Evaluation of Traffic Signals for Daily Traffic Pattern

• URL: http://arxiv.org/abs/2506.21469v1
• Date: Thu, 26 Jun 2025 16:56:59 GMT
• Title: Evaluation of Traffic Signals for Daily Traffic Pattern
• Authors: Mohammad Shokrolah Shirazi, Hung-Fu Chang,
• Abstract summary: The turning movement count data is crucial for traffic signal design, intersection geometry planning, traffic flow, and congestion analysis.<n>A vision-based tracking system is developed to estimate the TMC of six intersections in Las Vegas using traffic cameras.<n>Four intersections show better performance for dynamic signal timing configuration, and the other two with lower performance have a lower ratio of total vehicle count to total lanes of the intersection leg.
• Score: 0.8287206589886879
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The turning movement count data is crucial for traffic signal design, intersection geometry planning, traffic flow, and congestion analysis. This work proposes three methods called dynamic, static, and hybrid configuration for TMC-based traffic signals. A vision-based tracking system is developed to estimate the TMC of six intersections in Las Vegas using traffic cameras. The intersection design, route (e.g. vehicle movement directions), and signal configuration files with compatible formats are synthesized and imported into Simulation of Urban MObility for signal evaluation with realistic data. The initial experimental results based on estimated waiting times indicate that the cycle time of 90 and 120 seconds works best for all intersections. In addition, four intersections show better performance for dynamic signal timing configuration, and the other two with lower performance have a lower ratio of total vehicle count to total lanes of the intersection leg. Since daily traffic flow often exhibits a bimodal pattern, we propose a hybrid signal method that switches between dynamic and static methods, adapting to peak and off-peak traffic conditions for improved flow management. So, a built-in traffic generator module creates vehicle routes for 4 hours, including peak hours, and a signal design module produces signal schedule cycles according to static, dynamic, and hybrid methods. Vehicle count distributions are weighted differently for each zone (i.e., West, North, East, South) to generate diverse traffic patterns. The extended experimental results for 6 intersections with 4 hours of simulation time imply that zone-based traffic pattern distributions affect signal design selection. Although the static method works great for evenly zone-based traffic distribution, the hybrid method works well for highly weighted traffic at intersection pairs of the West-East and North-South zones.

Related papers

• Dynamic Graph Attention Networks for Travel Time Distribution Prediction in Urban Arterial Roads [5.849150965368483]
  We propose a structured framework for simultaneous modeling travel time distributions in both directions along arterial corridors.<n>The results demonstrate resilience to effective traffic variations, including cycle lengths, green percentages, traffic density, and counterfactual routes.<n>This framework supports dynamic signal timing, enhances congestion management, and improves travel time reliability in real-world applications.
  arXiv  Detail & Related papers  (2024-12-15T07:30:01Z)
• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• Applying Neural Monte Carlo Tree Search to Unsignalized Multi-intersection Scheduling for Autonomous Vehicles [7.32653612106583]
  We introduce a transformation model that maps sequences of potentially conflicting road-space reservation requests from platoons of vehicles into a series of board-game-like problems. We use NMCTS to search for solutions representing optimal road-space allocation schedules in the context of past allocations. We show that the proposed method maintained free-flow in light traffic when all intersections are under control of PNMCTS and outperformed state-of-the-art RL-based traffic-light controllers in average travel time by 74.5% and total throughput by 16% in heavy traffic.
  arXiv  Detail & Related papers  (2024-10-24T14:37:55Z)
• Graph Attention Network for Lane-Wise and Topology-Invariant Intersection Traffic Simulation [8.600701437207725]
  We propose two efficient and accurate "Digital Twin" models for intersections. These digital twins capture temporal, spatial, and contextual aspects of traffic within intersections. Our study's applications extend to lane reconfiguration, driving behavior analysis, and facilitating informed decisions regarding intersection safety and efficiency enhancements.
  arXiv  Detail & Related papers  (2024-04-11T03:02:06Z)
• A Holistic Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation [53.39174966020085]
  Traffic signal control (TSC) is crucial for reducing traffic congestion that leads to smoother traffic flow, reduced idling time, and mitigated CO2 emissions. In this study, we explore the computer vision approach for TSC that modulates on-road traffic flows through visual observation. We introduce a holistic traffic simulation framework called TrafficDojo towards vision-based TSC and its benchmarking.
  arXiv  Detail & Related papers  (2024-03-11T16:42:29Z)
• Traffic Reconstruction and Analysis of Natural Driving Behaviors at Unsignalized Intersections [1.7273380623090846]
  This research involved recording traffic at various unsignalized intersections in Memphis, TN, during different times of the day. After manually labeling video data to capture specific variables, we reconstructed traffic scenarios in the SUMO simulation environment. The output data from these simulations offered a comprehensive analysis, including time-space diagrams for vehicle movement, travel time frequency distributions, and speed-position plots to identify bottleneck points.
  arXiv  Detail & Related papers  (2023-12-22T09:38:06Z)
• DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback [109.84667902348498]
  Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road network. Most prior TSC methods leverage deep reinforcement learning to search for a control policy. We propose DenseLight, a novel RL-based TSC method that employs an unbiased reward function to provide dense feedback on policy effectiveness.
  arXiv  Detail & Related papers  (2023-06-13T05:58:57Z)
• RegTraffic: A Regression Based Traffic Simulator for Spatiotemporal Traffic Modeling, Simulation and Visualization [0.6531546527140474]
  This paper presents RegTraffic, a novel interactive traffic simulator that integrates dynamic regression-based traffic analysis. RegTraffic can effectively predict traffic congestion with a Mean Squared Error of 1.3 Km/h and a Root Mean Squared Error 1.71 Km/h.
  arXiv  Detail & Related papers  (2022-11-23T03:34:27Z)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction [63.470149585093665]
  arterial traffic prediction plays a crucial role in the development of modern intelligent transportation systems. Many existing studies on arterial traffic prediction only consider temporal measurements of flow and occupancy from loop sensors and neglect the rich spatial relationships between upstream and downstream detectors. We fill this gap by enhancing a deep learning approach, Diffusion Convolutional Recurrent Neural Network, with spatial information generated from signal timing plans at targeted intersections.
  arXiv  Detail & Related papers  (2020-12-25T01:40:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.