The evening commute with cars and transit: Duality results and user equilibrium for the combined morning and evening peaks

This paper extends Vickrey’s (1969) commute problem for commuters wishing to pass a bottleneck for both cars and transit that share finite road capacity. In addition to this more general framework considering two modes, the paper focuses on the evening rush, when commuters travel from work to home. Commuters choose which mode to use and when to travel in order to minimize the generalized cost of their own trips, including queueing delay and penalties for deviation from a preferred schedule of arrival and departure to and from work. The user equilibrium for the isolated morning and evening commutes are shown to be asymmetric because the schedule penalty in the morning is the difference between the departure and wished curves, and the schedule penalty in the evening is the difference between the arrival and wished curves. It is shown that the system optimum in the morning and evening peaks are symmetric because queueing delay is eliminated and the optimal arrival curves are the same as the departure curves.The paper then considers both the morning and evening peaks together for a single mode bottleneck (all cars) with identical travelers that share the same wished times. For a schedule penalty function of the morning departure and evening arrival times that is positive definite and has certain properties, a user equilibrium is shown to exist in which commuters travel in the same order in both peaks. The result is used to illustrate the user equilibrium for two cases: (i) commuters have decoupled schedule preferences in the morning and evening and (ii) commuters must work a fixed shift length but have flexibility when to start. Finally, a special case is considered with cars and transit: commuters have the same wished order in the morning and evening peaks. Commuters must use the same mode in both directions, and the complete user equilibrium solution reveals the number of commuters using cars and transit and the period in the middle of each rush when transit is used.     

A conceptual and methdological framework for the generation of activity-travel patterns

This paper develops a conceptual framework for the generation of activity and travel patterns in the context of more general structures and presents an integrated model system as a step toward development of an improved travel demand forecasting model system. We propose a two-stage structure to model activity and travel behavior. The first stage, the stop generation and stop/auto allocation models, consists of the choices for the number of household maintenance stops and the allocation of stops and autos to household members. The second stage, the tour formation model, includes the choices for the number of tours and the assignment of stops to tours for each individual, conditional on the choices in the first stage. Empirical results demonstrate that individual and household socio-demographics are important factors affecting the first stage choices, the generation of maintenance stops and the allocation of stops and autos among household members, and the second stage choices, the number of tours and the assignment of stops to tours. This revised version was published online in June 2006 with corrections to the Cover Date.     

The effect of social comparisons on commute well-being

We study the effect of social comparisons on travel happiness and behavior. Social comparisons arise from exchanges of information among individuals. We postulate that the social gap resulting from comparisons is a determinant of “comparative happiness” (i.e. happiness arising from comparisons), which in turn affects subsequent behavior. We develop a modeling framework based on the Hybrid Choice Model that captures the indirect effect of social comparisons on travel choices through its effect on comparative happiness.We present an empirical analysis of one component of this framework. Specifically, we study how perceived differences between experienced commute attributes and those communicated by others affect comparative happiness and consequently overall commute satisfaction. We find that greater comparative happiness arising from favorable comparisons of one’s commute to that of others (e.g. shorter commute time than others, same mode as others for car commuters, and different mode than others for non-motorized commuters) increases overall commute satisfaction or utility.The empirical model develops only the link between social comparisons and happiness in the comparisons-happiness-behavior chain. It is anticipated that the theoretical framework that considers the entire chain will enhance the behavioral realism of “black box” models that do not account for happiness in the link between comparisons and behavior.     

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

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

     

Not All Transfers Are Created Equal: Towards a Framework Relating Transfer Connectivity to Travel Behaviour

Abstract

Walking from origins to transit stops, transferring between transit lines and walking from transit stops to destinations—all add to the burden of transit travel, sometimes to a very large degree. Transfers in particular can be stressful and/or time‐consuming for travellers, discouraging transit use. As such, transit facilities that reduce the burdens of walking, waiting and transferring can substantially increase transit system efficacy and use. In this paper, we argue that transit planning research on transit stops and stations, and transit planning practice frequently lack a clear conceptual framework relating transit waits and transfers with what we know about travel behaviour. Therefore, we draw on the concepts of transfer penalties and value of time in the travel behaviour/economics literature to develop a framework that situates transfer penalties within the total travel generalized costs of a transit trip. For example, value of time is important in relating actual time of waiting and walking to the perceived time of travel. We also draw on research to classify factors most important to users’ perspectives and travel behaviour—transfer costs, time scheduling and five transfer facility attributes: (1) access, (2) connection and reliability, (3) information, (4) amenities, and (5) security and safety. Using this framework, we seek to explicitly relate improvements of transfer stops/stations with components of transfer penalties and changes in travel behaviour (through a reduction in transfer penalties). We conclude that the employment of such a framework can help practitioners better apply the most effective improvements to transit stops and transfer facilities.     

Importance of traveler attitudes in the choice of public transportation to work: findings from the Regional Transportation Authority Attitudinal Survey

The commute mode choice decision is one of the most fundamental aspects of daily travel. Although initial research in this area was limited to explaining mode choice behavior as a function of traveler socioeconomics, travel times, and costs, subsequent studies have included the effect of traveler attitudes and perceptions. This paper extends the existing body of literature by examining public transit choice in the Chicago area. Data from a recent Attitudinal Survey conducted by the Regional Transportation Authority (RTA) in Northeastern Illinois were used to pursue three major steps. First, a factor analysis methodology was used to condense scores on 23 statements related to daily travel into six factors. Second, the factor scores on these six dimensions were used in conjunction with traveler socioeconomics, travel times, and costs to estimate a binary logistic regression of public transit choice. Third, elasticities of transit choice to the six factors were computed, and the factors were ranked in decreasing order of these elasticities. The analysis provided two major findings. First, from a statistical standpoint, the attitudinal factors improved the intuitiveness and goodness-of-fit of the model. Second, from a policy standpoint, the analysis indicated the importance of word-of-mouth publicity in attracting new riders, as well as the need for a marketing message that emphasizes the lower stress level and better commute time productivity due to transit use.     

Transit-based smart parking: An evaluation of the San Francisco Bay area field test

This paper presents an evaluation of the first transit-based smart parking project in the US at the San Francisco Bay Area Rapid Transit (BART) District station in Oakland, California. The paper begins with a review of the smart parking literature; next the smart parking field test is described including its capital, operational, and maintenance costs; and finally the results of the participant survey analysis are presented. Some key user response results are: (1) most participants used the smart parking system 1–3 days a month for commute travel and (2) 37% of respondents had seen the changeable message signs with parking information, but only 32% of those used this information to decide whether to continue driving or take BART. Some key changes in participant travel behavior include: (1) increases in BART mode share, (2) reductions in drive alone modal share, (3) decreased average commute time, and (4) an overall reduction in total vehicle miles of travel.     

A zonal inference model based on observed smart-card transactions for Santiago de Chile

The collection of origin–destination data for a city is an important but often costly task. This way, there is a need to develop more efficient and inexpensive methods of collecting information about citizens’ travel patterns. In this line, this paper presents a generic methodology that allows to infer the origin and destination zones for an observed trip between two public transport stops (i.e., bus stops or metro stations) using socio-economic, land use, and network information. The proposed zonal inference model follows a disaggregated Logit approach including size variables. The model enables the estimation of a zonal origin–destination matrix for a city, if trip information passively collected by a smart-card payment system is available (in form of a stop-to-stop matrix). The methodology is applied to the Santiago de Chile’s morning peak period, with the purpose of serving as input for a public transport planning computational tool. To estimate the model, information was gathered from different sources and processed into a unified framework; data included a survey conducted at public transport stops, land use information, and a stop-to-stop trip matrix. Additionally, a zonal system with 1176 zones was constructed for the city, including the definition of its access links and associated distances. Our results shows that, ceteris paribus, zones with high numbers of housing units have higher probabilities of being the origin of a morning peak trip. Likewise, health facilities, educational, residential, commercial, and offices centres have significant attraction powers during this period. In this sense, our model manages to capture the expected effects of land use on trip generation and attraction. This study has numerous policy implications, as the information obtained can be used to predict the impacts of changes in the public transport network (such as extending routes, relocating their stops, designing new routes or changing the fare structure). Further research is needed to improve the zonal inference formulation and origin–destination matrix estimation, mainly by including better cost measures, and dealing with survey and data limitations.     

Modeling the choice continuum: an integrated model of residential location,auto ownership,bicycle ownership,and commute tour mode choice decisions

The integrated modeling of land use and transportation choices involves analyzing a continuum of choices that characterize people’s lifestyles across temporal scales. This includes long-term choices such as residential and work location choices that affect land-use, medium-term choices such as vehicle ownership, and short-term choices such as travel mode choice that affect travel demand. Prior research in this area has been limited by the complexities associated with the development of integrated model systems that combine the long-, medium- and short-term choices into a unified analytical framework. This paper presents an integrated simultaneous multi-dimensional choice model of residential location, auto ownership, bicycle ownership, and commute tour mode choices using a mixed multidimensional choice modeling methodology. Model estimation results using the San Francisco Bay Area highlight a series of interdependencies among the multi-dimensional choice processes. The interdependencies include: (1) self-selection effects due to observed and unobserved factors, where households locate based on lifestyle and mobility preferences, (2) endogeneity effects, where any one choice dimension is not exogenous to another, but is endogenous to the system as a whole, (3) correlated error structures, where common unobserved factors significantly and simultaneously impact multiple choice dimensions, and (4) unobserved heterogeneity, where decision-makers show significant variation in sensitivity to explanatory variables due to unobserved factors. From a policy standpoint, to be able to forecast the “true” causal influence of activity-travel environment changes on residential location, auto/bicycle ownership, and commute mode choices, it is necessary to capture the above-identified interdependencies by jointly modeling the multiple choice dimensions in an integrated framework.     

Household travel attributes transferability analysis: application of a hierarchical rule based approach

This paper presents a system of hierarchical rule-based models of trip generation and modal split. Travel attributes, like trip counts for different transportation modes and commute distance, are among the modeled variables. The proposed framework could be considered as an alternative for several modules of the traditional travel demand modeling approach, while providing travel attributes at the highly disaggregate level that can be also used in activity-based micro-simulation modeling systems. Nonetheless, the modeling framework of this study is not considered as a substitute for activity-based models. The explanatory variables set ranges from socio-economic and demographic attributes of the household to the built environment characteristics of the household residential location. Another important contribution of the study is a framework in which travel attributes are modeled in conjunction with each other and the interdependencies among them are postulated through a hierarchical system of models. All the models are developed using rule-based decision tree method. Moreover, the models developed in this study present a useful improvement in increasing the practicality and accuracy of the rule-based travel data simulation models.     

Using a stages of change approach to explore opportunities for increasing bicycle commuting

Given the potential benefits of bicycling to the environment, the economy, and public health, many U.S. cities have set ambitious goals for increasing the bicycle share of commute trips. The Transtheoretical Model of Behavior Change, which seeks to describe how positive and permanent change can be fostered in individuals, may shed light on how cities can most effectively increase bicycle commuting. We use the model’s “stages of change” framework to explore the potential for increased bicycle commuting to the UC Davis campus in Davis, California. Our analysis uses data from the 2012 to 2013 UC Davis Campus Travel Survey, an annual online survey that is randomly administered to students and employees at UC Davis. Based on their responses to questions about current commute mode and contemplation of bicycle commuting, respondents are divided into five stages of change: Pre-contemplation, Contemplation, Preparation, Action, and Maintenance. We construct a Bayesian multilevel ordinal logistic regression model to understand how differences in socio-demographic characteristics, travel attributes, and travel attitudes between individuals explain their membership in different stages of change. In addition, we use this model to explore the potential of various intervention strategies to move individuals through the stages of change toward becoming regular bicycle commuters. Our results indicate that travel attitudes matter more to progression toward regular commute bicycling than travel attributes, tentatively supporting the efficacy of “soft” policies focused on changing travel attitudes.     

A dynamic route choice model for public transport networks with boarding queues

Abstract

The concepts of optimal strategy and hyperpath were born within the framework of static frequency-based public transport assignment, where it is assumed that travel times and frequencies do not change over time and no overcrowding occurs. However, the formation of queues at public transport stops can prevent passengers from boarding the first vehicle approaching and can thus lead to additional delays in their trip. Assuming that passengers know from previous experience that for certain stops/lines they will have to wait for the arrival of the 2nd, 3rd, …, k-th vehicle, they may alter their route choices, thus resulting in a different assignment of flows across the network. The aim of this paper is to investigate route choice behaviour changes as a result of the formation and dispersion of queues at stops within the framework of optimal travel strategies. A new model is developed, based on modifications of existing algorithms.     

Enjoyment of commute: A comparison of different transportation modes

This study investigates how socio-demographic and attitudinal variables of university students affect their desire to increase or decrease their daily commute. The case study is McMaster University in Hamilton, Canada, and data was obtained by means of a web-based survey that included questions regarding travel behavior, socio-demographic information, and attitudes toward travel, land use, and the environment. The objective variable is defined as the ratio of ideal to actual commute time, and regression analysis is implemented to test the relationship between this variable and socio-demographic variables and attitudinal scores. The impact of different attitudes on the gap between ideal and actual commute time is expanded to include three different modes, active travel (walk/cycle), transit, and personal automobile. Interestingly, the results indicate that active travelers tend to be less dissatisfied with their commute, followed by those who travel in a personal vehicle and transit users. A number of attitudinal responses are shown to impact the desire to travel more or less, including variables that relate to the social environment, availability of local activities, quality of facilities, productive use of the commute, and the intrinsic value found in the commute travel. The picture emerges of a traveler who would like to spend more time commuting, as someone who is an active traveler, thinks that getting there is half the fun, dislikes traveling alone, but rather likes to live in an active neighborhood where there is a sense of community. The results suggests that enjoyment of commuting, while a challenge from the perspective of motorized mobility, may provide valuable policy opportunities from the perspective of active transportation.     

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.     

Impact of telecommuting on spatial and temporal patterns of household travel

A spatial and temporal analysis of travel diary data collected during the State of California Telecommuting Pilot Project is performed to determine the impacts of telecommuting on household travel behavior. The analysis is based on geocoded trip data where missing trips and trip attributes have been augmented to the extent possible. The results confirm the earlier finding that the Pilot Project telecommuters substantially reduced travel; on telecommuting days, the telecommuters made virtually no commute trips, reduced peak-period trips by 60%, total distance traveled by 75%, and freeway miles by 90%. The spatial analysis of the trip records has shown that the telecommuters chose non-work destinations that are closer to home; they exhibited contracted action spaces after the introduction of telecommuting. Importantly, this contraction took place on both telecommuting days and commuting days. The telecommuters distributed their trips, over the day and avoided peak-period travel on telecommuting days. Non-work trips, however, show similar patterns of temporal distribution on telecommuting days and commuting days. Non-work trips continued to be made during the lunch period and late afternoon and evening hours.     

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.     

On the structure of weekly activity/travel patterns

Understanding the process of activity scheduling is a critical pre-requisite to an understanding of changes in travel behavior. To examine this process, a computerized survey instrument was developed to collect household activity scheduling data. The instrument is unique in that it records the evolution of activity schedules from intentions to final outcomes for a weekly period. This paper summarizes an investigation of the structure of activity/travel patterns based on data collected from a pilot study of the instrument. The term “structure” refers to the sequence by which various activities enter one’s daily activity scheduling process. Results of the empirical analyses show that activities of shorter duration were more likely to be opportunistically inserted in a schedule already anchored by their longer duration counterparts. Additionally, analysis of travel patterns reveals that many trip-chains were formed opportunistically. Travel time required to reach an activity was positively related to the scheduling horizon for the activity, with more distant stops being planned earlier than closer locations.     

Uncertain benefits: Application of Bayesian melding to the Alaskan Way Viaduct in Seattle

Uncertainty is inherent in major infrastructure projects, but public decision-making for such projects ignores it. We investigate the uncertainty about the future effects of tearing down the Alaskan Way Viaduct in downtown Seattle, using an integrated model of housing, jobs, land use and transportation, on outcomes including average commute times. Our methodology combines the urban simulation model UrbanSim with the regional transportation model. We assess uncertainty using Bayesian melding, yielding a full predictive distribution of average commute times on 22 different routes in 2020. Of these routes, 14 do not include the viaduct and eight do. For the 14 base routes that do not include the viaduct, the predictive distributions overlap substantially, and so there is no indication that removing the viaduct would increase commute times for these routes. For each of the eight routes that do include the viaduct, the 95% predictive interval for the difference in average travel times between the two scenarios includes zero, so there is not strong statistical support for the conclusion that removing the viaduct would lead to any increase in travel times. However, the median predicted increase is positive for each of these routes, with an average of 6 min, suggesting that there may be some measurable increase in travel time for drivers that use the viaduct as a core component of their commute.     

What determines commute time choices? A structural equation modelling approach

Abstract

Many researchers have been attracted by the phenomenon of constant travel time, and the time spent on travel has been an important indicator of understanding travellers’ behaviours. This paper is based on a survey conducted in a university in London which includes both objective and subjective variables in relation to commute time and some demographic characteristics. Two conceptual structural models are examined in order to explore the factors determining travellers’ choices. Results of the analysis reveal some interesting relationships: (1) a positive relationship between age and commute time; (2) females are more likely to read or listen to music during their journeys, and their ideal commute time (ICT) and current commute time (CCT) generally tend to be longer; (3) academic staff tend to have the habit of working during their commute, administrative staff tend to commute longer while students tend to spend a shorter time commuting; (4) normally, a habit while travelling is significantly associated with CCT; those with a habit of reading or working during their commute journey tend to have longer commute times and (5) the relationship between CCT and commuters’ ICT and tolerable commute time is positive; both hypothesised causal relationships are significant so that a loop is formed between subjective and objective variables, and thus a dynamic modelling process could be envisaged as temporal sequences of those variables.     

Hierarchical travel demand estimation using multiple data sources: A forward and backward propagation algorithmic framework on a layered computational graph

Aiming to develop a theoretically consistent framework to estimate travel demand using multiple data sources, this paper first proposes a multi-layered Hierarchical Flow Network (HFN) representation to structurally model different levels of travel demand variables including trip generation, origin/destination matrices, path/link flows, and individual behavior parameters. Different data channels from household travel surveys, smartphone type devices, global position systems, and sensors can be mapped to different layers of the proposed network structure. We introduce Big data-driven Transportation Computational Graph (BTCG), alternatively Beijing Transportation Computational Graph, as the underlying mathematical modeling tool to perform automatic differentiation on layers of composition functions. A feedforward passing on the HFN sequentially implements 3 steps of the traditional 4-step process: trip generation, spatial distribution estimation, and path flow-based traffic assignment, respectively. BTCG can aggregate different layers of partial first-order gradients and use the back-propagation of “loss errors” to update estimated demand variables. A comparative analysis indicates that the proposed methods can effectively integrate different data sources and offer a consistent representation of demand. The proposed methodology is also evaluated under a demonstration network in a Beijing subnetwork.