Journals / Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi / 2022 / Cilt: 28 - Sayı: 6

The relationship between transportation demand and supply: Granger-Causality test using time-series data

Ulaşım talebi ve arzı arasındaki bağıntı: Zaman-Seri veri ile Granger nedensellik testi

Pages
786–801
Publication Date
2022-11-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Using the Turkish statistics, the GCT results showed that, in the short/medium run, overwhelmingly the supply variables preceded (mostly in railway mode), mostly unidirectional (one-way causality) manner, however, in the long-run almost no relationship was found.

📌 3 Key Takeaways

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Objective & Hypothesis:Transport demand and supply are deemed to determine each other in a cyclic manner.
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Key Findings & Data:In other transportation modes, no significant relationship is observed.
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Conclusion & Impact:Using the Turkish statistics, the GCT results showed that, in the short/medium run, overwhelmingly the supply variables preceded (mostly in railway mode), mostly unidirectional (one-way causality) manner, however, in the long-run almost no relationship was found.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Travel demand · Transport(-ation) supply · Grangercausality test

Abstract

Transport demand and supply are deemed to determine each other in a cyclic manner. The major idea has been that the demand is usually the preceding one. However, in urban cases, usually the land use variables in place of supply interfere this process. Cleansing the land use variables, the regional/national level variable pairs of demand and supply are employed to analyze the cause-effect mechanism. For objectivity, the Granger-causality test (GCT) is used to understand the relationship between transportation demand and supply. The Analyses were made at four dimensions; (a)whether the nexus is one-directional or bidirectional, (b)its significance level, (c)whether demand or supply is the preceding, (d)whether the effects are short-term or long-term. Using the Turkish statistics, the GCT results showed that, in the short/medium run, overwhelmingly the supply variables preceded (mostly in railway mode), mostly unidirectional (one-way causality) manner, however, in the long-run almost no relationship was found. In other transportation modes, no significant relationship is observed. Finally, bi-directional relations were usually observed in suburban rail. The investments then should be made according to known demand. Usually, the effects of supply (especially of railways and roadways) could rather fade away in the long-run. Still, no general statement can be made for the demand/supply causality especially in terms of which one is preceding and of the direction of causality. The chaotic nature of the process reigns over with the changing conditions.

Özet

Ulaşım arz ve talebinin karşılıklı ve dönüşümlü biçimde birbirlerini belirlediği düşünülür. Aslolan talebin belirlemede öncül olmasıdır. Fakat kentsel bölgelerde, genellikle arz yerine kullanılan arazi kullanım değişkenleri bu sürecin arasına karışmaktadır. Arazi kullanım değişkenlerini temizleyerek, bölgesel/milli arz-talep değişken çiftleri sebep-sonuç mekanizması analizinde kullanılmıştır. Nesnel bir analiz için, Granger-nedensellik testi (GCT), tek-yön ve çift-yön için zaman seri veri kullanılarak, hem öncel olan tarafın ve en etken değişkenlerinin tespitinde kullanılmıştır. Analizler dört seviyede yapılmıştır; (a)bağıntının tek-yönlü veya çift-yönlü olup olmadığı, (b)istatistiki anlamlılık, (c)talep veya arzın başlatıcı olup olmadığı, (d) etkilerin kısa vade veya uzun vade olup olmadığı. Ülkemizin bölge istatistikleri ile GCT sonuçları göstermiştir ki, arz-talep etkileşimi tartışmasına açıklık getirebilecek şekilde tek-yön ilişkide arz tarafı değişkenleri özellikle demiryolları bakımından daha önceldir. Buna mukabil, uzun vadede anlamlı sonuçlar hemen hemen yoktur. Sonuçta, çift-yönlü ilişkiler banliyö tren ulaşımında gözlemlenmiştir. Yatırımlar mutlaka talep bilgisi doğrultusunda olmalıdır. Genellikle, arz etkileri (bilhassa demiryolu ve karayolunda) uzun vadede kaybolma eğilimindedir. Hala, arz/talep nedenselliğinde hangisinin başat olduğu ve nedensellik yönlenimi konusunda genel bir hükme varılamamaktadır. Değişen koşullara göre sürecin karmaşık doğası etkin olmaktadır.

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Cite This Article

DUVARCI, Y., DURAN, H. E. (2022). The relationship between transportation demand and supply: Granger-Causality test using time-series data. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 28 - Sayı: 6, 786–801. https://ajindex.com/articles/862552

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References (15)

  1. Paleti R, Imani AF, Eluru N, Hu HH, Huang G. “An integrated model of intensity of activity opportunities on supply side and tour destination & departure time choices on demand side”. Journal of Choice Modelling, 24, 63-74, 2017
  2. European Commision. “White Paper, Roadmap to a Single European Transport Area-Towards a competitive and resource efficient transport system”. Brussels, Belgium, Report No: COM144 Final, 2011
  3. Holmgren J. “Demand and supply of public transport- the problem of cause and effect”. Competition and Ownership in Land Passenger Transport: Selected Refereed Papers From the 8th International Conference (Thredbo 8), Rio de Janeiro, Brasil, 12-19 September 2003
  4. Mert M. “Public Transportation Investments and Economic Growth in Turkey”. Eurasian Journal of Economics and Finance, 5(2), 17-35, 2017
  5. Algaic A. “An analysis of the causal relationship between transportation and GDP: a time-series approach for the United States”. Major Themes in Economics, 19(4), 17-37, 2017
  6. Beyzatlar MA, Karacal M, Yetkiner H. “Granger-causality between transportation and GDP: a panel data approach”. Transportation Research Part A: Policy and Practice, 63, 43-55, 2014
  7. Engle RF, Granger CWJ. “Co-Integration and error correction: representation, estimation, and testing.” Econometrica, 55(2), 251-276, 1987
  8. Greene WH. Econometric Analysis. 5th ed. New Jersey, USA, Prentice Hal, 2002
  9. Granger CWJ. “Investigating causal relation by econometric models and cross-spectral methods”. Econometrica, 37(3), 424-438, 1969
  10. EViews 4. “User’s Guide” https://www.researchgate.net/profile/Ahammad_Hossa in2/post/Is_there_any_evoews_code_for_MWALD_grange r_causality_test_for_bivariate_VAR_model/attachment/5 9d6242b79197b8077982805/AS%3A31158007441408 0%401451297887143/download/Users+Guide+4.0.pdf (20.09.2018)
  11. Gujarati DN, Porter DC. Basic Econometrics. 5th ed. Newyork, USA, McGraw-Hill Irvin, 2009
  12. Kitamura R. “The effects of added transportation capacity on travel: a review of theoretical and empirical results”. Transportation, 36, 745-762, 2009
  13. Cervero R. “Are induced travel studies inducing bad investments”. Access, 22, 22-27, 2003
  14. Cervero R, Kockelman K. “Travel demand and the 3Ds: density, diversity, and design”. Transportation Research Part D: Transport and Environment, 2(3), 199-219, 1997
  15. Kitamura R, Mokhtarian PL, Laidet L. “A micro-analysis of land use and travel in five neighborhoods in the San Francisco B