Advanced Traffic Management Systems Research Articles

A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory Model

Timely and accurate traffic prediction information is essential for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). Because of the characteristics of nonlinearity, nonstationarity, and randomness, short-term traffic flow prediction could be still a challenging task. In this study, a hybrid short-term traffic flow multistep prediction method is proposed by combining the variational mode decomposition (VMD) algorithm and long short-term memory (LSTM) model. Firstly, the VMD algorithm is employed to decompose the original traffic flow data into a series of intrinsic mode function (IMF) components. Secondly, different LSTM models are established to predict different IMF components. For each prediction model, one-step to three-step predictions are carried out. Finally, the component prediction results are aggregated to obtain the final traffic flow multistep prediction values. The prediction performance of the proposed hybrid model is investigated using inductive loop data measured from the north-south viaduct expressway in Shanghai. The experiment results show that (1) VMD algorithm could effectively avoid the problems of endpoint effects and modal aliasing, and the decomposition effect is better than empirical mode decomposition algorithm and wavelet decomposition algorithm; (2) among all the involved methods, the proposed hybrid model is more effective and robust in extracting the trend information, which has the best multistep prediction performance.

Air Support

Unmanned aerial vehicles are set to take over our skies in the future, offering everything from food delivery and traffic monitoring to emergency response. However, for cities to fully benefit and safety to be assured an advanced traffic management system will be essential. Lauren Dyson speaks to the leading experts in this area

Quantitative Evaluation of Advanced Traffic Management Systems using Analytic Hierarchy Process

With technological advancements in recent years, a series of intelligent transportation system (ITS) products have now become available to the transportation agencies to collect data and manage traffic conditions on the roadway network. Among ITS products, the advanced traffic management system (ATMS) has been effectively serving as the central nervous system of a traffic management center. ATMS serves as an integrated application for a wide variety of purposes ranging from data collection to implementing traffic management strategies. Owing to commercial popularity, a series of ATMS products are now available to transportation agencies and there is no consensus on selecting the best-suited product based on tailored requirements. Making a decision for a decision-critical item such as ATMS products on qualitative evidence can add risk to the decision-makers to justify their decision of choice. In this study, a multi-criteria decision analysis framework was proposed for quantitative evaluation of ATMS alternatives that can consider multiple and conflicting decision-making criteria using a real-world example. Moreover, the proposed framework was evaluated for different scenarios related to different applications of ATMS products to provide flexibility to the user in evaluating the ATMS alternatives. Results indicated that the proposed method can be considered as a viable alternative in contrast to a qualitative evidence-based decision-making strategy to minimize the risk associated with the decision-makers. Using the proposed quantitative framework, decision-makers can examine the weights of different criteria under consideration and evaluate multiple ATMS alternatives based on their jurisdiction-specific requirements. The proposed framework can be easily applied to other ITS technology selection processes.

Highway Travel Time Prediction of Segments Based on ANPR Data considering Traffic Diversion

Travel time is one of the most critical parameters in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid model named LSTM-CNN for predicting the travel time of highways by integrating the long short-term memory (LSTM) and the convolutional neural networks (CNNs) with the attention mechanism and the residual network. The highway is divided into multiple segments by considering the traffic diversion and the relative location of automatic number plate recognition (ANPR). There are four steps in this hybrid approach. First, the average travel time of each segment in each interval is calculated from ANPR and fed into LSTM in the form of a multidimensional array. Second, the attention mechanism is adopted to combine the hidden layer of LSTM with dynamic temporal weights. Third, the residual network is introduced to increase the network depth and overcome the vanishing gradient problem, which consists of three pairs of one-dimensional convolutional layers (Conv1D) and batch normalization (BatchNorm) with the rectified linear unit (ReLU) as the activation function. Finally, a series of Conv1D layers is connected to extract features further and reduce dimensionality. The proposed LSTM-CNN approach is tested on the three-month ANPR data of a real-world 39.25 km highway with four pairs of ANPR detectors of the uplink and downlink, Zhejiang, China. The experimental results indicate that LSTM-CNN learns spatial, temporal, and depth information better than the state-of-the-art traffic forecasting models, so LSTM-CNN can predict more accurate travel time. Moreover, LSTM-CNN outperforms the state-of-the-art methods in nonrecurrent prediction, multistep-ahead prediction, and long-term prediction. LSTM-CNN is a promising model with scalability and portability for highway traffic prediction and can be further extended to improve the performance of the advanced traffic management system (ATMS) and advanced traffic information system (ATIS).

A Dynamic Day-To-Day Departure Time and Route Choice Model for Bounded-Rational Individuals

Accurate prediction of travellers’ day-to-day departure time and route choice is critical in advanced traffic management systems. There have been several related works about route choice with the assumption that the departure time for individual travellers is known beforehand. With real-time traffic state information provided by navigation systems and previous historical experience, travellers will dynamically update their departure time, which is neglected in existing works. In this study, we aim to describe travellers’ spatial-temporary choice behaviour taking navigation information into account and propose a bounded-rational day-to-day dynamic learning and adjustment model. The new model contains three steps. First, the real-time navigation guidance on each discrete day is obtained, and the self-learned experience of travellers’ choices with navigation information is presented; then, the day-to-day revision process of the choices is derived to maximize departure and route choice prospect; next, by aggregating each individual’s behaviour and calculating route choice probability, a bounded-rational continuous day-to-day dynamic model is provided. Numerical experiments suggest that the proposed model converges to a spatial-temporal oscillating equilibrium not a fixed-point stable status, and the final equilibrium trend is different from classical user equilibrium. The findings of the study are helpful to improve the prediction accuracy of traffic state in urban street networks.

A Data-Driven Multi-Fidelity Approach for Traffic State Estimation Using Data From Multiple Sources

Accurate estimation of traffic state is crucial in the successful implementation of advanced traveler information systems and advanced traffic management systems. Currently, traffic data from various sources such as GPS, cell phones, and connected vehicles are increasingly available. Consequently, efforts have moved from estimating traffic using a single source of data to incorporating heterogeneous data, i.e. data from multiple sources, for traffic estimation. In these works, data from different sources are mostly considered to have the same level of fidelity or accuracy. This is while, in reality, different data sources may contain different levels of noise. In this work, we propose using multi-fidelity Gaussian processes to exploit data with different fidelities for traffic estimation. We demonstrate the performance of the proposed approach using a simulation study, where the results show that accounting for multi-fidelity nature of data can significantly improve the accuracy when multiple data sources are used for traffic state estimation.

Citywide traffic speed prediction: A geometric deep learning approach

Accurate traffic speed prediction is critical to modern internet of things-based intelligent transportation systems. It serves as the foundation of advanced traffic management systems and travel services. Nonetheless, the large number of roads and sensors impose great computational burden to existing forecast approaches, most of which can only handle one or few roads at a time. In this paper, a novel data-driven deep learning-based approach is proposed for citywide traffic speed prediction. The proposed approach is grounded on recent developments of geometric deep learning techniques to fully utilize the topological information of road networks in the learning process. Specifically, the approach captures the geometric traffic data dependency with graph convolution and attention mechanisms, and the temporal data correlation is extracted and expanded using the encoder–decoder architecture within a generative adversarial learning framework. Comprehensive case studies are conducted with real-world urban road networks and respective data to evaluate its performance, where consistent improvements can be observed over baseline approaches. Lastly, an architectural study is carried out to discover the best-performing structure of the proposed approach, whose sensitivity to data noise and sample frequency is also assessed.

Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices

The rising need for mobility, especially in large urban centers, consequently results in congestion, which leads to increased travel times and pollution. Advanced traffic management systems are being developed to take the advantage of increased mobility positive effects and minimize the negative ones. The first step dealing with congestion in urban areas is the detection of congested areas and the estimation of the congestion level. This paper presents a a method for a traffic state estimation on a citywide scale using the novel traffic data representation, named Speed Transition Matrix (STM). The proposed method uses traffic data to extract the STMs and to estimate the traffic state based on the Center Of Mass (COM) computation for every STM. The COM-based approach enables the simplification of the clustering process and provides increased interpretability of the resulting clusters. Using the proposed method, traffic data is analyzed, and the traffic state is estimated for the most relevant road segments in the City of Zagreb, which is the capital and the largest city in Croatia. The traffic state classification results are validated using the cross-validation method and the domain knowledge data with the resulting accuracy of 97% and 91%, respectively. The results indicate the possible application of the proposed method for the traffic state estimation on macro- and micro-locations in the city area. In the end, the application of STMs for traffic state estimation, traffic management, and anomaly detection is discussed.

Verifiable and Privacy-Preserving Traffic Flow Statistics for Advanced Traffic Management Systems

Crowdsourcing-based traffic monitoring plays an important role in advanced traffic management systems due to its high accuracy and low costs, but it may expose drivers real identities and sensitive locations that results in the privacy leakage of drivers. In this paper, we propose a crowdsourcing-based traffic monitoring scheme that enables a transportation management center (TMC) to achieve traffic flow statistics at road intersections in an efficient, verifiable, and privacy-preserving manner. Specifically, by integrating a homomorphic encryption primitive and a super-increasing sequence, traffic flow can be flexibly structured and encrypted by drivers, i.e., each drivers travel direction at T-junctions or crossroads is protected. As a middle-ware between drivers and TMC, roadside units (RSUs) are introduced to aggregate and further perturb the aggregated encrypted traffic flow based on a differential privacy mechanism. In this way, TMC is capable of acquiring the traffic flow statistics by decrypting the perturbed encrypted traffic flow, without disclosing each individual drivers traffic information. In addition, based on a lightweight commitment proof, the correctness of the encrypted drivers data can be guaranteed, i.e., a selfish driver cannot arbitrarily manipulate his data to poison the aggregated traffic flow. Finally, security analysis demonstrates that the proposed scheme satisfies all desirable security properties, including confidentiality, verifiability, unlinkability, and traceability. Extensive simulations are also conducted to show that the proposed scheme is efficient in terms of low computation and communication costs.

RETRACTED ARTICLE: Smart traffic management system in metropolitan cities

Now-a-days, traffic congestion is one of the troubles in metro cities. It is mainly due to escalate in vehicle, population etc. Due to the raise in vehicle, the road is incapable of handling the traffic. Consequently it is very important to revamp the traffic management system. The advanced traffic management system intention is to ameliorate the safety and efficiency of the transportation system. Advanced traffic management system permit opportunities for new methods of evaluations and continuing assessment is provided. A few scenarios were analysed in the traffic issues and better performance is acquired in this research. The traffic map in New York City is taken as a database model and to discover the shortest path an advanced Dijkstra’s algorithm is applied. The advanced form of Hadoop known as Apache spark is used. Spark is the open standard and it is easy to program as it has tons of high level operators which can be flexible in-memory data processing that enables batch, real-time, and advanced analytics on the Apache Hadoop platform. R-tool is an open source programming language and software environment for statistical computing is also employed. By using time forecasting algorithm, for each scenarios the vehicle speed, count, collusion, time, etc. is calculated and the Map Analysis is done with good performance by using R-tool. The overall performance of the system that enhances the traffic control efficiency is 96.23%

Optimal combined traffic routing and signal control in simple road networks: an analytical solution

ABSTRACT Advanced traffic management systems aim at enhancing the traffic performance of urban networks. In addition to traffic signal control, the current paper includes traffic routing as another control measure in the traffic management system. Combined model of traffic routing and traffic signal control has a long history. In this paper, similar to previous works, both traffic routing and signal control are coupled in a continuous-time model for simple traffic networks. Unlike previous works, (i) the developed model considers dynamic transient periods, rather than steady-state periods or equilibrium conditions, and (ii) system optimum via optimal control is found analytically. The optimal analytical solutions, which are derived based on Pontryagin Maximum Principle, are verified via numerical solutions. A feedback routing and signal control policy based on link queue lengths is presented.

Crash Classification by Congestion Type for Highways

Effective management of highway networks requires a thorough understanding of the conditions under which vehicular crashes occur. Such an understanding can and should inform related operational and resource allocation decisions. This paper presents an easily implementable methodology that can classify all reported crashes in terms of the operational conditions under which each crash occurred. The classification methodology uses link-based speed data. Unlike previous secondary collision identification schemes, it neither requires an a priori identification of the precipitating incident nor definition of the precipitating incident’s impact area. To accomplish this objective, the methodology makes use of a novel scheme for distinguishing between recurrent and non-recurrent congestion. A 500-crash case study was performed using a 274 km section of the I-40 in North Carolina. Twelve percent of the case study crashes were classified as occurring in non-recurrent congestion. Thirty-seven percent of the crashes in non-recurrent congestion classified were identified within unreported primary incidents or crashes influence area. The remainder was classified as primary crashes occurring in either uncongested conditions (84%) or recurrent congestion (4%). The methodology can be implemented in any advanced traffic management system for which crash time and link location are available along with corresponding archived link speed data are available.

Advanced Traffic Management System using a Fuzzy Logic Controller to Differentiate Parking Vehicle and Moving Vehicle

In smart cities, traffic congestion is one of the significant problems for citizens. Traffic management is an essential one for the quick development of populace and urban movement in metropolitan areas, and traffic blockage is often seeming on streets. To handle different issues for managing traffic on the streets and to help experts in inappropriate arrangement, a smart traffic management system with the IoT (Internet of Things) is proposed in this paper. Mechanisms to utilize IR sensors to distinguish traffic density isn't easy as smooth a solo vehicle recognized at the last sensor so that it can suggest traffic density in high in any event, even if there is free space before it. A technique to be proposed to solve the previously mentioned issues efficiently is by utilizing the Internet of things for traffic management systems. This paper aims to propose a Fuzzy controller to deal with traffics in smart cities. Fuzzy induction used to compute exact traffic, which separates the parking vehicle and moving vehicle. There is an issue of separating parking and un-parking vehicles in the existing systems. So, we planned to solve this using fuzzy logic.

Consideration of Users’ Needs in Regard to Planning of Intelligent Transport Solutions in an African Metropolis

Population growth rates in the least industrialized countries are the highest and will add pressure to transport systems in the future, making travel unimaginably more difficult, especially in locations where road infrastructure is already inadequate or dilapidated. It is, therefore, imperative to plan and deploy intelligent solutions that can intuitively adapt to future traffic demands and inclusively improve transport efficiency and safety. By considering modernization theory and technology transfer, the authors weigh transport system users’ perspectives on the desirability of deployable intelligent transport systems (ITSs) in an African metropolis. The study shows that users recognize the benefits of technology in improving transport performance and travel experience in the metropolis with regard to the precursory ITS solutions already in existence. Although, at different degrees of interest, the participants expressed their desire for deployable solutions in all categories and applications of ITS surveyed. For example, an advanced public transport system (APTS) and advanced traffic management system (ATMS) are more desired. An advanced vehicle communication system (AVCS) tied for the third spot with an advanced travelers’ information system (ATIS). An intelligent transport pricing system (ITPS) was rated as least desirable for a probable reason that the supply and maintenance of road infrastructure are believed to be the government’s responsibility. Differential opinions were recorded, depending on respondents’ age group, profession and most-used means of transport. The authors conclude that the metropolis, and indeed, countries, could develop user-oriented, context-specific and evidence-based ITS policies toward the goal of deploying solutions for a more efficient transport system and pleasing travel experience. The paper also provides a conceptual ITS policy framework that considers transport users’ needs in the metropolis.

Dynamic Origin-Destination Demand Estimation From Link Counts, Cellular Data And Travel Time Data

Accurate real-time Origin-Destination (O-D) demand estimation is critical for development of Advanced Traveller Information Systems (ATIS) as well as Advanced Traffic Management Systems (ATMS). Traditional O-D estimation techniques typically pose it as optimization problems involving offline calibration of O-D priors from historical data and updating these prior based on real-time data sources such as link counts, link travel times and Global Positioning System (GPS) probe data. However, two major drawbacks have emerged from this approach. Firstly, the generation of effective priors requires extensive population data with high temporal sampling frequency. Such data is generally hard to obtain and process with low memory and compute power. Secondly, the optimization problems proposed for are often non-convex in nature. Therefore, convergence to an optimal solution is not guaranteed. Moreover, the algorithms suggested don’t scale well with increase in the size of the transportation network. In order to tackle these challenges, we first generate priors through a convex optimization framework by Wu et al. (2015) for O-D estimation in quasi-static settings from cell phone Call Detail Records (CDRs). We extend this approach to a completely dynamic setting by utilizing real-time link counts and link travel times through an Entropy Maximization framework proposed by Janson et al. (1992). Approximate solution to the traffic assignment sub-problem is also achieved from Wu et al. (2015). We test this procedure for estimating commute demand for a simplified freeway network representing nine counties of the San Francisco Bay Area. We estimate demands for 2,916 O-D pairs for three hours at five-minute intervals, making it a 104,976 dimensional problem. We analyze the spatio-temporal distribution of errors, the effects of wider cell-phone coverage and updating estimates over time. Results indicate that an increase in cell-phone coverage from 0% to 100% leads to a reduction in average Root Mean Squared Error (RMSE) from 15.7 to 7.2 while updating estimates also leads to reductions in RMSE when O-D demand is high.

Exploratory Study on Approaches for Traffic count Prediction; Using Toll-Way Traffic Count

The process of predicting or simulating traffic conditions, based on current and past traffic observations is an important component of any of the Intelligent Transportation System (ITS) applications. There are several methods to predict traffic count. However, each method in different literatures may use different datasets, different time intervals of input traffic flow/count. One of the aims of this study is to provide a review and performance analysis of parametric and non-parametric approaches on traffic prediction. Second, to explore the possibilities in the implementation of Advanced Traffic Management Systems (ATMS), one of the functional areas of Intelligent Transportation Systems (ITS), by predicting traffic count on toll plazas for optimizing of toll-plaza operations. An RFID (Radio-Frequency Identification) based Electronic Toll Collection (ETC) system gives timely varying traffic counts observed at toll plazas, which has been utilized to develop prediction models based on historic data. An empirical differentiation of four methods, namely Seasonal Autoregressive Integrated Moving Average (SARIMA) model based on time series analysis, Monte Carlo Simulation (MCS), Random Forest (RF) based on tree ensemble learning technique and KNN non-parametric regression-based machine learning technique, are proposed. Performance analysis at varying time intervals (5, 10, and 15 minutes) of input traffic count, for all the aforesaid models were compared with Simple Average Technique (SAT) using the historic data collected from two different toll-plazas in India. It was observed that K Nearest Neighbors (KNN) non-parametric regression performed better than other methods in most of the cases.

Travel Time Prediction under Mixed Traffic Conditions Using RFID and Bluetooth Sensors

Travel time information is an integral part in various ITS applications such as Advanced Traveler Information System, Advanced Traffic Management Systems etc. Travel time data can be collected manually or by using advanced sensors. In this study, suitability of Bluetooth and RFID (Radio Frequency Identifier) sensors for data collection under mixed traffic conditions as prevailing in India is explored. Reliability analysis was carried out using Cumulative Frequency Diagrams (CFDs) and buffer time index along with evaluation of penetration rate and match rate of RFID and Bluetooth sensors. Further, travel time of cars for a subsequent week was predicted using the travel time data obtained from RFID sensors for the present week as input in ARIMA modeling method. For predicting the travel time of different vehicle categories, relationships were framed between travel time of different vehicle categories and travel time of cars determined from RFID sensors. The stream travel time was then determined considering the travel time of all vehicle categories. The R-Square and MAPE values were used as performance measure for checking the accuracy of the developed models and were closer to one and lower respectively, indicating the suitability of the RFID sensors for travel time prediction under mixed traffic conditions. The developed estimation schemes can be used as part of travel time information applications in real time Intelligent Transportation System (ITS) implementations.

Grouped travel time estimation in signalized arterials using point-to-point detectors

Travel time, an essential parameter in Advanced Traffic Management Systems and Advanced Traveller Information Systems, has been studied for decades of years, especially on freeways. However, the traffic flows in signalized arterials are more complex and stochastic, which enables arterial travel times to be multistate due to the interruptions from signal controls. Therefore, the signal effects need to be considered in the estimation of arterial travel times. In this paper, a grouped travel time estimation method is proposed to consider the signal interruptions and estimate the arterial travel times on a more micro level. With the data obtained from point-to-point detectors, the method analyzes vehicles’ traffic states before signals and recognizes those vehicles sharing the same states in both upstream and downstream signals as vehicle groups. Then the estimation problem is formulated as a clustering process to recognize the vehicle groups, and the grouped travel times are represented by the centers of vehicle groups. The field tests under ten different scenarios show that the grouped travel times can capture the multistate properties of the travel times in signalized arterials and represent the observed travel times of most vehicles within minor deviations.

Exploiting Vehicular Social Networks and Dynamic Clustering to Enhance Urban Mobility Management.

Transport authorities are employing advanced traffic management system (ATMS) to improve vehicular traffic management efficiency. ATMS currently uses intelligent traffic lights and sensors distributed along the roads to achieve its goals. Furthermore, there are other promising technologies that can be applied more efficiently in place of the abovementioned ones, such as vehicular networks and 5G. In ATMS, the centralized approach to detect congestion and calculate alternative routes is one of the most adopted because of the difficulty of selecting the most appropriate vehicles in highly dynamic networks. The advantage of this approach is that it takes into consideration the scenario to its full extent at every execution. On the other hand, the distributed solution needs to previously segment the entire scenario to select the vehicles. Additionally, such solutions suggest alternative routes in a selfish fashion, which can lead to secondary congestions. These open issues have inspired the proposal of a distributed system of urban mobility management based on a collaborative approach in vehicular social networks (VSNs), named SOPHIA. The VSN paradigm has emerged from the integration of mobile communication devices and their social relationships in the vehicular environment. Therefore, social network analysis (SNA) and social network concepts (SNC) are two approaches that can be explored in VSNs. Our proposed solution adopts both SNA and SNC approaches for alternative route-planning in a collaborative way. Additionally, we used dynamic clustering to select the most appropriate vehicles in a distributed manner. Simulation results confirmed that the combined use of SNA, SNC, and dynamic clustering, in the vehicular environment, have great potential in increasing system scalability as well as improving urban mobility management efficiency.

Short-term prediction of intersection turning volume using seasonal ARIMA model

ABSTRACT Accurate prediction of turning volumes at urban intersections is useful in Advanced Traffic Management Systems (ATMS). Existing studies on traffic flow prediction have mostly focused on midblock sections and only limited studies were undertaken on urban intersections. In the present study, models based on Seasonal Autoregressive Integrated Moving Average (SARIMA) were developed to predict the direction-wise turning volumes at an unsignalized three-leg intersection. Preceding three days of direction-wise turning volumes were used in the model to predict the following day’s turning volumes. Short term prediction of turning volumes was also experimented using both historic (preceding three days data) and real time data on the day of prediction. The results were promising with Mean Absolute Percentage Error (MAPE) of less than 10 in majority of the cases. The prediction scheme requires only limited data as input and open source software package R for estimation of model parameters and prediction.