A dynamic discrete choice modelling approach for forward-looking travel mode choices

This paper investigates two mode-related dimensions at the household level, namely, mode choice and modal use intensity (as reflected by modal expenditures). These dimensions are analyzed jointly in the context of Chennai city in India, by using a large disaggregate database consisting of more than 2,000 households. Specifically, the objectives of this study are to analyze mode choice decisions at the household level, to integrate mode choice and mode usage using a suitable model, and to analyze the effect of contextual factors relevant to developing countries on the mode choice propensity and mode use intensity. At the household level, the mode choice problem is a multiple discrete choice problem (multiple alternatives may be selected) in contrast to the singly discrete nature of the individual mode choice problem. Therefore, a multiple discrete–continuous extreme value model is formulated to integrate choice and usage, based on a coherent utility maximization framework. To the authors’ knowledge, this study is the first to model mode choice as a multiple discrete choice problem. The results reveal that several unique and context-specific features in developing countries affect household-level mode choice significantly. Further, the mode use intensity of alternative modes is influenced by prior mode choice decisions, inertia, and user's perception of safety and congestion. The results have important planning and policy implications for transit improvement and congestion mitigation strategies.

A discrete choice analysis of transport mode choice causality and perceived barriers of sustainable mobility in the MENA region

An investigation of commuting trip timing and mode choice in the Greater Toronto Area: Application of a joint discrete-continuous model

A nested logit model of mode choice for inland movement of export shipments: A case study of containerised export cargo from India

Empirical studies on the link between land use characteristics and travel behavior may generate biased estimates when the choice of residential locations is influenced by mode preference (i.e., residential self-selection). Without appropriately addressing this matter, the effects of land use characteristics on travel behavior may be overestimated. This study aims specifically to address the residential self-selection problem related to transit service. It also aims to take into account the effects of density and transit service at the origin and destination on mode choice because the level of transit service is often correlated with density. Mode choice effects associated with density may actually be due to levels of transit service. The two-day activity data from the 1994/1995 Portland Household Activity and Travel Behavior survey are used to analyze discrete choice models of commuting mode and residential location choices. Generally omitted in many studies, different transit service characteristics around both residential and employment locations are identified at a micro scale level. A cluster analysis is employed as a way to classify areas that have similar transit service characteristics into distinct groupings, or levels of transit service. Levels of transit service derived from the cluster analysis characterize different dimensions of transit service. No evidence was found that the residential choice regarding the levels of transit service and commuting mode choice were jointly made. People do not appear to choose residential locations with regard to levels of transit service to match their preferred mode. Although this relationship (the self-selection) could not be found, the findings suggest that it is necessary to include transit service characteristics in mode choice models because they help to separate transit service effects from density. With the exception of the self-selection question, the findings suggest that the levels of transit service at the employment locations (not the ones at the residential locations) encourage transit use and non-motorized modes. High levels of transit service at the residential location do not appear to influence transit use. As a result, land use policies and improvements of transit service emphasizing employment locations may be more effective than those that target residential locations.

This paper analysed automobile commuters' mode choice behaviour under the influence of simulated multimodal traveller information by developing two logit models. A combined revealed preference (RP)/stated preference (SP) travel behaviour survey was administered to drivers to gather individual commuters' travel decisions under integrated multimodal traveller information. Two SP scenarios were designed where the first scenario is to test the mode choice preference in a basic situation involving a congested work/school trip with information on several travel options, and the second scenario is to investigate the mode choice decision when certain incentives are given for public transport. Results showed that integrated multimodal traveller information can influence a traveller's mode choice decision. The influencing factors that significantly affect the mode choice decision include socio-economic characteristics – for example, gender, age, level of education and level of income – and multimodal traveller information attributes – for example, access mode to mass rapid transit (MRT) station, access time to MRT station and transit seat availability. The findings are useful to traffic management agencies for designing better operational policy and information publication strategies.

A considerable shift has occurred in mode choice behaviour and escort pattern of students for their school trips around the world during the last few decades. This shift towards using more non-active modes has undesirable consequences, including physical inactivity among students, traffic jams during peak hours, and adverse environmental impacts. Hence, understanding the behaviour of decision-makers with regard to the mode choice and escorting pattern selection is crucial for controlling the trend of the shift and promoting active modes. This study is the first effort to mathematically model the mode choice and accompaniment pattern selection by parents for the school trip of students, in a joint modelling structure as it is believed that these two decisions are jointly made by parents. Two modelling formulations are used: a nested logit (NL) model and a copula-based model. Results showed that the copula model outperforms the NL model. It was also found that modelling these two decisions in an independent manner can result in misleading policy assessments.

A tour-based microsimulation approach to modeling destination choice and mode choice of San Francisco residents is presented. These models were developed as part of an overall tour-based travel demand forecasting model (SF model) for the San Francisco County Transportation Authority to provide detailed forecasts of travel demand for various planning applications. The models described represent two of the nine primary components of the SF model. Both model components consist of multiple logit choice models and include both tour-level models (which refer to the primary activity of the tour) and trip-level models (other activities on the tour). A separate model was estimated for each tour purpose, including work, school, other, and work-based. The destination choice models combine the trip attraction and trip distribution components of the traditional four-step process and use a multinomial logit specification. The mode choice models utilize a nested logit formulation to capture the similarities among sets of similar modes. The two models are linked by incorporating the mode choice utility logsum in the destination choice models; the result is equivalent to a nested structure with a mode choice nest under destination choice. It is demonstrated that the microsimulation approach easily allows the inclusion of a number of key variables in destination and mode choice models that have a significant explanatory power compared with those in traditional models. It is also shown that this approach allows estimation of the effects of tour characteristics on the choice of destination and mode using widely available data and estimation procedures.

The transport sector has been hit hard by the COVID-19 pandemic disrupting travel behaviors and mobility patterns around the globe. The pandemic has also affected mode choice behavior. This research study modeled the mode choice behavior before and during the COVID-19 pandemic in Pakistan. Data was collected through an online questionnaire survey consisting of questions about socio-economic characteristics, factors affecting mode choice, and mode chosen for shorter as well as longer distances for both before and during COVID-19 pandemic situations. The results indicated that public transport use declined, whereas walking and bicycling slightly increased during the pandemic. The respondents placed more priority on safety and security, comfort, cleanliness, infection concerns, personal social status, availability of hand-sanitizers, waiting, and paying more for less congested vehicles during the pandemic. Factor analysis was performed to explore the underlying factors affecting mode choice before and during the pandemic. Discrete choice models were developed to model the mode choice behavior. Monthly household income and pandemic-related underlying factor were significant predictors of mode choice for shorter distances (i.e., < 5 km) during the pandemic. Whereas, gender, car ownership and monthly household income were significant predictors of mode choice for longer distances (i.e., > 5 km) during the pandemic. Understanding the modal shift during a pandemic will surely help urban and transport planners to prepare better for effective transport management in the future. Policy implications are also presented to help policymakers in developing policies for post-pandemic mobility needs, particularly in developing countries.

Models for anticipating non-motorized travel choices, and the role of the built environment

Implementation of a new network equilibrium model of travel choices

Modelling life trajectories and mode choice using Bayesian belief networks

Trip destination and mode choice are highly influenced by travelers' perceptions and behaviors; selecting a destination and a vehicle for a trip are two interdependent problems. This paper presents and applies a disaggregate joint model for traveler destination and mode choice. The choice model uses fuzzy set and probability theory to deal with the uncertainty embedded in travelers' perceptions and behaviors. The model is structured as a decision tree in which the fuzzy and non-fuzzy classification of influential variables regarding destination selection and mode choice expand the tree. The most influential explanatory variables among all the variables categorized for travelers' household, trip, and living zone specifications are selected based on the maximizing information. An aggregation method is designed to provide aggregate estimates for transportation planning based on the suggested disaggregate choice model. A data-set of over 9000 home-based morning peak-hour trips in Shiraz, a large city in Iran, is used for model construction and evaluation. When compared with a multinomial logit (MNL) model, the suggested models' estimates are more accurate than the traditional MNL model.

Traditional mode choice models consider travel modes of an individual in a consecutive trip to be independent. However, a persons choice of the travel mode of a trip is likely to be affected by the mode choice of the previous trips, particularly when it comes to car driving. Furthermore, traditional travel mode choice models involve discrete choice models, which are largely derived from expert knowledge, to build rules or heuristics. Their approach relies heavily on a predefined specific model structure (utility model) and constraining it to hold across an entire series of historical observations. These studies also assumed that the travel diaries of individuals in travel survey data is complete, which seldom occurs. Therefore, in this chapter, we propose a data-driven methodology with artificial neural networks (ANNs) and fuzzy sets (to better represent historical knowledge in an intuitive way) to model travel mode choices. The proposed methodology models and analyses travel mode choice of an individual trip and its influence on consecutive trips of individuals. The methodology is tested using the Household Travel Survey (HTS) data of Sydney metropolitan area and its performance is compared with the state-of-the-art approaches such as decision trees. Experimental results indicate that the proposed methodology with ANN and fuzzy sets can effectively improve the accuracy of travel mode choice prediction.

ABSTRACTBoth, the capability of a transport system to grant access to a destination and a traveller’s ability to utilize its opportunities can be distinguished in objective and subjective factors. The paper analyses the potential for objective accessibility factors to explain mobility behaviour. In more detail, the paper (i) quantifies the explanatory power of objective factors of the transport system (supply-side) for mode choice decisions and (ii) analyses if these supply-side factors, as well as objective factors of the traveller (demand-side), affects the level of multimodality among Austrian employees. Following this approach, the paper estimates mode choice models that only consist of trip features and accessibility factors. The final model reaches a high explanatory power and includes the individual transport mode availability, travel time, travel cost, public transport service quality and the car parking situation. The model allows simulation, which estimates the modal shift with respect to adaptations of the accessibility factors. Car travel time is the variable impacting mode choice decisions the most; this suggests a push-strategy when aiming to make the transport system more sustainable. Additionally, we show that both transport demand and supply factors, but most of all their combination can explain multimodal travel behaviour, i.e., whether or not a person uses more than one transport mode within one week. The explanatory value increases significantly if one also considers mobility tools. The variables explaining multimodality best are ownership of a bike and a PT season card as well as the public transport network density.