Doubly dynamic traffic assignment: simulation modelling framework and experimental results

Properties of a doubly dynamic simulation assignment model were investigated. The model involves specifying a day-to-day route choice model as a discrete time stochastic process, combining a between-day driver learning and adjusting model with a continuous-time, within-day dynamic network loading. Such a simulation model may be regarded as the realization of a stochastic process, which under certain mild conditions admits a unique stationary probability distribution (i.e., an invariant probability distribution over time of network flows and travel times). Such a stationary state of the stochastic process is of interest to transport modelers, as one can then describe the stochastic process by its moments such as the means, variances, and covariances of the flow and travel time profiles. The results of a simulation experiment are reported in which the process of individual drivers' day-to-day route choices are based on the aggregate learning of the experienced within-day route costs by all drivers departing in the same period. Experimental results of the stationarity of the stochastic process are discussed, along with an analysis of the sensitivity of autocorrelations of the route flows to the route choice model parameters. The results illustrate the consistency of the link flow model with properties such as first in-first out, and a simple network is used to illustrate the properties

A model for integrating home-work tour scheduling with time-varying network congestion and marginal utility profiles for home and work activities.

The existing literature on activity-based modeling emphasizes that individuals schedule their activities by keeping the whole-day activity pattern in mind. Several attempts have been made to integrate this with network congestion; however, for explicit explanation of travel behavior of individuals, further improvements are required. The proposed model is a combined model that addresses the scheduling of the home-work tour with time-varied network congestion in a fixed-point problem frame-work. Marginal utility profiles that represent individual time-of-day preferences and satiation effect of the activities are incorporated for the measurement of the utility of activity engagement along with the disutility of travel. Consideration of only time-of-day dependent marginal utility profiles of activities in the utility function does not appropriately integrate activities and travel within the tour. The proof is shown analytically and numerically. This finding contradicts earlier research into integration of morning and evening commutes with network congestion. Additionally, the results of two numerical experiments are presented. In the first experiment, an arbitrary dynamic tolling strategy is assumed, and then a detailed analysis is performed to show variation in the balance of trade-offs involved in the process. The second experiment assesses the sensitivity of the combined model through incorporation of different dynamic traffic loading models. Some meaningful observations are drawn from these experiments and are discussed with the identification of avenues for future research

Investigating undesired spatial and temporal boundary effects of congestion charging.

Two types of reported problems are related to the existing congestion charging projects that levy traffic only in a certain area within one or a few time periods during the day. One is that travellers depart earlier or later than a charging period to avoid paying full or part of the congestion charging tolls, which creates two undesired demand peaks that are often greater than available capacity. One peak comes just before the start of congestion charging and the other follows the end of it. We term this phenomenon ‘temporal boundary effect’ of congestion charging. The other reported problem is that travellers would rather stay away from a charging zone than pay congestion charging tolls, which causes undesired congestion on those roads or paths on the edge of the charging zone. We call this phenomenon ‘spatial boundary effect’ generated by congestion charging. This research investigates these boundary effects in the context of simultaneous route and departure time choice dynamic user equilibrium (SRD-DUE) network flows with an aim to gain new insights into congestion charging design. Numerical experiments investigating constant and time-varying congestion charging toll profiles are presented in this paper. This investigation shows that congestion charging may not be able to eliminate hypercongestion efficiently if schemes are not well designed, and can unfortunately give rise to undesired boundary effects and that a simply designed congestion charging scheme with small level toll or time-varying toll profiles can reduce the magnitude of boundary effects but may not be able to fully eliminate such undesired effect

Interdependence between transportation system and power distribution system: a comprehensive review on models and applications

The rapidly increasing penetration of electric vehicles in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environment protection. Integrating charging facilities, especially high-power chargers in fast charging stations, into power distribution systems remarkably alters the traditional load flow pattern, and thus imposes great challenges on the operation of distribution network in which controllable resources are rare. On the other hand, provided with appropriate incentives, the energy storage capability of electric vehicle offers a unique opportunity to facilitate the integration of distributed wind and solar power generation into power distribution system. The above trends call for thorough investigation and research on the interdependence between transportation system and power distribution system. This paper conducts a comprehensive survey on this line of research. The basic models of transportation system and power distribution system are introduced, especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community. The modelling of interdependence across the two systems is highlighted. Taking into account such interdependence, applications ranging from long-term planning to short-term operation are reviewed with emphasis on comparing the description of traffic-power interdependence. Finally, an outlook of prospective directions and key technologies in future research is summarized.fi=vertaisarvioitu|en=peerReviewed

A Metaheuristic Framework for Bi-level Programming Problems with Multi-disciplinary Applications

Bi-level programming problems arise in situations when the decision maker has to take into account the responses of the users to his decisions. Several problems arising in engineering and economics can be cast within the bi-level programming framework. The bi-level programming model is also known as a Stackleberg or leader-follower game in which the leader chooses his variables so as to optimise his objective function, taking into account the response of the follower(s) who separately optimise their own objectives, treating the leader’s decisions as exogenous. In this chapter, we present a unified framework fully consistent with the Stackleberg paradigm of bi-level programming that allows for the integration of meta-heuristic algorithms with traditional gradient based optimisation algorithms for the solution of bi-level programming problems. In particular we employ Differential Evolution as the main meta-heuristic in our proposal.We subsequently apply the proposed method (DEBLP) to a range of problems from many fields such as transportation systems management, parameter estimation and game theory. It is demonstrated that DEBLP is a robust and powerful search heuristic for this class of problems characterised by non smoothness and non convexity

Algorithms for spatially competitive network facility-location

Earlier formulations of models for locating a firm's production facilities while simultaneously determining production levels at these facilities so as to maximize the firm's profit are reviewed. In these formulations, existing firms, as well as new entrants, are assumed to act in accordance with an appropriate model of spatial equilibrium. A heuristic algorithm is developed, and numerical results presented. An exact method is also presented and tested.