Application of Probe-Vehicle Data for Real-Time Traffic-State Estimation and Short-Term Travel-Time Prediction on a Freeway

2003 ◽  
Vol 1855 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Chumchoke Nanthawichit ◽  
Takashi Nakatsuji ◽  
Hironori Suzuki
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Traffic Information ◽  
Estimation Accuracy ◽  
Travel Time Estimation ◽  
Travel Time Prediction ◽  
Short Term ◽  
State Equations ◽  
Probe Vehicles ◽  
Real Time Traffic

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.

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

2000 ◽  
Vol 1719 (1) ◽  
pp. 61-68 ◽  
Author(s):  
Mei Chen ◽  
Steven I. J. Chien
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Accurate Estimate ◽  
Traffic Information ◽  
Travel Time Estimation ◽  
Probe Vehicles ◽  
Real Time Traffic ◽  
Minimum Number ◽  
Link Travel Time ◽  
The Impact

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.

Determination of Number of Probe Vehicles Required for Reliable Travel Time Measurement in Urban Network

1996 ◽  
Vol 1537 (1) ◽  
pp. 15-22 ◽  
Author(s):  
Karthik K. Srinivasan ◽  
Paul P. Jovanis
Keyword(s):  
Travel Time ◽  
Traffic Management ◽  
High Speed ◽  
Time Estimation ◽  
Traffic Information ◽  
Peak Period ◽  
Urban Network ◽  
Reliability Criterion ◽  
Probe Vehicles ◽  
Real Time Traffic

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.

Dynamic Travel Time Estimation Techniques for Urban Freight Transportation Networks Using Historical and Real-Time Data

2011 ◽  
pp. 252-273
Author(s):  
Vasileios Zeimpekis
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Real Life ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Travel Time Prediction ◽  
Time Data ◽  
Urban Freight ◽  
Real Time Traffic ◽  
Real Time Data

Effective travel time prediction is of great importance for efficient real-time management of freight deliveries, especially in urban networks. This is due to the need for dynamic handling of unexpected events, which is an important factor for successful completion of a delivery schedule in a predefined time period. This chapter discusses the prediction results generated by two travel time estimation methods that use historical and real-time data respectively. The first method follows the k-nn model, which relies on the non-parametric regression method, whereas the second one relies on an interpolation scheme which is employed during the transmission of real-time traffic data in fixed intervals. The study focuses on exploring the interaction of factors that affect prediction accuracy by modelling both prediction methods. The data employed are provided by real-life scenarios of a freight carrier and the experiments follow a 2-level full factorial design approach.

Accuracy of Link Travel Time Estimation Using Probe Vehicles

2003 ◽  
Vol 36 (14) ◽  
pp. 137-141 ◽  
Author(s):  
Alexandre Torday ◽  
André-Gilles Dumont
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Probe Vehicles ◽  
Link Travel Time

Travel time estimation in urban networks using limited probes data

2011 ◽  
Vol 38 (3) ◽  
pp. 305-318 ◽  
Author(s):  
Mohamed El Esawey ◽  
Tarek Sayed
Keyword(s):  
Simulation Model ◽  
Travel Time ◽  
Network Performance ◽  
Time Estimation ◽  
Performance Measure ◽  
Estimation Accuracy ◽  
Travel Time Estimation ◽  
Travel Times ◽  
Time Data ◽  
Travel Time Data

Travel time is a simple and robust network performance measure that is well understood by the public. However, travel time data collection can be costly especially if the analysis area is large. This research proposes a solution to the problem of limited network sensor coverage caused by insufficient sample size of probe vehicles or inadequate numbers of fixed sensors. Within a homogeneous road network, nearby links of similar character are exposed to comparable traffic conditions, and therefore, their travel times are likely to be positively correlated. This correlation can be useful in developing travel time relationships between nearby links so that if data becomes available on a subset of these links, travel times of their neighbours can be estimated. A methodology is proposed to estimate link travel times using available data from neighbouring links. To test the proposed methodology, a case study was undertaken using a VISSIM micro-simulation model of downtown Vancouver. The simulation model was calibrated and validated using field traffic volumes and travel time data. Neighbour links travel time estimation accuracy was assessed using different error measurements and the results were satisfactory. Overall, the results of this research demonstrate the feasibility of using neighbour links data as an additional source of information to estimate travel time, especially in case of limited coverage.

The Research of Travel Time Estimation Based on Vehicle License Plate Auto Recognition System

2014 ◽  
Vol 644-650 ◽  
pp. 1324-1329
Author(s):  
Ying Hong Li ◽  
Zhao Li
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Recognition System ◽  
Traffic Information ◽  
Travel Time Estimation ◽  
License Plate ◽  
Network Information ◽  
Data Matching ◽  
Huge Data ◽  
Database Technology

This Paper proposes a quick matching method for vehicle plate data based on NoSQL database technology considering the huge amounts of traffic information, the method uses the basic traffic network information database which built based on vehicle license plate auto recognition system and it improves the effectiveness and applicability of huge data-matching. Besides, the paper fits the travel time by the time priority principle, and estimates the average travel time by the mean estimation and median estimation according to sample capacity. The practical application shows that this method can effectively improve the efficiency of the mass data processing, and obtain travel time information quickly.

Estimating Travel Time Summary Statistics of Larger Intervals from Smaller Intervals Without Storing Individual Data

2002 ◽  
Vol 1804 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Dongjoo Park ◽  
Laurence R. Rilett ◽  
Parichart Pattanamekar ◽  
Keechoo Choi
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Transportation Systems ◽  
Identification System ◽  
Time Interval ◽  
Travel Time Estimation ◽  
Time Data ◽  
Probe Vehicles ◽  
Mean And Variance ◽  
Link Travel Time

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.

scholarly journals Urban Link Travel Time Estimation Based on Low Frequency Probe Vehicle Data

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Xiyang Zhou ◽  
Zhaosheng Yang ◽  
Wei Zhang ◽  
Xiujuan Tian ◽  
Qichun Bing
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Time Window ◽  
Low Frequency ◽  
Time Estimation ◽  
Estimation Accuracy ◽  
Travel Time Estimation ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Link Travel Time

To improve the accuracy and robustness of urban link travel time estimation with limited resources, this research developed a methodology to estimate the urban link travel time using low frequency GPS probe vehicle data. First, focusing on the case without reporting points for the GPS probe vehicle on the target link in the current estimation time window, a virtual report point creation model based on theK-Nearest Neighbour Rule was proposed. Then an improved back propagation neural network model was used to estimate the link travel time. The proposed method was applied to a case study based on an arterial road in Changchun, China: comparisons with the traditional artificial neural network method and the spatiotemporal moving average method revealed that the proposed method offered a higher estimation accuracy and better robustness.

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