DISPARITY OF ACCESS: VARIATIONS IN TRANSIT SERVICE BY RACE, ETHNICITY, INCOME, AND AUTO AVAILABILITY

This study explores the relationship between transit-based job accessibility and minority races and ethnicities, low- and middle-income households, and carless households at the block group level for the 50 largest by population metropolitan regions in the United States. A log-linear regression model is used to identify inequities in transit-based job accessibility across the US using data collected from the American Community Survey, the Environmental Protection Agency’s Smart Location Database, and the Access Across America database. The intra-metropolitan analyses reveal that accessibility is unevenly distributed across block groups that have different densities of race and levels of income. The differences in accessibility are especially apparent where there are denser pockets with higher percentages of African Americans, Hispanics, low-income households, and zero-car households. The inter-metropolitan analyses show that accessibility is unevenly distributed across metropolitan regions across the US when considering various sociodemographic populations. Different metropolitan regions provide different levels of accessibility for all investigated sociodemographic categories, whether considering racial minorities, levels of income, or car ownership. The results may inform recommendations for equitable transport planning and policy-making

Analyses of Bicycle and Pedestrian Trail Traffic: New Tools for Modeling User Expenditures and Demand

University of Minnesota MURP thesis. August 2017. Major: Urban and Regional Planning. Advisor: Greg Lindsey. 1 computer file (PDF); x, 93 pages.Despite the importance of multi-use trails in urban non-motorized transportation networks, transportation planners, engineers, and trail managers lack tools for describing economic activity associated with local trail use and for predicting bicycle and pedestrian demand for trails. New tools are needed to plan and prioritize investments in new facilities and to inform management and maintenance of trail infrastructure. Among other needs, they need tools to predict (1) expenditures by local users to support local economic development initiatives and assess neighborhood effects of proposals for trail development and (2) trail traffic demand for optimizing investments and managing maintenance of systems and facilities. This thesis responds to these needs and augments the burgeoning literature on trail traffic analysis by developing models of trail-related expenditures and mode-specific trail demand models. From the expenditures by local users side, using the results of intercept surveys completed by 1,282 trail users on the Central Ohio Greenway trail network in 2014, this thesis estimates the probabilities and patterns that different types of trail users will make expenditures. Approximately one-fifth of trail users reported spending between US$15 and US$20 for food, drink, and other incidental items. Across all trail users the average expenditure by individuals is about US$3 per visit. All else equal, cyclists are more than twice as likely than other users to report expenditures. Users visiting trails principally for recreation are 53% more likely to spend, while users visiting trails mainly for exercise were about 19% less likely. Both longer trips to and on the trails are associated with higher spending. From the trail traffic demand side, this thesis employs trail traffic volumes recorded at 15-minute intervals for 32 multi-use trails located in 13 urban areas across the United States from January 1, 2014 through February 16, 2016. The results of analyses indicate (1) daily trail traffic varies substantially – over three orders of magnitude – across the monitoring stations included in the study; (2) daily trail traffic is highly correlated with weather, and the parabola form of weather parameters works well for modeling variables such as temperature, where trail use is associated with warmer temperatures, but only up to a point at which higher temperatures then decrease use; (3) bicyclists and pedestrians respond differently to variations in weather, and their responses vary both within and across regions; (4) with only a few exceptions, average daily pedestrians (ADP) and average daily bicyclists (ADB) are correlated with different variables, and the magnitude of effects of variables that are the same varies significantly between the two modes; (5) the mean relative percentage error (MRPE) for bicyclist, pedestrian, and mixed-mode demand models, respectively, are 65.4%, 85.3%, and 45.9%; (6) although using multimodal monitoring networks enables us to juxtapose the bicyclist demand with pedestrian demand, there is not a significant improvement in predicting total demand using multimodal sensors; (7) a new post-validation procedure improves the demand models, reducing the MRPE of bicyclist, pedestrian, and mixed-mode models by 27.2%, 32.1%, and 14.1%. Transportation planners, engineers, and trail managers can use these results to estimate the effects of weather and climate on trail traffic and to plan and manage facilities more effectively. The developed models also can be used in practical applications such as selection of route corridors and prioritization of investments where order-of-magnitude estimates suffice

Choice of speed under compromised Dynamic Message Signs.

This study explores speed choice behavior of travelers under realistic and fabricated Dynamic Message Signs (DMS) content. Using web-based survey information of 4,302 participants collected by Amazon Mechanical Turk in the United States, we develop a set of multivariate latent-based ordered probit models participants. Results show female, African-Americans, drivers with a disability, elderly, and drivers who trust DMS are likely to comply with the fabricated messages. Drivers who comply with traffic regulations, have a good driving record, and live in rural areas, as well as female drivers are likely to slow down under fabricated messages. We highlight that calling or texting, taking picture, and tuning the radio are distracting activities leading drivers to slow down or stop under fictitious scenarios

Spatiotemporal Short-term Traffic Forecasting using the Network Weight Matrix and Systematic Detrending

This study examines the dependency between traffic links using a three-dimensional data detrend- ing algorithm to build a network weight matrix in a real-world example. The network weight matrix reveals how links are spatially dependent in a complex network and detects the competi- tive and complementary nature of traffic links. We model the traffic flow of 140 traffic links in a sub-network of the Minneapolis - St. Paul highway system for both rush hour and non-rush hour time intervals, and validate the extracted network weight matrix. The results of the modeling indi- cate: (1) the spatial weight matrix is unstable over time-of-day, while the network weight matrix is robust in all cases and (2) the performance of the network weight matrix in non-rush hour traffic regimes is significantly better than rush hour traffic regimes. The results of the validation show the network weight matrix outperforms the traditional way of capturing spatial dependency between traffic links. Averaging over all traffic links and time, this superiority is about 13.2% in rush hour and 15.3% in non-rush hour, when only the 1st -order neighboring links are embedded in modeling. Aside from the superiority in forecasting, a remarkable capability of the network weight matrix is its stability and robustness over time, which is not observed in spatial weight matrix. In addition, this study proposes a naïve two-step algorithm to search and identify the best look-back time win- dow for upstream links. We indicate the best look-back time window depends on the travel time between two study detectors, and it varies by time-of-day and traffic link

Traffic Flow Variation and Network Structure

This study defines and detects competitive and complementary links in a complex network and constructs theories illustrating how the variation of traffic flow is interconnected with network structure. To test the hypotheses, we extract a grid-like sub-network containing 140 traffic links from the Minneapolis - St. Paul highway system. We reveal a real-world traffic network comprises both competitive and complementary links, and there is a negative network dependency between a competitive link pair and a positive network dependency between a complementary link pair. We validate a robust linear relationship between standard deviation of flow in a link and its number of competitive links, its link correlation with competitive links, and its network dependency with both competitive and complementary links. The results indicate the number of competitive links in a traffic network is negatively correlated with the variation of traffic flow in congested regimes as drivers are able to take alternative paths. The results also signify that the more the traffic flow of a link is correlated to the traffic flow of its competitive links, the more the flow variation is in the link. Considering the network dependency, however, it is corroborated that the more the network dependency between a link and its competitive links, the more the flow variation in the link. This is also true for complementary links

Safety in Numbers: Pedestrian and Bicyclist Activity and Safety in Minneapolis

This investigation aims to evaluate whether the Safety in Numbers phenomenon is observable in the midwestern U.S. city of Minneapolis, Minnesota. Safety in Numbers (SIN) refers to the phenomenon that pedestrian safety is positively correlated with increased pedestrian traffic in a given area. Walking and bicycling are increasingly becoming important transportation modes in modern cities. Proper placement of non-motorized facilities and improvements has implications for safety, accessibility, and mode choice, but proper information regarding estimated non-motorized traffic levels is needed to locate areas where investments can have the greatest impact. Assessment of collision risk between automobiles and non-motorized travelers offers a tool that can help inform investments to improve non-motorized traveler safety. Models of non-motorized crash risk typically require detailed historical multimodal crash and traffic volume data, but many cities do not have dense datasets of non-motorized transport flow levels. Methods of estimating pedestrian and bicycle behavior that do not rely heavily on high-resolution count data are applied in this study. Pedestrian and cyclist traffic counts, average automobile traffic, and crash data from the city of Minneapolis are used to build models of crash frequencies at the intersection level as a function of modal traffic inputs. These models determine whether the SIN effect is observable within the available datasets for pedestrians, cyclists, and cars, as well as determine specific locations within Minneapolis where non-motorized travelers experience elevated levels of risk of crashes with automobiles

Safety in Numbers and Safety in Congestion for Bicyclists and Motorists at Urban Intersections

This study assesses the estimated crashes per bicyclist and per vehicle as a function of bicyclist and vehicle traffic, and tests whether greater traffic reduces the per-car crash rate. We present a framework for comprehensive bicyclist risk assessment modeling, using estimated bicyclist flow per intersection, observed vehicle flow, and crash records. Using a two-part model of crashes, we reveal that both the annual average daily traffic and daily bicyclist traffic have a diminishing return to scale in crashes. This accentuates the positive role of safety in numbers. Increasing the number of vehicles and cyclists decelerates not only the probability of crashes, but the number of crashes as well. Measuring the elasticity of the variables, it is found that a 1% increase in the annual average daily motor vehicle traffic increases the probability of crashes by 0.14% and the number of crashes by 0.80%. However, a 1% increase in the average daily bicyclist traffic increases the probability of crashes by 0.09% and the number of crashes by 0.50%. The saturation point of the safety in numbers for bicyclists is notably less than for motor vehicles. Extracting the vertex point of the parabola functions examines that the number of crashes starts decreasing when daily vehicle and bicyclist traffic per intersection exceed 29,568 and 1,532, respectively.Road Safety Institut

Traffic Impacts of Bicycle Facilities

Engineers need information about interactions between vehicles and bicyclists to design efficient, safe transportation systems. This study involved a review of design guidelines for bicycle facilities, observation of bicycle-vehicle interactions at nine roadways with different types of bicycle facilities, analysis of results, and description of design implications. Facilities observed included buffered and striped bicycle lanes, sharrows, signed shared lanes, and shoulders of various widths. Driver behaviors were categorized as no change in trajectory, deviation within lane, encroachment into adjacent lane, completion of a passing maneuver, and queuing behind cyclists. Drivers on roadways with bicycle lanes were less likely to encroach into adjacent lanes, pass, or queue when interacting with cyclists than drivers on roadways with sharrows, signs designating shared lanes, or no bicycle facilities. Queueing behind cyclists, the most significant impact on vehicular traffic flows, generally was highest on roads with no facilities or shared facilities without marked lanes. Statistical modeling confirmed the descriptive results. Given an objective of increasing predictability of driver behavior, buffered or striped bicycle lanes offer advantages over other facilities. Sharrows may alert drivers to the presence of cyclists, but traffic impacts on roadways with sharrows may not differ significantly from roadways with no facilities. Signs indicating bicyclists may occupy lanes also may alert drivers to the presence of cyclists, but this study provided no evidence that interactions on roadways marked only with signs differ from roadways with no facilities. From the perspective of reducing potential traffic impacts, bicycle lanes are to be preferred over sharrows or signage

Public transport equity in Shenyang: Using structural equation modelling

In China, with the rapid development of urbanisation, the contradiction between supply and demand has become increasingly severe, particularly in large and medium-sized cities. Improving public transport equity can help to reduce the social exclusion of lower-income and socially vulnerable groups in relation to the urban transport system, and guarantee that public transport systems are given priority in terms of development. Using the concept of transport-related social equity, this study aims to explore the effects of public transport equity in relation to the quality of public transport, public participation, and public transport-related policy using Shenyang as a case study. Data are analysed using Structural Equation Model (SEM). Our findings show that the three latent variables of accessibility, affordability, and social impacts can be seen as representing the main characteristics of public transport equity; while improvements in public transport quality, public participation, and public transport-related polices play a significant role in reducing public transport inequity. Moreover, the findings indicate that public participation has direct, significant, positive influences on public transport quality and public transport-related policies. In terms of policy implications, we suggest that policies designed to improve public transport service quality, extend public transport fare concessions, and promote public participation in the public transport policy decision-making process should be given priority in the next round of urban comprehensive planning in order to reduce public transport-related social inequity in Shenyang and China more generally