Toward Conceptualizing Trip-Chaining Behavior: A Review

This paper critically reviews the existing literature on multiple-trip journeys, and clarifies the need for an improved conceptualization and modeling strategy. It also suggests different ways that trip chaining can be treated and a theory constructed. Exploratory studies, from the 1960s and 1970s, are briefly surveyed (Section I). While they developed the existence and complexity of travel patterns, they lacked any comprehensive theoretical framework. However, they were useful in that they supported the conceptual framework of various kinds of stochastic processes used by transportation scientists to replicate trip-chaining behavior (Section II). Scientists subsequently improved their conceptualization of the issue by grounding individual behavior on the principle of utility maximization. In this latter area the economists have emphasized theoretical concerns while transportation researchers have emphasized operationality (Section III).     

Local Indicators of Network-Constrained Clusters in Spatial Point Patterns

The detection of clustering in a spatial phenomenon of interest is an important issue in spatial pattern analysis. While traditional methods mostly rely on the planar space assumption, many spatial phenomena defy the logic of this assumption. For instance, certain spatial phenomena related to human activities are inherently constrained by a transportation network because of our strong dependence on the transportation system. This article thus introduces an exploratory spatial data analysis method named l ocal i ndicators of n etwork-constrained c luster s (LINCS), for detecting local-scale clustering in a spatial phenomenon that is constrained by a network space. The LINCS method presented here applies to a set of point events distributed over the network space. It is based on the network K -function, which is designed to determine whether an event distribution has a significant clustering tendency with respect to the network space. First, an incremental K -function is developed so as to identify cluster size more explicitly than the original K -function does. Second, to enable identification of cluster locations, a local K -function is derived by decomposing and modifying the original network K -function. The local K -function LINCS, which is referred to as KLINCS, is tested on the distribution of 1997 highway vehicle crashes in the Buffalo, NY area. Also discussed is an adjustment of the KLINCS method for the nonuniformity of the population at risk over the network. As traffic volume can be seen as a surrogate of the population exposed to a risk of vehicle crashes, the spatial distribution of vehicle crashes is examined in relation to that of traffic volumes on the network. The results of the KLINCS analysis are validated through a comparison with priority investigation locations (PILs) designated by the New York State Department of Transportation.     

Ideology versus economy in culture change

John W. Cole and Eric R. Wolf. The Hidden Frontier: Ecology and Ethnicity in an Alpine Valley: Studies in Social Discontinuity. New York: Academic Press, 1974. xiv + 348 pp. Tables, figures, illustrations, appendixes, bibliography, and index. $12.50.     

Travel-Time Constraints on Destination-Choice Sets

The importance of travel-time constraints in spatial choice is widely recognized in the literature of geography and related disciplines, but little work has been done toward developing operational models of spatial choice wherein these constraints and their effects are made explicit. The purpose of the paper is to test the accuracy of predictions produced by a destination choice model that does not take explicit account of travel constraints under the assumption that observed choices are made from choice sets delineated by a constraint of maximum travel time. Observed choices are generated by simulation from a new random utility model consistent with the constrained nature of individual choice sets. Results show that the characteristics of constraints are a decisive factor in the accuracy of the unconstrained choice model. Choice probabilities of the constrained reality are predicted with a reasonably good accuracy in some instances, but predictions are less impressive, and even poor, in many others.