ORCID Profile
0000-0001-6969-7764
Current Organisations
Monash University
,
The Hong Kong Polytechnic University
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Publisher: Wiley
Date: 15-11-2010
Publisher: SAGE Publications
Date: 2015
DOI: 10.3141/2491-05
Abstract: The macroscopic fundamental diagram (MFD) relates space–mean density and flow. Because the MFD represents areawide network traffic performance, perimeter control strategies and networkwide traffic state estimation using the MFD concept have been studied. Most previous works used data from fixed sensors, such as inductive loops, to estimate the MFD, which can cause biased estimation in urban networks because of queue spillovers at intersections. To overcome this limitation, recent literature reported on the use of trajectory data obtained from probe vehicles. However, these studies were conducted with simulated data sets few works have discussed the limitations of real data sets and their impact on variable estimation. This study compares two methods for estimating traffic state variables of signalized arterial sections: a method based on cumulative vehicle counts (CUPRITE) and one based on vehicle trajectory from taxi GPS logs. The comparisons reveal some characteristics of taxi trajectory data available in Brisbane, Queensland, Australia. The current trajectory data have limitations in quantity (i.e., the penetration rate), because of which the traffic state variables tend to be underestimated. Nevertheless, the trajectory-based method successfully captures the features of traffic states, which suggests that the trajectories from taxis can be a good estimator for networkwide traffic states.
Publisher: Informa UK Limited
Date: 03-02-2022
Publisher: Informa UK Limited
Date: 15-02-2022
Publisher: Springer Science and Business Media LLC
Date: 22-04-2015
Publisher: WIT Press
Date: 17-08-2007
DOI: 10.2495/UT070151
Publisher: IEEE
Date: 04-2015
Publisher: Elsevier BV
Date: 07-2007
Publisher: Elsevier BV
Date: 08-2017
Publisher: Wiley
Date: 12-2016
DOI: 10.1002/ATR.1448
Publisher: Elsevier BV
Date: 11-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2015
Publisher: Elsevier BV
Date: 03-2018
Publisher: Wiley
Date: 12-2016
DOI: 10.1002/ATR.1440
Publisher: Informa UK Limited
Date: 04-06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 09-2010
Publisher: Thomas Telford Ltd.
Date: 10-2015
Publisher: Elsevier BV
Date: 2012
Publisher: Informa UK Limited
Date: 13-02-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 08-2017
Publisher: Informa UK Limited
Date: 22-07-2022
Publisher: Springer Science and Business Media LLC
Date: 17-09-2018
Publisher: Elsevier BV
Date: 06-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
Publisher: Hindawi Limited
Date: 05-08-2018
DOI: 10.1155/2018/6825205
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2019
Publisher: Informa UK Limited
Date: 09-04-2015
Publisher: IEEE
Date: 2005
Publisher: Springer Netherlands
Date: 2007
Publisher: Wiley
Date: 03-2016
DOI: 10.1002/ATR.1371
Publisher: SAGE Publications
Date: 2013
DOI: 10.3141/2396-06
Abstract: The primary objective of this study is to develop a robust queue estimation algorithm for motorway on-r s. Real-time queue information is a vital input for dynamic queue management on metered on-r s. Accurate and reliable queue information enables the management of on-r queues in a manner that adapts to the actual traffic queue size and thus minimizes the adverse impacts of queue flush while increasing the benefit of r metering. The proposed algorithm is based on the Kalman filter framework. The fundamental conservation model is used to estimate the system state (queue size) with the flow-in and flow-out measurements. These projection results are updated with the measurement equation by using the time occupancies from midlink and link entrance loop detectors. This study also proposes a novel singular-point correction method. This method resets the estimated system state to eliminate the counting errors that accumulate over time. In the performance evaluation, the proposed algorithm demonstrated accurate and reliable performance and consistently outperformed the benchmarked single-occupancy Kalman filter (SOKF) method. The improvements over the SOKF method were 62% and 63% on average for the estimation accuracy and reliability, respectively. The benefit of the innovative concepts of the algorithm is well justified by the improved estimation performance in conditions of congested r traffic, in which long queues may significantly compromise the benchmark algorithm's performance.
Publisher: MDPI AG
Date: 24-09-2021
DOI: 10.3390/FUTURETRANSP1030024
Abstract: Traffic safety studies need more than what the current micro-simulation models can provide, as they presume that all drivers exhibit safe behaviors. Therefore, existing micro-simulation models are inadequate to evaluate the safety impacts of managed motorway systems such as Variable Speed Limits. All microscopic traffic simulation packages include a core car-following model. This paper highlights the limitations of the existing car-following models to emulate driver behaviour for safety study purposes. It also compares the capabilities of the mainstream car-following models, modelling driver behaviour with precise parameters such as headways and time-to-collisions. The comparison evaluates the robustness of each car-following model for safety metric reproductions. A new car-following model, based on the personal space concept and fish school model is proposed to simulate more accurate traffic metrics. This new model is capable of reflecting changes in the headway distribution after imposing the speed limit from variable speed limit (VSL) systems. This model can also emulate different traffic states and can be easily calibrated. These research findings facilitate assessing and predicting intelligent transportation systems effects on motorways, using microscopic simulation.
Publisher: Elsevier BV
Date: 11-2022
Publisher: Wiley
Date: 04-04-2016
DOI: 10.1002/ATR.1373
Publisher: SAGE Publications
Date: 2014
DOI: 10.3141/2421-02
Publisher: Elsevier BV
Date: 05-2016
Publisher: Informa UK Limited
Date: 09-07-2019
Publisher: IEEE
Date: 2005
Publisher: SAGE Publications
Date: 2014
DOI: 10.3141/2442-09
Abstract: Travel time estimation and prediction on motorways has long been a topic of research. Prediction modeling generally assumes that the estimation is perfect. However good the modeling, errors in estimation can significantly weaken the accuracy and reliability of the prediction. Models have been proposed for estimating travel time from loop detector data. Generally, detectors are closely spaced (say, 500 m), and travel time can be estimated accurately. However, detectors are not always perfect, and even during normal running conditions a few detectors malfunction, with a resultant increase in the spacing between functional detectors. Under such conditions, an error in the travel time estimation is significant and generally unacceptable. This research evaluated the in-practice travel time estimation models during various traffic conditions. Existing models fail to estimate travel time accurately under large detector spacing and during shoulder congestion periods. To address this issue, an innovative hybrid model that considered loop data for travel time estimation was proposed. The model was tested with simulation and was validated with real Bluetooth data from the Pacific Motorway in Brisbane, Queensland, Australia. Results indicate that during non-free-flow conditions and larger detector spacing, the proposed model provides significant improvement in the accuracy of travel time estimation.
Publisher: SAGE Publications
Date: 2009
DOI: 10.3141/2121-05
Abstract: This paper presents a methodology for estimation of average travel time on signalized urban networks by integrating cumulative plots and probe data. This integration aims to reduce the relative deviations in the cumulative plots due to midlink sources and sinks. During undersaturated traffic conditions, the concept of a virtual probe is introduced, and therefore, accurate travel time can be obtained when a real probe is unavailable. For oversaturated traffic conditions, only one probe per travel time estimation interval–-360 s or 3% of vehicles traversing the link as a probe–-has the potential to provide accurate travel time.
Publisher: Elsevier BV
Date: 09-2019
Publisher: Wiley
Date: 03-11-2014
DOI: 10.1111/MICE.12101
Publisher: Elsevier BV
Date: 12-2014
Publisher: Springer Science and Business Media LLC
Date: 06-12-2011
Publisher: Informa UK Limited
Date: 29-05-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2017
Publisher: Elsevier BV
Date: 11-2014
Publisher: Hindawi Limited
Date: 2014
DOI: 10.1155/2014/508039
Abstract: Accurate prediction of incident duration is not only important information of Traffic Incident Management System, but also an effective input for travel time prediction. In this paper, the hazard based prediction models are developed for both incident clearance time and arrival time. The data are obtained from the Queensland Department of Transport and Main Roads’ STREAMS Incident Management System (SIMS) for one year ending in November 2010. The best fitting distributions are drawn for both clearance and arrival time for 3 types of incident: crash, stationary vehicle, and hazard. The results show that Gamma, Log-logistic, and Weibull are the best fit for crash, stationary vehicle, and hazard incident, respectively. The obvious impact factors are given for crash clearance time and arrival time. The quantitative influences for crash and hazard incident are presented for both clearance and arrival. The model accuracy is analyzed at the end.
Publisher: IEEE
Date: 06-2011
Publisher: Elsevier BV
Date: 07-2014
Publisher: Informa UK Limited
Date: 2008
Publisher: Informa UK Limited
Date: 26-05-2022
Publisher: Elsevier BV
Date: 09-2015
Publisher: Elsevier BV
Date: 2012
Publisher: WIT Press
Date: 17-08-2007
DOI: 10.2495/UT070151
Publisher: Elsevier BV
Date: 11-2015
Publisher: American Society of Civil Engineers (ASCE)
Date: 2015
Publisher: Informa UK Limited
Date: 24-09-2019
Publisher: Elsevier BV
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2015
Publisher: Elsevier BV
Date: 2017
Publisher: Elsevier BV
Date: 02-2019
Publisher: No publisher found
Date: 2007
Publisher: SAGE Publications
Date: 2012
DOI: 10.3141/2308-06
Abstract: This paper presents a methodology for real-time estimation of exit movement–specific average travel time on urban routes by integrating real-time cumulative plots, probe vehicles, and historic cumulative plots. Two approaches, component based and extreme based, are discussed for route travel time estimation. The methodology is tested with simulation and is validated with real data from Lucerne, Switzerland, that demonstrate its potential for accurate estimation. Both approaches provide similar results. The component-based approach is more reliable, with a greater chance of obtaining a probe vehicle in each interval, although additional data from each component is required. The extreme-based approach is simple and requires only data from upstream and downstream of the route, but the chances of obtaining a probe that traverses the entire route might be low. The performance of the methodology is also compared with a probe-only method. The proposed methodology requires only a few probes for accurate estimation the probe-only method requires significantly more probes.
Publisher: Elsevier BV
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: IEEE
Date: 06-2018
Publisher: No publisher found
Date: 2007
Publisher: Springer Science and Business Media LLC
Date: 07-06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2021
Publisher: Elsevier BV
Date: 12-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 21-09-2017
Publisher: Springer Science and Business Media LLC
Date: 22-02-2018
Publisher: Elsevier BV
Date: 08-2019
Publisher: Springer Science and Business Media LLC
Date: 22-04-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Start Date: 2021
End Date: 2023
Funder: Research Grants Council
View Funded ActivityStart Date: 2012
End Date: 2012
Funder: Australian Research Council
View Funded Activity