Statistical inference of probabilistic origin-destination demand using day-to-day traffic data

Abstract

Recent transportation network studies on uncertainty and reliability call for modeling the probabilistic O-D demand and probabilistic network flow. Making the best use of day-to-day traffic data collected over many years, this paper develops a novel theoretical framework for estimating the mean and variance/covariance matrix of O-D demand considering the day-to-day variation induced by travelers' independent route choices. It also estimates the probability distributions of link/path flow and their travel cost where the variance stems from three sources, O-D demand, route choice and unknown errors. The framework estimates O-D demand mean and variance/covariance matrix iteratively, also known as iterative generalized least squares (IGLS) in statistics. Lasso regularization is employed to obtain sparse covariance matrix for better interpretation and computational efficiency. Though the probabilistic O-D estimation (ODE) works with a much larger solution space than the deterministic ODE, we show that its estimator for O-D demand mean is no worse than the best possible estimator by an error that reduces with the increase in sample size. The probabilistic ODE is examined on two small networks and two real-world large-scale networks. The solution converges quickly under the IGLS framework. In all those experiments, the results of the probabilistic ODE are compelling, satisfactory and computationally plausible. Lasso regularization on the covariance matrix estimation leans to underestimate most of variance/covariance entries. A proper Lasso penalty ensures a good trade-off between bias and variance of the estimation.

Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method

“…Nigro M collected all the monitoring travel data of specific areas in Rome of Italy, and analyzed the travel time and route selection probability of the passenger's OD, which can improve the temporal and spatial reliability of the demand matrix [17]. Ma W used the daily traffic data collected over the years to propose a new theoretical framework, which is used to consider the daily changes caused by travelers' independent choice of routes and estimate OD demand, path selection probability, and travel cost [18]. Dai X proposed a data-driven short-term subway passenger flow prediction framework, which can be successfully used to describe different subway travel modes [19].…”

Crowd intelligence for sustainable futuristic intelligent transportation system: a review

“…The authors used feature fusion with the convolution for the demand prediction. A probabilistic inference model based on day‐to‐day traffic data is proposed by Ma and Qian [186] for travel demand prediction.…”

Estimating multi-year24/7origin-destination demand using high-granular multi-source traffic data

“…In this research, we speculate that there exist several typical repetitive traffic conditions at the network level, each of which carries weekday/weekend, seasonal or other demand/supply characteristics. In each typical traffic pattern, we assume the network condition follows a statistical equilibrium defined by Ma and Qian [37,38]. Travelers will select their route based on the traffic pattern they observe historically, and their route choice portions remains stable for those days with the same typical traffic pattern.…”