Multi-state Supernetworks Research Articles

A Micro-simulation Model of Updating Expected Travel Time in Provision of Travel Information: A Bayesian Belief Approach Implemented in a Multi-state Supernetwork

This study introduces a model of individual belief updating of subjective travel times as a function of the provision of different types of travel information. Travel information includes real-time prescriptive or descriptive, and public or personal information. The model is embedded in a start-of-the art multi-state supernetwork representation of individual daily activity-travel scheduling behavior. The belief updating process of subjective travel times under information provision is based on Bayes’ Theorem. The multi-state supernetwork predicts daily activity travel choices based on the minimization of generalized costs related to the full activity-travel pattern. These generalized costs are based on expected travel times across the network. Thus, the simulation model will capture changes in activity-travel scheduling decisions that are made by individuals after updated their beliefs about expected travel times when receiving new travel information.

Incorporating space–time constraints and activity-travel time profiles in a multi-state supernetwork approach to individual activity-travel scheduling

Activity-travel scheduling is at the core of many activity-based models that predict short-term effects of travel information systems and travel demand management. Multi-state supernetworks have been advanced to represent in an integral fashion the multi-dimensional nature of activity-travel scheduling processes. To date, however, the treatment of time in the supernetworks has been rather limited. This paper attempts to (i) dramatically improve the temporal dimension in multi-state supernetworks by embedding space–time constraints into location selection models, not only operating between consecutive pairs of locations, but also at the overall schedule at large, and (ii) systematically incorporate time in the disutility profiles of activity participation and parking. These two improvements make the multi-state supernetworks fully time-dependent, allowing modeling choice of mode, route, parking and activity locations in a unified and time-dependent manner and more accurately capturing interdependences of the activity-travel trip chaining. To account for this generalized representation, refined behavioral assumptions and dominance relationships are proposed based on an earlier proposed bicriteria label-correcting algorithm to find the optimal activity-travel pattern. Examples are shown to demonstrate the feasibility of this new approach and its potential applicability to large scale agent-based simulation systems.

Multi-state supernetwork framework for the two-person joint travel problem

Most travel behavior studies on route and mode choice focus only on an individual level. This paper adopts the concept of multi-state supernetworks to model the two-person joint travel problem (JTP). Travel is differentiated in terms of activity-vehicle-joint states, i.e. travel separately or jointly with which transport mode and with which activities conducted. In each state, route choice can be addressed given the state information and travel preference parameters. The joint travel pattern space is represented as a multi-state supernetwork, which is constructed by assigning the individual and joint networks to all possible states and connecting them via transfer links at joints where individuals can meet or depart. Besides route choice, the choices of where and when to meet, and which transport mode(s) to use can all be explicitly represented in a consistent fashion. A joint path through the supernetwork corresponds to a specific joint travel pattern. Then, JTP is reduced to an optimization problem to find the joint path with the minimum disutility. Three standard shortest path algorithm variants are proposed to find the optimal under different scenarios. The proposed framework further indicates the feasibility of multi-state supernetworks for addressing high dimensional problems and contributes to the design of a next generation of joint routing systems.

Constructing personalized transportation networks in multi-state supernetworks: a heuristic approach

An integrated view encompassing the networks for public and private transport modes as well as the activity programs of travelers is essential for accessibility analysis. In earlier research, the multi-state supernetwork has been put forward by the authors as a suitable technique to model the system in such an integrated fashion. An essential part of a supernetwork involving multi-modal and multi-activity is the personalized transportation network, which is an under-researched topic in the academic community. This article attempts to develop a heuristic approach to construct personalized transportation networks for an individual's activity program. In this approach, the personalized network consists of two types of network extractions from the original transportation system: public transport network and private vehicle network. Three examples are presented to illustrate that the public transport network and private vehicle network can represent an individual's attributes and be applied in large-scale applications for analyzing the synchronization of land-use and transportation systems.

Multistate supernetwork approach to modelling multi-activity, multimodal trip chains

Least-costs path finding methods belong to the standard toolkit of geographical information science and allow one to assign trips with known origin and destination to a transport network. An extension of the network representation is proposed that allows one to model complete trip chains (tours) that may involve multiple transport modes and multiple activities, as a least-cost path through the network. The extension is based on the notion that link costs may change as a consequence of activity and vehicle choices during the tour. The extended network or supernetwork contains as many copies of the network as there are possible activity–vehicle states and additional state transition links to interconnect them. In this way, not only the generalized link cost function, but also the start and end points of a path can be defined state dependently. A least-cost path through a multistate network defines an optimal sequence of travelling, transferring, parking, conducting activities and dropping off products of activities. An application shows how a multistate network can be constructed and used to assess the impacts of network design variables on path choice.