Joint analysis of urban shopping destination and travel mode choice accounting for potential spatial correlation between alternatives

Abstract

In recent years, there have been important developments in the joint analysis of the travel behavior based on discrete choice models as well as in the formulation of increasingly flexible closed-form models belonging to the generalized extreme value class. The objective of this work is to describe the simultaneous choice of shopping destination and travel-to-shop mode in downtown area by making use of the cross-nested logit (CNL) structure that allows for potential spatial correlation. The analysis uses data collected in the downtown areas of Maryland-Washington, D.C. region for shopping trips, considering household, individual, land use, and travel-related characteristics. The estimation results show that the dissimilarity parameter in the CNL model is 0.37 and significant at the 95% level, indicating that the alternatives have high spatial correlation for the short shopping distance. The results of analysis reveal detailed significant influences on travel behavior of joint choice shopping destination and travel mode. Moreover, a Monte Carlo simulation for a group of scenarios arising from transportation policies and parking fees in downtown area, was undertaken to examine the impact of a change in car travel cost on the shopping destination and travel mode switching. These findings have important implications for transportation demand management and urban planning.

Keywords

shopping destination / travel mode choice / joint choice / cross-nested logit / Monte Carlo simulation

References

[1] ReckerW W. Factors influencing destination choice for the urban grocery shopping trip [J]. Transportation, 1978, 7(1): 19-33

[2] BhatC R, GuoJ. A mixed spatially correlated logit model: formulation and application to residential choice modeling [J]. Transportation Research Part B, 2004, 38(2): 147-168

[3] SivakumarA, BhatC R. Comprehensive, unified framework for analyzing spatial location choice [J]. Journal of the Transportation Research Board, 2007, 2003: 103-111

[4] BhatC R. Work travel mode choice and number of non-work commute stops [J]. Transportation Research Part B, 1997, 31(1): 41-54

[5] PalmaA D, RochatD. Mode choices for trips to work in Geneva: An empirical analysis [J]. Journal of Transport Geography, 2000, 8(1): 43-51

[6] TringidesC A, YeX, PendyalaR M. Departure-time choice and mode choice for nonwork trips: Alternative formulations of joint model systems [J]. Journal of the Transportation Research Board, 2004, 1898: 1-9

[7] BajwaS, BekhorS, KuwaharaM, ChungE. Discrete choice modeling of combined mode and departure time [J]. Transportation, 2008, 4(2): 155-177

[8] VegaA, FeighanA R. A methodological framework for the study of residential location and travel-to-work mode choice under central and suburban employment destination patterns [J]. Transportation Research Part A, 2009, 43(4): 401-419

[9] YangL, ZhengG, ZhuX. Cross-nested logit model for the joint choice of residential location, travel mode, and departure time [J]. Habitat International, 2013, 38: 157-166

[10] ScheinerJ. Interrelations between travel mode choice and trip distance: Trends in Germany 1976–2002 [J]. Journal of Transport Geography, 2010, 18(1): 75-84

[11] JongG D, DalyA, PietersM, VellayC, BradleyM, HofmanF. A model for time of day and mode choice using error components logit [J]. Transportation Research Part E, 2003, 39(3): 245-268

[12] FengZ-x, YuanH-z, LiuJ, GaoX, ZhangW-hua. Selection model of trip time for rural population [J]. Journal of Central South University, 2013, 20(1): 274-278

[13] HessS, FowlerM, AdlerT, BahreinianA. A joint model for vehicle type and fuel type choice: Evidence from a cross-nested logit study [J]. Transportation, 2012, 39(3): 593-625

[14] McFaddenD, et al. KarlqvistA, et al. . Modeling the choice of residential location. [M]. Spatial Interaction Theory and Residential Location, 1978, Amsterdam, North-Holland: 75-96

[15] BekhorS, PrashkerJ N. GEV-based destination choice models that account for unobserved similarities among alternatives [J]. Transportation Research Part B, 2008, 42(3): 243-262

[16] WenC H, KoppelamF S. The generalized nested logit model [J]. Transportation Research Part B, 2001, 35(7): 627-641

[17] DalyA, BierlaireM. A general and operational representation of generalised extreme value models [J]. Transportation Research Part B, 2006, 40(4): 285-305

[18] SenerI N, PendyalaR M, BhatC R. Accommodating spatial correlation across choice alternatives in discrete choice models: an application to modeling residential location choice behavior [J]. Journal of Transport Geography, 2011, 19(2): 294-303

[19] PapolaA. Some developments on the cross-nested logit model [J]. Transportation Research Part B, 2004, 38(9): 833-851

[20] BierlaireM. A theoretical analysis of the cross-nested logit model [J]. Annals of Operations Research, 2006, 144(1): 287-300

[21] MishraS, YeX, DuccaF, KnaapG J. A functional integrated land use-transportation model for analyzing transportation impacts in the Maryland-Washington, DC Region [J]. Journal of Sustainability Science Policy and Practice, 2011, 7(2): 60-69

[22] HessS, PolakJ W. Exploring the potential for cross-nesting structures in airport-choice analysis: A case-study of the greater London area [J]. Transportation Research Part E, 2006, 42(2): 63-81

[23] CerveroR. Built environments and mode choice: toward a normative framework [J]. Transportation Research Part D, 2002, 7(4): 265-284

[24] EwingR, CerveroR. Travel and the built environment: A meta-analysis [J]. Journal of the American Planning Association, 2010, 76(3): 265-294

[25] BierlaireM. The network of GEV model [C]. Proceeding of the 2nd Swiss Transport Research Conference, 2002, Monte Verita, Ascona, Switzerland: 1-20

[26] BierlaireM. BIOGEME: A free package for the estimation of discrete choice models [C]. Proceeding of the 3rd Swiss Transport Research Conference. Monte Verita, Ascona, Switzerland, 20031-24

[27] HessS, RoseJ M, HensherD A. Asymmetric preference formation in willingness to pay estimates in discrete choice models [J]. Transportation Research Part E, 2008, 44(5): 847-863

[28] SalehW, FarrellS. Implication of congestion charging for departure time choice: work and non-work schedule flexibility [J]. Transportation Research Part A, 2005, 39(7/8/9): 773-791

[29] BhatC R, SardesaiR. The impact of stop-making and travel time reliability on commute mode choice [J]. Transportation Research Part B, 2006, 40(9): 709-730