Traffic Information Prediction Research Articles

Development of Highway Traffic Information Prediction Models Using the Stacking Ensemble Technique Based on Cross-validation

Development of Highway Traffic Information Prediction Models Using the Stacking Ensemble Technique Based on Cross-validation

Spatial–temporal feature extraction based on convolutional neural networks for travel time prediction

AbstractIn recent years, some traffic information prediction methods have been proposed to provide the precise information on travel time, vehicle speed and traffic flow for highways. However, huge errors may be obtained by these methods for urban roads or alternative roads of highways. Therefore, this study proposes a travel time prediction method based on convolutional neural networks to extract important factors for the improvement of traffic information prediction. In practical experimental environments, the travel time records on No. 5 Highway and its alternative roads were collected and used to evaluate the proposed method. The results showed that the mean absolute percentage error of the proposed method was about 5.69%. Therefore, the proposed method based on deep‐learning techniques can improve the accuracy of travel time prediction.

Real-time traffic congestion prediction using big data and machine learning techniques

PurposeThe study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.Design/methodology/approachThis study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.FindingsThe study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.Research limitations/implicationsThe model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.Practical implicationsTo help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.Social implicationsTo estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.Originality/valueThe proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.

On-Road Safety Prompt Framework for Platoon

In recent years, researchers are very interested in the development of autonomous vehicle platoons that can be commercialized on a large scale. The research progress in this field raises the new demand for real-time traffic flow information and safety prediction. Therefore, an On-road Safety Prompt Framework (OSPF) that combines the free space detection and lane change prediction has beed developed by this paper. This OSPF can predict the lane-change decision and lane-change trajectory around in a short period of time in the future according to the current state and historical trajectory of the surrounding vehicles, and combine these prediction information to provide the autonomous vehicle platoon with the predicted free space in the near future. The free space provided by this OSPF includes three types: continuing driving according to the current state or changing lanes to the left or right. At the same time, it also provides the required recommended driving trajectory. The evaluation test of this ADAS has been done through a simulation testbed of the NGSIM dataset built with Eclipse SUMO (Lopez et al. (2018)).

Fine-grained traffic information prediction at the turning-level based on low-frequency GNSS trajectory data

Fine-grained traffic information prediction at the turning-level based on low-frequency GNSS trajectory data

Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models

Accurate prediction of traffic information (i.e., traffic flow, travel time, traffic speed, etc.) is a key component of Intelligent Transportation System (ITS). Traffic speed is an important indicator to evaluate traffic efficiency. Up to date, although a few studies have considered the periodic feature in traffic prediction, very few studies comprehensively evaluate the impact of periodic component on statistical and machine learning prediction models. This paper selects several representative statistical models and machine learning models to analyze the influence of periodic component on short-term speed prediction under different scenarios: (1) multi-horizon ahead prediction (5, 15, 30, 60 minutes ahead predictions), (2) with and without periodic component, (3) two data aggregation levels (5-minute and 15-minute), (4) peak hours and off-peak hours. Specifically, three statistical models (i.e., space time (ST) model, vector autoregressive (VAR) model, autoregressive integrated moving average (ARIMA) model) and three machine learning approaches (i.e., support vector machines (SVM) model, multi-layer perceptron (MLP) model, recurrent neural network (RNN) model) are developed and examined. Furthermore, the periodic features of the speed data are considered via a hybrid prediction method, which assumes that the data consist of two components: a periodic component and a residual component. The periodic component is described by a trigonometric regression function, and the residual component is modeled by the statistical models or the machine learning approaches. The important conclusions can be summarized as follows: (1) the multi-step ahead prediction accuracy improves when considering the periodic component of speed data for both three statistical models and three machine learning models, especially in the peak hours; (2) considering the impact of periodic component for all models, the prediction performance improvement gradually becomes larger as the time step increases; (3) under the same prediction horizon, the prediction performance of all models for 15-minute speed data is generally better than that for 5-minute speed data. Overall, the findings in this paper suggest that the proposed hybrid prediction approach is effective for both statistical and machine learning models in short-term speed prediction.

Analyzing the Impact of Anticipatory Vehicle Routing on the Network Performance

Traffic congestion causes lost in time, fuel consumption and frustration of the drivers. Route guidance systems are considered as the most feasible solution to alleviate traffic congestion. However, existing route navigation systems provide route guidance based only on real-time traffic information, without considering the future evolvement of traffic in the network. These strategies, known as reactive routing strategies, react only when congestion has already occurred and do not prevent congestion from happening. Therefore, researchers have been investigating in anticipatory route guidance, to enable the integration of traffic prediction into route recommendation. The anticipatory route guidance is expected to improve the effectiveness of the route guidance systems, however its effects on the overall traffic on the network are unclear. The main aim of this paper is to analyze the impacts of the anticipatory routing strategy on the network performance. To achieve this, the performance of the system during anticipatory routing strategy is compared to current reactive routing strategy. The models of the routing strategies are built and tested in a microscopic simulation environment and the results are compared using suitable network performance indicators, such as average travel time, total delay time and the effects of anticipatory routing on different vehicle types. Evaluation results show that the anticipatory routing strategy can improve the network performance, but only for a limited penetration rate of the vehicles equipped with navigation system based on the predictive traffic information, the average travel time compared to reactive routing is reduced by up to 20 % in the best scenario and the vehicles with the predictive navigation system are the ones to profit the most from the predictive routing strategy.

Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles.

To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connectivity is needed. Accurate traffic information prediction can improve traffic congestion and operation efficiency, which helps to reduce commute times, noise and carbon emissions. In this study, we present a novel approach for predicting the traffic flow volume by using traffic data in self-organizing vehicular networks. The proposed method is based on using a probabilistic generative neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) auto-encoders. Time series data generated from the roadside units (RSUs) for five highway links are used by a three layer DBN to extract and learn key input features for constructing a model to predict traffic flow. Back-propagation is utilized as a general learning algorithm for fine-tuning the weight parameters among the visible and hidden layers of RBMs. During the training process the firefly algorithm (FFA) is applied for optimizing the DBN topology and learning rate parameter. Monte Carlo simulations are used to assess the accuracy of the prediction model. The results show that the proposed model achieves superior performance accuracy for predicting traffic flow in comparison with other approaches applied in the literature. The proposed approach can help to solve the problem of traffic congestion, and provide guidance and advice for road users and traffic regulators.

Modeling Day-to-Day Dynamics of Travelers’ Risky Route Choices under the Influence of Predictive Traffic Information

In stochastic transport networks, travelers tend to reserve a “travel time budget” (TTB) for every alternative route to assure their punctual arrival with a predefined confidence level. This paper incorporates the concept of TTB into a deterministic nonlinear dynamic system to model the day-to-day evolution process of travelers’ risk-taking route choices in stochastic degradable road networks. This paper also introduces consistent traffic information prediction, which can be provided by advanced traveler information systems (ATIS), into the dynamic system to investigate its influence on travelers’ route choice behaviors and the corresponding day-to-day network flow evolutions. In the proposed dynamic model, ATIS-equipped travelers are distinguished from those not so equipped to reflect traveler heterogeneity in access to traffic information. Some theoretical analyses are further conducted to investigate the existence and uniqueness of the fixed point, and the stability of the day-to-day dynamic model. The analysis results show that consistent predictive traffic information can help to stabilize day-to-day network flow fluctuations and thus can enhance fixed-point stability of the dynamic model. In addition, this paper investigates the effects of travelers’ attitudes to risk on the network flow evolution process. An illustrative example is presented to demonstrate the characteristics of the dynamic model as well as to verify the theoretical analysis results.

A multivariate short-term traffic flow forecasting method based on wavelet analysis and seasonal time series

Short-term traffic flow forecasting is a key step to achieve the performance of intelligent transportation system (ITS). Timely and accurate traffic information prediction is also the prerequisite of realizing proactive traffic control and dynamic traffic assignment effectively. Based on the fact that univariate forecasting methods have limited forecasting abilities when the data is missing or erroneous and that single models make no full use of information underline data, a new hybrid method with multivariate for short-term traffic flow forecasting is proposed. This method combines statistical analysis method with computational intelligence techniques to mine the characteristic of traffic flow as well as forecast short-term traffic state. First, the wavelet de-noising is employed to remove the noise information. Then, time series analysis is used to analyze time-varying and periodic characteristic of traffic flow. Furthermore, the seasonal auto-regressive moving average with external input (SARIMAX) is established to fit traffic flow with occupancy as exogenous variables. Finally, wavelet forecast is adopted to forecast the values of occupancy which are used as exogenous input, and a WSARIMAX is constructed to forecast traffic flow. Using the relationship of flow and occupancy at the same road section and taking traffic flow and occupancy data from freeway I-694 EB in the Twin Cities as endogenous variables and exogenous variables respectively, this paper studies the forecasting performance of the proposed method. The study results are encouraging. Compared with SARIMA newly proposed in literature, WSARIMA and SARIMAX improved method with wavelet analysis and multivariate modeling method, the proposed method gets improvements of 12.95%, 12.62% and 10.41% in forecasting accuracy of one-step ahead respectively. For ten-steps ahead forecasting, it gets improvements of 18.87%, 17.05% and 2.57% in forecasting accuracy respectively.

Travel Time Prediction by Providing Constraints on a Convolutional Neural Network

Traffic prediction is a complex, nonlinear spatiotemporal modeling problem. Data-based methods have been widely applied to traffic information prediction because of their ability to model the complex, nonlinear spatiotemporal traffic dynamics. These abilities are based on statistical methods and machine learning methods to extract features from historical traffic information. Despite the success of these methods, the local-level representation in the data-based methods is clearly limited because it does not directly reflect the relationship between each historical data point in the input sequence and the predicted information. Convolutional neural networks (CNNs) have also been widely applied in many other fields because of their feature extraction ability, which greatly enhances the learning ability of the standard, fully connected layers of neural networks by providing constraints in the task domain. Thus, we create a global-level representation to reflect the relationship between each historical traffic data point and the predicted information by using the proposed window that provides constraints in the task domain and exploits a max-pooled CNN to automatically extract global-level features for traffic information. To the best of our knowledge, our proposed method is the first example of using the new window to provide constraints in the task domain for CNNs. The proposed method is trained in an end-to-end way by the back-propagation approach in which the AdaGrad method is used to update the parameters of the proposed method. Based on the data set provided by Highways England, the experimental results show that the proposed method outperforms all baseline methods in terms of accuracy.

Optimal energy management strategy for parallel plug-in hybrid electric vehicle based on driving behavior analysis and real time traffic information prediction

Conclusive evidence has justified great importance of energy management strategies in the performance and economy of plug-in hybrid electric vehicles (PHEVs). This article pays attention to improve adaptive equivalent consumption minimization strategy (A-ECMS) for parallel PHEV based on driving behavior recognition and real time traffic information prediction. Three main efforts have been made to distinguish our work from exiting research. Firstly, a hierarchical driving behavior model is constructed, providing in-depth knowledge about behavior generation, transmission, and consequence. Secondly, an online driving behavior classification method is designed. The proposed method is the coefficient result of offline driving behavior study based on self-report driving behavior questionnaire (DBQ) and online driving behavior discrimination by BP neural network. Thirdly, an improved adaptive equivalent consumption minimization strategy (IA-ECMS) is formulated based on identified driving behavior and predicted real time traffic information. The IA-ECMS can realize equivalent factor tuning instantaneously and reasonably. The simulation results indicate the proposed energy management strategy holds potential in fuel economy improvement than A-ECMS.

Rainfall‐integrated traffic speed prediction using deep learning method

Traffic information prediction is one of the most essential studies for traffic research, operation and management. The successful prediction of traffic speed is increasingly significant for the benefits of both road users and traffic authorities. However, accurate prediction is challenging, due to the stochastic feature of traffic flow and shallow model structure. Furthermore, environmental factors, such as rainfall influence, should also be incorporated to improve accuracy. Inspired by deep learning, this paper investigates the performance of deep belief network (DBN) and long short-term memory (LSTM) to conduct short-term traffic speed prediction with the consideration of rainfall impact as a non-traffic input. The deep learning models have the ability to learn complex features of traffic flow pattern under various rainfall conditions. To validate the performance of rainfall-integrated DBN and LSTM, the traffic detector data from an arterial in Beijing are utilised for model training and testing. The experiment results indicate that with the combination input of speed and additional rainfall data, deep learning models have better prediction accuracy over other existing models, and also yields improvements over the models without rainfall input. Furthermore, the LSTM can outperform the DBN to capture the time-series characteristics of traffic speed data.

Neuro-Fuzzy Prediction of Cooperation Interaction Profile of Flexible Road Train Based on Hybrid Automaton Modeling

Accurate prediction of traffic information is important in many applications in relation to Intelligent Transport systems (ITS), since it reduces the uncertainty of future traffic states and improves traffic mobility. There is a lot of research done in the field of traffic information predictions such as speed, flow and travel time. The most important research was done in the domain of cooperative intelligent transport system (C-ITS). The goal of this paper is to introduce the novel cooperation behaviour profile prediction through the example of flexible Road Trains useful road cooperation parameter, which contributes to the improvement of traffic mobility in Intelligent Transportation Systems. This paper presents an approach towards the control and cooperation behaviour modelling of vehicles in the flexible Road Train based on hybrid automaton and neuro-fuzzy (ANFIS) prediction of cooperation profile of the flexible Road Train. Hybrid automaton takes into account complex dynamics of each vehicle as well as discrete cooperation approach. The ANFIS is a particular class of the ANN family with attractive estimation and learning potentials. In order to provide statistical analysis, RMSE (root mean square error), coefficient of determination (R2) and Pearson coefficient (r), were utilized. The study results suggest that ANFIS would be an efficient soft computing methodology, which could offer precise predictions of cooperative interactions between vehicles in Road Train, which is useful for prediction mobility in Intelligent Transport systems.

Dynamic Vehicular Route Guidance Using Traffic Prediction Information

We propose a dynamic vehicular routing algorithm with traffic prediction for improved routing performance. The primary idea of our algorithm is to use real-time as well as predictive traffic information provided by a central routing controller. In order to evaluate the performance, we develop a microtraffic simulator that provides road networks created from real maps, routing algorithms, and vehicles that travel from origins to destinations depending on traffic conditions. The performance is evaluated by newly defined metric that reveals travel time distributions more accurately than a commonly used metric of mean travel time. Our simulation results show that our dynamic routing algorithm with prediction outperforms both Static and Dynamic without prediction routing algorithms under various traffic conditions and road configurations. We also include traffic scenarios where not all vehicles comply with our dynamic routing with prediction strategy, and the results suggest that more than half the benefit of the new routing algorithm is realized even when only 30% of the vehicles comply.

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.

Design of Dynamic Vehicle Navigation Terminal Based on GPS/GPRS

A design scheme of combine dynamic vehicle navigation terminal based on GPS/GPRS is presented, according to some shortcomings about real-time and the digital map data updating in existing vehicle navigation terminal. The paper focuses on the basic working principle and software flow of work of the CPU core board and the main functional modules. Established TCP/IP communication mode based on GPRS and used data monitoring server, to realize the accurate prediction of traffic flow information. The design of the Vehicle navigation terminal provides an important prerequisite for realizing the urban traffic vehicle global optimization and dynamic navigation.

A Traffic Information Prediction System Based on Global Position System-Equipped Probe Car Reporting

A Traffic Information Prediction System Based on Global Position System-Equipped Probe Car Reporting

基于动态交通信息的公交到站预测服务

公交到站时间(BAT)服务是提高公众交通吸引力的关键服务之一。服务给使用者提供实时车辆到站信息能够使用户更好的安排自己的公共交通行程。因此，实时公交到站时间预测技术在智能交通领域已经成为研究的热点。在本文中采用了新的公交到站时间预测模型。该模型提出了一套完整的算法来解决BAT预测中大规模的实时交通信息的计算，采用了高效的算法来实时纠正车辆的行驶方向。动态交通信息作为数据基础支撑BAT的计算。同时，采用虚拟预测来处理GPS数据丢失的情况保证了服务提供的稳定性。实验结果表明模型拥有较高的准确率(超过85.1%)和计算速度(最高处理5000条每秒)。 The bus arrival time (BAT) service is a key service to improve public transport attractiveness by providing users with real-time bus arrival information which can help them to arrange their bus travel schedule intelligently. Thus the technique of real-time bus arrival prediction has become a research hotspot in the community of Intelligent Transport Systems (ITS) nowadays. In this paper, a novel model on bus arrival time prediction is proposed. The model proposes a complete set of programs to solve BAT prediction for large-scale real-time traffic information calculating. It adopts an effective algorithm calculating vehicle’s driving direction real-timely. BAT is calculated based on dynamic traffic information and visual prediction is a way to complement when GPS information is not arrived as expected. Experimental results indicate that the model has considerable efficiency in accuracy (over 85.1%) and computational speed (max 5000 GPS records per second).

A new model for studying the SO-based pre-trip information release strategy and route choice behaviour

In reality, the predictive traffic information is rarely perfect. It is thus a rational assumption that travellers would not be completely in compliance with the guidance by advanced traveller information systems (ATIS). This article presents a Markovian decision programming (MDP) model for investigating the day-to-day receiving and adjusting process to the pre-trip information released by ATIS at route level. The goal is to seek an optimal information release strategy (IRS) for minimising the overall disutility under the assumption that travellers’ route choice is governed by a logit model on the base of adopted travel times. The properties of the model solution are analysed. It is found that there exists an optimal IRS that can drive the flow pattern to a system optimum (SO) if the behaviour adjustment parameter satisfies a condition as . The predictive information provided to drivers results in the oscillation of traffic flow among alternative routes when . The solution of the MDP model is obtained by solving a series of sequential minimisation programs. The model and algorithm are numerically verified on a test network.