Judith Y.T. Wang

Travel Time Minimization Versus Reserve Capacity Maximization in the Network Design Problem

In the classical continuous network design problem, the most common approach is to determine optimal capacity enhancement by minimizing the total system cost under a budget constraint while taking into account the route choice behavior of network users. An alternative objective, maximization of network reserve capacity, is examined with the expectation that it is equivalent to total system cost minimization to a certain extent. The two approaches are tested and the results are compared in order to assess the level of equivalence and their effectiveness. The level of equivalence of the two objectives is found to vary by level of congestion. When the level of congestion is low, maximization of network reserve capacity is found to have the simultaneous effect of total cost minimization. This objective becomes more desirable when the level of congestion is low. When the level of congestion is high, maximizing the reserve capacity can only minimize the total travel cost to a certain extent. In this case, a combined objective can also be considered by applying different weightings on the two objectives.

Tolling Analysis with Bi-objective Traffic Assignment

We present a route choice model for a toll highway network based on bi-objective user equilibrium defined as the condition that traffic arranges itself in such a way that no individual trip maker can improve either their toll or travel time without worsening the other component by unilaterally switching routes. We develop heuristic traffic assignment procedures to find bi-objective user equilibrium solutions. Finally, we demonstrate that bi-objective user equilibrium is not equivalent to a bi-objective optimisation problem by studying a three link example network.

Modelling route choice behaviour in a tolled road network with a time surplus maximisation bi-objective user equilibrium model

In this paper, we propose a novel approach to model route choice behaviour in a tolled road network with a bi-objective approach, assuming that all users have two objectives: (1) minimise travel time; and (2) minimise toll cost. We assume further that users have different preferences in the sense that for any given path with a specific toll, there is a limit on the time that an individual would be willing to spend. Different users can have different preferences represented by this indifference curve between toll and time. Time surplus is defined as the maximum time minus the actual time. Given a set of paths, the one with the highest (or least negative) time surplus will be the preferred path for the individual. This will result in a bi-objective equilibrium solution satisfying the time surplus maximisation bi-objective user equilibrium (TSmaxBUE) condition. That is, for each O–D pair, all individuals are travelling on the path with the highest time surplus value among all the efficient paths between this O–D pair.

A framework for and empirical study of algorithms for traffic assignment

Traffic congestion is an issue in most cities worldwide. Transportation engineers and urban planners develop various traffic management projects in order to solve this issue. One way to evaluate such projects is traffic assignment (TA). The goal of TA is to predict the behaviour of road users for a given period of time (morning and evening peaks, for example). Once such a model is created, it can be used to analyse the usage of a road network and to predict the impact of implementing a potential project. The most commonly used TA model is known as user equilibrium, which is based on the assumption that all drivers minimise their travel time or generalised cost. In this study, we consider the static deterministic user equilibrium TA model.The constant growth of road networks and the need of highly precise solutions (required for select link analysis, network design, etc.) motivate researchers to propose numerous methods to solve this problem. Our study aims to provide a recommendation on what methods are more suitable depending on available computational resources, time and requirements on the solution. In order to achieve this goal, we implement a flexible software framework that maximises the usage of common code and, hence, ensures comparison of algorithms on common ground. In order to identify similarities and differences of the methods, we analyse groups of algorithms that are based on common principles. In addition, we implement and compare several different methods for solving sub-problems and discuss issues related to accumulated numerical errors that might occur when highly accurate solutions are required.

‘Resilience thinking’ in transport planning

Resilience has been discussed in ecology for over 40 years. While some aspects of resilience have received attention in transport planning, there is no unified definition of resilience in transportation. To define resilience in transportation, I trace back to the origin of resilience in ecology with a view of revealing the essence of resilience thinking and its relevance to transport planning. Based on the fundamental concepts of engineering resilience and ecological resilience, I define ‘comprehensive resilience in transportation’ as the quality that leads to recovery, reliability and sustainability. Observing that previous work in resilience analysis in transportation has focussed on addressing engineering resilience rather than ecological resilience, I conclude that transformability has been generally overlooked and needs to be incorporated in the analysis framework for comprehensive resilience in transportation.

On multi-objective stochastic user equilibrium

There is extensive empirical evidence that travellers consider many qualities (travel time, tolls, reliability, etc.) when choosing between alternative routes. Two main approaches exist to deal with this in network assignment models: Combine all qualities into a single (linear) utility function, or solve a multi-objective problem. The former has the advantages of a unique solution and efficient algorithms; the latter, however, is more general, but leads to many solutions and is difficult to implement in larger systems. In the present paper we present three alternative approaches for combining the principles of multi-objective decision-making with a stochastic user equilibrium model based on random utility theory. The aim is to deduce a tractable, analytic method. The three methods are compared both in terms of their theoretical principles, and in terms of the implied trade-offs, illustrated through simple numerical examples.

Bi-objective network equilibrium, traffic assignment and road pricing

Multi-objective equilibrium models of traffic assignment state that users of road networks travel on routes that are efficient with respect to several objectives, such as travel time and toll. This concept provides a general framework for modelling traffic flow in tolled road networks. We present the concept of time surplus maximisation as a way of handling user preferences. Given a toll, users have a maximum time they are willing to spend for a trip. Time surplus is this maximum time minus actual travel time. A rational user can be assumed to maximise time surplus, leading to the definition of time surplus maximisation bi-objective user equilibrium. We propose to use such models on the lower level of bi-level models for pricing in road networks under multiple upper level objectives such as minimising total travel time and emissions. In such a model a multi-objective optimisation problem at the upper level is combined with a multi-objective equilibrium problem at the lower level.

Numerical stability of path-based algorithms for traffic assignment

In this paper we study numerical stability of path-based algorithms for the traffic assignment problem. These algorithms are based on decomposition of the original problem into smaller sub-problems which are optimized sequentially. Previously, path-based algorithms were numerically tested only in the setting of moderate requirements to the level of solution precision. In this study we analyse convergence of these methods when the convergence measure approaches machine epsilon of IEEE double precision format. In particular, we demonstrate that the straightforward implementation of one of the algorithms of this group (projected gradient) suffers from loss of precision and is not able to converge to highly precise solution. We propose a way to solve this problem and test the proposed adjusted version of the algorithm on various benchmark instances.

Air Pollution Exposure in Relation to the Commute to School: A Bradford UK Case Study

Walking School Buses (WSBs) provide a safe alternative to being driven to school. Children benefit from the contribution the exercise provides towards their daily exercise target, it gives children practical experience with respect to road safety and it helps to relieve traffic congestion around the entrance to their school. Walking routes are designed largely based in road safety considerations, catchment need and the availability of parent support. However, little attention is given to the air pollution exposure experienced by children during their journey to school, despite the commuting microenvironment being an important contributor to a child’s daily air pollution exposure. This study aims to quantify the air pollution exposure experienced by children walking to school and those being driven by car. A school was chosen in Bradford, UK. Three adult participants carried out the journey to and from school, each carrying a P-Trak ultrafine particle (UFP) count monitor. One participant travelled the journey to school by car while the other two walked, each on opposite sides of the road for the majority of the journey. Data collection was carried out over a period of two weeks, for a total of five journeys to school in the morning and five on the way home at the end of the school day. Results of the study suggest that car commuters experience lower levels of air pollution dose due to lower exposure and reduced commute times. The largest reductions in exposure for pedestrians can be achieved by avoiding close proximity to traffic queuing up at intersections, and, where possible, walking on the side of the road opposite the traffic, especially during the morning commuting period. Major intersections should also be avoided as they were associated with peak exposures. Steps to ensure that the phasing of lights is optimised to minimise pedestrian waiting time would also help reduce exposure. If possible, busy roads should be avoided altogether. By the careful design of WSB routes, taking into account air pollution, children will be able to experience the benefits that walking to school brings while minimizing their air pollution exposure during their commute to and from school.