A Review of Models of Urban Traffic Networks (With Particular reference to the Requirements for Modelling Dynamic Route Guidance Systems)

This paper reviews a number of existing models of urban traffic networks developed in Europe and North America. The primary intention is to evaluate the various models with regard to their suitability to simulate traffic conditions and driver behavior when a dynamic route guidance system is in operation

Microsimulation models incorporating both demand and supply dynamics

There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework. The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makers’ decisions and individual vehicle movements across the network. To achieve this it represents directly individual drivers’ choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the space–time trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamic

Mixing quantitative and qualitative methods for sustainable transportation in Smart Cities

L'abstract è presente nell'allegato / the abstract is in the attachmen

Two Variables Algorithms for Solving the Stochastic Equilibrium Assignment with Variable Demand: Performance Analysis and Effects of Path Choice Models

In this paper a general fixed-point approach dealing with multi-user (stochastic) equilibrium assignment with variable demand is proposed. The main focus is on (i) the implementation and comparison of different algorithm solutions based on successive averages methods calculated on one (arc flows, arc costs) and on two variables (arc flows and path satisfaction; arc costs and demand flows); (ii) the effects of algorithm efficiency on different path choice models and/or travel demand choice models. In terms of the best performing algorithmic solution, the effects of different path choice models, such as Multinomial Logit model, C-Logit model and Multinomial Probit model were implemented, and algorithmic efficiency was investigated w.r.t. a real network

Forecasting in Mathematics

Mathematical probability and statistics are an attractive, thriving, and respectable part of mathematics. Some mathematicians and philosophers of science say they are the gateway to mathematics’ deepest mysteries. Moreover, mathematical statistics denotes an accumulation of mathematical discussions connected with efforts to most efficiently collect and use numerical data subject to random or deterministic variations. Currently, the concept of probability and mathematical statistics has become one of the fundamental notions of modern science and the philosophy of nature. This book is an illustration of the use of mathematics to solve specific problems in engineering, statistics, and science in general

A Second Order Stochastic Network Equilibrium Model, II: Solution Method and Numerical Experiments

Real traffic networks typically exhibit considerable day-to-day variations in traffic flows and travel times, yet these variations are commonly neglected in network performance models. Recently, two alternative theoretical approaches were proposed for incorporating stochastic flow variation in the equilibration of route choices: the stochastic process (SP) approach (Cantarella and Cascetta 1995) and the second order generalized stochastic user equilibrium (GSUE(2)) model (Watling 2002). The theoretical basis of the two approaches is contrasted, and the paper goes on to present a heuristic solution method for the GSUE(2) model, and two alternative simulation methods for the SP model, each applicable to the realistic case of probit-based choice probabilities. These solution methods are then applied to two realistic networks. Factors affecting convergence and reproducibility are first identified, followed by comparisons of the GSUE(2) and SP predictions. It is seen that a quasi-periodic behaviour commonly arises in the SP model, with the predictions radically different from the GSUE(2) model. However, by modifying the link performance functions in the overcapacity regime, the nature of the SP solution changes, and for a memory filter based on a large number of days' experience, its moments are seen to be approximated by those of the GSUE(2) model. Implications for the application of these models are discussed