Travel Choice Behavior Research Articles

Travel Behavior of Morning Commute with Staggered Work Hours

In this paper, an activity-based bottleneck model with staggered work hours (SWHs) is proposed. Commuters’ departure time choice is assumed to follows user equilibrium (UE) principle in terms of total activity utility. The equilibrium departure rates of the activity-based bottleneck model with both single work start time and double work start times are analytically derived. Analytical results show that there are three possible travel patterns for the bottleneck model with double work start times, and the formulations of the departure rates with respect to double work start times are the same as that with respect to single work start time. However, SWHs indeed change commuters’ travel choice behavior, and can result alternative cumulative departure flows.

Integration of a Capacity-Constrained Workplace Choice Model: Recent Developments and Applications with an Agent-Based Simulation in Singapore

Destination choice models can be embedded in transport and land use models to understand travel and location choice behavior and to forecast scenarios. Utility-maximizing destination choice models can account for individual behavior and make them suitable for agent-based models, while processing destination capacities is also in line with agent-based modeling. This paper addresses the possibility and impact of introducing capacity constraints, their effect on choice behavior, and the feasibility of applying an approach like this in agent-based microsimulations with individual characteristics for each agent. Here, a comprehensive workplace choice model and its application in a large-scale simulation case study for Singapore are described; one technical and one methodological achievement are highlighted. Technical achievement benefits from recent computational advances; the workplace choice model is estimated with a comprehensive utility function on a large data set with 103 destinations. Reasonable model fit and robust parameters are achieved while obviating sampling techniques; resulting parameters are efficiently applied to the entire 5.4 million Singapore population and validated with survey data. For methodological innovation, capacity limitations are introduced at workplaces to avoid oversaturation. A robust optimization method based on shadow prices is proposed to accommodate capacity limitations at all workplaces during the choice model application defined above. The proposed method efficiently assigns commuters to unused workplaces while respecting individual commuter preferences. Validation of the simulation results, by comparing travel time distributions for commuting trips reported in travel diary data, shows that the model fits well with observed data.

A cell-based multiple vehicle type dynamic user equilibrium model with physical queues

This paper proposes a cell-based multiple vehicle type dynamic user equilibrium model with physical queues. A single-type traffic flow model is extended to a general case with multiple vehicle types that can be partly solved by the time-space discretization method. Then, a network version of the multiple vehicle type cell transmission model is given. An integrated variational inequality (VI) formulation is presented to capture the complex traveler choice behaviors such as route and departure time choices. Furthermore, a genetic algorithm with a flow-swapping method is adopted to solve the VI problem. Two examples are used to evaluate the properties of this formulation. The results show that the model can reflect dynamic phenomena, such as multiple vehicle type speed consistent under congested conditions, queue formation and dissipation and so on. Moreover, the solutions can approximately follow the multiple vehicle type dynamic route and departure time user equilibrium conditions.

Stated Preference Analysis for New Public Transport in a Medium-sized Asian City: A Case Study in Malang, Indonesia

This study analyzes citizens' travel choice behavior in a medium-sized Southeast Asian city to observe their intention to use new public transport. The authors selected Malang in Indonesia as a case study. A travel behavior and intention survey including stated preference questions was conducted with university students. The results of the latent-class model of commute mode choices show that respondents could be divided into “cost and delay time” and “travel and access time” oriented classes. Respondents who were likely to convert to new public transport modes were male, had a higher income, and were interested in new public transport. The estimation results of the scheduling choices model confirm the significant difference in the sensitivity to recreation time between recreational activities.

Dynamic traffic analysis model of multiple passengers for urban public transport corridor

For researching travel choice behavior about different kinds of passenger-alone urban public transportation corridor, a dynamic multiple-passenger flow analysis model was proposed based on dynamic equilibrium assignment theory. Some factors which might influence passenger travel disutility were considered in the model, and they were closely related to the characteristic of passenger. And then a heuristic solution algorithm is developed to solve this model. Relying on MATLAB simulation, the model could reflect the time–space distribution of multiple-passenger flow along this corridor and gave a theoretically quantitative relationship among the passenger characteristics and departure time or path/mode choice. And some useful conclusions about travel choice behavior of different passengers were obtained finally. Compared with the actual data, the deviation of the simulation results is less than 20%, which shows the efficiency of the dynamic multiple-passenger flow analysis model clearly.

What Do you Do with Your App?

Provision of real-time passenger information (RTPI) increasingly is becoming a fundamental element of the service offered by transit agencies. RTPI changes how travelers perceive public transport services and can have a remarkable influence on travel choices and, consequently, system performance. Such consequences depend on the objectives pursued by the riders and the characteristics of the transit service. The existing knowledge about transit RTPI is extended by studying the decision-making process of bus passengers in the presence of multichannel descriptive and prescriptive real-time information. The use of different kinds of information, decision-making objectives, travel choices, and their associations (which define classes of travel choice behavior) was investigated by conducting a survey of passengers on Lothian Buses in Edinburgh, Scotland. Descriptive RTPI also was accessed before traveling and influenced decisions about route choice above all. The analysis demonstrated that RTPI was associated with more flexible behavior and that classes of behavior were well defined. Results emphasize the importance to transit agencies of providing RTPI that is tailored to customers. The development of models including the effects of RTPI is recommended to assess its impact on system performance.

Stochastic equilibrium assignment with variable demand: Theoretical and implementation issues

Recently, it has been pointed out that transport models should reflect all significant traveler choice behavior. In particular, trip generation, trip distribution, modal split as well as route choice should be modeled in a consistent process based on the equilibrium between transport supply and travel demand. In this paper a general fixed-point approach that allows dealing with multi-user stochastic equilibrium assignment with variable demand is presented. The main focus was on investigating the effectiveness of internal and external approaches and of different algorithmic specifications based on the method of successive averages within the internal approach. The vector demand function was assumed non-separable, non-symmetric cost functions were adopted and implementation issues, such updating step and convergence criterion, were investigated. In particular the aim was threefold: (i) compare the internal and the external approaches; (ii) investigate the effectiveness of different algorithmic specifications to solve the variable demand equilibrium assignment problem through the internal approach; (iii) investigate the incidence of the number of the links with non-separable and/or asymmetrical cost functions. The proposed analyses were carried out with respect to two real-scale urban networks regarding medium-size urban contexts in Italy.

Dynamic Mechanism of Effect Factors for Railway Passenger Volume

The railway passenger volume is a non-linear, dynamic and complicated issue which is affected by many factors. Using the principle and method of system dynamic, this paper has analyzed the effect factors for railway passenger volume, including the economic factors, rail infrastructure factors and travel choice behavior factor. Considering about the causality among the factors, a dynamic model has been built based on the relevant data from <China Statistical Yearbook (2000-2013)>. At the same time, the model has been run for making predictions (2014-2030). This paper focuses on the influences caused by the fare increase and the increase of GDP growth rate.

An advanced traveler general information system for Fresno, California

This study estimates the effects of an advanced traveler general information system (ATGIS), which includes fuel consumption and health-related emissions cost information on transportation network users’ travel choice behavior for recurrent congestion conditions. The effects are estimated using four different formulations based on four different behavioral assumptions. Incorporating stochastic features in link cost estimation rather than in route choice, we provide a novel modeling approach that enables us to use transportation planning models of major metropolitan areas without a need for major computationally-expensive changes in the existing models. We examined the effects of an ATGIS on the Fresno, CA, road network and found several interesting results. First, the ATGIS impact is closely related to pre-system (prior to the implementation of an ATGIS) perceived fuel and emissions costs. Total travel time in the city can be reduced by 17% (no pre-system perceived costs) to 1% (accurate pre-system perceived costs), and even increased by 1% (higher-than-actual pre-system perceived costs). Second, the addition of emissions costs, although negligible relative to fuel and time costs, can effectively reduce total system-wide travel time by up to 1% and fuel consumption by up to 0.6% during peak hours. Third, the ATGIS can reduce annual social costs by as much as $1053 million (high gas price, no pre-system perception) to $48 million (medium gas price, accurate pre-system perception), which are comparable to social cost savings by a congestion pricing (CP) scheme in the study area.

A Stochastic User Equilibrium Model and Optimal Congestion Pricing with Prospect Theory

In spite of the fact that expected utility theory is a main method to model people's travel choice behavior under uncertainty, the assumption that each decision maker (DM) is completely rational limits its wide application. This paper applies prospect theory, which can appropriately describe DMs’ bounded rational behavior, to route choice behavior analysis and develops an improved stochastic user equilibrium model and finally analyzes optimal congestion pricing. We find that the prospect in prospect theory could be regarded as a special utility in some degree. Based on this result we assume that the prospect of each route is constituted by a fixed term and a random one, and then proposed the concept of perceived prospect. Further assumption is that the random variable follows some certain normal distribution and then a probit model is put forward. Considering the complexity of probit model, Monte Carlo simulation is brought into the solution algorithm of MSA. A numerical example is given to show the impact of uncertainty, including subjective and objective uncertainty that travelers have to deal with. Congestion pricing is also taken into account to express the influence of traffic management policy on traffic flow on a network. At last, an approximately optimal charging standard is proposed through comparing one-link charging and two-links charging. The results prove the effectiveness of congestion pricing. Further research shows that there are 1.91% and 2.71% drops of total travel time and total travel cost respectively when achieving the optimal charging standard.

An integrated analysis of workers’ physically active activity and active travel choice behavior

Using Texas add-on sample data from the 2009 National Household Travel Survey, this study examines adult workers’ daily active choice decisions in the context of physical activity and attendant health benefits. The study looked at workers’ two choice behaviors: active activity and active travel. The first choice behavior, active activity, is developed as an ordered-response model based on the number of physically active recreational activities pursued during the workday. The second choice behavior, active travel, is developed as a binary-response model that examines workers’ active travel choices—whether or not the worker used any active mode of travel during the same workday. The study improves the understanding and knowledge of observed factors influencing workers’ physically active activity-travel behavior. The study also provides several observations regarding the role (and constraints) of employment in individuals’ active choices. Using a flexible copula modeling methodology, we explore the true correlation (or dependence) between the two behavior choices that could occur due to the presence of unobserved factors, suggesting a simultaneously low or simultaneously high propensity for being physically active across workers. The study findings suggest that transportation and public health policy makers can mutually benefit from encouraging workers to be physically active (from an activity and/or travel perspective). Overall, the study draws attention to the integrated nature of the public health and transportation fields, thereby providing a distinct view of active/inactive choice behavior. To our knowledge, this is the first study exploring a rich variety of components for workers’ active activity-travel behavior through a robust copula approach.

Making plans or “just thinking about the trip”? Understanding people’s travel planning in practice

ICT solutions have been proposed as a means for changing environmentally unfavourable traffic behaviour by providing better, real-time and more accessible travel information. However, prevailing models of travel choice and travel behaviour tend to overemphasise the impact and importance of information and the individualistic perspective. The issue of choice and travel planning in everyday life situations, and how information is used and acted on in these processes, was examined in a qualitative study in Stockholm, Sweden. Practice Theory was used as the theoretical framework for the study. Interviews were supplemented with an explorative diary and photo assignment to bring unreflected choices and actions of planning travel to the conscious level.The results showed that travel planning involves the immediate situation where planning and decisions are made, but also aspirations, cognitive/time/material limitations, social norms and social relations that extend widely in time and space. Definitions of travel planning and travel information based on the situated practices of planning are suggested. In the muddle of everyday life, travel planning takes place in the brief moments where circumstances at different levels – time, place, the social realm - interact and are considered or directly acted upon. In the development of new ICT-based travel information services, the role of technology in changing normal practices should be considered.

Applications of theories and models of choice and decision-making under conditions of uncertainty in travel behavior research

The overwhelming majority of models in travel behavior research assume implicitly or explicitly that individuals choose between alternatives under conditions of certainty. The validity of this assumption can be questioned as in reality urban and transportation networks are in a constant state of flux. It is important therefore that transportation researchers develop relevant approaches and models to analyze and predict decision-making under conditions of uncertainty. Transportation researchers have tended to adopt theories and models, originally developed in social psychology and decision sciences, and explore their applicability to travel choice behavior. This invited review paper summarizes the most important of these theories and models, and illustrates their application using examples of empirical research in travel behavior research. Arguing that some basic assumptions underlying these theories may not mimic the quintessence of day-to-day activity–travel behavior, the paper is completed by reflecting on the limitations of these models and identifying avenues of future research.

Park-and-Ride Network Equilibrium with Heterogeneous Commuters and Parking Space Constraint

A dynamic user equilibrium (DUE) model of heterogeneous commuters’ travel choice behaviors including departure time and path and travel mode choices in a schematic park-and-ride network is developed. Commuters have three transportation modes to choose from in a peak period: private car, rail transit, and a combination of private car and rail transit (i.e., an intermodal transportation mode). Two types of DUE patterns are derived with and without the parking space constraint. In this study, an optimal park-and-ride parking fee scheme is proposed: commuters’ travel choice behaviors are characterized by the developed trimodal multiclass DUE model. The optimal park-and-ride parking fee scheme is formulated as a bilevel program in which the upper-level problem is to find the optimal parking fee for improving network performance and the lower-level problem is to evaluate network performance in equilibrium. Because the DUE solution may not be unique, the authors aim to improve the network performance in the worst and best cases. Through numerical tests, it is demonstrated that the parking space constraint has a significant influence on the departure and route choices of commuters and should be considered as an important factor in park-and-ride commuting pattern analysis and parking fee management in order to avoid a misleading decision.

A Bilevel Programming Model to Optimize Train Operation Based on Satisfaction for an Intercity Rail Line

The passenger travel demands for intercity rail lines fluctuate obviously during different time periods, which makes the rail departments unable to establish an even train operation scheme. This paper considers an optimization problem for train operations which respond to passenger travel demands of different periods in intercity rail lines. A satisfactory function of passenger travelling is proposed by means of analyzing the passengers’ travel choice behavior and correlative influencing factors. On this basis, the paper formulates a bilevel programming model which maximizes interests of railway enterprises and travelling satisfaction of each passenger. The trains operation in different periods can be optimized through upper layer planning of the model, while considering the passenger flow distribution problem based on the Wardrop user equilibrium principle in the lower layer planning. Then, a genetic algorithm is designed according to model features for solving the upper laying. The Frank-Wolfe algorithm is used for solving the lower layer planning. Finally, a numerical example is provided to demonstrate the application of the method proposed in this paper.

From Personal Attitudes to Public Opinion

A model of a social network–based attitude diffusion system in the context of activity and travel choice behavior is presented. The principal mechanisms contributing to attitude formation were identified, and mathematical models to capture these processes were developed. The primary contributions of this research are (a) the modeling of attitude diffusion according to social and learning mechanisms and (b) the evolution of these attitudes over time in a lattice neighborhood social network. The agent-based framework presented is sufficiently general and flexible to allow the building of a more complete representation of information diffusion and attitude formation within activity and travel behavior choice dimensions, for example, mode choice or departure time choice. The framework allows the extension of the presented approach with additional social network structures, information sources, and social interaction mechanisms in the physical and virtual realms or the extension and modification of the presented approach to simulate the impact of information-based management strategies.

An Empirical Study of Parameter Estimation for Stated Preference Experimental Design

The stated preference experimental design can affect the reliability of the parameters estimation in discrete choice model. Some scholars have proposed some new experimental designs, such as D-efficient, Bayesian D-efficient. But insufficient empirical research has been conducted on the effectiveness of these new designs and there has been little comparative analysis of the new designs against the traditional designs. In this paper, a new metro connecting Chengdu and its satellite cities is taken as the research subject to demonstrate the validity of the D-efficient and Bayesian D-efficient design. Comparisons between these new designs and orthogonal design were made by the fit of model and standard deviation of parameters estimation; then the best model result is obtained to analyze the travel choice behavior. The results indicate that Bayesian D-efficient design works better than D-efficient design. Some of the variables can affect significantly the choice behavior of people, including the waiting time and arrival time. The D-efficient and Bayesian D-efficient design for MNL can acquire reliability result in ML model, but the ML model cannot develop the theory advantages of these two designs. Finally, the metro can handle over 40% passengers flow if the metro will be operated in the future.

Modeling and Application of Passenger Transport Network Capacity in New Town Based on Control Indicators

In order to reflect the uncertainty of traffic demand and the orienting of planning schemes in new town, a bi-level programming model of passenger transport network capacity is proposed here based on travel choice behavior model. In the model, the objective function is to maximize the road network capacity. The saturation of road sections, the community development intensity and the public transit trip contribution rate are defined as control indicators, among which the interactive control relationships are revealed. Furthermore, a corresponding solution algorithm is also presented. At last, a numeric example is studied to verify the effectiveness of the model and the algorithm. The results show that the model and the algorithm are of highly effective and available in practice.

An Analysis of Car and Motorcycle Ownership in Macao

ABSTRACT Reducing automobile dependency is one of the key challenges in the development of a sustainable transportation system. However, to evaluate the effectiveness of a policy, it is necessary to understand the factors that would influence the choice behavior of travelers. In this paper, the impacts of urban characteristics to the household vehicle ownership level for cars and motorcycles in Macao are investigated. A discrete choice approach is used to estimate the number of vehicles that a household would own using disaggregate household survey data. The result reveals that whereas income has positive effect on both car and motorcycle ownerships, the demographic attributes of the residential locations have different effects to the ownerships of the two vehicle types. We also propose that the motorcycle ownership should be represented at a personal decision level instead of a household level.

A network equilibrium approach for modelling activity-travel pattern scheduling problems in multi-modal transit networks with uncertainty

An understanding of the interaction between individuals’ activities and travel choice behaviour plays an important role in long-term transit service planning. In this paper, an activity-based network equilibrium model for scheduling daily activity-travel patterns (DATPs) in multi-modal transit networks under uncertainty is presented. In the proposed model, the DATP choice problem is transformed into a static traffic assignment problem by constructing a new super-network platform. With the use of the new super-network platform, individuals’ activity and travel choices such as time and space coordination, activity location, activity sequence and duration, and route/mode choices, can be simultaneously considered. In order to capture the stochastic characteristics of different activities, activity utilities are assumed in this study to be time-dependent and stochastic in relation to the activity types. A concept of DATP budget utility is proposed for modelling the uncertainty of activity utility. An efficient solution algorithm without prior enumeration of DATPs is developed for solving the DATP scheduling problem in multi-modal transit networks. Numerical examples are used to illustrate the application of the proposed model and the solution algorithm.