Transportation Research Board AHB45
Committee on Traffic Flow Theory and Characteristics

Greenshields Prize

On July 10, 2008 after the successful Greenshields 75 Symposium, the Committee on Traffic Flow Theory and Characteristics established the Greenshields Prize, named in honor of Dr. Bruce D. Greenshields, a pioneer in our field. The committee may award a Greenshields Prize annually for a paper submitted for both presentation and publication through the TRB Annual Meeting paper submission process. The Paper Review Subcommittee will annually forward a set of top papers to the Awards Subcommittee that will then review the papers and attend the presentations and will determine whether to make a selection after each Annual Meeting. Members of the subcommittee may also add papers for consideration when attending the presentations. The Prize will be announced at the following mid-year meeting and presented at the following Annual Meeting. The selected paper should be in the spirit of Dr. Greenshields’ work, basing sound theory on rigorous empirical analysis and will be reviewed based on these four main criteria:

    • Novelty and originality of material.
    • Experimental support of results.
    • Quality of writing and presentation.
    • Practical applications or implications of the study.
    • The committee will periodically review these criteria and procedures based on actual experience.

2011 Inaugural Greenshields Prize

TRB Paper No. 11-4034
Transportation Research Record No. 2249 pp. 62-77
Correlated Parameters in Driving Behavior Models: Car-Following Example and Implications For Traffic Microsimulation
By Ji-Won Kim and Hani S. Mahmassani

The paper presents a detailed analysis of the existence and extent of such correlation between behavioral parameters in car-following models and its impact on microsimulation results. The correlation is often disregarded when calibrating simulation models because individual parameters are independently and randomly drawn for each driver assuming they are uncorrelated leading to inaccurate output. This study quantifies the impact of ignoring correlation for three representative car-following models and proposes the use of a parametric distribution with known correlation structure to significantly improve the results. Model testing was undertaken using NGSIM datasets. The paper provides guidance on capturing correlation in miscrosimulation and will certainly lead to more accurate calibration procedures in the future.

2012 Greenshields Prize

Two papers shared the 2012 Greenshields Prize:

TRB Paper No. 12-0919
Transportation Research Record No. 2316, pp. 47-57
Integrated Lane Change Model with Relaxation and Synchronization
By Schakel, W.J., Knoop, V.L., and Van Arem, B.

TRB Paper No. 12-0299
Transportation Research Record No. 2315, pp 11-24
Can Results of Car-Following Model Calibration Based on Trajectory Data Be Trusted? 

By Punzo, V., Ciuffo, B.F., and Montanino, M.

2013 Greenshields Prize

TRB Paper No. 13-4853
Transportation Research Record No. 2391, pp. 32-43
Data-Fitted First-Order Traffic Models and Their Second-Order Generalizations 
By Shimao Fan and Benjamin Seibold

This paper proposes a cross-comparison of first and second order traffic flow models and assesses their capabilities to accurately reproduce experimental observations on freeways. This paper addresses a very relevant question in the traffic flow theory community because such kind of models are often separately studied and validated. The advantages and drawbacks of first order versus second order models have been debated for a long time within the traffic flow community and this paper provides an elegant contribution to a better definition of the domain of relevance of both approaches. This paper should then contribute to improve the knowledge on best practices when dynamic traffic simulation is needed.

2014 Greenshields Prize

TRB Paper No. 14-0816
Transportation Research Record No. 2422, pp. 1-11
Empirical observations of congestion propagation and dynamic partitioning with probe data for large scale systems
By Yuxuan Ji, EPFL; Jun Luo, Shenzhen Institutes of Advanced Technology; Nikolas Geroliminis, EPFL

This paper presents innovative computational methods to dynamically partition networks into homogeneous sub-regions. This work is supported by extensive data analysis based on 30 to 50 million GPS daily records corresponding to 20,000 taxis for the city of Shenhzen (China). The research shows that homogeneity conditions are not stable during the onset and the offset of congestion at a city scale. This is clearly illustrated with the results of the Macroscopic Fundamental Diagram (MFD) within the different sub-regions. The formation of congestion is modeled by investigating individual links and their connections between multiple dynamic clusters. Contributions from this paper are very significant because they open the door to the application of the MFD concept for designing efficient control strategies by only dynamically adapting the control perimeter. They also highlight that the key parameters (critical flow, critical density,…) of the MFD are remarkably stable when the congested region grows. Moreover, the paper provides the basis for a better understanding of the dynamics of congestion in dense urban areas.

2015 Greenshields Prize

TRB Paper No. 15-3916
Transportation Research Record No. 2490, pp 56-64
Real-Time Travel Time Prediction Framework for Departure Time and Route Advice

By Calvert, S.C., Snelder, M., Bakri, T., Heijligers, B., Knoop, V.

This paper proposes a real time travel time prediction framework designed for large urban area including both arterial and urban roads. This framework makes it possible to test a wide variety of prediction models based either on theoretical or data-driven approaches. The results are demonstrated in a large test case corresponding to the Amsterdam Practical Trial. Data-driven approaches were then favor because their are easier to calibrate and require less computations. For short-term prediction, it appears that the simplest data driven approach (naive approach) performs the best. For larger-time window, a refined method (historic median prediction) provides the more accurate results. In most cases, the average absolute relative error is below 20%. The main contributions of this paper are (i) the formulation of the global framework and (ii) the extensive test of different methods on a large and heterogeneous operational test cases. The operational feedbacks from this study provide a good state of the art of the performance of data-driven methods in a mixed context and pave the way of further methodological developments.

2016 Greenshields Prize

TRB Paper No. 16-0003
Transportation Research Record No. 2560, pp 1-9
Capacity Drops at Merges: Analytical Expressions for Multilane Freeways

By Leclercq, L., Marczak, F., Knoop, V., and Hoogendoorn, S.

This paper deals with the derivation of analytical formulae to estimate the effective capacity at freeway merges in a multilane context. Effective capacity means the capacity observed when the merge happens to be the head of the congestion. It extends two previous papers that are based on the same modeling framework but that are restricted to a single lane on the freeway (or to the analysis of the right lane only). The analytical expression for the one-lane capacity is recursively applied for all lanes. Lane-changing maneuvers (mandatory for the on-ramp vehicles and discretionary for others) are divided into two non-overlapping local merging areas.Usually, estimating the effective capacity at freeway merges requires a traffic simulator and multiple runs. Here, the analytical formulae provide a first estimation considering most of the important parameters related both to road design (e.g. length of the inserting length, number of lanes), and the traffic composition (e.g. truck proportion, vehicle acceleration capabilities). A sensitivity analysis shows that vehicle acceleration and the truck ratio are the most influential parameters for the total capacity. The analytical formulae are proven to provide very good estimates when compared to experimental data for an active merge on the M6 freeway in UK.

2017 Greenshields Prize

TRB Paper No. 17-6081
Transportation Research Record No. 2623, pp 29-39
Traffic State Estimation for Urban Road Networks Using A Link Queue Model

By Gu, Y., Qian, Z., and Zhang, G.

This paper is about traffic state estimation in complex urban networks. It takes benefit of the different data sources that may be available (cell phone, GPS, probe vehicles, inductive loops….) by stating a robust framework for data fusion. A Bayesian probabilistic model to estimate traffic states is proposed, along with Expectation-Maximization Extended Kalman Filter (EM-EKF) algorithm. The model is embedded with a macroscopic traffic flow propagation model (namely the link queue model) that is computationally efficient for large-scale networks. The Bayesian framework can seamlessly integrate multiple data sources for best inferring flow propagation and flow entry/exit along roads. A synthetic test bed is then investigated. The experiments show that the EM-EKF algorithm can effectively and promptly estimate traffic states. Another advantage is that the EM-EKF can update its model parameters in real time to adapt to unknown traffic incidents such as lane closures. Finally, the proposed methodology is applied to estimate travel speed in a small-scale urban network in the Washington DC area, resulting satisfactory estimation results with with a 8.5% error rate.




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