What is the market potential for on-demand services as a train station access mode?

Figures & data

Figure 1. Survey outline for the three choices within one choice set.

Table 1. SP survey prior values and attribute levels.

Access leg prior		Train leg priors
Cost	−0.6	Cost	−0.6
In-vehicle time	−0.1	In-vehicle time	−0.1
Out-of-vehicle time	−0.2	Headway	−0.1
		Transfer	−1.2
		InterCity station label	0.7
	Cost		In-vehicle time		Out-of-vehicle time
	Local station	InterCity station	Local station	InterCity station	Local station	InterCity station
Bicycle	€ 0.00	€ 0.00	12 min	30 min	1 min	1 min
	€ 1.00	€ 1.00	16 min	35 min	5 min	5 min
	€ 2.00	€ 2.00	20 min	40 min	9 min	9 min
Car	€ 1.00	€ 2.00	8 min	12 min	1 min	1 min
	€ 5.00	€ 6.00	12 min	21 min	5 min	5 min
	€ 9.00	€ 10.00	16 min	30 min	9 min	9 min
Public transport	€ 0.50	€ 1.00	8 min	12 min	1 min	1 min
	€ 2.00	€ 3.00	12 min	21 min	5 min	5 min
	€ 3.50	€ 5.00	16 min	30 min	9 min	9 min
Private FLEX	€ 5.00	€ 8.00	8 min	12 min	1 min	1 min
	€ 10.00	€ 13.00	12 min	21 min	5 min	5 min
	€ 15.00	€ 18.00	16 min	30 min	9 min	9 min
Shared FLEX	€ 2.00	€ 2.00	8 min	12 min	1 min	1 min
	€ 5.00	€ 6.00	12 min	21 min	5 min	5 min
	€ 8.00	€ 10.00	16 min	30 min	9 min	9 min
	Cost	In-vehicle time	Headway	Transfers
Local station	€ 17.00	60 min	10 min	1
	€ 20.00	75 min	15 min	2
	€ 23.00	90 min	30 min	3
InterCity station	€ 20.00	75 min	10 min	0
			15 min	1
			30 min

Table 2. Attitudinal statements on FLEX-related characteristics.

Category		Statement
Use of (travel planning) apps	1	I find it difficult to use travel planning apps.^a
	2	Using travel planning apps makes my travel more efficient.^a
	3	I am willing to pay for transport related services within apps.
	4	I do not like using GPS services in apps because I am concerned for my privacy.
Mobility integration	5	I am confident when travelling with multiple modes and multiple transfers.
	6	I do not mind infrequent public transport, if it is reliable.
	7	I do not mind having a longer travel time if I can use my travel time productively. ^b
	8	Not having to drive allows me to do other things in my travel time. ^b
Sharing a ride	9	I am willing to share a ride with strangers ONLY if I can pay a lower price. ^b
	10	I feel uncomfortable sitting close to strangers. ^b
	11	I see reserving a ride as negative, because I cannot travel spontaneously.
Sharing economy	12	I believe the sharing economy is beneficial for me.
	13	I believe the sharing economy is beneficial for society.
	14	Because of the sharing economy, I use traditional alternatives (taxis, public transport, hotels …) less often.
	15	Because of the sharing economy, I think more carefully when buying items that can be rented through online platforms.
	16	I think the sharing economy involves controversial business practices (AirBnB renting, Uber drivers’ rights …).

^a Adapted from (Lu et al. 2015).

^b Adapted from (Lavieri and Bhat 2019).

The remaining statements were formulated for the purpose of this study.

Table 3. Service of the sharing economy, including examples, as presented to respondents.

	Type of (sharing economy) service	Examples shown
1	How familiar are you with car sharing?	Snappcar, Greenwheels, car2go
2	How familiar are you with bike/scooter sharing?	Mobike, OV fiets, Felyx
3	How familiar are you with flexible public transport?	Twentsflex, Bravoflex, U-flex, Delfthopper
4	How familiar are you with ride-hailing?	Uber, ViaVan
5	How familiar are you with food delivery services?	Thuisbezorgd, Deliveroo, Foodora, UberEATS
6	How familiar are you with home rental services?	AirBnB, HomeStay, Couchsurfing

Table 4. Socio-demographics of the sample and the Dutch population (Centraal Bureau voor de Statistiek 2020).

Variable	Level	Sample	Population
Gender	Female	53%	50%
	Male	47%	50%
Age	18–34	22%	27%
	35–49	22%	23%
	50–64	30%	26%
	65+	26%	24%
Education^a	Low	25%	32%
	Middle	39%	37%
	High	36%	32%
Household income^b	Below average	21%	26%
	Average	48%	47%
	Above average	6%	27%
	Did not disclose	25%	0%
Employment status	Working	51%	51%
	Not working	49%	49%
Urbanisation level	Very highly urban	23%	24%
	Highly urban	31%	25%
	Moderately urban	17%	17%
	Low urban	21%	17%
	Not urban	8%	17%
Household size	One person	22%	17%
	2 or more	78%	83%

^a Low: no education, elementary education or incomplete secondary education; Middle: complete secondary education and vocational education; High: bachelor’s or master’s degree from a research university or university of applied sciences.

^b Below average: below modal income (< €29,500); Average: 1-2x modal income (€29,500–€73,000); Above average: Above 2x modal income (> €73,000).

Figure 2. Modal split of the initially selected access modes (inner ring) and access modes selected for the actually chosen station (outer ring), for the local and distant station separately and combined.

Figure 3. Results of the attitudinal statements.

Figure 4. Familiarity with different sharing economy services.

Figure 5. Exploratory factor analysis factor loadings for the four factors.

Figure 6. Mode-based and station-based nesting structure of choices.

Table 5. Segmentation structure and corresponding segment sizes.

	Higher WtP	Lower WtP	Σ
Mode-based	Segment 1	Segment 3
nesting	21.6%	30.2%	51.8%
Station-based	Segment 2	Segment 4
nesting	25.9%	22.3%	48.2%
Σ	47.5%	52.5%	100.0%

Table 6. Model fit, estimates of the taste, nest and class allocation parameters.

Model fit
Null LL	−14,627.17
Final LL	−9,699.74
Adj. Rho-square	0.3327
BIC	19,825.31
Taste parameters
	Segments 1 & 2		Segments 3 & 4
	47.5%		52.5%
Class size	Est.	t-stat	Est.	t-stat
Constants
Bike	0 (fixed)		0 (fixed)
Car	−2.08	−9.79***	1.17	3.43***
Public Transport	−1.56	−11.30***	0.85	3.81***
Shared FLEX	−3.27	−7.70***	−0.06	−0.36
Private FLEX	−6.43	−6.58***	−0.75	−2.44**
Local station	0 (fixed)		0 (fixed)
Distant station	−0.56	−2.73***	−0.44	−2.10**
Common parameters
Cost	−0.28	−7.71***	−0.18	−8.22***
Access leg
In-vehicle time	−0.10	−8.89***	−0.04	−6.84***
Park & walk [bike, car]	−0.07	−7.60***	−0.08	−5.37***
Walk & wait [PT]	−0.04	−1.97**	−0.02	−2.03**
Wait time [FLEX]	0.03	0.64	0.00	0.27
Train leg
In-vehicle time	−0.06	−10.20***	−0.04	−5.84***
Headway	−0.04	−7.61***	−0.04	−7.11***
Transfer	−1.02	−7.58***	−0.85	−4.77***
Nesting parameters and class allocation parameters
	Segment 1		Segment 2		Segment 3		Class 4
	21.6%		25.9%		30.2%		22.3%
Class size	Est.	t-stat	Est.	t-stat	Est.	t-stat	Est.	t-stat
Nesting parameters
Bike nest	10.00	4.65***			1.57	4.32***
Car nest	1.45	3.04***			1.00	14.30***
PT nest	7.43	1.01			10.00	3.43***
Private FLEX nest	1.00	0.38			1.04	3.42***
Shared FLEX nest	2.18	2.62***			2.82	4.77***
Local station nest			2.03	7.46***			2.38	6.73***
Distant station nest			1.00	5.15***			3.11	5.05***
Class allocation param.
Constant	4.08	5.07***	2.94	4.35***	2.22	3.13***
Age	−0.57	−3.38***	−0.19	−1.55	−0.42	−3.13***
BTM use	−1.12	−5.58***	−0.74	−4.78***	−1.24	−6.86***	Baseline
Car use	−0.23	−1.43	−0.38	−2.64***	0.64	3.50***
DRT averse	−0.01	−0.10	0.14	1.81*	0.00	0.00
SE positive	0.39	2.92***	0.24	2.31**	0.11	1.03
TT use	−0.21	−1.80*	0.00	−0.01	−0.30	−2.79***

***p ≤ 0.01, **p ≤ 0.05, *p ≤ 0.1.

Figure 7. Weekly mobility patterns of the entire sample and the 4 classes (values below 5% are not labelled).

Table 7. Parameter trade-offs for different segments.

	Segments 1 & 2	Segments 3 & 4
In-vehicle time
Access IVT [€/h]	20.62	13.86
Train IVT [€/h]	13.14	12.71
Ratio access/train IVT	1.57	1.09
Access segment
PT Walk + Wait [€/h]	9.33	6.88
Car & Bike Walk [€/h]	15.89	27.93
Train segment
Frequency [€/h]	8.95	12.81
Transfer [€]	3.66	4.80
Transfer [min]	16.69	22.64

Table 8. Average factor scores and socio-demographic characteristics of the sample and each segment.

		Sample	Segment 1	Segment 2	Segment 3	Segment 4
Factors	Digitally challenged	–	−0.11	0.03	0.05	−0.00
	DRT averse	–	−0.13	0.10	0.12	−0.16
	Unfamiliar with SE	Normalised	−0.01	0.00	0.01	−0.00
	Positive about SE	–	0.24	0.12	−0.24	−0.05
	Effective use of TT	–	−0.03	0.21	−0.34	0.24
Gender	Female	53%	55%	55%	49%	53%
	Male	47%	45%	45%	51%	47%
Age	18–34	22%	29%	19%	22%	16%
	35–49	22%	25%	20%	26%	16%
	50–64	30%	28%	31%	32%	30%
	65+	26%	18%	30%	20%	37%
Education level	Low	25%	21%	27%	23%	28%
	Middle	39%	39%	37%	43%	37%
	High	36%	40%	36%	34%	35%
Household income	Below average	21%	21%	24%	17%	22%
	Average	48%	47%	47%	51%	48%
	Above average	16%	17%	15%	17%	16%
	Did not say	14%	15%	14%	15%	14%
Employment status	Employed	51%	55%	44%	61%	40%
	Student	6%	8%	6%	4%	6%
	Retired	24%	17%	28%	18%	34%
	other	20%	20%	21%	17%	20%
Urbanisation level	Very highly urban	23%	22%	25%	17%	30%
	Highly urban	31%	31%	32%	31%	32%
	Moderately urban	17%	17%	17%	17%	16%
	Low urban	21%	22%	19%	26%	16%
	Not urban	8%	8%	7%	10%	7%
Household size	1	22%	21%	25%	17%	25%
	2	36%	31%	37%	34%	41%
	3+	42%	48%	38%	49%	34%
Household car ownership	Average	1.17	1.17	1.04	1.38	1.06
	0	15%	17%	21%	5%	19%
	1	56%	53%	56%	57%	59%
	2+	29%	30%	23%	38%	22%

Figure 8. The impact of the Introduction and Substitution scenarios on modal split for train station access.

Figure 9. Market share for FLEX and Distant station when varying the average travel speed of FLEX and the distance between the two stations.

Figure 10. Market share for FLEX and Distant station when varying the number of transfers saved by travelling via the distant station and the distance between the two stations.

Figure 11. Example choice set of Choice 1 (and Choice 2).

Figure 12. Example choice set of Choice 3.

Table 9. Model fits of non-nested, nested, cross-nested and latent-class nested logit models.

	MNL model	NL model (station-based nesting)	NL model (mode-based nesting)	CNL model	2-class LCCM with nesting
Final LL	−11,238	−11,134	−11,114	−10,853	−10,768
Adj. rho-square	0.2308	0.2388	0.2389	0.2556	0.2623
BIC	22,590.15	22,408.96	22,394.69	22,014.72	21,689.51

Figure 13. Example trip options (left): Via local train station; right via IC train station.

Table 10. Results of the sensitivity analysis.

		Local station					Distant station
		Bike	Car	PT	FLEX shared	FLEX private	Bike	Car	PT	FLEX shared	FLEX private
Initial market shares		0.28	0.04	0.40	0.03	0.01	0.01	0.02	0.18	0.03	0.00
Bike to Local station	Access time	−0.08	0.11*	0.03	0.03	0.02	0.01	0	0.01	0	0
	Parking cost	−0.02	0.02	0.01	0.01	0	0	0	0	0	0
	Parking search time	−0.02	0.02	0.01	0.01	0	0	0	0	0	0
Car to Local station	Access time	0.01	−0.08	0	0.01	0	0	0	0	0	0
	Parking and trip cost	0	−0.05	0	0	0	0	0	0	0	0
	Parking search time	0	−0.06	0	0	0.01	0	0	0	0	0
PT to Local station	Access time	0.03	0.03	−0.05	0.05	0.01	0.01	0.01	0.05	0.02	0.01
	Ticket price	0.02	0.02	−0.04	0.03	0.02	0.01	0.01	0.05	0.01	0.01
	Walking & waiting time	0.01	0.01	−0.01	0.01	0	0	0	0.02	0	0
Shared FLEX to Local station	Access time	0	0	0	−0.1*	0.01	0	0	0	0.01	0.01
	Ride price	0	0	0	−0.11*	0.03	0	0	0	0.01	0.02
	Waiting time	0	0	0	0.02	0	0	0	0	0	0
Private FLEX to Local station	Access time	0	0	0	0	−0.08	0	0	0	0	0.02
	Ride price	0	0	0	0.01	−0.16*	0	0	0	0	0.03
	Waiting time	0	0	0	0	0.01	0	0	0	0	0
Bike to Distant station	Access time	0	0	0	0	0	−0.33*	0.02	0.01	0.02	0.01
	Parking cost	0	0	0	0	0	−0.04	0	0	0	0
	Parking search time	0	0	0	0	0	−0.09	0	0	0	0.01
Car to Distant station	Access time	0	0	0	0	0	0.02	−0.13*	0.01	0.01	0.01
	Parking and trip cost	0	0	0	0	0	0.02	−0.16*	0.01	0.01	0.01
	Parking search time	0	0	0	0	0	0.02	−0.14*	0.01	0.01	0.01
PT to Distant station	Access time	0.01	0.02	0.03	0.01	0.04	0.12*	0.13*	−0.12*	0.05	0.13*
	Ticket price	0	0.01	0.01	0.01	0.01	0.07	0.07	−0.05	0.05	0.05
	Walking & waiting time	0	0	0.01	0	0.01	0.02	0.01	−0.02	0.01	0.02
Shared FLEX to Distant station	Access time	0	0	0	0.01	0.01	0.03	0.03	0.01	−0.1*	0.02
	Ride price	0	0	0	0.01	0.01	0.03	0.02	0.01	−0.11*	0.03
	Waiting time	0	0	0	0	0	0	0	0	0	0
Private FLEX to Distant station	Access time	0	0	0	0	0	0	0	0	0	−0.15*
	Ride price	0	0	0	0	0.01	0	0	0	0	−0.38*
	Waiting time	0	0	0	0	0	0	0	0	0	0
Travel via Local station	Train travel time	0	−0.03	−0.02	−0.06	−0.09	0.06	0.05	0.05	0.06	0.1
	Train ticket price	0	−0.04	−0.03	−0.08	−0.13*	0.07	0.07	0.07	0.06	0.13*
	Operating frequency	0	−0.01	0	−0.01	−0.03	0.01	0.01	0.01	0.01	0.02
	Transfers	0	−0.02	−0.01	−0.04	−0.05	0.03	0.04	0.03	0.03	0.05
Travel via Distant station	Train travel time	0.01	0.02	0.02	0.03	0.06	−0.06	−0.04	−0.04	−0.06	−0.1*
	Train ticket price	0.01	0.03	0.02	0.04	0.07	−0.08	−0.05	−0.06	−0.07	−0.13*
	Operating frequency	0	0.01	0.01	0.01	0.02	−0.01	−0.01	−0.01	−0.02	−0.03
	Transfers	0	0.01	0	0	0.01	−0.01	0	−0.01	−0.01	−0.02

Grey shade cells indicate differences greater than 0.1.

Lu, J., Z. Mao, M. Wang, and L. Hu. 2015. “Goodbye Maps, Hello Apps? Exploring the Influential Determinants of Travel app Adoption.” Current Issues in Tourism 18 (11): 1059–1079. doi:10.1080/13683500.2015.1043248.

 Web of Science ®Google Scholar

Lavieri, P. S., and C. R. Bhat. 2019. “Investigating Objective and Subjective Factors Influencing the Adoption, Frequency, and Characteristics of Ride-Hailing Trips.” Transportation Research Part C: Emerging Technologies 105: 100–125. doi:10.1016/j.trc.2019.05.037.

 Web of Science ®Google Scholar

Centraal Bureau voor de Statistiek. 2020. StatLine. Accessed 14 July 14. https://opendata.cbs.nl/statline/#/CBS/nl/navigatieScherm/thema.

 Google Scholar