Inferring origin–destination trip matrices from aggregate volumes on groups of links: a case study using volumes inferred from mobile phone data

Abstract

The origin-destination matrix is an important source of information describing transport demand in a region. Most commonly used methods for matrix estimation use link volumes collected on a subset of links in order to update an existing matrix. Traditional volume data collection methods have significant shortcomings because of the high costs involved and the fact that detectors only provide status information at specified locations in the network. Better matrix estimates can be obtained when information is available about the overall distribution of traffic through time and space. Other existing technologies are not used in matrix estimation methods because they collect volume data aggregated on groups of links, rather than on single links. That is the case of mobile systems. Mobile phones sometimes cannot provide location accuracy for estimating flows on single links but do so on groups of links; in contrast, data can be acquired over a wider coverage without additional costs. This paper presents a methodology adapted to the concept of volume aggregated on groups of links in order to use any available volume data source in traditional matrix estimation methodologies. To calculate volume data, we have used a model that has had promising results in transforming phone call data into traffic movement data. The proposed methodology using vehicle volumes obtained by such a model is applied over a large real network as a case study. The experimental results reveal the efficiency and consistency of the solution proposed, making the alternative attractive for practical applications. MAE, mean absolute error; MARE, mean absolute relative error; MedARE, median absolute relative error. 662 N. CACERES ET AL.Figure 4. Correlation between observed and modelled aggregate volumes of groups of links crossing inter-cell boundaries by assigning (a) the prior O-D matrix and (b) the adjusted one. 664 N. CACERES ET AL.

Updating origin–destination matrices with aggregated data of GPS traces

“…As far as the authors know, a few studies have used aggregated mobile phone data for estimating travel demand. For example, Caceres et al (2013) has shown that it is possible to adopt a bi-level estimator (e.g. Yang et al, 1992) to derive the OD matrix if viewing each aggregated area as a combination of links.…”

Potentialities of Autonomous Vehicles for Online Monitoring of Motorway Traffic Volume

“…A few studies for estimating traffic density [11] and hourly traffic volume [12,13] have been reported. A combination method of a kinematic wave model and a probability model for dynamically estimating traffic density was developed based on reconstructing individual vehicle trajectory using heterogeneous data (loop detector, AVI probe, and GPS probe data) in [11].…”

“…A combination method of a kinematic wave model and a probability model for dynamically estimating traffic density was developed based on reconstructing individual vehicle trajectory using heterogeneous data (loop detector, AVI probe, and GPS probe data) in [11]. An innovative methodology for estimating hourly traffic volumes using cellular-phone call count and its probability crossing intercell boundaries was proposed in [12,13]. The two studies demonstrated that cellular-phone call data can be employed for inferring hourly traffic volumes, where the resulting estimation error was 20%.…”

Statistical models for the estimation of the origin-destination matrix from traffic counts

“…Castillo et al (2008) develop a model which uses plate scanning data, Parry and Hazelton (2012) propose a model which uses samples of route lows obtained from tracking vehicles in addition to link volumes, and Caceres et al (2013) develop a methodology in order to estimate link volumes from mobile phone data.…”