Abstract

TRB 2014 - 93rd Transportation Research Board Annual Meeting, WASHINGTON DC, ETATS-UNIS, 12-/01/2014 - 16/01/2014; The travel time variability may significantly influence the driver's route choice. Estimating travel time reliability is thus really appealing to urban infrastructure managers. This could provide better information to drivers, encourage their reassignment on the network, and optimize its use. In this paper, we study any arterial in under-saturated conditions with known fixed-time traffic signals. The kinematic wave model is chosen to simply represent traffic dynamics. A semi-analytical (grid-free) method is applied to determine the separation between vehicle groups at each signal of the arterial. The main contribution of this paper is to present an aggregated diagram that describes the functioning of this arterial. It graphically provides the direct assessment of vehicle travel times with respect to their departure and the traffic flow. This tool only depends on signal settings and is cyclic and invariant for fixed-time signals. We show how to use this diagram to generate probabilistic travel time distributions when some input parameters are uncertain. Furthermore, this diagram appears insightful when the traffic flow is not accurately known.

Abstract

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks. Document type: Article