Accuracy Of Traffic Information Research Articles

An intelligent optimization‐based traffic information acquirement approach to software‐defined networking

AbstractInternet of things (IoT) is a global information infrastructure that supports access to thousands of monitoring devices and user terminals. A large amount of monitoring data generated by IoT is integrated to cloud computing through the network to improve the quality of life of citizens. Fine‐grained and accurate traffic information is important for IoT network management. Software‐defined networking (SDN) is a centralized control plane as a logical control center, making network management more flexible and efficient. Then, we collect fine‐grained traffic information in SDN‐based IoT networks to improve network management. To acquire the traffic information with low overhead and high accuracy, first, we collect the statistics of coarse‐grained traffic of flows and fine‐grained traffic of links, and then we utilize the intelligent optimization methods to estimate the network traffic. To improve the granularity and accuracy of the acquired traffic information, we construct an optimization function with constraints to decrease the estimation errors. As the optimization function of traffic information is a non‐deterministic polynomial‐hard problem, we present a heuristic algorithm to obtain the optimal solution of the fine‐grained measurement. Finally, we conduct some simulations to verify the proposed measurement scheme. Simulation results show that our approach can improve the granularity and accuracy of traffic information with intelligent optimization methods.

Detection Spacing of DSRC-Based Data Collection System for Real-Time Highway Travel Time Estimation

A DSRC (Dedicated Short-Range Communication)-based traffic data collection system has been introduced to enhance the accuracy of real-time traffic information. However, due to the lack of studies for the DSRC system on interrupted flow condition, there is no installation guideline of DSRC RSEs on the national highways in Korea. This study presented an appropriate spacing of DSRC RSEs for the national highways based on the accuracy of traffic information and the required minimum sample size. By analyzing the field data collected on the national highways, the optimal spacing of DSRC RSEs was selected for each type of road. According to the result, the optimal spacing for controlled-access highway was different from that for multi-lane road and urban and suburban arterial. It implies that detection spacing should be determined considering the type of road. These results would be utilized as a guideline for installing DSRC RSEs on the national highways.

Formulation and Validation of Model for Calculating the Number of Probe Cars Considering Traffic Information Update Frequency

Probe cars are very effective tools in traffic information gathering. Probe cars can collect the traffic condition data without using roadside sensors but with only using their own equipped sensors. By using probe cars, it is expected that the accuracy of traffic information will be improved and the area coverage of the traffic information service will be increased, and many experimental probe car data collection projects have been implemented. However, the number of probe cars required for adequate information update frequency has not been theoretically discussed. This paper formulates the probabilistic relationships among the traffic volume of probe cars, the area coverage, and the update frequency of traffic information provision. This formulation calculates the adequate number of probe cars to realize traffic information services in advance considering two major service factors: area coverage and update frequency. This paper also validates the theoretical formulation with experimental probe car data collected in a taxi monitoring system.