Designing Better Public Transport: Understanding Mode Choice Preferences Following the COVID-19 Pandemic
Abstract
:1. Introduction—The Importance of Public Transport Usage
1.1. COVID-19 in the UK Context
1.2. Aim and Objectives
- Identifying, through literature searching and a focus group, the five most important factors that drive the choice of public transport;
- Designing and running an online discrete choice decision-making experiment;
- Conducting analyses of the data using a multinomial logit model;
- Deriving the relative importance of the different factors as well as a marginal willingness to pay estimate for each.
2. Literature Review
2.1. Discrete Choice Methodology
2.2. DCEs in the Transport Context
3. Method
3.1. DCE Scenario
- Personal journeys—journeys for personal reasons, such as shopping, holidays, family visits, healthcare, etc.
- Commuting journeys—journeys taken to travel to either work or education, typically taken on a regular basis.
- Expensed journeys—journeys where the traveller would not indirectly pay for their travel option. For example, business journeys where travel expenses could be claimed back, or a situation where a parent/guardian has paid for their child’s ticket.
3.2. Selection of Attributes and Levels
3.2.1. Transport Type
- Bus;
- Taxi;
- Tram/Underground;
- Train.
3.2.2. Fare Cost
3.2.3. Travel Time (on Journey)
3.2.4. Additional Travel Time
3.2.5. Information Provision
- None—no information provided on the journey;
- Some—some information, such as time of arrival;
- Much—much information is provided, such as real-time location information and the next upcoming station/stop.
3.3. Experimental Design
3.4. Data Collection and Sample
3.5. Data Analysis
4. Results
4.1. Estimated Parameters
4.2. Marginal Willingness to Pay
5. Discussion
5.1. Practical Implications for Public Transport Service Design
5.1.1. Fare Cost
5.1.2. Travel Time
5.1.3. Additional Travel Time
5.1.4. Information Provision
5.1.5. Taxi Travel
5.2. Limitations
6. Conclusions
- Journey time savings should not be implemented at the expense of higher fare costs. Fare cost had a stronger negative coefficient in all travel scenarios than journey time.
- If fare cost remains consistent, then providing faster travel times has a greater utility compared to decreasing the additional travel time (i.e., time travelling to stops or stations). This was most notable for commuters.
- Commuter and expensed traveller-focussed transport options should provide some level of information provision on board, such as next stop and delays. Some information provision was found to be the most significant positive factor for utility for commuters, but there was no further utility gained from providing a lot of detailed information.
- The strong preference towards taxis for those on expensed journeys suggest taxi drivers should focus their businesses on addressing the needs of these customers. On a wider level, this indicates a preference towards road vehicle travel when cost is not a concern.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Type | Fare Cost Levels (GBP/£) | Data Source |
---|---|---|
Bus | 1.2/2.7/4.2 | National Bus Fares Survey (TAS, 2018) and market data from transit services |
Taxi | 13/19/25 | National Taxi Price Index (Reg Transfers, 2019) |
Tram/ Underground | 1.5/3.75/6 | DfT data (Department for Transport, 2018) and market values from transit services |
Train | 5/9/13 | Market values from train search engines (National Rail, 2021) |
Type | Travel Time (Minutes) | Data Source |
---|---|---|
Bus | 50/60/70 | DfT average journey time data [46] along with values from mapping services |
Taxi | 25/30/35 | |
Tram/Underground | 35/45/55 | |
Train | 10/15/20 |
Type | Additional Travel Time (Minutes) | Justification |
---|---|---|
Bus | 10/20/30 | Transport for London data (Transport for London, 2019) and data from mapping services |
Taxi | 0/5/10 | DfT data (Department for Transport, 2019) and mapping services |
Tram/Underground | 10/20/30 | DfT data (Department for Transport, 2018) and mapping services |
Train | 20/30/40 | DfT data (Department for Transport, 2019) and mapping services |
Sociodemographic Variable | Total Sample Breakdown | Total (n = 1138) | Personal (n = 382) | Commute (n = 364) | Expensed (n = 392) |
---|---|---|---|---|---|
Gender | Female | 593 | 205 | 170 | 218 |
Male | 544 | 177 | 193 | 174 | |
Other | 1 | 0 | 1 | 0 | |
Age | 18–24 | 125 | 14 | 97 | 14 |
25–34 | 217 | 56 | 100 | 61 | |
35–44 | 212 | 74 | 70 | 68 | |
45–54 | 244 | 93 | 51 | 100 | |
55–64 | 182 | 70 | 30 | 82 | |
65+ | 158 | 75 | 16 | 67 | |
Ethnicity | Asian/Asian British | 67 | 18 | 35 | 14 |
Black/African/Caribbean Black British | 37 | 7 | 14 | 16 | |
Mixed/Multiple ethnic groups | 26 | 6 | 14 | 6 | |
White/White British | 1004 | 349 | 301 | 354 | |
Other ethnic group | 4 | 2 | 0 | 2 | |
Region | Northern England | 270 | 91 | 78 | 101 |
Mid England | 272 | 98 | 79 | 95 | |
Southern England | 263 | 94 | 84 | 85 | |
Greater London | 142 | 35 | 69 | 38 | |
Wales | 54 | 15 | 20 | 19 | |
Scotland | 104 | 37 | 28 | 39 |
Personal Travel (PERSONAL) (n = 382) | Commuter Travel (COMMUTE) (n = 364) | Expensed Travel (EXPENSED) (n = 392) | |||||||
---|---|---|---|---|---|---|---|---|---|
Coefficient | SE | z-Value | Coefficient | SE | z-Value | Coefficient | SE | z-Value | |
Intercept | 0.245 | 0.569 | 0.572 | 0.214 | 0.270 | 0.791 | 0.465 | 0.308 | 1.508 |
Type:Taxi (TAXI) | −0.0399 | 0.306 | −0.130 | 0.143 | 0.240 | 0.595 | 0.682 * | 0.212 | 3.202 |
Type:Tram (TRAM) | −0.00351 | 0.144 | −0.0243 | 0.0320 | 0.112 | 0.285 | 0.100 | 0.106 | 0.942 |
Type:Train (TRAIN) | −0.405 | 0.277 | −1.458 | −0.351 | 0.215 | −1.627 | 0.0342 | 0.201 | 0.170 |
Fare Cost (FARE) | −0.144 ** | 0.0137 | −11.909 | −0.161 ** | 0.0106 | −15.120 | −0.119 ** | 0.00874 | −13.708 |
Travel Time (TIME) | −0.0331 ** | 0.00556 | −6.851 | −0.0359 ** | 0.00427 | −8.416 | −0.0399 ** | 0.00398 | −10.026 |
Ad. Travel Time (ATIME) | −0.0304 ** | 0.00649 | −3.663 | −0.0138 ** | 0.00513 | −2.699 | −0.0247 ** | 0.00431 | −5.741 |
Some Information Provision (SINFO) | 0.0413 | 0.121 | 0.339 | 0.246 * | 0.0972 | 2.537 | 0.158 * | 0.0797 | 1.991 |
Much Information Provision (MINFO) | −0.145 | 0.105 | −1.372 | −0.155 | 0.0819 | −1.892 | 0.126 | 0.0741 | 1.705 |
Log-likelihood | −988.35 | −1632.1 | −1998.9 | ||||||
McFadden’s R2 | 0.202 | 0.171 | 0.119 | ||||||
AIC | 2101.55 | 3344.121 | 4085.779 |
Personal | Commute | Expensed | |
---|---|---|---|
TYPE:TAXI | n/a | n/a | 5.73 * |
TIME (£/min) | 0.23 ** | 0.22 ** | 0.33 ** |
ATIME (£/min) | 0.21 ** | 0.09 ** | 0.21 ** |
SINFO (£) | n/a | 1.52 * | 1.33 * |
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Ulahannan, A.; Birrell, S. Designing Better Public Transport: Understanding Mode Choice Preferences Following the COVID-19 Pandemic. Sustainability 2022, 14, 5952. https://doi.org/10.3390/su14105952
Ulahannan A, Birrell S. Designing Better Public Transport: Understanding Mode Choice Preferences Following the COVID-19 Pandemic. Sustainability. 2022; 14(10):5952. https://doi.org/10.3390/su14105952
Chicago/Turabian StyleUlahannan, Arun, and Stewart Birrell. 2022. "Designing Better Public Transport: Understanding Mode Choice Preferences Following the COVID-19 Pandemic" Sustainability 14, no. 10: 5952. https://doi.org/10.3390/su14105952
APA StyleUlahannan, A., & Birrell, S. (2022). Designing Better Public Transport: Understanding Mode Choice Preferences Following the COVID-19 Pandemic. Sustainability, 14(10), 5952. https://doi.org/10.3390/su14105952