Changes In Traffic Flow Research Articles

Macroscopic Model on Driver Physiological and Psychological Behavior at changes in Traffic

A model is presented which can accurately characterize macroscopic traffic evolution. This model is based on analogies with the ideal gas law. A traffic constant is proposed which considers both physiological and psychological driver behavior. Physiological behavior is the time taken to observe and process local traffic conditions and then initiate actions while psychological behavior includes the attitude and awareness of a driver. Thus, the traffic constant encompasses the perception, awareness, attitude, and reaction of a driver. The proposed model is evaluated for changes in traffic flow caused by an inactive bottleneck. The results are compared with those for the Payne-Whitham (PW) model. This shows that the temporal and spatial evolution of traffic with the proposed model is more realistic.

Analysis and prediction of traffic flow based on Wavelet-BP neural network

Through on-the-spot investigation, collected data of traffic flow from a certain section of 3rd Ring Road in Beijing, the investigation results were analyzed from the perspective of time and space, macro rules of traffic flow and the influence of incidental traffic events on roads was summarized. The MATLAB software was used to decompose traffic flow data by three-layer wavelet, and the variation characteristics of the detail coefficients were obtained by decomposing the parameters of traffic flow by wavelet. Analyzed the abrupt change phenomenon of the traffic flow parameters, so as to judge whether some traffic incident occurred and which kind of it. With the level of road traffic service, the same moment which anomalous traffic flow data produced theoretically and traffic service levels were greater than or equal to 4 levels was chosen, so as to verify whether the anomalous time point analyzed by wavelet-analysis were precise. Used MATLAB software to establish the prediction model by a neural network, and determined the various parameters, functions, training and testing samples. Coefficients of different parameters in the BP neural network were used to predict the changes of traffic flow in the future. Compared the predicted data with the actually observed traffic flow, the starting time of traffic incident and the anomalous mutation starting point of three traffic flow parameters was consistent. This research had great significance to the future intelligent traffic decision-making.

Design and preliminary testing of demand-responsive transverse rumble strips

Transverse rumble strips are common practice to alert drivers by engaging their auditory and tactile senses in addition to visual senses by traffic signals. However, continuous exposure to noise and vibration by transverse rumble strips often results in diminished effectiveness and erratic behaviors, leading to additional safety challenges. In response, demand-responsive transverse rumble strips were developed as traffic safety countermeasures that reduce unnecessary noise and vibration associated with transverse rumble strips by incorporating active control of the rumble strips. Rather than staying static, demand-responsive transverse rumble strips are activated based on the presence of pedestrians, at predesignated times, or in response to abrupt changes in traffic flow. To evaluate the effectiveness of demand-responsive transverse rumble strips, the research team assessed noise and vibration data, both inside the vehicles and on the roadside, for various types of vehicles traveling at different speeds. The test data indicate that demand-responsive transverse rumble strips produced noticeable in-vehicle noise and vibration that could alert drivers to downstream events. Furthermore, demand-responsive transverse rumble strips generated sufficient noise to alert roadside pedestrians to vehicle presence but at low enough level to be considered as acceptable for a residential neighborhood use. Accordingly, demand-responsive transverse rumble strips could address the challenges that static transverse rumble strips face, by providing a design with relatively limited noise while enhancing safety.

A dynamic optimization method for adaptive signal control in a connected vehicle environment

In a connected vehicle environment, vehicle location, speed, and other traffic information are readily available; hence, such environments provide new data sources for traffic signal control optimization. Existing adaptive signal control systems based on fixed detectors cannot directly obtain vehicle location and speed information, and thus, cannot provide accurate information about real-time traffic flow changes. This study presents a dynamic optimization method for adaptive signal control in a connected vehicle environment. First, the proposed method developed a dynamic platoon dispersion model to predict vehicle arrivals by using connected vehicle data. Then, a signal timing optimization model is constructed by regarding the minimization of average vehicle delay as the optimization objective, and setting the green time duration of each phase as a constraint. To achieve real-time adaptive signal control, a genetic algorithm is adopted to solve the optimization model through rolling optimization. Finally, a real-world road network was modeled in Vissim to validate the proposed method. Simulation results show that compared with the classical adaptive signal control algorithm, the proposed method is able to reduce vehicle delays and queue lengths at least 50% penetration rates. At 100% penetration rate, the proposed method improved the average vehicle delay and the average queue length by 22.7% and 24.8%, respectively. Moreover, it catered to all directions in a balanced manner.

Traffic Safety Evaluation Approach for Reconstructed Freeway Medium Strip Opening Area

The remaining of the medium strip of the freeway in reconstruction and widening project reduces the cost of renovation, however brings potential driving risks. In this study, the characteristics of traffic flow changes caused by the opening of the medium strip were analyzed. The safety of the opening length of the medium strip was evaluated based on the interweaving and conflicting driving behavior characteristics, and the opening length of the interweaving area of the medium strip was calculated. In addition, the location of the exit traffic sign was advised for this new traffic flow characteristic. Appropriate opening length of the medium strip and the optimized traffic signs can effectively guide drivers to enter and exit the closed area of the central separation zone safely and comfortably.

Ukraine: an Example of Limits of Balance and Asymmetric Reaction in the Maritime Trade Market

This article deals with the principles of reaction of shipowners’ structures on the set of changes in the maritime trade market’s parameters. There are features of decision-making within the uncertain variability of the maritime trade market. Thus there are subsystems clearly realizing prioritizing strategies, and subsystems that choose the form of reaction to changes in the conditions of limited resources in the structure of the global maritime transport industry. Maintaining a balance, as the most effective condition of any market, plays a special role in maritime transport due to the high cost of fleet deadweight and the intensity of the crews’ work. Therefore, the clarity and timeliness of the response to external changes in traffic flows and carrying capacity of competing shipping companies becomes paramount.

The Influence of the Weather Conditions on Traffic Accidents With the Most Severe Consequences in the Republic of Srpska

Traffic accidents with the most severe consequences, i.e. killed in traffic accidents and great material damage, are the result of numerous complex impact factors. Among these important factors, there are different time parameters, that is, meteorological parameters. Research on the impact of weather parameters on the occurrence of traffic accidents was the subject of research in the early 70s of the twentieth century. Weather conditions usually have a double impact on the occurrence of traffic accidents such as: the impact on the risk of a traffic accident and the impact on the exposure to the risk of an accident. In other words, it can be said that the time parameters influence directly and indirectly on the safety of traffic, or the occurrence of traffic accidents. Namely, it has been proven that the weather conditions influence the change in the structure of the traffic flow (change of mode of transport), as well as the total number of realized trips. Although the impression is that the impact of weather conditions on the occurrence of traffic accidents is unambiguous and clear, concrete research on the impact of weather conditions is quite rare and the results sometimes differ from the expected ones. Through a review report, a special aspect of the analysis will focus on identifying concrete changes in the characteristics of time, with the aim of recognizing their impact on the number of traffic accidents with the most severe consequences in the Republic of Srpska.

A Novel Traffic Flow prediction model based on the Improved Extreme Learning Machine

The changes of urban traffic flow is affected by many factors, and during predicting the traffic flow, it should consider each factor. The paper considers the characteristic between time and traffic flow, and introduces the time factor as a separated input parameter. Besides, some unexpected situations have large impacts on the predicting results, so, the unexpected factor is introduced as an improved factor to the input samples. Based on these, a novel prediction model is proposed, called at-ELM, which determines the hidden node number by using PCA, and reduces the impacts of the hidden node numbers on the predicting results. After performance tests and analysis, the novel model is a little improved in prediction accuracy and completely acceptable.

Estimation of traffic flow changes using networks in networks approaches

Understanding traffic flow in urban areas has great importance and implications from an economic, social and environmental point of view. For this reason, numerous disciplines are working on this topic. Although complex network theory made their appearance in transportation research through empirical measures, the relationships between dynamic traffic patterns and the underlying transportation network structures have scarcely been investigated so far. In this work, a novel Networks in Networks (NiN) approach is presented to study changes in traffic flows, caused by topological changes in the transportation network. The NiN structure is a special type of multi-layer network in which vertices are networks themselves. This embedded network structure makes it possible to encode multiple pieces of information such as topology, paths, and origin-destination information, within one consistent graph structure. Since each vertex is an independent network in itself, it is possible to implement multiple diffusion processes with different physical meanings. In this way, it is possible to estimate how the travellers’ paths will change and to determine the cascading effect in the network. Using the Sioux Falls benchmark network and a real-world road network in Switzerland, it is shown that NiN models capture both topological and spatial-temporal patterns in a simple representation, resulting in a better traffic flow approximation than single-layer network models.

Roadway traffic crash prediction using a state-space model based support vector regression approach.

Conventional traffic crash analyzing methods focus on identifying the relationship between traffic crash outcomes and impact risk factors and explaining the effects of risk factors, which ignore the changes of roadway systems and can lead to inaccurate results in traffic crash predictions. To address this issue, an innovative two-step method is proposed and a support vector regression (SVR) model is formulated into state-space model (SSM) framework for traffic crash prediction. The SSM was developed in the first step to identify the dynamic evolution process of the roadway systems that are caused by the changes of traffic flow and predict the changes of impact factors in roadway systems. Using the predicted impact factors, the SVR model was incorporated in the second step to perform the traffic crash prediction. A five-year dataset that obtained from 1152 roadway segments in Tennessee was employed to validate the model effectiveness. The proposed models result in an average prediction MAPE of 7.59%, a MAE of 0.11, and a RMSD of 0.32. For the performance comparison, a SVR model and a multivariate negative binomial (MVNB) model were developed to do the same task. The results show that the proposed model has superior performances in terms of prediction accuracy compared to the SVR and MVNB models. Compared to the SVR and MVNB models, the benefit of incorporating a state-space model to identify the changes of roadway systems is significant evident in the proposed models for all crash types, and the prediction accuracy that measured by MAPE can be improved by 4.360% and 6.445% on average, respectively. Apart from accuracy improvement, the proposed models are more robust and the predictions can retain a smoother pattern. Furthermore, the results show that the proposed model has a more precise and synchronized response behavior to the high variations of the observed data, especially for the phenomenon of extra zeros.

Development of Dynamic Platoon Dispersion Models for Predictive Traffic Signal Control

As the development of traffic detection technology, recent research is directed to a new generation of signal control systems supported by new traffic data. One of these directions is dynamic predictive control by incorporating short-term prediction capability. This paper focuses on investigating dynamic platoon dispersion models which could capture the variability of traffic flow in a cross-sectional traffic detection environment. The dynamic models are applied to predict the evolution of traffic flow, and further used to produce signal timing plans that account not only for the current state of the system but also for the expected short-term changes in traffic flows. We investigate factors affecting model accuracy, including time-zone length, position of upstream traffic detection equipment, road section length, traffic volume, turning percentages, and computation time. The impact of these factors on the model’s performance is illustrated through a simulation analysis, and the computation performance of models is discussed. The results show that both the dynamic speed-truncated normal distribution model and dynamic Robertson model with dynamics outperform their respective static versions, and that they can be further applied for dynamic control.

The Real-time Traffic Signal Control System for the Minimum Emission using Reinforcement Learning in V2X Environment

As the population and vehicle ownership increase, emission of pollutants is also increasing. The percentage of GHG emission by transportation sector is about 21 % in 2015 (OECD), and this may be caused by frequent stop-and-go phenomenon or delay time of vehicles in signalized intersection. Generally, these could be minimised by driving in constant speed or decreasing the delay times with an efficient traffic signal control. On the other hand, researches try to decrease vehicles’ delay time and to exclude the unnecessary stop-and-go phenomenon in an urban signalized intersection with an advent of V2X (Vehicle-to-Everything) technology development. Especially, in traditional pre-timed traffic signal control situation, even the autonomous vehicles would be impossible to exhibit their own maximum performance. Thus, the development of the traffic signal control system could have effects not only on the traffic flow but also on environmental aspects, which optimizes the signalized traffic flow based on the real-time vehicle information. In this research, on the premise of V2X environment, changes in traffic flow and the emission are analysed based on microscopic traffic information. In specific, the reinforcement learning model is constructed based on Deep Learning which learns the real-time traffic information and displays the optimal traffic signal. The performance of the system was analysed through microscopic traffic simulator - Vissim. The proposed system is expected to contribute on analysing the traffic flow and the environmental effects. Also, it is expected to contribute on constructing the green smart cities with an advent of autonomous vehicle operation in future V2X environment.

Impact of oil price fluctuations on tanker maritime network structure and traffic flow changes

Understanding maritime network structure and traffic flow changes is a challenging task that must incorporate economic, energy, geopolitics, maritime transportation, and network sciences. The reported research examines whether there are linkages between oil price fluctuations, maritime network structure and traffic flow changes, and investigates whether tanker maritime network structure and traffic flow changes are driven by oil price fluctuations. This is essential knowledge for developing strategies energy policies. To address this, a systems-based approach is introduced to construct a maritime transportation network based on trajectory data. The maritime network structure and traffic flow changes are derived from monthly transport. Supporting methods were then used to reveal two-way linkages, long-term balance relationships and shock responses attributable to oil price fluctuations, the maritime network structure and traffic flow changes. Evidence suggests that there are different two-way linkages between oil price fluctuations, maritime network structure and traffic flow changes in oil import- dependent and export-dependent countries. International crude oil price fluctuations contribute to maritime network structure changes for many of the countries examined, and showed rapid growth peaks in the second or third month. Our results have important policy implications for national tanker transportation strategies within the context of oil price fluctuations, suggesting diversification of import origins and export destinations as well as adjustment of shipping mode to cope with the supply shocks caused by oil price fluctuations.

Traffic Speed Prediction Under Non-Recurrent Congestion: Based on LSTM Method and BeiDou Navigation Satellite System Data

The full utilization of Location-Based Vehicle Sensor Data (LB-VSD) can improve the efficiency of traffic control and management. Currently, LB-VSD is widely applied to the prediction of traffic speed. Like the GPS system, BeiDou satellite navigation system (BDS) can collect LB-VSD. In China, the key operation vehicles on the expressway are equipped with BDS to monitor the travel path. This provides a basis for predicting the traffic speed on expressway accurately. In this paper, considering the abnormal data collected by BDS, the screening and processing rules are made, and then the traffic speed sequence is extracted. Considering the data-missing problem caused by equipment failure or abnormal data elimination and the data sparse problem caused by small size of sample, a filling method based on trend-historical data is proposed. Traffic flow evolution is a complex process. Sudden accidents or bad weather can cause a sudden change in traffic flow and non-recurrent traffic congestion. The prediction accuracy of traditional machine learning methods is low when non-recurrent congestion occurred. In order to solve this problem, this paper adopts a deep learning model?Long Short-Term Memory (LSTM) to predict the traffic speed. Moreover, three-regime algorithm is used while building the prediction model. The prediction method is compared with Support Vector Regression (SVR) method. The results show that the prediction accuracy of the proposed method is higher than that of SVR algorithm, and the robustness is better in the case of non-recurrent traffic congestion.

Development and Application of Dynamic Timing Optimization Platform for Big Data Intelligent Traffic Signals

As the number of car ownership increases, road traffic flow continues to increase. At the same time, traffic pressure at intersections is increasing as well. At present, most of the traffic lights in China are fixed cycle control. This timing control algorithm obviously cannot make timely adjustments according to changes in traffic flow. In this case, a large number of transportation resources would be wasted. It is very necessary to establish a dynamic timing system for Big data intelligent traffic signals. In this research, the video recognition method was used to acquire the number of vehicles at the intersection in real time, and the obtained data was processed by the optimization algorithm to make a reasonable dynamic timing of the traffic signals. The test results show that after using the big data intelligent traffic signal dynamic timing optimization control platform, in the experimental area, the overall total delay time was reduced by 23%, and the travel time was reduced by 15%. During the off-peak period, the overall total delay time in the experimental region was reduced by 17% and travel time was reduced by 10%. The big data intelligent traffic signal dynamic timing optimization platform would improve the operational efficiency and traffic supply capacity of the existing transportation infrastructure, and could provide real convenience for citizens.

Cellular Automata Model for Analysis of Lane-Changing Behavior on Damaged Pavement

Drivers often perform lane-changing behavior for driving benefits on a broken road. Accordingly, a lane-changing model for two-lane traffic under pavement damage conditions was established based on the cellular automata NaSch model by introducing slow start and new lane-changing rules. To simulate the characteristics of a driver, traffic flow, and lane change under different road conditions, a simulation process was established in four steps, namely, lane-changing demand, lane selection, clearance detection, and lane change execution. Road damage level is classified from the perspective of vehicle operation. Vehicle driving benefit is calculated through utility theory to establish a lane selection model. A lane-changing coefficient is also introduced to study the effect of pavement damage on vehicle operating characteristics. The driver is divided into four categories based on the differences among drivers' behavior, namely, adventurous, alert, cautious, and slow. The characteristics of the different types of drivers under pavement damage conditions were analyzed by setting simulation parameters. Results showed that the lane-changing coefficient would increase with the increase of pavement damage level. A high level of damage would result in a low benefit of the vehicle traveling on damaged roads, which further increases the probability of lane change that can properly simulate the effect of pavement damage on vehicle lane-changing behavior. The highest lane-changing rate was obtained by the adventurous driver in medium density, and the speed variance and lane-changing rate gradually increases with the increase of damage level. The high lane-changing rate also indicates that pavement distress aggravates lane-changing behavior and interferes with the normal flow of traffic operation, thereby providing a theoretical basis for strengthening traffic safety management of damaged road sections.

Airworthiness Support Measures Analogy to the Prospective Roundabouts Alternatives: Theoretical Aspects

The goal of this paper is to investigate theoretically the possible directions of some specified methods for the alternative roundabouts effectiveness modeling and optimization. The out-coming criteria have an economical interpretation. Those are the objective functionals of the alternative roundabouts effectiveness as the profit gained in the course of the traffic flow changes in the view of the integral form. This is modeled taking into consideration the transport infrastructure functioning elements such as the traffic flow of a capacity model. It takes into account two major components of the transportation services which are the alternative roundabouts business’ incomes and expenses relating to the roundabouts transportation worthiness support. The prototypic approach is that one from the aircraft airworthiness support measures models. Corresponding managerial influences with respect to environmental, safety, utility, and other issues, as well as probable impacts, are modeled with the construction of the relevant under-integral expressions, equations, and appropriate coefficients and parameters of the mathematical models. The achieved theoretical results, on the basis of the Euler-Lagrange equation and accepted assumptions, have been checked for the sufficiency of the objective functional maximum presence at the “point” with the use of the conducted computer simulation. The necessary diagrams are plotted in order to illustrate the theoretical contemplations and speculations, as well as to check the correctness of the applied mathematical derivations and visualize the models’ preciseness and abilities. The theoretically constructed mathematical models have a significance of the prognostic values applicability required at the alternative roundabouts effectiveness modeling and optimization ensuring their design progress and evolutions.

Research for Urban Traffic Simulation Model of Cross-street Pedestrian Influence

To study the influence of cross-street pedestrian on the urban traffic flow, traffic characteristic of vehicle and pedestrian are investigated under horizontal interference conditions. With resetting the rules of vehicle operation behaviour such as deceleration, acceleration, lane change, etc, a new simulation model of two-lane urban traffic flow is established, which is also called cellular automata model. To simulate the traffic conditions of signal control road sections and non-signal control road sections and generate the space-time diagram, the softwares MATLAB and ORIGIN is used for acquiring numerical simulating results and relational graphs of the traffic flow parameters. Comparative analysis is carried on between traffic state with horizontal interference and traffic state without horizontal interference. Finally, simulation results show that pedestrian crossing street has less obvious impact on traffic flow in larger density (higher than 0.22), signal control and acceptable crossing gaps for pedestrian have certain influence on traffic flow and lane change of vehicle. The proposed simulation model is in better consistence with actual situation and has some practical and theoretical significance for traffic flow research.

Dynamic traffic diversion model based on dynamic traffic demand estimation and prediction

Traffic diversion is an effective measure to solve the incidental traffic congestion in urban expressway traffic system. By adopting the macroscopic traffic flow model METANET, this study analyses the state change of traffic flow on the road network and establishes the dynamic traffic diversion model, inducing the redistribution of traffic demand. Considering the changes in the amount of origin-destination ( O - D ) demand, diversion rate is introduced into the basic theory of dynamic O - D model, and then established a dynamic traffic flow model based on dynamic demand change. The genetic algorithm is used to solve the non-linearity problem of the objective function in the traffic diversion model. This study sets up five cases for numerical analyses, and gets the optimal diversion scheme.

Modeling handicapped pedestrians considering physical characteristics using cellular automaton

In disaster situations, the evacuation of pedestrians is very important. In particular, handicapped pedestrians experience difficulties in evacuation situations because they cannot move quickly. Therefore, the evacuation of handicapped pedestrians is considered to be an important factor in disaster situations. In this study, we use a walking model based on a cellular automaton (CA) to study the effect of handicapped walking movement related to traffic flow. For the purpose of this research, handicapped walking encompasses wheelchairs, crutches, children, and elderly people. A new model is constructed and analyzed that reflects the physical characteristics of handicapped pedestrians. In this study, the change of pedestrian traffic flow was observed while simulating a 30% ratio of handicapped to normal people. The experimental results showed that the average walking speed decreased by approximately 12% for each simulation step. Therefore, the results of this study suggest that it is necessary to consider handicapped pedestrians in order to conduct realistic evacuation simulations.