Current Traffic Conditions Research Articles

A Lane Change Model Considering the Stability of Cooperative Adaptive Cruise Control Platoon Fleet

In this article, lane change models for mixed traffic flow under cooperative adaptive cruise control (CACC) platoon formation are established. The analysis begins by examining the impact of lane changes on traffic flow stability. The influences of various factors such as lane change locations, timing, and the current traffic state on stability are discussed. In this analysis, it is assumed that the lane change location and the entry position in the adjacent lane have already been selected, without considering the specific intention behind the lane change. The speeds of the involved vehicles are adjusted based on an existing lane change model, and various conditions are analyzed for traffic flow disturbances, including duration, shock amplitude, and driving delays. Numerical calculations are provided to illustrate these effects. Additionally, traffic flow stability is factored into the lane change decision-making process. By incorporating disturbances to the fleet into the lane change income model, both a lane change intention model and a lane change execution model are constructed. These models are then compared with a model that does not account for stability, leading to the corresponding conclusions.

Real-time bus arrival delays analysis using seemingly unrelated regression model

AbstractTo effectively manage and control public transport operations, understanding the various factors that impact bus arrival delays is crucial. However, limited research has focused on a comprehensive analysis of bus delay factors, often relying on single-step delay prediction models that are unable to account for the heterogeneous impacts of spatiotemporal factors along the bus route. To analyze the heterogeneous impact of bus arrival delay factors, the paper proposes a set of regression equations conditional on the bus location. A seemingly unrelated regression equation (SURE) model is developed to estimate the regression coefficients, accounting for potential correlations between regression residuals caused by shared unobserved factors among equations. The model is validated using bus operations data from Stockholm, Sweden. The results highlight the importance of developing stop-specific bus arrival delay models to understand the heterogeneous impact of explanatory variables. The significant factors impacting bus arrival delays are primarily associated with bus operations, such as delays at consecutive upstream stops, dwell time, scheduled travel time, recurrent congestion, and current traffic conditions. Factors like the calendar and weather have significant but marginal impacts on arrival delays. The study suggests that different bus operating management strategies, such as schedule adjustments, route optimization, and real-time monitoring and control, should be tailored to the characteristics of stop sections since the impacts of these factors vary depending on the stop location.

Improving traffic light systems using Deep Q-networks

As our cities become more complex and traffic demand grows, managing such traffic efficiently becomes challenging. Hence, solutions that allow building upon the current traffic light systems and that can be readily deployed are of global interest. In this work, we address the challenge of improving traffic light management at intersections. We propose an agent-based traffic light control system where an agent, one per intersection, dynamically regulates the light’s phase cycle depending on the current traffic conditions. To this end, we will rely on Deep Networks to adequately train agents to make good decisions. Simulation results in a realistic scenario using SUMO show that our proposed approach can significantly reduce waiting times, improving transit times by 44% compared to the standard fixed-timing method. Additionally, to assess the effectiveness and reliability of our control algorithm, we introduce new performance metrics.

ROBUST LEARNING AND ACCURATE PREDICTION OF WIRELESS TRAFFIC USING IMPROVED NEURAL NETWORK

The proposed system introduces a novel Deep Metric Algorithm as a cornerstone for enhancing the feasibility and optimality of network traffic prediction within the dynamic landscape of cloud technologies. The Deep Metric Algorithm is designed to operate as a physically interpretable probabilistic model that captures network-wide traffic patterns. Unlike traditional methods that rely solely on current traffic states observed at specific intervals, this algorithm leverages an initially expensive set of measurements to construct a network-specific traffic model.The algorithm's innovation lies in its ability to utilize this network-specific model alongside readily available, less expensive measurements, thereby significantly improving the accuracy of traffic predictions. By employing the Deep Metric Algorithm, the system can predict traffic fluctuations not only over observed links but also extend its predictions to unobserved links. This is a crucial advancement as it enables the system to offer more comprehensive insights into network behavior, even where direct measurements might be limited.One notable strength of the proposed Deep Metric Algorithm is its demonstrated applicability across various traffic periods within the same network. This adaptability ensures that the learned model remains effective over time, showcasing its potential utility in optimizing network performance as it evolves. The algorithm's predictive capabilities, coupled with its ability to adapt to changing network conditions, position it as a valuable tool for load-aware resource management and predictive control, thereby contributing to the efficient management of multi-provider networks in the evolving cloud landscape. KEYWORDS: Quality of Service (QoS), network traffic, accurate predictions, network-specific traffic model, Deep Metric Algorithm.

An improved eco-driving strategy for mixed platoons of autonomous and human-driven vehicles

The emergence of autonomous vehicles (AVs) could transform traffic flow characteristics and affect energy consumption subsequently. The primary objective of this study is to propose an improved eco-driving strategy for the mixed platoons by exploring the impacts of AVs on energy consumption characteristics. Initially, leveraging precise AV control, this study utilizes a Model Predictive Control (MPC) system to optimize AV trajectories, aiming to minimize energy consumption within mixed platoons of AVs and human-driven vehicles (HDVs). Given the differing collaborative capabilities of HDVs and AVs in mixed platoons, exceptional situations may arise at intersections where not all platoon vehicles cross the stop line during the same green light cycle (GLC). To address these challenges, we then propose a splitting approach to optimize the platoon operation mode. This approach allows for flexible splitting decisions based on AVs market penetration and current traffic conditions. The simulation results indicate that the energy consumption reduction ranges from 15.35% to 35.39% and 2.75–3.64% compared to the traditional mode and the eco-driving mode, respectively. In addition, through an analysis of the energy consumption of various vehicle orders within a mixed platoon, we determined that positioning the second AV in the middle platoon maximizes stability, resulting in a more consistent energy consumption pattern. These findings highlight the advantages of the strategy in terms of energy consumption, providing theoretical support management of mixed platoons.

Traffic light control with reinforcement learning

Urban traffic signal optimization is important for alleviating congestion in urban transportation systems. This study proposes a real-time traffic light control algorithm based on deep Q learning with a reward function that accounts for queue lengths, delays, travel times, and throughput. The model dynamically decides phase changes based on current traffic conditions. The training of the deep Q network involves an offline stage from pre-generated data with fixed signal timing and an online stage using real-time traffic data. A deep Q network structure with aphase gate component is used to simplify the model's learning task under different phases. Amemory palace" mechanism is used to address sample imbalance during the training process. Both synthetic and real-world traffic flow data are used to validate our approach under an urban road intersection scenario in Hangzhou, China. Results demonstrate significant performance improvements of the proposed method in reducing vehicle waiting time (57.1% to 100%), queue lengths (40.9% to 100%), and total travel time (16.8% to 68.0%) compared to traditional fixed signal timing plans.

Freeway congestion management on multiple consecutive bottlenecks with RL‐based headway control of autonomous vehicles

AbstractAdaptive cruise control (ACC) is the core building block of future full autonomous driving. Numerous recent research demonstrated that Autonomous Vehicles (AVs) adopting shorter headways generally increase road capacity and may relieve congestion at bottlenecks for moderate demand scenarios. However, with high demand scenarios, bottlenecks can still be activated causing capacity breakdown. Therefore, extra control measures as dynamic traffic control near bottlenecks is necessary. The challenge is harder on urban freeways with consecutive bottlenecks which affect each other. This paper aims to improve the performance of ACC systems in a high demand scenario. A multi‐bottleneck dynamic headway control strategy based on deep reinforcement learning (DRL) that adapts headways to optimize traffic flow and minimize delay is proposed. The controller dynamically assigns an optimal headway for each controlled section, based on state measurement representing the current traffic conditions. The case study is a freeway stretch with three consecutive bottlenecks which is then extended to include eight bottlenecks. Three different RL agent configurations are presented and compared. It is quantitatively demonstrated that the proposed control strategy improves traffic and enhances the system delay by up to 22.30%, and 18.87% compared to shortest headway setting for the three‐bottleneck and the eight‐bottleneck networks, respectively.

Assessment of Traffic Characteristic at Major Urban Road Intersection of Kathmandu Valley: A Case Study of Khanivivag, Kesarmahal, and Narayanhiti Intersections

This study addresses the prevalent issue of traffic congestion at critical intersections in urban areas, leading to slowed traffic and increased queue lengths. The centralization of the Kathmandu Valley has contributed to a gradual rise in population thus impacting the efficiency of the traffic management system. This study focuses on three of the busiest intersections of the valley i.e. Kesharmahal, Narayanhiti, and Khanibhivag intersections and aims to provide insights into current traffic conditions and propose measures for enhancing traffic flow. Data collected is analyzed to determine current traffic flow, saturation flow, existing capacity, Level of Service (LOS), and intersection delay using the HCM Manual. The analysis involves assessing present conditions and making comparisons with standardized data. Three days of traffic volume data during peak hours and geometric features of the intersection were gathered. Results indicate that the intersections are experiencing over-saturation, exceeding their capacity based on existing geometry and signalization. Additionally, the vehicle replacement strategy showed limited effectiveness in reducing saturation flow, with the flow persisting at an over-saturated level. These findings highlight the need for targeted measures to address the challenges posed by congestion and enhance the efficiency of traffic management at these intersections for smooth traffic flow.

Fatigue study on improved composite box girder bridge with corrugated steel webs

The purpose of the study is to estimate the fatigue behavior of an improved composite box girder bridge with corrugated steel webs (ICBGBCSW) based on a three-dimensional vehicle-bridge coupled vibration system (VBCVS). In the VBCVS, the benchmark of the ICBGBCSW widely used in the highway bridges of Gansu Province of China was adopted as the bridge model, and a 6-axle fatigue load model established based on the traffic data of China was adopted as the fatigue vehicle loading. Based on the established VBCVS, the critical fatigue details of the ICBGBCSW were determined and the effects of pavement roughness condition (PRC) and vehicle speed on the stress ranges experienced by critical fatigue details were first investigated. Then, the fatigue live of critical fatigue details were evaluated based on the deterministic analysis method and reliability analysis method. The results show that the fatigue life of the ICBGBCSW can reach more than 375 years from a deterministic perspective under the current traffic conditions of Gansu Province of China and that the fatigue reliability index of the ICBGBCSW sharply decreases to the target fatigue reliability index of 2 if the PRC stays in the “Poor” class and the “Very poor” class for eight years and two years, respectively. The results obtained in the study can provide a technique support for the wide application of the ICBGBCSW and provide a reference for the bridge management agency to make plans for bridge pavement maintenance.

DRL-based routing algorithm with guaranteed loss, latency and bandwidth in SDN networks: Application of online video conferencing

Technology development in traffic engineering has led to the re-architecting of Online Video Conferencing Services (OVCS) through Software-Defined Networking (SDN) to provide scalability and low-latency performance. Since OVCS requires significant network bandwidth, routing algorithms must consider issues such as path redundancy, network congestion, and the rate of requested resources. With this motivation, this paper proposes a routing algorithm based on Deep Reinforcement Learning (DRL) to handle online video conference connection requests in SDN, which can simultaneously guarantee packet loss, latency, and bandwidth. The learning agent is configured based on the Asynchronous Advantage Actor-Critic (A3C) algorithm, which can adapt to the current traffic conditions and reduce the service latency as much as possible. We adjust the weights of the links in the weighted shortest path algorithm based on a set of critical nodes to improve the convergence ability of A3C and reduce the dependence of the learning agent on the network topology. Here, the link weight is measured by considering the critical nodes under maximum flow and minimum latency, configured to reduce interference in future routings. In addition, a postponement strategy is embedded in the SDN controller to prioritize requests based on the resources demanded for routing. This strategy is activated during network congestion and provides the ability to reserve more resources for future requests. The experimental results show that the proposed algorithm has a promising performance and convergence compared to the existing solutions and guarantees the QoS of OVCS. Specifically, the proposed algorithm can improve the throughput (up to 2.8%) and admission rate (up to 1.8%) of OVCS connection compared to the best existing solution.

Machine Learning Techniques for Sustainable Smart Cities Traffic Management

The issue of traffic congestion is one of the problems that disrupt the smoothness and comfort of the community in carrying out daily affairs. Rapid development, with an expanding population and vehicles in cities, has caused traffic congestion to become one of the most pressing concerns. As a result, this study proposed vehicle tracking method for traffic system management to improve and keep up with the rising demand for better traffic on the road. In the proposed system, cameras were placed at traffic intersections and along the road or highway to gather real-time traffic data. Image processing and machine learning algorithm were applied to calculate the number of vehicles based on the traffic flow and vehicle speed on the road to reflect the current traffic condition so that it helps general public to plan their trip and less pollution is produced in long run. This study focused on the cities of Cyberjaya, Putrajaya and selected Klang Valley areas to study the traffic condition during the peak and off-peak hour. Experimental results showed that the proposed machine learning solution provided high accuracy and effective detection on real-time traffic congestion. Current method used variables such as vehicle speed and number of vehicles to calculate the traffic flow on the road. As future direction, this method can be expanded to incorporate vehicle density as one of the variables for more precise traffic congestion reporting.

Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling Approach

The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people&#x2019;s lives. Hence, it is significant to propose an efficient scheduling approach to help EVs arrive faster. Existing vehicle-centric scheduling approaches aim to recommend the optimal paths for EVs based on the current traffic status while the road-centric scheduling approaches aim to improve the traffic condition and assign a higher priority for EVs to pass an intersection. With the intuition that real-time vehicle-road information interaction and strategy coordination can bring more benefits, we propose <i>LEVID</i>, a <u>LE</u>arning-based cooperative <u>V</u>eh<u>I</u>cle-roa<u>D</u> scheduling approach including a real-time route planning module and a collaborative traffic signal control module, which interact with each other and make decisions iteratively. The real-time route planning module adapts the artificial potential field method to address the real-time changes of traffic signals and avoid falling into a local optimum. The collaborative traffic signal control module leverages a graph attention reinforcement learning framework to extract the latent features of different intersections and abstract their interplay to learn cooperative policies. Extensive experiments based on multiple real-world datasets show that our approach outperforms the state-of-the-art baselines.

Artificial neural network based load balancing scheme for top of rack switches in optical data centers

Abstract Data centers serve as dedicated facilities for housing computer systems and their related components, including telecommunications and storage systems. They typically have high levels of security and environmental controls to ensure that the equipment housed within them functions optimally. Data center networks (DCNs) often employ load balancing algorithms to handle large volumes of traffic and ensure that all servers and switches are utilized equally, keeping the network running smoothly. However, as load on the server varies, therefore dynamic traffic management systems that can adjust traffic flow in real-time based on the current traffic state is required. This study presents an artificial neural network-based load balancing method. By training a feed-forward artificial neural network (ANN) using a back propagation (BP) learning algorithm, it evenly distributes workload over all of the nodes. Simulation results are also presented to prove the usefulness of the proposed load balancing mechanism. It is found that the load balancing scheme can reduce the packet blocking probability (PBP) by 10 folds and delay by about nearly 11 percent.

Generic Dynamic Graph Convolutional Network for traffic flow forecasting

In the field of traffic forecasting, methods based on Graph Convolutional Network (GCN) are emerging. But existing methods still have limitations due to insufficient sharing patterns, inflexible temporal relations and static relation assumptions. To address these issues, a Generic Dynamic Graph Convolutional Network (GDGCN) for traffic flow forecasting is proposed. A generic framework with both parameter-sharing and independent blocks across stacked layers is proposed to explore parameter sharing systematically in all data dimensions, which can exploit distinct patterns from layer to layer and stable patterns across layers simultaneously. Then, we design a novel temporal graph convolutional block to view historical time slots as nodes in graph perspective and handle temporal dynamics with graph convolution. This temporal convolutional block can capture flexible and global temporal relations to have a better understanding of current traffic conditions. Lastly, a dynamic graph constructor is proposed to model not only the time-specific spatial dependencies between nodes, but also the changing temporal interactions between time slots to discover dynamic relations from data thoroughly. Experimental results on four real-world datasets show that GDGCN not only outperforms state-of-the-art methods, but also obtains interpretable dynamic spatial relations of segments. Codes are available at https://anonymous.4open.science/r/GDGCN.

Two-Stage Fuzzy Traffic Congestion Detector

This paper presents a two-stage fuzzy-logic application based on the Mamdani inference method to classify the observed road traffic conditions. It was tested using real data extracted from the Padua–Venice motorway in Italy, which contains a dense monitoring network that provides continuous measurements of flow, occupancy, and speed. The data collected indicate that the traffic flow characteristics of the road network are highly perturbed in oversaturated conditions, suggesting that a fuzzy approach might be more convenient than a deterministic one. Furthermore, since drivers have a vague notion of the traffic state, the fuzzy method seems more appropriate than the deterministic one for providing drivers with qualitative information about current traffic conditions. In the proposed method, the traffic states are analysed for each road section by relating them to average speed values modelled with fuzzy rules. An application using real data was carried out in Simulink MATLAB. The empirical results show that the proposed study performs well in estimation and classification.

A novel traffic optimization method using GRU based deep neural network for the IoV system.

At present, China is moving towards the direction of "Industry 4.0". The development of the automobile industry, especially intelligent automobiles, is in full swing, which brings great convenience to people's life and travel. However, at the same time, urban traffic pressure is also increasingly prominent, and the situation of traffic congestion and traffic safety is not optimistic. In this context, the Internet of Vehicles (also known as "IoV") opens up a new way to relieve urban traffic pressure. Therefore, in order to further optimize the road network traffic conditions in the IoV environment, this research focuses on the traffic flow prediction algorithm on the basis of deep learning to enhance traffic efficiency and safety. First, the study investigates the short-time traffic flow prediction by combining the characteristics of the IoV environment. To address the issues that existing algorithms cannot automatically extract data features and the model expression capability is weak, the study chooses to build a deep neural network using GRU model in deep learning for short-time traffic flow prediction, thereby improving the prediction accuracy of algorithm. Secondly, a fine-grained traffic flow statistics approach suitable for the IoV situation is suggested in accordance with the deep learning model that was built. The algorithm sends the vehicle characteristic data obtained through GRU model training into the fine-grained traffic flow statistics algorithm, so as to realize the statistics of traffic information of various types of vehicles. The advantage of this algorithm is that it can well count the traffic flow of multiple lanes, so as to better predict the current traffic status and achieve traffic optimization. Finally, the IoV environment is constructed to confirm the effectiveness of the prediction model. The prediction results prove that the new algorithm has good performance in traffic flow statistics in different scenarios.

Characterizing the Impact of Autonomous Vehicles on Macroscopic Fundamental Diagrams

With the rapid development of autonomous driving, Autonomous Vehicles (AVs) have started to appear on public roads, which has inevitably affected current traffic conditions and the operations of Manual Vehicles (MVs). Current research on AVs’ influence has mainly been conducted at individual level of driving behaviors, while few studies have focused on the overall network level to consider the traffic flow pattern due to mixed traffic. In this work, considering varying signal control schemes and demand loading patterns, we conducted simulation experiments based on a grid network and a real-world network in Beijing using SUMO. Traffic flow with the mixture of MVs, low-level AVs (LAVs), and high-level AVs (HAVs) were emulated so to investigate how the network performs at various levels of mixed traffic. Driving behaviors between the three types of vehicles were calibrated using driving data drawn from OpenACC dataset, and Waymo Open Dataset. The capacity and critical accumulation of the Macroscopic Fundamental Diagram (MFD) were chosen as the key indicators of network performance. We found that AVs positively boost network capacity (up to 19.0% increase) but a negative influence on critical accumulation was also observed (up to 9.0% decrease). However, the positive impact of OpenACC and Waymo’s AVs on macroscopic traffic is still far from ideal since they may be too conservative. AVs can boost flow when traffic is in unsaturated or saturated states. However, when traffic flow is oversaturated, AVs can instead cause flow and average speed to drop faster than that in the MV-only scenario.

Ridesharing Price Prediction: Exploring the strategies of Dynamic Pricing

This paper explores the application of dynamic pricing algorithms in rideshare industries and examines the key variables that influence trip prices by analyzing Uber and Lyft Dataset of Boston in 2018. This dataset contains Uber and Lyft data mainly from November and December 2018. It contains essential variables such like distance and price for rideshare price prediction, together with many other variables which could be included in regression model such as weather and hours in the day. The Ordinary Least Square regression method is used in this paper, and results shows that the most influential and statistically significant variables on price are the distance of trip and surge multiplier which indicates traffic surge. The regression result is in accordance with the intuition that price is positively correlated with distance and price goes up during surge time. The result implies that the dynamic pricing algorithm that adjusts live prices heavily relies on the current traffic condition and distance of the trip, while the price is not determined by hour of the trip during the day and temperature.

Dynamic Route Guidance in Vehicle Networks by Simulating Future Traffic Patterns

Roadway congestion leads to wasted time and money and environmental damage. Since adding more roadway capacity is often not possible in urban environments, it is becoming more important to use existing road networks more efficiently. Toward this goal, recent research in real-time, schedule-driven intersection control has shown an ability to significantly reduce the delays incurred at signalized intersections. Such approaches operate by building a predictive model of when locally sensed approaching traffic is expected to arrive at a given intersection, and then using the model to generate a signal timing plan (a phase schedule) that minimizes the cumulative delay of this traffic as it moves through the intersection. In this paper, we consider whether further reduction in delay could be gained by applying this predictive model to dynamically reroute some portion of vehicles willing to share their destinations along less congested paths. We developed an algorithm that simulates the current traffic state forward at each vehicle decision point, based on knowledge of other vehicles’ current routes and traffic signals’ control algorithms, and evaluated it using the SUMO microscopic simulator on different road networks (one as a simple synthetic example and the other taken from the real world) using different traffic signal control algorithms (fixed-timing plans and schedule-driven intersection control). Experiments carried out on combinations of networks and traffic signal control algorithms show that our rerouting protocol reduces delay for both vehicles participating in route guidance (adopters) and those that do not (non-adopters) and that the reduction in delay generally increases as the proportion of adopters does.

Characterizing Behavioral Differences of Autonomous Vehicles and Human-Driven Vehicles at Signalized Intersections Based on Waymo Open Dataset

Autonomous vehicles (AVs) are being introduced to the traffic system with the promise of improving current traffic status. However, the empirical data also indicate contrary effects with estimated higher crash rate and change of crash patterns. Therefore, it is necessary to investigate the driving behavior of AVs and human-driven vehicles (HDVs) in real mixed traffic. Current studies have analyzed the driving behavior of AVs and HDVs, as well as behavioral adaptations of drivers of HDVs based on empirical data. While they play an important role in traffic systems, signalized intersections have not been studied sufficiently in this context. Therefore, this study aims to utilize the Waymo open dataset to characterize and quantify the behavioral differences of AVs and HDVs at signalized intersections. Five parameters of driving behavior related to signalized intersections were characterized according to five critical maneuver phases, which were identified by wavelet transform and threshold-based method. Statistically significant differences in driving behavior between AVs and HDVs were found, from three categorized situations: vehicle approaching the red light/queue, vehicle responding to the green light (as the first vehicle), and vehicle responding to its preceding vehicle (in the queue). Further, behavioral adaptations of HDV drivers were revealed in that they tended to keep closer to the stopped AVs in a queue and to react more strongly to AV start-up maneuvers when the traffic light turns to green.