28-08-2014, 02:42 PM
Designing Heterogeneous Sensor Networks for Estimating and
Predicting Path Travel Time Dynamics: An Information-Theoretic
Modeling Approach
Designing Heterogeneous.pdf (Size: 533.52 KB / Downloads: 16)
Introduction
1.1. Motivation
To provide effective traffic congestion mitigation strategies, transportation planning organizations and traffic
management centers need to (1) reliably estimate and predict network-wide traffic conditions and (2) effectively
inform and divert travelers to avoid recurring and non-recurring congestion. Traffic monitoring systems provide
fundamental data inputs for public agencies to measure time-varying traffic network flow patterns and accordingly
generate coordinated control strategies. In this paper, we focus on a series of critical and challenging modeling
issues in traffic sensor network design, in particular, how to locate different types of detectors to improve path travel
time prediction accuracy. Reliable end-to-end trip travel time information is critically needed in a wide range of
intelligent transportation system applications, such as personalized route guidance and pre-trip traveler information
provision
Introduction
1.1. Motivation
To provide effective traffic congestion mitigation strategies, transportation planning organizations and traffic
management centers need to (1) reliably estimate and predict network-wide traffic conditions and (2) effectively
inform and divert travelers to avoid recurring and non-recurring congestion. Traffic monitoring systems provide
fundamental data inputs for public agencies to measure time-varying traffic network flow patterns and accordingly
generate coordinated control strategies. In this paper, we focus on a series of critical and challenging modeling
issues in traffic sensor network design, in particular, how to locate different types of detectors to improve path travel
time prediction accuracy. Reliable end-to-end trip travel time information is critically needed in a wide range of
intelligent transportation system applications, such as personalized route guidance and pre-trip traveler information
provision
Proposed approach
While significant progress has been made in formulating and solving the sensor location problem for travel time
estimation and prediction, a number of challenging theoretical and practical issues remain to be addressed.
First, the optimization criteria used in the existing sensor location models typically differ from those used in
travel time estimation and prediction. Due to the inconsistency between the two models, the potential of scarce
sensor resources might not be fully achieved in terms of maximizing information gain for travel time
estimation/prediction. For example, an AVI sensor location plan that maximizes sensor coverage does not
necessarily yield the least end-to-end travel time estimation and prediction uncertainty if there are multiple likely
paths between pairs of AVI sensors. As a result, a simplified but unified travel time estimation and prediction model
for utilizing different data sources is critically required as the underlying building block for the sensor network
design problem.
Conceptual framework and data flow
Focusing on predicting end-to-end path travel time applications and considering future availability of GPS
probe data on links
. Conclusions
To provide effective congestion mitigation strategies, transportation engineers and planners need to
systematically measure and identify both recurring and non-recurring traffic patterns through a network of sensors.
The collected data are further processed and disseminated for travelers to make smart route and departure decisions.
There are a variety of traditional and emerging traffic monitoring techniques, each with ability to collect real-time
traffic data in different spatial and temporal resolutions. This study proposes a theoretical framework for the
heterogeneous sensor network design problem. In particular, we focus on how to better construct network-wide
historical travel time databases, which need to characterize both mean and estimation uncertainty of end-to-end path
travel time in a regional network.
A unified Kalman filtering based travel time estimation and prediction model is first proposed in this research to
integrate heterogeneous data sources through different measurement mapping matrices. Specifically, the travel time
estimation model starts with the historical travel time database as prior estimates. Point-to-point sensor data and
GPS probe data are mapped to a sequence of link travel times along the most likely travelled path. Through an
analytical information updating equation derived from Kalman filtering, the variances of travel times on different
links are estimated for possible sensor design solutions with different degree of sampling or measurement
errors. The variance of travel time estimates for spatially distributed links are further assembled to calculate the
overall path travel time estimation uncertainty for the entire network as the single-valued information measure. The
proposed information quantification model and beam search solution algorithm can assist decision-makers to select
and integrate different types of sensors, as well as to determine how, when, where to integrate them in an existing
traffic sensor infrastructure.
A unified Kalman filtering based travel time estimation and prediction model is first proposed in this research to
integrate heterogeneous data sources through different measurement mapping matrices. Specifically, the travel time
estimation model starts with the historical travel time database as prior estimates. Point-to-point sensor data and
GPS probe data are mapped to a sequence of link travel times along the most likely travelled path. Through an
analytical information updating equation derived from Kalman filtering, the variances of travel times on different
links are estimated for possible sensor design solutions with different degree of sampling or measurement
errors. The variance of travel time estimates for spatially distributed links are further assembled to calculate the
overall path travel time estimation uncertainty for the entire network as the single-valued information measure. The
proposed information quantification model and beam search solution algorithm can assist decision-makers to select
and integrate different types of sensors, as well as to determine how, when, where to integrate them in an existing
traffic sensor infrastructure.