Investigating the Impact of Public Transport Service Disruptions upon Passenger Travel Behaviour—Results from Krakow City
Abstract
:1. Introduction
1.1. Literature Review
1.2. Objectives and Contribution
2. Materials and Methods
2.1. Case Study—Public Transport (PT) System in Krakow
2.2. Data on Registered PT Service Disruptions
- A total of 577 unplanned service suspension events were registered for the 6-month analysis period, which gives a monthly average of 97 suspension events.
- The vast majority of service suspension events were short-term in their duration (Figure 1): 52% of suspensions took no longer than 15 min, and just 22% of them were longer than 30 min. Note that this only includes the time duration of suspension itself, excluding the subsequent service recovery period.
- No significant differences were observed in terms of temporal distribution. Disruption events tend to occur at fairly similar frequencies both throughout the daytime (with 78% of them taking place between 6 a.m. and 6 p.m.) and across the consecutive months (82 to 108 events registered per month).
2.3. SP/RP Survey Setup and Design
- The first (RP-based) part of the questionnaire focuses on their historical experience of travel disruptions, trip circumstances, information sources and reported travel choices in the event of the most recent disruption. In the case when a passenger did not experience any disruption at all, this survey part is omitted.
- In the second (SP-based) part, passengers are asked to state their maximum (tolerable) time thresholds and ensuing travel decisions for two hypothetical situations, i.e., service suspension for two distinct trip purposes: a time-critical journey and a non-time critical journey (i.e., depending on the propensity to arrive on-time).
- The final part contains questions on the judgement of the existing RTI system quality in the city of Krakow and desirable future RTI content during service suspensions.
3. Results
3.1. General Experience of PT Disruptions
3.2. Revealed Impact of Past PT Disruptions
3.3. Long-Term Travel Behaviour Shifts Due to PT Disruptions
3.4. Stated Travel Choices during PT Disruptions
3.5. Travel Information during PT Disruptions
4. Discussion
- Majority of surveyed PT users (68%) recall having experienced sudden, unplanned service disruptions in their PT trips and 23% of them report such experience on a frequent basis (at least once a week). Importantly, we observe that recency of experienced PT disruptions has significant influence upon passengers’ travel memory. Only 9% of passengers interviewed in the midst of a typical commuting season (May–June), recall not having experienced any disruption before. However, the same survey conducted just after holiday break (September) yields a corresponding rate of 56% respondents. This is all the more remarkable, given that registered PT disruptions exhibit similar characteristics (frequency and duration) across consecutive months, and both samples are controlled against the steady share of regular PT users.
- Travel choices revealed by passengers during the latest (most recent) PT disruption primarily involve using an alternative PT (bus or tram) route (39% of travellers), followed by waiting at the stop (29%), walking towards the destination (27%), shifting to private transport (4%) and resigning from travelling (2%). Passengers more accustomed to frequent experience of PT disruptions are less likely to change their current PT travel routine and instead tend to wait further at the current stop.
- Furthermore, 77% of respondents admit to having made long-term adjustments in their travel behaviour as a consequence of recurrent disruption experience. These mostly involve using an alternative PT route or adjusting current PT trip itinerary, by changing the origin departure time or an alighting stop (40–50% of travellers). In contrast, increased frequency of car usage is reported only by ca. 20% of respondents.
- In the hypothetical (SP) disruption scenarios, trip time-criticality stands out as a major factor, influencing the stated choices and maximum acceptable wait time at the current PT stop before taking further action. For time-critical trips (e.g., work, study), only 7% of travellers would accept a max. waiting time longer than 15 min—whereas for non-time-critical trips (e.g., shopping, leisure) this rate increases to 37%.
- Our findings also expose relevant differences between passengers’ preferred vs. actual choices during PT disruptions. Stated preferences (SP) vary with time-criticality, with higher propensity to use private transport for time-critical trips (19%), and to resign from travelling for non-time-critical trips (7%). Corresponding rates in the revealed preferences (RP), meanwhile, are equal to ca. 6% and 2% respectively, suggesting that SP answers overestimate the probability of modal shifts and trip cancellations. In contrast, RP answers exhibit uniform patterns regardless of trip time-criticality. Revealed travel choices primarily involve taking an alternative PT route (55%) or walking to a destination (38%—remarkably, also for time-critical trips).
- The main information sources which help travellers to recognise the emerging PT service disruptions are travellers’ own observations (34%), and electronic RTI displays at stops (20%), followed by PT staff announcements or observations of other passengers. At the time of this research, on-line and internet resources were the least popular means of recognising the PT disruption. However, smartphone apps and RTI displays are the principal (most consulted) travel information sources for planning an onward journey once the PT disruption takes place, even despite limited information content utility.
Implications and Recommendations for RTI Policy in Krakow
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Zhu, S.; Levinson, D.M. Disruptions to transportation networks: A review. In Network Reliability in Practice; Levinson, D., Liu, H., Bell, M., Eds.; Springer: New York, NY, USA, 2012; pp. 5–20. [Google Scholar] [CrossRef] [Green Version]
- Marsden, G.; Anable, J.; Shires, J.; Doherty, I. Travel Behaviour Response to Major Transport System Disruptions: Implications for Smarter Resilience Planning; OECD Discussion Paper: Paris, France, 2016; p. 9. [Google Scholar]
- Ziółkowski, R.; Dziejma, Z. Investigations of the Dynamic Travel Time Information Impact on Drivers’ Route Choice in an Urban Area—A Case Study Based on the City of Bialystok. Energies 2021, 14, 1645. [Google Scholar] [CrossRef]
- Cairns, S.; Atkins, S.; Goodwin, P. Disappearing traffic? The story so far. Proc. Inst. Civ. Eng. Munic. Eng. 2003, 151, 13–22. [Google Scholar] [CrossRef]
- Passenger Focus UK. Bus Passengers’ Experience of Delays and Disruption; Research Report; Passenger Focus UK: London, UK, 2013.
- Clegg, R. Empirical studies on road traffic responses to capacity reduction. In Proceedings of the International Symposium on Transportation and Traffic Theory (ISTTT17), London, UK, 13–15 July 2007. [Google Scholar]
- Goodwin, P.B. Enhancing the Effectiveness of Transport Policy by Better Understanding of Travel Choices; Centre for Transport and Society, UWE Bristol: Bristol, UK, 2009. [Google Scholar]
- Papangelis, K.; Corsar, D.; Sripada, S.; Beecroft, M.; Nelson, J.D.; Edwards, P.; Velaga, N.; Anable, J. Examining the effects of disruption on travel behaviour in rural areas. In Proceedings of the 13th World Conference on Transportation Research (WCTR), Rio de Janeiro, Brazil, 15–18 July 2013. [Google Scholar]
- Maréchal, S. Modelling the acquisition of travel information and its influence on travel behaviour. In Proceedings of the 48th Universities’ Transport Study Group (UTSG) Conference, Bristol, UK, 6–8 January 2016. [Google Scholar]
- Guiver, J. Modal talk: Discourse analysis of how people talk about bus and car travel. Transp. Res. Part A Policy Pract. 2007, 41, 233–248. [Google Scholar] [CrossRef]
- Goodwin, P. Habit and Hysteresis in Mode Choice. Urban Stud. 1977, 14, 95–98. [Google Scholar] [CrossRef]
- Shires, J.D.; Cabral, M.; Marsden, G.; Wardman, M. The Impact of Disruption on Rail Demand. In Proceedings of the 44th European Transport Conference (ETC), Barcelona, Spain, 5–7 October 2016. [Google Scholar]
- Islam, M.F.; Fonzone, A.; MacIver, A.; Dickinson, K. Modelling factors affecting the use of ubiquitous real-time bus passenger information. In Proceedings of the 5th IEEE International Conference on Models and Technologies in Intelligent Transport Systems, MT-ITS 2017, Naples, Italy, 26–28 June 2017. [Google Scholar] [CrossRef]
- Islam, M.F.; Fonzone, A.; MacIver, A.; Dickinson, A. Use of ubiquitous real-time bus passenger information. IET Intell. Transp. Syst. 2019, 14, 139–147. [Google Scholar] [CrossRef] [Green Version]
- Kattan, L.; de Barros, A.G.; Saleemi, H. Travel behavior changes and responses to advanced traveler information in prolonged and large-scale network disruptions: A case study of west LRT line construction in the city of Calgary. Transp. Res. Part F Traffic Psychol. Behav. 2013, 21, 90–102. [Google Scholar] [CrossRef]
- Caulfield, B.; O’Mahony, M. A stated preference analysis of real-time public transit stop information. J. Public Transp. 2009, 12, 1–20. [Google Scholar] [CrossRef] [Green Version]
- Fonzone, A. What do you do with your app? study of bus rider decision making with real-time passenger information. Transp. Res. Rec. 2015, 2535, 15–24. [Google Scholar] [CrossRef]
- Cats, O.; Jenelius, E. Dynamic vulnerability analysis of public transport networks: Mitigation effects of real-time information. Netw. Spat. Econ. 2014, 14, 435–463. [Google Scholar] [CrossRef]
- Fonzone, A.; Schmöcker, J.D. Effects of transit real-time information usage strategies. Transp. Res. Rec. 2014, 2417, 121–129. [Google Scholar] [CrossRef]
- Evans, J.S. Bias in Human Reasoning: Causes and Consequences; Lawrence Erlbaum Associates, Inc.: Mahwah, NJ, USA, 1989. [Google Scholar]
Total Sample | Survey Period | ||
---|---|---|---|
May/June ‘17 | September ‘17 | ||
More than once a week | 10% | 21% | 2% |
Ca. once a week | 13% | 18% | 5% |
Less than once a week | 45% | 53% | 37% |
Never experienced yet | 32% | 9% | 56% |
Share of regular PT users (i.e., 2 + PT trips per week) | 92% | 91% | 91% |
Total Sample | Frequency of Experienced PT Disruptions | |||
---|---|---|---|---|
More Often | Ca. Once a Week | Less Often | ||
Wait at the stop | 29% | 45% | 29% | 26% |
Use an alternative PT route | 39% | 26% | 39% | 41% |
Walk down to destination | 27% | 25% | 20% | 29% |
Shift to private transport | 4% | 1% | 8% | 4% |
Resign from travelling | 2% | 2% | 3% | 1% |
Stated Choices Potential Trips | Revealed Choices Past Trips | |||||
---|---|---|---|---|---|---|
Need to arrive on-time? | yes | no | yes | no | yes | no |
Wait at the stop | (n/a) | (excl. waiting): | 25% | 35% | ||
Use an alternative PT route | 58% | 57% | 54% | 54% | 40% | 35% |
Walk down to destination | 22% | 34% | 38% | 36% | 29% | 23% |
Shift to private transport | 19% | 2% | 5% | 7% | 3% | 5% |
Resign from travelling | ~0% | 7% | 3% | 1% | 2% | 1% |
Total Sample | Frequency of Experienced PT Disruptions | |||
---|---|---|---|---|
More Often | Ca. Once a Week | Less Often | ||
Driver or PT staff | 20% | 15% | 12% | 24% |
Other passengers | 13% | 4% | 17% | 13% |
Internet, social media | 7% | 9% | 10% | 6% |
RTI displays at stops | 26% | 20% | 20% | 30% |
Just notice them myself | 34% | 52% | 41% | 27% |
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Drabicki, A.A.; Islam, M.F.; Szarata, A. Investigating the Impact of Public Transport Service Disruptions upon Passenger Travel Behaviour—Results from Krakow City. Energies 2021, 14, 4889. https://doi.org/10.3390/en14164889
Drabicki AA, Islam MF, Szarata A. Investigating the Impact of Public Transport Service Disruptions upon Passenger Travel Behaviour—Results from Krakow City. Energies. 2021; 14(16):4889. https://doi.org/10.3390/en14164889
Chicago/Turabian StyleDrabicki, Arkadiusz Adam, Md Faqhrul Islam, and Andrzej Szarata. 2021. "Investigating the Impact of Public Transport Service Disruptions upon Passenger Travel Behaviour—Results from Krakow City" Energies 14, no. 16: 4889. https://doi.org/10.3390/en14164889
APA StyleDrabicki, A. A., Islam, M. F., & Szarata, A. (2021). Investigating the Impact of Public Transport Service Disruptions upon Passenger Travel Behaviour—Results from Krakow City. Energies, 14(16), 4889. https://doi.org/10.3390/en14164889