Exploring the impacts of COVID-19 on travel behavior and mode preferences

This paper presents the first representative estimates of the marginal willingness to pay for a reduction in travel time (VTTS) for Austria, being of great importance for transport policy appraisals.The main focus is to investigate mode and user-type effects using a pooled RP/SP modeling approach for mode, route and destination choice data, revealing average VTTS estimates for car (9.90 Euro/h), public transport (3.90 Euro/h), bike (7.30Euro/h) and walk (11.40 Euro/h).The only user characteristic being able to decompose this large difference in average VTTS between car and public transport into a smaller part, that can be purely attributed to the mode-specific valuation of in-vehicle travel time, is urban residential location area:When controlling for it, the VTTS difference becomes 5.5 Euro/h, which, compared to the total average VTTS difference of about 6 Euro/h, is still relatively high.As our results indicate that in the case of Austria, characteristics of the mode are more important than characteristics of the users, and that the conditions of travel time spent in public transport are perceived as more pleasant than in a car, the investigation of the value of time assigned to travel (VTAT) is a fundamental next research step.

Analysis of travel mode and departure time choice for urban shopping trips

Modelling residential mobility decision and its impact on car ownership and travel mode

The neighborhood effect on keeping non-commuting trips inside neighborhoods has not yet been investigated in developing countries. The modeling of non-commuting trips inside neighborhoods helps understand how to avoid unnecessary journeys by car into different parts of the city. This paper, therefore, attempts to clarify (1) the similarities and differences in the socioeconomic characteristics and the perceptions of people in sprawled and compact neighborhoods, (2) correlations between, on the one hand, the choice of destinations of non-commuting trips for shopping and entertainment activities and, on the other, the socioeconomic features, travel behavior, and perceptions of residents in the two large Pakistani cities of Lahore and Rawalpindi, (3) the similarities and differences in the determinants of non-commuting destinations inside neighborhoods in compact and sprawled districts. The paper develops four Binary Logistic (BL) regression models, with two models for each type of neighborhood. The findings show that trips to shopping areas inside compact districts are correlated with a sense of belonging to the neighborhood, frequency of public transport use, residential location, and mode choice of non-commuting trips to destinations both inside and outside the neighborhood. On the other hand, the number of non-commuting trips, mode choice for non-commuting trips outside the neighborhood, frequency of public transport use, the attractiveness of shops, and monthly income (please see the Note) are significant determinants for trips to the shopping area in sprawled districts. Age, gender, possession of a driver’s license, income, number of non-commuting trips, mode choice for non-commuting trips outside of the neighborhood, car ownership, and attractiveness of shops in a neighborhood are correlated with trips to entertainment locations inside the neighborhood in compact districts. Finally, the attractiveness of shops, quality of social and recreational facilities, a sense of belonging to a neighborhood, choice of residential location, gender, age, possession of a driver’s license, number of cars in the household, and income are determinants of trips to entertainment locations in sprawled districts. A chi-square test confirms the differences across gender, daily activity, monthly income, frequency of public transport use, residential location choice, and the quality of social and recreational facilities for sprawled and compact districts in Pakistan.

Examining the effects of the neighborhood built environment on CO2 emissions from different residential trip purposes: A case study in Guangzhou, China

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.

Investigating the Performance of Rule-based Models with Increasing Complexity on the Prediction of Trip Generation and Distribution

Clearly, the built environment has certain effects on the travel behavior of individuals and households, but Dutch research findings indicate that this relationship is quite complex. Although the argumentation behind these spatial concepts sounds plausible, other mechanisms may complicate the relationship between travel behavior and the built environment. Rather than the built environment having a straightforward influence on trip frequencies, travel distances and mode choices, we may be dealing with endogenous relationships, in which individuals take into account other preferences, other activities and travel, other people, and other locations. The goal of this study is to review primarily Dutch empirical work that goes beyond direct associations and address these feedback effects in the relationship between the built environment and travel behavior. It is argued that second order effects, such as rebound effects and residential self-selection are largely responsible for this.

Because of technology penetration in the transportation system, the automated vehicle (AV) is set to be a future mode of transport. Given the major implications of AVs, investigation of the potential impact of these vehicles on travel behavior is vital for a wide range of purposes, especially for policy making. In this study, we report the results of a stated preference survey distributed in the Netherlands in which respondents had to choose between conventional cars, public transportation, and AVs for different travel distances and trip purposes. Having collected information from 663 respondents we conducted an integrated study incorporating classic trip attributes (such as travel time and travel costs), attitudinal factors, and socio-economic variables to understand people’s choices. We studied a particular form of AVs, automated driving transport service (ADTS), which we defined as an automatically controlled door-to-door transport service provided by a vehicle with similar dimensions to a conventional car, albeit driverless. Results suggest that travelers’ mode preferences vary significantly for different travel distances and purposes. We found that conventional cars and public transportation are perceived as being the least attractive alternatives in relation to in-vehicle travel time on short- and long-distance commuting trips, respectively. Preference for ADTS lay between the car and public transportation, neither the best nor the worst alternative in all scenarios. Our findings suggest that ADTS adopters are likely to prefer this mode for long-distance leisure trips rather than short-distance commuting trips.

The lack of efficient application of transportation planning process in developing cities, such as Gaza, leads to deficiency in adopting the suitable transport policies to mitigate the transportation problems resulting from urbanization and rapid increase of population. The mode choice model is probably the most important element in transportation planning and policy making. The aim of this study is to develop mode choice model for work trips in Gaza city and therefore investigating the factors that affect the employed people’s choice for transport modes. The model was developed using about two thirds of 552 questionnaires distributed for this purpose. The rest remaining third of questionnaires were used to validate the chosen models. The results of this research show that the factors that significantly affect the choice of transport modes are: total travel time, total cost divided by personal income, ownership of means of transport, distance, age, and average family monthly income. The developed model is able to predict the choice behavior of employed people in Gaza city as it is valid at 95% confidence level. This study can be used by transportation planners to predict the employed people’s behavior and travel demand analysis. The developed model can be used for predicting the future modal split by inputting predicted future value of exploratory variables.

Exploring the role of daily “modality styles” and urban structure in holidays and longer weekend trips: Travel behaviour of urban and peri-urban residents in Greater Copenhagen

Intervening in the built environment is a key way for land-use and transport planning and related policies to promote low-carbon development and low-carbon travel. It is of significance to explore and recognize the actual impact of the neighborhood built environment on travel-related CO2 emissions. This study calculated the CO2 emissions from four purposes of trips, which were within the urban region, using Travel O-D Point Intelligent Query System (TIQS) and 1239 residents’ travel survey questionnaires from 15 neighborhoods in Guangzhou. It measured the direct and indirect effects of built environments on CO2 emissions from different purposes of trips by developing structural equation models (SEMs). The results showed that for different purposes of trips, the effects of the neighborhood built environments on CO2 emissions were inconsistent. Almost all built environment elements had significant total effects on CO2 emissions, which were mainly indirect effects through mediators such as car ownership and trip distance, then affecting CO2 emissions indirectly. Most of the direct effects of neighborhood built environments on CO2 emissions were not significant, especially those from non-commuting trips. These findings suggest that in the process of formulating low-carbon oriented land-use and transport planning and policies, the indirect effects of the built environments should not be ignored, and the differences of the effects of the neighborhood built environments among different purposes of the trip should be fully considered.

Mobility and travel behavior in urban China: The role of institutional factors

This report describes the analysis of the choice of travel mode for trips to work or school through the study of attitudinal and behavioral responses. Travel behavior (mode choice) is linked to attitudes about the alternative modes through an intermediate preference construct. Individual attitudes are analyzed to obtain measures of perceptions of and feelings toward available transportation modes. These measures are related to mode preference, and preferences and situational constraints are related to choice. Differences in the perception-feelings-preference-choice formulation between travelers making local work trips and those making suburb-to-CBD work trips are identified and interpreted. The paper identifies (1) similarities and differences in the way perceptions are formulated and used to evaluate travel alternatives for local and suburb-to-CBD commuter trips and (2) the importance of including feelings measures in models of travel mode preference and choice.

Based on the RP-SP fusion data obtained from the holiday travel behavior survey in 2012, this paper takes the integrated multimodal travel information (IMTI) as the research condition and develops the Nested Logit model under IMTI based on the trip chain complexity and trip mode choices (Chain-Mode). The effectiveness of the model was identified by the inclusive value and dissimilarity parameter. The result shows that 9% of the travelers will cancel their travel in holidays and 13% will change their trip modes under the influence of IMTI. At the meantime, travel cost, parking fee, car ownership, gender, information demand, travel time and trip distance are dominant factors that influence the Chain-Mode choices in holidays. Higher parking fee and longer travel time and trip distance will encourage people to choose a complex chain. Besides, the trip mode with low travel cost and high information demand will have the high probability to be chosen.