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Traffic accident on inter-city expressways might cause large-scale traffic congestion. It might increase travel times of many drivers and it produces a large social loss. This study aims to estimate the social loss of travel time of drivers caused by traffic accidents on inter-city expressway using traffic simulation model, and to evaluate the effects of outflow recommendations when an accident occurs on the expressway. The traffic simulation model on Tomei Expressway is constructed to estimate the dynamic traffic congestion. Travel time losses of drivers are estimated by the simulation results with hypothetical traffic accidents. It is understood that the total losses of travel times are depending on the positions of accident spots and the occurrence times of accidents, because it might influence to congestion at the bottlenecks of flow capacity. Moreover, the effect of traffic control in emergency situation is discussed. The influences of outflow recommendations for drivers are estimated using the traffic simulation model.

This paper aims to present a linear mathematical framework for modeling and optimizing road transport infrastructure. The framework assesses and optimizes performance of existing transport facility rather than relying on building new roads for the ever-increasing travel demand.

The mathematical framework is built upon a traffic model called Cell Transmission Model (CTM). CTM describes the relationship and evolution of traffic flow and concentration over space and time. The model is parsimonious and accurate in predicting traffic dynamics. More importantly, the traffic flow model is piecewise linear with which the corresponding transport facility optimization problem can be formulated as a Linear Programming (LP) problem and solved by established solution algorithm for global optimality.

We select a section on England Motorway M25 as a case study. With traffic data, we first calibrate the CTM, and we are able to produce traffic estimation with a reasonable error rate of 12 per cent. The corresponding LP then seeks an optimal ramp metering strategy that minimizes the delay on the motorway. It is shown that an optimal and practical strategy can be derived which reduces the motorway delay by 10 per cent without significantly hurting the surrounding connectors.

Instead of the tedious microscopic models used by many traditional tools, the underlying CTM is parsimonious and reliable. The tools developed herein are based upon plausible traffic theory and will be accessible for a wide range of users. The LP formulation can be easily implemented and solved for optimal and practical control strategies for real-world transport networks by using existing computer software (CPLEX) within reasonable computational time. The present work will certainly contribute to the sustainable development of transport facility.

The purpose of this paper is to explore the impact of the mixed traffic flow, self-stabilization effect and the lane changing behavior on traffic flow stability.

An extended two-lane lattice hydrodynamic model considering mixed traffic flow and self-stabilization effect is proposed in this paper. Through linear analysis, the stability conditions of the extended model are derived. Then, the nonlinear analysis of the model is carried out by using the perturbation theory, the modified Kortweg–de Vries equation of the density of the blocking area is derived and the kink–antikink solution about the density is obtained. Furthermore, the results of theoretical analysis are verified by numerical simulation.

The results of numerical simulation show that the increase of the proportion of vehicles with larger maximum speed or larger safe headway in the mix flow are not conducive to the stability of traffic flow, while the self-stabilization effect and lane changing behavior is positive to the alleviation of traffic congestion.

This paper does not take into account the factors such as curve and slope in the actual road environment, which will have more or less influence on the stability of traffic flow, so there is still a certain gap with the real traffic environment.

The existing two-lane lattice hydrodynamic models are rarely discussed in the case of mixed traffic flow. The improved model proposed in this paper can better reflect the actual traffic, which can also provide a theoretical reference for the actual traffic governance.

This study aims to consider the influence of density difference integral and relative flow difference on traffic flow, a novel two-lane lattice hydrodynamic model is proposed. The stability criterion for the new model is obtained through the linear analysis method.

The modified Korteweg de Vries (KdV) (mKdV) equation is derived to describe the characteristic of traffic jams near the critical point. Numerical simulations are carried out to explore how density difference integral and relative flow difference influence traffic stability. Numerical and analytical results demonstrate that traffic congestions can be effectively relieved considering density difference integral and relative flow difference.

The traffic congestions can be effectively relieved considering density difference integral and relative flow difference.

Novel two-lane lattice hydrodynamic model is presented considering density difference integral and relative flow difference. Applying the linear stability theory, the new model’s linear stability is obtained. Through nonlinear analysis, the mKdV equation is derived. Numerical results demonstrate that the traffic flow stability can be efficiently improved by the effect of density difference integral and relative flow difference.

Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps.

This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing.

Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction.

The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations.

The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications.

This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.