A path-based algorithm is developed for the static traffic assignment problem (TAP). In each iteration, it decomposes the problem into origin-destination (OD) pairs and solves each subproblem separately using the Wolfe reduced gradient (RG) method. This method reduces the dimensions of each single-OD subproblem by selecting a basic path between the OD pair and reformulating the subproblem in terms of the nonbasic paths. A column generation technique is included to avoid path enumeration in large scale networks. Also, some speed-up techniques are designed to improve the computational efficiency. The algorithm shifts flows from costlier paths to cheaper paths; however, the amount of flow shifted from a costlier path is proportional to not only the travel time but also the flow on the path. It is applied to the Philadelphia and Chicago test problems, while different strategies for choosing the basic paths are examined. The RG algorithm shows an excellent convergence to relative gaps of the order of 1.0E-14 when compared against several reference TAP algorithms.

In this chapter, the different basic assumptions for the development of assignment models to transit networks (frequency-based, schedule-based) are presented together with the possible approaches to the simulation of the dynamic system (steady state, macroscopic flows, agent-based).

Purpose– This paper aims to elucidate perceptions of safe driving and social norms in relation to driving motorbikes in the Vietnamese context.Design/methodology/approach– A series of focus groups was undertaken in relation to driving practices from a number of groups: adolescents, families and adult males and females. The discussion centred on how driving behaviours were socialised within the various groups.Findings– The research highlighted some very interesting social dynamics in relation to how safe driving habits are established and supported within the social context. In particular, the separation of descriptive and injunctive norms and the role such norms play in socialising driving behaviours, safe or otherwise.Practical implications– The implications for social marketing practice are considerable, especially in the Vietnamese context where injunctive norms are difficult to portray, given the dynamics of the media landscape. Social marketing campaigns will need to have a broader consideration of how to establish descriptive norms, bearing in mind the social milieu in which the behaviours occur.Originality/value– This research is the first of its kind in the Vietnamese context. While much practice-led innovation is occurring in the region, there is little extant research on the topic of social norms and the socialisation of behaviours within the Southeast Asian region.

Toll road operators and other toll facility stakeholders require analysis tools to estimate the ridership and projected income for an increasing variety of tolling schemes. Some tolling schemes commonly considered include link-based tolls as well as derived schemes, such as charging both a minimum and a maximum toll (or cap) for the use of the facility. In addition, different entry ramps may incur different tolls, which may be added to a link-based toll and subject the total toll to a toll cap value. Network equilibrium models that consider such tolls result in nonadditive costs on the modeled network due to the capping. To obtain a more tractable equivalent model, a network transformation is used. The model uses the addition of a set of temporary links to the network, which inherit the delays and tolls of the original links. It considers the toll cost per link, as well as minimum and a maximum value of the tolls paid. It is shown that the modified and the original network formulations are equivalent. To solve the resulting multi-class network equilibrium model, a multi-threaded bi-conjugate variant of the linear approximation method has been adapted for the particular network model used. The method is illustrated with a small example as well as an instance of capped link-based toll modeling on a network originating from practice that employs the toll structure considered.

on Congestion Management of Transportation Systems on the Ground and in the Air and was organized by Michael Ball of the University of Maryland, Michael Florian of INRO and the Universite ´de Montre ´al and Siriphong (Toi) Lawphongpanich of the University of Florida.The main goal of the workshop was to bring together researchers from both the road and air transportation domains.Typically, close interactions among researchers from these two disciplines are uncommon.Thus, this workshop provided an opportunity for rich discussions and cross fertilization of ideas.Moreover, the workshop's broader theme, congestion management, is of great interest and importance to transportation planners and government policy makers around the world.There were 35 presentations organized into ten sessions and two panel discussions.Among the 45 attendees were 12 graduate students whose room, board and registration

Over the past few years, much attention has been paid to computing flows for multi-class network equilibrium models that exhibit uniqueness of the class flows and proportionality (Bar-Gera et al., 2012). Several new algorithms have been developed such as bush based methods of Bar-Gera (2002), Dial (2006), and Gentile (2012) that are able to obtain very fine solutions of network equilibrium models. These solutions can be post processed (Bar-Gera, 2006) in order to ensure proportionality and class uniqueness of the flows. Recently developed, the TAPAS, algorithm (Bar Gera, 2010) is able to produce solutions that have proportionality embedded, without requiring post processing. It was generally accepted that these methods for solving UE traffic assignment are the only way to obtain unique path and class link flows. The purpose of this paper is to show that the linear approximation method and some of its variants satisfy these conditions as well. In addition, some analytical results regarding the relation between steps of the linear approximation algorithm and the path flows entropy are presented. (C) 2014 Elsevier Ltd. All rights reserved.

This chapter presents four categories of optimization models: spatial interaction models, network balancing models and multimodal multiproduct goods transport network planning models. Spatial interaction models are used to establish origin–destination demand matrices. Network balancing models are used to model the route choice on congested networks, while transit route choice models study the frequency of the service offered on public transport lines. Traffic assignment models were designed to describe the traffic flows formed by the users of a transport network, such as an urban road network. The models and methods presented in the chapter have a wide scope: they are applied to strategic planning problems on an international, national and regional scale, where the transportation of several products using the networks and services of several transporters are considered simultaneously.

Since it was first developed [see Spiess and Florian, Transp Res 23:83–102 ( 1989 )], the strategy-based transit assignment has been extensively used and its properties are well understood now. The computation of an optimal strategy is relatively fast and is comparable to the computation of a shortest path tree for one destination. However, since it is the solution of a linear program, it produces extremal solutions. As a consequence, the sensitivity analysis of strategy flows is not smooth. This work parallels the contribution of Nguyen et al. (Transp Sci 32:54–64 1998 ) who developed a logit choice of strategies following a basic idea due to Dial (Transp Res 5:88–111, 1971 ), in order to consider a larger variety of strategies by allowing walk choices at nodes of the transit network. Nevertheless, since the network representation used is different from the one used by Nguyen et al. (Transp Sci 32:54–64, 1998 ), the development is different. This modified logit strategy transit assignment algorithm was shown to produce more realistic results in dense transit networks where relatively short walks are required for access to attractive alternative transit paths. It also models better access from centroids representing large zones.

Nowadays ecological issues are of high public priority. Within industries namely the automotive sector, often new machines, facilities or technological innovations are the key to ecological improvements. Although it is seen less prominent, logistics play an important role in optimizing the ecological system. Due to the high amount of transport traffic in inbound logistics, small changes lead to substantial savings in CO2 emissions. Through transport-oriented scheduling this potential savings can be realized. By means of smoothing and bundling demands in scheduling, transport planning can be optimized resulting in increased utilization, avoided transports and reduced CO2 emissions. The developed concept was evaluated by means of a simulation model using real scheduling data.

The temporal demand matrix is an essential input to both on-line and off-line applications of dynamic traffic assignment (DTA). This paper presents a new method to solve the simultaneous adjustment of a dynamic traffic demand matrix, searching for a reliable solution with acceptable computational times for off-line applications and using as an input traffic counts and speeds, prior O–D matrices and other aggregate demand data (traffic demand productions by zone). The proposed solving procedure is a modification of the basic Simultaneous Perturbation Stochastic Approximation (SPSA) path search optimization method; it can find a good solution when the starting point (the seed matrix) is assumed to be “near” the optimal one, working with a gradient approximation based on a simultaneous perturbation of each demand variable.

The proper representation of route choice on transit networks that are subject to congestion is a continuous challenge in developed and developing countries. Theoretical contributions to the methodology for transit assignment methods that consider congestion have appeared in the literature (see for instance Cepeda et al. (2006) and Spiess and Florian (1989)). There are few documented successful applications of large scale applications of these methods and their validations using counts. The purpose of this paper is to present two large scale applications of the method described in Cepeda et al. (2006), which is based on optimal strategies, that considers discomfort functions aboard the vehicles and increased waiting times due to inability of transit travelers to board the first vehicle of a transit line to arrive. The applications described in detail in the paper are related to the planning networks of Santiago, Chile and London, UK.

In this chapter a gradient approximation approach has been applied to solve the dynamic origin-destination (O-D) simultaneous adjustment problem. It is formulated as an optimization problem aiming at minimizing the error between actual and estimated link observations (speed and volumes) and the distance between estimated and a priori O-D demand flows in a dynamic context. Different novelties, both in the problem formulation and in the solution procedure, have been introduced with the aim of improving the solution and reducing the computational times.

The mainstay method of equilibrium assignment methods is based on adaptation of the linear approximation algorithm. Practically all commercial software packages for transportation planning offer a version of this algorithm. In the early days of personal computing, when random-access memory (RAM) was limited, this method was the most appropriate one to use because it requires little intermediate storage. As personal computers became more powerful and RAM became plentiful, the drawbacks of the linear approximation method became evident to practitioners. A measure of convergence is the relative gap, which measures the relative difference between total travel time and total travel time on the shortest paths. Relative gaps of less than 10 –4 are difficult to reach with this method. Alternative assignment methods, based on algorithms that have better convergence rates, are known and can obtain finer solutions. Changing to new algorithms would appear to be a trivial task; however, it is not the case. The issues related to changing assignment algorithms pertain to the uniqueness of equilibrium paths, flows, and times. Examples of the expected changes in results for both standard multiclass assignments and for one complex model, which is equilibrated with feedback procedures, are presented. An adaptation of the projected gradient with path flows is used to represent the modern algorithms, which can reach relative gaps of 10 –6 or better. Differences in relevant results are relatively small. Nevertheless, practitioners are careful to reproduce results and may face a challenge to accept slightly different results with a faster converging algorithm.