A Bayesian Method for Dynamic Origin–Destination Demand Estimation Synthesizing Multiple Sources of Data

Abstract

In this paper a Bayesian method is proposed to estimate dynamic origin–destination (O–D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O–D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O–D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O–D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O–D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen–Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O–D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O–D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and “true” O–D demands is relatively small, and the O–D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O–D demands with fine accuracy.

Simulation-Based Origin-Destination Matrix Reduction: A Case Study of Helsinki City Area

“…For the case when data from urban sensors are available (such as link counts, flows and travel times), more sophisticated methods of data fusion are usually applied. In [8], time-dependent demand is estimated via Bayesian approach when the posterior distribution of OD matrices is updated based on traffic counts data. Traffic counts are also used for OD matrix estimation in [9], when an iterative bilevel framework is proposed to minimize the deviation between estimated and real-time link counts.…”

Hourly Origin–Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning

“…The static OD matrix generally represents travel demand in a proposed region for a given period of time [14]. Many studies have estimated the static OD matrix from various data sources, such as traffic volumes [15][16][17][18][19], smart cards [20], cell phones [7,21], and GPS data [22][23][24]. Dynamic or time-dependent OD matrix represents travel demand in different periods.…”

Bayesian Disaggregate and Aggregate Calibration of Path Logit Choice Models

“…Bayesian methods have recently been adopted for travel demand estimation and trafc models in dynamic context. Te dynamic travel demand is estimated in Yu et al [29] considering normal prior distribution and posterior distribution obtained from added observed trafc counts; the errors decrease with the observations. Considering the generalized Bayesian approach and path choice, Zhu et al [30,31] analysed the trafc models in stochastic transportation systems with user equilibrium and non-user equilibrium conditions and the convergences with numerical studies and day-to-day dynamics for path.…”