A study on possibility of commuting trip using private motorized modes in cities around the world: Application of multilevel model

The focus of the current research was to evaluate how the individual’s social characteristics and urban infrastructure impacts the usage of Private Motorized Modes (PMM). Based on individual and urban characteristics a multilevel analysis was conducted on the possibility of commuting trip by private motorized modes on the rush time of 78 cities around the world. Also the selected cities were classified through a principal component analysis, and based on the classification the impact of and urban variables on the possibility of commuting trips made by private motorized modes (PCTP) was verified. Results showed a diverse range of variables related to the usage of PMM, as well as the urban structure and railway lengths being an important variable in travel behavior.     

Economic and service comparison of conventional bus and automated transit—a case study

Three origin‐destination matrices of inter‐zonal person trips for a section of the Los Angeles metropolitan region are analyzed using principal component analysis. The matrices represent total person trips, journey‐to‐work trips, and shopping trips. This allows for the identification of a number of sub‐regional travel fields or functional regions within the area. The composition of and interrelationships between these fields and the spatial coincidence of fields defined for different travel purposes are compared with existing and proposed public transit facilities.     

Dynamic stochastic journey time estimation and reliability analysis using stochastic cell transmission model: Algorithm and case studies

This paper proposes a framework for evaluating the distributions of stochastic dynamic link travel time and journey time as well as assessing the journey time reliability. Due to the stochastic nature of the flow profiles, the paper devises a sampling process to estimate the probability mass function (PMF) of the link travel time. This sampling process defines a likelihood concept that measures the probability of the difference between the cumulative stochastic link inflow and outflow profiles to be less than or equal to a prescribed bound. Based on this likelihood measure, the probability mass function (PMF) of the link travel time is evaluated over an appropriate sampling interval. The PMF of the journey time is then evaluated by extending the deterministic nested delay operator to a stochastic version which is defined as a series of “nested” conditional probabilities of the link travel time PMFs along the route. This paper also proposes a method to fit the PMF of the journey time to a class of statistical distribution to determine its skewness, which is useful in the analysis of journey time reliability. The paper then analyzes journey time reliability via the properties of dynamic travel time distributions such as confidence intervals and shape parameters. The proposed algorithm is applied to estimate the stochastic journey time on a freeway corridor from the stochastic cumulative inflow and outflow profiles generated from the stochastic cell transmission model. This methodology is validated with two empirical studies: (i) estimations of journey time distribution and reliability analysis for one short freeway segment in California during a specific time period and (ii) the effects of traffic incidents on journey time reliability for a long expressway corridor of Hanshin expressway (between Osaka and Kobe) in Japan.     

Using structural equations modeling to unravel the influence of land use patterns on travel behavior of workers in Montreal

This paper addresses the relations between travel behavior and land use patterns using a Structural Equations Modeling (SEM) framework. The proposed model structure draws on two earlier models developed for Lisbon and Seattle which show significant effects of land use patterns on travel behavior. The travel behavior variables included here are multifaceted including commuting distance, car ownership, the amount of mobility by mode (car, transit and non-motorized modes), both in terms of total kilometers travelled and number of trips. The model also includes a travel scheduling variable, which is the total time spent between the first and last trips to reflect daily constraints in time allocation and travel.The modeled land use variables measure the levels of urban concentration and density, diversity, both in terms of types of uses and the mix between jobs and inhabitants/residents, the transport supply levels, transit and road infrastructure, and accessibility indicators. The land use patterns are described both at the residence and employment zones of each individual included in the model by using a factor analysis technique as a data reduction and multicollinearity elimination technique. In order to explicitly account for self selection bias the land use variables are explicitly modeled as functions of socioeconomic attributes of individuals and their households.The results obtained show that people with different socioeconomic characteristics tend to work and live in places of substantially different urban environments. But besides these socioeconomic self-selection effects, land use variables significantly affect travel behavior. More precisely the effects of land use are in great part passed thru variables describing long term decisions like commuting distance, and car ownership. These results point to similar conclusions from the models developed for Lisbon and Seattle and thus give weight to the use of land use policies as tools for changing travel behavior.     

Trip misreporting forecast using count data model in a GPS enhanced travel survey

As Global Positioning System (GPS) technology advances, it has been increasingly used to supplement traditional self-reported travel surveys due to its promising features in capturing travel data with better accuracy and reliability. Realizing the limitations of diary-based surveys, this paper presents a study that directly accounts for trip misreporting behavior in trip generation models. Travel data were obtained from prompted-recall assisted GPS survey along with a diary-based survey. Negative Binomial models for count data were developed to accommodate misreporting behavior by introducing interaction effects of the sample-indicator variable with various personal and household variables. The interaction effects indicate how the impacts of the socioeconomic and demographic variables on trip-making vary across the two samples. Assuming that the GPS sample represents the ground truth, the interaction effects actually capture the likelihood and the extent of trip misreporting by detailed personal and household characteristics. The model results reveal significant interaction effects of several personal and household variables, indicating misreporting behavior associated with these attributes. The addition of interaction coefficients to the main effect model represents the real impacts of the independent variables, after compensating for trip misreporting behavior, if any.     

The determinants of long distance travel in Great Britain

This study analyses of the determinants of long distance travel in Great Britain using data from the 1995-2006 National Travel Surveys (NTSs). The main objective is to determine the effects of socio-economic, demographic and geographic factors on long distance travel. The estimated models express the distance travelled for long distance journeys as a function of income, gender, age, employment status, household characteristics, area of residence, size of municipality, type of residence and length of time living in the area. A time trend is also included to capture common changes in long distance travel over time not included in the explanatory variables. Separate models are estimated for total travel, travel by each of four modes (car, rail, coach and air), travel by five purposes (business, commuting, leisure, holiday and visiting friends and relatives (VFRs)) and two journey lengths (<150 miles and 150+ miles one way), as well as the 35 mode-purpose-distance combinations.The results show that long distance travel is strongly related to income: air is most income-elastic, followed by rail, car and finally coach. This is the case for most journey purposes and distance bands. Notable is the substantial difference in income elasticities for rail for business/commuting as opposed to holiday/leisure/VFR. In addition, the income elasticity for coach travel is very low, and zero for the majority of purpose-distance bands, suggesting coach travel to be an inferior mode in comparison to car, rail and air. Regarding journey distance, we find that longer distance journeys are more income elastic than shorter journeys.For total long distance travel, the study indicates that women travel less than men, the elderly less than younger people, the employed and students more than others, those in one adult households more than those in larger households and those in households with children less than those without. Long distance travel is also lowest for individuals living in London and greatest for those in the South West, and increases as the size of the municipality declines.     

Travel-time ratios for visits to the workplace: the relationship between commuting time and work duration

For a better understanding of commuting behavior, the home-to-work journey has to be addressed in the context of daily time use. Although many studies have analyzed commuting times, the influence of the time spent working on the home-to-work travel time has only been investigated indirectly. This paper uses the travel-time ratio concept to investigate the association between work duration and commuting. We describe the theoretical framework of the travel-time ratio and analyze realized travel-time ratios for work activities with data from the 1998 Dutch National Travel Survey. It is shown that workers, on average, spend 10.5% of the time available for work and travel on commuting, which corresponds to 28 min (single trip) for an 8-h workday. The travel-time ratio varies systematically with sociodemographic variables; urban form is of rather limited importance in the explanation of travel-time ratio values.     

Urban activity spaces: Illustrations and application of a conceptual model for integrating the time and space dimensions

This work extends the conceptual argument for the use of ellipses to portray activity spaces and offers one example of how the ellipse construct can be used to analyze urban travel characteristics, based on observed trip making behavior and socio-economic variables. A problem in characterizing activity spaces has been in integrating the time and space dimensions into the same analytical framework while maintaining an understandable graphical representation of the space-time geographies envisioned by Hagerstrand and others. The ellipse allows this, as well as providing several quantifiable measures to be used for analyzing and characterizing activity spaces and urban travel behavior. In the current application, analysis of variance is used to analyze the resulting elliptic variables of 653 travelers. The results indicate that home location and household size are important factors in determining activity space characteristics and that the ellipse variables provide a different and useful approach for understanding urban travel behavior.     

Incorporating the extended theory of planned behavior in a school travel mode choice model: a case study of Shaoxing,China

Travel to and from school can have social, economic, and environmental implications for students and their parents. Therefore, understanding school travel mode choice behavior is essential to find policy-oriented approaches to optimizing school travel mode share. Recent research suggests that psychological factors of parents play a significant role in school travel mode choice behavior and the Multiple Indicators and Multiple Causes (MIMIC) model has been used to test the effect of psychological constructs on mode choice behavior. However, little research has used a systematic framework of behavioral theory to organize these psychological factors and investigate their internal relationships. This paper proposes an extended theory of planned behavior (ETPB) to delve into the psychological factors caused by the effects of adults’ cognition and behavioral habits and explores the factors’ relationship paradigm. A theoretical framework of travel mode choice behavior for students in China is constructed. We established the MIMIC model that accommodates latent variables from ETPB. We found that not all the psychological latent variables have significant effects on school travel mode choice behavior, but habit can play an essential role. The results provide theoretical support for demand policies for school travel.     

Characteristics analysis for travel behavior of transportation hub passengers using mobile phone data

The travel behavior of passengers from the transportation hub within the city area is critical for travel demand analysis, security monitoring, and supporting traffic facilities designing. However, the traditional methods used to study the travel behavior of the passengers inside the city are time and labor consuming. The records of the cellular communication provide a potential huge data source for this study to follow the movement of passengers. This study focuses on the passengers’ travel behavior of the Hongqiao transportation hub in Shanghai, China, utilizing the mobile phone data. First, a systematic and novel method is presented to extract the trip information from the mobile phone data. Several key travel characteristics of passengers, including passengers traveling inside the city and between cities, are analyzed and compared. The results show that the proposed method is effective to obtain the travel trajectories of mobile phone users. Besides, the travel behavior of incity passengers and external passengers are quite different. Then, the correlation analysis of the passengers’ travel trajectories is provided to research the availability of the comprehensive area. Moreover, the results of the correlation analysis further indicate that the comprehensive area of the Hongqiao hub plays a relatively important role in passengers’ daily travel.

     

The importance of the multi-purpose journey to work in urban travel behavior

This study examines the journey to work as a multiple-purpose trip (home-to-home circuit). Using disaggregate travel diary data collected over 35 consecutive days, the study shows the importance of the multi-purpose work trip in the overall travel pattern of the urban household. A large proportion of many households' total travel is undertaken in conjunction with the journey to and from work. The paper also examines the nature of these work-induced travel linkages and finds that many types of urban establishments depend heavily upon stops made in connection with the work trip. In fact, there is a group of urban functions that have stronger travel links with the workplace than with the home or with any other type of urban establishment. The study examines the implications of the multi-purpose journey to work for policies regarding mode use and the viability of centrally-located urban functions.     

Evidence on the distribution of values of travel time savings from a six-week diary

This paper presents the results of a series of mixed logit models focusing on the distribution of the values of travel time savings. The parametric assumptions include both normal and various bounded distributions derived from the normal (log-normal, Johnson’s S_b and a censored normal). The full model, which also incorporates a number of time budget related variables, indicates that a small, but relevant share of the respondents might not value time savings, or would rather extend the journey. This share is consistent with results from other studies. A series of models for the different types of tours indicate even higher potential shares in situations with typically fewer binding time constraints.The RP data used is derived from the six week travel diary Mobidrive. The observations from Karlsruhe are summarised at the level of the tour.     

Exploring key determinants of travel satisfaction for multi-modal trips by different traveler groups

Using a primary dataset from an experimental survey in eight European cities, this study identified the key determinants of satisfaction with individual trip stages as well as overall journey experience for different travel modes and traveler groups. Multivariate statistical analyses were used to examine the relationships between overall satisfaction and travel experience variables, trip complexity, subjective well-being indices, travel-related attitudes as well as individual- and trip-specific attributes. The results indicate that for certain traveler groups, such as women, young and low-income or unemployed travelers, there are distinctive determinants of satisfaction with trip stages for various travel modes. The results also indicate that satisfaction with the primary trip stage is strongly linked to overall trip satisfaction, while satisfaction levels with access and egress trip stages are strongly related to satisfaction with the primary trip stage. Past experience, traveler expectations and attitudes, and the emotional state of travelers are also significant explanatory variables for travel satisfaction. The results indicate that when an individual consciously chooses a particular travel mode, they will report a higher level of satisfaction with that chosen mode. Notwithstanding, while past experience highly influences an individual’s current travel satisfaction, the more they travel with the current mode, the less satisfied they are with their choice. The results of this study highlight the importance of gaining a better understanding of the interaction between instrumental variables and non-instrumental variables at different trip stages and the influence on user preferences, satisfaction and decision-making processes.     

Access to railway stations and its potential in increasing rail use

The propensity to travel by rail, and not, for example by car, can be considered to be a factor of the rail service offered, the access to it and the characteristics of the population served. Efforts to increase rail use usually focus on the rail service itself while the accessibility of the rail network receives less attention. In this context, the paper has two broad aims. First, to evaluate how important the ‘access-to-the-station’ part of a rail journey is to passengers in their overall satisfaction with the rail journey and second, to investigate the balance between characteristics of the service, the access to it and the population served in determining rail use in different parts of the rail network. The analysis is carried out for the Netherlands. To achieve the first aim, we use the Dutch Railways customer satisfaction survey and apply principal component analysis and derived importance techniques to assess the relative importance of accessibility in determining the overall satisfaction with the rail journey. For the second aim, we use regression analysis to explain, at the Dutch postcode level, the propensity to use rail. We find that satisfaction with the level and quality of the access to the station is an important dimension of the rail journey which influences the overall satisfaction from that journey and that the quality and level of accessibility is an important element in explaining rail use. The conclusion reached is that in many parts of the rail network improving and expanding access services to the railway station can substitute for improving and expanding the services provided on the rail network and that it is probably more cost efficient when the aim is to increase rail use. These parts of the network are mainly in the periphery where the current level of rail service is relatively low.     

Modeling the behavioral determinants of travel behavior: An application of latent transition analysis

This paper applies the relatively new method of latent class transition analysis to explore the notion that qualitative differences in travel behavior patterns are substantively meaningful and therefore relevant from explanatory point of view. For example, because the bicycle may function as an important access and egress mode, a car user who also (occasionally) uses the bicycle may be more likely to switch to a public transit profile than someone who only uses the car. Data from the Dutch mobility panel are used to inductively reveal travel behavior patterns and model transitions in these patterns over time. Additionally, the effects of seven exogenous variables, including two important life events (i.e. moving house and changing jobs), on cluster membership and the transition probabilities are assessed. The results show that multiple-mode users compared to single-mode users are more likely to switch from one behavioral profile to another. In addition, age, the residential environment, moving house and changing jobs have strong influences on the transition probabilities between the revealed behavioral patterns over time.     

The use of travel time by rail passengers in Great Britain

In the face of a society that exhibits an increasing dependence on motorised mobility, the response of transport policy is one that remains grounded in the pursuit of quicker journey times. Less time spent travelling is assumed to convert ‘unproductive’ time into economically valuable time. This paper explores an alternative perspective on travel time. It seeks to examine the notion that travel time, rather than being wasted, can and does possess a positive utility. This brings into question the extent of assumed economic benefits derived from schemes and policies intended to reduce journey times. Specifically the paper reports on a national mail-back questionnaire survey of 26,221 rail passengers in Great Britain conducted in autumn 2004. The survey examined how passengers used their time on the train, how worthwhile that time use was considered to be and the role of mobile technologies. The results paint a picture of travel time use in which the behaviour and opinions of commuters, business travellers and leisure travellers are compared and contrasted. A substantial if not overwhelming incidence of positive utility of travel time use is revealed, especially for business travel but also for commuting and leisure travel. In light of the survey evidence the paper points to the challenge of understanding the notion of productivity and offers some critical comments concerning the current approach to economic appraisal in Britain.     

Built environment,travel behavior,and residential self-selection: a study based on panel data from Beijing,China

The influence of the built environment on travel behavior has been the subject of considerable research attention in recent decades. Scholars have debated the role of residential self-selection in explaining the associations between the built environment and travel behavior. The purpose of this study is to make a contribution to the literature by adopting the cross-lagged panel modeling approach to analyze a panel data, which scholars have recommended as the ideal design for studying the influence of the built environment on travel behavior accounting for the residential self-selection. To that objective, we collected activity-travel diary data from a sample of 229 households in Beijing before and after they moved from one residential location to another. We developed a two-wave structural equation model linking the residential built environment to travel behavior and taking into consideration travel-related attitudes before and after residential change. The modeling results show that individuals’ travel attitudes may change after a home relocation. We found no evidence of residential self-selection, but significant influence of the built environment on travel preference. Nevertheless, the direct influence of travel preference on travel behavior seems to be stronger than that of the built environment. As one of the very few studies to use panel data, this research presents new insights into the relationship between the built environment and travel behavior and the role of residential self-selection.

     

Modeling travel behavior by the structural relationships between lifestyle,built environment and non-working trips

In the context of sustainable urban transport in developing countries, individuals’ travel behavior faces multiple factors which influence their mobility patterns. Recognizing these factors could be a favorable method to organize more regular and sustainable trip patterns. This study aims to identify the less well-known lifestyle along with more popular built environment as the main factors which shape travel behaviors. Employing data from 900 respondents of 22 urban areas in city of Shiraz, Iran, this paper explores travel behaviors as non-working trip frequencies by different modes. Results of structural equation model indicate a strong significant effect of individual’s lifestyle patterns on their non-working trips. However, built environment impact on travel behavior is small compared to lifestyle. Besides, other variables such as travel attitudes and socio-economic factors stay crucial in the mode choice selection. These findings indicate the necessity of regarding lifestyle orientations in travel studies as well as objective factors such as land use attributes.     

The impacts of built environment on home-based work and non-work trips: An empirical study from Iran

This paper aims to explore the impact of built environment attributes in the scale of one quarter-mile buffers on individuals’ travel behaviors in the metropolitan of Shiraz, Iran. In order to develop this topic, the present research is developed through the analysis of a dataset collected from residents of 22 neighborhoods with variety of land use features. Using household survey on daily activities, this study investigates home-based work and non-work (HBW and HBN) trips. Structural equation models are utilized to examine the relationships between land use attributes and travel behavior while taking into account socio-economic characteristics as the residential self-selection. Results from models indicate that individuals residing in areas with high residential and job density, and shorter distance to sub-centers are more interested in using transit and non-motorized modes. Moreover, residents of neighborhoods with mixed land uses tend to travel less by car and more by transit and non-motorized modes to non-work destinations. Nevertheless, the influences of design measurements such as street density and internal connectivity are mixed in our models. Although higher internal connectivity leads to more transit and non-motorized trips in HBW model, the impacts of design measurements on individuals travel behavior in HBN model are significantly in contrast with research hypothesis. Our study also shows the importance of individuals’ self-selection impacts on travel behaviors; individuals with special socio-demographic attributes live in the neighborhoods with regard to their transportation patterns. The findings of this paper reveal that the effects of built environment attributes on travel behavior in origins of trips do not exactly correspond with the expected predictions, when it comes in practice in a various study context. This study displays the necessity of regarding local conditions of urban areas and the inherent differences between travel destinations in integrating land use and transportation planning.     

Analyzing high speed rail passengers’ train choices based on new online booking data in China

This study explores two nonparametric machine learning methods, namely support vector regression (SVR) and artificial neural networks (ANN), for understanding and predicting high-speed rail (HSR) travelers’ choices of ticket purchase timings, train types, and travel classes, using ticket sales data. In the train choice literature, discrete choice analysis is the predominant approach and many variants of logit models have been developed. Alternatively, emerging travel choice studies adopt non-utility-based methods, especially nonparametric machine learning methods including SVR and ANN, because (1) those methods do not rely on assumptions on the relations between choices and explanatory variables or any prior knowledge of the underlying relations; (2) they have superb capabilities of iteratively identifying patterns and extracting rules from data. This paper thus contributes to the HSR train choice literature by applying and comparing SVR and ANN with a real-world case study of the Shanghai-Beijing HSR market in China. A new normalized metric capturing both the load factor and the booking lead time is proposed as the target variable and several train service attributes, such as day of week, departure time, travel time, fare, are identified as input variables. Computational results demonstrate that both SVR and ANN can predict the train choice behavior with high accuracy, outperforming the linear regression approach. Potential applications of this study, such as rail pricing reform, have also been identified.