Xuesong Zhou

Dynamic origin-destination demand estimation using automatic vehicle identification data

This paper proposes a dynamic origin-destination (OD) estimation method to extract valuable point-to-point split-fraction information from automatic vehicle identification (AVI) counts without estimating market-penetration rates and identification rates of AVI tags. A nonlinear ordinary least-squares estimation model is presented to combine AVI counts, link counts, and historical demand information into a multiobjective optimization framework. A joint estimation formulation and a one-sided linear-penalty formulation are further developed to take into account possible identification and representativeness errors, and the resulting optimization problems are solved by using an iterative bilevel estimation procedure. Based on a synthetic data set, this study shows the effectiveness of the proposed estimation models under different market-penetration rates and identification rates

Dynamic Origin-Destination Demand Estimation with Multiday Link Traffic Counts for Planning Applications

A dynamic origin–destination demand estimation model for planning applications with real-time link counts from multiple days is presented. Based on an iterative bilevel estimation framework, the upper-level problem is to minimize both the deviation between estimated link flows and real-time link counts and the deviation between estimated time-dependent demand and given historical static demand. These two types of deviations are combined into a weighted objective function, where the weighting value is determined by an interactive approach to obtain the best compromise solution. The single-day formulation is further extended to use link counts from multiple days to estimate the variation in traffic demand over multiple days. A case study based on the Irvine test bed network is conducted to illustrate the methodology and estimate day-to-day demand patterns. The application illustrates considerable benefits in analyzing the demand dynamics with multiday data.

Number and Location of Sensors for Real-Time Network Traffic Estimation and Prediction: Sensitivity Analysis

Installing and maintaining sensors in a transportation network can be expensive. The motivation for this research is finding the best way to deploy finite resources and generate a network detection system in a manner that produces minimal estimation errors. The analysis uses a simulation-based real-time network traffic estimation and prediction system based on dynamic traffic assignment (DTA) methodology to analyze different levels of detection and different sensor locations in a portion of the Chesapeake Highway Advisories Routing Traffic (CHART) network (between Washington, D.C., and Baltimore, Maryland). This study provides a conceptual framework of the sensor location problem and a theoretical description of the objectives associated with the sensor location problem. A sensitivity analysis of the estimation and prediction quality with the DYNASMART-X real-time DTA system in relation to sensor number and location is conducted in the Maryland CHART network. The analysis considers both randomly generated location scenarios and scenarios based on engineering judgment. The analysis reveals the importance of providing detection in specific locations of the network and the dependence of the value of additional detection on the specific location selected.

Toll pricing and heterogeneous users: Approximation algorithms for finding bicriterion time-dependent efficient paths in large-scale traffic networks

This paper presents both exact and approximation algorithms for finding extreme efficient time-dependent shortest paths for use with dynamic traffic assignment applications to networks with variable toll pricing and heterogeneous users (with different value of time preferences). A parametric least-generalized cost path algorithm is presented to determine a complete set of extreme efficient time-dependent paths that simultaneously consider travel time and cost criteria. However, exact procedures may not be practical for large networks. For this reason, approximation schemes are devised and tested. Based on the concept of ϵ-efficiency in multiobjective shortest path problems, a binary search framework is developed to find a set of extreme efficient paths that minimize expected approximation error, with the use of the underlying value of time distribution. Both exact and approximation schemes (along with variants) are tested on three actual traffic networks. The experimental results indicate that the computa...

Dynamic Origin—Destination Trip Demand Estimation for Subarea Analysis

Subarea analysis capability is needed in conjunction with dynamic network analysis models to allow consideration and rapid evaluation of a large number of scenarios and to support transportation network planning and operations decisions for situations that may not require analysis on a complete network representation. With a focus on how to provide an up-to-date time-dependent origin-destination (O-D) demand matrix for the subarea network, a two-stage subarea demand estimation procedure is described. The first stage uses path-based traffic assignment results from the original network to generate an induced O-D demand matrix for the subarea network. The second stage incorporates an iterative bilevel subarea O-D updating procedure to find a consistent network flow pattern, using the induced O-D demand information and archived traffic measurements in the subarea network. An excessdemand traffic assignment formulation is adopted to model the external trips that traverse or bypass the subarea network. This for...

Variable toll pricing and heterogeneous users : Model and solution algorithm for bicriterion dynamic traffic assignment problem

A dynamic traffic assignment model and its solution algorithm for the bicriterion dynamic user equilibrium (BDUE) problem that allows for heterogeneous users with different value-of-time (VOT) preferences are presented. Assuming the VOT as a continuously distributed random variable across the population of trips, the BDUE problem is formulated as a system of infinite-dimensional variational inequalities (VIs). Rather than solving the VI formulation directly, this study employs a generalized Frank-Wolfe algorithm to find the BDUE flow pattern. A bicriterion time-dependent least-cost path algorithm is applied to generate the extreme efficient path set, and the corresponding breakpoints naturally define the multiple user classes and thereby generate the descent direction for a multiclass dynamic network loading. A traffic simulator is used to describe the traffic flow propagation and the spatial and temporal interactions. To circumvent the difficulty of storing the memory-intensive path set and routing polic...

Online Consistency Checking and Origin-Destination Demand Updating: Recursive Approaches with Real-Time Dynamic Traffic Assignment Operator

To maintain an internally consistent representation with actual traffic conditions, this paper presents an origin-destination (O-D) demand consistency-checking and updating model for online dynamic traffic assignment operation. Both predictive and reactive approaches are proposed to minimize the deviations between simulated states and real-world observations and O-D demand adjustment magnitude. The two objectives are combined into a weighted linear quadratic function to construct a guaranteed overdetermined optimization problem. Alternative recursive solution algorithms are presented to design an efficient feedback controller that regulates the demand input for the real-time dynamic traffic assignment simulator. The proposed model is tested with field data from the Irvine test bed network.