Modelling Planner-Carrier Interactions in Road Freight Transport: Optimization of Road Maintenance Costs Via Overloading Control

A bi-level modelling approach is proposed to represent the interaction between the vehicle loading practices of road freight transport carriers, and the decisions of a road planning authority responsible both for road maintenance and for the enforcement of overloading control. At the lower (reactive) level, the overloading decisions of the carriers impact on road maintenance expenditure, while at the upper (anticipatory) level the planner decides fine and enforcement levels by anticipating the responses of the carriers. A case study using data from Mexico is used to illustrate the method

A route-swapping dynamical system and Lyapunov function for stochastic user equilibrium

An analysis of the continuous-time dynamics of a route-swap adjustment process is presented, which is a natural adaptation of that which was presented in Smith (1984) for deterministic choice problems, for a case in which drivers are assumed to make perceptual errors in their evaluations of travel cost, according to a Random Utility Model. We show that stationary points of this system are stochastic user equilibria. A Lyapnuov function is developed for this system under the assumption of monotone, continuously differentiable and bounded cost-flow functions and a logit-based decision rule, establishing convergence and stability of trajectories of such a dynamical system with respect to a stochastic user equilibrium solution

Reliable Network Design Problem: case with uncertain demand and total travel time reliability

In the reliable network design problem (RNDP) the main sources of uncertainty are variable demand and route choice. The objective is to maximize network total travel time reliability (TTR), which is defined as the probability that the network total travel time will be less than a threshold. A framework is presented for a stochastic network model with Poisson-distributed demand and uncertain route choice. The travelers are assumed to choose their routes to minimize their perceived expected travel cost following the probit stochastic user equilibrium condition. An analytical method is presented for approximation of the first and second moments of the total travel time. These moments are then fitted with a log-normal distribution. Then the design problem is tackled in which the analytical derivative of the TTR is derived with the sensitivity analysis of the equilibrated path choice probability. This derivative is then supplied to a gradient-based optimization algorithm to solve the RNDP. The algorithm is tested with a small network exampl

Influence of Experience on Users’ Behaviour: A Day-to-Day Model for Route Choice Updating

The performance of a transportation network is the consequence of users’ choices and the interaction of these choices. Several aspects influence users’ choices. For the analyst of the transportation system, being able to forecast users’ behaviour is necessary in order to obtain a simulation close to the reality, and in order to predict the impact of hypothetical policy measures. In private car transport networks, the route choice of drivers is one of the key choice dimensions to be considered. We suppose that three main elements influence route choice: i) the subjective perception of travel times, ii) experience directly obtained on the network by the user, and iii) information acquired through information systems like Advanced Traveler Information Systems (ATIS). In particular, in the present paper we delve into the influence of information on choices, by exploring the influence of the process by which the perception of travel times is updated day-by-day due to experience obtained on the network. In our mathematical model, the users’ awareness/knowledge of actual conditions on the network changes through an evolution process. Although this kind of problem has been previously studied, a novel feature of our approach is that as a consequence of actual experience or information, users may become aware of new alternatives: hence the ‘choice set’ perceived to be available may change day-by-day, not only the perception of travel times within a given choice set. In this paper, therefore, the time-evolution of both the choice (the alternative actually chosen) and the choice set (the alternative considered) is investigated. An illustrative specification of the model is proposed, and a series of simulation experiments conducted

The Restricted Stochastic User Equilibrium with Threshold model: Large-scale application and parameter testing

This paper presents the application and calibration of the recently proposed Restricted Stochastic User Equilibrium with Threshold model (RSUET) to a large-scale case-study. The RSUET model avoids the limitations of the well-known Stochastic User Equilibrium model (SUE) and the Deterministic User Equilibrium model (DUE), by combining the strengths of the Boundedly Rational User Equilibrium model and the Restricted Stochastic User Equilibrium model (RSUE). Thereby, the RSUET model reaches an equilibrated solution in which the flow is distributed according to Random Utility Theory among a consistently equilibrated set of paths which all are within a threshold relative to the cost on the cheapest path and which do not leave any attractive paths unused. Several variants of a generic RSUET solution algorithm are tested and calibrated on a large-scale case network with 18,708 arcs and about 20 million OD-pairs, and comparisons are performed with respect to a previously proposed RSUE model as well as an existing link-based mixed Multinomial Probit (MNP) SUE model. The results show that the RSUET has very attractive computation times for large-scale applications and demonstrate that the threshold addition to the RSUE model improves the behavioural realism, especially for high congestion cases. Also, fast and well-behaved convergence to equilibrated solutions among non-universal choice sets is observed across different congestion levels, choice model scale parameters, and algorithm step sizes. Clearly, the results highlight that the RSUET outperforms the MNP SUE in terms of convergence, calculation time and behavioural realism. The choice set composition is validated by using 16,618 observed route choices collected by GPS devices in the same network and observing their reproduction within the equilibrated choice sets generated by the RSUET model. Relevantly, the RSUET model is very successful in reproducing observed link

Updating of travel behavior parameters and estimation of vehicle trip-chain data based on plate scanning

This article proposes a maximum-likelihood method to update travel behavior model parameters and estimate vehicle trip chain based on plate scanning. The information from plate scanning consists of the vehicle passing time and sequence of scanned vehicles along a series of plate scanning locations (sensor locations installed on road network). The article adopts the hierarchical travel behavior decision model, in which the upper tier is an activity pattern generation model, and the lower tier is a destination and route choice model. The activity pattern is an individual profile of daily performed activities. To obtain reliable estimation results, the sensor location schemes for predicting trip chaining are proposed. The maximum-likelihood estimation problem based on plate scanning is formulated to update model parameters. This problem is solved by the expectation-maximization (EM) algorithm. The model and algorithm are then tested with simulated plate scanning data in a modified Sioux Falls network. The results illustrate the efficiency of the model and its potential for an application to large and complex network cases

Asymptotic approximations of transient behaviour for day-to-day traffic models

We consider a wide class of stochastic process traffic assignment models that capture the day-to-day evolving interaction between traffic congestion and drivers’ information acquisition and choice processes. Such models provide a description of not only transient change and ‘steady’ behaviour, but also represent additional variability that occurs through probabilistic descriptions. They are therefore highly suited to modelling both the disturbance and subsequent ‘drift’ of networks that are subject to some systematic change, be that a road closure or capacity reduction, new policy measure or general change in demand patterns. In this paper we derive analytic results to probabilistically capture the nature of the transient effects following such a systematic change. This can be thought of as understanding what happens as a system moves from varying about one equilibrium state to varying about a new equilibrium state. The results capture analytically the changes over time in descriptors of the system, in terms of link flow means, variances and covariances. Formally, the analytic results hold asymptotically as approximations, as we imagine demand increasing in tandem with capacities; however, our interest is in general cases where such tandem increases do not occur, and so we provide conditions under which our approximations are likely to work well. Numerical results of applying the methods are reported on several examples. The quality of the approximations is assessed through comparisons with Monte Carlo simulations from the true underlying process

The Long Term Behaviour of Day-to-Day Traffic Assignment Models

The dynamical behaviour of deterministic process, day-to-day traffic assignment models is sometimes characterised by convergence to a variety of different fixed equilibrium points dependent upon the initial flow pattern, even though individual trajectories are unique for a given start point. This non-uniqueness is seemingly in sharp contrast to the evolution of stochastic process, day-to-day models; under certain assumptions these converge in law to a unique stationary distribution, irrespective of the start point. In this article, we show how models may be constructed which exhibit characteristics of both deterministic models and stochastic models, and illustrate the ideas by using a simple example network