Application of Discrete 3-level Nested Logit Model in Travel Demand Forecasting as an Alternative to Traditional 4-Step Model

Document Type : Original Article

Authors

1 Transportation Department, Faculty of Civil Engineering, Istanbul Technical University, Istanbul, Turkey

2 Transportation Department, Faculty of civil engineering, Istanbul Technical University

Abstract

This paper aims to introduce a new modelling approach that represents departure time, destination and travel mode choice under a unified framework. Through it, it is possible to overcome shortages of the traditional 4-step model associated with the lack of introducing actual travellers’ behaviours. This objective can be achieved through adopting discrete 3-level Nested Logit model that represents different potential correlation (cross elasticity) among departure time, destination and travel mode alternatives. The proposed model has been estimated and tested by using discretionary trips’ data from Eskisehir city, Turkey. In the light of the estimation results, individuals tend to jointly decide on discretionary travel dimensions rather than separately as assumed by the traditional 4-step model. Moreover, the proposed approach shows more flexibility in considering attributes of alternatives along with characteristics of decision makers. That results in a more behavioural travel demand modelling, more accurate future forecasting and more trusted policy implications. The proposed model represents a more accurate and reliable alternative for the first 3-steps of the traditional 4-step model in small-scale planning issues. Finally, the proposed approach is a significant milestone toward obtaining a consistent, efficient and integrated full-scale behavioural-model that consists of all travel demand dimensions.

Keywords


 1. Sumi, L. and Ranga, V., "Intelligent traffic management system
for prioritizing emergency vehicles in a smart city", International
Journal of Engineering- Transaction A: Basics,  Vol. 31, No. 2,
(2018), 278-283. 
2. Ghasemi, J. and Rasekhi, J., "Traffic signal prediction using
elman neural network and particle swarm optimization", 
International Journal of Engineering-Transactions B:
Applications,  Vol. 29, No. 11, (2016), 1558-1564. 
3. Johnston, R., "The urban transportation planning process", The
Geography of Urban Transportation,  Vol. 3, (2004), 115-140. 
4. Morehouse, T.A., "The 1962 highway act: A study in artful
interpretation", Journal of the American Institute of Planners, 
Vol. 35, No. 3, (1969), 160-168. 
5. Boyce, D., "Is the sequential travel forecasting paradigm
counterproductive?", Journal of Urban Planning and
Development,  Vol. 128, No. 4, (2002), 169-183. 
6. Gu, Y., "Integrating a regional planning model (transims) with an
operational model (corsim)", Virginia Tech,  (2004),  
7. Bhat, C.R., "Analysis of travel mode and departure time choice
for urban shopping trips", Transportation Research Part B:
Methodological,  Vol. 32, No. 6, (1998), 361-371. 
8. Stopher, P.R., "Deficiencies of travel-forecasting methods
relative to mobile emissions", Journal of Transportation
Engineering,  Vol. 119, No. 5, (1993), 723-741. 
9. Weiner, E., "Upgrading travel demand forecasting capabilities",
in 4th National Conference on Transportation Planning Methods
Applications, Volumes I and II. A Compendium of
PapersTransportation Research Board Committee on
Transportation Planning Applications-A1C07; Federal Highway
Administration; Federal Transit Administration; McTrans Center,
University of Florida; and hosted by the Florida Department of
Transportation., (1993). 
10. Setak, M., Dastaki, M.S. and Karimi, H., "Investigating zone
pricing in a location-routing problem using a variable
neighborhood search algorithm", International Journal of
Engineering-Transactions B: Applications,  Vol. 28, No. 11,
(2015), 1624-1633. 
11. Ben-Akiva, M.E., Lerman, S.R. and Lerman, S.R., "Discrete
choice analysis: Theory and application to travel demand, MIT
press,  Vol. 9,  (1985). 
12. McNally, M.G., "The four step model",  In: Handbook of
Transport Modelling, ed. David A. Hensher and Kenneth J.
Button, (2000), 35-52. 
13. Oppenheim, N., "Urban travel demand modeling: From
individual choices to general equilibrium, John Wiley and Sons, 
(1995). 
14. Vuchic, V.R., "Urban transit: Operations, planning, and
economics, John Wiley & Sons,  (2017). 
15. Donnelly, R., "Advanced practices in travel forecasting,
Transportation Research Board,  Vol. 406,  (2010). 
16. Small, K.A., "The scheduling of consumer activities: Work trips",
The American Economic Review,  Vol. 72, No. 3, (1982), 467479.
17. Hendrickson, C. and Plank, E., "The flexibility of departure times
for work trips", Transportation Research Part A: General,  Vol.
18, No. 1, (1984), 25-36. 
18. Wilson, P.W., "Scheduling costs and the value of travel time",
Urban Studies,  Vol. 26, No. 3, (1989), 356-366. 
19. Noland, R. and Small, K.A., "Travel-time uncertainty, departure
time choice, and the cost of morning commutes", Transportation
Research Record,  Vol. 1493, (1995), 150-158. 
20. Abu-Eisheh, S. and Mannering, F.L., "Discrete/continuous
analysis of commuters' route and departure time choices",
Transportation Research Record,  Vol. 1138, (1987), 27-34. 
21. Hamed, M.M. and Mannering, F.L., "Modeling travelers'
postwork activity involvement: Toward a new methodology",
Transportation Science,  Vol. 27, No. 4, (1993), 381-394. 
22. Bhat, C.R. and Steed, J.L., "A continuous-time model of
departure time choice for urban shopping trips", Transportation
Research Part B: Methodological,  Vol. 36, No. 3, (2002), 207224.
23. Wang, J.J., "Timing utility of daily activities and its impact on
travel", Transportation Research Part A: Policy and Practice, 
Vol. 30, No. 3, (1996), 189-206. 
24. Yamamoto, T., Fujii, S., Kitamura, R. and Yoshida, H., "Analysis
of time allocation, departure time, and route choice behavior
under congestion pricing", Transportation Research Record, 
Vol. 1725, No. 1, (2000), 95-101. 
25. Ettema, D. and Timmermans, H., "Modeling departure time
choice in the context of activity scheduling behavior",
Transportation Research Record,  Vol. 1831, No. 1, (2003), 3946.
26. Bhat, C.R., "A hazard-based duration model of shopping activity
with nonparametric baseline specification and nonparametric
control for unobserved heterogeneity", Transportation Research
Part B: Methodological,  Vol. 30, No. 3, (1996), 189-207. 
27. Bhat, C.R., "A multiple discrete–continuous extreme value
model: Formulation and application to discretionary time-use
decisions", Transportation Research Part B: Methodological, 
Vol. 39, No. 8, (2005), 679-707. 
28. Bhat, C.R., "The multiple discrete-continuous extreme value
(mdcev) model: Role of utility function parameters, identification
considerations, and model extensions", Transportation Research
Part B: Methodological,  Vol. 42, No. 3, (2008), 274-303. 
29. Pinjari, A.R. and Bhat, C., "A multiple discrete–continuous
nested extreme value (MDCNEV) model: Formulation and
application to non-worker activity time-use and timing behavior
on weekdays", Transportation Research Part B:
Methodological,  Vol. 44, No. 4, (2010), 562-583. 
30. Bowman, J.L. and Ben-Akiva, M.E., "Activity-based
disaggregate travel demand model system with activity
schedules", Transportation Research Part A: Policy and
Practice,  Vol. 35, No. 1, (2001), 1-28. 
31. You, D., Garikapati, V.M., Konduri, K.C., Pendyala, R.M.,
Vovsha, P. and Livshits, V., Multiple discrete-continuous model
of activity type choice and time allocation for home-based
nonwork tours. (2013). 
32. Bhat, C.R., "Accommodating flexible substitution patterns in
multi-dimensional choice modeling: Formulation and application
to travel mode and departure time choice", Transportation
Research Part B: Methodological,  Vol. 32, No. 7, (1998), 455466.
33. McFadden, D., "Modeling the choice of residential location",
Transportation Research Record,  Vol. 673, (1978). 
34. Jrew, B., Msallam, M. and Momani, M., "Strategic development
of transportation demand management in jordan", Civil
Engineering Journal,  Vol. 5, No. 1, (2019), 48-60. 
35. Moradzaeh, A. and Khaffafi, K., "Comparison and evaluation of
the performance of various types of neural networks for planning
issues related to optimal management of charging and discharging
electric cars in intelligent power grids", Emerging Science
Journal,  Vol. 1, No. 4, (2018), 201-207. 
36. Shafiei, S., Vaelizadeh, R., Bertrand, F. and Ansari, M.,
"Evaluating and ranking of travel mode in metropolitan; a
transportation economic approach", Civil Engineering Journal, 
Vol. 4, No. 6, (2018), 1303-1314. 
37. Koppelman, F. and Bhat, C., "A self instructing course in mode
choice modeling: Multinomial and nested logit models. Prepared
for us department of transportation federal transit administration",
Federal Transit Administration, (2006).