Impacts of automated passenger cars on the capacity of a freeway basic section: applicability in the determination of vehicle adjustment factors in mixed traffic

The highway capacity manual(HCM)provides a formula to calculate the heavy vehicle adjustment factor(fHV)as a function of passenger car equivalent factors for the heavy vehicle(ET).However,a significant drawback is that the methodology was established solely based on human-driven passenger cars(HDPC)and human-driven heavy vehicles(HDHV).Due to automated passenger cars(APCs),a new adjustment factor(fAV)might be expected.This study simulated traffic flows at different percentages of HDHVs and APCs to investigate the impacts of HDHVs and APCs on freeway capacity by analyzing their influence on fHV and fAV values.The simulation determined observed adjustment factors at different percentages of HDHVs and APCs(fobserved).The HCM formula was used to calculate(fHCM).Modifications to the HCM formula are proposed,and vehicle adjustment factors due to HDHVs and APCs were calculated(fproposed).Results showed that,in the presence of APCs,while fobserved and fHCM were statistically significantly different,fobserved and fproposed were statistically equal.Hence,this study recommends using the proposed formula when determining vehicle adjustment factors(fproposed)due to HDHVs and APCs in the traffic stream.

Effects of rainy weather on traffic accidents of a freeway using cellular automata model

The aim of this work is to investigate the influence of rainy weather on traffic accidents of a freeway. The micro-scale driving behaviors in rainy weather and possible vehicle rear-end and sideslip accidents are analyzed. An improved CA model of two lanes one-way freeway is presented, where some vehicle accidents will occur when the necessary conditions are simultaneously satisfied. The characteristics of traffic flow under different rainfall intensities are discussed and the accident probabilities are analyzed via the simulation experiments by using variable speed limit （VSL） and incoming flow control. The results indicate that the measures are effective especially during heavy rainstorms or short-time heavy rainfall. According to different rainfall intensities, an appropriate strategy should be adopted in order to reduce the probability of vehicle accidents and enhance traffic flux as well.

Modeling of optimal control for urban freeway corridor under incident conditions

Traffic control and management are effective measures to solve the problem of traffic congestion. The optimal control model for freeway corridor is developed under incident conditions, which is in the form of minimization of the sum of the square of the difference between traffic demand and capacity at each intersection and on the freeway bottleneck section. The model optimizes control parameters of phase splits at arterial intersections, off-ramp diversion rates at upstream off-ramps and on-ramp diversion rates at downstream on ramps. Finally, the objective function is discussed and it is showed that the optimal control model is simple and practical.

Real-time rear-end crash potential prediction on freeways

This study develops new real-time freeway rear-end crash potential predictors using support vector machine(SVM) technique. The relationship between rear-end crash occurrences and traffic conditions were explored using historical loop detector data from Interstate-894 in Milwaukee, Wisconsin, USA. The extracted loop detection data were aggregated over different stations and time intervals to produce explanatory features. A feature selection process, which addresses the interaction between SVM classifiers and explanatory features, was adopted to identify the features that significantly influence rear-end crashes. Afterwards, the identified significant explanatory features over three separate time levels were used to train three SVM models. In the end, the multi-layer perceptron(MLP) artificial neural network models were used as benchmarks to evaluate the performance of SVM models. The results show that the proposed feature selection procedure greatly enhances the accuracy and generalization capability of SVM models. Moreover, the optimal SVM classifier achieves 81.1% overall prediction precision rate. In comparison with MLP artificial neural networks, SVM models provide better results in terms of crash prediction accuracy and false positive rate, which confirms the superior performance of SVM technique in rear-end crash potential prediction analysis.

The Use of Dynamic Message Signs (DMSs) on the Freeways: An Empirical Analysis of DMSs Logs and Survey Data

This study evaluates the Dynamic Message Signs (DMSs) use to dissipate incident information on the freeways in Las Vegas, Nevada. It focuses on the DMSs message timing, extent, and content, from the operators’ and drivers’ perspectives, considering the variability in drivers’ freeway experience. Two-week incidents data with fifty-nine incidents, DMS log data, and responses from a survey questionnaire were used. The descriptive analysis of the incidents revealed that about 54% of the incidents had their information posted on the DMSs;however, information of only 18.6% of the incidents was posted on time. The posted information covered the incident type (54.2%), location (49.2%), and lane blockage (45.8%), while the expected delay or the time the incident has lasted are rarely posted. Further, the standard DMSs are the most preferred sources of traffic information on the freeway compared to the travel time only DMSs, and the graphical map boards. The logistic regression applied to the survey responses revealed that regular freeway users are less likely to take an alternative route when they run into congestion, given no other information is available. Conversely, when given accurate information through DMSs, regular freeway users are about 2.9 times more likely to detour. Furthermore, regular freeway users perceive that the DMSs show clear information about the incident location. Upon improving the DMSs usage, 73% of respondents suggested that the information be provided earlier, and 54% requested improvements on congestion duration and length information. These findings can be used by the DMSs operators in Nevada and worldwide to improve freeway operations.

Evaluating impacts of different car-following types on rear-end crashes at freeway weaving section

The impacts of four different car-following types onrear-end crash risks at a freeway weaving section wereevaluated using trajectory data, in which Type 1 represents carfollowing car, Type 2 represents car following truck, Type 3represents truck following car and Type 4 represents truckfollowing truck. The time to collision （TIC） was introducedas the surrogate safety measure to determine the rear-end crashrisks. Then, the trajectory data at a freeway weaving sectionwas used for the case-controlled analysis. Three logisticregression models were developed with different TICthresholds to quantify the impacts of different car-followingtypes. The explanatory factors were alSO analyzed toinvestigate possible reasons for the results of logisticregressions. Results show that the rear-end crash risk of Type3 is 3, 167 times higher than that of Type 1 when the TICthreshold is 2 s. However, the odds ratios of Type 2 and Type4 are both smaller than 1, which indicates a safer condition.The analysis of explanatory factors also shows that Type 3 hasthe largest speed differences and the smallest net gaps. This isconsistent with vehicle operation features at a weaving sectionand is also the reason for the larger rear-end crash risks. Theresults of this study reflect the mechanism of rear-end crashrisks of different car-following types at the freeway weavingsection.

Speed Spatial Distribution Models for Traffic Accident Section of Freeway Based on Computer Simulation

Simulation models for accident section on freeway are built in microscopic traffic flow simulation environment. In these models involving 2-lane,3-lane and 4-lane freeway,one detector is set every 10 m to measure section running speed. According to the simulation results,speed spatial distribution curves for traffic accident section on freeway are drawn which help to determine dangerous sections on upstream of accident section. Furthermore,the speed spatial distribution models are obtained for every speed distribution curve. The results provide theoretical basis for determination on temporal and spatial influence ranges of traffic accident and offer reference to formulation of speed limit scheme and other management measures.

Micro-simulation model of two-lane freeway vehicles for obtaining traffic flow characteristics including safety condition

Unidirectional two-lane freeway is a typical and the simplest form of freeway. The traffic flow char- acteristics including safety condition on two-lane freeway is of great significance in planning, design, and manage- ment of a freeway. Many previous traffic flow models are able to figure out flow characteristics such as speed, den- sity, delay, and so forth. These models, however, have great difficulty in reflecting safety condition of vehicles. Besides, for the cellular automation, one of the most widely used microscopic traffic simulation models, its discreteness in both time and space can possibly cause inaccuracy or big errors in simulation results. In this paper, a micro-simula- tion model of two-lane freeway vehicles is proposed to evaluate characteristics of traffic flow, including safety condition. The model is also discrete in time but continu- ous in space, and it divides drivers into several groups on the basis of their preferences for overtaking, which makes the simulation more aligned with real situations. Partial test is conducted in this study and results of delay, speed, volume, and density indicate the preliminary validity of our model, based on which the proposed safety coefficient evaluates safety condition under different flow levels. It is found that the results of this evaluation coincide with daily experience of drivers, providing ground for effectiveness of the safety coefficient.

Understanding the Contributing Factors to Nighttime Crashes at Freeway Mainline Segments

This study investigated the crash contributing factors to the injury outcomes and the characteristics of the night time crashes at freeway mainline segments. Multinomial logit model (MNL) was selected to estimate the explanatory variables at a 95% confidence level. The six-year crash data (2005-2010) were obtained in the State of Florida, USA and five injury level outcomes, no injury, possible injury, non-incapacitating injury, capacitating injury, and fatal injury, were considered. The no injury level was selected as the baseline category.

A Literature Review on Freeway Traffic Incidents and Their Impact on Traffic Operations

Congestion on the freeway is more frequent due to several traffic incidents, namely traffic accidents, debris on the road, vehicle breakdown, and collision with guardrails than any other incidents. These, in turn, affect the operational performance of the freeway by increasing queue length, volume, and density. Consequently, effective freeway management strategies can help to minimize these impacts. The study investigates and summarizes existing studies to identify the reasons for and effects of the traffic incidents. Attention is given to the available solutions of the freeway traffic incidents management. The ultimate goal of this study is to identify the gaps which are not yet addressed to improve the operational effectiveness of the freeway. This study was conducted through a comprehensive literature review of existing refereed publications, established standards, and formal guidelines. Literature was sought through the Transport Research International Documentation (TRID) database, IEEE Transactions database, and google scholar search engine. Research focusing on freeway traffic incidents is a growing concern in transportation operations, as transportation network performance depends on it. Due to the advancement of technology, emerging vehicle technologies like connected vehicles have the potential to address these problems affecting the US transportation system and revolutionize mobility in the future. The study can serve as a reference for the researchers that are involved in freeway traffic operations.

Assessing the Safety Impacts of Increased Speed Limits on Kansas Freeways

Suitable speed limit is important for providing safety for road users. Lower-than-required posted speed limits could cause the majority of drivers non-compliant and higher-than-required posted speed limits may also increase the number of crashes with related severities. The speed limit raised in Kansas from 70 mph to 75 mph on a number of freeway segments in 2011. The goal of this study is to assess the safety impacts of the freeway sections influenced by speed limit increase. Three years before and three years after speed limit increase was considered and three methods were used: 1-Empirical Bayes (EB), 2-before-and-after with comparison group, and 3-cross-sectional study. The Crash Modification Factors (CMFs) were estimated and showed 16 percent increase for total crashes according to EB method. Further, the before-and-after with comparison group method showed 27 percent increase in total crashes and 35 percent increase on fatal and injury crashes. The cross- sectional method also presented 25 percent increase on total crashes and 62 percent increase on fatal and injury crashes. It was seen that these increases were statistically significant.

Investigating the Existence of Second Order Spatial Autocorrelation in Crash Frequency across Adjacent Freeway Segments

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.

Estimating Crash Rate of Freeway Segments Using Simultaneous Equation Model

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.

Study and Application of Evaluation Method for Weather Impact of Traffic Accident on Zhejiang Freeway

Occurrence degree of the accident on Zhejiang freeway is graded. Evaluation indicator system of weather impact on freeway is established. We use principal component analysis to extract meteorological indicators,and use Logistic regression to establish evaluation model of meteorological indicator,thereby determining evaluation grade of traffic weather impact. Via application test,it is proved that the evaluation on traffic weather condition by the model corresponds with actual situation,which can provide certain decision-making basis for traffic department and the public.

FREEWAY DT-061

点评：DT-061在机内预置了最佳均衡模式，多种图象控制（亮度、对比度、色调），屏幕角度与位置记忆，4路RCA前置输出，卡片式全功能遥控器。此外还有后显示视频输出。

FREEWAY DVD-0186OEL

点评：这款DVD拥有四路前置输出，卡片式全功能遥控器，操控非常方便。标准的安装尺寸，为车量身定做。数码同轴输出（被选）

FREEWAY MT-0051DVD

点评：HT-0051DVD可播放DVD／VCD／CD-R／CD-RW／MP3，先进的电子抗震系统，适合行车的颠簸环境。内置AH／FM／MPX立体声收音系统、30个预置电台、可浏览／自动记忆。机内预置最佳均衡模式，电子编码音量控制，标准的安装尺寸，方便车内安装使用。

Hazard-based duration modelling of merging time interval on freeway on-ramps

Freeway on-ramps suffer high crash risks due to frequent merging behaviours.This study developed hazard-based duration models to investigate the merging time interval on freeway on-ramps based on microscopic trajectory data.Fixed effect,random effect and random parameters Weibull distributed accelerated failure time models were developed to capture merging time as a function of various dynamic variables.The random parameters model was found to outperform the two counterparts since the unobserved heterogeneity of individual drivers was captured.Modelling estimation results indicate that drivers along the merging section with an auxiliary lane perform a smooth merging process and are easily affected by speed variables.Dynamics of leading and following vehicles on the merging and target lanes are found to influence the merging time interval for merging without an auxiliary lane,whereas the influence of surrounding vehicles ismarginal for thosewith an auxiliary lane.The findings of this study identify potential countermeasures for improving safety during the merging process.

Predicting the severity of traffic accidents on mountain freeways with dynamic traffic and weather data

Traffic accident severity prediction is essential for dynamic traffic safety management.To explore the factors influencing the severity of traffic accidents on mountain freeways and to predict the severity of traffic accidents,four models based on machine learning algorithms are constructed using support vector machine(SVM),decision tree classifier(DTC),Ada_SVM and Ada_DTC.In addition,random forest(RF)is used to calculate the importance degree of variables and the accident severity influences with high importance levels form the RF dataset.The results show that rainfall intensity,collision type,number of vehicles involved in the accident and toad section type are important variables influencing accident severity.The RF feature selection method improves the classification performance of four machine leaming algorithms,resulting in a 9.3%,5.5%,7.2% and 3.6% improvement in prediction accuracy for SVM,DTC,Ada_SVM and Ada_DTC,respectively.The combination of the Ada_SVM integrated algorithm and RF feature selection method has the best prediction performance,and it achieves 78.9% and 88.4% prediction precision and accuracy,respectively.

Quality assessment between calibrated highway safety manual safety performance functions and calibration functions for predicting crashes on freeway facilities

Crash prediction models are commonly used for network screening in highway safety management process,where potential impacts of highway safety treatments are quantified.The Highway Safety Manual(HSM)provides crash prediction models for various types of highway facilities that are often referred to as safety performance functions(SPFs).Freeway facility SPFs in the HSM were developed using data gathered from the states of California,Maine,and Washington.When applying these HSM-default SPFs to a local jurisdiction,the HSM recommends calibration of HSM-default SPFs or development of jurisdiction-specific SPFs to improve the accuracy of crash predictions.This study first calibrated the HSM-default freeway SPFs and for further accuracy and comparison purposes calibration functions were developed using Kansas freeway data.The performance of calibrated HSM-default SPFs was then compared with developed calibration functions concerning the accuracy in crash prediction.Freeway facility calibration dataset included521 freeway segments,351 entrance-related speed-change lanes,and 366 exit-related speed-change lanes.Cumulative residual plots and several other goodness-of-fit measures were used to assess the quality of calibrated HSM-default SPFs and calibration functions.Calibration functions fitted better compared to calibrated HSM-default SPFs for Kansas freeway data.The methodology used in this study could be beneficial and practiced to any jurisdiction.Calibration functions could be used as an alternative to jurisdiction-specific SPFs or a replacement for HSM-default SPFs,which are frequently used in comparing alternatives,in calculating economic benefits of project improvements,and in estimating economic effectiveness of crash reduction in highway safety-related decision making.