Trip Choice Research Articles

Factors Affecting Mode Choice of Work Trips in Developing Cities—Gaza as a Case Study

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.

Overloaded Vehicle Choice Behavior Analysis Based on Nested Logit Model

To effectively control the overloaded transportation, the trip choice behavior of the overloaded vehicle should be deeply examined. A overloaded vehicle usually makes a series of decisions according to the overloaded level, trip line, etc., to get the maximized economic benefit. Different trip decisions often generate different trip cost of overloaded vehicles and trip benefit. Based on the study of decision behavior and trip utility of carriers who use overloaded vehicles to travel, the nested Logit model is developed considering the hierarchical relationship between the degree of road overloaded vehicles and the trip route for the carriers travel. Based on the probability and quantitative method, the trip decision behaviors of those overloaded vehicles are analyzed, which offers scientific references for the management of overloaded vehicles and the theories of compensation rate of pricing.

Network Equilibrium Model with Dogit and Nested Logit Structures

This paper develops a combined equilibrium model for the simultaneous prediction of trip distribution, mode choice, and trip assignment. Trip distribution is formulated as a dogit model to take into account travel behavior of compulsory (work) and discretionary (nonwork) trips. Mode choice is given by a two-level nested logit model to avoid the independence from irrelevant alternatives restriction. Trip assignment is based on Wardrop's user-equilibrium principle. The log sums of the mode choice model were computed and fed back into the trip distribution step. Under this iterative process, destination choice of discretionary trips would respond to changing travel cost conditions occurring in mode choice and trip assignment steps, whereas destination choice of compulsory trips would remain fixed in the study time period. The proposed combined trip distribution, mode choice, and trip assignment (CDMA) model was reformulated as an equivalent convex programming problem with linear constraints and applied empirically to a county-level network in New Jersey. The results indicate that the CDMA model is better than the commonly used logit combined model in reproducing the observed link flows, origin–destination (O-D) trip flows, and O-D trip flows by mode.

An Assessment of Psychological Noise Reduction by Landscape Plants

The emphasis in the term ‘Green Transportation’ is on the word ‘green’. Green transportation focuses on the construction of a slow transport system with a visually pleasing, easy and secure trip environment composed of urban parks, green roadside spaces and some other space that is full of landscape plants. This trip environment encourages residents to make trip choices that reduce fuel consumption and pollution and is one of the most important ways of popularizing green transportation. To study the psychological benefits provided by urban parks and other landscape environments, we combined a subjective approach (a questionnaire) with an objective quantitative approach (emotional tests using an electroencephalogram; EEG). Using a questionnaire survey, we found that 90% of the subjects believed that landscape plants contribute to noise reduction and that 55% overrated the plants’ actual ability to attenuate noise. Two videos (showing a traffic scene and a plant scene) were shown to 40 participants on video glasses. We detected and recorded EEG values with a portable electroencephalograph, and a comparison between the results of the two groups revealed that there was a highly significant asymmetry between the EEG activity of the vegetation scene and traffic scene groups. The results suggest that the emotions aroused by noise and visual stimuli are manifested in the synchronization of beta frequency band and the desynchronization of alpha frequency band, indicating that landscape plants can moderate or buffer the effects of noise. These findings indicate that landscape plants provide excess noise attenuating effects through subjects’ emotional processing, which we term ‘psychological noise reduction’.

Comparison of Agent-Based Transit Assignment Procedure with Conventional Approaches

The public transportation system, a key part of a multimodal transportation network, has been widely viewed as an efficient way to reduce road congestion and pollution. Public transportation planners use transit assignment models to forecast travel demand and service performance. As technologies evolve and smart transit systems become more prevalent, it is important that assignment models adapt to new policies, such as traveler information provision. This paper investigates three transit assignment tools that represent three approaches to modeling transit trip distribution over a network of fixed routes. These tools are the EMME/2 Transit Assignment Module (Module 5.35), commonly used by planners; Toronto, Canada, Transit Commission's transit assignment tool, MADITUC; and the newly developed Microsimulation Learning-based Approach to Transit Assignment (MILATRAS). These approaches range from aggregate, strategy-based frameworks to fully disaggregate microscopic platforms. MILATRAS presents a stochastic process approach (i.e., nonequilibrium based) for modeling within-day and day-to-day variations in the transit assignment process in which aggregate travel patterns can be extracted from individual choices. Although MILATRAS presents a different standpoint for analysis in comparison with equilibrium-based models, it still gives the steady state run loads. MILATRAS performs comparatively well with EMME/2 and MADITUC. In addition, MILATRAS presents a policy-sensitive platform for modeling the effects of smart transit system policies and technologies on passengers’ travel behavior (i.e., trip choices) and transit service performance.

An integrated behavioral model of the land-use and transport systems with network congestion and location externalities

The agents’ decisions, from their residential location to their members’ trip choices through the network, are jointly analyzed as an integrated long term equilibrium in which the location, travel decisions, and route choices are represented by logit or entropy models. In this approach, consumers optimize their combined residence and transport options represented as paths in an extended network built by connecting the transport sub-network to a fictitious sub-network that represents land-use and transport demand options. We model a static land- use and transport equilibrium by considering road congestion and location externalities. The latter include trip destination choices based on land-use attractions, as well as endogenous neighborhood characteristics that determine residential choices and segregation phenomena. The model can deal with heterogeneous populations and locations as well as multiple trip purposes, though it assumes only private transport modes. In a previous paper we studied the case with road congestion externalities only, characterizing equilibria by a strictly convex and coercive unconstrained minimization problem. This characterization fails for more general externalities, so we restate the model as a fixed-point problem, establishing the existence of equilibria, providing sufficient conditions for its uniqueness and for the convergence of a fixed-point iteration. A small numerical example is used to illustrate the model.

Convergent Validity of Revealed and Stated Recreation Behavior with Quality Change: A Comparison of Multiple and Single Site Demands

We consider the convergent validity of several demand models using beach recreation data. Two models employ multiple site data: a count data demand system model and the Kuhn–Tucker demand system model. We explore the role of existing variation in beach width in explaining trip choices, and analyze a hypothetical 100 foot increase in beach width. We compare these models to a single equation model where we jointly estimate revealed and stated preference trip data, and focus on a hypothetical scenario considering a 100 foot increase in beach width. In each case we develop estimates of the change in beach visits and the welfare impacts from the increase in width. The trip change estimates from two of the three models are similar and convergent valid, though the willingness to pay estimates differ in magnitude.

Mode Choice of Older People Before and After Shopping: A Study with London Data

With the population aging in many countries, older people’s travel is recently getting more attention in the transportation literature. However our understanding of factors influencing their mode choice is still limited. In this research the focus is on mode choice for shopping trips as these are the most frequent trips of older people. The study is not limited to trips to the shopping centre, but investigates the combined mode choice of trips to and from the shop in order to understand also which factors influence mode changes. Two types of models - the multinomial logit (MNL) and the nested logit (NL) - are fitted to data from the London Area Travel Survey. The nesting structure is used to test the correlation in mode choice before and after shopping. A particular focus of the models is on the importance of accessibility variables such as bus and rail stop density and service quality for specific areas of London. The results show that mode choice combinations such as “walk to shop and take the bus back” are not as frequent as sometimes thought and that bus stop density is of more significance to older people than attributes describing the quality of the bus services like service frequency.

An illustration of the quest genre as spiritual metaphor in adventure travel narratives

Adventure travel narratives are often written within a quest genre. The quest as genre is a romantic narrative that follows a pattern of sequential steps: the call to journey, preparation, the journey, and returning home. This paper proposes that the quests in which adventure travellers embark upon are spiritual in nature. Therefore, the quest genre is a metaphorical vehicle for narrating a spiritual journey. The term “spiritual,” in this context, refers to connecting to something outside of oneself. The purpose of this paper is to describe adventure travel as spiritual questing and to illustrate this idea with three popular adventure travel narratives. It is important to understand the common themes and constructions within adventure travel narratives because of their ability to frame travel expectations and influence various factors in the travel and tourism industry (trip choice, travel mode, destination choice, and intention to travel).

A dynamic model of the Bay of Biscay pelagic fleet simulating fishing trip choice: the response to the closure of the European anchovy (Engraulis encrasicolus) fishery in 2005

The scope of this paper is to describe, evaluate, and forecast fishing trip choices of the Bay of Biscay pelagic fleet using random utility modeling (RUM). First, alternative fishing trip choices of this fleet were identified using multivariate statistical methods based on species landings weighted by value and defined as distinct fishing activity or fisheries (termed métiers). A RUM was specified, which included trip components as attributes during the period 2001–2004 (a lagged percentage of the value per unit of effort of the main species caught, total value per unit of effort, and inertia in terms of changes from one métier to another). For the main métiers, the proportion of correct effort allocation is 90% during the calibration period of 2001–2004. The results from the RUM are used to parameterize a simulation model of trip choice. The model is used to predict trip choices in 2005, throughout most of which fishing was constrained by the closure of the European anchovy (Engraulis encrasicolus) fishery. Simulation results are compared with observed trip choices following the fishing ban: 80% of observed trip choices are correctly predicted by the model. The capacity of the behavioral model to predict responses to the closure is then discussed.

Social Activities and Travel Demand

Social activities are responsible for an important share of trips conducted by individuals. This paper contributes to a rapidly increasing stream of transportation research into individuals’ choice of social relations and trips made to maintain their social relations. A method that was used to collect data on ego-centered social networks in a large-scale survey conducted in the Netherlands in the late 1980s is described, and a model-based framework for analysis of these data is proposed. The framework consisted of a set of linked regression and choice models for analysis and prediction in a coherent fashion of the travel demands resulting from a chain of decisions, including the size and composition of the social network and the travel time and contact frequency related to each member of the network. The results indicate that the sociodemographic attributes of an individual have only a modest influence on network size and a stronger impact on the choice of relationship type, travel time, and contact frequency. Furthermore, significant relationships exist among these social network variables. It was concluded that the framework provides a straightforward and useful way to analyze social network data and identify fruitful directions for future research.

Valuing Changes in the Quality of Coral Reef Ecosystems: A Stated Preference Study of SCUBA Diving in the Bonaire National Marine Park

We estimated the economic value of changes in the quality of a coral reef ecosys- tem to SCUBA divers in the Caribbean using a stated preference mail survey. Our sampling frame was all divers with U.S. home addresses who purchased a tag required for diving in the Bonaire National Marine Park in 2001. Divers were asked how they might have altered their trip choice had the quality of the coral reef system been different from what they experienced. From these responses we inferred the value of three different levels of quality defined by vis- ibility, species diversity, and percent coral cover. We used random utility theory and mixed logit to analyze the choice questions. Our sample size was 211, and our survey response rate was 75%. For modest changes in quality we estimated per person annual losses at $45. For larger losses the value was $192.

Factors affecting purse seine catches: an observer-based analysis

A total of 72 fi shing trips were carried out by fi shery scientists onboard a purse seiner in the Pagasitikos Gulf (Greece) in an attempt to identify factors affecting total and individual species’ catches. As trawling is completely banned in the study area, purse seining is the main fi shing method. It was found that month, water depth and fi shing area affected total and Trachurus spp. catches of the purse seine fi shery, with month and water depth alone explaining 77.8% and 6.4% respectively of the total variation of the data. Signifi cant interactions observed indicate that the fl uctuations in total catches differed by month and fi shing area as well as fi shing area and water depth. Fishing area and water depth interaction were found to affect Trachurus spp. catches signifi cantly. Results also suggested that generalized linear modelling of the purse seine catches can be used to obtain representative abundance indices by reducing the observed variability. Fishery scientists’ onboard observations alongside fi sh market recordings during the same period confi rmed that each fi sher has developed an individual decision-making fi shing process. Fisher’s trip choice behaviour was found to be modulated by several factors, such as distance of fi shing grounds from the port, market demands (both in terms of species and market prices), weather conditions, alternative fi shing strategies, previously gathered information, economic pressure and personal skills. These fi ndings are discussed in the light of the need to incorporate additional quantitative information to stock abundance estimates if improved fi sheries management scenarios are to be advanced.

Direct Demand Forecasting Model for Small Urban Communities Using Multiple Linear Regression

Forecasting traffic volumes to support infrastructure decisions is the heart of the travel demand modeling process. The most commonly used methodology for obtaining these forecasted traffic volumes is the four-step process that considers generation, distribution, mode choice, and route assignment of trips. Each step of the process is performed independently, almost always through the use of computer software, to achieve the final traffic volumes. This paper examines the possibility of forecasting traffic volumes by using a multiple linear regression model to perform what is termed direct demand forecasting. The direct demand forecasting model generates traffic volumes for roadways through the development of a functional relationship between roadway characteristics and socioeconomic influences. A direct demand travel forecasting model has been developed and applied, with a small urban area as a case study community. Results are consistent with those obtained from the traditional four-step methodology.

MICRO-SCALE SPACE-TIME MODELLING OF EXPOSURES AND HEALTH RISKS IN A MOBILE POPULATION

ISEE-209 Introduction: Transport systems represent an important source of exposure and health risk, not only from air pollution but also noise and traffic accidents. In order to inform local transport management and policy, methods are needed to enable these exposures and their consequent health effects to be modelled and assessed. As part of the EU-funded HEARTS (Health Effects and Risks of Transport Systems) study, an integrated GIS-based system has been constructed to model exposures to air pollution, noise and traffic accident risks. Methods: An integrated system for micro-scale modelling of trip behaviour, and exposures to air pollution, noise and traffic accidents has been built in ArcGIS. Trip choices and behaviours (including pedestrian road crossing behaviours) are modelled using a stochastic model, informed by data on time activity patterns, modal preferences and route characteristics. Air pollution and noise are modelled either via external, loose-coupled dispersion models, or purposely-designed internal models, making use of GIS functionality. A specially designed accident risk model has also been incorporated, based on Routledge’s formula, and a user-friendly front-end provides access to the various model components. Models have been validated in Leicester, Lille and Florence, using observational data on time-activity patterns and trip behaviour, measured pollutant concentrations and personal exposure measurements. Results: This paper describes and demonstrates the system, presents results from validation studies in Leicester, UK, and illustrates applications of the modelling system to assess the impacts of different policy scenarios. Validation results show acceptable levels of performance for all the main model components. Results of applying the methodology to assess the potential effects of a local walk-to-school policy suggest conflicting costs and benefits from such interventions that are likely to be masked by more aggregate impact assessments. Discussion: GIS offer powerful tools for modelling of exposures and health risks. On the one hand, if these models are to provide realistic representations of the risks involved, and the consequences of intervention, they need to operate at the micro-scale, and to be capable of simulating complex behaviour patterns. On the other hand, GIS may also provide a dangerous pathway into fantasy!

Microsimulation models incorporating both demand and supply dynamics

There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework. The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makers’ decisions and individual vehicle movements across the network. To achieve this it represents directly individual drivers’ choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the space–time trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamics.

Multiagent Learning-Based Approach to Transit Assignment Problem

This paper presents an operational prototype of an innovative framework for the transit assignment problem, structured in a multiagent way and inspired by a learning-based approach. The proposed framework is based on representing passengers and their learning and decision-making activities explicitly. The underlying hypothesis is that individual passengers are expected to adjust their behavior (i.e., trip choices) according to their experience with transit system performance. A hypothetical transit network, which consists of 22 routes and 194 stops, has been developed within a microsimulation platform (Paramics). A population of 3,000 passengers was generated and synthesized to model the transit assignment process in the morning peak period. Using reinforcement learning to represent passengers’ adaptation and accounting for differences in passengers’ preferences and the dynamics of the transit network, the prototype has demonstrated that the proposed approach can simultaneously predict how passengers will choose their routes and estimate the total passenger travel cost in a congested network as well as loads on different transit routes.

Multiagent Learning-Based Approach to Transit Assignment Problem: A Prototype

This paper presents an operational prototype of an innovative framework for the transit assignment problem, structured in a multiagent way and inspired by a learning-based approach. The proposed framework is based on representing passengers and their learning and decision-making activities explicitly. The underlying hypothesis is that individual passengers are expected to adjust their behavior (i.e., trip choices) according to their experience with transit system performance. A hypothetical transit network, which consists of 22 routes and 194 stops, has been developed within a microsimulation platform (Paramics). A population of 3,000 passengers was generated and synthesized to model the transit assignment process in the morning peak period. Using reinforcement learning to represent passengers' adaptation and accounting for differences in passengers' preferences and the dynamics of the transit network, the prototype has demonstrated that the proposed approach can simultaneously predict how passengers will...

Gender-Based Analysis of Work Trip Mode Choice of Commuters in Suburban Montreal, Canada, with Stated Preference Data

Transportation literature suggests that men and women differ in their commuting patterns and in their propensity to switch between travel options. In North America, it is expected that women will have an increasing impact on travel demand. As such, differences in female responses to travel demand management strategies are likely to become increasingly important as governments try to curtail travel demand. This paper uses a 1994 stated preference survey of suburban commuters in Montreal, Canada, to determine whether there is evidence for differences between men and women in the factors that affect work trip choices, to quantify those differences, and to suggest what the differences imply for travel demand management in the future in Montreal. The main conclusions of this paper are as follows. First, women and men should be modeled separately for work trip mode choice. Second, three main differences appear from the econometric models: women are less likely to choose public transit than men; women are more likely to choose to rideshare; and women are less time-sensitive in regard to commuting than men are.

Gender-Based Analysis of Work Trip Mode Choice of Commuters in Suburban Montreal, Canada, with Stated Preference Data

Transportation literature suggests that men and women differ in their commuting patterns and in their propensity to switch between travel options. In North America, it is expected that women will have an increasing impact on travel demand. As such, differences in female responses to travel demand management strategies are likely to become increasingly important as governments try to curtail travel demand. This paper uses a 1994 stated preference survey of suburban commuters in Montreal, Canada, to determine whether there is evidence for differences between men and women in the factors that affect work trip choices, to quantify those differences, and to suggest what the differences imply for travel demand management in the future in Montreal. The main conclusions of this paper are as follows. First, women and men should be modeled separately for work trip mode choice. Second, three main differences appear from the econometric models: women are less likely to choose public transit than men; women are more l...