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Speeding was a factor in over a quarter of crash fatalities annually in the U.S. from 2009 to 2018. In some cities, automated speed camera enforcement is being used to curb speeding and improve roadway safety in instrumented areas. This paper reviews the effectiveness of automated speed cameras and some of the considerations informing the public debate around them in the United States. It then employs data from the City of Chicago, Illinois and the empirical Bayes approach to examine how effective speed cameras have been at reducing injury crashes and fatalities. Chicago installed most of its currently operating speed cameras in the 2013 to 2014 period. From 2015 to 2017, we estimate a 12% reduction in fatal and injury crashes across treated locations included in our analysis. Fatality and severe injury crashes declined by 15%. Some treated sites did not see the expected safety benefits, however. Recommendations to improve efficacy are made.

Much new work in urban and regional economics has emphasized the importance of place prosperity. This study focuses on the determinants of adult poverty and the contribution of place prosperity in damping the intergenerational transmission of poverty. Childhood poverty is a major predictor of adult poverty. We consider how such intergenerational transmission is affected by metropolitan and neighborhood (census tract) prosperity. To capture the temporal dynamics of this process, the model explored here is recursive in nature. We use longitudinal microdata from the Panel Study of Income Dynamics. Location variables at the census tract and metropolitan levels, family variables, and poverty status are observed for our subjects over multiple years both in childhood and adulthood. Neighborhood and metropolitan prosperity are measured in terms of average incomes adjusted for purchasing power parity differences. The standardized neighborhood prosperity direct effect on adult poverty is strongly significant and its total effect is twice as large. On the other hand, the standardized direct effect of metropolitan prosperity and its total effect are small and insignificant. But even neighborhood effects are modest compared to standardized effects of childhood poverty, race, mother’s education and own education. At least with respect to these data, the recent emphasis on place variables would seem to be overstated.

Taxis play an indispensable role as the main mode of paratransit in many cities in the developing countries. To the best of our knowledge, research on taxi service quality, and certainly studies fo...

We investigate changes in the allocation of discretionary time to social activities between 2003 and 2013 in the United States using data from the American Time Use Survey (ATUS). Within this period, several changes particularly around communication technologies have occurred, including the mass adoption of web and video enabled smart phones. Other advances include social networking platforms, low cost subscription based streaming movies, multi-person online gaming etc., which collectively can alter how people spend their discretionary time and their social activity time use. Summary statistics from the ATUS show a decline in the number of people reporting face-to-face social activities and a growth in the average leisure time spent alone between 2003 and 2013. At the same time, we observe modest declines in average time allotted to social activities and to mandatory activities. We separate time spent on discretionary activities into social activity time use and non-social activity time use and employ five doubly censored Tobit models to investigate time allocation for social and non-social activities between these two time points. These models show that the relative proportion of discretionary time allotted to face-to-face social activities has declined between 2003 and 2013. The apportionment of social to non-social time is also influenced by other sociodemographic variables such as gender, age, household structure, education and work hours. We summarize the implications of these findings to transportation and beyond.

This study explored how disability, mobility, social and leisure engagement, and travel behavior influence older people’s life satisfaction. The study used the 2013 Disability and Use of Time data for people ages 50 years and older, many of whom reported physical impairments. The study developed a model that related life satisfaction with various time use, disability, and mobility variables. Summary statistics of time use showed that as people aged, they spent more time on solitary, passive leisure activities; social face-to-face time did not seem to change very much. Alone passive leisure time use was especially large for those who experienced a physical mobility-related disability and were carless. The study used an ordinal logistic regression and found that longer alone leisure time uses were associated with lower life satisfaction. Life satisfaction was positively affected by transportation variables, such as vehicle availability. The study also found that social face-to-face time use had a weak positive relationship with life satisfaction, and technology-mediated social activities had a strong negative relationship with life satisfaction.

Purpose This study examined potential sources of selection and information biases when using residence history information from a commercial database to construct residential histories for cancer research. Methods We searched the LexisNexis database for residence data on 3473 adults diagnosed with cancers of the prostate, colon/rectum, and female breast in a single health-care system between 2005 and 2016 using the name and address at diagnosis and the birth date. Residential histories were generated from the results using open-source statistical programs from the National Cancer Institute. Multivariable regression models analyzed the associations of the search results with demographic characteristics and all-cause mortality. Results Racial/ethnic minorities were less likely to match to vendor residence data compared with non-Hispanic whites (odd ratios [95% confidence intervals (CIs)] for non-Hispanic blacks, Hispanics, and Asian/Pacific Islander were 1.66 [1.30, 2.12], 2.92 [2.18, 3.90], and 4.53 [2.72, 7.55], respectively). Being non-Hispanic black was negatively associated with years of residential history (vs. non-Hispanic whites, β coefficient [95% CI] = −2.57 [−3.40, −1.73]). Not matching to residence data was associated with an increased 5-year odds of death from any cause (vs. matched subjects, odd ratios [95% CI] = 5.92 [4.29, 8.50]). Conclusions Differential ascertainment of residence history by race/ethnicity and association of ascertainment with prognosis are potential sources of selection and information biases when using residence data from a commercial database.

This article studies the equity of transit accessibility in the City of Chicago. We measure the accessibility of different cohorts including minority and low-income populations, the elderly, people with disabilities, those with lower education levels, and households without a car to six different destinations by public transit. The destinations are jobs, parks, groceries, hospitals, schools, and libraries. We show that there are clear inequalities across cohorts in the distribution of benefits that the transit system provides as measured by the number of reachable valued destinations. The results indicate that areas of low accessibility have a higher percentage of African-Americans, Hispanics, Asians, low-income workers, low-educated citizens, and the elderly. The most affected cohort are low-income workers, for whom access to jobs, parks, groceries, hospitals, and libraries decline as their number grows. The findings also highlight that inequities are most severe, in order, to jobs, hospitals, and grocery stores when examining the different cohorts. While transit agencies must deploy service with the existing demand in mind, the observed inequities behoove decision makers to make accessibility and equity considerations explicit in transit service decisions.

The last-mile problem refers to challenges that travelers experience in accessing transit stations from their activity locations. The objective of this study was to find the contributing factors that reduced people's propensity to walk and take transit. A stated preference study was conducted in the Chicago, Illinois, area with an online survey composed of questions based on the actual travel experience of the respondents. The data were used to estimate a logit choice model. The findings showed that access time, safety from crime, and sidewalk availability were important factors that influenced people's choice to walk to transit. The model was used to estimate time-based values associated with reduction in crime and sidewalk availability. The study also estimated the propensity to walk and use transit for a representative resident in each tract of the Chicago metropolitan area. These values were then used to identify census tracts where acute to minimal barriers to walking to transit existed. In addition to suburban areas that were not well suited for walking to transit, the results identified areas that were well served by transit but had other barriers that inhibited walking access to transit.

This study looks at questions of regional transit job accessibility in an urban area making significant changes to its transit system. The study area is the Minneapolis–St. Paul (MSP) metropolitan region, which also has several initiatives to build/expand different competitive economic clusters —export-oriented, interconnected firms in the region. We analyze current transit accessibility to the existing clusters in the region and find significant sector to sector differences that highlight both the poor level of transit access to some economic sectors and the need of automobile ownership to be able to reliably access these jobs. Further, given changes that are being made to the transit system, we conduct scenario analyses and ask which population and employment growth patterns the region should follow to maximize transit accessibility for its residents. The results suggest that a strategy which focuses growth along transitways, particularly the growth of jobs along transitway corridors, will achieve the best regional transit accessibility gains. The research helps to bridge the separate bodies of literature on competitive clusters and transit, tests alternative land use scenarios to enhance accessibility, and investigates the importance of transit for jobs in regional competitive clusters.

Weather information is frequently requested by travelers. Prior literature indicates that inclement weather is one of the most important factors contributing to traffic congestion and crashes. This paper proposes a methodology to use real-time weather information to predict future speeds. The reason for doing so is to ultimately have the capability to disseminate weather-responsive travel time estimates to those requesting information. Using a stratified sampling technique, cases with different weather conditions (precipitation levels) were selected and a linear regression model (called the base model) and a statistical learning model [using support vector machines for regression (SVR)] were used to predict 30-min-ahead speeds. One of the major inputs into a weather-responsive short-term speed prediction method is weather forecasts; however, weather forecasts may themselves be inaccurate. The effects of such inaccuracies are assessed by means of simulations. The predictive accuracy of the SVR models show that statistical learning methods may be useful in bringing together streaming forecasted weather data and real-time information on downstream traffic conditions to enable travelers to make informed choices.

Most economic models assume that individuals act out their preferences based on self-interest alone. However, there have also been other paradigms in economics that aim to capture aspects of behavior that include fairness, reciprocity, and altruism. In this study we empirically examine preferences of travel time and income distributions with and without the respondent knowing their own position in each distribution. The data comes from a Stated Preference experiment where subjects were presented paired alternative distributions of travel time and income. The alternatives require a tradeoff between distributional concerns and the respondent’s own position. Choices also do not penalize or reward any particular choice. Overall, choices show individuals are willing forgo alternatives where they would be individually well off in the interest of distributional concerns in both the travel time and income cases. Exclusively self-interested choices are seen more in the income questions, where nearly 25 % of respondents express such preferences, than in the travel time case, where only 5 % of respondents make such choices. The results also suggest that respondents prioritize their own position differently relative to regional distributions of travel time and income. Estimated choice models show that when it comes to travel time, individuals are more concerned with societal average travel time followed by the standard deviation in the region and finally their own travel time, while in the case of income they are more concerned with their own income, followed by a desire for more variability, and finally increasing the minimum income in their region. When individuals do not know their fate after a policy change that affects regional travel time, their choices appear to be mainly motivated by risk averse behavior and aim to reduce variability in outcomes. On the other hand, in the income context, the expected value appears to drive choices. In all cases, population-wide tastes are also estimated and reported.

Big Data is the term being used to describe a wide spectrum of observational or “naturally-occurring” data generated through transactional, operational, planning and social activities that are not specifically designed for research. Due to the structure and access conditions associated with such data, their use for research and analysis becomes significantly complicated. New sources of Big Data are rapidly emerging as a result of technological, institutional, social, and business innovations. The objective of this background paper is to describe emerging sources of Big Data, their use in urban research, and the challenges that arise with their use. To a certain extent, Big Data in the urban context has become narrowly associated with sensor (e.g., Internet of Things) or socially generated (e.g., social media or citizen science) data. However, there are many other sources of observational data that are meaningful to different groups of urban researchers and user communities. Examples include privately held transactions data, confidential administrative micro-data, data from arts and humanities collections, and hybrid data consisting of synthetic or linked data.

The chapters in this book were first presented in a 2-day workshop on Big Data and Urban Informatics held at the University of Illinois at Chicago in 2014. The workshop, sponsored by the National Science Foundation, brought together approximately 150 educators, practitioners and students from 91 different institutions in 11 countries. Participants represented a variety of academic disciplines including Urban Planning, Computer Science, Civil Engineering, Economics, Statistics, and Geography and provided a unique opportunity for discussions by urban social scientists and data scientists interested in the use of Big Data to address urban challenges. The papers in this volume are a selected subset of those presented at the workshop and have gone through a peer-review process.

This paper examines the role that public transport last mile problems play in mode choice decisions of commuters, while controlling for trip, built environment, and decision maker related variables. Last-mile problems arise due to lack of adequate connectivity between transit stops and trip origin or termination points. The paper is motivated by previous literature which has pointed out that high-quality public transit needs to consider end-to-end connectivity from trip origins to destinations. In contrast to previous work on transit last mile problems, which has focused on physical distance and sidewalks to transit stops, we consider a wider range of area factors including transit availability, job accessibility, parking costs, the quality of the pedestrian environment and risks to pedestrians from vehicular traffic, and social characteristics such as street-level crime. Using a discrete choice model, our goal is to unpack ways in which such factors contribute to the last-mile problem in home-based work trips, while controlling for these wider range of factors as well as the usual variables such as cost and trip time that inform mode choice. We find that the prevalence of non-domestic violent crimes reduces the odds of using all types of non-motorized alternatives as well as transit that is accessed either by walking or driving. Using compensating variation to measure welfare changes, we show that there are significant benefits that could be brought to transit service users through increasing safety in the transit access trip. By separately controlling for origin and destination transit accessibility, we show that improved destination accessibility significantly boosts transit use to a greater degree than increases in origin level accessibility. These findings argue for improving accessibility and related job densities at employment centers.

This research explores to what extent people's work locations are similar to that of those who live around them. Using the Longitudinal Economic and Household Dynamics data set and the US census for the Twin Cities (Minneapolis-St. Paul) metropolitan area, we investigate the home and work locations of different census block residents. Our aim is to investigate if people who live close to one another, also work close to one another to a degree beyond what would be expected at random. We find a significantly non-random correlation between joint home and joint work locations. Further, we show what features of particular neighborhoods are associated with comparatively higher incidences of people sharing work locations. One reason for such an outcome can be the role neighborhood level social networks play in locating jobs; or conversely work place social networks play in choosing the home location or both. Such findings should be used to refine work trip distribution models that otherwise depend mainly on impedance between the origin and destination.

The aging of the American population has raised a number of concerns about senior mobility. The New Freedom (NF) program, which was designed to go above and beyond the transportation requirements of the Americans with Disabilities Act, has been used to fund transportation for seniors. The primary purpose of this paper is to present the results of an exploratory analysis of the social, physical, and functional health and mobility outcomes experienced by seniors who are clients of NF services and the associated Coordinated Human Services Transportation Plan (CHSTP) processes. The paper uses data from primary surveys of the following groups from seven urban, suburban, and rural locations: (a) NF-funded transportation service users, (b) program managers operating the services, and (c) locally selected lead and partner organizations involved in developing the CHSTPs. Results show that concerns about senior mobility vary significantly by a number of functional, social, and physical disability indicators, including instrumental activities of daily living. These indicators are substantially more complex than a chronological accounting of age. A principal component analysis yielded two constructs underlying seniors’ mobility: a transportation deprivation component and an independence and health deprivation component. Each component has implications for different types of services. To the extent that the NF program has supported transportation services that are supplemental to integrated care for seniors and thereby enable a continuum of care, the program has enhanced community-based interventions to meet the complex mobility needs of seniors in the seven locations.

This study evaluates individual preferences for five different cycling environments by trading off a better facility with a higher travel time against a less attractive facility at a lower travel time. The tradeoff of travel time to amenities of a particular facility informs our understanding of the value attached to different attributes such as bike-lanes, off-road trails, or side-street parking. The facilities considered here are off-road facilities, in-traffic facilities with bike-lane and no on-street parking, in-traffic facilities with a bike-lane and on-street parking, in-traffic facilities with no bike-lane and no on-street parking and in-traffic facilities with no bike-lane but with parking on the side. We find that respondents are willing to travel up to twenty minutes more to switch from an unmarked on-road facility with side parking to an off-road bicycle trail, with smaller changes associated with less dramatic improvements.

This study considers the influence of the knowledge of existing mode shares on travelers mode choice. This contrasts with traditional mode choice models, where the main objective is to predict the overall mode shares as the aggregate of individual mode choices according to variables encompassing attributes of the modes, and characteristics of the travelers. In this study, a computer-administered adaptive stated preference survey is developed and applied to a sample of subjects selected from the University of Minnesota. The results indicate that the presence of mode shares in the mode choice model does influence the decision of travelers.

Major network disruptions have significant impacts on local travelers. Understanding the behavioral reactions to such incidents is crucial for traffic management and planning. This study investigates travelers’ reaction to both the collapse and reopening of the I-35W Mississippi River Bridge in Minneapolis, Minnesota. Web-based surveys conducted at residences in several communities across the metropolitan area supplement hand-out/mail-back paper-based surveys distributed to workers in areas around the bridge collapse (downtown Minneapolis and the University of Minnesota). Findings from the four surveys highlight differences in travel impacts and behavioral reactions after the unplanned bridge collapse and the planned bridge reopening.

The study of travel for social activities presents layered challenges because of the temporal and spatial flexibility with which such activities can be undertaken and the changing set of decision makers involved in each activity episode. This paper seeks to answer a set of questions based on empirical data about how relationship, social network variables, purpose, personal and household constraints, location attributes, and interdependence between meeting duration, distance, and other meetings provide some structure to the observed social activity location and duration decisions. In particular, we investigate what attributes determine whether a meeting takes place in or out of home, and what explains the distance travelled and the duration of meetings. Empirically we show that in-home meetings tend to occur most often with close contacts and less often with distant contacts. When looking at duration and distance travelled, we find that relationship related variables have some of the largest impacts on the distance travelled and the duration of meetings as compared to other variables. We find that meeting durations with close contacts are on average almost 45 min longer, and that respondents are willing to hold these meetings about 1.5 miles (2.4 km) further away from their residences than they would with non-close contacts. Overall the paper illustrates that relationship type, as well as other meeting specific and demographic variables are important in explaining the location, duration and distance travelled for social meetings.

This paper empirically explores the relationship between (i) job finding and commuting outcomes and (ii) the relationship between job search and the commute and location outcomes of relocation decisions after finding employment. The relationship between commute outcomes when finding a new job and the job search method that one employs are explored first. That is followed by an analysis of how long one stays at their residence after finding work, and where they eventually relocate relative to their new employment site as well as their previous residence. Along with the usual socio-demographic variables, the analysis takes on the job search method as well as the local contacts that one has in their residential area as important variables informing these choices. The findings indicate that jobs found through the use of internet and newspapers were on average farther away from the searchersÕ residence as compared to those found through contacts and formal means. On relocation after employment, we find that being a renter and moving to a rental unit were important in how quickly one relocated. In addition those that used the internet to find their jobs also relocated faster after controlling for demographic variables such as age. The distribution of ones social contacts were also found to be important in how far away from the previous location a person relocated.

Major network disruptions have significant impacts on local travelers. A good understanding of behavioral reactions to such incidents is crucial for traffic management and planning. Existing research on such topics is limited. This study investigates travelers' reaction to both the collapse and reopening of the I-35W Bridge crossing the Mississippi River at the Twin Cities, Minnesota. A web-based survey conducted at residences in several communities across the metropolitan area supplements the hand-out/mail-back paper-based survey distributed to workers in areas around the bridge collapse (downtown Minneapolis and the University of Minnesota). Findings from the survey highlight differences in travel impacts and behavioral reactions after the bridge collapse and the bridge reopening.

Relocation decisions are complex. Each household has a bundle of attributes that make a location attractive to it, including the ability to access different activity locations easily, neighborhood quality, house amenities etc. Relocating households have an opportunity to find housing closer to their work. Using data collected in the Twin Cities area, we investigate how distance to home and travel time to home change among individuals who have changed their residence since they started their current job. Comparing the home-to-work distance after the move to the previous-home-to-work distance, we find that the average home to work distance is reduced as a result of the move. We also find that the reduction depend on the previous home to work distance as well as the previous homesÕ proximity to downtown Minneapolis. The findings show that households that are either very close to their work, or very close to downtown, or both did not significantly increase or decrease their commute after relocation. This suggests that access to work as well as access to the opportunities that proximity to downtown offers (to jobs, urban spaces, etc.) are important in the decision making process.

On August 1st, 2007, the I-35W bridge crossing the Mississippi river fell into the Mississippi river. In addition to the human tragedy that it caused, the bridge failure also impacted how people moved in the area. The bridge carried 140,000 cars daily. As such it required a significant amount of traffic find new routes to reach their destinations. Traffic impacts may also have led to changes in mode, time, or some trips being foregone. Those who changed routes were not just the ones that previously used the bridge. With the I-35 traffic using alternate routes, those who saw or anticipated higher traffic also found it necessary to re route their trips. In this study we ask a sample of people that were recruited for another study, if their travels had been impacted by the failure of the bridge, how they coped and what impacts it had on their travels and other activities.

This study evaluates individual preferences for five different cycling environments by trading off a higher travel time as a cost incurred when choosing a better facility while letting the user have the option of selecting a less attractive facility at a lower travel time. The tradeoff of travel time to amenities of a particular facility informs our understanding of the value attached to different attributes such as bike-lanes, off-road trails, or side-street parking. The facilities considered here are off-road facilities, in-traffic facilities with bike-lane and no side street parking, in-traffic facilities with a bike-lane and side street parking, in-traffic facilities with no bike-lane and no side street parking and in-traffic facilities with no bike-lane but with parking on the side. We find that respondents are willing to travel up to twenty minutes more to switch from an unmarked on-road facility with side parking to an off-road bicycle trail, with smaller changes associated with less dramatic improvements.

This study estimates Value of Time differences between people who arrived at their destination as planned and those that were delayed. The analysis is based on the I-394 MnPASS High Occupancy/Toll (HOT) lane project recently implemented in the Minneapolis/St. Paul region. Using a Stated Preference survey, the individuals are asked about a trip they have taken before, and asked if they would opt for the free route or pay and go on the HOT lanes. The analysis groups the travelers into subscribers and non-subscribers of the MnPASS (electronic toll collection transponder) system and further decomposes choices into categories based on trip time and experience (delayed or not). Trip times were divided into morning peak, afternoon peak, and off peak and trip experience was divided into delayed and not delayed, creating six categories. The findings suggest an increased willingness to pay among subscribers who were late to reduce travel time in the PM rush hour. As well, we find some evidence that individuals who were late during the AM peak have a lower VOT as compared to their on-time counterparts.