Traffic Management Strategies Research Articles

Achieving social routing via navigation apps: User acceptance of travel time sacrifice

Trip information and navigation systems are expected to become key components of future traffic management strategies, which, if properly exploited, may contribute to the mitigation of car usage externalities. In this study, we investigate social routing recommendations, which could be associated with nudges, and delivered via a navigation app, aiming at promoting sustainable routing behavior, where some drivers are asked to take longer routes and make travel time sacrifices (TTS). In particular, we propose a framework including data collection and behavioral modeling to identify the impacts of various types of information delivered to drivers, goals of the detour, and personal characteristics on drivers’ TTS behavior. The methodology includes stated choice and revealed choice experiments in two European cities, Amsterdam and Helsinki, and a mixed ordered-response logit model to provide insights into TTS behavior. Our analyses show that delivering different information and nudges results in different levels of TTS. However, regardless of the goal of the detour, offering incentives to drivers enables achieving a higher level of TTS. Comparing the stated and revealed data, regarding TTS and compliance rate, also clarifies significant differences between these two types of data.

Trajectory Data-Driven Network Representation for Traffic State Prediction using Deep Learning

In this study, we propose a trajectory data-driven network representation method, specifically leveraging directional statistics. This approach allows us to extract major intersections and define links from observed trajectories, thereby mitigating the reliance on existing network data and map matching. We apply Graph Convolutional Networks and Long-Short Term Memory models to the trajectory data-driven network representation, suggesting the potential for fast and accurate traffic state prediction. The results imply significant reduction in computational complexity while demonstrating promising prediction accuracy. Our proposed method offers a valuable approach for analyzing and modeling transportation networks using real-world trajectory data, providing insights into traffic patterns and facilitating the exploration of more efficient traffic management strategies.

Estimation and prediction of freeway traffic congestion propagation using tagged vehicle positioning data

Effective estimation and prediction of freeway congestion propagation is the basis for formulating a traffic management strategy. A new estimation and prediction model for congestion propagation based on coordinated vehicle positioning data collected from fixed detectors is proposed. A shockwave-based method is established to model the propagation trajectory of congestion in time and space using data filtered by the Savitzy-Golay. Bayesian ridge regression is applied to determine the probability range of the propagation path of the congestion. The proposed method was tested with Wi-Fi positioning data from a section of the Beijing-Kunming Freeway in China. The results of our method were compared with filed traffic conditions estimated by loop data. The results show a difference of approximately 200s between the start time of the estimated congestion and the observed time on the selected road section. The predicted congestion start time differs from the observed results by around 100s.

Towards self-organizing railway traffic management: concept and framework

Railway traffic management requires a timely and accurate redefinition of routes and schedules in response to detected perturbations of the original timetable. To date, most of the (automated) solutions to this problem require a central authority to make decisions for all the trains in a given control area. An appealing alternative is to consider trains as intelligent agents able to self-organize and determine the best traffic management strategy. This could lead to more scalable and resilient approaches, that can also take into account the real-time mobility demand. In this paper, we formalize the concept of railway traffic self-organization and we present an original design that enables its real-world deployment. We detail the principles at the basis of the sub-processes brought forth by the trains in a decentralized way, explaining their sequence and interaction. Moreover, we propose a preliminary proof of concept based on a realistic setting representing traffic in a French control area. The results allow conjecturing that self-organizing railway traffic management may be a viable option, and foster further research in this direction.

Assessing crash severity of urban roads with data mining techniques using big data from in-vehicle dashcam

<abstract> <p>The factors that affect the severity of crashes must be identified for pedestrian and traffic safety in urban roads. Specifically, in the case of urban road crashes, these crashes occur due to the complex interaction of various factors. Therefore, it is necessary to collect high-quality data that can derive these various factors. Accordingly, this study collected crash data, which included detailed crash factor data on the huge urban and mid-level roads. Using this, various crash factors including driver, vehicle, road, environment, and crash characteristics are constructed to develop a crash severity prediction model. Through this, this study identified more detailed factors affecting the severity of urban road crashes. The crash severity model was developed using both machine learning and statistical models because the insights that can be obtained from the latest technology and traditional methods are different. Therefore, the binary logit model, a support vector machine, and extreme gradient boosting were developed using key variables derived from the multiple correspondence analysis and Boruta-SHapley Additive exPlanations. The main result of this study shows that the crash severity decreased at four-street intersections and when traffic segregation facilities were installed. The findings of this study can be used to establish a traffic safety management strategy to reduce the severity of crashes on urban roads.</p> </abstract>

Computational Architecture of an Integrated Urban Model Considering Physical-Virtual Activity Spaces

This study presents the computational architecture of the integrated transport, land-use, and emission (iTLE) modeling system. It incorporates the effects of physical-virtual activity-space interactions within the microsimulation framework, making it a comprehensive tool for predicting travel demand and evaluating the impacts of evolving activity-travel behavior on transport and land-use systems. The model systematically integrates households’ and individuals’ long-term, medium-term, and short-term travel related decisions, and traffic assignment allowing for more reliable estimates of travel demand. Application of the iTLE prototype in the Halifax Regional Municipality (HRM), Canada reveals notable trends, including a reduction in travel time for work activities, highlighting the potential of teleworking as a pragmatic strategy for traffic management. The model’s flexibility is demonstrated through its ability to capture the trade-offs between physical and virtual activity environments, aligning with the intricacies of modern digitalized societies. Integration of iTLE with the traffic assignment system enhances its applicability across diverse urban contexts, streamlining the process of setting origin-destination trip matrices and significantly improving computational efficiency. By systematically coupling the micro-behavioral decision processes, iTLE generates robust estimates of traffic flow, emissions, and energy use, providing reliable insights for sustainable and efficient transportation planning. This study contributes to advancing integrated urban modeling, offering a valuable tool that can be replicable for multiple cities in Canada and beyond.

Prediction of Traffic Congestion in Vehicular Ad-Hoc Networks Employing Extreme Deep Learning Machines (Edrlm)

Vehicular Ad-Hoc Networks (VANETs) represent a crucial component of intelligent transportation systems (ITS), enabling vehicles to communicate with each other and with roadside infrastructure. Predicting traffic congestion in VANETs is essential for enhancing road safety, optimizing traffic flow, and improving overall transportation efficiency. Traditional machine learning methods have shown promise in this domain; however, they often fall short in handling the complex, high-dimensional data typical of VANETs. To address these challenges, this study employs Extreme Deep Learning Machines (EDRLM), an advanced deep learning technique, for traffic congestion prediction. The EDRLM framework leverages the strengths of deep neural networks and extreme learning machines, offering a robust and scalable solution for processing the dynamic and heterogeneous data in VANETs. By integrating feature extraction, selection, and prediction into a unified model, EDRLM can capture intricate patterns and temporal dependencies within traffic data. The proposed model is trained and validated using real-world VANET datasets, incorporating various traffic parameters such as vehicle speed, density, and inter-vehicular distances. Our experimental results demonstrate that EDRLM outperforms conventional machine learning algorithms in terms of prediction accuracy, computational efficiency, and robustness to noise and missing data. The model's ability to provide timely and precise congestion predictions can facilitate proactive traffic management strategies, including dynamic routing and adaptive traffic signal control, ultimately leading to reduced travel times and enhanced road safety. This study underscores the potential of EDRLM in transforming traffic management in VANETs, paving the way for more intelligent and adaptive ITS solutions. Future research directions include exploring hybrid models combining EDRLM with other advanced machine learning techniques and expanding the framework to accommodate emerging vehicular communication technologies such as 5G and Internet of Things (IoT) devices.

The Influence of the Stationarity Property on Traffic Flow Control

This study provides an overview of traffic control methods using automated traffic control systems (ATCS). The main approach highlighted during the analysis is the application of rigid multi-program management, including a library of coordination programs. The study also found a dependence of the period of operation of each coordination program on the state and intensity of traffic flow (TF), noting an increase in the period of stationarity with an increase in the intensity of TF. Based on the system analysis, a mathematical dependence of the control parameter on the intensity affecting the efficiency of TF management is proposed. The results obtained can be applied to optimize the operation of the automated control system and develop effective traffic management strategies.

MARLens: Understanding Multi-Agent Reinforcement Learning for Traffic Signal Control Via Visual Analytics.

The issue of traffic congestion poses a significant obstacle to the development of global cities. One promising solution to tackle this problem is intelligent traffic signal control (TSC). Recently, TSC strategies leveraging reinforcement learning (RL) have garnered attention among researchers. However, the evaluation of these models has primarily relied on fixed metrics like reward and queue length. This limited evaluation approach provides only a narrow view of the model's decision-making process, impeding its practical implementation. Moreover, effective TSC necessitates coordinated actions across multiple intersections. Existing visual analysis solutions fall short when applied in multi-agent settings. In this study, we delve into the challenge of interpretability in multi-agent reinforcement learning (MARL), particularly within the context of TSC. We propose MARLens, a visual analytics system tailored to understand MARL-based TSC. Our system serves as a versatile platform for both RL and TSC researchers. It empowers them to explore the model's features from various perspectives, revealing its decision-making processes and shedding light on interactions among different agents. To facilitate quick identification of critical states, we have devised multiple visualization views, complemented by a traffic simulation module that allows users to replay specific training scenarios. To validate the utility of our proposed system, we present three comprehensive case studies, incorporate insights from domain experts through interviews, and conduct a user study. These collective efforts underscore the feasibility and effectiveness of MARLens in enhancing our understanding of MARL-based TSC systems and pave the way for more informed and efficient traffic management strategies.

Scalable and Robust Mobility Prediction with Traffic Management Strategy for Connected and Autonomous Vehicles

Scalable and Robust Mobility Prediction with Traffic Management Strategy for Connected and Autonomous Vehicles

Time Series Prediction with Digital Twins in Public Transportation Systems

Classical traffic and transportation control centres are becoming insufficient with the rapid spread of electric, intelligent, autonomous, and software-defined vehicles. Existing traffic management strategies have significant drawbacks in public safety, predictive maintenance, tuning the core functionality of vehicles, and managing mobility. We can renovate this system with next-generation intelligent Digital Twin (DT) technologies. This research proposes a timeseries prediction system through Digital Twins to manage public transportation system with Facebook’s Prophet. This study presents a model framework to build Digital Twin application in Intelligent Public Transportation Systems and used a public data set to validate model with Facebook’s Prophet library by forecasting metro lines usage. Obtained results presented in the study and it is shown that forecasting error interms of Mean Absolute Percentage Error (MAPE) is 0.017 for 1 day horizon.

Simulation of Urban Dynamic Traffic Network Based on Greedy Algorithm

This study explores the intricate dynamics of urban traffic flow under varying degrees of intersection and road section carrying capacity. We simulate a city-wide traffic network and examine the impact of extreme situations such as a decline in urban road carrying capacity on the entire traffic system. Our experimental procedure involved simulating traffic flow through intersections and road sections, artificially reducing their maximum carrying capacity, and observing the resultant changes in traffic patterns. Our data reveals a robust correlation between the decrease in carrying capacity and the actual flow percentage across intersections and adjacent sections, with a marked decrease in flow observed when the capacity reduction exceeds 40%. The actual flow percentage of nearby intersections increased significantly under the same conditions, suggesting a degree of redistribution of traffic flow. This study contributes to the broader understanding of urban traffic dynamics, offering valuable insights for urban planning and traffic management strategies.

Reducing traffic congestion in makkah during Hajj through the use of AI technology

This research addresses the pervasive issue of traffic congestion during the Hajj, where approximately 250,000 vehicles substantially exacerbate travel times and road accidents, while also escalating pollution levels, thereby adversely affecting public health. Aimed at bolstering the Kingdom's Vision 2030, the study focuses on the incorporation of artificial intelligence (AI) and advanced communication technologies to optimize traffic management in Mecca. Through the innovative deployment of smart cameras and real-time data analytics, the proposed system seeks to predict, manage, and alleviate traffic congestion by providing alternative routes and facilitating smoother vehicular movement. An exploration into the myriad benefits of this AI-integrated system reveals potentials such as enhanced road safety, improved emergency response efficiencies, and elevated air quality, thereby contributing to the overall wellbeing of the community and environment. In addition, the research anticipates that reducing traffic bottlenecks will indirectly invigorate local businesses and augment tourism revenues, aligning with the objectives of enhancing economic prosperity. By advocating a multi-faceted approach to crowd monitoring and management, this study underscores the indispensable role of AI in revolutionizing traffic management strategies, despite the challenges posed by the complexity of real-time data simulation and the unique intricacies of the Hajj.

A graph multi-attention network for predicting airport delays

Predicting airport delays is of great importance for aviation operations, from the development of effective air traffic management strategies to the reallocation of airline resources. In this paper, the long-term prediction of the next 24 h of network-wide delays is investigated. The sensitivity to error propagation over long time periods as well as dynamic spatial correlations and non-linear temporal correlations of the aviation network are considered. An external impact modeling module is introduced to account for the influence of weather on flight delay patterns. A Spatial-Temporal Gated Multi-Attention Graph Network (STGMAGNet) considering external impact to predict airport delays is then developed. We validate our model on a flight delays dataset collected from the Bureau of Transportation Statistics of US, covering January 1, 2019, to December 31, 2019. In long-term (input-24-predict-24 setting) forecasting, STGMAGNet provides state-of-the-art accuracy, with a MAE reduction of at least 21% averaged in arrival delay prediction, 18% averaged MAE reduction in departure delay prediction compared to MLP, LSTM, Seq2Seq and Transformer. Our model can enable aviation management to shift from a reactive to proactive approach, thus enhancing its operational efficiency and overall performance.

Intelligent Traffic Congestion Control Using Black Widow Optimization with Hybrid Deep Learning on Smart City Environment

Abstract Intelligent traffic congestion control is an integral aspect of making sustainable and efficient smart cities. With the increasing count of vehicles on the road and rapid urbanization, traffic congestion is a main concern nowadays that hinders the growth of the economy and affects the quality of life. In smart cities, an intelligent transportation solution (ITS) is enhance traffic flow by adjusting traffic signal timing and observing traffic patterns. Currently, one of the vital dilemmas in terms of transportation systems was traffic congestion which needs to be resolved for minimizing driver frustration, traffic jams, fuel waste, and accidents. Due to the high count of vehicles, most of the traffic interruptions in metropolitan cities arise. With the advancements in Artificial Intelligence (AI) and Machine Learning (ML), smart environments monitored in smart cities observe the influencing issues of the environment correctly, with the best control of traffic congestion, pollution, and other negative effects. Therefore, this study presents an intelligent traffic congestion control using Black Widow Optimization with Hybrid Deep Learning (ITC-BWOHDL) technique in Smart City Environment. The main aim of the ITC-BWOHDL technique is to utilize feature subset selection with parameter-tuning strategies for effective traffic congestion management. To obtain this, the ITC-BWOHDL technique primarily designs the emperor penguin optimizer-based feature selection (EPO-FS) approach for selecting a useful set of features. For the detection of traffic congestion, the ITCBWOHDL technique makes use of the HDL model which incorporates convolutional neural network (CNN) with gated recurrent unit (GRU) approach. To improve the classification results of the HDL model, the BWO-based hyperparameter tuning process gets executed. For exhibiting the improved classification outcome of the ITC-BWOHDL system, a comprehensive range of experiments was executed. The obtained outcome described the betterment of ITC-BWOHDL method over other existing techniques.

A multi-fidelity approach for reliability-based risk assessment of single-vehicle crashes

Road vehicles are highly susceptible to single-vehicle crashes (SVCs) under complex road geometry and inclement weather, which can significantly threaten traffic safety and mobility of the whole traffic system. Most existing studies involve various simplifications and approximations to assess the associated SVC risks promptly, and therefore the assessment accuracy is often compromised. A novel multi-fidelity approach is developed for the reliability-based risk assessment of SVCs to balance the simulation accuracy and efficiency. Specifically, a high-fidelity transient dynamic vehicle model is introduced for a robust estimation of the vehicle dynamics under various driving environments, assisted by a low-fidelity simplified physics-based vehicle model to improve the computational efficiency. Based on the simulations of the two models, a new multi-fidelity improved cross entropy-based importance sampling (MFICE) algorithm is proposed for integrating multi-fidelity information and facilitating accurate and efficient reliability analysis. Five demonstrative cases are studied to evaluate the performance of the proposed approach, including the comparison with existing representative approaches. The results show that the proposed innovative multi-fidelity approach can provide a reliability evaluation of SVCs both accurately and efficiently, with obviously superior performance over typical state-of-the-art counterparts. Therefore, the proposed approach bears great potential on developing proactive and near real-time intelligent traffic operation and management strategies against SVCs in both normal and hazardous conditions.

Intelligent traffic management strategy for traffic congestion in underground loop

In recent years, there has been a growing trend of developing and constructing network-type underground loops in urban central business districts (CBDs) to complement interconnected underground spaces. However, the high volume of arrival and departure traffic, the impact of traffic patterns, and the complexity of traffic organization in CBD areas pose challenges to the efficient operation of underground loops. Accurate identification of congestion patterns and effective traffic control strategies are crucial to enhance capacity and ensure safe operations of underground loop systems. This paper presents a simulation analysis of morning and evening peak traffic scenarios in a designed underground loop to identify congestion patterns and their underlying causes. Based on the analysis, a combination of traffic control strategies, including ramp control, plot control, and fast entry without railing, is proposed and evaluated through simulations. The findings of this study serve as a reference for developing traffic congestion control measures in underground loop systems, contributing to improved capacity and safe operations.

Dynamic Bayesian hierarchical peak over threshold modeling for real-time crash-risk estimation from conflict extremes

Using traffic conflict-based extreme value theory (EVT) models to quantify real-time crash-risk of road facilities is a promising direction for developing proactive traffic safety management strategies. Existing EVT real-time crash-risk analysis studies have only focused on using block maxima models. This study proposes a dynamic Bayesian hierarchical peak over threshold modeling approach to estimate real-time crash-risk based on traffic conflicts. The proposed approach combines quantile regression, dynamic updating approach, Bayesian hierarchical structure, and the peak over threshold method to generate time-varying generalized Pareto distributions to derive real-time crash-risk measures (i.e., crash probability and return level). The derived real-time crash-risk measures are applied to estimate cycle-level crash-risk at three signalized intersections in Surrey, British Columbia. Five approaches are used to dynamically update the model parameters, including time trend model, generalized autoregressive conditional heteroskedasticity process approach, as well as the first-order, second-order, and third-order dynamic linear models. For comparison, static models are also developed. All the developed models are compared in terms of statistical fit and predictive performance. Based on the best fitted dynamic model, cycle-level crash probability and return level are calculated to measure signalized intersection safety at cycle level. The results show that dynamic models considerably outperform static models in terms of statistical fit and predictive performance. Further, the third-order dynamic model has the best performance, which is probably due to that the model incorporates two linear trends to respectively describe the variation of the coefficients as well as its change to better account for the variation in the effect of time-varying covariates. However, it should be noted that the third-order dynamic model development needs more computation time than other dynamic models, which may limit the application of the model.

A decentralized federated learning-based spatial–temporal model for freight traffic speed forecasting

Accurately understanding the spatial–temporal information of future freight traffic speed in the metropolitan area is of vital importance to formulate freight-related traffic management strategies. In this study, we develop a novel decentralized federated learning-based spatial–temporal model for freight traffic speed forecasting while implementing collaborative training among multiple participants instead of requiring an exclusive cloud center server for centralized data processing. First, a tailored spatial–temporal transformer network is proposed to substitute the existing graph convolutional network for local personalized learning of each participant. Second, a decentralized federated learning model is designed to fuse local personalization models for freight traffic speed forecasting. The associated convergence properties are theoretically illustrated. rgb]0,0,0Finally, the experiments based on a real-world freight dataset of member cities in the Nanjing Metropolitan Area demonstrate that the proposed approach can accurately forecast freight traffic speed and outperform existing traffic speed forecasting methods, with the average improvement of 8.3% for MAE, 8.2% for RMSE, and 8.6% for MAPE respectively. The visualization results reveal that the proposed approach is able to effectively capture the internal spatial–temporal dependencies among urban regions from various neighboring cities, providing the insights for collaboratively developing proactive freight-related traffic management strategies.

Traffic Congestion Detection and Alternative Route Provision Using Machine Learning and IoT-Based Surveillance

The Automated Dynamic Traffic Assignment (ADTA) system introduces a novel approach to urban traffic management, merging the power of IoT with machine learning. This research assessed the system's performance in comparison to traditional traffic management strategies across various real-world scenarios. Findings consistently showcased the ADTA's superior efficiency: during peak traffic, it reduced vehicle wait times by half, and in scenarios with unexpected road closures, congestion detection was almost five times quicker, rerouting traffic with a remarkable 95% efficiency. The system's adaptability was further highlighted during weather challenges, ensuring safer vehicle speeds and substantially reducing weather-induced incidents. Large-scale public events, known disruptors of traffic flow, witnessed significantly reduced backlogs under the ADTA. Moreover, emergency situations benefitted from the system's rapid response, ensuring minimal delays for critical vehicles. This research underscores the potential of the ADTA system as a transformative solution for urban traffic woes, emphasizing its scalability and real-world applicability. With its integration of innovative technology and adaptive mechanisms, the ADTA offers a blueprint for the future of intelligent urban transport management.