A Methodology for Estimating Vehicle Route Choice from Sparse Flow Measurements in a Traffic Network

Alex A Kurzhanskiy

doi:10.3390/math10030527

Abstract

While traffic speed data and travel time estimates are increasingly more available from commercial vendors, they are not sufficient for proper management and performance evaluation of transportation networks. Effective traffic control and demand management requires information about volumes, which is provided by fixed location sensors, such as loop detectors or cameras, and those are sparse. This paper proposes a method for estimating route choice using sparse flow measurements and estimated speed on the road network based on compressed sensing technology widely used in image processing, where from a handful of scattered pixels, a full image is recovered. What is known includes flows at origins and at selected links of the road network, where the detection is present; speed estimates are available for all network links. We find coefficients that split origin flows among routes starting at those origins. The advantage of the proposed methodology is that it does not rely on simulation that is prone to calibration errors but only on measured data. We also show how vehicle flows can be estimated at links with no detection, which enables computing performance measures for road networks lacking complete sensor coverage. Finally, we propose a method for selecting plausible routes between origins and destinations.

Highlights

One of the main challenges of urban traffic management is the shortage of traffic volume measurements
There are three main approaches to the O-D estimation [5]: the first one assumes that trips follow a gravity type pattern and the problem is reduced to calibrating the parameters of such a model from the observed counts [6]; the second approach estimates the O-D matrix by using user equilibrium traffic assignment based on Wardrop’s first principle [7,8]; and the third is an entropy maximising approach, in which the most likely trip matrix compatible with the observed flows is sought [1]
Suppose for a moment that vehicle flows at every origin, and vehicle flows at some links that belong to one or more routes in the road network are known: these flows are measured by inductive loops, magnetic sensors, radars or cameras

Summary

Introduction

One of the main challenges of urban traffic management is the shortage of traffic volume measurements. The combination of the second and third approaches entails the use of various traffic data sources and the invocation of traffic assignment, which employs one or another traffic model. 5. The adjusted O-D matrix is used again in step 1 until convergence of the assigned and measured flows and/or the a priori and estimated matrix. This paper proposes a method of estimating route choice using sparse flow measurements and estimated speed on the road network. The proposed approach is new, and it promises efficient technology for road network performance evaluation and traffic flow estimation required for traffic control and demand management.

Problem Statement

Computing Split Coefficients

Static Case

Accounting for Traffic Dynamics

Selecting Plausible Routes

Conclusions