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.

Extended speed gradient model for traffic flow on two-lane freeways

In this paper, the speed gradient （SG） model is extended to describe the traffic flow on two-lane freeways. Terms related to lane change are added into the continuity equations and velocity dynamic equations. The empirically observed two-lane phenomena, such as lane usage inversion and lane change rate versus density, are reproduced by extended SG model. The local cluster effect is also investigated by numerical simulations.

A continuum traffic flow model with the consideration of coupling effect for two-lane freeways

A new higher-order continuum model is proposed by considering the coupling and lane changing effects of the vehicles on two adjacent lanes. A stability analysis of the proposed model provides the conditions that ensure its linear stability. Issues related to lane changing, shock waves and rarefaction waves, local clustering and phase transition are also investigated with numerical experiments. The simulation results show that the proposed model is capable of providing explanations to some particular traffic phenomena commonly observable in real traffic flows.

A viscous continuum traffic flow model with consideration of the coupling effect for two-lane freeways

In this paper, the viscous continuum traffic flow model for a single lane is extended to the traffic flow for two-lane freeways. The proposed model is a higher-order continuum model considering the coupling and lane changing effects of the vehicles on two adjacent lanes. It results from integrating the Taylor series expansion of the viscous continuum traffic flow model proposed by Ge （2006 Physiea A 371 667） into the multi-lane model presented by Daganzo （1997 Transpn. Res. B 31 83）. Our proposed model may be used to describe non-anisotropic behaviour because of lane changing in multi-lane traffic. A linear stability analysis is given and the neutral stability condition is obtained. Also, issues related to lane changing, shock waves and rarefaction waves, local clustering and phase transition are investigated through a simulation experiment. The simulation results show that the proposed model is capable of explaining some particular traffic phenomena commonly observable in real world traffic flow.

Continuum modeling for two-lane traffic flow

In this paper, we study the continuum modeling of traffic dynamics for two-lane freeways. A new dynamics model is proposed, which contains the speed gradient-based momentum equations derived from a car-following theory suited to two-lane traffic flow. The conditions for securing the linear stability of the new model are presented. Numerical tests are can＇ied out and some nonequilibrium phenomena are observed, such as small disturbance instability, stop-and-go waves, local clusters and phase transition.

Analysis of the wave properties of a new two-lane continuum model with the coupling effect

A multilane extension of the single-lane anisotropic continuum model （GK model） developed by Gupta and Katiyar for traffic flow is discussed with the consideration of the coupling effect between the vehicles of different lanes in the instantaneous traffic situation and the lane-changing effect. The conditions for securing the linear stability of the new model are presented. The shock and the rarefaction waves, the local cluster effect and the phase transition are investigated through simulation experiments with the new model and are found to be consistent with the diverse nonlinear dynamical phenomena observed in a real traffic flow. The analysis also focuses on empirically observed two- lane phenomena, such as lane usage inversion and the density dependence of the number of lane changes. It is shown that single-lane dynamics can be extended to multilane cases without changing the basic properties of the single-lane model. The results show that the new multilane model is capable of explaining some particular traffic phenomena and is in accordance with real traffic flow.

Placement distance of freeway exit advance guide sign and its safety impacts

To remedy the empirical pitfalls of current chinese specifications and MUTCD 2009 guidelines in determining the placement distance of freeway exit advance guide signs,the driving maneuver of exiting traffic is analyzed and the factors influencing placement distance are explored.Variables including the number of lanes,lane width,lane-changing time,driver＇s visual characteristics,sign installation methods and operating speeds on both freeway mainlines and exit ramps are found significant in explaining exit safety.Three different installation methods,namely ground installation,overhead installation and median installation,are introduced and their applicable conditions are given.Models,with the same structure among the three installation methods,are developed to compute the placement distance under different roadway geometric and traffic conditions.Taking overhead installation as an example,simulation results in TSIS-CORSIM show that the proposed distance reduces the number of lane changes in the area from the ramp nose to 500 m upstream by 58.93% compared with current Chinese specifications and 27.35% compared with MUTCD 2009 guidelines.Thus,the distances recommended in this paper have a better safety performance.

Optimal dispatching method of traffic incident rescue resource for freeway network

An optimal resource dispatching method is proposed to solve the multiple-response problem under the conditions of potential incidents on freeway networks.Travel time of the response vehicle is selected instead of route distance as the weight to reflect the impact of traffic conditions on the decisions of rescue resources.According to the characteristics of different types of rescue vehicles the dispatching decision-making time is revised to show the heterogeneity among different rescue vehicle dispatching modes. The genetic algorithm is used to obtain the solutions to the rescue resources dispatching model. A case study shows that the proposed method can accurately reveal the impact of potential incidents on the costs of rescues according to the variations in the types and quantities of rescue resources and the optimal dispatching plan with respect to potential incidents can be obtained.The proposed method is applicable in real world scenarios.

Hierarchical decision-making system for real-time freeway incident response

The artificial intelligence technique is used to generate a freeway incident response plan. The incident response framework based on rule-based reasoning, case-based reasoning and Bayesian networks reasoning is presented. First, a freeway incident management system （RK-IMS） based on rule-based reasoning is developed and applied for incident management in the northern section of the Nanjing-Lianyunguang Freeway. Then, field data from the two-year long operations of the RK-IMS are analyzed. Representations of incident case structures and Bayesian networks（BNs） structures related to incident responses are deduced. Finally, the k-nearest neighbor （k-NN） algorithm is applied to calculate the similarities of the cases. The preplan generation and the control strategy by integrating the k-NN algorithm are also developed. The model is validated by using incident data of the year 2006 from the RK-IMS. The comparison results indicate that the proposed algorithm is accurate and reliable.

Construction of crash prediction model of freeway basic segment based on interactive influence of explanatory variables

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.

Evaluation model for freeway incident management system

In order to evaluate the general situation and find special problems of the freeway incident management system, an evaluation model is proposed. First, the expert appraisal approach is used to select the primary evaluation index. As a result, 81 indices and the hierarchical structures of the index such as the object layer, the sub-object layer, the criterion layer and the index layer are determined. Then, based on the fuzzy characteristics of each index layer, the analytical hierarchy process（AHP）and the fuzzy comprehensive evaluation are applied to generate the weight and the satisfaction of the index and the criterion layers. When analyzing the relationship between the sub-object layer and the object layer, it is easy to find that the number of sub-objects is too large and sub-objects are significantly redundant. The partial least square （PLS） is proposed to solve the problems. Finally, an application example, whose result has already been accepted and employed as the indication of a new project in improving incident management, is introduced and the result verifies the feasibility and efficiency of the model.

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.

Case Study of Plant Selection in Freeway Landscape Design

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.

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.

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

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 3represents 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 showthat the rear-end crash risk of Type3 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 Type4 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.

Measuring speed consistency for freeway diverge areas using factor analysis

Although either absolute speed or speed difference can be considered as a measure for speed consistency, few researches consider both in practice. The factor analysis method was introduced to extract an optimal number of factors from numerous original measures. The freeway diverging zone was divided into four elements, namely the upstream, the diverge area, the downstream and the exit ramp. Operating speeds together with individual vehicle speeds were collected at each element with radar guns. Following the factor analysis procedure, two factors, which explain 96.722% of the variance in the original data, were retained from the initial seven speed measures. According to the loadings after Varimax rotation, the two factors are clearly classified into two categories. The first category is named "speed scale" reflecting the absolute speed, and the other one is named "speed dispersion" interpreting speed discreteness. Then, the weighted score of speed consistency for each diverge area is given in terms of linear combination of the two retained factors. To facilitate the level classification of speed consistency, the weighted scores are normalized in the range of (0, 1.0). The criterion for speed consistency classification is given as 0≤F N <0.30, good consistency; 0.30≤F N <0.60, fair consistency; 0.60≤ F N ≤1.00, poor consistency. The validation by comparing with previously developed measures shows that the proposed measure is acceptable in evaluating speed consistency.

Travel time prediction model of freeway based on gradient boosting decision tree

To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in current period Q i , speed in current period V i , density in current period K i , the number of vehicles in current period N i , occupancy in current period R i , traffic state parameter in current period X i , travel time in previous time period T i -1 , etc.) are selected to predict the travel time for 10 min ahead in the proposed model. Data obtained from VISSIM simulation is used to train and test the model. The results demonstrate that the prediction error of the GBDT model is smaller than those of the back propagation (BP) neural network model and the support vector machine (SVM) model. Travel time in current period T i is the most important variable among all variables in the GBDT model. The GBDT model can produce more accurate prediction results and mine the hidden nonlinear relationships deeply between variables and the predicted travel time.

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.

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.