Determinants of ground transport modal choice in long-distance trips in Spain

In transport economics, modeling modal choice is a fundamental key for policy makers trying to improve the sustainability of transportation systems. However, existing empirical literature has focused on short-distance travel within urban systems. This paper contributes to the limited number of investigations on mode choice in medium- and long-distance travel. The main objective of this research is to study the impacts of socio-demographic and economic variables, land-use features and trip attributes on long-distance travel mode choice. Using data from 2007 Spanish National Mobility Survey we apply a multilevel multinomial logit model that accounts for the potential problem of spatial heterogeneity in order to explain long-distance travel mode choice. This approach permits us to compute how the probability of choosing among private car, bus and train varies depending on the traveler spatial location at regional level. Results indicate that travelers characteristics, trip features, cost of usage of transport modes and geographical variables have significant impacts on long-distance mode choice.     

Mode choice analysis with imprecise location information

Several large-scale person trip surveys include the information of the origin and destination of the trip only at the TAZ (traffic analysis zone) level, so the accuracy of location information is not enough to examine the effect of access and egress conditions on mode choice. Two approaches are applied in this study to complement the imprecise information; one for access to public transit from home, and the other for egress from public transit to destination. Home-based trip data with the destinations as university, governmental office, and hospitals are used in this study. About the information of the egress, the precise location of the destination are identified within TAZ from GIS database using the purpose of the trip and the type of the destination reported by the respondent, and the distance from the nearest train station and bus stop are calculated. About the access to the public transit form home, the distance from home to the public transit is treated as a probabilistic variable in estimating the mode choice model in this study. The model has the same structure as the latent class model. Census data which contain the population distribution within TAZ at city block level is used for the distribution of origin. The results of empirical analysis show that the proposed model has a better log-likelihood at convergence than those with TAZ centroids as the ends of the trip. The results suggest that the proposed model has the same effect as obtaining the precise location information, and that it enables to better represent mode choice behavior than using TAZ centroid. The results also suggest that imprecise location information provides smaller coefficient estimates for the effect of access and egress conditions, resulting the underestimate on the elasticity of the access and egress conditions for promoting public transit.     

Energy consumption,pollutant production,and dollar costs of diesel suburban commuter trains

The results presented in this report are based on data obtained from Chicago's three largest diesel commuter railroads. Those aspects of their operations that relate to energy and pollution are described. Service characteristics, such as average occupancy and average trip distance, are presented. Energy consumption results are presented and discussed. With energy efficiency measured in passenger-miles per Btu, it is found that trips by diesel commuter train are 3.5 times more energy efficient than Chicago Central Area auto trips. The total trip from home to suburban station, then by train to a downtown terminal, is found to be 2.2 times more energy efficient than Chicago Central Area auto trips. Pollutant production rates are presented for five pollutants. For every pollutant except sulfur oxides, trains are found to be less polluting per passenger-mile than autos. Per passenger-mile pollutant emissions from trains are, overall, less damaging by a factor of 5.5 than the per passenger-mile emissions from autos. Travel on these diesel commuter trains is less costly to society than auto travel (1972 suburban-based autos). This is the case whether one compares the train trip alone with an auto trip or the home-to-suburb an-station-tlien-to-a-downtown-terminal trip with a home-to-downtown auto trip.     

Modelling route choice behaviour in multi-modal transport networks

The paper presents new findings on the influence of multi-modal trip attributes on the quality and competitiveness of inter-urban multi-modal train alternatives. The analysis covers the entire trip from origin to destination, including access and egress legs to and from the train network. The focus is on preferences for different feeder modes, railway station types and train service types as well as on the relative influence of time elements and transfer penalties. Data from dedicated surveys are used including individual objective choice sets of 235 multi-modal homebound trips in which train is the main transport mode. The observed trips have origins and destinations within the Rotterdam–Dordrecht region in The Netherlands with an average total trip time of 50 minutes. Hierarchical Nested Logit models are estimated to take account of unobserved similarities between alternatives at the home-end and the activity-end of the trip respectively, resulting in two-level nesting structures which differentiate between intercity (IC) and non-intercity railway station types at the upper level and between transit and private access modes at the lower level. In order to reflect the multi-dimensional structure of the data a more advanced so-called Multi-Nested GEV model according to the Principles of Differentiation has been estimated which significantly improves the explanatory power and stresses the importance of the home-end of the multi-modal trip.     

Structural equation approach to investigate trip-chaining and mode choice relationships in the context of developing countries

This paper investigates empirical relationships between trip chain type and mode class choice for developing countries. To formulate these two sets of decisions, four empirical models are developed using structural equation modeling (SEM). Those models are calibrated using one-month travel diary data collected in Dhaka city. SEM correlates the observed variables and identifies their relationship with trip-chaining type utility and mode class choice utility. The fitted models are selected based on statistical results and similarity with the real-life situation. Direct relationships between trip-chaining and mode choice utilities are found insignificant. However, several socio-demographic factors influence both simultaneously. Consequently, it is essential to consider mode class choice concurrently for modeling trip chains. This study also investigates the influencing factors for work-based and non-work-based trip chains separately and effects of road users’ heterogeneity. The research results can be utilized to perceive trip chain-mode choice patterns for developing countries.     

Trip chaining propensity and tour mode choice of out-of-home workers: evidence from a mid-sized Canadian city

Suburban development patterns, flexible work hours, and increasing participation in out-of-home activities are making the travel patterns of individuals more complex, and complex trip chaining could be a major barrier to the shift from drive-alone to public transport. This study introduces a cohort-based approach to analyse trip tour behaviors, in order to better understand and model their relationships to socio-demographics, trip attributes, and land use patterns. Specifically, it employs worker population cohorts with homogenous activity patterns to explore differences and similarities in tour frequency, trip chaining, and tour mode choices, all of which are required for travel demand modeling. The paper shows how modeling of these important tour variables may be improved, for integration into an activity-based modeling framework. Using data from the Space–Time Activity Research (STAR) survey for Halifax, Canada, five clusters of workers were identified from their activity travel patterns. These were labeled as extended workers, 8 to 4 workers, shorter work-day workers, 7 to 3 workers, and 9 to 5 workers. The number of home-based tours per day for all clusters were modeled using a Poisson regression model. Trip chaining was then modeled using an Ordered Probit model, and tour mode choice was modeled using a Multinomial logit (MNL) model. Statistical analysis showed that socio-demographic characteristics and tour attributes are significant predictors of travel behavior, consistent with existing literature. Urban form characteristics also have a significant influence on non-workers’ travel behavior and tour complexity. The findings of this study will assist in the future evaluation of transportation projects, and in land-use policymaking.

     

An analysis of the commuter departure time decision

Transportation planners and transit operators alike have become increasingly aware of the need to diffuse the concentration of peak period travel in an effort to improve gasoline economy and reduce peak load requirements. An evaluation of the potential effectiveness of strategies directed to achieve this end requires an understanding of factors which affect commuter trip timing decisions. The research discussed in this article addresses this particular problem through the development and estimation of a commuter departure time (to work) choice model.A number of conclusions were drawn based on the departure time model results and related analyses. It was found that work schedule flexibility, mode, occupation, income, age, and transportation level of service all influence departure time choice. The uncertainty in work arrival time and the consequences of various work arrival times may also be determinants of commuter departure time choice.The estimated model represents improvements over previous work in that it more explicitly considers work arrival time uncertainty and travelers' perceived loss associated with varying work arrival times, and additional socio-demographic factors which can potentially affect departure time choice. Furthermore, the estimated model includes consideration of transit commuters, in addition to single occupant auto and carpool work travelers. The inclusion of transit commuters represents a particularly important contribution for policy analysis, since the model could potentially be used to study the effect of service and employment policies on transit system peak load requirements.     

The effect of neighbourhood characteristics,accessibility, home–work location,and demographics on commuting distances

The goal of this paper is to better understand home-to-work travel distances throughout the Montréal Metropolitan region. A simultaneous equation modelling analysis is carried out to jointly explain commuter trip length and home–work location as a function of neighbourhood typologies, commuter socio-demographics and measures of job and worker accessibility. First, a factor and cluster analysis of urban form is performed over the entire region on a fine-scale grid pattern. The outcome of this analysis is the classification of typologies at both home and job locations. Different measures of accessibility and commuter socio-demographics are then incorporated into the analysis. Varied data sources including a detailed Montréal Origin–Destination Survey on over 30,000 home-to-work automobile trips are analyzed. Among other results, commuters that live and work in a different sub-region almost double the average trip distance and although socio-economic factors have a statistically significant correlation with commuter distance, these factors have a marginal effect. Interestingly, our results highlight the importance of urban form and job accessibility. Deciding on whether to live and work in the same sub-region was modelled as an endogenous binary random utility model; unobserved heterogeneities seem to be simultaneously influencing both the home–work location choice and trip-to-work distances. Our results underscore the importance of home–work location with respect to urban form and job accessibility. Hence, policies that support more dense and mixed land-use in suburban areas would not be enough to reduce commuter distances. These actions should be accompanied by other policy initiatives to discourage long car trips.     

The bicycle as a feedering mode: experiences from three European countries

Bike-and-ride, or the combined use of bicycle and public transport for one trip, is a multimodal alternative for the car. This paper discusses the use of bike-and-ride in three countries with widely differing bicycle cultures and infrastructures: the Netherlands, Germany and the UK. The share of the bicycle in access trips is comparable to general levels of bicycle ridership in each country, but only for train services and other fast modes of public transport. Strong similarities are found in the characteristics of bike-and-ride trips and users, in terms of travel distances, travel motives, and the impact of car availability. The majority of bike-and-ride users travels between 2 and 5 km to a public transport stop, with longer access distances reported for faster modes of public transport. Work and education are the main travel motives, with the first dominating the faster modes and the second the slower modes of public transport. Car availability hardly influences the choice for a combined use of bicycle and train, but strongly affects the levels of bike-and-ride for slower modes of transport.     

Model development for metro station access mode choice

This paper analyses the behaviour of metro users in choosing their access mode to a metro station. Multinominal logit models with satisfactory predictive power were developed for access mode choice on the basis of data collected by interviewing metro users at existing metro stations. A population segmentation approach was adopted and models referring to individuals having the same set of alternative access modes were developed. Trip purpose was found to have significant effects on the access mode choice. Thus, for each population segment different models are proposed for work and education and other trip purpose. Various conclusions concerning the importance of the variables included in the proposed models were drawn through comparisons carried out across the models.     

Development of railway station choice models to improve the representation of station catchments in rail demand models

ABSTRACT

This paper describes the development of railway station choice models suitable for defining probabilistic station catchments. These catchments can then be incorporated into the aggregate demand models typically used to forecast demand for new rail stations. Revealed preference passenger survey data obtained from the Welsh and Scottish Governments was used for model calibration. Techniques were developed to identify trip origins and destinations from incomplete address information and to automatically validate reported trips. A bespoke trip planner was used to derive mode-specific station access variables and train leg measures. The results from a number of multinomial logit and random parameter (mixed) logit models are presented and their predictive performance assessed. The models were found to have substantially superior predictive accuracy compared to the base model (which assumes the nearest station has a probability of one), indicating that their incorporation into passenger demand forecasting methods has the potential to significantly improve model predictive performance.     

Modeling the modal choice decision process

Most models of modal choice are macroanalytic in nature — focusing on the behavior of large groups of travelers — and have limited explanatory power. Transportation managers need to know more about the decision processes of individual travelers in selecting a mode for a particular trip, if they are to be able to develop strategies for influencing these decisions. A microanalytic model of modal choice is therefore developed in flow-chart form, clarifying the stages in the modal choice decision process for any given trip. Individual consumers are seen as trying to satisfy a particular travel need by first specifying the characteristics of the trip itself and then specifying the “ideal” modal attributes required for this trip. Next, the perceived characteristics of a limited number of modes are evaluated against this “ideal” solution and the consumer is assumed to select that mode which provides the best match. The model explicitly recognizes the impact of psychological variables on modal choice as well as the consumer's need for information if he or she is to evaluate realistically all alternatives.     

Influences on commuter trip departure time decisions in Singapore

This paper provides empirical evidence to support the widely held view that institutional factors such as official work start times and staggered working hours are powerful policy tools in traffic management and in influencing travel behaviour. This approach is to be preferred over continued investment in infrastructure given the scarcity of land in Singapore. A more efficient use of existing infrastructure could be achieved by spreading peak travel. Full utilisation of the Mass Rapid Transit will depend on changing the commuter's perception on multi mode travel in addition to using public transport. While many studies have been carried out on modal choice, research on commuter trip departure decisions have been few and remain largely least understood. This paper employs multinomial logit and simultaneous nested logit analysis to model the choice of departure time (using household data collected in Singapore in 1983). Preliminary findings show that schedule delay, travel cost, and journey time to be important influences on commuter's choice of trip departure time to work. Some difficulties are highlighted and suggestions for further research are made.     

Modelling users’ behaviour in inter-urban carsharing program: A stated preference approach

In this paper, the effects of a inter-urban carsharing program on users’ mode choice behaviour were investigated and modelled through specification, calibration and validation of different modelling approaches founded on the behavioural paradigm of the random utility theory. To this end, switching models conditional on the usually chosen transport mode, unconditional switching models and holding models were investigated and compared. The aim was threefold: (i) to analyse the feasibility of a inter-urban carsharing program; (ii) to investigate the main determinants of the choice behaviour; (iii) to compare different approaches (switching vs. holding; conditional vs. unconditional); (iv) to investigate different modelling solutions within the random utility framework (homoscedastic, heteroscedastic and cross-correlated closed-form solutions). The set of models was calibrated on a stated preferences survey carried out on users commuting within the metropolitan area of Salerno, in particular with regard to the home-to-work trips from/to Salerno (the capital city of the Salerno province) to/from the three main municipalities belonging to the metropolitan area of Salerno. All of the involved municipalities significantly interact each other, the average trip length is about 30 km a day and all are served by public transport. The proposed carsharing program was a one-way service, working alongside public transport, with the possibility of sharing the same car among different users, with free parking slots and free access to the existent restricted traffic areas. Results indicated that the inter-urban carsharing service may be a substitute of the car transport mode, but also it could be a complementary alternative to the transit system in those time periods in which the service is not guaranteed or efficient. Estimation results highlighted that the conditional switching approach is the most effective one, whereas travel monetary cost, access time to carsharing parking slots, gender, age, trip frequency, car availability and the type of trip (home-based) were the most significant attributes. Elasticity results showed that access time to the parking slots predominantly influences choice probability for bus and carpool users; change in carsharing travel costs mainly affects carpool users; change in travel costs of the usually chosen transport mode mainly affects car and carpool users.     

Effect of environmental awareness on electric bicycle users’ mode choices

Travellers’ environmental awareness can affect their mode choices. The primary objective of this study is to identify the effect of electric bicycle (e-bike) users’ environmental awareness on their mode choice when the use of e-bikes is prohibited in urban areas in China. The data were collected via a questionnaire survey administered at ten locations in Nanjing, China. Using mixed multinomial logit (MMNL) models, we examined the relationship between the e-bike users’ mode choice and their environmental awareness, combined with socioeconomic and demographic characteristics and trip attributes. The results show that the level of environmental awareness, gender, age, education, income, the ownership of car and conventional bike, and trip distance affect e-bike users’ choices significantly. Those with a high level of environmental awareness are more likely to choose zero-emission transport modes. A stratified analysis reveals that the effect of environmental awareness is associated with their original transport mode choice prior to their use of the e-bike. With a high level of environmental awareness, original car users tend to opt for moderate- or zero-emission modes; original bus and metro users incline to choose a zero-emission mode or their original mode; and few original cyclists and walkers favour moderate- or high-emission modes. The results of the current study provide transport authorities with insights to establish sustainable urban transportation management policies and strategies to increase the share of zero- and low-emission transport modes.     

Defining transit accessibility with environmental inputs

This paper defines transit access incorporating the system attributes, viz, availability of access modes, their availability distance from residence, the difference in detour and airline distance to transit station and the condition of fixed facilities and flow entities, the shift potential under the effect of policies and the economic implications of pollution loading. The analysis is based on revealed and stated preference data. A maximum information procedure is developed to identify the shift potential due to the provision of better access conditions. A model is developed using joint estimation approach for the prediction of mode shares under policy effects. The environmental transit accessibility index, defined in this study, considers the effect of socioeconomic characteristics of the commuter, the economic and environmental implications and the behaviour of commuter under the hypothetical scenarios. The index is defined both for base year and for scenario year. A scale for evaluating environment transit accessibility is proposed. The approach is found defining transit accessibility satisfactorily for the study area.     

Rewarding rush-hour avoidance: A study of commuters’ travel behavior

Spitsmijden, peak avoidance in Dutch, is the largest systematic effort to date to study, in the field, the potential of rewards as a policy mean for changing commuter behavior. A 13 week field study was organized in The Netherlands with the purpose of longitudinally investigating the impacts of rewards on commuter behavior. Different levels and types of rewards were applied and behavior was tracked with state-of-the art detection equipment. Based on the collected data, which included also pre and post-test measurements, a mixed discrete choice model was estimated. The results suggest that rewards can be effective tools in changing commuting behavior. Specifically rewards reduce the shares of rush-hour driving, shift driving to off-peak times and increase the shares of public transport, cycling and working from home. Mediating factors include socio-demographic characteristics, scheduling constraints and work time flexibility, habitual behavior, attitudes to commuting alternatives, the availability of travel information and even the weather. The success of this study has encouraged adoption of rewards, as additional policy tools, to alleviate congestion, especially during temporary road closures.     

A direct demand model for downtown work trips

This article documents the development of a direct travel demand model for bus and rail modes. In the model, the number of interzonal work trips is dependent on travel times and travel costs on each available mode, size and socioeconomic characteristics of the labor force, and the number of jobs. In estimating the models’ coefficients constraints are imposed to insure that the travel demand elasticities behave according to the economic theory of consumer behavior. The direct access time elasticities for both transit modes are estimated to be approximately minus two, and the direct linehaul time elasticities approximately minus one. The cross-elasticities with respect to the travel time components are estimated to be less than the corresponding direct elasticities. In general, the time cross-elasticities are such that rail trip characteristics but not car trip characteristics affect bus travel, and car trip characteristics but not bus trip characteristics affect rail travel. The cost elasticities lie between zero and one-half. Thus, the success of mass transit serving a strong downtown appears to depend on good access arrangements. This success can be confirmed with competitive linehaul speeds. The cost of travel appears to assume a minor role in choice of mode and tripmaking decisions. In the paper, a comparison is also made between the predictive performance of the models developed and that of a traditional transit model. The results indicate that the econometric models developed attain both lower percent error and lower variation of the error than the traditional model.     

The relationship between land use and intrazonal trip making behaviors: Evidence and implications

This paper addresses the relationship between land use, destination selection, and travel mode choice. Specifically, it focuses on intrazonal trips, a sub-category of trip making where both trip origin and trip destination are contained in the same geographic unit of analysis, using data from the 1994 Household Activity and Travel Diary Survey conducted by Portland Metro. Using multinomial logit and binary logistic models to measure travel mode choice and decision to internalize trips, the evidence supports the conclusions that (1) intrazonal trips characteristics suggest mode choice for these trips might be influenced by urban form, which in turn affects regional trip distribution; (2) there is a threshold effect in the ability of economic diversity/mixed use to alter travel behavior; and (3) greater emphasis to destinations within the area where an individual’s home is located needs to be given in trip distribution models.