pith. sign in

arxiv: 2606.29756 · v1 · pith:URKBG33Wnew · submitted 2026-06-29 · 💰 econ.EM

Modeling Mode and Departure Time Responses to Congestion Pricing: A Spatial and Behavioral Analysis Using Cross-Nested Logit Model

Pith reviewed 2026-06-30 04:13 UTC · model grok-4.3

classification 💰 econ.EM
keywords congestion pricingcross-nested logitmode choicedeparture time choicestated preferencespatial analysiselasticitytravel behavior
0
0 comments X

The pith

The Cross-Nested Logit model better captures how commuters simultaneously adjust modes and departure times under congestion pricing than standard logit models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper compares three discrete choice models built on stated preference survey data from Calgary commuters to examine responses to congestion pricing in both mode and departure time. It shows that the Cross-Nested Logit structure allows overlapping substitutions between the two decisions, producing more realistic behavioral patterns than the Multinomial Logit or Nested Logit alternatives. Spatial and elasticity results indicate stronger price sensitivity for trips to central locations and during peak periods. The analysis supports the use of targeted cordon and time-variable tolls, along with transit improvements, to shift travel behavior and manage congestion.

Core claim

Utilizing stated preference survey data from commuters in Calgary, Canada, three discrete choice models including Multinomial Logit, Nested Logit, and Cross-Nested Logit are developed and compared. Results indicate that the Cross-Nested Logit model provides superior behavioural realism and flexibility by capturing simultaneous substitutions across modes and departure times. Spatial analysis and elasticity assessments reveal substantial geographic variation in traveller sensitivity to pricing, particularly highlighting stronger responses among commuters travelling to high-demand central locations and during peak travel periods.

What carries the argument

Cross-Nested Logit model that permits overlapping nests between mode and departure time choices to represent joint substitutions.

If this is right

  • Cordon-based pricing combined with time-specific toll adjustments reduces peak-period congestion.
  • Traveler groups differ in flexibility, so policies can be tailored to high-sensitivity segments.
  • Improved transit services and targeted discounts are required to achieve equitable outcomes.
  • Distance-based and travel-time-based tolls produce distinct behavioral effects that can be used selectively.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same model structure could be applied to stated preference data from other cities to test whether the advantage over simpler logit models holds elsewhere.
  • Collecting revealed preference data after pricing is implemented would allow direct comparison of predicted versus observed responses.
  • The documented geographic variation implies that city-wide uniform tolls may miss opportunities for more efficient location-specific pricing.

Load-bearing premise

Stated preference survey answers from Calgary commuters accurately predict how people would actually change their mode and departure time under real congestion pricing.

What would settle it

Measure actual shifts in mode shares and departure times after a congestion pricing scheme is introduced in Calgary and compare those observed changes to the model's forecasts.

read the original abstract

Effective congestion management strategies require a detailed understanding of how travellers respond to different pricing interventions. This paper presents an in-depth analysis of traveller behaviour under congestion pricing scenarios, focusing specifically on mode and departure time decisions. Utilizing stated preference survey data from commuters in Calgary, Canada, three discrete choice models including Multinomial Logit, Nested Logit, and Cross-Nested Logit are developed and compared. Results indicate that the Cross-Nested Logit model provides superior behavioural realism and flexibility by capturing simultaneous substitutions across modes and departure times. Spatial analysis and elasticity assessments reveal substantial geographic variation in traveller sensitivity to pricing, particularly highlighting stronger responses among commuters travelling to high-demand central locations and during peak travel periods. Further elasticity analyses clarify behavioural patterns, identifying traveller groups with varying degrees of flexibility. Policy analyses underscore the effectiveness of targeted, dynamic tolling, particularly cordon-based pricing combined with time-specific toll adjustments, in reducing congestion levels. Additionally, the findings highlight the necessity of complementary measures, including improved transit services and targeted discounts, to ensure equitable outcomes. The findings offer targeted insights into how specific pricing strategies such as cordon, distance, and travel time-based tolls can be used to influence travel behaviour, reduce peak-period congestion, and guide equitable policy design in urban transportation planning.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper develops and compares three discrete choice models (Multinomial Logit, Nested Logit, and Cross-Nested Logit) estimated on stated-preference survey data from Calgary commuters to examine mode and departure-time responses to congestion pricing scenarios. It claims that the CNL specification supplies superior behavioral realism through its ability to capture simultaneous substitutions across modes and times, supported by spatial analysis of geographic variation in price sensitivity and elasticity calculations that inform policy recommendations on cordon, distance, and time-based tolls plus complementary transit improvements.

Significance. If the CNL superiority and elasticity patterns hold under the maintained assumptions, the work adds to the transport-econometrics literature by illustrating the practical value of cross-nested structures for joint mode-time choice and by documenting spatially heterogeneous responses that could guide targeted congestion pricing. The explicit comparison of three nested structures and the policy discussion of dynamic tolling are constructive contributions, though the exclusive reliance on SP data limits external validity claims.

major comments (1)
  1. [Abstract and Results] Abstract and Results sections: The headline claim that CNL supplies superior behavioural realism and flexibility is demonstrated solely via fit statistics and substitution patterns recovered from the stated-preference survey; no revealed-preference validation, before-after field data, or external policy benchmark is referenced. This assumption is load-bearing for the policy implications drawn from the elasticities and spatial patterns.
minor comments (1)
  1. [Abstract] Abstract: The assertion of CNL superiority is stated without accompanying model-fit numbers (log-likelihood, rho-squared, or likelihood-ratio tests), which would help readers evaluate the claim before reaching the full results.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comment on the reliance on stated-preference data. We address the point below and agree that revisions are warranted to qualify our claims.

read point-by-point responses
  1. Referee: [Abstract and Results] Abstract and Results sections: The headline claim that CNL supplies superior behavioural realism and flexibility is demonstrated solely via fit statistics and substitution patterns recovered from the stated-preference survey; no revealed-preference validation, before-after field data, or external policy benchmark is referenced. This assumption is load-bearing for the policy implications drawn from the elasticities and spatial patterns.

    Authors: We agree that the model comparison and superiority claims rest on the stated-preference (SP) survey data alone. SP data is appropriate here because the congestion pricing scenarios are hypothetical and not yet implemented in Calgary, allowing controlled variation in prices, modes, and times that would be difficult to observe in revealed-preference data. Nevertheless, the referee is correct that this limits external validity for the policy recommendations. In the revision we will (i) revise the abstract and results sections to state explicitly that CNL superiority is shown within the SP context via fit statistics and substitution patterns, (ii) add a dedicated limitations paragraph noting the absence of RP validation or before-after benchmarks, and (iii) qualify the elasticity-based policy implications as illustrative rather than definitive. These changes will be reflected in both the abstract and the concluding discussion. revision: yes

Circularity Check

0 steps flagged

No circularity: standard CNL estimation on SP survey data

full rationale

The paper estimates MNL, NL and CNL models directly on stated-preference survey responses using conventional maximum-likelihood procedures. Model superiority is assessed via log-likelihood, rho-squared and derived elasticities; no derivation reduces a fitted parameter to a prediction by construction, no uniqueness theorem is imported via self-citation, and no ansatz is smuggled in. All load-bearing steps are external to the paper's own equations and rest on observable choice data.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Central claim depends on validity of stated preference data as proxy for real behavior and on the CNL nesting structure correctly representing choice correlations; no free parameters or invented entities are identifiable from abstract.

axioms (1)
  • domain assumption Stated preference survey responses reflect actual traveler behavior under congestion pricing
    Models are developed and compared using hypothetical choice data without cross-validation against observed post-policy behavior.

pith-pipeline@v0.9.1-grok · 5770 in / 1144 out tokens · 45571 ms · 2026-06-30T04:13:01.370335+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

41 extracted references

  1. [1]

    Lindsney, R., Verhoef, E. (2001). Traffic congestion and congestion pricing. In:Handbook of Transport Systems and Traffic Control, pp. 77–105. Emerald Group Publishing Limited

  2. [2]

    De Palma, A., Lindsey, R. (2011). Traffic congestion pricing methodologies and technologies.Trans- portation Research Part C: Emerging Technologies, 19(6), 1377–1399

  3. [3]

    Gu, Z., Liu, Z., Cheng, Q., Saberi, M. (2018). Congestion pricing practices and public acceptance: A review of evidence.Case Studies on Transport Policy, 6(1), 94–101

  4. [4]

    Simoni, M.D., Kockelman, K.M., Gurumurthy, K.M., Bischoff, J. (2019). Congestion pricing in a world of self-driving vehicles: An analysis of different strategies in alternative future scenarios.Transportation Research Part C: Emerging Technologies, 98, 167–185

  5. [5]

    Wen, C.-H., Koppelman, F.S. (2001). The generalized nested logit model.Transportation Research Part B: Methodological, 35(7), 627–641

  6. [6]

    Papola, A. (2004). Some developments on the cross-nested logit model.Transportation Research Part B: Methodological, 38(9), 833–851

  7. [7]

    Heimgartner, D., & Axhausen, K. W. (2025). Multimodality in the Swiss new normal: data collec- tion methods and response behavior in a multi-stage survey with linked stated preference designs. Transportation, 1-44

  8. [8]

    Brauer, M., Freedman, G., Frostad, J., Van Donkelaar, A., Martin, R.V., Dentener, F., van Dingenen, R., Estep, K., Amini, H., Apte, J.S., et al. (2016). Ambient air pollution exposure estimation for the global burden of disease 2013.Environmental Science & Technology, 50(1), 79–88

  9. [9]

    Gately, C.K., Hutyra, L.R., Peterson, S., Wing, I.S. (2017). Urban emissions hotspots: Quantifying vehicle congestion and air pollution using mobile phone GPS data.Environmental Pollution, 229, 496– 504

  10. [10]

    Ecola, L., Light, T. (2009). Equity and congestion pricing.Rand Corporation, 1–45

  11. [11]

    Vickrey, W.S. (1969). Congestion theory and transport investment.The American Economic Review, 59(2), 251–260. 32

  12. [12]

    (2007).The Economics of Urban Transportation

    Small, K.A., Verhoef, E.T. (2007).The Economics of Urban Transportation. Routledge, New York, NY

  13. [13]

    He, B.Y., Ma, T., Dong, K., Scott, D.M., Miller, E.J. (2021). A MATSim-based test bed for congestion pricing: Insights from the New York City case.Transport Policy, 108, 28–42

  14. [14]

    Schuitema, G., Steg, L., Forward, S. (2010). Explaining differences in acceptability before and acceptance after the implementation of a congestion charge in Stockholm.Transportation Research Part A: Policy and Practice, 44(2), 99–109

  15. [15]

    Leape, J. (2006). The London congestion charge.Journal of Economic Perspectives, 20(4), 157–176

  16. [16]

    Eliasson, J. (2009). A cost–benefit analysis of the Stockholm congestion charging system.Transportation Research Part A: Policy and Practice, 43(4), 468–480

  17. [17]

    Daganzo, C.F., Lehe, L.J. (2015). Distance-dependent congestion pricing for downtown zones.Trans- portation Research Part B: Methodological, 75, 89–99

  18. [18]

    (2002).Transport Policy and the Environment

    Banister, D. (2002).Transport Policy and the Environment. Routledge

  19. [19]

    Aboudina, A., Abdelgawad, H., Abdulhai, B., Habib, K.N. (2016). Time-dependent congestion pricing system for large networks: Integrating departure time choice, dynamic traffic assignment and regional travel surveys in the Greater Toronto Area.Transportation Research Part A: Policy and Practice, 94, 411–430

  20. [20]

    Li, Z., Wang, H., Yang, J. (2019). Impacts of congestion pricing and reward strategies on automobile travelers’ mode shift decisions: A stated preference study in Beijing.Transport Policy, 81, 234–246

  21. [21]

    Geng, K., Wang, Y., Cherchi, E., Guarda, P. (2023). Commuter departure time choice behavior under congestion charge: Analysis based on cumulative prospect theory.Transportation Research Part A: Policy and Practice, 168, 103564

  22. [22]

    Zhang, W., Liu, C., Zhang, H. (2023). Public acceptance of congestion pricing policies in Beijing: The roles of neighborhood built environment and air pollution perception.Transport Policy, 143, 106–120

  23. [23]

    Sparrow, R., Howard, M. (2020). Make way for the wealthy? Autonomous vehicles, markets in mobility, and social justice.Mobilities, 15(4), 514–526

  24. [24]

    Yang, H., Huang, H.-J. (1997). Analysis of the time-varying pricing of a bottleneck with elastic demand using optimal control theory.Transportation Research Part B: Methodological, 31(6), 425–440

  25. [25]

    Bajwa, S., Bekhor, S., Kuwahara, M., Chung, E. (2008). Discrete choice modeling of combined mode and departure time.Transportmetrica, 4(2), 155–177

  26. [26]

    Yang, L., Zheng, G., Zhu, X. (2013). Cross-nested logit model for the joint choice of residential location, travel mode, and departure time.Habitat International, 38, 157–166

  27. [27]

    Ma, S., Yu, Z., Liu, C. (2020). Nested logit joint model of travel mode and travel time choice for urban commuting trips in Xi’an, China.Journal of Urban Planning and Development, 146(2), 04020020

  28. [28]

    Hossain, S., Hasnine, M.S., Habib, K.N. (2021). A latent class joint mode and departure time choice model for the Greater Toronto and Hamilton Area.Transportation, 48(3), 1217–1239

  29. [29]

    Ding, C., Mishra, S., Lin, Y., Xie, B. (2015). Cross-nested joint model of travel mode and departure time choice for urban commuting trips: Case study in Maryland–Washington, DC region.Journal of Urban Planning and Development, 141(4), 04014036. 33

  30. [30]

    Karlstr¨ om, A., Franklin, J.P. (2009). Behavioral adjustments and equity effects of congestion pricing: Analysis of morning commutes during the Stockholm Trial.Transportation Research Part A: Policy and Practice, 43(3), 283–296

  31. [31]

    Government of Alberta. (2025). Calgary Region Profile — Regional Dashboard. Available at: https: //regionaldashboard.alberta.ca/region/calgary/#/. Accessed April 11, 2025

  32. [32]

    (2024).Ngene 1.4 User Manual and Reference Guide: The Cutting Edge in Experimental Design

    ChoiceMetrics. (2024).Ngene 1.4 User Manual and Reference Guide: The Cutting Edge in Experimental Design. Available at: https://files.choice-metrics.com/NgeneManual140.pdf. Accessed May 2024

  33. [33]

    (2009).Discrete Choice Methods with Simulation

    Train, K.E. (2009).Discrete Choice Methods with Simulation. Cambridge University Press

  34. [34]

    Hess, S., Fowler, M., Adler, T., Bahreinian, A. (2012). A joint model for vehicle type and fuel type choice: Evidence from a cross-nested logit study.Transportation, 39, 593–625

  35. [35]

    Bierlaire, M. (2006). A theoretical analysis of the cross-nested logit model.Annals of Operations Research, 144, 287–300

  36. [36]

    Hess, S., Bierlaire, M., Polak, J.W. (2004). Development and application of a mixed cross-nested logit model. In:Proceedings of the XXIth European Transport Conference

  37. [37]

    Fridstrøm, L., Østli, V. (2021). Direct and cross price elasticities of demand for gasoline, diesel, hybrid and battery electric cars: The case of Norway.European Transport Research Review, 13, 1–24

  38. [38]

    Bierlaire, M. (2018). Calculating indicators with PandasBiogeme. Technical report, Transport and Mobility Laboratory, ´Ecole Polytechnique F´ ed´ erale de Lausanne

  39. [39]

    Macrotrends. (2025). Calgary, Canada Metro Area Population 1950–2024. Available at: https://www. macrotrends.net/global-metrics/cities/20370/calgary/population. Accessed April 11, 2025

  40. [40]

    Thorhauge, M., Cherchi, E., Rich, J. (2016). How flexible is flexible? Accounting for the effect of rescheduling possibilities in choice of departure time for work trips.Transportation Research Part A: Policy and Practice, 86, 177–193

  41. [41]

    Cook, C., Kreidieh, A., Vasserman, S., Allcott, H., Arora, N., van Sambeek, F., Tomkins, A., Turkel, E. (2025). The short-run effects of congestion pricing in New York City. Technical Report, National Bureau of Economic Research. 34