Effectiveness of link and path information on simultaneous adjustment of dynamic O-D demand matrix

Ernesto Cipriani Marialisa Nigro Gaetano Fusco Chiara Colombaroni 0 ) Department of Engineering, Roma Tre University , Via Vito Volterra 62, 00146 Rome, Italy Introduction The paper deals with the adjustment of timedependent Origin-destination (O-D) demand matrix, which is the fundamental input of ITS application for traffic predictions. The usual problem is to search for temporal O-D matrices that are near an a priori estimate (seed matrices) and that best fit traffic counts. However information on link flows is not fully effective in describing the state of the network; recent technologies for tracking vehicles provide a new kind of information on route travel times that can integrate usual information on traffic flows at count sections. Objective The object of the paper is to analyse the effectiveness of different types of information in the off-line simultaneous adjustment of dynamic O-D demand, starting from seed matrices with different degrees of reliability. Dynamic estimation of Origin-destination (O-D) matrix is a fundamental input for ITS systems, which need to identify the current traffic state and predict future traffic conditions at realtime level. In fact, demand patterns vary from day to day and - congested networks are heavily affected by even small changes of O-D demand flows. So, high level of accuracy on demand can lead to successful ITS systems [1] as well as to effective strategies for implementing route guidance, congestion pricing and network-based traffic signal control [2]. On the other hand, knowledge of space-temporal structure of demand is the necessary input for a dynamic traffic assignment model that simulates congestion evolution. Without correcting errors in O-D demand estimation, the inconsistency in O-D flows would accumulate and propagate in the traffic simulation process, making the network state estimation and prediction highly unreliable [3]. Usual methods for O-D estimation combine some a priori information, like historical O-D matrices, with real-time traffic measurements. Since dynamic traffic assignment models for ITS applications require a very detailed representation of O-D matrix in time and space, the O-D estimation problem is highly undetermined. So, any possible information on demand structure can be useful to reduce the complexity of the problem. Information on prior O-D matrices (the so-called seed matrix) are usually reported in any formulation, both static and dynamic; however, differently from other measures, they are not directly observable [4] and solution procedures for demand adjustment are usually irrespective of their quality [5]. Current technologies can provide a great amount of traffic data collected on links and nodes of the transportation network: pavement-embedded sensors, roadside radars and cameras provide measures of flows and speeds at nodes and along links; Advanced Vehicle Identification (AVI), ground-based radio navigation, cellular geo-location and GPS provide a new kind of information about travel times and route choices that integrate usual information on traffic flows at count sections. Moreover, it is well known that traffic counts are not fully effective in discerning between congested and uncongested traffic state of a link, because of non-monotone flow-density relationship. Thus, it is important to formulate effective methods for O-D estimation combining several heterogeneous sources of information and to assess the relative importance of each of them. On the other hand, optimization methods can applied to individuate the best locations of measurement sections (see, for example, [6]). Many authors dealt with the problem of increasing the amount of information required by dynamic O-D estimation problem and included, for example, speed and link occupancy [79], probe data from vehicle equipped by AVI tags [1014, 15,16], aggregate demand data such as traffic emissions and attractions by zones [8,9,17], total demand for sub-networks, or the temporal distribution of trips in some areas on the network. In this paper we want to investigate the contribution of different kinds of information to improve the accuracy of time-dependent O-D matrix estimation. Specifically, with respect to previous studies, we introduce information on travel times, which are assumed to be provided by a fleet of floating cars. In order to focus on basic issues of the problem, we tackle off-line simultaneous estimation of time-dependent OD demand, which is the basis for a suitable development of ITS applications in on-line context. The paper is organized into five sections including this introduction: Section 2 reports different methodologies developed in the last years for the dynamic OD estimation and after defines the one adopted in the study; in Section 3 the case study is presented, while the results of the application are reported in Section 4; finally Section 5 summarizes the main conclusions. 2 Problem formulation Different approaches and solution algorithms have been developed in the last years for both off-line and on-line dynamic OD estimation: in the following the most recent contributes are reported. Zhou et al. [18] formulated the dynamic OD estimation problem as a single level nonlinear optimization model, solved with a relaxation algorithm of the lagrangian extension of the original one, taking into account route choice in order to work in the path- flow dimension. Frederix et al. [23] adopted a linear approximation of the relationship between O-D flows and link flows, taking into account link flows being not separable. This approximation has been obtained with the marginal computation (MaC) method that performs a perturbation analysis in a computationally efficient way, using the kinematic wave theory principles for traffic simulation. Toledo and Kolechkina [19] proposed a method based on a linear approximation of the assignment matrix; they apply different iterative algorithms, performing a mesoscopic traffic simulation to conduct network loadings. Djukic et al. [20] proposed the reduction and approximation of OD demand variables based on principal component analysis (PCA). The new transformed set of variables (demand principal components) is then updated online from traffic counts in a novel reduced state space model for real time estimation of OD demand. The problem of off-line simultaneous estimation of temporal O-D matrices is tackled in this paper adopting a simulation approach, which avoids introducing assignment matrices [9]. The O-D estimation problem is formulated as an optimization problem aiming at minimizing a linear combination of the distance between estimated and a priori O-D demand flows and the errors between detected and estimated traffic measurements in a dynamic (i.e., time-dependent) off-line context. The objective function includes different kinds of data collected with different types of techniques: simple traffic counts and speed measurements detected at fixed road se (...truncated)