Feixiong Liao

Supernetwork Approach for Multimodal and Multiactivity Travel Planning

Multimodal and multiactivity travel planning is a practical but thorny problem in transportation research. This paper develops an improved supernetwork model to address this problem. The supernetwork is constructed mainly in three steps: a personalized network is first split into two types of networks with all links mode-specified; these are then assigned to all possible activity-vehicle states by means of state spreading from the beginning activity state. Finally, these discrete networks are connected into a supernetwork by state-labeled transition links. The proposed supernetwork is easier to construct than previous proposals and reduces the size needed to embody all combinations of choice facets explicitly. It can be proved for any activity program that any tour is a feasible solution in this representation. Consequently, every transport and transition link can be defined mode and activity-state dependent; thus standard shortest path algorithms can be used to find the most desirable tour. A case study is presented to show that the supernetwork model can be applied in a real-time manner for practical travel planning.

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 individuals 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 individuals attributes and be applied in large-scale applications for analyzing the synchronization of land-use and transportation systems.

Dynamic activity-travel assignment in multi-state supernetworks

The integration of activity-based modeling and dynamic traffic assignment for travel demand analysis has recently attracted ever-increasing attention. However, related studies have limitations either on the integration structure or the number of choice facets being captured. This paper proposes a formulation of dynamic activity-travel assignment (DATA) in the framework of multi-state supernetworks, in which any path through a personalized supernetwork represents a particular activity-travel pattern (ATP) at a high level of spatial and temporal detail. DATA is formulated as a discrete-time dynamic user equilibrium (DUE) problem, which is reformulated as an equivalent variational inequality (VI) problem. A generalized dynamic link disutility function is established with the accommodation of different characteristics of the links in the supernetworks. Flow constraints and non-uniqueness of equilibria are also investigated. In the proposed formulation, the choices of departure time, route, mode, activity sequence, activity and parking location are all unified into one time-dependent ATP choice. As a result, the interdependences among all these choice facets can be readily captured. A solution algorithm based on the route-swapping mechanism is adopted to find the user equilibrium. A numerical example with simulated scenarios is provided to demonstrate the advantages of the proposed approach. 2015 Elsevier Ltd. All rights reserved.

Supernetwork Approach for Modeling Traveler Response to Park-and-Ride

Park-and-ride has been identified by transport planners as a key element of any sustainability package to promote multimodal trips, improve air quality, and alleviate congestion in urban areas. This paper presents a supernetwork approach that can assess traveler response to park-and-ride in an integrated fashion. The supernetwork is constructed to include all the choices of each travelers activity program in terms of individual preferences and, thus, is capable of representing the travelers action space. The choices of park-and-ride facilities are embedded into the full activity and trip chains. Within this framework, the trade-off between the use of private vehicles and public transport and the trade-off between car-and-ride and bike-and-ride can both be captured. In addition, sensitivity analysis of the design of services or facilities is possible. A series of scenario studies is presented to demonstrate that the proposed supernetwork approach can be applied as a systemic analytical tool to examine traveler response to park-and-ride at a high level of detail.

Incorporating activity-travel time uncertainty and stochastic space–time prisms in multistate supernetworks for activity-travel scheduling

Multistate supernetwork approach has been advanced recently to study multimodal, multi-activity travel behavior. The approach allows simultaneously modeling multiple choice facets pertaining to activity-travel scheduling behavior, subject to space–time constraints, in the context of full daily activity-travel patterns. In that sense, multistate supernetworks offer an alternative to constraints-based time-geographic activity-based models. To date, most research on time-geographic models and supernetworks alike has represented time and space in a deterministic fashion. To enhance the validity and realism of the scheduling process and the underlying space–time decisions, this paper pioneers incorporating time uncertainty in multistate supernetworks for activity-travel scheduling. Solutions based on the concept of the -shortest path are proposed to find the reliable activity-travel pattern with confidence level. An algorithm combining label correcting and Monte-Carlo integration is proposed to finding the-shortest paths in the presence of time window constraints. An example of a typical daily activity program is executed to demonstrate the applicability of the proposed extension.

Modeling Context-Sensitive, Dynamic Activity Travel Behavior by Linking Short- and Long-Term Responses to Accumulated Stress: Results of Numerical Simulations

As existing activity-based models of travel demand simulate activity travel patterns for a typical day, dynamic models simulate behavioral response to endogenous or exogenous change along various time horizons. Prior research predominantly addressed a specific kind of change, which usually affected a specific time horizon. In contrast, the current study aims to develop a dynamic model of activity travel decisions that links short- and long-term adaptation decisions in a hierarchical manner. Specifically, this study focuses on the bottom-up process of influence, in which problems with rescheduling on a daily basis may induce a long-term change. The authors assume that travelers will first explore short-term adjustments of their habitual activity travel patterns so as to cope with change and increasing stress. Only when travelers recognize that such adaptation strategies are ineffective will they consider long-term decisions. The proposed framework integrates three key concepts: aspiration, activation, and expected utility. Moreover, both rational and emotional mechanisms are taken into account. The study demonstrates model properties by using numerical simulation. Individual travelers are represented as agents, each with their cognition of the environment, habits, preferences, and aspirations. The results offer insight into the dynamics of traveler learning–adaptation and into the evolution of long-term decisions.

A user equilibrium model for combined activity–travel choice under prospect theoretical mechanisms of decision-making under uncertainty

ABSTRACT Rather than considering single trips as the unit of analysis, the activity-based modeling paradigm of travel demand analysis has led to reconceptualizations and innovations in traffic flow models by focusing on complete daily activity–travel patterns. The vast majority of these travel demand and traffic flow models have either implicitly or explicitly assumed that travelers choose between alternatives by maximizing their utility under a deterministic representation of the choice alternatives. While this behavioral assumption leads to tractable, easy-to-apply models, the validity of the assumption largely went untested. This paper investigates the user equilibrium of activity–travel patterns under uncertainty from the perspective of prospect theory. A formulation of the static activity-based user equilibrium model is proposed. In particular, we adopt the concept of a multi-state supernetwork to represent the choice space of activity–travel patterns. A numerical example using hypothetical scenarios is presented to illustrate the proposed model and solution algorithm.

Multi-state supernetworks： recent progress and prospects

Abstract: Supernetworks have long been adopted to address multi-dimensional choice problems, which are thorny to solve for classic singular networks. Originated from combining transport mode and route choice into a multi-modal network, supernetworks have been extended into multi-state networks to include activity-travel scheduling, centered around activity-based models of travel demand. A key feature of the network extensions is that multiple choice facets pertaining to conducting a full activity program can be modeled in a consistent and integrative fashion. Thus, interdependencies and constraints between related choice facets can be readily captured. Given this advantage of integrity, the modeling of supernetwork has become an emerging topic in transportation research. This paper summarizes the recent progress in modeling multi-state supernetworks and discusses future prospects.

Dynamic activity-travel assignment in multi-state supernetworks under transport and location capacity constraints

ABSTRACT A unified framework coupling activity-based modelling and dynamic traffic assignment has recently been proposed. It formulates dynamic activity-travel assignment (DATA) in multi-state supernetworks as a dynamic user equilibrium. Choices of departure time, route, mode, activity sequence, and activity/parking location are determined endogenously, reflected in time-dependent activity-travel patterns (ATPs). However, capacity constraints associated with activities, parking, and public transit vehicles have not taken into account. This paper extends this approach by formulating these constraints and incorporating them into the DATA framework. Three numerical examples are presented to illustrate the effectiveness of the approach. It is shown that capacity constraints have significant effects on the choice facets.

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.