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.

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.

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.

Tunnel bottleneck management with high-occupancy vehicle priority on intelligent freeways

Tunnels on freeways,as one of the critical bottlenecks,frequently cause severe congestion and passenger delay.To solve the tunnel bottleneck problem,most of the existing research can be divided into two types.One is to adopt variable speed limits(VSLs)to regulate a predetermined speed for vehicles to get through a bottleneck smoothly.The other is to adopt high-occupancy vehicle(HOV)lane management.In HOV lane management strategies,all traffic is divided into HOVs and low-occupancy vehicles(LOVs).HOVs are vehicles with a driver and one or more passengers.LOVs are vehicles with only a driver.This kind of research can grant priority to HOVs by providing a dedicated HOV lane.However,the existing research cannot both mitigate congestion and maximize passenger-oriented benefits.To address the research gap,this paper leverages connected and automated vehicle(CAV)technologies on intelligent freeways and develops a tunnel bottleneck management strategy with a dynamic HOV lane(DHL).The strategy bears the following features:1)enables tunnel bottleneck management at a microscopic level;2)maximizes passenger-oriented benefits;3)grants priority to HOVs even when the HOV lane is open to LOVs;4)allocates right-of-way segments for HOVs and LOVs in real time;and 5)performs well in a mixed-traffic environment.The proposed strategy is evaluated through comparison against the non-control baseline and a VSL strategy.Sensitivity analysis is conducted under different congestion levels and penetration rates.The results demonstrate that the proposed strategy outperforms in terms of passenger-oriented delay reduction and HOVs’priority level improvement.

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.

Differential variable speed limits to improve performance and safety of car-truck mixed traffic on freeways

This study develops a differential variable speed limit(DVSL)which assigns different speed limits for car and truck,and varies speed limits based on traffic conditions.The proposed DVSL algorithm changes speed limits in real time based on truck percentage and occupancy immediately upstream of the ramp and the average speed of the control road sections upstream of the ramp.DVSL algorithm also considers spatial coordination of speeds,which gradually changes the speed limits in successive road sections upstream of the ramp when the severe congestion occurs.The study tested the impacts of DVSL and three other speed limit strategies on delay and safety for a section of the Gardiner Expressway in Toronto,Canada using the VISSIM traffic simulation model.The other strategies are 1)uniform speed limit(USL),2)differential speed limit for car and truck(DSL),and 3)USL&DSL(U&D)-i.e.,USL at low truck percentage and DSL at high truck percentage.It was found that DVSL showed the lowest delays for both car and truck among the four strategies.This is mainly because DVSL increased the spacing between vehicles in the right lane upstream of the on-ramp and facilitated vehicles’merging into the mainline freeway.It was also found that DVSL showed the lowest likelihood of rear-end crash between the lead and following vehicles among the four strategies.This study demonstrates that the proposed DVSL algorithm can better control car and truck speeds to reduce delay and improve safety of car-truck mixed traffic flow on freeways.

A comprehensive analysis on the pavement condition indices of freeways and the establishment of a pavement management system

In Taiwan, both the engineers and temporary employees serving in National Freeway Bureau are graduates of civil engineering. The lack of specialization in the pavement domains as students and the increasing financial requirement of pavement maintenance are hindering new engineers from further improving their knowledge on pavement. The present study aimed to improve the comfort index of national freeways. First, the inspection aspects of national freeways, such as road testing items and methods, are analyzed. Subsequently, the lack of previous literature on this subject prompted the researchers to organize relevant information pertaining to the comfort index and inspection processes of national freeways. The inspection and analysis of national freeways are beneficial for maintaining and enhancing the comfort and quality of national freeways. Moreover, the International Roughness Index is adopted to elucidate the differences exhibited at different speeds. In addition, the present study endeavored to determine skid number （SN） degradation equations to improve road traffic safety. First, a correlation analysis between SN and traffic flow, as well as between SN and climate data, was performed. Findings revealed an increased correlation between SN and traffic flow, while the correlation between SN and climate （temperature and rainfall） is less prevalent. Subsequently, traffic flow and climate were adopted as the factors for the SN degradation equations. The present study divided the traffic flow data of the entire national freeway system into five groups, enabling researchers to present individual SN degradation equations for various traffic flow conditions, thereby contributing to maintain and elevate the road traffic safety of national freeways.

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.

Macroscopic Equation and Its Application for Free Ways

Effective transportation systems lead to the efficient movement of goods and people, which significantly contribute to the quality of life in every society. In the heart of every economic and social development, there is always a transportation system. Mathematically the problem of modeling vehicle traffic flow can be solved at two main observation scales: The microscopic and the macroscopic levels. In the microscopic level, every vehicle is considered individually, and therefore, for every vehicle, we have an equation that is usually an ordinary differential equation (ODE). At a macroscopic level, we use from the dynamics models, where we have a system of partial differential equation, which involves variables such as density, speed, and flow rate of traffic stream with respect to time and space. Therefore, considering above content, this study has tried to compare solution of equation of macroscopic flow considering linear form (speed-density) and applying boundary condition that resulting to form solved is non-linear one-order partial differential equation (sharpy method) with non-linear assuming (speed and density) and consequently homographic nonlinear relation (speed-density). The recent case clearly gives more significant speeds than linear case of speed and density that can be a good scientific basis. In terms of safety for accidents and traffic signal, just as a reminder, but it is resulted of the reality that generally solutions of partial differential equations can have different forms. Therefore, the solution of partial differential equation (macroscopic flow) can have different answers and solutions so that all of these solutions apply in PDE (equation of macroscopic flow). Thus, under this condition, we can have solution of linear equation similar to greenberg or greenshield & android that are explained in logarithm and exponential function, but this article is based mostly on nonlinear solution of macroscopic equation, provided that existing nonlinear relationship between speed and density (homographic the second degree function). As mentioned above, as it gives more reliable and reasonable speeds than greenshield case, it will have more safety. This article has been provided in this field.

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.

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.

Iterative learning control approach for ramp metering

An iterative learning control scheme is developed to the traffic densitycontrol in a macroscopic level freeway environment. With rigorous analysis, the proposed intelligentcontrol scheme guarantees the asymptotic convergence of the traffic density to the desired one. Thecontrol scheme is applied to a freeway model, and simulation results confirm the efficacy of theproposed approach.

高速公路景观设计中植物选用案例分析(英文)

Along with China's rapid economic development and continuous extension of freeways,greening and beautifying technologies of high-grade freeways become a new topic in landscape work in the new era.Through the analysis of landscape plants along Line B of the freeway from Wansheng to Nanchuan in Chongqing and the introduction of characteristics of plants suitable for freeways,the conclusion upon the advantages of landscape design of this section of the freeway is deduced.

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.

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.