An ensemble Kalman filtering approach to highway traffic estimation using GPS enabled mobile devices

Abstract

Traffic state estimation is a challenging problem for the transportation community due to the limited deployment of sensing infrastructure. However, recent trends in the mobile phone industry suggest that GPS equipped devices will become standard in the next few years. Leveraging these GPS equipped devices as traffic sensors will fundamentally change the type and the quality of traffic data collected on large scales in the near future. New traffic models and data assimilation algorithms must be developed to efficiently transform this data into usable traffic information.In this work, we introduce a new partial differential equation (PDE) based on the Lighthill-Whitham-Richards PDE, which serves as a flow model for velocity. We formulate a Godunov discretization scheme to cast the PDE into a Velocity Cell Transmission Model (CTM-v), which is a nonlinear dynamical system with a time varying observation matrix. The Ensemble Kalman Filtering (EnKF) technique is applied to the CTMv to estimate the velocity field on the highway using data obtained from GPS devices, and the method is illustrated in microsimulation on a fully calibrated model of I880 in California. Experimental validation is performed through the unprecedented 100-vehicle Mobile Century experiment, which used a novel privacy-preserving traffic monitoring system to collect GPS cell phone data specifically for this research.

Proactive Vehicular Traffic Rerouting for Lower Travel Time

“…Projects such as Mobile Millennium [42], [20], CarTel [11], JamBayes [19], Nericell [30], and surface street estimation [16] use vehicle probe data collected from onboard GPS devices to reconstruct the state of traffic and to estimate the shortest travel time. This paper moves beyond this idea.…”

“…Several methods can be employed to estimate the travel time over a road segment. For instance, using vehicle probe data collected from onboard GPS devices to reconstruct the state of traffic is a well-studied topic [24], [42]. We use Greenshield's model [6] to estimate the travel time since it is extensively used in DTA models by transportation researchers.…”

“…Recent research has proven that real-time traffic flow data and road travel time can be determined based on data reported by vehicles or roadside sensors [19], [30], [42]. The question is how to utilize this knowledge in an intelligent fashion to avoid congestion and to reduce the drivers' travel times.…”

The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data

“…Building reliable and cost-effective traffic monitoring systems is a prerequisite to addressing this phenomenon. Historically, the estimation of traffic congestion has been limited to highways, and has relied mostly on a static, dedicated sensing infrastructure such as loop detectors or cameras [41]. The estimation problem is more challenging in the case of the secondary road network, also called the arterial network, due to the cost of deploying a wide network of sensors in large metropolitan areas.…”

A hybrid HMM model for travel path inference with sparse GPS samples

“…Although historical average speed computed from data is commonly used in different traffic-related problems (Gonzalez et al 2007;Li and McDonald 2002;Work et al 2008), to the best of our knowledge, most of the path inference algorithms assume constant legal speed limits according to road types and transportation modes (Chen and Bierlaire 2015;Hunter et al 2014;Lou et al 2009;Yuan et al 2010). In this study, we use average speed computed from historical speed data of a given road.…”