Hyunsoo Noh

Integrated Land Use-Transport Model System with Dynamic Time-Dependent Activity-Travel Microsimulation

The development of integrated land use–transport model systems has long been of interest because of the complex interrelationships between land use, transport demand, and network supply. This paper describes the design and prototype implementation of an integrated model system that involves the microsimulation of location choices in the land use domain, activity–travel choices in the travel demand domain, and individual vehicles on networks in the network supply modeling domain. Although many previous applications of integrated transport demand–supply models have relied on a sequential coupling of the models, the system presented in this paper involves a dynamic integration of the activity–travel demand model and the dynamic traffic assignment and simulation model with appropriate feedback to the land use model system. The system has been fully implemented, and initial results of model system runs in a case study test application suggest that the proposed model design provides a robust behavioral framework for simulation of human activity–travel behavior in space, time, and networks. The paper provides a detailed description of the design, together with results from initial test runs.

Approach to Modeling Demand and Supply for a Short-Notice Evacuation

As part of disaster mitigation and evacuation planning, planners must be able to develop effective tactical and operational strategies to manage traffic and transportation needs during an evacuation. One aspect of evacuation planning is the estimation of how many people must be evacuated to provide strategies that are responsive to the number and location of these people. When such estimates are available, it may be possible to implement tactical and operational strategies that closely match the likely demand on the road network during the evacuation. With short notice for an evacuation, people may need to be evacuated directly from current locations. In addition, for some disasters, the spatial extent of the evacuated area may change over time. This problem may be exacerbated by congestion around the evacuated area. An estimation process is proposed for a short-notice evacuation. The method uses on-hand data typically generated through existing travel demand models at many metropolitan planning organizations. It estimates demand using convenient models for trip generation, trip distribution, and travel time generation for these trips, considering a staged evacuation. These demand estimates feed a dynamic simulation model, DynusT, that is used to model the supply characteristics of the roadway network during the evacuation. Such models can be applied using a case study based on a short-notice flooding scenario for Phoenix, Arizona.

Intermodal Path Algorithm for Time-Dependent Auto Network and Scheduled Transit Service

A simple but efficient algorithm is proposed for finding the optimal path in an intermodal urban transportation network. The network is a general transportation network with multiple modes (auto, bus, rail, walk, etc.) divided into the two major categories of private and public, with proper transfer constraints. The goal was to find the optimal path according to the generalized cost, including private-side travel cost, public-side travel cost, and transfer cost. A detailed network model of transfers between modes was used to improve the accounting of travel times during these transfers. The intermodal path algorithm was a sequential application of specific cases of transit and auto shortest paths and resulted in the optimal intermodal path, with the optimal park-and-ride location for transferring from private to public modes. The computational complexity of the algorithm was shown to be a significant improvement over existing algorithms. The algorithm was applied to a real network within a dynamic traffic and transit assignment procedure and integrated with a sequential activity choice model.

Algorithm for Intermodal Optimal Multidestination Tour with Dynamic Travel Times

This paper presents an efficient algorithm that finds the intermodal optimal tour (origin to origin) in a time-dependent transportation network while the algorithm implicitly solves the park-and-ride facility choice problem with the inherent park-and-ride constraints for a traveler with a sequence of destinations to visit. To solve the problem, a network expansion technique that captures the constraints of park-and-ride behavior in the model and that transforms the park-and-ride choice problem into a dynamic network flow problem is introduced. An efficient iterative labeling algorithm that finds the optimal intermodal tour to serve the sequence of activities is also introduced. Multisource shortest-path runs are used in the iterative labeling algorithm to find the optimal tour with several intermediate destinations in an efficient manner. The performance of the algorithm is compared with the performance of existing approaches, and improvement is indicated. The solution method proposed benefits from the advantages of Dijkstras shortest-path algorithm, which is made possible by (a) a nontrivial transformation of the original problem into a dynamic network flow problem and (b) an innovative use of a multisource shortest path in the context of origin–destination choice. The solution algorithm integrates time-dependent auto and transit shortest-path algorithms to find the optimal tour. The algorithm is implemented, coded, and tested on a real network, and the results are promising.

Hyperpaths in Network Based on Transit Schedules

The concept of a hyperpath was introduced for handling passenger strategies in route choice behavior for public transit, especially in a frequency-based transit service environment. This model for handling route choice behavior has been widely used for planning transit services, and hyperpaths are now applied in areas beyond public transit. A hyperpath representing more specific passenger behaviors on a network based on transit schedules is proposed. A link-based time-expanded (LBTE) network for transit schedules is introduced; in the network each link represents a scheduled vehicle trip (or trip segment) with departure time and travel time (or arrival time) between two consecutive stops. The proposed LBTE network reduces the effort to build a network based on transit schedules because the network is expanded with scheduled links. A link-based representation of a hypergraph with existing hyperpath model properties that is directly integrated with the LBTE network is also proposed. Transit passenger behavior was incorporated for transfers in the link-based hyperpath. The efficiency of the proposed hyperpath model was demonstrated. The proposed models were applied on a test network and a real transit network represented by the general specification of Googles transit feed.

Modeling Transit and Intermodal Tours in a Dynamic Multimodal Network

A fixed-point formulation and a simulation-based solution method were developed for modeling intermodal passenger tours in a dynamic transportation network. The model proposed in this paper is a combined model of a dynamic traffic assignment, a schedule-based transit assignment, and a park-and-ride choice model, which assigns intermodal demand (i.e., passengers with drive-to-transit mode) to the optimal park-and-ride station. The proposed model accounts for all segments of passenger tours in the passengers’ daily travel, incorporates the constraint on returning to the same park-and-ride location in a tour, and models individual passengers at a disaggregate level. The model has been applied in an integrated travel demand model in Sacramento, California, and feedback to the activity-based demand model is provided through separate time-dependent skim tables for auto, transit, and intermodal trips.