Anupriya, Daniel J. Graham, Daniel Hörcher, Prateek Bansal
arXiv 6 Apr 2021 · Econometrics · 1 citations (OpenAlex)
arXiv:2104.02399 · PDF · DOI · OpenAlex · Extracted main text
The fundamental relationship of traffic flow is empirically estimated by fitting a regression curve to a cloud of observations of traffic variables. Such estimates, however, may suffer from the confounding/endogeneity bias due to omitted variables such as driving behaviour and weather. To this end, this paper adopts a causal approach to obtain an unbiased estimate of the fundamental flow-density relationship using traffic detector data. In particular, we apply a Bayesian non-parametric spline-based regression approach with instrumental variables to adjust for the aforementioned confounding bias. The proposed approach is benchmarked against standard curve-fitting methods in estimating the flow-density relationship for three highway bottlenecks in the United States. Our empirical results suggest that the saturated (or hypercongested) regime of the estimated flow-density relationship using correlational curve fitting methods may be severely biased, which in turn leads to biased estimates of important traffic control inputs such as capacity and capacity-drop. We emphasise that our causal approach is based on the physical laws of vehicle movement in a traffic stream as opposed to a demand-supply framework adopted in the economics literature. By doing so, we also aim to conciliate the engineering and economics approaches to this empirical problem. Our results, thus, have important implications both for traffic engineers and transport economists.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Anderson \ Davis (2020) `An empirical test of hypercongestion in highway bottlenecks', Journal of Public Economics 187, 104197 | 1.000 | 12 | 4 | 100% |
| 2 | Oh \ Yeo (2012) `Estimation of capacity drop in highway merging sections', Transportation Research Record 2286(1), 111–121 | 1.000 | 8 | 4 | 100% |
| 3 | Cassidy \ Bertini (1999) `Some traffic features at freeway bottlenecks', Transportation Research Part B: Methodological 33(1), 25–42 | 0.928 | 4 | 3 | 100% |
| 4 | Chung, Rudjanakanoknad \ Cassidy (2007) `Relation between traffic density and capacity drop at three freeway bottlenecks', Transportation Research Part B: Methodologica… | 0.928 | 4 | 3 | 100% |
| 5 | Wiesenfarth, Hisgen, Kneib \ Cadarso-Suarez (2014) `Bayesian nonparametric instrumental variables regression based on penalized splines and dirichlet process mixtures', Journal of… | 0.874 | 8 | 2 | 100% |
| 6 | Cassidy (1998) `Bivariate relations in nearly stationary highway traffic', Transportation Research Part B: Methodological 32(1), 49–59 | 0.874 | 6 | 2 | 100% |
| 7 | Daganzo (1997) Fundamentals of transportation and traffic operations, Vol. 30, Pergamon Oxford | 0.811 | 4 | 2 | 100% |
| 8 | May (1990) Traffic flow fundamentals, Englewood Cliffs, N.J | 0.811 | 4 | 2 | 100% |
| 9 | Srivastava \ Geroliminis (2013) `Empirical observations of capacity drop in freeway merges with ramp control and integration in a first-order model', Transporta… | 0.811 | 4 | 2 | 100% |
| 10 | Anderson \ Davis (2018) `Does hypercongestion exist?: New evidence suggests not', National Bureau of Economic Research | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 84 scored citations.