Route Choice and the Value of Motorists’ Travel Time: Theoretical and Methodological Issues

In June 1985, a survey of motorists makng urban journeys within Tyne and Wear was undertaken as part of the Department of Transport's research project into the value of time. This paper considers the theoretical and methodological issues involved in estimating the value that motorists' place upon travel time savings from their actual route choices and their responses to a simulated route choice experiment. The reasons for undertaking this survey and for choosing this particular location are discussed. The experimental design and the modelling technique used in the stated preference analysis are examined and the problems which face both a revealed preference and a stated preference investigation of motorists' route choices are considered. One of the aims of the study is to consider variations in the value of time according to socio-economic factors and journey characteristics. The theoretical sources of variations in the value of time are discussed as is the modelling approach which was adopted to analyse these potential variations. The empirical findings from the actual survey of motorists making urban journeys is the subject of a subsequent working paper

User equilibrium traffic network assignment with stochastic travel times and late arrival penalty

The classical Wardrop user equilibrium (UE) assignment model assumes traveller choices are based on fixed, known travel times, yet these times are known to be rather variable between trips, both within and between days; typically, then, only mean travel times are represented. Classical stochastic user equilibrium (SUE) methods allow the mean travel times to be differentially perceived across the population, yet in a conventional application neither the UE or SUE approach recognises the travel times to be inherently variable. That is to say, there is no recognition that drivers risk arriving late at their destinations, and that this risk may vary across different paths of the network and according to the arrival time flexibility of the traveller. Recent work on incorporating risky elements into the choice process is seen either to neglect the link to the arrival constraints of the traveller, or to apply only to restricted problems with parallel alternatives and inflexible travel time distributions. In the paper, an alternative approach is described based on the ‘schedule delay’ paradigm, penalising late arrival under fixed departure times. The approach allows flexible travel time densities, which can be fitted to actual surveillance data, to be incorporated. A generalised formulation of UE is proposed, termed a Late Arrival Penalised UE (LAPUE). Conditions for the existence and uniqueness of LAPUE solutions are considered, as well as methods for their computation. Two specific travel time models are then considered, one based on multivariate Normal arc travel times, and an extended model to represent arc incidents, based on mixture distributions of multivariate Normals. Several illustrative examples are used to examine the sensitivity of LAPUE solutions to various input parameters, and in particular its comparison with UE predictions. Finally, paths for further research are discussed, including the extension of the model to include elements such as distributed arrival time constraints and penalties

Investigating the potential of the combination of random utility models (CoRUM) for discrete choice modelling and travel demand analysis

People make choices every day. Many choices have a strong impact on the quality or their life.Each day a person wakes up and chooses which action he wants to do before, what to have for breakfast, what to wear, what time to go outside, how to manage his/her day by virtue of the budget and time constraints, which place to move to and how, which activity to do, which one to do before or after and so forth. There are choices that are not made every day, but they have a strong impact on the decision maker’s well-being. In fact, sooner or later, a person will decide his household location, whether to own a car, the typology and the vehicle model, whether to own a pet, which breed or size, how many children to have, in which school register them and much more. Some of the above-mentioned choice examples involve mobility. Thus, it is easy to recognize why these kind of choices form the basis for the planning and policy actions in the transport field. What is called, at aggregate level, congestion or traffic, represents the sum of individual choices that everyone makes at different levels: do I move? What time do I move? Where I want to go? Which transport mode do I want to use? What itinerary do I travel? This kind of choices, that can be termed transport choices, relating the so-called travel behaviour, are characterized by a significant modelling complexity. The random utility theory represents the most widely used paradigm in modelling the behaviour of people who make choices. This thesis investigates the potential of the combination of random utility models (CoRUM; Papola, 2016) for travel demand analysis and discrete choice modelling in general. In the current work, several theoretical advances and some specific transport-field applications are carried out. The CoRUM framework, in fact, is very general and allows for handling several discrete choice modelling crucial issues. The thesis is structured as follows: Chapter 2 reviews the state of the art on random utility theory and its application to route choice. In particular, the Section 2.1 provides the basic setup for the description of RUMs; Section 2.2 reviews the random utility models available in the literature, with reference to the two main problems of the error structure (inter-correlations and heteroskedasticity problems) and the inter/intra-respondent taste variation; Section 2.3 briefly summarizes the main applications of the random utility theory to the route choice problem; Section 2.4 describes the main assumptions of the Combination of random utility models (CoRUM) as a general framework for modelling discrete choices, with particular reference to travel choices. Chapter 3 investigates more general specifications of the CoRUM than those previously analysed, allowing accommodating also the taste heterogeneity and the heteroscedasticity, in particular by combining mixtures of RUMs. To this end, the chapter proposes a theoretical generalization of the CoRUM framework and a real-world application on data collected on a stated survey of 1688 observations of 211 respondents. Chapter 4 represents an estimation exercise with applications on future scenarios on the main closed form random utility models, on synthetic datasets with variable sample sizes and complex underlying correlation scenarios. Such correlation scenarios, on the other hand, can be representative of typical mode choice or route choice contexts. The aim of this chapter is investigating the potential of the CoNL (and the other models) in terms of forecasting, and comparing it with the models goodness of fit performances. Chapter 5 proposes a new route choice model obtained under the CoRUM framework. It describes an algorithm to generate a CoNL specification, allowing detecting a set and a composition for the components of the model, and a way to compute all the structural model parameters, whatever the network. Chapter 6 is currently an original contribution of this thesis and describes several advance compared to the published work in Chapter 5. In particular, an implicit enumeration algorithm theoretically consistent with the CoRUM route choice model,is proposed and tested on toy networks; an in-depth analysis of the complex route choice models is carried out on their ability to reproduce complex correlation scenarios, drawing important conclusions, both theoretical and applicative, on the novel CoNL route choice model, proposed in Chapter 5, and on the existent Link Nested Logit model; some practical advance on the original route choice model is proposed and tested both on toy networks and on a real network (Region Campania network). The goodness of fit of the CoNL route choice has been analysed and compared with the one of the other route choice models, using real observations collected by means of GPS detection of about 200 trajectories. Chapter 7 reports a summary of the conclusions reached in the whole thesis and proposes several ideas for future research steps