Advanced Traffic Management Systems Research Articles

A heterogeneous traffic spatio-temporal graph convolution model for traffic prediction

Smart cities require advanced traffic management systems. Traffic forecasting is an essential task of the advanced transportation system. Traffic spatio-temporal data are often heterogeneous. Most existing traffic prediction models predominantly use separate components to extract the temporal and spatial features of traffic data. However, this overlooks the intrinsic connections between the spatio-temporal features of traffic data. To directly mine the spatio-temporal heterogeneity, this study constructs a global heterogeneous traffic spatio-temporal graph and proposes the Heterogeneous Traffic Spatio-Temporal Graph Convolution (HTSTGC). To reduce the complexity of the model, Simple Graph Convolution (SGC) is used to extract semi-structured meta-graph information. The receptive fields that capture temporal and spatial features can be flexibly adjusted separately through clever design, which can balance the performance and efficiency of the model. Finally, the feature fusion module applies Gated Graph Neural Network (GGNN) to fuse temporal and spatial features. The results on the PEMS datasets reveal that jointly modeling different types of relationships can improve the traffic prediction performance of the model. The proposed HTSTGC has better performance than the baseline methods in most cases. The research results can support urban traffic control, traffic pollution reduction, and sustainable urban development.

Detection of Vehicle Number Plate and Speed Using Machine Learning

In an era marked by a significant upsurge in vehicular traffic and the resultant increase in road accidents, the need for advanced traffic management systems is paramount. This paper presents an innovative solution, "Detection of vehicle number plate and speed using machine learning" which aims to enhance road safety by addressing the persistent problem of over-speeding. The project leverages state-of-the-art technologies, including machine learning, deep learning, and computer vision, to develop a smart system that can detect speeding vehicles, recognize license plates, and record crucial data related to speed limit violations. The primary motivation for this endeavor is the escalating rate of road accidents, particularly in India, driven by speeding in areas susceptible to accidents. The system employs image segmentation, corner detection algorithms, filtering algorithms, and automatic vehicle plate recognition techniques to achieve its objectives. By using machine learning and deep learning models, the system accurately identifies license plates, segments characters on these plates, and recognizes them. In summary, this project represents a significant advancement in road safety and traffic management. By deploying an intelligent system that can proactively detect over-speeding vehicles and maintain comprehensive records, it strives to create a safer and more responsible driving environment, ultimately working towards the prevention of accidents and saving lives. Key Words: Automatic Number Plate Recognition, Vehicle Speed Detection, Machine Learning, Computer Vision, Traffic Management

Detection of Vehicle Number Plate and Speed Using Machine Learning

In an era marked by a significant upsurge in vehicular traffic and the resultant increase in road accidents, the need for advanced traffic management systems is paramount. This paper presents an innovative solution, "Detection of vehicle number plate and speed using machine learning" which aims to enhance road safety by addressing the persistent problem of over-speeding. The project leverages state-of-the-art technologies, including machine learning, deep learning, and computer vision, to develop a smart system that can detect speeding vehicles, recognize license plates, and record crucial data related to speed limit violations. The primary motivation for this endeavor is the escalating rate of road accidents, particularly in India, driven by speeding in areas susceptible to accidents. The system employs image segmentation, corner detection algorithms, filtering algorithms, and automatic vehicle plate recognition techniques to achieve its objectives. By using machine learning and deep learning models, the system accurately identifies license plates, segments characters on these plates, and recognizes them. In summary, this project represents a significant advancement in road safety and traffic management. By deploying an intelligent system that can proactively detect over-speeding vehicles and maintain comprehensive records, it strives to create a safer and more responsible driving environment, ultimately working towards the prevention of accidents and saving lives. Key Words: Automatic Number Plate Recognition, Vehicle Speed Detection, Machine Learning, Computer Vision, Traffic Management.

An empirical investigation of driver car-following risk evolution using naturistic driving data and random parameters multinomial logit model with heterogeneity in means and variances

This study aims to address the questions of how driving risk evolves during car-following processes and what factors contribute to the underlying evolution patterns. An empirical study is conducted using real world car-following data collected in the Shanghai Naturalistic Driving Study (SH-NDS). The evolution of the driving risk induced by the dynamic coupling between the leading and following vehicles during the car-following process is characterized by how an instantaneous crash-risk measure - rear crash risk index (RCRI) - changes by time. A spectral clustering analysis is first conducted to classify the driving risk evolution of the observed car-following maneuvers, showing the existence of five distinctive risk evolution patterns in the car-following processes. In order to investigate the relationship between the identified driving risk evolution clusters and their contributing factors, a regression analysis employing a random parameter multinomial logit model with heterogeneity in means and variances is followed, revealing several significant contributing factors to the car-following risk evolution patterns, such as congestion level, driver’s ability to maintain stable headways, and vehicle deceleration. This study has provided important insights into driving risk from the new perspective of risk evolution patterns, which is expected to have significant implications for the future development of advanced traffic management and traveler information systems (ATMS/ATIS) strategies, advanced driver assistance systems (ADAS), and connected and autonomous vehicles (CAV).

Optimizing Transportation Management with a Hybrid GNN-RL Approach

The process of rapid urbanization in contemporary cities has resulted in a rise in traffic congestion, accidents, and pollution, leading to negative impacts on transportation efficiency and urban development. In response, Intelligent Transportation Systems (ITS) have emerged as a crucial field of research and development, with a goal to leverage technological advancements to enhance transportation safety, efficiency, and sustainability. However, traditional expert systems and traffic control methods have displayed limitations in handling the complexities of urban transportation. To address these challenges in Advanced Traffic Management Systems (ATMSs), this paper pro- poses a new approach utilizing Graph Neural Network-Reinforcement Learning (GNN-RL). By using GNNs for pre-training and information aggregation from the entire grid, our method enables downstream RL tasks, allowing for fine-tuning of effective global information aggregation and dynamic traffic signal control policy adjustment. The GNN-RL approach offers enhanced adapt- ability and performance in managing long-term dependencies, rendering it a promising alternative for traffic control in contemporary urban transportation systems.

Spatio-Temporal vehicle traffic flow prediction using multivariate CNN and LSTM model

Traffic congestion is a major problem in developing and developed countries vehicle traffic management systems. Traffic control system works based on the idea of removing instabilities and avoid accidents in order to minimize the traffic and maximize the vehicle flow. To control the congestion need to predict the upcoming traffic flow and it will be useful for Advanced Traffic Information Systems (ATIS), Advanced Traffic Management Systems (ATMS) and traffic analytics. Non–linear historical data and uncertain factors influence the vehicle congestion at peak hours which cannot be considered in existing algorithms. This study proposes hybrid neural network algorithms such as Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) network for short term traffic flow prediction based on multivariate analysis. Widely referred datasets Performance Measurement Systems (PEMS) and Mesowest have been used to evaluate this model. Experiment results shows that CNN-LSTM Hybrid prediction model achieves high accuracy compared with other models.

Short-Term Traffic Congestion Prediction Using Hybrid Deep Learning Technique

A vital problem faced by urban areas, traffic congestion impacts wealth, climate, and air pollution in cities. Sustainable transportation systems (STSs) play a crucial role in traffic congestion prediction for adopting transportation networks to improve the efficiency and capacity of traffic management. In STSs, one of the essential functional areas is the advanced traffic management system, which alleviates traffic congestion by locating traffic bottlenecks to intensify the interpretation of the traffic network. Furthermore, in urban areas, accurate short-term traffic congestion forecasting is critical for designing transport infrastructure and for the real-time optimization of traffic. The main objective of this paper was to devise a method to predict short-term traffic congestion (STTC) every 5 min over 1 h. This paper proposes a hybrid Xception support vector machine (XPSVM) classifier model to predict STTC. Primarily, the Xception classifier uses separable convolution, ReLU, and convolution techniques to predict the feature detection in the dataset. Secondarily, the support vector machine (SVM) classifier operates maximum marginal separations to predict the output more accurately using the weight regularization technique and a fine-tuned binary hyperplane mechanism. The dataset used in this work was taken from Google Maps and comprised snapshots of Bangalore, Karnataka, taken using the Selenium automation tool. The experimental outcome showed that the proposed model forecasted traffic congestion with an accuracy of 97.16%.

Machine Learning Framework for Real-Time Assessment of Traffic Safety Utilizing Connected Vehicle Data

Assessment of roadway safety in real-time is a necessary component for providing proactive safety countermeasures to ensure the continued safety and efficiency of roadways. A framework for utilizing data from connected vehicles and other probe sources is proposed in this study. Connected vehicles present an opportunity to provide live fingerprinting and activity monitoring on roadways. Taking advantage of high-resolution trajectory data streaming directly from connected vehicles, variables are extracted and the relationship with crashes are explored utilizing statistical and machine learning models. Hard acceleration events, in conjunction with segment miles are shown to have strong positive correlations with historical crash outcomes as proven by OLS, Poisson and Gradient Booster regression models. An XGBoost classification model is then trained to predict the real-time instances of crash outcomes at 5 min temporal bins with high levels of accuracy when trained with data including the real-time segment speed, reference speed, segment miles, a segment crash risk factor and other variables related to the difference in speeds between consecutive segments as well as the hour of the day. A weighted ensemble model achieved the best performance with an accuracy of 0.95. The results present evidence that the framework can capitalize on the richness of data available via connected vehicles and is implementable as a component in Advanced Traffic Management Systems for the analysis of safety critical situations in real-time.

Addressing unobserved heterogeneity at road user level for the analysis of conflict risk at tunnel toll plaza: A correlated grouped random parameters logit approach with heterogeneity in means

Toll plaza is a designated area of controlled-access roads like expressway, bridge, and tunnel for toll collection. A number of toll booths are often placed at the toll plaza accommodating high passing traffic and multiple payment methods. Traffic and safety characteristics of toll plazas are different from that of other road entities. Different conflict risk indicators, which are usually longitudinal, have been adopted for real-time safety assessment. In this study, correlated grouped random parameter logit models with heterogeneity in the means are established to capture the unobserved heterogeneity, with additional flexibility, at road user level for the association between conflict risk and influencing factors. In addition, modified conflict risk indicator is developed to assess the safety of diverging, merging, and weaving movements of traffic, with which vehicles’ dimensions (width and length), and longitudinal and angular movements are considered. Also, prevalence and severity of both rear-end and sideswipe conflicts are assessed. Results indicate that toll collection type, vehicle’s location, average longitudinal speed, angular speed, acceleration, and vehicle class all affect the risk of traffic conflicts. Furthermore, there are significant correlation among the random parameters of severe traffic conflicts. Proposed analytic method can accommodate the conflict risk analysis for different conflict types and account for the correlation of unobserved heterogeneity. Findings should shed light on appropriate remedial measures like traffic signs, road markings, and advanced traffic management system that can improve the safety at tunnel toll plazas.

What is the Root Cause of Congestion in Urban Traffic Networks: Road Infrastructure or Signal Control?

Identifying the root cause of congestion and taking appropriate strategies to improve traffic network performance are important goals of Advanced Traffic Management Systems (ATMS). On many occasions, the causes of congestion are not necessarily attributable to road infrastructures themselves. Instead, signal control strategies at intersections are very often the major contributors of congestion. In lieu of this, in this paper, a root cause identification method is developed with consideration of the impact from both road infrastructure and traffic signal control. Firstly, we differentiate congestion effects between road segments and intersections to attribute the causes of congestion to road infrastructure and signal control respectively. Then, we construct causal congestion trees to model congestion propagation and quantify congestion costs for each road segment and intersection in the whole road network. A Markov model is utilized to capture congestion spatio-temporal correlation among multiple road segments and intersections simultaneously, with which the most critical root cause can be located. Furthermore, a gradient boosting decision tree based method is presented to predict the root cause of congestion according to traffic flows, signal control strategies and road topology in traffic networks. Finally, simulations based on Simulation of Urban Mobility (SUMO) validate the effectiveness of our proposed method in identifying and predicting the congestion root cause. Experiments are further conducted using inductive loop detector data to identify the root cause for the road network of Taipei.

Alleviating Traffic Signal Design at Kacherippadi Junction

Abstract: Improved traffic management and control systems are widely reported to be cost effective investments. As vehicular traffic began to increase, the congestion on the street to begin to hamper thus safe and efficient movement of traffic. The problem of traffic accidents and congestion in urban roads is being viewed with grave concern in the recent years. The main cause for this problem improper planning of road network and other roadway facilities and poor traffic planning. Simply retiming signals can provide significant benefits by reducing vehicle stops, travel times, and fuel consumption. The installation of advanced traffic management systems (ATMS) can provide even greater savings. However, many hardware and software obstacles are impeded the actual implementation of advanced traffic management systems. For this project we have chosen an intersection at Kacherippadi, where traffic congestion is more and we are planning to do a traffic signal, for that we counted traffic in that area, after analyzing and examining the speed data, average vehicle at a peak time, number of vehicles per day. Pedestrian movements, to reduce the congestion problems

Identification and Mitigation of Blackspots and Implementation of An Advanced Traffic Management System on NH-216A (A Spur of National Highway-16)

Abstract: As the population is increasing day by day the number of vehicles is also increasing. As the number of vehicles is increasing, the number of accidents also increases. Analysis of previous data indicates that 66% of the accidents occur due to human error and 33% due to road parameters such as road and vehicle interaction, other road users, and environmental factors. Road safety has recently become a major concern in most modern societies. The identification of sites (black spots) that are more dangerous from an accident point of view can help in better scheduling road safety policies. The study includes the collection of accident data and prioritizing the accident-prone locations by using the Weighted Severity Index (WSI) method. This study includes the identification of Accidental Black Spots and improving these Accidental Black Spots by using ATMS. This project emphasizes accident studies on the National Highway-216A (Diwancheruvu to Siddantham) in East Godavari District, State of Andhra Pradesh, India. So the main aim of this study is, therefore, to identify the major accident black spots on National Highway -16 and mitigation of Accidental Black Spots by using Advanced Traffic Management System. Keywords: Accident, National Highway, Black Spots, Advanced Traffic Management System (ATMS), Weighted Severity Index (WSI).

Low-Cost Connected Work Zone Devices with Simplified Architecture

Connected work zone devices for safety, traffic monitoring, and performance evaluation are important assets in road construction projects. Challenges for implementation and connectivity present a barrier to their use and full realization of benefits. Typical connected work zone devices require dedicated cellular modems with monthly data charges for each device. The numerous connections or proprietary platforms hinder integration with a central advanced traffic management system. The described architecture presents considerable costs in data ownership and security concerns. This paper presents an alternative architecture wherein many inexpensive monitoring and incident detection devices read and process sensor data and communicate information by radio link to a single internet-connected hub that forwards information to a central server. The server then notifies client applications that support work zone monitoring and incident response. From a field test of the radio units in an active work zone, the effective transmission range was found to be 3 km. All devices in a work zone up to 6 km in length could be serviced by a single internet-connected base receiver station centered within it. Prototype devices were created using readily and inexpensively available sensors and microcontrollers. An example dashboard was created to show real-time work zone device and sensor information on a mapping interface. This project shows how use of connected work zone devices can be expanded at low cost using commonly available hardware and skills with a simplified architecture and encapsulated complexity from the standpoint of work zone management personnel while retaining data ownership.

A GATs-GAN framework for road traffic states forecasting

Short-term traffic states forecasting of road networks based on real-time data is an important component of intelligent transportation systems, especially advanced traffic management systems and traveller information systems. By considering the influence of both space and time dimensions, we proposed a novel GATs-GAN framework for the forecasting of traffic states. First, to capture spatial traffic relationships, the traffic topological graph network is set up based on the connection of traffic sections. Then, the first-order neighbours and high-order neighbours of traffic networks can be structured. Graph attention networks (GATs) are used to obtain the hidden features of input traffic data by training the attention between nodes in high-order neighbours. Based on two traffic networks in California and Seattle in the United States, we find that the GATs-GAN with high-order neighbours can satisfactorily estimate the traffic data and performs better than the baseline methods and comparative experiments.

Applying Clustered KNN Algorithm for Short-Term Travel Speed Prediction and Reduced Speed Detection on Urban Arterial Road Work Zones

This study developed and verified a travel speed prediction model based on the travel speed and work zone statistics collected from the advanced traffic management system (ATMS) real-time data in Daegu, South Korea. A clustered K-nearest neighbors (CKNN) algorithm was used to predict travel speed, resulting in a 6.9% average mean absolute percentage error (MAPE) using the data from 1,815 work zones. Furthermore, road network impact due to road work was calculated by comparing the travel speed prediction results obtained from the historical speed data. The predicted travel speed data in a work zone generated from this study is expected to allow drivers to select optimized paths and use them for traffic management strategies to operate in a work zone efficiently.

Examining the factors effecting severity of two-wheeler crashes at intersections

For short-distance travellers, two-wheelers have become a standard transportation mode and an ideal alternative (compact size, economical, easy parking), to four-wheelers particularly in developing countries. However, two-wheelers are associated with different safety concerns. In India, two-wheelers are more vulnerable to fatal and serious injuries per kilometres of travel in contrast to drivers of other vehicles. Most of these accidents occur on junctions and are likely to be fatal and more severe in contrast to nonjunction accidents. This research article aims to examines different risk variables associated with motorized two-wheeler crashes at intersections (signalized and un-signalized) in Haridwar and Dehradun districts of Uttarakhand state, India. A multinominal logistic approach was used to analyse a dataset of 680 two-wheeler crashes occurred within four years (2016–2019). The accident severities were categorized into three groups, i.e. fatal injury, serious injury and normal injury. The risk variables were categorized into five significant classes, i.e. riders' attributes, environmental attributes, junction crash attributes and land-use attributes. However, the majority of this study's findings endorses the concepts derived from earlier researches that used alternative (for instance; random forest, ordered logit, etc.) analytical methodologies in crash severity analysis. The model results showed that some variables (e.g. age, gender, adverse environmental conditions, time of crash and location of the junction) increases the possibility of mortal injuries. In contrast, some variables (day of crash, type of intersection and type of controls at intersections) decrease the probability of mortal injuries compared with minor or severe injuries. Based on the model's findings, policy recommendations are made, such as enforcing rules, advanced traffic management systems and road safety campaigns. For instance, education of young drivers can be an effective way to reduce crash severity as they are physiologically less sensitive towards the risk associated with crashes, thus, their involvement results in more severe crashes. Additionally, the results suggested that treatment measures such as transforming an un-controlled junction into signalized or all-way stop controls can be a productive approach for improving the safety of uncontrolled junctions.

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).