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Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real - Time Bus Arrival Information
Open accessAbstract
This study quantifies the relationship between perceived and actual waiting times experienced by passengers awaiting the arrival of a bus at a bus stop. Understanding such a relationship would be useful in quantifying the value of providing realtime information to passengers on the time until the next bus is expected to arrive at a bus stop. Data on perceived and actual passenger waiting times, along with socioeconomic characteristics, were collected at bus stops where no real-time bus arrival information is provided, and relationships between perceived and actual waiting times are estimated. The results indicate that passengers do perceive time to be greater than the actual amount of time waited. However, the hypothesis that the rate of change of perceived time does not vary with respect to the actual waiting time could not be rejected (over a range of 3 to 15 minutes). Assuming that a passenger’s perceived waiting time is equal to the actual time when presented with accurate real-time bus arrival information, the value of the eliminated additional time is assessed in the form of reduced vehicle hours per day resulting from a longer headway that produces the same mean passenger waiting time. The eliminated additional time is also assessed in the form of uncertainty in the headway resulting in the same extra waiting time. Naturally, such benefits of passenger information can only be confirmed when the actual effect of information on the perception of waiting time is quantified.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This pilot study quantifies how bus passengers’ perceived waiting times differ from actual waiting times when no real-time arrival information is provided. It finds that passengers overestimate waiting time by 0.84 minutes on average, and translates that overestimate into potential operating and service-quality benefits of real-time information.
Key findings
- Passengers perceived waiting time as greater than actual: mean perceived waiting time was 6.6 minutes versus mean actual waiting time of 5.77 minutes, a difference of 0.84 minutes.
- In an OLS model with 83 observations, perceived waiting time increased with actual waiting time, but the slope was not statistically different from 1; the intercept was significantly greater than 0, and a joint test rejected perceived time equaling actual time. The data did not show that the additional perceived time varied with actual waiting time over the 3-to-15-minute range.
- In a model adding socioeconomic variables, walking time to the destination and presence of a time constraint had explanatory value: longer walking time was associated with greater exaggeration of perceived waiting time, while a time constraint brought perceived waiting time closer to actual time. Other socioeconomic variables were not significant at the 0.05 level.
- Eliminating the 0.84-minute overestimate via real-time information was equivalent to a reduction of 7.77 vehicle-hours per day, or a 4.4% saving, for a route with 30-minute running time, 10-minute headway, and 8 hours of weekday service. For a 10-minute headway, the equivalent headway coefficient of variation was 0.40, corresponding to a headway standard deviation of 4.0 minutes.
Methods
- Three students surveyed 83 passengers waiting for Campus Area Bus Service buses at five stops on three routes over approximately one year, with a response rate close to 100%. Data included perceived waiting time, actual waiting time, and socioeconomic characteristics.
- The main analysis estimated an OLS regression of perceived waiting time on actual waiting time, tested whether the slope equaled 1 and the intercept equaled 0, and jointly tested perceived time equaling actual time. A second model added walking time to destination and a time-constraint dummy variable.
- The estimated 0.84-minute mean difference was then converted into equivalent vehicle-hours per day saved and an equivalent headway coefficient of variation, assuming deterministic headways and random passenger arrivals.
Limitations
- The paper states this was a pilot study on a university campus-based transit system; it calls for larger transit systems, longer routes, more heterogeneous traveling populations, a wider range of actual waiting times, larger data sets, and more complete socioeconomic variables. Some socioeconomic variables had as few as 46 observations because data were not collected for every passenger due to survey time constraints.
- The paper states that the key assumption—that accurate real-time information makes perceived waiting time equal actual waiting time—requires further validation, ideally through before-and-after surveys.
- Evident from the text: the sample is small (83 passengers), the observed actual waiting times are limited to 3–15 minutes, and the study does not directly test the effect of real-time information on perceptions.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“with a t-statistic of .77, the estimate of .33 is significantly different from 0 at the 0.0 level.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > ^^
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“the presence of a time constraint brings the perceived waiting time closer to the actual time”
- Also stated in: § Socioeconomic Variables · hedged
“The negative value of the parameter estimate corresponding to the time constraint dummy variable supports the dominance of the former effect.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Summary and Future Research
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § Socioeconomic Variables · hedged
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A longer walking time produces a greater exaggeration in the perceived waiting time”
- Also stated in: § Socioeconomic Variables · plain
“the positive parameter value of the walking time to destination variable”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Summary and Future Research
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § Socioeconomic Variables · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the walking time to the destination from the bus stop at which the passenger is waiting and the presence of a time constraint do exhibit some explanatory value.”
- Also stated in: § Socioeconomic Variables · boosted
“The t-statistics of the estimates of the intercept and the parameters of the two additional variables reflect values significantly different from 0 at the 0.0 level.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Socioeconomic Variables
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § Socioeconomic Variables · boosted
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In light of the quantified difference of 0.84 minutes”
- Also stated in: § Summary and Future Research · plain
“The mean difference between the perceived waiting time by passengers and the actual time is 0.84 minutes.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Potential Value of Providing Passenger Information
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § Summary and Future Research · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Investigating the socioeconomic variables listed above, none are found to have parameter estimates significantly different from 0 at the 0.05 level.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Socioeconomic Variables
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Elimination of increased perceived waiting times on a bus route that does not exhibit any variability in its headway could conceptually be traded off against increased variability in headways.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Equivalent Coefficient of Variation
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: When accurate real-time bus arrival information is provided, passengers will perceive their waiting time to be equal to the actual waiting time.
- The authors state this as an assumption: Passengers arrive at bus stops in a totally random manner (arrivals follow the Poisson process).
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a result, the percent reduction Z in the vehicle hours per day required for service provision is given by the following: Z = .68(h+.68) -(3)”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Equivalent Vehicle-Hours
Tests? Asserted: unclear✓ Checkable: passes✓ Load-bearing: passes? Based: unclear- The authors state this as an assumption: When accurate real-time bus arrival information is provided, passengers will perceive their waiting time to be equal to the actual waiting time.
- The authors state this as an assumption: The headway is deterministic (headway variance is 0) for the vehicle-hours analysis.
- The authors state this as an assumption: Passengers arrive at bus stops in a totally random manner (arrivals follow the Poisson process).
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because passengers perceive waiting times to be greater than actual waiting times at a bus stop, real-time passenger information systems could potentially reduce the perceived waiting time for buses when providing accurate information.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Summary and Future Research
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: When accurate real-time bus arrival information is provided, passengers will perceive their waiting time to be equal to the actual waiting time.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Based on this pilot study, there is good evidence that a difference between perceived and actual waiting times does exist to motivate a more comprehensive data collection and modeling effort.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Summary and Future Research
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- The quote only partly states this claim. 4
- It may state two separate claims as one. 4
- Probably not a substantial claim. 6
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results indicate that passengers do perceive time to be greater than the actual amount of time waited.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, the hypothesis that the rate of change of perceived time does not vary with respect to the actual waiting time could not be rejected (over a range of 3 to 15 minutes).”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Abstract
Load-bearingStrength:hedged· not yet calibratedTests? Asserted: unclear✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Empirically, the mean perceived waiting time of the passengers surveyed is 6.6 minutes, while their mean actual waiting time is 5.77 minutes.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Potential Value of Providing Passenger Information
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The equivalent coefficient of variation is seen to vary between 0.580 and 0.37.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Equivalent Coefficient of Variation
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe quote only partly states this claim.
- The authors state this as an assumption: Passengers arrive at bus stops in a totally random manner (arrivals follow the Poisson process).
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, under deterministic headways and running times, the vehicle-hours per day required for service provision is the ratio of the running time to the headway multiplied by the total hours of service provided per day.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Equivalent Vehicle-Hours
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
- The authors state this as an assumption: The headway is deterministic (headway variance is 0) for the vehicle-hours analysis.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This study demonstrated the feasibility of examining the questions of interest and points to several directions regarding future research.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Summary and Future Research
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the exaggeration in the perceptions of waiting time is eliminated by providing real-time passenger information, the quantified difference of 0.84 minutes will be reduced to 0. Under such a condition, the transit agency can achieve the same level of service, as measured by the mean passenger waiting time, by employing fewer vehicle-hours per day.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Equivalent Vehicle-Hours
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
- The authors state this as an assumption: When accurate real-time bus arrival information is provided, passengers will perceive their waiting time to be equal to the actual waiting time.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The equivalent coefficient of variation of headway is a measure of the bus service quality as perceived by passengers.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Equivalent Coefficient of Variation
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Assuming that the headway is deterministic (i.e., the headway variance is 0) and passengers arrive in a totally random manner (i.e., arrivals follow the Poisson process), the mean actual passenger waiting time is equal to half the deterministic headway.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Equivalent Vehicle-Hours
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- The authors state this as an assumption: The headway is deterministic (headway variance is 0) for the vehicle-hours analysis.
- The authors state this as an assumption: Passengers arrive at bus stops in a totally random manner (arrivals follow the Poisson process).
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The transit service used in this pilot study is small enough to be manageable, yet large enough to reflect situations pertinent to more extensive transit services in urban areas.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Data Collection
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The reduction in perceived waiting times could potentially be translated into reduced operating costs or increased passenger satisfaction and, ultimately, into increased ridership for public transit, depending on the policies adopted by the transit agency in conjunction with the introduction of real-time passenger information systems.”
§ Passenger Wait Time Perceptions at Bus Stops 89 Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information > Summary and Future Research
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
- The authors state this as an assumption: When accurate real-time bus arrival information is provided, passengers will perceive their waiting time to be equal to the actual waiting time.
The authors state this as an assumption.
When accurate real-time bus arrival information is provided, passengers will perceive their waiting time to be equal to the actual waiting time.
“When accurate bus arrival information is provided, it is assumed that passengers will perceive their waiting time to be equal to the actual waiting time.”
“it is assumed that real-time passenger information will eliminate exaggerated perceptions of waiting time.”
“the assumption that such information will produce accurate perceived waiting times.”
The authors state this as an assumption.
The headway is deterministic (headway variance is 0) for the vehicle-hours analysis.
“Assuming that the headway is deterministic (i.e., the headway variance is 0)”
“assuming a deterministic headway”
The authors state this as an assumption.
Passengers arrive at bus stops in a totally random manner (arrivals follow the Poisson process).
“passengers arrive in a totally random manner (i.e., arrivals follow the Poisson process)”
“Assuming that passengers arrive in a totally random manner”
Citations
Cites
Econometric Models and Economic Forecasts
Pindyck, R. S., & Rubinfeld, D. L. (1990) · eYLS (Yale Law School) · Cited by 4,973
Ben-Akiva (1985) · Medical Entomology and Zoology · Cited by 1,379
(1982) · Journal of Criminal Justice · doi:10.1016/0047-2352(82)90013-7 · Cited by 696
Passenger travel time and path choice implications of real-time transit information
Hickman, M. D., & Wilson, N. H. M. (1995) · Transportation Research Part C Emerging Technologies · doi:10.1016/0968-090x(95)00007-6 · Cited by 141
Gass, S. I. (1982) · Transportation Research Part A General · doi:10.1016/0191-2607(82)90036-x · Cited by 115
de Jong, G., & Kroes, E. (2014) · SxI Springer per l'Innovazione · doi:10.1007/978-3-319-04786-7_8 · Cited by 40
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2 of the 8 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Wirtz, J. 00. Passenger perceptions of waiting time at bus stops. BS thesis, Ohio State University, Department of Civil and Environmental Engineering and Geodetic Science.
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Aleta, A., Meloni, S., & Moreno, Y. (2017) · Scientific Reports · doi:10.1038/srep44359 · Cited by 170
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Roy, S., & Basu, D. (2020) · Journal of Traffic and Transportation Engineering (English Edition) · doi:10.1016/j.jtte.2018.08.002 · Cited by 7
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Chou, S., & Ekawati, Y. (2009) · Advanced concurrent engineering · doi:10.1007/978-1-84882-762-2_52 · Cited by 6
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Freitas, A. L. P., & dos Reis, T. B. (2013) · Revista Produção Online · doi:10.14488/1676-1901.v13i3.1010 · Cited by 5
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Ma, W., Lin, N., Chen, X., & Zhang, W. (2018) · PROMET - Traffic&Transportation · doi:10.7307/ptt.v30i5.2609 · Cited by 5
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Fisal, S. F. M., & Sukor, N. S. A. (2021) · Jurnal Kejuruteraan · doi:10.17576/jkukm-2021-33(3)-20 · Cited by 5
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Arhin, S. A., PTOE, P. E., Gatiba, A., Anderson, M. F., Ribbisso, M., & Manandhar, B. (2019) · Open Journal of Civil Engineering · doi:10.4236/ojce.2019.94019 · Cited by 4
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Reich, T., Budka, M., & Hulbert, D. (2021) · Soft Computing Letters · doi:10.1016/j.socl.2021.100029 · Cited by 4
Sukri, F. H., Chew, B. C., Hamid, S. R., & Loo, H. S. (2017) · AIP conference proceedings · doi:10.1063/1.4976917 · Cited by 4
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Nour, A. (2009) · UWSpace (University of Waterloo) · Cited by 4
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Arhin, S. A. (2019) · doi:10.31979/mti.2019.1801 · Cited by 4
Georgiadis, G., Politis, I., Verani, E., Kopsacheilis, A., Sdoukopoulos, A., & Fyrogenis, I. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.100590 · Cited by 3
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ETA HTC: Estimating time of arrival under heterogeneous traffic conditions using crowdsensing
Singh, G., Bansal, D., & Sofat, S. (2017) · International Conference on Inventive Computing and Informatics (ICICI) · doi:10.1109/icici.2017.8365337 · Cited by 3
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Metadata
- Authors
- Mishalani, R., McCord, M., & Wirtz, J.
- Affiliations
- The Ohio State University (US)
- Venue
- Journal of Public Transportation
- Published
- 2006-05
- Volume
- 9
- Issue
- 2
- Pages
- 89-106
- ISSN
- 1077-291X (ISSN-L)
- 2375-0901
- OpenAlex
- W2022832686
- Semantic Scholar
- 37448ef48b3e6c5f728a43816df7f89f2e103b73
- Cited by
- 154
- References
- 8
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 4 · 2023: 2 · 2022: 3 · 2021: 13 · 2020: 8 · 2019: 9 · 2018: 15 · 2017: 14 · 2016: 13 · 2015: 10 · 2014: 13 · 2013: 9 · 2012: 7
- FWCI
- 5.62
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Public Transportation (publishedVersion)
- Digital Commons - University of South Florida (University of South Florida) (submittedVersion)
- Funders
- Ohio State University
- Topics
- Social Sciences › Transportation › Transportation Planning and Optimization
- Social Sciences › Transportation › Transportation and Mobility Innovations
- Physical Sciences › Engineering › Civil and Structural Engineering › Urban Transport Systems Analysis
- Keywords
- perceived waiting time, bus stops, passenger information, headway, value of information, public transport
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-11-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
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