Use of Probe Vehicles to Increase Traffic Estimation Accuracy in Brisbane (original) (raw)
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The lack of proficient real-time traffic monitoring systems is one of the major bottlenecks of Advanced Traveller Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS). In this report we describe a method of collecting real-time traffic data from probe vehicles automatically sending traffic reports to one or more base stations, connected to a traffic center by a wired communications network. Analyzing and computing road traffic and message traffic flows in the San Francisco Bay Area, we study several multi-disciplinary aspects of this data collection technique, such as the relation between vehicle traffic and message traffic, the influence of road traffic congestion on communication performance, the reliability of road traffic estimates on radio network throughput and the location of base stations. The results presented in this report reveal that random access (ALOHA) transmission of traffic messages is a (spectrum) efficient, inexpensive and flexible method for collecting road traffic data and that this approach can provide reliable traffic monitoring. Not only highly accurate real time link travel times can be estimated, but also Automatic Incident Detection (AID) can performed.
Optimization of probe vehicle deployment for traffic status estimation
2013 IEEE International Conference on Automation Science and Engineering (CASE), 2013
Traffic congestion in urban areas is posing many challenges, and traffic flow model provides accurate traffic status estimation and prediction can be beneficial for congestion management. With the limitation of infrastructure, probe data from individual vehicles is an attractive alternative to inductive loop detectors as a mean to collect traffic data for traffic flow modelling. This paper investigates the optimal deployment strategy of probe vehicles. Data assimilation technique, Newtonian relaxation method, is used to incorporate probe data into macroscopic traffic flow model, and synthetic traffic is used to study the optimization problem. The tradeoff between the quality of traffic density estimation and operation cost of probing are investigating using multi-objective genetic algorithm. The results indicates that it is possible to decrease probe data for congested traffic with negligible degradation on the quality of traffic status estimation.
Towards Area-Wide Traffic Monitoring-Applications Derived from Probe Vehicle Data Probe Vehicle Data
Applications of Advanced Technologies in Transportation Engineering (2004), 2004
Comprehensive, up-to-date traffic monitoring is the basis for mobility information and traffic management systems. However, conventional stationary traffic data measurements are hardly able to provide the necessary data for an area-wide monitoring and cannot deliver enough information for many traffic related services. Therefore an alternative approach using positioning data of commercial vehicle fleets for traffic monitoring issues has been established. This paper surveys different prototype applications based on this probe vehicle data. Continuous monitoring and information of traffic situation via the World Wide Web accomplished by jam detection and highlighting is the basic service. Further on, vehicle route guidance systems using current and historic data achieve superior performance. Such guidance systems have been tested as modules for dynamic navigation and fleet disposition system. Finally a method to derive digital road maps and street characteristics from positioning data is presented.
Using commercial vehicle fleets as probes may be a cost-effective method for obtaining real-time traffic information. Because the taxi dispatch system automatically records the location of a taxi traveling in an urban network, large quantities of real-time travel data can be obtained at low cost. The reliability of this information about real-time traffic conditions, however, has not been investigated. This study examines the feasibility of using taxi dispatch systems as probes for realtime traffic surveillance. The examination focuses on the uncertainties in the data provided by the two types of wireless communications systems-analog and digital Multi Channel Access (MCA)-that are used for dispatching taxis. The study reveals that the digital MCA wireless system is much better than the analog wireless system for data transmission. This article exemplified that selection of longer road segments helps to greatly reduce traffic surveillance errors. Finally, this article discusses the lessons learned from this study and the limitations that must be overcome before the system can be widely deployed.
Probe vehicle based real-time traffic monitoring on urban roadways
Transportation Research Part C: Emerging Technologies, 2014
Travel time estimation and prediction on urban arterials is an important component of Active Traffic and Demand Management Systems (ATDMS). This paper aims in using the information of GPS probes to augment less dynamic but available information describing arterial travel times. The direction followed in this paper chooses a cooperative approach in travel time estimation using static information describing arterial geometry and signal timing, semi-dynamic information of historical travel time distributions per time of day, and utilizes GPS probe information to augment and improve the latter. First, arterial travel times are classified by identifying different travel time states, then link travel time distributions are approximated using mixtures of normal distributions. If prior travel time data is available, travel time distributions can be estimated empirically. Otherwise, travel time distribution can be estimated based on signal timing and arterial geometry. Real-time GPS travel time data is then used to identify the current traffic condition based on Bayes Theorem. Moreover, these GPS data can also be used to update the parameters of the travel time distributions using a Bayesian update. The iterative update process makes the posterior distributions more and more accurate. Finally, two comprehensive case studies using the NGSIM Peachtree Street dataset, and GPS data of Washington Avenue in Minneapolis, were conducted. The first case study estimated prior travel time distributions based on signal timing and arterial geometry under different traffic conditions. Travel time data were classified and corresponding distributions were updated. In addition, results from the Bayesian update and EM algorithm were compared. The second case study first tested the methodologies based on real GPS data and showed the importance of sample size. In addition, a methodology was proposed to distinguish new traffic conditions in the second case study.
Estimation of Urban Traffic State With Probe Vehicles
IEEE Transactions on Intelligent Transportation Systems
We present in this paper a method to estimate urban traffic state with communicating vehicles. Vehicles moving on the links of the urban road network form queues at the traffic lights. We assume that a proportion of vehicles are equipped with localization and communication capabilities, and name them probe vehicles. First, we propose a method for the estimation of the penetration ratio of probe vehicles, as well as the vehicles arrival rate on a link. Moreover, we show that turn ratios at each junction can be estimated. Second, assuming that the turn ratios at each junction are given, we propose an estimation of the queue lengths on a 2-lanes link, by extending a 1-lane existing method. Our extension introduces vehicles assignment onto the lanes. Third, based on this approach, we propose control laws for the traffic light and for the assignment of the arriving vehicles onto the lane queues. Finally, numerical simulations are conducted with Veins framework that bi-directionally couples microscopic road traffic and communication simulators. We illustrate and discuss our propositions with the simulation results.
Integration of road traffic data collected by means of non-infrastructure based probe vehicles and infrastructure based induction loop detectors allows enhancement of the quality of real-time traffic information that can be obtained for ATMIS. Appropriate fusion of both data sources requires knowledge about deficiencies in present data collection and processing techniques based on infrastructure based traffic detectors and fundamental insight in techniques for processing probe vehicle data. In this research report methods for estimating realtime travel times and performing automatic incident detection for ATMIS based on induction loop or probe vehicle data alone are developed. By properly incorporating additional traffic data from the other source the performance of the developed methods is shown to improve.
2015
Urban traffic congestion is a problem that plagues many cities in the United States. Testing strategies to alleviate this congestion is especially challenging due to the difficulty of modeling complex urban traffic networks. However, recent work has shown that these complicated systems can be modeled in relatively simple ways by leveraging consistent relationships that exist between network-wide averages of pertinent traffic properties, such as average network flow, network density and the rate at which trips are completed. Using these “macroscopic” traffic models, various control strategies can be developed to mitigate congestion and improve network performance. However, the effectiveness of many of these strategies depends on the ability to estimate traffic conditions on the network in real-time. This jointly proposed research between Penn State and Virginia Tech investigated how real-time mobile vehicle probes can be combined with macroscopic urban traffic models to inform more e...