Genetics of traffic assignment models for strategic transport planning

This paper presents a review and classification of traffic assignment models for strategic transport planning purposes by using concepts analogous to genetics in biology. Traffic assignment models share the same theoretical framework (DNA), but differ in functionality (genes). We argue that all traffic assignment models can be described by two genes. The first gene determines the spatial functionality (unrestricted, capacity restrained, capacity constrained, capacity and storage constrained) described by five spatial interaction assumptions, while the second gene determines the temporal functionality (static, semi-dynamic, dynamic) described by two temporal interaction assumptions. This classification provides a deeper understanding of the often implicit assumptions made in traffic assignment models described in the literature, particularly with respect to networking loading where the largest differences occur. It further allows for comparing different models in terms of functionality, and opens the way for developing novel traffic assignment models

Traffic Signal Optimization Using Cyclically Expanded Networks

Traditionally, the coordination of multiple traffic signals and the traffic assignment problem in an urban street network are considered as two separate optimization problems. However, it is easy to see that the traffic assignment has an influence on the optimal signal coordination and, vice versa, a change in the signal coordination changes the optimal traffic assignment. In this paper we present a cyclically time-expanded network and a corresponding mixed integer linear programming formulation for simultaneously optimizing both the coordination of traffic signals and the traffic assignment in an urban street network. Although the new cyclically time-expanded network provides a model of both traffic and signals close to reality, it still has the advantage of a linear objective function. Using this model we compute optimized signal coordinations and traffic assignment on real-world street networks. To evaluate the practical relevance of the computed solutions we conduct extensive simulation experiments using two established traffic simulation tools that reveal the advantages of our model

Traffic assignment optimization models

Optimalizace toku v síti je klasickou aplikací matematického programování. Tyto modely mají, mimo jiné, široké uplatnění také v logistice, kde se tak snažíme docílit optimálního rozdělení dopravy, např. vzhledem k maximalizaci zisku, či minimalizaci nákladů. Toto pojetí ovšem často problém idealizuje, poněvadž předpokládá existenci jediného rozhodovatele. Takový přístup je možný ve striktně organizovaných sítích jako např. v logistických sítích přepravních společností, železničních sítích či armádním zásobování. Úloha ''Traffic Assignment Problem'' (TAP) se zaměřuje na dopady teorie her na optimalizaci toku, tj. zkoumá vliv více rozhodovatelů na celkové využití sítě. V práci se zaobíráme úlohou TAP s působením náhodných vlivů, k čemuž využíváme metod stochastické a vícestupňové optimalizace. Dále zkoumáme možnosti zlepšení stávajícího využití sítě za rozhodnutí autoritativního rozhodovatele, kterému je umožněn zásah do samotné struktury sítě, k čemuž využíváme víceúrovňové programování.The class of network flow problems is one of the traditional applications of mathematical optimization. Such problems are widely applicable for example in logistics to achieve an optimal distribution of flow with respect to maximization of profit, or minimization of costs. This approach often leads to simplified models of real problems as it supposes the existence of only one decision maker. Such approach is possible in centralised networks, where an authority exists (such as railway network, military supply, or logistic network used by any company). The Traffic Assignment Problem (TAP) deals with impact of game theory to the network flow problem. Hence, we assume multiple decision makers, where each one of them wants to find his optimal behaviour. In this thesis, we focus on stochastic influences in TAP, for which we use methods of stochastic and multi-stage programming. Further, we concentrate on improvement options for the utilization of the system. Hereby, we consider possible actions of the master decision maker, and discuss them by the presence of multi-level mathematical programming.

An Investigation on Computational Methods of Traffic Assignment in Road Networks

In this paper some computational methods of traffic assignment are proposed. At first solutions of traffic assignment by the equal travel times principle used nonlinear programmings are showed. They are a method to approximate by a quadratic programming, a method to use the SUMT transformation, a method to apply Rosen's gradient projection method and a method to apply the conjugate gradient projection method. Through a computational example the efficiency of them are compared. To decide route flows uniquely in traffic assignment by the equal travel times principle, a method requesting route flows such that the joint probability is a maximum under supposed a priori probabilities is proposed. In case of a traffic assignment in a large scale road network, it is significant to improve the efficiency of route searches. For this purpose a technique of minimum time route search by division of a network is proposed