What neighborhood are you in? Empirical findings of relationships between household travel and neighborhood characteristics

In recent years, there have been studies of the influence of neighborhood or built environment characteristics on residential location choice and household travel behavior. Interestingly, there is no uniform definition of neighborhood in the literature and the definition is often vague. This paper presents an alternative way of defining neighborhood and neighborhood type, which involves innovative usage of public data sources. Furthermore, the paper investigates the interaction between neighborhood environment and household travel in the US. A neighborhood here is spatially identical to a census tract. A neighborhood type identifies a group of neighborhoods with similar neighborhood socio-economic, demographic, and land use characteristics. This is accomplished by performing log-likelihood clustering on the Census Transportation Planning Package (CTPP) 2000 data. Five household travel measures, i.e., number of trips per household, mode share, average travel distance and time per trip, and vehicle miles of travel (VMT), are then compared across the resulting 10 neighborhood types, using the 2001 National Household Travel Survey (NHTS) household and trip files. It is found that household life cycle status and residential location are not independent. Transit availability at place of residence tends to increase the transit mode share regardless of household automobile ownership and income level, and job-housing trade-offs are evident when mobility is not of concern. The study also reveals racial preference in residential location and contrasting travel characteristics among ethnic groups.

Re-cycling a city – Examining the growth of cycling in Dublin

In the past few decades much research has been conducted on the increasing numbers of commuters taking up cycling to work. This modal shift has been encouraged by pro-cycling policies to increase the attractiveness of cycling and the construction of new cycling infrastructure. In Dublin, several policies have been applied such as a bike rental scheme, bicycle-purchasing schemes, reducing speed limits and the construction of segregated cycle lanes to promote cycling. This paper seeks to examine what, if any, impact these policies have had on cycling rates in Dublin. This paper compares census data from 2006 and 2011 to determine how cycling rates have changed and if the demographics of cyclists have changed in the city. The results presented in the paper show that cycling rates have increased in Dublin and that a greater percentage of females, those in higher age and socio-economic groups are cycling to work on a regular basis. The analysis presented in this paper identifies groups of individuals that have recently shifted to cycling to work, by identifying who these people are, policymakers can tailor strategies to target these groups to encourage others in these groups to take up cycling.     

Explaining the “immigrant effect” on auto use: the influences of neighborhoods and preferences

Since immigrants will account for most urban growth in the United States for the foreseeable future, better understanding their travel patterns is a critical task for transportation and land use planners. Immigrants initially travel in personal vehicles far less than the US-born, even when controlling for demographics, but their reliance on autos increases the longer they live in the US. Cultural or habitual differences, followed by assimilation to auto use, could partly explain this pattern; and it may also be partly due to changes in locations and characteristics of home and work neighborhoods. Previous studies have rarely investigated non-work travel, and have not tested workplace land use measures, compared the relative influences of enclave and home neighborhood measures, or looked at the role of culturally-bound residential preferences or motivations for migration. This study relies on a unique and rich dataset consisting of a survey of US residents born in South Asia, Latin America, and the US, joined to spatial information in a GIS. I find that the home built environment is the most consistently influential factor in explaining the lower auto use of both recent and settled Latin American immigrants. Indian immigrants use autos less than would be expected given their home and work neighborhoods. There is little evidence that either ethnic enclaves, or cultural differences, play a role in lower auto use by immigrants. These results suggest there may be a role for neighborhood built environment policies in delaying immigrant assimilation to auto use in the US.     

长输管道油品泄漏监控检测方法

输油管道的泄漏会造成很大的危害,及时发现并准确找出泄漏点可以最大程度地降低损失。文中主要介绍了负压波法泄漏监控定位的原理、计算方法。对出现的误差及其不能正常工作的原因加以说明。并对现今的几种监测方法进行比较。     

Telecommuting and urban sprawl

This study examines what happens to city size when telecommuting occurs. It assumes that more telecommuting occurs when telecommuters’ labor cost share increases and/or workers adopt a more favorable attitude toward working from home. The study shows that telecommuting produces opposing forces that regulate the city size, one centralizing and the other decentralizing urban activities. These forces are examined in a city where workers and firms are given the option to freely mix working at the office and at home, and the city’s land use is endogenously determined. A rise in the productivity of an economy due to telecommunications technology could work to centralize urban activities, while urban contraction can occur with a fixed city population.     

Fleet purchase behavior: decision processes and implications for new vehicle technologies and fuels

Vehicle fleets are a poorly understood part of the economy. They are important, though, in that they purchase a large share of light-duty vehicles and are often targeted by governments as agents of change. We investigate fleet purchase behavior, using focus groups, interviews, and mail and telephone surveys. We categorize fleets into four different decision-making structures (autocratic, bureaucratic, hierarchic, and democratic), determine what share of the market sector each represents, describe salient features of each behavioral model, and explore implications of that behavior for industry investment and public policy.     

Optimal housing supply in a bid-rent equilibrium framework

In this study, we examine how the spatial distribution of housing supply impacts people’s residential choices and developers’ profitability. By optimally providing housing supply in a region, developers attempt to maximize their profits; on the other hand, if residents were given a chance to decide on housing supply, what patterns of housing supply they would prefer in order to maximize their consumer surpluses. This paper studies the interplay between these two perspectives. A nested multinomial-logit choice structure that encapsulates the bid-rent process is used to capture residents’ location and travel choices simultaneously, and the resultant rents at different locations. To investigate the optimal housing supply for these two stakeholders, we conduct sensitivity analyzes to explore the impact of different housing supply patterns on total rental profit and total consumer surplus. Specifically, analytical results are derived for a simple linear network with two residential locations and one destination under homogeneous and heterogeneous value(s) of time. The results of the sensitivity analyzes indicate that segregation of housing supplies at different locations for different income classes is, surprisingly, a “preferred” outcome by residents under consumer surplus maximization, whereas creating housing supply shortages at convenient locations is a natural outcome under housing profit maximization. These results provide insights on revealing the differences and tradeoffs in performance between these two different perspectives, and on where land use regulations may be needed to balance these two objectives.     

Crash analysis at intersections in the CBD: A survival analysis model

Enhancing the safety level of urban roads especially in CBDs is paramount. Due to a large number of intersections in what is usually a grid road system in the CBDs, we investigate crashes occurring in and around an intersection. The question of interest in this study is: does the nature of crashes at intersections differ from those of the roads at midblock? Stated more precisely, considering the intersection as a reference point, does the distance to the reference point (i.e. midblock locations on the roads) correlate with different types of crashes compared to that of the intersection? A right answer can lead traffic engineers and safety auditors to propose different safety measures at intersections and the midblock locations. As a pilot study, we collected the last 9 years crash data of the CBD of Melbourne, Australia. For the first time, we employ Survival Analysis models -including Exponential, Weibull, and Log-logistic- to investigate a space-dependent phenomenon (i.e. accidents at proximity to the intersection). Of the outcome, highlights are: (i) police presence at busy intersections during busy night outs and weekends highly improves the pedestrian safety (ii) raised crossings at midblock locations lower likelihood of crashes of pedestrians as well as cars, (iii) lighting conditions at intersections must be watched and kept at a high level. (iv) Severity, likelihood, and location have no known association with the level of congestion. In other words, safety is first, always and everywhere. The results can be of interest to traffic authorities and policy makers in reinforcing traffic calming measures in the cities. The codes developed in this study are made available to the research community to be used in further studies.     

A new role for planners: Working with community residents in formulating alternative plans for street patterns—Before decision making

Citizen participation at the neighborhood level will become effective only if reliable procedures can be developed for involving residents in the formulation of alternative plans before official decisions are made. Therefore, a research project in transportation planning was conducted in the Pico-Union neighborhood of Los Angeles, California, in order to design and test methods for producing reliable information that could be used by the neighborhood as well as by city transportation planners.The transportation problem of most concern to the residents of Pico-Union was the patterns and uses of their local streets, rather than the usual trip-to-work problem. Two independent methods were designed for determining what street patterns and uses were most preferred by the resident: a home-interview method and a photo-comparison method. The second enabled residents to compare photographic simulations of many possible re-designs of one of their local streets, and to make individual and group decisions about them. Results of the two methods generally agreed. However, one part of the research revealed that significant differences in decisions occur when simple changes are made in the orientation and sequence of the same two photographic simulations.Methods can be developed for obtaining information useful in urban planning at the neighborhood level by involving residents in an iterative learning and decision process. Broadly representative interdisciplinary teams are needed for further research to improve the reliability of such methods and for putting them into practice.     

From traces to trajectories: How well can we guess activity locations from mobile phone traces?

Passively generated mobile phone dataset is emerging as a new data source for research in human mobility patterns. Information on individuals’ trajectories is not directly available from such data; they must be inferred. Many questions remain in terms how well we can capture human mobility patterns from these datasets. Only one study has compared the results from a mobile phone dataset to those from the National Household Travel Survey (NHTS), though the comparison is on two different populations and samples. This study is a very first attempt that develops a procedure to generate a simulated mobile phone dataset containing the ground truth information. This procedure can be used by other researchers and practitioners who are interested in using mobile phone data and want to formally evaluate the effectiveness of an algorithm.To identify activity locations from mobile phone traces, we develop an ensemble of methods: a model-based clustering method to identify clusters, a logistic regression model to distinguish between activity and travel clusters, and a set of behavior-based algorithms to detect types of locations visited. We show that the distribution of the activity locations identified from the simulated mobile phone dataset resembles the ground truth better than the existing studies. For home locations, 70% and 97% of identified homes are within 100 and 1000 m from the truth, respectively. For work places, 65% and 86% of the identified work places are within 100 and 1000 m from the true ones, respectively. These results point to the possibility of using these passively generated mobile phone datasets to supplement or even replace household travel surveys in transportation planning in the future.     

Non-commuters: the people who walk to work or work at home

The paper focuses on the socioeconomic characteristics of workers at home and those who walk to work and these are compared with commuters (those who travel to work by motorized transportation). Understanding of such characteristics of these people is useful for purposes of designing policies that encourage these forms of "travel" to work, if it is believed desirable for planning or environmental purposes. For example, subsidizing public transportation may also have an impact on the proclivity to work at home or walk to work. Using a large census data set for Israel, separate subsamples are analyzed for heads of household and for their spouses. Metropolitan areas as well as peripheral urbanized areas are analyzed separately. Logit analysis is used to identify those variables that affect the likelihood of different groups of people to walk to work or to work at home.It is shown that walkers to work tend to be lower-income, less-educated people with lower asset ownership rates. Females are overrepresented amongst them, while "high-status" professionals are underrepresented. Workers at home appear to be a more complex group. They tend to have higher levels of education and wealth than commuters, but earn less on average. They include proportionately more females. The likelihood of working at home increases with home size and with ownership of some durable goods. The workers at home may in fact be comprised of two or more differing groups with contrasting characteristics, one higher-income and higher-educated, the other with lower socio-economic indicators. Because they may be a heterogeneous group, development of planning policies to encourage non-vehicle commuting may require different policy tools for the different subgroups.     

Parking demand and responsiveness to supply,pricing and location in the Sydney central business district

This paper investigates the role of parking pricing and supply by time of day in whether to drive and park in the central business district (CBD). A stated preference survey of car drivers and public transport users was undertaken at a number of parking locations, public transit interchanges, and shopping centres in Sydney CBD during 1998. In the context of a current trip to the CBD, respondents were asked to consider six alternatives, including three parking locations in the CBD, park outside of the CBD with public transport connection to the CBD, switch to public transport, or forego that trip to the CBD. The three parking locations were defined by hours of operation, a tariff schedule, and access time to the final destination from the parking station. Data from the survey were then used to estimate a nested logit model of mode and parking choices, which was then used to simulate the impacts of supply pricing scenarios on CBD parking share. The change in CBD parking share attributable to supply by time of day is less than 3%, compared to 97% attributable to parking prices.     

The intra-household choices regarding commuting and housing

This paper is an empirical analysis of the relationships between commuting decisions of spouses in dual-income households, where the role of housing and housing tenure is taken into account. The study is based on a large survey of US commuters and actual commuting and housing choices. Household commuting decisions are analyzed together with housing choices using the data from the 2001 American Housing Survey. A sample of dual-career spouses who commute to work by car only is used to explore the inter-relationship between male and female commuting decisions in such households and the effect of housing choices on their commuting. The inter-relationships between spousal commuting decisions are examined separately for the two forms of housing tenure: ownership and renting. In general, men commute further than women and owners commute further than renters.It is shown that for both renters and homeowners, journeys to work by men and women in such dual-earner households appear to be “complements” and not “substitutes”. This means that commute trips are jointly chosen to be longer (or shorter) for both spouses, as part of household selection of preferred housing and neighborhood characteristics. The residuals of equations explaining commute times and commute distances for men are strongly positively correlated with those for women. Many of the explanatory factors affect the men and women in similar ways. In particular, housing value is positively correlated with commute times and distances for both genders. The commute distance for women owners appears more sensitive to housing value than that of men, meaning women adjust their trips to work even more so than men do, as part of obtaining better housing.For both homeowners and renters, longer commute distances were correlated with higher salaries for both spouses. Somewhat surprisingly, it was found that commuting distances for women are similarly or even more sensitive to income increments than are those of men. Household size appears to affect women who own housing in ways opposite from those who rent. Other housing and neighborhood factors also affect the joint commuting decisions of these men and women.     

Repeatability & reproducibility: Implications of using GPS data for freight activity chains

As transport modellers we are interested in capturing the behaviour of freight vehicles that includes the locations at which vehicles perform their activities, the duration of activities, how often these locations are visited, and the sequence in which they are visited. With disaggregated freight behaviour data being scarce, transport modellers have identified vehicle tracking and fleet management companies as ideal third party sources for GPS travel data. GPS data does not provide us with behavioural information, but allows us to infer and extract behavioural knowledge using a variety of processing techniques. Many researchers remain sceptical as specific human intervention, referred to as ‘expert knowledge’, is often required during the processing phase: each GPS data set has unique characteristics and requires unique processing techniques and validation to extract the necessary behavioural information. Although much of the GPS data processing is automated through algorithms, human scrutiny is required to decide what algorithmic parameters as considered ‘best’, or at least ‘good’. In this paper we investigate the repeatability and reproducibility (R&R) of a method that entails variable human intervention in processing GPS data. More specifically, the judgement made by an observer with domain expertise on what clustering parameters applied to GPS data best identify the facilities where commercial vehicles perform their activities. By studying repeatability we want to answer the question ‘if the same expert analyses the GPS data more than once, how similar are the outcomes?’, and with reproducibility we want to answer the question ‘if different experts analyse the same GPS data, how similar are their outcomes?’ We follow two approaches to quantify the R&R and conclude in both cases that the measurement system is accurate. The use of GPS data and the associated expert judgements can hence be applied with confidence in freight transport models.     

Methods using belief functions to manage imperfect information concerning events on the road in VANETs

Different models using belief functions are proposed and compared in this article to share and manage imperfect information about events on the road in vehicular networks. In an environment without infrastructure, the goal is to provide to driver the synthesis of the situation on the road from all acquired information. Different strategies are considered: discount or reinforce towards the absence of the event to take into account messages agings, keep the original messages or only the fusion results in vehicles databases, consider the world update, manage the spatiality of traffic jams by taking into account neighborhood. Methods are tested and compared using a Matlab™ simulator. Two strategies are introduced to tackle fog blankets spatiality; they are compared through an example.     

Testing electric vehicle demand in ‘hybrid households’ using a reflexive survey

The debate over electric vehicles (EVs) pivots largely on issues of market demand: will consumers purchase a vehicle that provides substantially less driving range, yet can be refueled at home, than an otherwise comparable gasoline vehicle? Also, what role do other unique attributes of EVs play in the purchase decision? Most previous studies find that limited driving range is a serious market barrier; many of those same studies ignore or under-value other novel attributes. To probe these future consumer decision processes deeply and robustly, we first devised and conducted detailed, interactive and experiment-oriented interviews. Then, incorporating what we learned, we designed an innovative mail survey and administered it to 454 multi-car households in California. The four-stage mail survey included a video of EV use and recharging and other informational material, completion of a 3-day trip diary and map of activity locations, and vehicle choice experiments. In addition to propulsion systems, respondents made choices of body styles, driving ranges, and other features. We formalized and tested what we call the hybrid household hypothesis: households who choose EVs will be purposefully diversifying their vehicle holdings to achieve the unique advantages of different propulsion systems. The hypothesis is supported, given the assumptions in our experimental design. In fact, a significantly larger number of EVs are chosen than the minimum number that would support our hypothesis. We find that purchases of battery-powered EVs by hybrid households would account for between 7 and 18% of annual light duty vehicle sales in California. EVs sold to fleets and other households would be in addition to those identified by this study.     

Active school travel: homogeneity or heterogeneity? That is the question

ABSTRACT

To explain and predict active school travel (AST), most studies have not investigated to what extent considering taste heterogeneity is an important influence on AST share. The main aim of the present study was to evaluate whether considering unobserved taste heterogeneity through mixed logit models – including random coefficient and random coefficient analysis (RCA) – materially improves/influences the AST prediction compared to a simpler model – the multinomial logit (MNL) model. The database comprises 735 valid observations. The results show that, with a 10% increase in perceived walking time to school, the MNL model predicts that the AST share would decrease by 7.8% (from 18.9% to 17.4%) while the RCA model predicts that it would decrease by 8.5% (from 18.9% to 17.3%). Thus, the expected share of AST is overestimated by MNL by one-tenth of a percentage point. Although there might be random taste variations around perceived distance to school, it seems the other important policy-sensitive variables, such as safety perception, homogeneously impacts on the AST share across households with different socioeconomic and built environment characteristics. Our empirical assessment suggests that considering taste heterogeneity does not necessarily improve the accuracy of analysis for the aggregate share of the AST concerning policy-sensitive variables.     

A household-level activity pattern generation model with an application for Southern California

This paper develops and estimates a multiple discrete continuous extreme value model of household activity generation that jointly predicts the activity participation decisions of all individuals in a household by activity purpose and the precise combination of individuals participating. The model is estimated on a sample obtained from the post census regional household travel survey conducted by the South California Association of Governments in the year 2000. A host of household, individual, and residential neighborhood accessibility measures are used as explanatory variables. The results reveal that, in addition to household and individual demographics, the built environment of the home zone also impacts the activity participation levels and durations of households. A validation exercise is undertaken to evaluate the ability of the proposed model to predict participation levels and durations. In addition to providing richness in behavioral detail, the model can be easily embedded in an activity-based microsimulation framework and is computationally efficient as it obviates the need for several hierarchical sub-models typically used in extant activity-based systems to generate activity patterns.     

Disparities in childhood asthma hospitalizations: A spatial analysis of contextual effects

Transportation and health inequities have been subjects of study by scholars from various disciplines, but the linkages among traffic exposure, race, class, and asthma is neither sufficient nor conclusive. Previous studies suggest that the relationships are multifaceted and geographically specific. We investigate neighborhood social and transportation impacts on childhood asthma disparities in Tarrant County, Texas, using geographical information systems. Results reveal distinctive socio-demographic characteristics of the patients; clear spatial patterns of neighborhoods with and without asthma hospitalizations; and significant contextual effects of traffic density, race, and class on neighborhood childhood asthma hospitalizations. The findings generally support the notion of contextual effects.     

The Share-a-Ride problem with stochastic travel times and stochastic delivery locations

We consider two stochastic variants of the Share-a-Ride problem: one with stochastic travel times and one with stochastic delivery locations. Both variants are formulated as a two-stage stochastic programming model with recourse. The objective is to maximize the expected profit of serving a set of passengers and parcels using a set of homogeneous vehicles. Our solution methodology integrates an adaptive large neighborhood search heuristic and three sampling strategies for the scenario generation (fixed sample size sampling, sample average approximation, and sequential sampling procedure). A computational study is carried out to compare the proposed approaches. The results show that the convergence rate depends on the source of stochasticity in the problem: stochastic delivery locations converge faster than stochastic travel times according to the numerical test. The sample average approximation and the sequential sampling procedure show a similar performance. The performance of the fixed sample size sampling is better compared to the other two approaches. The results suggest that the stochastic information is valuable in real-life and can dramatically improve the performance of a taxi sharing system, compared to deterministic solutions.