On the use of Lagrangian observations from public transport and probe vehicles to estimate car space-mean speeds in bi-modal urban networks

Dakic, Igor; Menéndez, Mónica

doi:10.1016/j.trc.2018.04.004

Cited by 43 publications

(15 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various kernel-based estimation schemes have also been proposed to interpolate spatio-temporal macroscopic traffic speeds from heterogeneous data sources, mainly, loop detectors and probe vehicles [23]- [27]. However, these interpolation methods need field calibration of static model parameters (offline or online) such as shockwave speeds and free flow speeds, which can dynamically change depending on the local-traffic conditions, leading to biased results.…”

Section: Introductionmentioning

confidence: 99%

Traffic Data Imputation Using Deep Convolutional Neural Networks

et al. 2020

Self Cite

View full text Add to dashboard Cite

We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, we show that a well trained neural network can learn spatio-temporal traffic speed dynamics from time-space diagrams. We demonstrate this for a homogeneous road section using simulated vehicle trajectories and then validate it using real-world data from the Next Generation Simulation (NGSIM) program. Our results show that with probe vehicle penetration levels as low as 5%, the proposed estimation method can provide a sound reconstruction of macroscopic traffic speeds and reproduce realistic shockwave patterns, implying applicability in a variety of traffic conditions. We further discuss the model's reconstruction mechanisms and confirm its ability to differentiate various traffic behaviors such as congested and free-flow traffic states, transition dynamics, and shockwave propagation. We also provide a comparison against a widely used adaptive smoothing technique used for the same purpose and demonstrate the superiority of the proposed approach, even with probe vehicle lower penetration levels.

show abstract

Section: Introductionmentioning

confidence: 99%

Traffic Data Imputation Using Deep Convolutional Neural Networks

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…the frequency a vehicle reports information, which can also be calculated as the inverse of the time interval between two consecutive reports. Some of the existing literature requires that the equipped vehicles report information every second [15]- [17]. However, the sampling rate could be lower in reality due to the transmission and storage capacity.…”

Section: Introductionmentioning

confidence: 99%

Queue Estimation in a Connected Vehicle Environment: A Convex Approach

Yang

Menéndez

2019

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

This paper proposes a convex optimization based algorithm for queue profile estimation in a connected vehicle environment, which can also be used for trajectory reconstruction, delay evaluation, etc. This algorithm generalizes the widelyadopted assumption of a linear back of queue (BoQ) curve to a piecewise linear BoQ curve to consider more practical scenarios. The piecewise linear BoQ curve is estimated via a convex optimization model, ensuring efficient computation. Moreover, this paper explicitly handles cases with low penetration rates and low sampling rates, as well as measurement noises. Additionally, the proposed methodology is extended to an urban arterial, reusing the estimated departure information from the upstream intersections to further improve the estimation accuracy. Finally, two online implementation approaches are presented to perform real-time queue estimation.The proposed methodology is tested with two datasets: the Lankershim dataset in the NGSIM project and the simulated dataset of Wehntalerstrasse, Zurich, Switzerland. Results show that the error is less than 1.5 cars in undersaturated scenarios and 5.2 cars in oversaturated scenarios if the penetration rates are larger than 0.1 and sampling rates are higher than 0.05s −1 . It is demonstrated that by considering a piecewise linear BoQ curve, the estimation accuracy can be improved by up to 16%. Incorporating flow successfully can also reduce the estimation error by up to 16%. Results further show that the proposed methodology is robust to measurement errors. It is finally shown that the proposed framework can be solved within a reasonable time (0.8s), which is sufficient for most real-time applications.

show abstract

“…The bus data is collected from the automated vehicle location devices (AVL), used to reconstruct the trajectories of vehicles and to estimate the averages of speed and density. The data from Zurich has been previously used by Loder et al (2017) and Dakic and Menendez (2018). All required parameters for applying the proposed functional form on both simulation and empirical data sets are listed in Table 3, where those related to the network topology of London and Zurich are obtained from OpenStreetMap.…”

Section: Validation Of the 3d-mfdmentioning

confidence: 99%

“…Multi-modal MFDs are a powerful tool to investigate and understand the multimodal performance of entire urban road networks (Ampountolas et al, 2017;Zheng et al, 2017;Amirgholy et al, 2017). They can be estimated using both simulation and empirical observations (Geroliminis et al, 2014;Loder et al, 2017;Castrillon and Laval, 2018;Dakic and Menendez, 2018) or derived numerically (Boyaci and Geroliminis, 2011;Chiabaut, 2015;Dakic et al, 2019). So far, however, no particular functional form for multi-modal MFD exists, and no proposal has been made to link the physical properties of the road and bus network topology as well as the traffic operations to the shape of the 3D-MFD.…”

Section: Introductionmentioning

confidence: 99%

Capturing network properties with a functional form for the multi-modal macroscopic fundamental diagram

Loder

Dakic

Bressan

et al. 2019

Transportation Research Part B: Methodological

Self Cite

View full text Add to dashboard Cite

In urban road networks, the interactions between different modes can clearly impact the overall travel production. Although those interactions can be quantified with the multi-modal macroscopic fundamental diagram; so far, no functional form exists for this diagram to explicitly capture operational and network properties. In this paper, we propose a methodology to generate such functional form, and we show its applicability to the specific case of a bi-modal network with buses and cars. The proposed functional form has two components. First, a three dimensional lower envelope limits travel production to the theoretical best-case situation for any given number of vehicles for the different modes. The lower envelopes parameters are derived from topology and operational features of the road network. Second, a smoothing parameter quantifies how interactions between all vehicle types reduce travel production from the theoretical best-case. The smoothing parameter is estimated with network topology and traffic data. In the case no traffic data is available, our functional form is still applicable. The lower envelope can be approximated assuming fundamental parameters of traffic operations. For the smoothing parameter, we show that it always hold similar values even for different networks, making its approximation also possible. This feature of the proposed functional form is an advantage compared to curve fitting, as it provides a reasonable shape for the multi-modal macroscopic fundamental diagram irrespective of traffic data availability. The methodology is illustrated and validated using simulation and empirical data sets from London and Zurich.

show abstract

On the use of Lagrangian observations from public transport and probe vehicles to estimate car space-mean speeds in bi-modal urban networks

Cited by 43 publications

References 63 publications

Traffic Data Imputation Using Deep Convolutional Neural Networks

Traffic Data Imputation Using Deep Convolutional Neural Networks

Queue Estimation in a Connected Vehicle Environment: A Convex Approach

Capturing network properties with a functional form for the multi-modal macroscopic fundamental diagram

Contact Info

Product

Resources

About