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:
2011 Inaugural Greenshields Prize
TRB Paper No. 11-4034
2012 Greenshields Prize
Two papers shared the 2012 Greenshields Prize:
TRB Paper No. 12-0919
TRB Paper No. 12-0299
2013 Greenshields Prize
TRB Paper No. 13-4853
2014 Greenshields Prize
TRB Paper No. 14-0816
2015 Greenshields Prize
TRB Paper No. 15-3916
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
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
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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