On Transportation Equity Implications of Connected and Autonomous Vehicles (CAV) A Review of Methodologies

This review brings together the bodies of literature on transportation equity analysis, travel behavior forecasting, and impacts of CAVs, with the ultimate objective of highlighting important research needs for measuring the transportation equity implications of CAVs. In comparison to previous reviews of social impacts of CAV our focus is specifically on the state of current methods for accessing the transportation equity implications of CAVs. We seek to provide a summary of the methods used to assess potential impacts of CAV’s and how these impacts are likely to apply for transport disadvantaged communities. Our review highlight some critical gaps in available methods for measuring potential equity impacts of CAVs. We find that Our while a range of methods exists that may be applied to begin building our understanding of CAV equity implications, clear guidance how to appropriately apply these is warranted.http://deepblue.lib.umich.edu/bitstream/2027.42/162824/5/On Measuring the Transportation Equity Implications of Connected and Autonomous Vehicles (CAV) A Review of Methodologies.pd

CommuniSense: Crowdsourcing Road Hazards in Nairobi

Nairobi is one of the fastest growing metropolitan cities and a major business and technology powerhouse in Africa. However, Nairobi currently lacks monitoring technologies to obtain reliable data on traffic and road infrastructure conditions. In this paper, we investigate the use of mobile crowdsourcing as means to gather and document Nairobi's road quality information. We first present the key findings of a city-wide road quality survey about the perception of existing road quality conditions in Nairobi. Based on the survey's findings, we then developed a mobile crowdsourcing application, called CommuniSense, to collect road quality data. The application serves as a tool for users to locate, describe, and photograph road hazards. We tested our application through a two-week field study amongst 30 participants to document various forms of road hazards from different areas in Nairobi. To verify the authenticity of user-contributed reports from our field study, we proposed to use online crowdsourcing using Amazon's Mechanical Turk (MTurk) to verify whether submitted reports indeed depict road hazards. We found 92% of user-submitted reports to match the MTurkers judgements. While our prototype was designed and tested on a specific city, our methodology is applicable to other developing cities.Comment: In Proceedings of 17th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI 2015

Case Studies of Travel Demand Analysis on Transport Disadvantaged Communities

Travel Demand Models are the backbone of decision-making for public transportation infrastructure investment. Yet, critiques of these models with respect to their usefulness and performance for transport disadvantage communities are rare in the academic literature. These disadvantage communities may include (but are not limited to) low income travelers, transit dependents, un/underemployed, and the elderly. With the objective of promoting travel demand models that are better equipped for assessing transportation impacts for disadvantaged communities, this presentation highlights lessons learned from two case studies of applying travel demand analysis to understand the transportation accessibility of low income, elderly, and transit dependent communities. The case studies take place in two Michigan cities, Benton Harbor and Detroit.https://pdxscholar.library.pdx.edu/trec_seminar/1197/thumbnail.jp

Enhancing Transportation Equity Analysis for Long Range Planning and Decision Making

Metropolitan Planning Organizations (MPOs) regularly perform equity analyses for their long-range transportation plans, as this is required by Environmental Justice regulations. These regional-level plans may propose hundreds of transportation infrastructure and policy changes (e.g. highway and transit extensions, fare changes, pricing schemes, etc.) as well as land-use policy changes. The challenge is to assess the distribution of impacts from all the proposed changes across different population segments. In addition, these agencies are to confirm that disadvantaged groups will share equitably in the benefits and not be overly adversely affected. While there are a number of approaches used for regional transportation equity analyses in practice, approaches using large scale travel models are emerging as a common existing practice. However, the existing methods used generally fail to paint a clear picture of what groups benefit or do not benefit from the transportation improvements. In particular, there are four critical shortcomings of the existing transportation equity analysis practice. First, there is no clear framework outlining the key components of a transportation equity analysis at the regional-level. Second, the existing zonal-level group segmentation used for identifying target and comparison groups are problematic and can lead to significant biases. Third, the use of average equity indicators can be misleading, as averages tend to mask important information about the underlying distributions. Finally, there is no clear guidance on implementing scenario ranking based on the equity objectives. In addressing the first shortcoming of existing equity analysis practices, we present a guiding framework for transportation equity analysis that organizes the components of equity analysis in terms of transportation priorities, the model, and the equity indicators. The first component emphasizes the need to identify the priority transportation improvement(s) relevant for communities, as this guides the transportation benefits (or costs) to be evaluated. The second component is the model to be used for facilitating scenario analysis and measuring the expected transportation and land-use changes. The third component refers to the selection of equity indicators (ideally selected based on the transportation priorities identified), and the evaluation of these indicators. This three-part framework is also useful for outlining the research needs for transportation equity analysis. Among other key research needs, the literature indicates that the development of meaningful distributional comparison methods for transportation planning and decision-making and the use of more comprehensive measures of transportation benefit (for use as equity indicators) are critical.The primary contributions of this dissertation relate to the third component; we develop an advanced approach for evaluating transportation equity outcomes (as represented by the equity indicator(s)). Our proposed analytical approach to transportation equity analysis addresses the existing shortcomings with respect to zonal-level group segmentation and average measures of transportation equity indicators. In addition, our approach emphasizes the importance of scenario ranking using explicit equity criteria. Our approach leverages the disaggregate functionality of activity-based travel demand models and applies individual-level data analysis to advance the existing equity analysis practices.There are four steps in our proposed equity analysis process. The first step is to select the equity indicators to be evaluated and segment the population into a target group and comparison group(s). In this case we advocate for an individual -unit of segmentation and therefore individual-level equity indicators. This minimizes the biases associated with aggregate group segmentation and average equity indicators. The second step is to calculate the indicators from the model data output, which involves determining the exact measures (formulas) for the selected equity indicators. Here we advocate for measures that are comprehensive and sensitive to both transportation system changes and land-use factors, such as the logsum accessibility and consumer surplus measure. The third step in the process is to generate and evaluate distributions of the individual-level equity indicators. In particular, we advocate for the use of what we refer to as the "Individual Difference Density" comparison, which compares distributions of individual-level changes for the population segments across the planning scenarios. This comparison allows for the "winners" and "losers" resulting from the transportation and land-use plans to be identified. The fourth and final step in the process is to identify equity criteria (associated with the chosen equity standard (objective)) and rank the planning scenarios based on the degree to which they meet the equity criteria.We present two conceptual demonstrations of the advantages of distributional comparisons, relative to average measures. The first case uses a synthetic data set and simple binary mode choice model to show and the second case uses an empirical data set (the 2000 Bay Area Travel Survey) and more sophisticated mode choice model. These demonstrations show that distributional comparisons are capable of revealing a much richer picture of how different population segments are affected by transportation plans, in comparison with average measures. Further, distributional comparison provides a framework for evaluating what population's characteristics and conditions lead to certain distribution transportation outcomes.Our proposed process for regional transportation equity analysis is subsequently applied in a case study for the San Francisco Bay Area. We evaluate joint transportation and land-use scenarios modeled using the Metropolitan Transportation Commission's state-of-the-art activity-based travel demand model. We demonstrate the power of individual-level data analysis in a real-world setting. We calculate individual-level measures of commute travel time and logsum-based accessibility/consumer surplus using the model output and compare the scenario changes across income segments. We generate empirical distributions of these indicators and compare the changes associated with the planning scenarios for low and high income commuters. Further, we apply criteria for a set of equity standards (which represent alternative equity objectives) and rank the planning scenarios. There are four key takeaways from this case study. First is that our results show a significant difference in equity outcomes when using the individual-level population segmentation approach, compared to using the zonal segmentation approach done in practice. In fact we find opposite results. For average commute travel time, the Metropolitan Transportation Commission's zonal segmentation approach indicates that low income commuters are worse off than all other commuters, while the individual segmentation approach (in our case) indicates that low income commuters are significantly better off than high income commuters. While the underlying causes for these results warrant further investigation, we hypothesize that this difference is due to the fact that the zone-based approach only captures 40% of the target (low income) group. The individual-level segmentation approach is able to capture 100% of the target group. Second is regarding the equity indicators evaluated. The commute travel time indicator results indicate that low income commuters are better off than high income commuters, while the accessibility/consumer surplus results indicate that low income commuters are worse off than high income commuters. The underlying causes for these results warrant further investigation, but we hypothesize that this difference in results to due to the fact that the logsum accessibility/consumer surplus measure by design is able to capture transportation and land-use related factors, while the travel time measure only captures one dimension of transportation user factors. Focusing on travel time may be misleading because it does not fully capture the true benefits of the transportation scenarios. Third is regarding the use of distributional comparisons, relative to average measures. We find that distributional comparisons are much more informative than average measures. The distributional measures are capable of providing a much richer picture of individuals-level transportation impacts, in terms of who gains and who loses due the transportation planning scenarios. Using the accessibility/ consumer surplus measure, the Individual Difference Densities show that as many as 33.3% of low income commuters experience losses, compared to 13.4% for high income commuters. Finally, we make the case that the use of equity standards for scenario ranking plays an important role in the equity analysis process. Our results show that different equity standards result in different rankings for the transportation planning scenarios. This points to the need for agencies (and communities) to make conscious decisions on what equity standard(s) should be used and apply this/these in the scenario ranking process.This dissertation work includes the first known full-scale application of a regional activity-based travel model for transportation equity analysis that involves distributional comparisons of individual-level equity indicators and scenario ranking based on equity criteria. We find that while the existing practice is to use average measures to represent how difference are affected by transportation plans, distributional comparison are able to provide for a richer evaluation of individual-level transportation impacts. Distributional comparisons provide a framework for quantifying the "winners" and "losers" of transportation plans, while average measures and be misleading and uninformative. We make significant progress with regard to evaluating equity indicators (part three of the guiding framework). However, our proposed process is flexible and can be extended to include a number of additional advances, including more environmental and long-term land-use related equity indicators (e.g. emissions exposure, gentrification and displacement risk, employment participation, etc.) and additional population segments (e.g. age, ethnicity, household type, auto-ownership class, etc.). Among other important research directions, our analytical framework for regional transportation equity analysis can be applied to investigating why certain groups are more likely to be "losers" and what factors of transportation planning scenarios to modify in order to arrive at a more equitable transportation and land-use plan

Enhancing Transportation Equity Analysis for Long Range Planning and Decision Making

Metropolitan Planning Organizations (MPOs) regularly perform equity analyses for their long-range transportation plans, as this is required by Environmental Justice regulations. These regional-level plans may propose hundreds of transportation infrastructure and policy changes (e.g. highway and transit extensions, fare changes, pricing schemes, etc.) as well as land-use policy changes. The challenge is to assess the distribution of impacts from all the proposed changes across different population segments. In addition, these agencies are to confirm that disadvantaged groups will share equitably in the benefits and not be overly adversely affected. While there are a number of approaches used for regional transportation equity analyses in practice, approaches using large scale travel models are emerging as a common existing practice. However, the existing methods used generally fail to paint a clear picture of what groups benefit or do not benefit from the transportation improvements. In particular, there are four critical shortcomings of the existing transportation equity analysis practice. First, there is no clear framework outlining the key components of a transportation equity analysis at the regional-level. Second, the existing zonal-level group segmentation used for identifying target and comparison groups are problematic and can lead to significant biases. Third, the use of average equity indicators can be misleading, as averages tend to mask important information about the underlying distributions. Finally, there is no clear guidance on implementing scenario ranking based on the equity objectives. In addressing the first shortcoming of existing equity analysis practices, we present a guiding framework for transportation equity analysis that organizes the components of equity analysis in terms of transportation priorities, the model, and the equity indicators. The first component emphasizes the need to identify the priority transportation improvement(s) relevant for communities, as this guides the transportation benefits (or costs) to be evaluated. The second component is the model to be used for facilitating scenario analysis and measuring the expected transportation and land-use changes. The third component refers to the selection of equity indicators (ideally selected based on the transportation priorities identified), and the evaluation of these indicators. This three-part framework is also useful for outlining the research needs for transportation equity analysis. Among other key research needs, the literature indicates that the development of meaningful distributional comparison methods for transportation planning and decision-making and the use of more comprehensive measures of transportation benefit (for use as equity indicators) are critical.The primary contributions of this dissertation relate to the third component; we develop an advanced approach for evaluating transportation equity outcomes (as represented by the equity indicator(s)). Our proposed analytical approach to transportation equity analysis addresses the existing shortcomings with respect to zonal-level group segmentation and average measures of transportation equity indicators. In addition, our approach emphasizes the importance of scenario ranking using explicit equity criteria. Our approach leverages the disaggregate functionality of activity-based travel demand models and applies individual-level data analysis to advance the existing equity analysis practices.There are four steps in our proposed equity analysis process. The first step is to select the equity indicators to be evaluated and segment the population into a target group and comparison group(s). In this case we advocate for an individual -unit of segmentation and therefore individual-level equity indicators. This minimizes the biases associated with aggregate group segmentation and average equity indicators. The second step is to calculate the indicators from the model data output, which involves determining the exact measures (formulas) for the selected equity indicators. Here we advocate for measures that are comprehensive and sensitive to both transportation system changes and land-use factors, such as the logsum accessibility and consumer surplus measure. The third step in the process is to generate and evaluate distributions of the individual-level equity indicators. In particular, we advocate for the use of what we refer to as the "Individual Difference Density" comparison, which compares distributions of individual-level changes for the population segments across the planning scenarios. This comparison allows for the "winners" and "losers" resulting from the transportation and land-use plans to be identified. The fourth and final step in the process is to identify equity criteria (associated with the chosen equity standard (objective)) and rank the planning scenarios based on the degree to which they meet the equity criteria.We present two conceptual demonstrations of the advantages of distributional comparisons, relative to average measures. The first case uses a synthetic data set and simple binary mode choice model to show and the second case uses an empirical data set (the 2000 Bay Area Travel Survey) and more sophisticated mode choice model. These demonstrations show that distributional comparisons are capable of revealing a much richer picture of how different population segments are affected by transportation plans, in comparison with average measures. Further, distributional comparison provides a framework for evaluating what population's characteristics and conditions lead to certain distribution transportation outcomes.Our proposed process for regional transportation equity analysis is subsequently applied in a case study for the San Francisco Bay Area. We evaluate joint transportation and land-use scenarios modeled using the Metropolitan Transportation Commission's state-of-the-art activity-based travel demand model. We demonstrate the power of individual-level data analysis in a real-world setting. We calculate individual-level measures of commute travel time and logsum-based accessibility/consumer surplus using the model output and compare the scenario changes across income segments. We generate empirical distributions of these indicators and compare the changes associated with the planning scenarios for low and high income commuters. Further, we apply criteria for a set of equity standards (which represent alternative equity objectives) and rank the planning scenarios. There are four key takeaways from this case study. First is that our results show a significant difference in equity outcomes when using the individual-level population segmentation approach, compared to using the zonal segmentation approach done in practice. In fact we find opposite results. For average commute travel time, the Metropolitan Transportation Commission's zonal segmentation approach indicates that low income commuters are worse off than all other commuters, while the individual segmentation approach (in our case) indicates that low income commuters are significantly better off than high income commuters. While the underlying causes for these results warrant further investigation, we hypothesize that this difference is due to the fact that the zone-based approach only captures 40% of the target (low income) group. The individual-level segmentation approach is able to capture 100% of the target group. Second is regarding the equity indicators evaluated. The commute travel time indicator results indicate that low income commuters are better off than high income commuters, while the accessibility/consumer surplus results indicate that low income commuters are worse off than high income commuters. The underlying causes for these results warrant further investigation, but we hypothesize that this difference in results to due to the fact that the logsum accessibility/consumer surplus measure by design is able to capture transportation and land-use related factors, while the travel time measure only captures one dimension of transportation user factors. Focusing on travel time may be misleading because it does not fully capture the true benefits of the transportation scenarios. Third is regarding the use of distributional comparisons, relative to average measures. We find that distributional comparisons are much more informative than average measures. The distributional measures are capable of providing a much richer picture of individuals-level transportation impacts, in terms of who gains and who loses due the transportation planning scenarios. Using the accessibility/ consumer surplus measure, the Individual Difference Densities show that as many as 33.3% of low income commuters experience losses, compared to 13.4% for high income commuters. Finally, we make the case that the use of equity standards for scenario ranking plays an important role in the equity analysis process. Our results show that different equity standards result in different rankings for the transportation planning scenarios. This points to the need for agencies (and communities) to make conscious decisions on what equity standard(s) should be used and apply this/these in the scenario ranking process.This dissertation work includes the first known full-scale application of a regional activity-based travel model for transportation equity analysis that involves distributional comparisons of individual-level equity indicators and scenario ranking based on equity criteria. We find that while the existing practice is to use average measures to represent how difference are affected by transportation plans, distributional comparison are able to provide for a richer evaluation of individual-level transportation impacts. Distributional comparisons provide a framework for quantifying the "winners" and "losers" of transportation plans, while average measures and be misleading and uninformative. We make significant progress with regard to evaluating equity indicators (part three of the guiding framework). However, our proposed process is flexible and can be extended to include a number of additional advances, including more environmental and long-term land-use related equity indicators (e.g. emissions exposure, gentrification and displacement risk, employment participation, etc.) and additional population segments (e.g. age, ethnicity, household type, auto-ownership class, etc.). Among other important research directions, our analytical framework for regional transportation equity analysis can be applied to investigating why certain groups are more likely to be "losers" and what factors of transportation planning scenarios to modify in order to arrive at a more equitable transportation and land-use plan

Transit Accessibility Measurement Considering Behavioral Adaptations to Reliability

Accessibility measures are necessary for evaluating the benefits of proposed transportation improvements. However, they often do not account for travel time unreliability, but instead incorporate deterministic and time-invariant travel times. This approach risks mischaracterizing the accessibility experienced by travelers. In this paper, we review recent literature on accessibility and travel time reliability with a focus on transit and introduce an approach to joint accessibility-reliability measurement that relies on a behavioral perspective. Using this behavioral perspective, we propose that existing accessibility measures be implemented using travelers’ total travel time budget as a measure of travel time, and that varying departure time strategies depending on service characteristics be considered. The total travel time budget can reasonably be quantified with a high percentile of the total travel time distribution. However, we note that different percentiles may be more appropriate for different traveler types, as these percentiles correspond to varying tolerances for late arrivals. This behavioral perspective can be operationalized with commonly used accessibility measures, such as the cumulative opportunity measure, and with real-time vehicle location data. We include a demonstration of the potential changes in accessibility estimates when accounting for travel time unreliability, with a simplified case study of a transit route in San Francisco. The results show a considerable reduction of the number of opportunities available to travelers when the calculation is based on the latter—between 5.9% and 37.9% less, depending on various factors. Such differences have the potential to significantly affect the accessibility benefits of transit capital investments

Towards transit equity in Detroit: An assessment of microtransit and its impact on employment accessibility

Several studies cite great potential for microtransit services to improve transportation equity by providing disadvantaged communities with a more flexible and reliable option relative to traditional fixed route transit. However, few have attempted to forecast these equity benefits. This study seeks to contribute to the ongoing discussion by exploring potential equity impacts of a microtransit service in Metropolitan Detroit. Using a logsum accessibility measure calculated from a regional travel demand model, we simulate accessibility changes due to a hypothetical regional microtransit service and demonstrate a regional transportation equity analysis using a typical regional travel demand model. Our results show that accessibility gains would be slightly higher for lower income communities (17% increase compared with 13% for high income) and transit-dependent households (21% compared with 15% for car-owning households). Further, we find that Microtransit may help to reduce gaps in accessibility between disadvantaged and more advantaged traveler groups

Identifying Factors that Determine Bicyclist and Pedestrian-Involved Collision Rates and Bicyclist and Pedestrian Demand at Multi-Lane Roundabouts

This project examined the safety and demand issues for pedestrians and bicyclists at multi-lane roundabouts through a literature review, case studies, in-field counts and surveys, focus groups, and video analysis. This document presents research findings, synthesizes current information on best practices, and makes recommendations to assist local agencies planning and designing safer multi-lane roundabouts. These findings should help local agencies and Caltrans create roundabouts that better and more safely address the needs of bicyclists and pedestrians. The current literature is referred to throughout the document to augment the research team’s findings, especially for issues that were beyond the scope of this project. Key findings in the areas of pedestrian and bicyclist avoidance of, behavior around, and collisions at multi-lane roundabouts are presented along with recommendations for geometric design, design speed, sight distance, width of lanes, signage and pavement markings, and operational recommendations.