own travel times (first scheme).
Nonlinear feedback ramp controller in freeway based on fuzzy logic

LIANG Xin-rong,LIU Zhi-yong,MAO Zong-yuan,

控制理论与应用 , 2006,
Abstract: The on-ramp control is considered as an important component of freeway traffic control and intelligent transport systems,but the results of existing on-ramp control are not satisfactory.A nonlinear feedback method is proposed for on-ramp metering by using fuzzy logic.The freeway traffic flow dynamic model is built.Based on the model and in conjunction with the fuzzy logic theory,the nonlinear feedback ramp controller is designed. The ramp metering rate is determined by the fuzzy control based on the density tracking error and error variation.Triangle curves are used for the membership functions of the fuzzy variables.The rule base including 56 fuzzy rules is also established.Finally,the control system is simulated in MATLAB software.The result shows that the controller designed has good dynamic and steady-state performance,and can achieve a desired traffic density along the mainline of a freeway.This method is effective to the on-ramp control.
Critical Crashes  [PDF]
Anders Johansen,Didier Sornette
Quantitative Finance , 1999,
Abstract: We argue that the word ``critical'' in the title is not purely literary. Based on our and other previous work on nonlinear complex dynamical systems, we summarize present evidence, on the Oct. 1929, Oct. 1987, Oct. 1987 Hong-Kong, Aug. 1998 global market events and on the 1985 Forex event, for the hypothesis advanced four years ago that stock market crashes are caused by the slow buildup of long-range correlations between traders leading to a collapse of the stock market in one critical instant. We qualify the log-periodic oscillations using a novel non-parametric method that does not rely on any fit: the corresponding log-periodogram exhibits a strong statistically significant peak for all six crashes examined, pointing at approximately the same prefered scaling ratio around 2.
Large financial crashes  [PDF]
Didier Sornette,Anders Johansen
Physics , 1997, DOI: 10.1016/S0378-4371(97)00318-X
Abstract: We propose that large stock market crashes are analogous to critical points studied in statistical physics with log-periodic correction to scaling. We extend our previous renormalization group model of stock market prices prior to and after crashes [D. Sornette et al., J.Phys.I France 6, 167, 1996] by including the first non-linear correction. This predicts the existence of a log-frequency shift over time in the log-periodic oscillations prior to a crash. This is tested on the two largest historical crashes of the century, the october 1929 and october 1987 crashes, by fitting the stock market index over an interval of 8 years prior to the crashes. The good quality of the fits, as well as the consistency of the parameter values obtained from the two crashes, promote the theory that crashes have their origin in the collective ``crowd'' behavior of many interacting agents.
Stock market crashes are outliers  [PDF]
A. Johansen,D. Sornette
Physics , 1997, DOI: 10.1007/s100510050163
Abstract: We call attention against what seems to a widely held misconception according to which large crashes are the largest events of distributions of price variations with fat tails. We demonstrate on the Dow Jones Industrial index that with high probability the three largest crashes in this century are outliers. This result supports suggestion that large crashes result from specific amplification processes that might lead to observable pre-cursory signatures.
Crashes : symptoms, diagnoses and remedies  [PDF]
M. Ausloos,K. Ivanova,N. Vandewalle
Quantitative Finance , 2001,
Abstract: A brief historical perspective is first given concerning financial crashes, - from the 17th till the 20th century. In modern times, it seems that log periodic oscillations are found before crashes in several financial indices. The same is found in sand pile avalanches on Sierpinski gaskets. A discussion pertains to the after shock period with illustrations from the DAX index. The factual financial observations and the laboratory ones allow us some conjecture on symptoms and remedies for discussing financial crashes along econophysics lines.
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