Joseph Y.J. Chow

Activity-Based Travel Scenario Analysis with Routing Problem Reoptimization

Household travel behavior is a complex modeling challenge because of the difficulty in handling daily routing and scheduling choices that individuals make with respect to activity and time use decisions. Activity-based travel scenario analysis and network design using a household activity pattern problem (HAPP) can face significant computational cost and inefficiency. Reoptimization makes use of an optimal solution of a prior problem instance to find a new solution faster and more accurately. Although the method is generally NP-hard as well, the approximation bound has been shown in the literature to be tighter than a full optimization for several traveling salesman problem variations. To date, however, there have not been any computational studies conducted with the method for scenario analysis with generalized vehicle routing problems, nor has there been any metaheuristics designed with reoptimization in mind. A generalized, selective household activity routing problem (G-SHARP) is presented as an extension of the HAPP model to include both destination and schedule choice for the purpose of testing reoptimization. The article proposes two reoptimization algorithms: (1) a simple swap heuristic, and (2) a new class of evolutionary algorithms designed for reoptimization, called a Genetic Algorithm with Mitochondrial Eve (GAME). The two algorithms are tested against a standard genetic algorithm in a computational experiment involving 100 zones that include 400 potential activities (resulting in a total of 802 nodes per single-traveler household). Five hundred households are synthesized and computationally tested with two base scenarios. One scenario where an office land use in one zone is dezoned and another scenario where a freeway is added onto the physical network. GAME and the capability of G-SHARP demonstrate the effectiveness of reoptimization to capture reallocations.

Real Option Pricing of Network Design Investments

A real option solution and method are used to quantify the value of flexibility for deferral and design strategies in investments made in a network. The model is proposed to address managerial flexibility in transportation planning and can be applied to any network design problem under uncertainty. The core framework is based on the assumption that some variable such as travel demand can be characterized by nonstationary, multi-dimensional stochastic processes, such as geometric Brownian motion. By exploiting that characterization, it is possible to determine the value of the flexibility to defer the investment or to redesign the network by solving a dynamic program with network design subproblem using a least squares Monte Carlo simulation algorithm. The option premium is shown to decompose into a basic deferral premium and a flexible network design premium, which quantifies the cost of committing to preferred alternative in transportation planning. The proposed model is tested on the classic Sioux Falls, South Dakota, network, showing that a traditional discounted cash-flow analysis may support an immediate investment of a continuous network design but that under some conditions, deferral with the option to redesign can offer the greatest option value.

A multi-day activity-based inventory routing model with space–time–needs constraints

We extend activity routing problems to consider ‘needs’ satisfaction over multiple days using an inventory routing problem concept. The resulting inventory-based selective household activity routing problem allows activity type choice, duration choice, activity destination choice, departure time, and scheduling of activities, all within space–time–needs constraints. A Lagrangian relaxation-based algorithm is proposed to solve the model for multiple days. A computational study is conducted. The model can measure several effects: heterogeneity in impacts of travel time changes on different days; the use of dual price as a threshold when needs play a role in the utility maximisation objectives; the possibility for activity participation consolidation due to increased travel time; and non-uniform impacts of travel time increase on utilities across a heterogeneous population. Comparison of the algorithms performance against a commercial package showed average objective values that differed by only 1.8% or less.

Generalized profitable tour problems for online activity routing system

A next-generation system for online route guidance and activity recommendations was studied to support decisions that considered multiple activity itineraries whose utilities account for their spatial proximities for a user. For the solution of the underlying problem, the problem of the profitable tour and the problem of the prize-collecting traveling salesman were extended to generalized cases with the expansion of single nodes to clusters to handle various activity types. The generalized formulations addressed several uses, including routing with refueling, the pub crawl problem, and the romantic date problem. Test cases compared an insertion heuristic and a multisolution genetic algorithm with exact solutions. Both algorithms worked well even with the constraints of time windows and with the fast computational times that are necessary for online decision support. The multisolution genetic algorithm tended to be slower than the insertion heuristic was, but the multisolution algorithm could handle a wider variety of problems and could provide a set of solutions from which a user could browse to account for unobserved preferences.

Resource Location and Relocation Models with Rolling Horizon Forecasting for Wildland Fire Planning

Abstract A location and relocation model are proposed for air tanker initial attack basing in California for regional wildland fires that require multiple air tankers that may be co-located at the same air base. The Burning Index from the National Fire Danger Rating System is modeled as a discrete mean-reverting process and estimated from 2001–2006 data for select weather stations at each of 12 California Department of Forestrys units being studied. The standard p-median formulation is changed into a k-server p-median problem to assign multiple servers to a node. Furthermore, this static problem is extended into the time dimension to obtain a chance-constrained dynamic relocation problem. Both problems are solved using branch and bound in the numerical example. The relocation model is shown to perform better than the static location model by as much as 20–30% when using fire weather data to forecast short term future demand for severe fires, whereas relocating without rolling horizon forecasting can be less cost-effective than a static location model. The results suggest that state fire agencies should identify the threshold beyond which it would be more cost-effective to adopt a regional relocation model with forecasting from fire weather data, especially in a global warming environment.

Agent-based day-to-day adjustment process to evaluate dynamic flexible transport service policies

ABSTRACT Advances in information and communications technologies, connected vehicle technologies, and Big Data have made it viable for public agencies to offer efficient flexible transit services for travel demand that is predominantly dynamic to the system. There is a clear gap in methodologies to evaluate the user equilibrium for flexible transport services (FTS). In this study we lay the groundwork for studying the equilibrium of these systems and propose an agent-based adjustment process to evaluate the properties of a stable state as an agent-based stochastic user equilibrium. To validate the proposed process and illustrate its effectiveness in measuring the effect of changes in FTS operating parameters on ridership three sets of experiments are conducted: (1) illustration with a simple 2-link network, (2) evaluation of a dynamic dial-a-ride problem, and (3) illustration using real data from Oakville, Ontario consisting of 57 zones and 2000 commuters.

Multi-Criteria Sustainability Assessment in Transport Planning for Recreational Travel

ABSTRACT A transport planning framework is considered that incorporates a multi-criteria, composite sustainability index (CSI) with elastic decision-maker preferences, and applied to a case study of an outdoor recreational destination. A stated preference survey is conducted on transit alternatives to access the United States. Mojave National Preserve from Barstow, California, located 160 kilometers away. A binary logit model is developed to relate policy variables to sustainability dimensions. A revised CSI is applied to evaluate eight alternatives under three decision-making schemes. Findings suggest that a zero-emissions train service with two round trips per day is preferred over the other alternatives under all three schemes.

Inverse vehicle routing for activity-based urban freight forecast modeling and city logistics

ABSTRACT Goods movement is one of the fastest growing transportation sectors, affecting both economic and environmental sustainability, particularly in dense urban areas with traffic congestion and air pollution. To meet this challenge, urban public agencies have paid attention to policies and systems to facilitate efficient and sustainable city logistics. This paper proposes a modeling framework to consider both spatial–temporal constraints and a means to calibrate the model from observable data, based on an adaptation of an activity-based passenger model called the household activity pattern problem. Conceptual comparisons with a state-of-the-art freight forecasting methodology are made using an example. Application of the model is illustrated through formulating and implementing a Sequential Selective Vehicle Routing Problem associated with drayage truck activities at the San Pedro Bay Ports in Southern California.

Genetic Algorithm to Estimate Cumulative Prospect Theory Parameters for Selection of High-Occupancy-Vehicle Lane

Recent literature suggests a need for a more realistic representation of driver behavior. In an effort to integrate prospect theory as a potential descriptive method into traveler behavior, it is important to determine the validity of previous estimated parameters obtained from empirical studies and interviews of people who are not in the same fast-paced, dynamic, travel time disutility setting as real drivers. A genetic algorithm is used to simultaneously estimate the parameters of the cumulated prospect theory (CPT) value and weight functions as well as the coefficients of the random utility model; this procedure leads to estimates that have a higher likelihood value and statistical significance than an equivalent expected utility–based logit model or a CPT-based logit model using the empirical values developed earlier. The value function parameters generally conform to conclusions from previous literature. The weight function parameters, however, suggest that drivers in a fast-paced changing environment with multiple subjects for prospect evaluation may become overwhelmed by the certainty effect.

Online Data Repository for Statewide Freight Planning and Analysis

Freight transportation has a multifaceted impact on the economy, and the importance of understanding freight demand is increasing. There is a significant need to access a wide array of data sources for freight modeling and analysis. However, current data sources are not always easily accessible even with the availability of the Internet. Among the reasons are differing user interfaces, unavailability of data type definition, data format incompatibility, and inability to assess the scope of data conveniently. The repository developed in this study, the California Freight Data Repository, is a user-centered online tool designed from a systems perspective with several objectives. First, it facilitates convenient access, standardized interface, and a centralized location for obtaining freight data. Data dictionaries and lookup tables are provided for each data source to allow users to understand the scope of the data source and to give a clear definition of terms found in the data. A quality assessment summary is also provided to inform users of the strengths and limitations associated with each data source. Second, the repository is equipped with several geographic information system–based visualization tools intended to allow users to perform preliminary evaluation of data to determine their suitability for specific modeling or analysis needs. Third, the repository is designed with a customized search engine to retrieve web resources specifically associated with freight modeling and analysis. This paper presents the metadata architecture used for identifying data sources, the assessment framework used to evaluate selected data sources, and the system and interface design of the California Freight Data Repository. Several use cases are presented to demonstrate the applicability of this resource.