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The use of soft computing methods in the field of transportation is rather new and unexplored in comparison with discrete choice models. Most of the soft computing applications have been based on fuzzy logic and neural networks. The share of the empirical studies that are based on fuzzy logic and neural networks in traffic and transportation studies is around 72% in 2004 (Avineri 2005). Among soft computing methods, Bayesian belief networks are rarely used in transportation modeling. In this section, soft computing methods and Bayesian belief networks in mode choice modeling are discussed.

2.3.1. Soft Computing Methods in Travel Demand Modeling

The presented study intends to compare performance of mode choice models. Discrete choice models, especially logit models have been the workhorse for empirical analysis. However, soft computing methods have emerged as an alternative approach to conventional models in travel demand modeling and transport economics, over the last 15 years. Relative literature suggests that soft computing methods may need less information about problem domain. However, they may give more information and better model performance than conventional approaches. For this reason, soft computing methods can be more suitable and robust models than conventional models. In this part of the literature review, soft computing literature in mode choice modeling has been discussed over empirical studies. These studies represented in Table 2.5 have been pioneer of soft computing approaches to conventional models in mode choice modeling. The important point is that the research question, how land use attributes

affect mode choice, generally has been ignored. In other words, the potential effects of

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Nijkamp et al. (1996, 1997) analyzed the impact of high speed train in Italy using logit and neural network model at aggregate level. Nijkamp et al. (2004) studied interregional European freight transport flows by comparing discrete choice models (logit and probit) and the neural network at aggregate level. Abdelwahab and Sayed (1999) introduced neural networks to behavioral choice modeling to analyze U.S. freight transport market at disaggregate level. Hensher and Ton (2000) compared neural networks and nested logit models for commuter mode choice at disaggregate level in the Australian cities. They did not find enough evidence to recommend that ANN is better than Nested Logit models. Cantarella and Luca (2005) analyzed mode choice for commuting trips within the Italian region of Veneto using Multi Layer Feed Forward Network (MLFFN) and random utility models (multinominal and nested logit models) at disaggregate level. Vythoulkas and Koutsopoulos (2003) studied modeling discrete choice behavior using fuzzy set theory, approximate reasoning, and neural networks in The Netherlands at disaggregate level. Celikoglu (2006) studied radial basis function neural network and generalized regression neural network in Istanbul using only time and cost input variables at aggregate level for home-based work (HBW) trips. Xie et al. (2003) compared the capability and performance of data mining methods (decision trees and neural networks) and multinomial logit (MNL) models for work trips in San Francisco Bay Area at disaggregate level. Demir and Gercek (2006) studied mode choice behavior in urban passenger transportation using with soft computing methods (fuzzy logic, neural networks, and neuro-fuzzy logic) and binary logit in EskiĢehir. Torres and Huber (2003) performed BBNs to trip generation as a function of socioeconomic variables for home - based work trips at disaggregates level using with 1996 Dallas Household Travel Survey. The study used found that accessibility variables have causal links with the trip generation variables. Janssens et al. (2006) examined the predictive capabilities of decision tree and Bayesian networks for modeling individual choice in The Netherlands. Scuderi and Clifton (2005) investigated the relationship between mode choice and land use using with BBNs in Baltimore metropolitan area at disaggregate level. The study found that the strongest relationships for mode choice are the availability of a private car, the driver status, age, and how empty the land-space looks around the point of origin. Household size, income, and number of commercial spaces are the least influential variables associated with mode choice. The performance of BBNs in the study was not measured.

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Empirical studies mentioned above found that soft computing methods outperform conventional models. On the other hand, Hensher and Ton (2000) did not found enough evidence about which approach is better. Xie et al. (2003) found that data mining methods (decision tree and neural networks) are slightly better performance than MNL. Nijkamp et al. (2004) found that the predictive performance of ANN is higher than that of logit model. Cantarella and Luca (2005) found that ANN outperformed random utility models. Celikoglu (2006) found that the performance of neural networks is higher than multivariate linear regression. In the majority of these studies using alternative approaches, land use variables were omitted from the input variables and travel characteristics (time and cost) only entered into the models. Also, neural networks, fuzzy logic, and hybrid approaches are common models in travel demand modeling. Different algorithms and hybrid approaches can be tested in future studies. Therefore, better performance and low error term can be obtained. Bayesian belief networks are one of the alternative methods that rarely used in mode choice modeling.

Table 2.5. Literature review of empirical studies employing soft computing methods used in mode choice

Case Study Data Type Empirical Models Compared Variables Land Use

Characteristics

Hensher and Ton (2000), in six Australian cities.

Commute Mode Choice, Disaggregate Level.

Artificial Neural Networks (ANN), Nested Logit Models.

Travel Characteristics (Cost and Time), Socieconomic and level of service (LOS)

attributes, and ASC.

Not included. Vythoulkas and

Koutsopolos (2003), in The Netherlands.

Analyzing choice behavior between rail and car, Disaggregate Level.

Fuzzy Logic, Neuro-Fuzzy, and Binary Logit Models.

Cost, Time, and Rail Access

Time. Not included.

Nijkamp, Reggiani, and Tritapepe (2004).

European Freight Flows, Aggregate Level.

ANN, Probit, and Logit

Models. Distance and Cost Not included.

Cantarella and Luca (2005),

two cases in Italy. Commuter trips.

Disaggregatye Level. ANN and MNL Models.

Travel Characteristics (Cost and Time), socieconomic and level of service (LOS) attributes, ASC

Whether destination zone is inside the urban center or not (only used in logit models). Celikoglu (2006),

in Istanbul. Home-based work trips. Aggregate Level

Neural Networks, Linear Regression, and Binary Logit Models.

Time and Cost. Not included. Demir and Gercek (2006),

in Eskisehir.

Mode choice for different income group,

Disaggregate Level

ANN, Fuzzy Logic,Neuro- Fuzzy, and MNL Models.

Time, Cost, and

Socioeconomic Attributes. Not included.

Scuderi and Clifton (2005), in Baltimore metropolitan region.

Disaggregate Level Only Bayesian Belief Networks. Socioeconomic Characteristics. Population density, road density index, commercial, industrial, vacant land rates. 41

2.4. The Methods for Performance Comparison of Mode Choice