Where do cyclists ride? A route choice model developed with revealed preference GPS data

2012 ◽  
Vol 46 (10) ◽  
pp. 1730-1740 ◽  
Author(s):  
Joseph Broach ◽  
Jennifer Dill ◽  
John Gliebe
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shin-Hyung Cho ◽  
Seung-Young Kho

Modelling route choice behaviours are essential in traffic operation and transportation planning. Many studies have focused on route choice behaviour using the stochastic model, and they have tried to construct the heterogeneous route choice model with various types of data. This study aims to develop the route choice model incorporating travellers’ heterogeneity according to the stochastic route choice set. The model is evaluated from the empirical travel data based on a radio frequency identification device (RFID) called dedicated short-range communication (DSRC). The reliability level is defined to explore the travellers’ heterogeneity in the choice set generation model. The heterogeneous K-reliable shortest path- (HK α RSP-) based route choice model is established to incorporate travellers’ heterogeneity in route choice behaviour. The model parameters are estimated for the mixed path-size correction logit (MPSCL) model, considering the overlapping paths and the heterogeneous behaviour in the route choice model. The different behaviours concerning the chosen routes are analysed to interpret the route choice behaviour from revealed preference data by comparing the different coefficients’ magnitude. There are model validation processes to confirm the prediction accuracy according to travel distance. This study discusses the policy implication to introduce the traveller specified route travel guidance system.


Author(s):  
Danique Ton ◽  
Oded Cats ◽  
Dorine Duives ◽  
Serge Hoogendoorn

Nowadays, the bicycle is seen as a sustainable and healthy substitute for the car in urban environments. The Netherlands is the leading country in bicycle use, especially in urban environments. Yet route choice models featuring inner-city travel that includes cyclists are lacking. This study estimated a cyclists’ route choice model for the inner city of Amsterdam, Netherlands, on the basis of 3,045 trips collected with GPS data. The main contribution of this study was the construction of the choice set with an empirical approach, which used only the observed trips in the data set to compose the choice alternatives. The findings suggested that cyclists were insensitive to separate cycle paths in Amsterdam, a city characterized by a dense cycle path network in which cycling was the most prominent mode of travel. In addition, cyclists were found to minimize travel distance and the number of intersections per kilometer. The impact of distance on route choice increased during the morning peak when schedule constraints were more prevalent. Furthermore, overlapping routes were more likely to be chosen by cyclists, everything else being the same.


Author(s):  
Akimasa Fujiwara ◽  
Junyi Zhang

Focusing on car tourists’ 1-day tours, a new scheduling model combines a nested paired combinatorial logit (NPCL) type of destination and route choice model and a time allocation (TA) model. The NPCL model, developed previously from the generalized extreme value family of discrete choice models to represent the similarity between pairs of alternatives in the same choice nest as well as the influence of inclusive value, indicates destination choice in the bottom level and route choice in the top level. The TA model applies Becker's theory to determine the time allocated to each touring site. Utility of destination choice is influenced by the time spent at each site. Different route choices result in a level of service for the road network that varies hourly, varying available time used in the TA model. The TA model endogenously incorporates the influence of hourly variance in level of service at the site of interest, which is affected by the allocated time. An iteration estimation procedure is proposed to estimate the parameters consistently in both models. Finally, revealed preference tourist travel survey data collected in a tourist attraction region near the Sea of Japan indicate that the proposed scheduling model is effective in representing car tourists’ scheduling behavior for 1-day tours.


2021 ◽  
Author(s):  
Aliaksandr Malokin ◽  
Giovanni Circella ◽  
Patricia L. Mokhtarian

AbstractMillennials, the demographic cohort born in the last two decades of the twentieth century, are reported to adopt information and communication technologies (ICTs) in their everyday lives, including travel, to a greater extent than older generations. As ICT-driven travel-based multitasking influences travelers’ experience and satisfaction in various ways, millennials are expected to be affected at a greater scale. Still, to our knowledge, no previous studies have specifically focused on the impact of travel multitasking on travel behavior and the value of travel time (VOTT) of young adults. To address this gap, we use an original dataset collected among Northern California commuters (N = 2216) to analyze the magnitude and significance of individual and household-level factors affecting commute mode choice. We estimate a revealed-preference mode choice model and investigate the differences between millennials and older adults in the sample. Additionally, we conduct a sensitivity analysis to explore how incorporation of explanatory factors such as attitudes and propensity to multitask while traveling in mode choice models affects coefficient estimates, VOTT, and willingness to pay to use a laptop on the commute. Compared to non-millennials, the mode choice of millennials is found to be less affected by socio-economic characteristics and more strongly influenced by the activities performed while traveling. Young adults are found to have lower VOTT than older adults for both in-vehicle (15.0% less) and out-of-vehicle travel time (15.7% less), and higher willingness to pay (in time or money) to use a laptop, even after controlling for demographic traits, personal attitudes, and the propensity to multitask. This study contributes to better understanding the commuting behavior of millennials, and the factors affecting it, a topic of interest to transportation researchers, planners, and practitioners.


2013 ◽  
Vol 45 (2) ◽  
pp. 263-275 ◽  
Author(s):  
Dominik Papinski ◽  
Darren M Scott
Keyword(s):  

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