Future Traffic Flow Research Articles

클라우드 경로탐색을 이용한 미래 교통정보 예측 방법

최근 내비게이션에서는 실시간 교통정보와 과거의 교통정보를 가공하여 미래의 교통정보를 예측하는 패턴 교통정보를 같이 활용하여 빠른 길을 안내해주고 있다. 그러나 현재 사용되는 패턴 교통정보는 과거의 정보를 가공하여 교통정보를 예측하기 때문에 특별한 상황(유고, 날씨 등)에서는 예측이 정확하지 않는 문제점을 가지고 있다. 그래서 본 연구에서는 빠른 길을 찾기 위해 실시간으로 운전자들이 요청하는 경로탐색 데이터를 분석하여 가까운 미래 운전자들이 위치할 도로의 교통 혼잡도를 미리 파악하여 패턴 교통정보 보다 정확한 예측 교통정보를 제시하였다. 연구결과 첫째, 연구지역의 정체경로인 양재에서 마포간 차량속도 비교에서는 기존 상습정체 도로의 속도가중치 정확도가 3km/h에서 18km/h의 오차율이 발생하였지만, 본 연구의 Real 예측 교통 정보를 적용한 결과는 1km/h에서 5km/h의 오차율이 발생하였다. 둘째, 경로 품질에서 기존의 경로보다 최대 약 9분, 평균 약 3분 일찍 목적지에 도착하여 예측 교통정보 결과의 신뢰성을 입증할 수 있었다. 셋째, 기존의 경로탐색 결과 보다 혼잡도를 미리 예측하여 혼잡이 발생할 도로에 대해 회피되는 경로탐색 결과를 도출할 수 있었다. 따라서 본 연구결과의 경로탐색 비교를 통해 교통량에 대한 예측정보를 획득할 수 있었으며 이를 활용하여 실시간 빠른 길 탐색이 가능하고, 향후 교통 흐름을 분산 시키는데도 도움이 될 것으로 판단된다. Recent navigation systems provide quick guide services, based on processing real-time traffic information and past traffic information by applying predictable pattern for traffic information. However, the current pattern for traffic information predicts traffic information by processing past information that it presents an inaccuracy problem in particular circumstances(accidents and weather). So, this study presented a more precise predictive traffic information system than historical traffic data first by analyzing route search data which the drivers ask in real time for the quickest way then by grasping traffic congestion levels of the route in which future drivers are supposed to locate. First results of this study, the congested route from Yang Jae to Mapo, the analysis result shows that the accuracy of the weighted value of speed of existing commonly congested road registered an error rate of 3km/h to 18km/h, however, after applying the real predictive traffic information of this study the error rate registered only 1km/h to 5km/h. Second, in terms of quality of route as compared to the existing route which allowed for an earlier arrival to the destination up to a maximum of 9 minutes and an average of up to 3 minutes that the reliability of predictable results has been secured. Third, new method allows for the prediction of congested levels and deduces results of route searches that avoid possibly congested routes and to reflect accurate real-time data in comparison with existing route searches. Therefore, this study enabled not only the predictable gathering of information regarding traffic density through route searches, but it also made real-time quick route searches based on this mechanism that convinced that this new method will contribute to diffusing future traffic flow.

Research on Traffic Flow Optimization of Flight Route and Ground Delay Program

This paper mainly discusses the traffic flow optimization of flight route and the problem of ground delay program. the author introduces the concept, procedure, method and the meaning of air traffic flow management. The author utilizes Eulerian method to build the model of air traffic networks, and solves the problem with adjoint method, then uses it to optimize the flow of the route, The author also builds the model of ground delay program with fair factor, and solves it with particle swarm optimization method. The author also uses the real data to prove the feasibility of the above two methods. Also, the author designs the original system of air traffic flow management, and gives the flow of operation and the output of interface. In the end, the author summarizes the study meaning of the paper and puts forward the opinion of the research of future air traffic flow management.

Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

Traffic flow forecasting is useful for controlling traffic flow, traffic lights, and travel times. This study uses a multi-layer perceptron neural network and the mutual information (MI) technique to forecast traffic flow and compares the prediction results with conventional traffic flow forecasting methods. The MI method is used to calculate the interdependency of historical traffic data and future traffic flow. In numerical case studies, the proposed traffic flow forecasting method was tested against data loss, changes in weather conditions, traffic congestion, and accidents. The outcomes were highly acceptable for all cases and showed the robustness of the proposed flow forecasting method.

Research on Short-term Traffic Forecast Algorithm based on Cloud Model

Short-term traffic flow is difficult to predict because of high uncertainty. This paper proposes a short-term traffic forecast algorithm based on cloud similarity. By taking advantage of quantitative and qualitative cloud model mutual conversion function and traffic flow predictability, the historical traffic data can be processed with cloud transformation. Set the current traffic cloud as a standard, traverse the historical traffic cloud to find the best traffic flow period which is with best similarities to the current traffic clouds. Set the future short-term traffic flow of this very period of time as the prediction result of the current period of time. Experiments show that the average prediction error was 3.25 (vehicles) and the prediction error distribution probability was 0.29. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4655 Full Text: PDF

The Influence of Future Vehicle Safety Network Impact on Traffic Flow

With the rapid development of high-speed mobile networks, the mobile applications related to vehicle safety, navigation systems are increasingly present in our lives, it is more and more easy for the driver to understand the situation on the road ahead, and this kind of change will greatly affect future traffic conditions. This article uses cellular automaton to simulate basic road sections, considering two modes of vehicle network safety applications may affect the future traffic flow, through the simulation, analysis the basic traffic flow data, conclude how the future vehicle network safety applications impact on traffic flow.

Traffic Flow Prediction for Road Transportation Networks With Limited Traffic Data

Obtaining accurate information about current and near-term future traffic flows of all links in a traffic network has a wide range of applications, including traffic forecasting, vehicle navigation devices, vehicle routing, and congestion management. A major problem in getting traffic flow information in real time is that the vast majority of links is not equipped with traffic sensors. Another problem is that factors affecting traffic flows, such as accidents, public events, and road closures, are often unforeseen, suggesting that traffic flow forecasting is a challenging task. In this paper, we first use a dynamic traffic simulator to generate flows in all links using available traffic information, estimated demand, and historical traffic data available from links equipped with sensors. We implement an optimization methodology to adjust the origin-to-destination matrices driving the simulator. We then use the real-time and estimated traffic data to predict the traffic flows on each link up to 30 min ahead. The prediction algorithm is based on an autoregressive model that adapts itself to unpredictable events. As a case study, we predict the flows of a traffic network in San Francisco, CA, USA, using a macroscopic traffic flow simulator. We use Monte Carlo simulations to evaluate our methodology. Our simulations demonstrate the accuracy of the proposed approach. The traffic flow prediction errors vary from an average of 2% for 5-min prediction windows to 12% for 30-min windows even in the presence of unpredictable events.

An intermodal freight transport system for optimal supply chain logistics

Complexity in transport networks evokes the need for instant response to the changing dynamics and uncertainties in the upstream operations, where multiple modes of transport are often available, but rarely used in conjunction. This paper proposes a model for strategic transport planning involving a network wide intermodal transport system. The system determines the spatio-temporal states of road based freight networks (unimodal) and future traffic flow in definite time intervals. This information is processed to devise efficient scheduling plans by coordinating and connecting existing rail transport schedules to road based freight systems (intermodal). The traffic flow estimation is performed by kernel based support vector mechanisms while mixed integer programming (MIP) is used to optimize schedules for intermodal transport network by considering various costs and additional capacity constraints. The model has been successfully applied to an existing Fast Moving Consumer Goods (FMCG) distribution network in India with encouraging results.

The Urban Road Short-Term Traffic Flow Prediction Research

To predict the future traffic flow status more accurately is of great significance to alleviate urban traffic congestion for a short period of time and avoid the waste of social resources. At first, this paper summarizes the characteristics of urban expressway traffic flow. Then establishes BP neural network short-term traffic flow evaluation model based on MATLAB, and finally through the instance, verify the validity of the model.

Weather Adaptive Traffic Prediction Using Neurowavelet Models

Climate change is a prevalent issue facing the world today. Unexpected increase in rainfall intensity and events is one of the major signatures of climate change. Rainfall influences traffic conditions and, in turn, traffic volume in urban arterials. For improved traffic management under adverse weather conditions, it is important to develop a traffic prediction algorithm considering the effect of rainfall. This inclusion is not intuitive as the effect is not immediate, and the influence of rainfall on traffic volume is often unrecognizable in a direct correlation analysis between the two time-series data sets; it can only be observed at certain frequency levels. Accordingly, it is useful to employ a multiresolution prediction framework to develop a weather adaptive traffic forecasting algorithm. Discrete wavelet transform (DWT) is a well-known multiresolution data analysis methodology. However, DWT imparts time variance in the transformed signal and makes it unsuitable for further time-series analysis. Therefore, the stationary form of DWT known as stationary wavelet transform (SWT) has been used in this paper to develop a neurowavelet prediction algorithm to forecast hourly traffic flow considering the effect of rainfall. The proposed prediction algorithm has been evaluated at two urban arterial locations in Dublin, Ireland. This paper shows that the rainfall data successfully augments the traffic flow data as an exogenous variable in periods of inclement weather, resulting in accurate predictions of future traffic flow at the two chosen locations. The forecasts from the neurowavelet model outperform the forecasts from the standard artificial neural network (ANN) model.

On-Road Sensor Configuration Design for Traffic Flow Prediction Using Fuzzy Neural Networks and Taguchi Method

On-road sensors provide proactive traffic control centers with current traffic flow conditions in order to forecast the future conditions. However, the number of on-road sensors is usually huge, and not all traffic flow conditions captured by these sensors are useful for predicting future traffic flow conditions. The inclusion of all captured traffic flow conditions is an ineffective means of predicting future traffic flow. Therefore, the selection of appropriate on-road sensors, which are significantly correlated to future traffic flow, is essential, although the trial and error method is generally used for the selection. In this paper, the Taguchi method, which is a robust and systematic optimization approach for designing reliable and high-quality models, is proposed for determinations of appropriate on-road sensors, in order to capture useful traffic flow conditions for forecasting. The effectiveness of the Taguchi method is demonstrated by developing a traffic flow predictor based on the architecture of fuzzy neural networks which can perform well on traffic flow forecasting. The case study was conducted based on traffic flow data captured by on-road sensors located on a Western Australia freeway. The advantages of using the Taguchi method can be indicated: 1) Traffic flow predictors with high accuracy can be designed, and 2) development time of traffic flow predictors is reasonable.

Dynamical functional prediction and classification, with application to traffic flow prediction

Motivated by the need for accurate traffic flow prediction in transportation management, we propose a functional data method to analyze traffic flow patterns and predict future traffic flow. In this study we approach the problem by sampling traffic flow trajectories from a mixture of stochastic processes. The proposed functional mixture prediction approach combines functional prediction with probabilistic functional classification to take distinct traffic flow patterns into account. The probabilistic classification procedure, which incorporates functional clustering and discrimination, hinges on subspace projection. The proposed methods not only assist in predicting traffic flow trajectories, but also identify distinct patterns in daily traffic flow of typical temporal trends and variabilities. The proposed methodology is widely applicable in analysis and prediction of longitudinally recorded functional data.

Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method

Over the past two decades, neural networks have been applied to develop short-term traffic flow predictors. The past traffic flow data, captured by on-road sensors, is used as input patterns of neural networks to forecast future traffic flow conditions. The amount of input patterns captured by the on-road sensors is usually huge, but not all input patterns are useful when trying to predict the future traffic flow. The inclusion of useless input patterns is not effective to developing neural network models. Therefore, the selection of appropriate input patterns, which are significant for short-term traffic flow forecasting, is essential. This can be conducted by setting an appropriate configuration of input nodes of the neural network; however, this is usually conducted by trial and error. In this paper, the Taguchi method, which is a robust and systematic optimization approach for designing reliable and high-quality models, is proposed for the purpose of determining an appropriate neural network configuration, in terms of input nodes, in order to capture useful input patterns for traffic flow forecasting. The effectiveness of the Taguchi method is demonstrated by a case study, which aims to develop a short-term traffic flow predictor based on past traffic flow data captured by on-road sensors located on a Western Australia freeway. Three advantages of using the Taguchi method were demonstrated: 1) short-term traffic flow predictors with high accuracy can be designed; 2) the development time for short-term traffic flow predictors is reasonable; and 3) the accuracy of short-term traffic flow predictors is robust with respect to the initial settings of the neural network parameters during the learning phase.

Application of Reinforcement Learning as a Tool of Adaptive Traffic Signal Control on Isolated Intersections

For a long time it was believed that the systems responding to real time traffic would enable significant benefits. However, numerous limitations have appeared such as the existence of the models with high level of detail precision, the uncertainty in predicting future traffic flows, the difficulty in arrival time estimation, the lack of self-adjusting mechanism. The difficulties in optimising the signal control strategy have initiated new researches. The results highlight the artificial intelligence methods as a possible solution. These systems are characterized with the ability to accumulate and use knowledge, set a problem, learn, process, conclude, solve the problem and exchange knowledge. The research presented in this paper proposes an adaptive signal control performed by a control agent able to adapt to an optimal policy by learning from the environment. The goal to be achieved is minimization of the delays in the system. First, the problem of reinforcement learning has been set. The first computation results of the Q-learning application for adaptive traffic signal control are presented. It is concluded that the results obtained are in favor of the adaptive signal control strategy compared to the fixed and actuated signal control.

Combined dynamic traffic assignment and urban traffic control models

Urban traffic control systems evolved through three generations. The first generation of such systems has been based on historical traffic data. The second generation took advantage of detectors, which enabled the collection of real-time traffic data, in order to re-adjust and select traffic signalization programs. The third generation provides the ability to forecast traffic conditions, in order to have traffic signalization programs and strategies pre-computed and applied at the most appropriate time frame for the optimal control of the current traffic conditions. Nowadays, the fourth generation of traffic control systems is already under development, based among others on principles of artificial intelligence and having capabilities of on-time information provision, traffic forecasting and incident detection is being developed according to principles of large-scale integrated systems engineering. Although these systems are largely benefiting from the developments of various information technology and computer science sectors, it is obvious that their performance is always related to that of the underlying optimization and control methods. Until recently, static traffic assignment (route choice) modes were used in order to forecast future traffic flows, considering that the parameters which affect the network capacity are fixed over a given origin-destination matrix. Traffic engineering considers traffic flows as being constant and tries to optimize the control parameters in order to optimize certain parameters and measures of effectiveness. These two procedures, although largely depend on each other and known as the combined traffic assignment and control problem, are usually handled separately. The recent scientific and research developments in the fields of traffic assignment, with the rapid development of the advantageous Dynamic Traffic Assignment models, the new dynamic traffic control strategies and the evolution of ITS tend to modify the way with which networks are being modelled and their efficiency is measured. The current paper aims to present the major findings out of a critical review of existing scientific literature in the fields of dynamic traffic assignment and traffic control. Combined traffic assignment and traffic control models are discussed both in terms of the underlying mathematical formulations, as well as in terms of algorithmic solutions, in order to better evaluate their applicability in large scale networks. In addition, a generic and easily transferable scheme, in the form of a methodological framework for the Combined Dynamic Traffic Assignment and Urban Traffic Control problem is presented and applied on a realistic urban network, so as to provide numerical results and to highlight the applicability of such models in cases, which differ from the standard test networks of the related bibliography, which are usually of simple nature and form.

A Neural Network Based Traffic-Flow Prediction Model

Prediction of traffic-flow in Istanbul has been a great concern for planners of the city. Istanbul as being one of the most crowded cities in the Europe has a rural population of more than 10 million. The related transportation agencies in Istanbul continuously collect data through many ways thanks to improvements in sensor technology and communication systems which allow to more closely monitor the condition of the city transportation system. Since monitoring alone cannot improve the safety or efficiency of the system, those agencies actively inform the drivers continuously through various media including television broadcasts, internet, and electronic display boards on many locations on the roads. Currently, the human expertise is employed to judge traffic-flow on the roads to inform the public. There is no reliance on past data and human experts give opinions only on the present condition without much idea on what will be the likely events in the next hours. Historical events such as school-timings, holidays and other periodic events cannot be utilized for judging the future traffic-flows. This paper makes a preliminary attempt to change scenario by using artificial neural networks (ANNs) to model the past historical data. It aims at the prediction of the traffic volume based on the historical data in each major junction in the city. ANNs have given very encouraging results with the suggested approach explained in the paper.

Fuzzy Rule-Based System Approach to Combining Traffic Count Forecasts

Current advances in artificial intelligence are providing new opportunities for utilizing the enormous amount of data available in contemporary urban road surveillance systems. Several approaches, methodologies, and techniques have been presented for analyzing and forecasting traffic counts because such information has been identified as vital for the deployment of advanced transportation management and information systems. In this paper, a meta-analysis framework is presented for improving forecasted information of traffic counts, based on an adaptive data processing scheme. In particular, a framework for combining traffic count forecasts within a Mamdani-type fuzzy adaptive optimal control scheme is presented and analyzed. The proposed methodology treats the uncertainty pertaining to such circumstances by augmenting qualitative information of future traffic flow states (and values) with a knowledge base and a heuristic optimization routine that provides dynamic training capabilities, resulting in an efficient real-time forecasting mechanism. Results from the application of the proposed framework on data acquired from realistic signalized urban network data (of Athens, Greece) and for a diversity of locations exhibit its potential.

A FRAMEWORK FOR REAL-TIME CRASH PREDICTION: STATISTICAL APPROACH VERSUS ARTIFICIAL INTELLIGENCE

The attempts to predict crashes on freeways through statistical modeling involving capacity driven measures of traffic flow (e.g., AADT) and road geometry have spanned for more than two decades. However, success in crash prediction involving these static data has so far been limited. In recent times, some researchers made efforts to accommodate the weather conditions and seasonal effects to better predict crashes through time series analysis. So far, these models have shown their incapability to accommodate the ever complex human factors, which, to a great extent, are believed to be directly or indirectly responsible for most of the crash cases. Moreover, the prediction outcome of these models is long-term and mainly used to forecast yearly crash, their seasonal variation, identify black-spots or to assess the impact of road improvement initiatives, etc. However, these models overlook the fact that crash can happen even on geometrically correct roads with excellent weather condition due to human factors. One of the latest additions to the crash prediction modeling is the approach to predict crashes based on real-time traffic data which considers the human factors in macroscopic level from the traffic engineering perspective. The assumption in these models is, instantaneous traffic flow data are indirect representation of the human factors. The concept of real-time crash prediction modeling is gaining momentum due to its proactive nature of application and the growing implementation of ITS, which ensures the future availability of real-time traffic data. However, being at its primitive stage and due to scarcity of past real-time data, the present models are yet theoretical and largely prone to unrealistic data requirements and lack of reliability. Moreover, there has not been any well established norm for modeling approach as well. Some researchers preferred using well known statistical methods while some others opted for artificial intelligence based methods. However, the basic concept in modeling crash in real-time has remained the same, in which, the traffic flow data are separated into two groups – condition leading to crash and normal condition, based on specified assumptions, and then future traffic flow data are evaluated to estimate how likely they are to fit into each of these groups. As crash is considered as a rare event, these models need to have the capability of being frequently updated as soon as new data are available. In future, we can expect that the models will also incorporate nontraffic flow variables to attain higher prediction accuracy. Moreover, the operation of these models is highly dependent on the reliability of the detectors, and hence, is expected to predict even when some data are missing. Considering these requirements, in this paper, we have investigated the applicability of Bayesian Network (BN), a popular real-time prediction method in the field of information science, as a probable method to predict crash in real-time. Bayesian Network is a graphical probabilistic modeling approach which can be calibrated in real-time with limited effort, can handle integration of new variables in future with little effort and also suitable in predicting with missing data. Although its inherent features are suitable for real-time crash prediction, it is important to make sure that BN exhibits satisfactory level of accuracy in predicting crashes, too. For this, after developing the model with BN we developed another model with Binary Logistic Regression and used it as a baseline to investigate the prediction capability of BN. We have organized the paper into four parts. In the first part, we conducted a literature review on real-time crash prediction models and explain the steps involved in modeling as well as making prediction in practical situation. Afterwards, we provided a short introduction to BN, clarifying the concepts important for the readers for understanding the paper. Then we have developed a prototype real-time crash prediction model with artificially generated data and compared its performance in crash prediction with Binary Logistic Regression. Lastly, we discussed the results, elaborated the limitations of the study and evaluated the possibilities of using BN for real-time crash prediction.

On Sociological Writing*

It is widely thought, both inside and outside our profession, that sociologists do not express themselves very well. The critic Malcolm Cowley complained years ago that the wisdoms of sociology, such as they are, tend to be conveyed "in a language that has to be learned almost like Esperanto." And Edmund Wilson, who once toyed with the idea that the writing of every specialist in the university should be reviewed by professors of English, doubted that sociologists would ever be able to survive such a test. Exposing the defects of sociological writing is a literary sport of long standing.

Phase transition at an on-ramp in the Nagel–Schreckenberg traffic flow model

Recent empirical observations show that traffic flow is related to various phase transitions, especially at bottlenecks. This paper studies traffic flow induced by an on-ramp in the Nagel–Schreckenberg model. It is shown that when considering the situation that vehicles on main road and on-ramp interact in a relatively long road section, first-order phase transition from free flow to congested flow occurs. In contrast, it is second-order phase transition from free flow to congested flow if the interaction road section is short. We have investigated the reason why first-order phase transition occurs. The interaction of vehicles may be viewed as a kind of cooperation behavior of drivers. Therefore, our results indicate that in future traffic flow research, investigation on cooperation behavior should be carried out.

Bayesian Inference for Origin-Destination Matrices of Transport Networks Using the EM Algorithm

Information on the origin-destination (OD) matrix of a transport network is a fundamental requirement in much transportation planning. A relatively inexpensive method for updating an OD matrix is to draw inference about the OD matrix based on a single observation of traffic flows on a specific set of network links, where the Bayesian approach is a natural choice for combining the prior knowledge about the OD matrix and the current observation of traffic flows. The existing approaches of Bayesian modeling of OD matrices include using normal approximations to Poisson distributions, which leads to the posterior being intractable even under some simple special cases, and using Markov chain Monte Carlo simulation, which incurs extreme demand of computational efforts. In this article, through the EM algorithm, Bayesian inference is reinvestigated for a transport network for estimating the population means of traffic flows, reconstructing traffic flows, and predicting future traffic flows. It is shown that the resultant estimates have very simple forms with minimal computational costs.