Using Global Positioning System Data to Understand Variations in Path Choice

Abstract

A comprehensive set of Global Positioning System (GPS) vehicle location data from Lexington, Kentucky, households was analyzed to determine if such data can be helpful in improving path choice assumptions in traffic assignment models. The portion of the data used consisted primarily of a reconstruction of the street network and the lists of street segments in each path. Analysis was based on matches of trips (e.g., pairs of trips with similar origins and destinations). Matches were obtained for trips within households and for trips across households. Statistics used to compare trips in matches were a path deviation index and the percentage of identical links. It was found that the path chosen on a trip was quite sensitive to the location of the origin and destination and that the chosen path most often differed considerably from the shortest time path across the network. Paths for trips made by the same driver were very consistent over time; paths by different drivers showed more deviations even when the trip ends were the same or very similar. As a result of this research, recommendations are made as to how GPS data on path choice can be better collected in the future for improvement of traffic assignment models.

Modelling Route Choice Decisions of Car Travellers Using Combined GPS and Diary Data

“…In a study of Papinski et al (2009), route directness is indicated as the second most important factor when choosing a route. Jan et al (2000) state that in most instances the chosen path differs considerably from the shortest path across the network. According to Zhang and Levinson (2008) efficiency-related attributes such as travel time, distance and number of stops are considered more important for commute, event and visit trips and less important or even insignificant for shopping and recreational trips.…”

“…This combination of GPS and diary responses provides great insight into the route choice decision-making process (Papinski et al, 2009). Jan et al (2000) showed that GPS is a viable tool to study travellers' route choice decisions as GPS can reveal important travel behavioural information that is impossible to discern with earlier conventional survey methods such as interviews, respondent-administered questionnaires, or driver simulators. Moreover, conventional methods have proved burdensome, time consuming, and error prone (Wolf et al, 1999).…”

“…Efficiencyrelated attributes (time, distance, number of stops) are important for commute, event and visit trips and insignificant for shopping and recreational trips Goldenbeld et al, 2007 'Shortest path' is one of the main reasons for choosing a route (in a study in the Netherlands) and is more important for route choice of work trips than for other purposes Levinson and El-Geneidy, 2009 Workers are more likely to commute with lower circuity Huang and Levinson, 2012 Non-work non-home destinations that are farther away, and discontinuous or more circuitous routes are less attractive Papinski et al, 2009 Route directness as second most important factor for route choice Jan et al, 2000 Mostly, the chosen path differs considerably from shortest path Bekhor et al, 2006 37% of respondents follow shortest time path, 22% follow shortest distance path Prato and Bekhor, 2006 43.3% of respondents follow shortest time path, 53.5% follow shortest distance path…”

“…Furthermore, trip-related attributes are considered. In literature these attributes are often pinpointed as predominant variables including: trip purpose (de Palma and Picard, 2005), trip distances (Scheiner, 2010) and congestion (Jan et al, 2000). Five types of trip purposes are distinguished: work, leisure, shopping, home and other.…”

“…An important limitation in both traditional four-step and present-day activity-based models is the fact that current route choice models have been developed largely in the absence of objective empirical evidence of actual route choices. Theories of utility maximization have proven useful (Ramadurai and Ukkusuri, 2010), but the underlying behaviour realism of these theories, when applied to modelling route choices, has not been extensively validated by empirical studies (Jan et al, 2000). After all, confronted with a multitude of route choice facets, travellers may not be able to make optimal decisions, especially when the deliberation process of various possible routes involves the anticipation of congestion (Han et al, 2008).…”