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

Abstract

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.

c-SPSA: Cluster-wise simultaneous perturbation stochastic approximation algorithm and its application to dynamic origin–destination matrix estimation

“…However, although forward differencing can reduce the number of function evaluations it increases the bias of the estimated gradient compared to the central differences method. Subsequently, Cipriani et al (2014) used the approach to evaluate the impact of different data sources on the accuracy of the dynamic OD matrix estimation. Cipriani et al (2013) conducted a sensitivity analysis of various implementations aspects of SPSA.…”

The use of microsimulation models for the planning and management of metro systems

“…Details on phases 1 and 2 can be found in [34]. Likewise details on phase 3 are provided by [36][37][38] and [39].…”

“…These sets of data were adopted to implement the procedures described in detail by [36][37][38] and [39]. Figure 1 provides the Gamma distribution function by highlighting the three considered levels of travel demand.…”

Dynamic O-D Demand Estimation: Application of SPSA AD-PI Method in Conjunction with Different Assignment Strategies

“…As far as the second research line, considerable attention has been given to the role of different traffic measures adopted inside the O-D estimation procedure, for offline and online applications in addition to the usually adopted link counts, specifically speed and link occupancy [7,8,13], probe data from vehicle equipped by AVI tags [14][15][16][17][18][19][20][21]; aggregate demand data, such as traffic emissions and attractions by zones [7,22,23]; measure link speeds and path travel times [24].…”