Behavioral Analysis of Choice of Daily Route with Data from Global Positioning System

Spissu, Erika; Meloni, Italo; Sanjust, Benedetta

doi:10.3141/2230-11

Cited by 19 publications

(12 citation statements)

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“…However, the exploration of observed route choice behaviour is crucial because the underlying decision-making process is more complex and dynamic for route choice than for other travel choice dimensions. The difficulties associated with the collection of data on route choice are reflected in the scarcity of studies on observed behaviour and the major simplifications made in traffic assignment models developed for the most common commercial software (Spissu et al, 2011).…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2013

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The aim of this research is to identify the relationship between activity patterns and route choice decisions. The focus is twofold: on the one hand, the relationship between the purpose of a trip and the road categories used for the relocation is investigated; on the other hand, the relationship between the purpose of a trip and the deviation from the shortest path is studied. The data for this study were collected in 2006 and 2007 in Flanders, the Dutch speaking and northern part of Belgium. To estimate the relationship between the primary road category travelled on and the corresponding activity-travel behaviour a multinomial logit model is developed. To estimate the relationship between the deviation from the shortest path and the corresponding activity-travel behaviour a Tobit model is developed. The results of the first model point out that route choice is a function of multiple factors, not just travel time or distance. Crucial for modelling route choices or in general for traffic assignment procedures is the conclusion that activity patterns have a clear influence on the road category primarily driven on. Particularly, it was shown that the likelihood of taking primarily through roads is highest for work trips and lowest for leisure trips. The second model shows a significant relationship between the deviation from the shortest path and the purpose of the trip. Furthermore, next to trip-related attributes (trip distance), also socio-demographic variables and geographical differences play an important role. These results certainly suggest that traffic assignment procedures should be developed that explicitly take into account an activity-based segmentation. In addition, it was shown that route choices were similar during peak and off-peak periods. This is an indication that car drivers are not necessarily utility maximizers, or that classical utility functions in the context of route choices are omitting important explanatory variables.

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Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2013

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“…In this study we focused the attention on a particular aspect of route choice behavior, known as route-switching. It consists on the fact that users, moving between the same OD pair, do not always use the same route, but change, depending on the level of satisfaction of several elements that are not directly known to the researcher (Abdel-Aty, et al, 1994;Li, et al, 2005;Zhu and Levinson, 2010;Spissu, et al, 2011). Several studies have shown the particular behavior in relation to the choice of different routes for the same OD trip.…”

Section: Introductionmentioning

confidence: 96%

“…Zhu and Levinson (2009) report 67.5% route changers (GPS data for 35 morning commuters and 351 trips in the Twin Cities area), while Levinson e Zhu (2013) observed about 40% (GPS survey, 95 commuters and 657 home to work trips, Twin Cities area). Spissu et al (2011), based on a GPS survey in the metropolitan area of Cagliari (Italy, all purpose, 12 users and 293 trips) observed 7% route changers, while Arifin and Axhausen (2011) found that 34.5% of users change routes (Jakarta, 93 users, 601 trips, 212 of which by car and 195 by bike, collected with GPS). Thus, clearly there is substantial variability which depends on both the context in which data are collected and on trip characteristics, as well as on the users themselves.…”

Section: Introductionmentioning

confidence: 97%

“…Technological progress, combined with the rapid advances in GPS devices, has resulted in major benefits for data collection, which now can be recorded automatically, in electronic format and, more importantly, with greater accuracy than those collected by means of individual interviews (Murakami and Wagner, 1999;Nakazato, et al, 2006;Hato, et al, 2006;Bricka, et al, 2009;Barbeau, et al, 2009). GPS has also been used to improve the knowledge of the attributes governing route choice (Jan, et al, 2000;Li, et al, 2005;Parkany, et al, 2006;Papinski, et al, 2009;Zhu and Levinson, 2009;Papinski and Scott, 2010;Zhu and Levinson, 2010;Spissu, et al, 2011;Levinson and Zhu, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Understanding Route Switch Behavior: An Analysis Using GPS Based Data

Vacca

Meloni

2015

Transportation Research Procedia

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The objective of this paper is to study route switch behavior to detect which trip and individual characteristics most influence the choice of multiple routes for the same origin-destination (OD) trip. In this study we used a database of 361 morning commute trips, regarding 66 users, collected in the metropolitan area of Cagliari (Italy) during the "Casteddu Mobility Styles" survey. Data were collected for a 14 days period through a personal probe system called Activity Locator , a smartphone that integrates a GPS logger for the acquisition of the routes and an activity/travel diary. Mixed logit models are estimated, in order to take into account the variability of user perception. Results show that route switch behavior is influenced by the number of traffic lights per km, percent of highways, time perception, gender, age, individual income and driving experience in relation with the minutes per km.

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“…From the research of Li et al (2013), route choice behavior changes relating to the familiarity to OD pairs while Hoogendoorn-Lanser et al (2005) focused on overlap in multimodal transport networks using the path size modeling. Knorring et al (2005) had an empirical analysis of long-haul truck drivers, with the result showing that time is a significant factor in the decision-making process and Spissu et al (2011) proved that higher levels of intra-individual variability were found for discretionary trips, whereas higher levels of inter-individual variability, as well as greater deviation from minimum-cost routes, are associated with work or study trips. Most of the above studies focused on the algorithm to generate alternative routes and to select the optimal one, and drivers' route preference is only considered in the selection step, but not in the generation.…”

Section: Introductionmentioning

confidence: 97%

Urban travel behavior analyses and route prediction based on floating car data

Sun

Zhang

et al. 2014

Transportation Letters

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The lack of sufficient data is the result of the inherent complexity of gathering and subsequently analyzing route choice behavior, which unfortunately hasn't been revealed much by existing literatures. With the assistance of GIS technology and taxi-based floating car data, the authors found that the majority of urban drivers would not travel along the shortest or the fastest paths. This paper studies the factors that influence commuters' route choice and route switching based on objective real-world observations of travel behavior. Possible factors that may affect driver's route choice are then analyzed and regression methods were introduced to attain if there existing a clear quantitative relationship between drivers' route choice and these factors. The result indicates that such connection is difficult to be established. Consequently, eight scenarios were proposed to quantify the influence of various potential factors. Analysis shows that travel distance, travel time and road preference have comparable higher influence on drivers' route choice. To this end, a new route prediction model is proposed, adopting the road usage as the weight and the shortest route's length and the fastest route's time as the constraints. The proposed model was implemented and validated using the FCD data of Shenzhen, China. The results indicate that by combining the external influence with the driver's personal preference, the predicted travel route has a higher matching ratio with the actual one, which consequently indicates the effectiveness of the model.

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Behavioral Analysis of Choice of Daily Route with Data from Global Positioning System

Cited by 19 publications

References 19 publications

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

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

Understanding Route Switch Behavior: An Analysis Using GPS Based Data

Urban travel behavior analyses and route prediction based on floating car data

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