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  • Yu, Chunhui  (3)
  • Mobility and traffic research  (3)
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  • Mobility and traffic research  (3)
  • 1
    Online Resource
    Online Resource
    SAGE Publications ; 2019
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2673, No. 6 ( 2019-06), p. 84-93
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2673, No. 6 ( 2019-06), p. 84-93
    Abstract: Traffic congestion causes traveler delay, environmental deterioration, and economic loss. Most studies on congestion mitigation focus on attracting travelers to public transportation and expanding road capacity. Few studies have been found to analyze the contribution of different traffic flows to the congestion on roads of interest. This study proposes an approach to driver back-tracing on the basis of automated vehicle identification (AVI) data for congestion mitigation. Driver back-tracing (DBT) aims to estimate the sources of the vehicles on roads of interest in both spatial and temporal dimensions. The spatial DBT model identifies the origins of vehicles on the roads and the temporal DBT model estimates the travel time from the origins to the roads. The difficulty lies in that vehicle trajectories are incomplete because of the low coverage of AVI detectors. Deep neural network classification and regression are applied to the spatial and temporal DBT models, respectively. Simulation data from VISSIM are collected as the dataset because of the lack of field data. Numerical studies validate the promising application and advantages of deep neural networks for the DBT problems. Sensitivity analyses show that the proposed models are robust to traffic volumes. However, turning ratios, and the number and layout of AVI detectors may have noticeable impacts on the model performance.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2019
    detail.hit.zdb_id: 2403378-9
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  • 2
    Online Resource
    Online Resource
    SAGE Publications ; 2022
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2676, No. 10 ( 2022-10), p. 207-219
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2676, No. 10 ( 2022-10), p. 207-219
    Abstract: Mixed traffic control with connected and autonomous vehicles (CAVs) and human driving vehicles (HVs) is becoming a hot topic. CAV trajectory planning at work zones under the mixed traffic environment is a big challenge. Existing studies only focus on longitudinal trajectories (e.g., acceleration profiles), ignoring lateral trajectories (lane changing). This study proposes a trajectory planning model for CAVs at work zones under mixed traffic environment. Both longitudinal and lateral trajectories are considered. On the basis of the states of CAVs and of HVs observed by CAVs, the number and initial states of unobservable HVs in the planning horizon are estimated considering the interactions between vehicle driving behaviors. A trajectory planning model is then formulated to optimize acceleration profiles and lane choices of CAVs in the planning horizon in a centralized way. The minimization of total vehicle delay and fuel consumption is adopted as the objective function. A car-following model and a lane-changing model are adopted to capture the driving behaviors of HVs. The proposed model is a mixed-integer linear program. Numerical studies validate the advantages of the proposed trajectory planning model over late merge control for vehicle delay and fuel consumption.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
    detail.hit.zdb_id: 2403378-9
    Location Call Number Limitation Availability
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  • 3
    Online Resource
    Online Resource
    SAGE Publications ; 2019
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2673, No. 5 ( 2019-05), p. 538-547
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2673, No. 5 ( 2019-05), p. 538-547
    Abstract: The time-of-day (TOD) mode is the most widely used strategy for traffic signal control with fluctuating flows. Most studies determine TOD breakpoints based on traffic volumes collected by infrastructure-based detectors (e.g., loop detectors). However, these infrastructure-based detectors have low coverage and high maintenance cost. With the deployment of probe vehicles, vehicle trajectory data has become available, providing a new data source for signal control. This paper proposes an approach to identify TOD breakpoints at an isolated intersection based on the trajectory data of probe vehicles, instead of conventional traffic volumes, with under-saturated traffic conditions. It is shown that the speeds of queueing shockwaves capture the characteristics of the traffic volumes according to the queueing shockwave theory. Data from multiple sampling days are aggregated to compensate for the limitations of low market penetration rates and long sampling intervals. Queue joining vehicles are then identified to obtain the speeds of queueing shockwaves. The bisecting K-means algorithm is applied to cluster periods, which are characterized by queueing shockwave speeds, to identify TOD breakpoints. The numerical studies validate that the speeds of queueing shockwaves capture the trend of traffic volumes. The clustering algorithm identifies the same TOD breakpoints for queueing shockwave speeds and traffic volumes. As long as the number of sampling days is large enough, the proposed method can handle low penetration rates (e.g., 2%) and long sampling intervals (e.g., 20 s), and thus achieve a comparable performance to the ideal conditions with high penetration rates (e.g., 100%) and short sampling intervals (e.g., 1 s).
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2019
    detail.hit.zdb_id: 2403378-9
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
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