Advanced Traffic Management Systems Research Articles

Simulation of the Bluetooth Inquiry Process for Application in Transportation Engineering

In recent years, Bluetooth technology has been adapted for use as a sensor for measuring vehicle travel times along a segment of roadway. However, using the Bluetooth technology in advanced traffic management systems has been limited in part, because there is a lack of tools, such as simulation, to predict the behavior of the system before it is developed and deployed. A number of studies have been published describing the Bluetooth technology, inquiry, and pairing process. Most of these studies have focused on the simulation of the first successful inquiry and reduction of the pairing time. However, in many traffic sensing applications, Bluetooth detectors are designed to stay in the inquiry stage and will continuously perform inquiry scans. These detectors will not proceed to the paring stage and multiple inquiry scans may occur on each device during the time these devices remain in the detection zone of the detector. In this paper, we propose a simulation framework that considers multiple inquiry scans and the effect of distance from the detector on the inquiry process. The simulation model was calibrated and validated using the field data collected from two custom-built Bluetooth detectors. The simulation framework has been made into a simulation software tool entitled blue synthesizer, which can be combined with commercially available trafficion microsimulation models to evaluate the use of Bluetooth technology within advanced traffic management systems.

Vehicle–space traffic-state estimation of a motorway corridor with slip roads

Traffic-state estimation is a key component of an advanced traffic management system. The Eulerian traffic-state estimation based on an X-T traffic-flow model has proved successful in processing traditional X-T data sources such as inductive loop data. However, over the past decade, the emergence of probe vehicle data from paired sensors has created challenges for X-T traffic-state estimation. The vehicle–space (N-X) model has shown potential benefits in modelling paired-probe data. In this paper, an original N-X traffic-state estimation framework is proposed and validated by numerical experiments. In addition, an original first-order conservation formulation is derived to address the entry and exit slip road issue for motorway corridors. An evaluation is conducted by comparing the proposed N-X estimation with the X-T estimation. The results show that the proposed model is superior to the X-T estimation in terms of accuracy and efficiency. It performs well, particularly during congestion, and can track traffic breakdowns and recoveries effectively.

Advanced Traffic Management Systems supporting resilient smart cities

Advanced Traffic Management Systems supporting resilient smart cities

Evaluating the Emission of CO2 at Traffic Intersections with the Purpose of Reducing Emission Rate, Case Study: The University of Nigeria, Nsukka

Traffic congestion is a major problem around the world that results in slower speeds, increased trip time, and a longer queuing of vehicles. The production and use of fuels for vehicles results in emissions of greenhouse gases (GHSs), besides carbon dioxide, which include methane and nitrous oxide. Traffic lights that wirelessly keep track of vehicles could reduce journey time and fuel consumption thereby reducing carbon emissions. In view of the importance of vehicles as an emitter of GHGs, namely CO2, with the growing concern about climate change, this paper aims to explore the emission of CO2 from vehicles at a traffic intersection for the purpose of reducing emission rate. Realizing this reduction, points to the implementation of an Advanced Traffic Management System (ATMS) with Wireless Sensor Networks (WSNs) on the road network of a region will be discussed. With such a technology, a region can experience lower queue lengths at an intersection and therefore lower CO2 emission surrounding the area. The University of Nigeria, Nsukka (UNN) is used as a case study in exploring this phenomenon which over the years has seen a drastic increase on the amount of cars on the campus area. With the assumption that an ATM system with WSNs is deployed on the UNN campus area, the paper looks into the traffic dynamics that makes it possible to evaluate CO2 emission at traffic light intersections to ensure a cleaner environment. Throughout the paper, it will be made clear that with the relevant equation of CO2 emission and the arrival time per vehicle, CO2 emission rate can be evaluated at a traffic intersection depending on the volume of cars at the intersection. With such evaluation, further analysis can be made on ways to actually reduce CO2 emission and techniques for implementation with an ATM system.

Classifying the traffic state of urban expressways: A machine-learning approach

The classification of the urban traffic state and its application is an important part of intelligent transportation systems (ITS), which can not only help traffic managers grasp the traffic operation situation and analyze congestion, but also provide travelers with more traffic information and help them avoid congestion. Thus, an accurate traffic state classification method would be very practical for urban traffic management. The primary objective of this study is to classify the urban traffic state using a machine-learning method (i.e., the FCM clustering method, and the classification results can be determined from the corresponding clustering labels). In this approach, two parts are developed. First, a new classification indicator, i.e., the ample degree of road network is proposed, and it will make up a comprehensive classification indicator system with other parameters such as traffic flow, speed and occupancy. Then, the traditional fuzzy c-means (FCM) clustering approach is improved in two regards, i.e., the fuzzy membership function improvement and weighting processing of the samples, and these improvements can enhance the clustering performance. As a result, an improved machine-learning method (i.e., the improved FCM clustering approach) is developed and used to conduct the clustering analysis with real-world traffic flow data. Next, a case study of Shanghai is used to guide the study process, which consists of data processing, clustering analysis and method comparison. The other methods (e.g., the support vector machines (SVM) method, the decision tree method, the k-Nearest Neighbor (KNN) method and the traditional FCM clustering approach) are introduced to compare with the improved FCM clustering approach. The discussion shows the superiority of the proposed method (e.g., compared with the traditional FCM clustering approach, the objective function value of the improved method decreased by 31.11%, and cluster center error also show a descending trend), and it outperforms the other methods in classification performance (e.g., the overall classification accuracy of the improved FCM method increased by 10.10%, 5.45%, 30.92% and 35.66% in comparison with the traditional FCM method, SVM method, decision tree method and KNN method, respectively). Additionally, the NMI, ROC curve results also illustrated the superiority of the improved FCM method to other methods. These comaprison results suggest that the improved FCM clustering approach is feasible and the results can be well used in the advanced traffic management system, which may have the potential to serve as a reference for releasing accurate traffic state information and preventing traffic congestion and risk.

Elaborating the Human Aspect of the NIST Framework for Cyber-Physical Systems.

The National Institute of Standards and Technology (NIST) has developed a Framework for Cyber-Physical Systems (CPS Framework) that supports system engineering analysis, design, development, operation, validation and assurance of CPS. Cyber-physical systems (CPS) comprise interacting digital, analog, physical, and human components engineered for function through integrated physics and logic. For instance, a city implementing an advanced traffic management system including real-time predictive analytics and adaptation/optimization must consider all aspects of such a CPS system of systems' functioning and integrations with other systems, including interactions with humans. One Aspect (or grouping of stakeholder concerns) of the CPS Framework is the Human Aspect. NIST is engaging HFES in a panel discussion to elaborate Human Aspect concerns, especially relevant constructs, measures, methods, and tools.

Evaluating the Reliability, Coverage, and Added Value of Crowdsourced Traffic Incident Reports from Waze

Traffic managers strive to have the most accurate information on road conditions, normally by using sensors and cameras, to act effectively in response to incidents. The prevalence of crowdsourced traffic information that has become available to traffic managers brings hope and yet raises important questions about the proper strategy for allocating resources to monitoring methods. Although many researchers have indicated the potential value in crowdsourced data, it is crucial to quantitatively explore its validity and coverage as a new source of data. This research studied crowdsourced data from a smartphone navigation application called Waze to identify the characteristics of this social sensor and provide a comparison with some of the common sources of data in traffic management. Moreover, this work quantifies the potential additional coverage that Waze can provide to existing sources of the advanced traffic management system (ATMS). One year of Waze data was compared with the recorded incidents in the Iowa’s ATMS in the same timeframe. Overall, the findings indicated that the crowdsourced data stream from Waze is an invaluable source of information for traffic monitoring with broad coverage (covering 43.2% of ATMS crash and congestion reports), timely reporting (on average 9.8 minutes earlier than a probe-based alternative), and reasonable geographic accuracy. Waze reports currently make significant contributions to incident detection and were found to have potential for further complementing the ATMS coverage of traffic conditions. In addition to these findings, the crowdsourced data evaluation procedure in this work provides researchers with a flexible framework for data evaluation.

A hybrid method for short-term freeway travel time prediction based on wavelet neural network and Markov chain

Short-term travel time prediction is an essential input to intelligent transportation systems. Timely and accurate traffic forecasting is necessary for advanced traffic management systems and advanced traveler information systems. Despite several short-term travel time prediction approaches have been proposed in the past decade, especially for hybrid models that consist of machine learning models and statistical models, few studies focus on the over-fitting problem brought by hybrid models. The over-fitting problem deteriorates the prediction accuracy especially during peak hours. This paper proposes a hybrid model that embraces wavelet neural network (WNN), Markov chain (MAR), and the volatility (VOA) model for short-term travel time prediction in a freeway system. The purpose of this paper is to provide deeper insights into underlining dynamic traffic patterns and to improve the prediction accuracy and robustness. The method takes periodical analysis, error correction, and noise extraction into consideration and improve the forecasting performance in peak hours. The proposed methodology predicts travel time by decomposing travel time data into three components: a periodic trend presented by a modified WNN, a residual part modeled by Markov chain, and the volatility part estimated by the modified generalized autoregressive conditional heteroscedasticity model. Forecasting performance is investigated with freeway travel time data from Houston, Texas and examined by three measures: mean absolute error, mean absolute percentage error, and root mean square error. The results show that the travel times predicted by the WNN-MAR-VOA method are robust and accurate. Meanwhile, the proposed method is able to capture the underlying periodic characteristics and volatility nature of travel time data.

Space-Time Hybrid Model for Short-Time Travel Speed Prediction

Short-time traffic speed forecasting is a significant issue for developing Intelligent Transportation Systems applications, and accurate speed forecasting results are necessary inputs for Intelligent Traffic Security Information System (ITSIS) and advanced traffic management systems (ATMS). This paper presents a hybrid model for travel speed based on temporal and spatial characteristics analysis and data fusion. This proposed methodology predicts speed by dividing the data into three parts: a periodic trend estimated by Fourier series, a residual part modeled by the ARIMA model, and the possible events affected by upstream or downstream traffic conditions. The aim of this study is to improve the accuracy of the prediction by modeling time and space variation of speed, and the forecast results could simultaneously reflect the periodic variation of traffic speed and emergencies. This information could provide decision-makers with a basis for developing traffic management measures. To achieve the research objective, one year of speed data was collected in Twin Cities Metro, Minnesota. The experimental results demonstrate that the proposed method can be used to explore the periodic characteristics of speed data and show abilities in increasing the accuracy of travel speed prediction.

A novel arterial travel time distribution estimation model and its application to energy/emissions estimation

ABSTRACTArterial travel time information is crucial to advanced traffic management systems and advanced traveler information systems. An effective way to represent this information is the estimation of travel time distribution. In this paper, we develop a modified Gaussian mixture model in order to estimate link travel time distributions along arterial with signalized intersections. The proposed model is applicable to traffic data from either fixed-location sensors or mobile sensors. The model performance is validated using real-world traffic data (more than 1,400 vehicles) collected by the wireless magnetic sensors and digital image recognition in the field. The proposed model shows high potential (i.e., the correction rate are above 0.9) to satisfactorily estimate travel time statistics and classify vehicle stop versus non-stop movements. In addition, the resultant movement classification application can significantly improve the estimation of traffic-related energy and emissions along arterial.

THE SENSOR LOCATION PROBLEM: METHODOLOGICAL APPROACH AND APPLICATION

The sensor location problem is of particular importance when planning the allocation of limited field equipment intended to be used for advanced traffic management systems and traveller information services. The locations within a network that satisfy specific goals need to be carefully selected, based on predefined goals related to the effective collection of data and the subsequent estimation of traffic related information. The detection of traffic volumes is mainly associated with two purposes, the travel time and the Origin–Destination (O–D) trip matrix estimation. In this context, this paper presents a quadratic programing model, able to determine the optimal location of tracking sensors. The model is implemented in the urban road network of the city of Thessaloniki (Greece) in which specific number of sensors is installed and utilized for real-time travel time information provision. The proposed methodology models the sensor location problem under the general framework of a set covering problem, which is one of the most popular optimization problems and has been applied in many industrial problems. The results of the case study in Thessaloniki reveal that the proposed model defines the optimal location of the limited number of sensors in such a way that the network, which is created having all sensors as origin or destination of all possible paths, represents to great extent (87% of the traffic flow along the major paths) the traffic volumes of the whole road network of the city.

Real-time estimation of secondary crash likelihood on freeways using high-resolution loop detector data

This study aimed to develop a secondary crash risk prediction model on freeways using real-time traffic flow data. The crash and traffic data were collected on the I-880 freeway for five years in California, United States. The secondary crashes were identified by a method based on speed contour plot. The random effect logit model was used to link the probability of secondary crashes with the real-time traffic flow conditions, primary crash characteristics, environmental conditions, and geometric characteristics. The results showed that real-time traffic variables significantly affect the likelihood of secondary crashes. These traffic variables include the traffic volume, average speed, standard deviation of detector occupancy, and volume difference between adjacent lanes. In addition, the primary crash characteristics, environmental conditions and geometric characteristics also significantly affect the risks of secondary crashes. The model evaluation results showed that the predictive performance of the developed model was deemed satisfactory. The inclusion of traffic flow variables and random effect increases prediction accuracy by 16.6% and 7.7%, respectively. These results have the potential to be used in advanced traffic management systems to develop proactive traffic control strategies to prevent the occurrences of secondary crashes on freeways.

Stream travel time prediction using particle filtering approach

Travel-time information is an integral part of Advanced Traveler Information Systems and Advanced Traffic Management Systems. Real-time estimation of stream travel time will be helpful in making trip decisions such as route choice and departure time. The present study proposes a method to predict stream travel time using particle filtering approach which considers the predicted stream travel time as the sum of the median of historical travel times, random variations in travel time over time, and a model evolution error. The present model hypothesizes the median of historical travel times obtained from the collected data as a priori estimate and hence predicting the actual travel time will be equivalent to forecasting the variations in travel time according to the current measurements. In order to capture the random variations in travel time, a dynamic mathematical modeling approach with particle filtering technique is used. The results obtained from the implementation of the above method are compared with the measured travel time data and the prediction accuracy, quantified using the Mean Absolute Percentage Error and Mean Absolute Error was observed to be satisfactory. Performance of this method was compared to a basic speed-based approach, where travel time is obtained by time–distance relationship using space-mean speed, and the proposed method showed better performance.

A real-time multiple vehicle tracking method for traffic congestion identification

Traffic congestion is a severe problem in many modern cities around the world. Real-time and accurate traffic congestion identification can provide the advanced traffic management systems with a reliable basis to take measurements. The most used data sources for traffic congestion are loop detector, GPS data, and video surveillance. Video based traffic monitoring systems have gained much attention due to their enormous advantages, such as low cost, flexibility to redesign the system and providing a rich information source for human understanding. In general, most existing video based systems for monitoring road traffic rely on stationary cameras and multiple vehicle tracking method. However, most commonly used multiple vehicle tracking methods are lack of effective track initiation schemes. Based on the motion of the vehicle usually obeys constant velocity model, a novel vehicle recognition method is proposed. The state of recognized vehicle is sent to the GM-PHD filter as birth target. In this way, we relieve the insensitive of GM-PHD filter for new entering vehicle. Combining with the advanced vehicle detection and data association techniques, this multiple vehicle tracking method is used to identify traffic congestion. It can be implemented in real-time with high accuracy and robustness. The advantages of our proposed method are validated on four real traffic data.

A Comparative Study of Three Multivariate Short-Term Freeway Traffic Flow Forecasting Methods With Missing Data

Short-term traffic flow forecasting is a critical function in advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS). Accurate forecasting results are useful to indicate future traffic conditions and assist traffic managers in seeking solutions to congestion problems on urban freeways and surface streets. There is new research interest in short-term traffic flow forecasting due to recent developments in intelligent transportation systems (ITS) technologies. Previous research involves technologies in multiple areas, and a significant number of forecasting methods exist in the literature. However, most studies used univariate forecasting methods, and they have limited forecasting abilities when part of the data is missing or erroneous. While the historical average (HA) method is often applied to deal with this issue, the forecasting accuracy cannot be guaranteed. This article makes use of the spatial relationship of traffic flow at nearby locations and builds up two multivariate forecasting approaches: the vector autoregression (VAR) and the general regression neural network (GRNN) based forecasting models. Traffic data collected from U.S. Highway 290 in Houston, TX, were used to test the model performance. Comparison of performances of the three models (HA, VAR, and GRNN) in different missing ratios and forecasting time intervals indicates that the accuracy of the VAR model is more sensitive to the missing ratio, while on average the GRNN model gives more robust and accurate forecasting with missing data, particularly when the missing data ratio is high.

Freeway Travel Time Prediction Based on Seamless Spatio-temporal Data Fusion: Case Study of the Freeway in Taiwan

Travel time prediction has been the fundamental brick for developing both Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS). The problem of travel time prediction is primarily shaped by the data available for model development and the needs from the perspectives of both travelers and operators. A distance-based Electronic Toll Collection (ETC) system over the freeway system in Taiwan has been fully implemented since December 2013, which provides more comprehensive online data acquisition of freeway traffic conditions, thereby enabling the capability to more reliably predict of travel time over freeway segments. Based on the data from both the ETC system and traditional Vehicle Detectors (VDs), this research proposes a travel time prediction approach whose core technique of data fusion seeks to seamlessly capture the spatio-temporal pattern of freeway traffic flows by matching traffic dynamics revealed from the ETC and VD data. Further, prediction models are constructed thereupon, where the Kalman filter is employed for short-term prediction and the Fourier transform for long-term prediction based on the continuous parameterized modeling of spot travel speed. The proposed approach is implemented as an online deployable system using Java, together with real-world data collected from the freeway in Taiwan, for the numerical experiments. The prediction errors are no greater than 10% in most cases, which illustrates the high accuracy of prediction capability of the model. The encouraging results also highlight the benefits of the pre-processing of data and data fusion in improving data quality and applicability.

A Study on Network Based Traffic Signal Optimization Using Traffic Prediction Data

An increasing number of vehicles is causing various traffic problems such as chronic congestion of highways and air pollution. Local governments have been managing traffic by constructing systems such as Intelligent Transport Systems (ITS) and Advanced Traffic Management Systems (ATMS) to relieve such problems, but construction of an infrastructure-based traffic system is insufficient in resolving chronic traffic problems. A more sophisticated system with enhanced operational management capabilities added to the existing facilities is necessary at this point. As traffic patterns of the urban traffic flow is time-specific due to the different vehicle populations throughout the time of the day, a local network-wide signal operation plan that can manage such situation-specific traffic patterns is deemed to be necessary. Therefore, this study is conducted for the purpose of establishment of a plan for contextual signal control management through signal optimization at the network level after setting the Frame Signal in accordance to the traffic patterns gathered from the short-term traffic forecast data as a means to mitigate the problems with existing standardized signal operations.

Traffic flow forecasting based on a hybrid model

Real-time traffic flow forecasting is of great importance in the development of advanced traffic management systems and advanced traveler information systems. Traffic flow is evaluated using time series, and the Autoregressive Integrated Moving Average (ARIMA) model has been commonly used for determining the regression-type relationship between historical and future data. However, the performance of the ARIMA model is limited by the difficulty of capturing nonlinear patterns and the challenges of diagnosing permanent white noises. Hence, a hybrid method of ARIMA-EGARCH-M-GED was developed with the intent to address those limitations. It combines the linear ARIMA model with a nonlinear model of Exponent Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) to capture heteroscedasticity (the variance of random error varying across the data) of traffic flow series. EGARCH in Mean (EGARCH-M), which corrects the expression of conditional variance by connecting the conditional mean directly with the variance, was introduced to better restrain the influence of abnormal data. Moreover, the tail of the generalized error distribution (GED) is better than that of the normal distribution in characterizing the features of time series, especially heteroscedasticity of residual sequences. Data collected from an interstate highway (I-80 in California) with a sampling period of 5 minutes were used to evaluate the performance of the proposed model. The results from the hybrid model were compared with ARIMA, an artificial neural network, and a K-nearest neighbor model. The results showed that the hybrid model outperformed the other methods in terms of accuracy and reliability. Overall, the proposed model performed well in tracking the features of measured data and controlling the impact of abnormal data.

A spatial–temporal-based state space approach for freeway network traffic flow modelling and prediction

Effective traffic flow prediction is an essential component of any proactive traffic control system and one of the pillars of an advanced traffic management system. Hence, the main objective of this study is to develop a general and flexible prediction model that simulates the freeway system as it responds to traffic state transition. To this end, a spatial–temporal-based model without the prior need for off-line parameter calibration is proposed for traffic flow prediction. The model predicts flow rates at a given freeway network based on contributions from their recent temporal profiles as well as the spatial contributions from neighbouring sites. An in-depth investigation of the variables pertinent to traffic flow prediction is conducted to examine the extent of the temporal and spatial contributions and their effects on prediction accuracy. The predictions of the proposed models have been compared with the field-measured flow rates, and show that the results predicted by the proposed model are quite close to the measurement values for all the investigated sites. The benefit from full-spatial contribution as opposed to no contribution at all (ARIMA model) is evident in all investigated sites, and the accuracy can be improved by 5% as an average. Compared with the linear regression model with only spatial contributions, the average prediction error of the proposed model is 9% lower. Through comparison, the result shows that the temporal input factor provides more accurate information to the flow rate prediction, but it can hardly track the flow rate drops during the congestion. During the flow rate drops, the spatial input factor would secure the prediction accuracy.

Recent trends in intelligent transportation systems: a review

Managing the increasing traffic is a big problem all over the world. Intelligent Transportation System (ITS) provides solution to these problems with the help of new technologies. ITS is an integrated system that implements a broad range of communication, control, vehicle sensing and electronics technologies to solve and manage the traffic problems. ITS is being used in the developed countries since past two decades, but it is still a new concept when developing countries like India, Brazil, China, South Africa etc. are concerned. In the present study we have studied four major parts of the ITS i.e., Advanced Traveler Information System (ATIS), Advanced Traffic Management System (ATMS), Advanced Public Transportation System (APTS), and Emergency Management System (EMS). Objective of the paper is to study various ITS architecture and model and review such models to get in-depth of their architecture. Hence architecture and developed models over the years of four major branches of ITS have been reviewed here to make a comparison analysis of different models that have been developed by the researchers in their studies. It will lead to the gaps in the knowledge which can be further studied. The paper highlights the conclusions extracted from the studies of different systems and also gives the future scope in the field of ITS to make it more user friendly and accessible.