Determining the Number of Probe Vehicles for Freeway Travel Time Estimation by Microscopic Simulation

Author(s):  
Mei Chen ◽  
Steven I. J. Chien

Using probe vehicles to collect real-time traffic information is considered an efficient method in real-world applications. How to determine the minimum number of probe vehicles required for accurate estimate of link travel time is a question of increasing interest. Although it usually is assumed that link travel time is normally distributed, it is shown, on the basis of simulation results, that sometimes this is not true. A heuristic of determining the minimum number of probe vehicles required is developed to accommodate this situation. In addition, the impact of traffic volume on the required probe vehicle number is discussed.

Author(s):  
Chumchoke Nanthawichit ◽  
Takashi Nakatsuji ◽  
Hironori Suzuki

Traffic information from probe vehicles has great potential for improving the estimation accuracy of traffic situations, especially where no traffic detector is installed. A method for dealing with probe data along with conventional detector data to estimate traffic states is proposed. The probe data were integrated into the observation equation of the Kalman filter, in which state equations are represented by a macroscopic traffic-flow model. Estimated states were updated with information from both stationary detectors and probe vehicles. The method was tested under several traffic conditions by using hypothetical data, giving considerably improved estimation results compared to those estimated without probe data. Finally, the application of the proposed method was extended to the estimation and short-term prediction of travel time. Travel times were obtained indirectly through the conversion of speeds estimated or predicted by the proposed method. Experimental results show that the performance of travel-time estimation or prediction is comparable to that of some existing methods.


2003 ◽  
Vol 36 (14) ◽  
pp. 137-141 ◽  
Author(s):  
Alexandre Torday ◽  
André-Gilles Dumont

Author(s):  
Karthik K. Srinivasan ◽  
Paul P. Jovanis

Several intelligent vehicle–highway system demonstration projects are currently assessing the feasibility of using probe vehicles to collect realtime traffic data for advanced traffic management and information systems. They have used a variety of criteria to determine the number of probes necessary, but few generalizable algorithms have been developed and tested. The described algorithm explicitly considers the time period for travel time estimation (e.g., 5, 10, or 15 min), the number of replications of travel time desired for each link during each measurement period (reliability criterion), the proportion of links to be covered, and the length of the peak period. This algorithm is implemented by using a simulation of the Sacramento Network (170 mi2) for the morning peak period. The results indicate that the number of probe vehicles required increases non-linearly as the reliability criterion is made more stringent. More probes are required for shorter measurement periods. As the desired proportion of link coverage in the network increases, the number of probes required increases. With a given number of probes a greater proportion of freeway links than of major arterials can reliably be covered. Probe vehicles appear to be an attractive source of real-time traffic information in heavily traveled, high-speed corridors such as freeways and major arterials during peak periods, but they are not recommended for coverage of minor arterials or local and collector streets or during off-peak hours.


Author(s):  
Dongjoo Park ◽  
Laurence R. Rilett ◽  
Parichart Pattanamekar ◽  
Keechoo Choi

Historically, real-time intelligent transportation systems data are aggregated into discrete periods, typically of 5 to 10 min duration, and are subsequently used for travel time estimation and forecasting. In a previous study of link and corridor travel time estimation and forecasting by using probe vehicles, it was shown that the optimal aggregation interval size is a function of the traffic condition and the application. It is expected that traffic management centers will continue to collect travel time statistics (e.g., mean and variance) from probe vehicles and archive this data at a minimum time interval. Statistical models are developed for estimating the mean and variance of the link and route or corridor travel time for a larger interval by using only the observed mean travel time and variance for each smaller or basic interval. The proposed models are demonstrated by using travel time data obtained from Houston, Texas, which were collected as part of the automatic vehicle identification system of the Houston TranStar system. It was found that the proposed models for estimating link travel time mean and variance for a larger interval were easy to implement and provided results that had minimal error. The route or corridor travel time mean and variance model had considerable error compared with the link travel time mean and variance models.


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