Dynamic platoon dispersion model based on real-time link travel time

2019 ◽  
Vol 13 (11) ◽  
pp. 1694-1700 ◽  
Author(s):  
Zhihong Yao ◽  
Taorang Xu ◽  
Yang Cheng ◽  
Lingqiao Qin ◽  
Yangsheng Jiang ◽  
...  
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Dispersion Model ◽  
Model Based ◽  
Platoon Dispersion ◽  
Link Travel Time

Calibration of Platoon Dispersion Parameters on the Basis of Link Travel Time Statistics

2000 ◽  
Vol 1727 (1) ◽  
pp. 89-94 ◽  
Author(s):  
Lei Yu
Keyword(s):  
Travel Time ◽  
Traffic Management ◽  
Dispersion Model ◽  
Time Data ◽  
Dispersion Parameters ◽  
Smoothing Factor ◽  
Travel Time Data ◽  
Platoon Dispersion ◽  
Advanced Traffic Management System ◽  
Link Travel Time

A calibration technique for platoon dispersion parameters for the widely used TRANSYT platoon dispersion model is presented. This technique calibrates platoon dispersion factor, travel time factor, and smoothing factor directly from the average link travel time and its standard deviation and can capture practically all of the roadway and traffic conditions in the field such as road grades, curvature, parking, opposing flow interference, traffic volume, and other sources of impedance. The technique is especially suited for applications in advanced traffic management system networks in which the required link travel time data could be obtained on a real-time basis. TRANSYT’s implementation of two scenarios is examined. The first scenario inputs the calibrated platoon dispersion parameter, with the result being that the smoothing factor used by TRANSYT is different from the calibrated parameter. The second scenario inputs a revised platoon dispersion factor, which is designed to make the smoothing factor used by TRANSYT identical to the calibrated parameter. This examination induces a recommendation that the TRANSYT input card or its internal calculation procedure for platoon dispersion be revised so that the average link travel time in the geometric distributed platoon dispersion model is consistent with those from the same model. The calibration of platoon dispersion parameters with field-collected link travel time data shows that platoon dispersion parameters are different for different standard deviations of link travel times even on the same street, and, therefore, the platoon dispersion parameters must be calibrated on a site-specific basis.

Minimum Sampling Size of Floating Cars for Urban Link Travel Time Distribution Estimation

2019 ◽  
Vol 2673 (3) ◽  
pp. 24-43 ◽  
Author(s):  
Meiping Yun ◽  
Wenwen Qin
Keyword(s):  
Sample Size ◽  
Travel Time ◽  
Real Time ◽  
Time Distribution ◽  
Ground Truth ◽  
Sample Size Determination ◽  
Minimum Sample Size ◽  
Travel Time Distribution ◽  
Minimum Sample ◽  
Link Travel Time

Despite the wide application of floating car data (FCD) in urban link travel time estimation, limited efforts have been made to determine the minimum sample size of floating cars appropriate to the requirements for travel time distribution (TTD) estimation. This study develops a framework for seeking the required minimum number of travel time observations generated from FCD for urban link TTD estimation. The basic idea is to test how, with a decreasing the number of observations, the similarities between the distribution of estimated travel time from observations and those from the ground-truth vary. These are measured by employing the Hellinger Distance (HD) and Kolmogorov-Smirnov (KS) tests. Finally, the minimum sample size is determined by the HD value, ensuring that corresponding distribution passes the KS test. The proposed method is validated with the sources of FCD and Radio Frequency Identification Data (RFID) collected from an urban arterial in Nanjing, China. The results indicate that: (1) the average travel times derived from FCD give good estimation accuracy for real-time application; (2) the minimum required sample size range changes with the extent of time-varying fluctuations in traffic flows; (3) the minimum sample size determination is sensitive to whether observations are aggregated near each peak in the multistate distribution; (4) sparse and incomplete observations from FCD in most time periods cannot be used to achieve the minimum sample size. Moreover, this would produce a significant deviation from the ground-truth distributions. Finally, FCD is strongly recommended for better TTD estimation incorporating both historical trends and real-time observations.

An efficient heterogeneous platoon dispersion model for real-time traffic signal control

2020 ◽  
Vol 539 ◽  
pp. 122982 ◽  
Author(s):  
Zhihong Yao ◽  
Bin Zhao ◽  
Lingqiao Qin ◽  
Yangsheng Jiang ◽  
Bin Ran ◽  
...  
Keyword(s):  
Real Time ◽  
Dispersion Model ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Real Time Traffic ◽  
Platoon Dispersion

Calibration of the Platoon Dispersion Model by Considering the Impact of the Percentage of Buses at Signalized Intersections

2017 ◽  
Vol 2647 (1) ◽  
pp. 93-99 ◽  
Author(s):  
Youan Wang ◽  
Xumei Chen ◽  
Lei Yu ◽  
Yi Qi
Keyword(s):  
Dispersion Model ◽  
Signalized Intersections ◽  
Signal Timing ◽  
Time Step ◽  
Traffic Conditions ◽  
Flow Profiles ◽  
Platoon Dispersion ◽  
Link Travel Time ◽  
Downstream Flow ◽  
The Impact

Despite the wide use of Robertson’s platoon dispersion model, few studies have calibrated this model under different traffic conditions at signalized intersections. This study calibrated the platoon dispersion model on the basis of field data collected from Beijing; the impact of the percentage of buses was considered in the calibration. First, a video was made of the platoon dispersion at an intersection. The link travel time of the vehicles in the platoon was extracted from the video. Then, the parameters of the platoon dispersion model were estimated with the average and standard deviations of the fleet link travel times. It was found that the derived parameters varied, with the observed percentage of buses ranging from 0% to 6% or 15% to 22%. This factor showed the impact of the percentage of buses on platoon dispersion under specific conditions. Regression models were developed to reflect such an impact. To evaluate the effectiveness of the calibrated platoon dispersion model, the downstream flow profiles derived from the calibrated model were compared with the field-observed downstream flow profiles within the dispersion process. Finally, the influence of the time step on the calibrated platoon dispersion model was analyzed. The results show that the calibrated model has high accuracy. The calibrated platoon dispersion model can be used to represent the process of platoon dispersion at signalized intersections where the impact is affected by the percentage of buses. It can contribute to signal timing optimization of the intersections.

Evaluating Effectiveness of Real-Time Advanced Traveler Information Systems Using a Small Test Vehicle Fleet

1997 ◽  
Vol 1588 (1) ◽  
pp. 41-48 ◽  
Author(s):  
Christopher L. Saricks ◽  
Joseph L. Schofer ◽  
Siim Sööt ◽  
Paul A. Belella
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Time Of Day ◽  
Guidance System ◽  
Effective Characteristics ◽  
Local Network ◽  
Route Guidance ◽  
Time Data ◽  
Traveler Information ◽  
Link Travel Time

ADVANCE was an in-vehicle advanced traveler information system (ATIS) providing route guidance in real time that operated in the northwestern portion and northwest suburbs of Chicago, Illinois. It used probe vehicles to generate dynamically travel time information about expressways, arterials, and local streets. Tests to evaluate the subsystems of ADVANCE, executed with limited availability of test vehicles and stringent scheduling, are described; they provided useful insights into both the performance of the ADVANCE system as a whole and the desirable and effective characteristics of ATIS deployments generally. Tests found that the user features of an in-route guidance system must be able to accommodate a broad range of technological sophistication and network knowledge among the population likely to become regular users of such a system. For users who know the local network configuration, only a system giving reliable real-time data about nonrecurrent congestion is likely to find a market base beyond specialized applications. In general, the quality and usefulness of systemwide real-time route guidance provided by other means are enhanced significantly by even a small deployment of probes: probe data greatly improve static (archival average) link travel time estimates by time of day, although the guidance algorithms that use these data should also include arterial traffic signal timings. Moreover, probe- and detector-based incident detection on arterial networks shows considerable promise for improved performance and reliability.

Forecasting Multiple-Period Freeway Link Travel Times Using Modular Neural Networks

1998 ◽  
Vol 1617 (1) ◽  
pp. 163-170 ◽  
Author(s):  
Dongjoo Park ◽  
Laurence R. Rilett
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Transportation Systems ◽  
Time Profile ◽  
Identification System ◽  
Route Guidance ◽  
Travel Times ◽  
Test Bed ◽  
Link Travel Time ◽  
Time Information

With the advent of route guidance systems (RGS), the prediction of short-term link travel times has become increasingly important. For RGS to be successful, the calculated routes should be based on not only historical and real-time link travel time information but also anticipatory link travel time information. An examination is conducted on how realtime information gathered as part of intelligent transportation systems can be used to predict link travel times for one through five time periods (of 5 minutes’ duration). The methodology developed consists of two steps. First, the historical link travel times are classified based on an unsupervised clustering technique. Second, an individual or modular artificial neural network (ANN) is calibrated for each class, and each modular ANN is then used to predict link travel times. Actual link travel times from Houston, Texas, collected as part of the automatic vehicle identification system of the Houston Transtar system were used as a test bed. It was found that the modular ANN outperformed a conventional singular ANN. The results of the best modular ANN were compared with existing link travel time techniques, including a Kalman filtering model, an exponential smoothing model, a historical profile, and a real-time profile, and it was found that the modular ANN gave the best overall results.

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