Link travel time inference using entry/exit information of trips on a network

This paper studies link travel time estimation using entry/exit time stamps of trips on a steady-state transportation network. We propose two inference methods based on the likelihood principle, assuming each link associates with a random travel time. The first method considers independent and Gaussian distributed link travel times, using the additive property that trip time has a closed-form distribution as the summation of link travel times. We particularly analyze the mean estimates when the variances of trip time estimates are known with a high degree of precision and examine the uniqueness of solutions. Two cases are discussed in detail: one with known paths of all trips and the other with unknown paths of some trips. We apply the Gaussian mixture model and the Expectation–Maximization (EM) algorithm to deal with the latter. The second method splits trip time proportionally among links traversed to deal with more general link travel time distributions such as log-normal. This approach builds upon an expected log-likelihood function which naturally leads to an iterative procedure analogous to the EM algorithm for solutions. Simulation tests on a simple nine-link network and on the Sioux Falls network respectively indicate that the two methods both perform well. The second method (i.e., trip splitting approximation) generally runs faster but with larger errors of estimated standard deviations of link travel times.     

Latent temporal preferences: An application to airline travel

An essential element of demand modeling in the airline industry is the representation of time of day demand—the demand for a given itinerary as a function of its departure or arrival times. It is an important datum that drives successful scheduling and fleet decisions. There are two key components to this problem: the distribution of the time of day demand and how preferred travel time influences itinerary choice. This paper focuses on estimating the time of day distribution. Our objective is to estimate it in a manner that is not confounded with air travel supply; is a function of the characteristics of the traveler, the trip, and the market; and accounts for potential measurement errors in self-reported travel time preferences. We employ a stated preference dataset collected by intercepting people who were booking continental US trips via an internet booking service. Respondents reported preferred travel times as well as choices from a hypothetical set of itineraries. We parameterize the time of day distribution as a mixture of normal distributions (due to the strong peaking nature of travel time preferences) and allow the mixing function to vary by individual characteristics and trip attributes. We estimate the time of day distribution and the itinerary choice model jointly in a manner that accounts for measurement error in the self-reported travel time preferences. We find that the mixture of normal distributions fits the time of day distribution well and is behaviorally intuitive. The strongest covariates of travel time preferences are party size and time zone change. The methodology employed to treat self-reported travel time preferences as potentially having error contributes to the broader transportation time of day demand literature, which either assumes that the desired travel times are known with certainty or that they are unknown. We find that the error in self-reported travel time preferences is statistically significant and impacts the inferred time of day demand distribution.     

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.     

Trip chain complexity: a comparison among latent classes of daily mobility patterns

This paper studies the relationship between trip chain complexity and daily travel behaviour of travellers. While trip chain complexity is conventionally investigated between travel modes, our scope is the more aggregated level of a person’s activity-travel pattern. Using data from the Netherlands Mobility Panel, a latent class cluster analysis was performed to group people with similar mode choice behaviour in distinct mobility pattern classes. All trip chains were assigned to both a travel mode and the mobility pattern class of the traveller. Subsequently, differences in trip chain complexity distributions were analysed between travel modes and between mobility pattern classes. Results indicate considerable differences between travel modes, particularly between multimodal and unimodal trip chains, but also between the unimodal travel modes car, bicycle, walking and public transport trip chains. No substantial differences in trip chain complexity were found between mobility pattern classes. Independently of the included travel modes, the distributions of trip chain complexity degrees were similar across mobility pattern classes. This means that personal circumstances such as the number of working hours or household members are not systematically translated into specific mobility patterns.

     

Effects of household structure and accessibility on travel

The concept of accessibility has been widely used in the transportation field, commonly to evaluate transportation planning options. The fundamental hypothesis of many studies related to accessibility could be “greater accessibility leads to more travel”. However, several studies have shown inconsistent results given this common hypothesis, finding instead that accessibility is independent of the trip/tour frequency. In addition, empirical aggregate urban modeling applications commonly produce either non-significant or negative (wrong sign) relationships between accessibility and the trip/tour frequency. For this reason, many practitioners rarely incorporate a measure of accessibility into trip/tour generation models out of consideration of the induced demand. In this context, this study examined the effect of accessibility in urban and suburban residences on the maintenance and discretionary activity tour frequencies of the elderly and the non-elderly using household travel survey data collected in the Seoul Metropolitan Area of Korea. The major finding of this study is that a higher density of land use and better quality of transportation service do not always lead to more tours due to the presence of intra-household interactions, trip chaining, and different travel needs by activity type. This finding implies that accessibility-related studies should not unquestioningly accept the common hypothesis when they apply accessibility measures to evaluate their transportation planning options or incorporate them into their trip/tour generation models.     

Gender differentials in travel pattern – The case of a mid-sized city,Rajkot, India

This paper is at the conjunction of arguments for gender equity in transport services on one hand and need to move towards low carbon transport on the other in a developing country city. The case study city, Rajkot, a mid-sized Indian city with mixed land use and limited public transport at the time of this study, has short trip lengths and low trip rates. But, even in this city there is gender disparity in travel pattern. Thus, trip rates and trip lengths of women in the city in each of the income group are lower than that of their male counter parts. With the increase in income, both, the trip rates and lengths increase, but, the increase is higher for men than for women. While the large proportions of women in each income group walk, that among the lowest income group walking is the predominant mode, as they are ‘no-choice’ or ‘forced’ walkers. With the increase in household income, women tend to shift to para-transit and men to personal motorized transport. If sustainability arguments or climate policies are to target retaining the current low ecological footprint of the women as one of the strategies, it needs to cater to expansion of their mobility while improving infrastructure to support the same.     

Probabilistic model for destination inference and travel pattern mining from smart card data

Transportation - Inferring trip destination in smart card data with only tap-in control is an important application. Most existing methods estimate trip destinations based on the continuity of trip...     

Intrapersonal variability in daily urban travel behavior: Some additional evidence

Researchers have used multiday travel data sets recently to examine day-to-day variability in travel behavior. This work has shown that there is considerable day-to-day variation in individuals' urban travel behavior in terms of such indicators of behavior as trip frequency, trip chaining, departure time from home, and route choice. These previous studies have also shown that there are a number of important implications of the observed day-to-day variability in travel behavior. For example, it has been shown that it may be possible to improve model parameter estimation precision, without increasing the cost of data collection, by drawing a multiday sample (rather than a single day sample) of traveler behavior, if there is considerable day-to-day variability in the phenomenon being modeled. This paper examines day-to-day variability in urban travel using a three-day travel data set collected recently in Seattle, WA. This research replicates and extends previous work dealing with day-to-day variability in trip-making behavior that was conducted with data collected in Reading, England, in the early 1970s. The present research extends the earlier work by examining day-to-day variations in trip chaining and daily travel time in addition to the variation in trip generation rates. Further, the present paper examines day-to-day variations in travel across the members of two-person households. This paper finds considerable day-to-day variability in the trip frequency, trip chaining and daily travel time of the sample persons and concludes that, in terms of trip frequency, the level of day-to-day variability is very comparable to that observed previously with a data set collected almost 20 years earlier in Reading, England. The paper also finds that day-to-day variability in daily travel time is similar in magnitude to that in daily trip rates. The analysis shows that the level of day-to-day variability is about the same for home-based and non-homebased trips, thus indicating that day-to-day variability in total trip-making is attributable to variation in both home-based and non-home-based trips. Day-to-day variability in the travel behaviors of members of two-person households was also found to be substantial.     

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.     

Development of a probabilistic model to optimize disseminated real‐time bus arrival information for pre‐trip passengers

In the advent of Advanced Traveler Information Systems (ATIS), the total wait time of passengers for buses may be reduced by disseminating real‐time bus arrival times for the next or series of buses to pre‐trip passengers through various media (e.g., internet, mobile phones, and personal digital assistants). A probabilistic model is desirable and developed in this study, while realistic distributions of bus and passenger arrivals are considered. The disseminated bus arrival time is optimized by minimizing the total wait time incurred by pre‐trip passengers, and its impact to the total wait time under both late and early bus arrival conditions is studied. Relations between the optimal disseminated bus arrival time and major model parameters, such as the mean and standard deviation of arrival times for buses and pre‐trip passengers, are investigated. Analytical results are presented based on Normal and Lognormal distributions of bus arrivals and Gumbel distribution of pre‐trip passenger arrivals at a designated stop. The developed methodology can be practically applied to any arrival distributions of buses and passengers.     

Detecting trip purposes from smartphone-based travel surveys with artificial neural networks and particle swarm optimization

Travel surveys based on global positioning system (GPS) data have exponentially increased over the past decades. Trip characteristics, including trip ends, travel modes, and trip purposes need to be detected from GPS data. Compared with other trip characteristics, studies on trip purpose detection are limited. These studies struggle with three types of limitations, namely, data validation, classification approach-related issues, and result comparison under multiple scenarios. Therefore, we attempt to obtain full understanding and improve these three aspects when detecting trip purposes in the current study. First, a smartphone-based travel survey is employed to collect GPS data, and a surveyor-intervened prompted recall survey is used to validate trip characteristics automatically detected from the GPS data. Second, artificial neural networks combined with particle swarm optimization are used to detect trip purposes from the GPS data. Third, four scenarios are constructed by employing two methods for land-use type coding, i.e., polygon-based information and point of interest, and two methods for selecting training dataset, i.e., equal proportion selection and equal number selection. The accuracy of trip purpose detection is then compared under these scenarios. The highest accuracies of 97.22% for the training dataset and 96.53% for the test dataset are achieved under the scenario of polygon-based information and equal proportion selection by comparing the detected and validated trip purposes. Promising results indicate that a smartphone-based travel survey can complement conventional travel surveys.     

Intercept and recall: Examining avidity carryover in on-site collected travel data

This study estimates trip demand and economic benefits for visitors to recreation sites when past season trip information is elicited from travelers intercepted on-site. We use a weighting function for past season counts that is different from, but nests, the standard on-site correction appropriate for current season counts. We find that for our sample of lake visitors relatively stronger preference or “avidity” for the interview site carries over across seasons. We further show that the appropriate weighting of past trip counts is critical in deriving meaningful estimates of travel demand and economic benefits.     

Short-term travel time prediction

Effective prediction of travel times is central to many advanced traveler information and transportation management systems. In this paper we propose a method to predict freeway travel times using a linear model in which the coefficients vary as smooth functions of the departure time. The method is straightforward to implement, computationally efficient and applicable to widely available freeway sensor data.We demonstrate the effectiveness of the proposed method by applying the method to two real-life loop detector data sets. The first data set––on I-880––is relatively small in scale, but very high in quality, containing information from probe vehicles and double loop detectors. On this data set the prediction error ranges from 5% for a trip leaving immediately to 10% for a trip leaving 30 min or more in the future. Having obtained encouraging results from the small data set, we move on to apply the method to a data set on a much larger spatial scale, from Caltrans District 12 in Los Angeles. On this data set, our errors range from about 8% at zero lag to 13% at a time lag of 30 min or more. We also investigate several extensions to the original method in the context of this larger data set.     

Effects of household structure and selected travel characteristics on trip chaining

This paper analyzes trip chaining, focusing on how households organize non-work travel. A trip chaining typology is developed using household survey data from Portland, Oregon. Households are organized according to demographic structure, allowing analysis of trip chaining differences among household types. A logit model of the propensity to link non-work trips to the work commute is estimated. A more general model of household allocation of non-work travel among three alternative chain types — work commutes, multi-stop non-work journeys, and unlinked trips — is also developed and estimated. Empirical results indicate that the likelihood of linking work and non-work travel, and the more general organization of non-work travel, varies with respect to household structure and other factors which previous studies have found to be important. The effects of two congestion indicators on trip chaining were mixed: workers who commuted in peak periods were found to have lower propensity to form work/non-work chains, while a more general congestion indicator had no effect on the allocation of non-work trips among alternative chains.     

A comparative analysis of traditional four-step and activity-based travel demand modeling: a case study of Tampa,Florida

Activity-based travel demand modeling (ABTDM) has often been viewed as an advanced approach, due to its higher fidelity and better policy sensitivity. However, a review of the literature indicates that no study has been undertaken to investigate quantitatively the differences and accuracy between an ABTDM approach and a traditional four-step travel demand model. In this paper we provide a comparative analysis against each step – trip generation, trip distribution, mode split, and network assignment – between an ABTDM developed using travel diary data from the Tampa Bay Region in Florida and the Tampa Bay Regional Planning Model (TBRPM), an existing traditional four-step model for the same area. Results show salient differences between the TBRPM and the ABTDM, in terms of modeling performance and accuracy, in each of the four steps. For example, trip production rates calculated from the travel diary data are found to be either double or a quarter less than those used in the TBRPM. On the other hand, trip attraction rates computed from activity-based travel simulations are found to be either more than double or one tenth less than those used in the TBRPM. The trip distribution curves from the two models are similar, but both average and peak trip lengths of the two are significantly different. Mode split analyses show that the TBRPM may underestimate driving trips and fail to capture any usage of alternative modes, such as taxi and nonmotorized (e.g., walking and bicycling) modes. In addition, the ABTDMs are found to be less capable of reproducing observed traffic counts when compared to the TBRPM, most likely due to not considering external and through trips. The comparative results presented can help transportation engineers and planners better understand the strengths and weaknesses of the two types of model and this should assist decision-makers in choosing a better modeling tool for their planning initiatives.     

A model for integrated analysis of household location and travel choices

We develop a model for integrated analysis of household location and travel choices and investigate it from a theoretical point of view.Each household makes a joint choice of location (zone and house type) and a travel pattern that maximizes utility subject to budget and time constraints. Prices for housing are calculated so that demand equals supply in each submarket. The travel pattern consists of a set of expected trip frequencies to different destinations with different modes. The joint time and budget constraints ensure that time and cost sensitivities are consistent throughout the model. Choosing the entire travel pattern at once, as opposed to treating travel decisions as a series of isolated choices, allows the marginal utilities of trips to depend on which other trips are made.When choosing trip frequencies to destinations, households are assumed to prefer variation to an extent varying with the purpose of the trip. The travel pattern will tend to be more evenly distributed across trip ends the less similar destinations and individual preferences are. These heterogeneities of destinations and individual preferences, respectively, are expressed in terms of a set of parameters to be estimated.     

Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands

In the past few decades, travel patterns have become more complex and policy makers demand more detailed information. As a result, conventional data collection methods seem no longer adequate to satisfy all data needs. Travel researchers around the world are currently experimenting with different Global Positioning System (GPS)-based data collection methods. An overview of the literature shows the potential of these methods, especially when algorithms that include spatial data are used to derive trip characteristics from the GPS logs. This article presents an innovative method that combines GPS logs, Geographic Information System (GIS) technology and an interactive web-based validation application. In particular, this approach concentrates on the issue of deriving and validating trip purposes and travel modes, as well as allowing for reliable multi-day data collection. In 2007, this method was used in practice in a large-scale study conducted in the Netherlands. In total, 1104 respondents successfully participated in the one-week survey. The project demonstrated that GPS-based methods now provide reliable multi-day data. In comparison with data from the Dutch Travel Survey, travel mode and trip purpose shares were almost equal while more trips per tour were recorded, which indicates the ability of collecting trips that are missed by paper diary methods.     

Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues

With the availability of Global Positioning System (GPS) receivers to capture vehicle location, it is now feasible to easily collect multiple days of travel data automatically. However, GPS-collected data are not ready for direct use in trip rate or route choice research until trip ends are identified within large GPS data streams. One common parameter used to divide trips is dwell time, the time a vehicle is stationary. Identifying trips is particularly challenging when there is trip chaining with brief stops, such as picking up and dropping off passengers. It is hard to distinguish these stops from those caused by traffic controls or congestion. Although the dwell time method is effective in many cases, it is not foolproof and recent research indicates use of additional logic improves trip dividing. While some studies incorporating more than dwell time to identify trip ends having been conducted, research including actual trip ends to evaluate the success of trip dividing methods used have been limited. In this research, 12 ten-day real-world GPS travel datasets were used to develop, calibrate and compare three methods to identify trip start points in the data stream. The true start and end points of each trip were identified in advance in the GPS data stream using a supplemental trip log completed by the participants so that the accuracy of each automated trip division method could be measured and compared. A heuristic model, which combines heading change, dwell time and distance between the GPS points and the road network, performs best, correctly identifying 94% of trip ends.     

Time use in travel surveys and time use surveys – Two sides of the same coin?

An in-depth understanding of travel behaviour determinants, including the relationship to non-travel activities, is the foundation for modelling and policy making. National Travel Surveys (NTS) and time use surveys (TUS) are two major data sources for travel behaviour and activity participation. The aim of this paper is to systematically compare both survey types regarding travel activities and non-travel activities. The analyses are based on the German National Travel Survey and the German National Time Use Survey from 2002.The number of trips and daily travel time for mobile respondents were computed as the main travel estimates. The number of trips per person is higher in the German TUS when changes in location without a trip are included. Location changes without a trip are consecutive non-trip activities with different locations but without a trip in-between. The daily travel time is consistently higher in the German TUS. The main reason for this difference is the 10-min interval used. Differences in travel estimates between the German TUS and NTS result from several interaction effects. Activity time in NTS is comparable with TUS for subsistence activities.Our analyses confirm that both survey types have advantages and disadvantages. TUS provide reliable travel estimates. The number of trips even seems preferable to NTS if missed trips are properly identified and considered. Daily travel times are somewhat exaggerated due to the 10-min interval. The fixed time interval is the most important limitation of TUS data. The result is that trip times in TUS do not represent actual trip times very well and should be treated with caution.We can use NTS activity data for subsistence activities between the first trip and the last trip. This can potentially benefit activity-based approaches since most activities before the first trip and after the last trip are typical home-based activities which are rarely substituted by out-of-home activities.     

Changes in perceived travel cost and time for the work trip during a period of increasing gasoline costs

This study was designed to examine the relationship between actual and perceived values of cost and time for the work trip and to examine how perceptions have changed over a period of dramatically increased travel costs. Variations in the relationship between perceived and actual values were examined as a function of situational and attitudinal variables. Two telephone surveys were conducted one year apart (Fall 1978 and Fall 1979). On the next working day following a survey, a research assistant recreated the respondent's work trip, recorded time values and used distance measures, car type information and parking costs to compute travel cost. The first survey revealed that most auto users were unable to articulate dollars-and-cents driving costs for the work trip, but auto users in the second survey were able to provide fairly accurate cost estimates. Dramatic changes in fuel prices between surveys is probably the main reason for the change in driving cost awareness. Auto users were also asked to rate relative costs of driving a car compared to using the bus for the work trip. These ratings showed that auto users tended to underestimate driving costs relative to bus costs, but this tendency decreased from the first to the second time period. Commuters in all modal groups at both time periods tended to overestimate travel times. Perception of travel time varied as a function of mode, perceived comfort (for car users), and perceived convenience and number of transfers (for bus users).To whom correspondence should be addressed.