Modelling observed and unobserved factors in cycling to railway stations: application to transit-oriented-developments in the Netherlands

La Paix Puello and Geurs, 2015, in European Journal of Transport and Infrastructure Research

doi:nan
Location Netherlands
Population Other (specify)
Sample size 12000
Factor analysis type confirmatory factor analysis, none rotation
Stepwise regression nan
Removal of insignificant variables nan
Reviewed by MWC

Abstract

Typically, mode choice behaviour is studied as a function of observed travel factors. Given the importance of unobservable factors on choice behaviour, this paper deviates from this approach. We analysed cycling as mode choice to access railway stations, incorporating latent variables and psychometric data to capture relatively intangible factors that influence mode choice. Such factors are not observable, but can manifest themselves through adjustable indicators. The database used for this paper contains 12000 observations of journeys carried out in the Rotterdam – The Hague area in the Netherlands, covering thirty-five railway stations. In addition to using a traditional binary logit model, we estimated three hybrid choice models for access mode choice. These hybrid choice models represented observed and unobserved factors simultaneously, including the train users’ perception of connectivity, attitude towards station environment and perceived quality of bicycle facilities. The results show that both attitudes and observable travel-related elements are important in the decision to cycle to the station or not. Variations in these perceptions and attitudes significantly affect the bicycle-train share. At the same time, improvements in unguarded bicycle parking facilities may increase the number of people who cycle to the train station more than improvements in guarded bicycle parking would. Moreover, the availability of the parking facilities is crucial during rush hours. Another conclusion is that transport strategies to encourage bicycle-train use must be implemented by station type, i.e. measures to encourage bicycle access at larger stations. Further research would develop a hybrid choice model for egress, and a stated choice experiment would compare these results. © 2015 Editorial Board EJTIR. All Rights reserved.

Factors

Variable Pattern loading
Social safety at departure station during the night (Safety) 0.297
Quality of lighting at departure station (Safety) Unknown
General judgment of the departure station (Comfort) Unknown
Variable Pattern loading
Punctuality of the trains (Convenience) Unknown
Frequency of the trains (Convenience) 0.368
Variable Pattern loading
Quality of guarded bicycle parking (Safety) 0.731
Quality of unguarded bicycle parking (Safety) Unknown

Models

Dependent variable Bicycle to train station
Model type Binary logit
Sample size 12000.0
R2 nan
Adjusted R2
Pseudo R2 (nan) 0.157
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Variable Coefficient p-value
Constant -0.457 0.153
Age -0.01 0.0
Male 0.04 0.772
Work trip 0.48 0.0
Business trip 0.63 0.0
School/study trip 0.41 0.0
Discount 0.26 0.0
Student card -0.16 0.011
Rush hour 0.27 0.0
Car -0.08 0.063
Income level in residence area divided by travel time 0.07 0.121
Density of bicycle network -0.24 0.0
Population density -0.01 0.246
Dwelling density -0.01 0.226
Job density 0.4 0.001
Employees in health near station -0.21 0.66
Public employees near station -3.23 0.0
Retail employees near station 1.18 0.211
Average road quality 2.4 0.0
Average traffic nuisance -1.04 0.795
More than 3.6km from station -0.35 0.0
Bus/tram/metro lines -0.03 0.0
Station type 1 (Very large station in the centre of a large city) -0.27 0.0
Bicycle parking spaces 0.002 0.0
Dependent variable Bicycle to train station
Model type Integrated choice and latent variable
Sample size 12000.0
R2 nan
Adjusted R2
Pseudo R2 (nan) 0.215
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Station perception
Variable Coefficient p-value
Constant 6.15 0.0
Station type 1 (Very large station in the centre of a large city) -0.24 0.067
Easy to find travel information 0.03 0.711
Bicycle parking 0.06 0.0
Lighting quality at station -0.003 0.968
Number of high-speed trains -0.15 0.0
Std. dev. 0.06 0.003
Utility
Variable Coefficient p-value
Constant -1.06 0.003
Station perception 0.1 0.0
Age -0.01 0.0
Male 0.04 0.332
Work trip 0.51 0.0
Business trip 0.63 0.0
School/study trip 0.27 0.0
Discount 0.36 0.0
Student card -0.21 0.001
Rush hour 0.28 0.0
Car -0.08 0.105
Income level in residence area divided by travel time 0.08 0.121
Density of bicycle network -0.24 0.0
Population density -0.01 0.234
Dwelling density -0.52 0.234
Job density 0.38 0.002
Employees in health near station -0.238 0.624
Public employees near station -3.32 0.0
Retail employees near station 1.05 0.263
Average road quality 2.4 0.0
Average traffic nuisance -1.04 0.0
More than 3km from station -0.35 0.0
Bus/tram/metro lines -0.03 0.0
Station type 1 (Very large station in the centre of a large city) -0.37 0.0
Bicycle parking spaces 0.002 0.038
Dependent variable Bicycle to train station
Model type Integrated choice and latent variable
Sample size 12000.0
R2 nan
Adjusted R2
Pseudo R2 (nan) 0.33
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Connectivity
Variable Coefficient p-value
Constant 5.87 0.0
Quality of bicycle access roads -0.02 0.208
Quality of guarded bicycle parking 0.003 0.43
Quality of unguarded bicycle parking 0.08 0.0
Number of InterCity trains -0.01 0.036
Number of Sprinter trains 0.05 0.003
Std. dev. 0.23 0.0
Utility
Variable Coefficient p-value
Constant -3.67 0.0
Connectivity 0.25 0.0
Age -0.01 0.0
Male 0.02 0.596
Work trip 0.55 0.0
Business trip 0.61 0.0
School/study trip 0.27 0.0
Discount 0.44 0.0
Student card -0.22 0.001
Rush hour 0.27 0.0
Car -0.1 0.026
Income level in residence area divided by travel time 0.0 0.497
Density of bicycle network -0.2 0.0
Population density -0.03 0.0
Dwelling density 0.002 0.569
Job density 0.2 0.012
Employees in health near station -0.185 0.704
Public employees near station -2.95 0.0
Retail employees near station 0.605 0.509
Average road quality 2.75 0.0
Average traffic nuisance -1.0 0.0
More than 3km from station -0.33 0.0
Bus/tram/metro lines -0.03 0.0
Station type 1 (Very large station in the centre of a large city) -0.11 0.007
Bicycle parking spaces 0.002 0.0
Dependent variable Bicycle to train station
Model type Integrated choice and latent variable
Sample size 12000.0
R2 nan
Adjusted R2
Pseudo R2 (nan) 0.155
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence nan
Perceived quality of bicycle facilities at the station
Variable Coefficient p-value
Constant 1.79 nan
Quality of bicycle access roads 0.05 nan
Number of InterCity trains -0.02 nan
Number of Sprinter trains 0.25 nan
Std. dev. 0.67 nan
Utility
Variable Coefficient p-value
Age -0.002 nan
Male 0.01 nan
Work trip 0.64 nan
Business trip 0.7 nan
School/study trip 0.27 nan
Discount 0.49 nan
Student card -0.25 nan
Rush hour 0.28 nan
Car -0.1 nan
Income level in residence area divided by travel time 0.08 nan
Density of bicycle network -0.31 nan
Population density 0.042 nan
Dwelling density -0.003 nan
Job density 1.23 nan
Employees in health near station -1.3 nan
Public employees near station -6.41 nan
Retail employees near station 1.61 nan
Average road quality 1.2 nan
Average traffic nuisance -1.52 nan
More than 3km from station -0.37 nan
Perceived quality of bicycle facilities at the station nan <0.05

The Attitudes and Travel Database is produced with support from the Center for Teaching Old Models New Tricks at Arizona State University, a University Transportation Center sponsored by the US Department of Transportation through Grant No. 69A3551747116.

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