Papers in Planning for the Transportation Impacts on Urban Form showcase how the many decisions of shippers (an example being warehouse placement), residents (example is housing location), passengers (mode selection), governments (e.g., criteria for transportation impact analyses or allowance of road diets), and suppliers (e.g., providers of autonomous vehicles) intersect with market forces (e.g., housing prices), geographical boundaries, and life events (birth of a child) to affect the urban environment. The resultant impacts affect the quality of life, whether measured in emissions, the ability to achieve a social goal (health care, enjoyment of parks, or employment), the feasibility of purchasing a home, or time spent commuting.
Examining the impacts of the Great Recession on the commuting and jobs-housing balance of public and private sector workers
Kyusik Kim (firstname.lastname@example.org), Florida State UniversityShow Abstract
Mark Horner, Florida State University
Research examining commuting-related phenomena remains a key area of geographical research. And although substantial research has attempted to explore the relationships between transportation dynamics and land use, little is known about how the impacts of major economic changes such as the Great Recession would affect related commuting outcomes. In addition, commuting studies examining the plight of workers in the private versus the public sectors are also virtually non-existent, though the two-groups’ commuting outcomes would potentially be affected very differently by the Great Recession. This study contributes to a better understanding of the impacts of the Great Recession on commuting outcomes and jobs-housing balance. We employ metrics from the excess commuting and jobs-housing balance literature in an effort to examine the commuting outcomes of private and public sector workers during the time of the Great Recession (2008-2009). Our analysis is conducted for the Atlanta, GA Metropolitan area. Findings of the study are that private workers experienced better jobs-housing balance over the study period, but they commute longer and more inefficiently when compared with public workers. While the Great Recession worsened both groups’ commuting situation, the effect was greater for public workers in terms of increasing their travel burdens. Since the public sector response to the Great Recession was delayed, policy implications suggest monitoring employment trends during and after economic shocks and recognizing the transportation disadvantages public sector workers may face in future crises.
Application of the Maximum Entropy Model in Urban Freight Transportation Planning: An Exploratory Analysis for Warehouse Location in Belo Horizonte Metropolitan Area
Isabela Oliveira, Universidade Federal de Minas GeraisShow Abstract
Leise Oliveira, Universidade Federal de Minas Gerais
Rodrigo Nóbrega, Universidade Federal de Minas Gerais
Urban freight transport has been neglected by the Belo Horizonte administration, leading to several negative externalities. In Brazil, neglect and lack of data have made transportation planning difficult. This paper combines modelling techniques with the maximum entropy formulation to model logistical probabilities at various metropolitan warehouses using urban planning factors. The phenomenon was analyzed using 19 factors, and the results were recorded on a map of logistical probabilities. Since this is exploratory research, the model validation was made by visual comparison, addressing the four study hypotheses: (i) Warehouses tend to be located near roads and railroads; (ii) Warehouses tend to be located close to their consumer markets; (iii) Warehouses are located in urban areas; and (iv) Warehouses tend to be located next to each other, forming agglomerations. All four hypotheses were derived from literary evidence, and all were proved here. Positive results were obtained, demonstrating this method to be suitable for modelling city freight transportation to support urban planning.
A Hybrid Latent Class Modeling Approach to Analyze Shared Automated Vehicles Acceptance
shelly etzioni (email@example.com), Technion Israel Institute of TechnologyShow Abstract
Ricardo Daziano, Cornell University
Eran Ben-Elia, Ben-Gurion University of the Negev
Yoram Shiftan, Technion Israel Institute of Technology
The flexibility of shared automated vehicles may engender new modes of transportation that will be a cross between private and public modes available today. We analyze user preferences towards shared automated vehicle services, including ride sharing, car sharing and automated transit, using a hybrid choice model. We use a discrete distribution to capture taste heterogeneity of distinct latent classes. More specifically, latent variables, socio-demographics and travel habits inform latent class assignment that is estimated simultaneously with the discrete choice kernel. Israeli respondents were asked to choose their preferred mode for going to work in a set of stated preference choice tasks, based on the attributes of their current commutes. We show that shared automated vehicles users can be segmented into two latent classes based on two latent factors that capture personal time style orientation and attitudes towards transit. We model the effects of trip cost, travel times, seating configuration and travel habits in a shared ride, among the two classes. Users who are more likely to dislike transit and don’t like ridesharing with strangers, are less likely to choose a shared ride if their designated seat is the middle seat, and less likely to choose automated transit. Individuals who are more likely to have organized time styles demonstrate higher marginal sensitivity to travel times and cost and are more likely to choose automated transit. Value of time analysis reveals that the value of wait time of services that offer convenient home pickup is lower than the value of in-vehicle time.
Clustering of Transport Measures for Effective Evaluation: A Port City Example using the Analytic Hierarchy Process
Steve Wright, University of AberdeenShow Abstract
Sarah Brooke, University of Aberdeen
Caitlin Cottrill (firstname.lastname@example.org), University of Aberdeen
Martin Kollingbaum, University of Aberdeen
John Nelson, University of Sydney
When evaluating the impact and effectiveness of transport measures introduced to address specific city-level objectives, it is important that the mutual influence among multiple measures introduced simultaneously be recognised and understood. In this paper, we outline a methodology for clustering transport measures for evaluation based on their contributions, both positive and negative, to the same strategic city-level objective. This methodology requires the identification of a single key outcome indicator associated with a city objective. In order to attribute the relative contribution of each measure to the overall objective-related outcome, the Analytical Hierarchy Process (AHP) is applied. An illustrative example from a European port city, undertaken as part of the European Union CIVITAS PORTIS (PORT-cities: Innovation for Sustainability) project, is used to demonstrate the measure clustering and attribution evaluation procedure and show how this approach allows for a pragmatic yet effective evaluation that is useful for city planners and policy makers. The proposed methodology outlined in this paper offers support to and enhancement of existing recognised evaluation approaches.
The Causal Influence of Built Form on Patterns of Household Consumption: Evidence from American Household Expenditure Surveys
Jason Hawkins (email@example.com), University of TorontoShow Abstract
Khandker Nurul Habib, University of Toronto
Transportation demand analysis can be said to be based on discerning the relationship between activity generation for individuals and the time to travel between those activities. Land use, often measured by population density, has been shown to have a significant effect on the length and frequency of travel. We present amenity consumption as an alternative perspective to this analysis, where it is assumed that individuals make trade-offs between spending time and money on activities. A causal identification strategy is proposed for the relationship between population density and amenity consumption based on geological features and historical population densities. Our analysis is based on a national sample of household expenditures for the United States. We measure amenity consumption as out-of-home entertainment and food, transit services, and replacement of home production. We find the strongest effect of density is on transit consumption. We also consider historical trends, which offer the critical forecasting insight that the relative distribution of consumption among aggregate categories has been relatively stable since the 1980s.
Car-sharing usage island, built environment, travel demand: Evidence from a correlation analysis
Hui Bi, Southeast UniversityShow Abstract
Zhirui Ye, Southeast University
Yi Zhang, Southeast Univesity
Car-sharing services offer a convenient mobility option, especially for door-to-door trips. As a non-stop transit mode, car-sharing usage regularity is more significantly interrelated with built environment due to the proximity of pick up and drop off locations to the trip’s origin and destination. Previous relevant studies employ direct ridership models to understand how built environment elements are associated with car-sharing demand at a global level. The models involved simply using a prior assumed linear or log-linear relationship. This study focuses only on the relationship between built environment elements and car-sharing usage in hot and cold spots to reveal the key mechanism of action. First, car-sharing usage islands, defined as geographical areas of interest with a relatively high or low concentration of car-sharing usage, are identified based on percolation theory. Next, this study innovatively adopts gradient boosting decision trees (GBDT) to explore the collective influence of built environment attributes, and their non-linear effects on ridership. On the one hand, the empirical results reveal a hierarchical structure of car-sharing usage islands, and regional imbalances of travel supply and demand are sporadically identified across several regions by comparing the spatial distribution of usage islands. On the other hand, home-work attributes have the strongest influences on car-sharing demand, and most other built environment variables as well as transport facility factors are associated with car-sharing ridership in a different and discontinuous non-linear way, regardless of island’s type. These threshold effects in all situations offer valuable implications for planners to achieve desirable environmental benefits efficiently.
Sharing the Road with Autonomous Vehicles: Perceived Safety and Regulatory Preferences
Gopindra Sivakumar Nair, UT Center for Transportation ResearchShow Abstract
Chandra Bhat (firstname.lastname@example.org), University of Texas, Austin
Technology providers, car manufacturers, and public agencies all need to work together to undertake extensive testing of fully autonomous vehicles (AVs) on public roads before such AVs are allowed to freely travel in ways similar to human-driven vehicles. This raises the importance of understanding public perceptions regarding safety considerations when traveling alongside AVs. This study makes use of a national survey conducted by the Pew Research Center to identify the affective, socio-demographic and technology-use attributes that affect an individual’s perception of the safety of sharing the road with AVs (PSSRAV) and identifies measures and interventions that can be undertaken to improve PSSRAV. Additionally, we evaluate individual preferences for AV regulations. Our results underscore the importance of the need for service providers and public agencies to be cognizant of the demographic and lifestyle/affective emotion considerations shaping AV safety perceptions and opinions about AV regulations. In particular, there is a need not only to focus on technological and other infrastructure components of AV development, but also to recognize the socio-technical considerations and human-related factors of the end-users. Our findings should be of substantial interest in the planning, design, deployment, and introduction of AVs within a safe and minimally regulated public operating arena.
Quantifying the effect of factors on bike-sharing demand: A regression model with spatially varying coefficients
Xudong Wang, McGill UniversityShow Abstract
Zhanhong Cheng, McGill University
Trépanier Martin, Ecole Polytechnique de Montreal
Lijun Sun (email@example.com), McGill University
As an emerging mobility service, bike-sharing has become increasingly popular around the world. A critical question in planning and designing bike-sharing services is to know how different land-use and built environment factors affect bike-sharing demand. Most existing research investigated this problem from a holistic view by regression models, where the regression coefficients are the same for all bicycle stations. However, a global regression model essentially ignores the local spatial effects of different factors. To address this problem, in this study we develop a regression model with spatially varying coefficients to investigate how land use attributes, social-demographic, and transportation infrastructure affect the bike-sharing demand at different stations. The regression coefficients in the model are station-specific and regularized by a graph structure that encourages nearby stations to have similar coefficients. We apply the model to the station-level bike-sharing demand data from the BIXI service in Montreal, Canada. We find that the obtained regression coefficients demonstrate clear spatially-varying patterns. The spatial distribution highlights areas that are more sensitive to the marginal change of a certain factor, which is very helpful to service planning. The proposed regression model also demonstrates its superior out-of-sample prediction power compared with traditional machine learning models and geostatistical models. This study can be used as a decision support tool to optimize and bike-sharing system design and other related planning tasks.
Evaluation of Joint Development of P&R and TOD near Metro Stations in Chengdu, China
Jinlong Li (firstname.lastname@example.org), Southwest Jiaotong UniversityShow Abstract
Haifeng Li, Southwest Jiaotong University
Yiyuan Zhang, Southwest Jiaotong University
luo xia, Southwest Jiaotong University
Aiming at the joint development issue of P&R (Park and Ride) and TOD (Transit Oriented Development) near metro stations in urban planning area, 24 indicators are selected and three comprehensive evaluation methods are proposed to judge the joint development level of metro stations from two dimensions: P&R characteristics and TOD characteristics, followed by entropy wright method and expert scoring method being combined to obtain the comprehensive weight values, P&R characteristics being subdivided into P&R facility service level and metro service level, meanwhile TOD characteristics consisting of density level, diversity level and refinement level. Through field investigation, API interface and APP data acquisition, the characteristic dataset of P&R and TOD near metro stations are obtained in one megacity (Chengdu, China), and three combined models are then tested. Furthermore, four categories results of joint development are analyzed and sorted: A-class, B-class, C-class and D-class near metro stations. Development characteristic and environment features of each category stations are summarized to provide theoretical and practical guidance for the joint development of P&R and TOD near metro stations in megacities.
Societal Impacts of a Complete Street Project in a Mixed Urban Corridor: A Case Study in Pittsburgh, PA
Rick Grahn (email@example.com), Carnegie Mellon UniversityShow Abstract
Chris Hendrickson, Carnegie Mellon University
H. Scott Matthews, Carnegie Mellon University
Sean Qian, Carnegie Mellon University
Corey Harper, Carnegie Mellon University
Complete streets facilitate multi-modal travel by improving both transportation access and safety by emphasizing the user, not the automobile. This case study evaluates the impacts of a complete street retrofit on a mixed urban corridor in Pittsburgh, PA. Forbes Avenue, originally a 4-lane urban arterial (2 lanes in each direction, with no dedicated bike lanes) was reduced to three lanes (one lane in each direction and a center turn lane), and two bikes lanes. A quantitative before-and-after analysis was conducted using multiple data sources. Results indicate that traffic volumes decreased by 15-32%, bicycle counts increased by 160% and 280% during the peak AM and PM hours, respectively, and average PM2.5 concentrations were reduced from 9.1μg/m3 to 7.6μg/m3 when compared to pre-retrofit conditions. During construction, we observe that vehicle and pedestrian safety were not adversely impacted. Results from this analysis can help inform the decision-making process for transportation planners exploring complete street projects with similar community and roadway traffic characteristics.
Calculating Place-Based Transit Accessibility: A Review of Methods and Comparative Analysis of Tools
Christopher Higgins (firstname.lastname@example.org), University of Toronto, ScarboroughShow Abstract
Amber DeJohn, University of Toronto
Steven Farber, University of Toronto, Scarborough
Matthew Palm, University of Toronto, Scarborough
James Vaughan, University of Toronto
Michael Widener, University of Toronto
Yang Xi, University of Toronto
Eric Miller, University of Toronto
To capture the complex relationships between transportation and land use, researchers and practitioners working in support of a broad range of planning goals are increasingly utilizing place-based measures of transportation accessibility. However, although they have been in use for several decades, there are number of issues and considerations that should be taken into account when performing practical accessibility analysis. This research reviews the state-of-the-art in applied transportation accessibility measurement and performs a comparative evaluation of software tools for calculating accessibility by walking and public transit including ArcGIS Pro, Emme, and OpenTripPlanner using R and Python, amongst others. Using a case study of the City of Toronto, we specify both local- and regional-scale analysis scenarios and generally find significant differences in tool performance. We then draw some conclusions about the relative strengths and weaknesses of each tool for their accessibility workflows. While a custom multiprocessing tool for ArcGIS Pro completed the regional analysis in the least amount of time, each software environment offers its own strengths and weaknesses based on the many technical considerations that inform applied accessibility analysis. Nevertheless, comparisons of computed travel times generally revealed that each tool produces different results for the same origin-destination pair. Such differences can have a significant effect on calculated accessibility scores and warrants further investigation.
What Do Residential Lotteries Show Us About Transportation Choices?
Adam Millard-Ball (email@example.com), University of California, Los AngelesShow Abstract
Jeremy West, University of California, Santa Cruz
Nazanin Rezaei, University of California, Santa Cruz
Garima Desai, AECOM
Credibly identifying how the built environment shapes behavior is empirically challenging, because people select residential locations based on differing constraints and preferences for site amenities. Our study overcomes these research barriers by leveraging San Francisco's affordable housing lotteries, which randomly allow specific households to move to specific residences. Using administrative data, we demonstrate that lottery-winning households' baseline preferences are uncorrelated with their allotted residential features such as public transportation accessibility, parking availability, and bicycle infrastructure---meaning that neighborhood attributes and a building's parking supply are effectively assigned at random. Surveying the households, we find that these attributes significantly affect transportation mode choices. Most notably, we show that essentially random variation in on-site parking availability greatly changes households' car ownership decisions and driving frequency, with substitution away from public transit. In contrast, we find that parking availability does not affect employment or job mobility. Overall, the evidence from our study robustly supports that local features of the built environment are important determinants of transportation behavior.
Assessing the Metropolitan Impacts of Teleworking Scenarios
Catherine Morency, Ecole Polytechnique de MontrealShow Abstract
Hubert Verreault (firstname.lastname@example.org), Ecole Polytechnique de Montreal
Transportation is the largest contributor to GHG emissions in Quebec. When looking at people's travel and activity behaviors, different strategies can be implemented to reduce the emissions resulting from trip distances, trip frequencies and mode choice. The total or partial virtualization of activities such as work (through teleworking) is one way of limiting the overall vehicle-kilometers travelled by reducing the frequency of work-related commuting trips. There is a general assumption that telework could help relieve traffic conditions and GHG emissions. In this context, and with the forced 100% telework state in which many workers have been put in with the pandemic, it is interesting to objectively assess the impacts of a massive adoption of home-based telework among workers in the medium term. This study aims to assess the plausible impacts on GHG emissions, trip numbers and distances for various telework adoption scenarios. Results show that each additional 1% of teleworkers could result in a decrease of 0.36% of the total number of trips and of 0.51% of kilometers travelled in the Montreal area. Each additional 1% of teleworkers may also result in a decrease of 0.53% of GHGs produced by daily trips in Montreal. The largest impacts are observed on the road network. They are assessed by estimating the carload on bridges linking the Montreal Island during peak periods. Therefore, each 1% increase in teleworkers results in a decrease in the number of vehicles on the bridges during peak hours of 0.84% inbound (AM peak) and 0.81% outbound (PM peak).
Traffic Trumps All: Examining the Effect of Traffic Impact Analyses on Urban Housing
Hao Ding (email@example.com), University of California, Los AngelesShow Abstract
Brian Taylor, University of California, Los Angeles
Traffic impact analysis (TIA) is often used to assess the traffic impacts of proposed developments and determine necessary mitigations to maintain acceptable levels of local traffic. Many have argued that this process tends to overestimate traffic impacts of higher density developments in urban areas where traffic is often congested and travel alternatives plentiful; this has important implications for housing supply and affordability, suburban sprawl, private vehicle dependence. This paper examines the understudied implication of the TIA process on housing. We describe three mechanisms through which TIA may discourage housing production and reduce housing affordability: 1) bias in trip generation, 2) elevated traffic impact fees and development costs, and 3) the central role of traffic in the land development process. We draw on empirical evidence from distinct bodies of research in the transportation and land use planning literatures, situate the issue in the wider criticism of mobility-based transportation planning, and discuss how a shift to an accessibility framework may alter the problematic link between TIA and housing affordability.
How Connected Are Major Canadian Cities? A Large-Scale Empirical Investigation
Fan Wu, University of AlbertaShow Abstract
Tae J. Kwon, University of Alberta
Tony Qiu, University of Alberta
Street network design is an important yet extremely complex problem in urban planning as it is shaped and influenced by human travel patterns, city planning, and local cultures. Modern cites combine multiple modes of transportation; however, each mode may prefer a certain type of network structure (e.g., aligned grids favor public transit while cul de sacs favor personal vehicles). Hence, it is of critical importance to understand the network patterns of our cities in general and street network connectivity in particular. Nevertheless, prior efforts concentrate on measuring street network structure from a holistic point of view using limited connectivity measures. Therefore, this study investigates network patterns using a wide range of metrics constructed upon graph theory, such as intersection density, street density, node degree, circuity, and street length. Large-scale geospatial data from 30 major Canadian cities were assimilated and used to quantify the connectedness of individual cities. Moreover, similarities and differences were explored between cities by utilizing a hierarchical clustering analysis. Street orientation was also analyzed to provide an in-depth understanding, which combines with the street connectivity together explain the matching degree between transportation system modes and the city street network. The findings of this study suggest that the method presented herein is effective in measuring network patterns of large cities, which in turn, will help us understand the characteristics of urban street networks and guide the cities towards building a more sustainable road network.
The Multimodal Accessibility Benchmark (MAB) in Transportation Planning
Zachary Patterson (firstname.lastname@example.org), Concordia UniversityShow Abstract
Aaron Bensmihen, Concordia University
Gavin Hermanson, Concordia University
Accessibility research in the transportation and land-use literature has been dominated by unimodal and comparative approaches to analyze accessibility. Little attention has been paid to quantifying multimodal accessibility or the interactions between modes, and how these can impact overall accessibility. Moreover, the notion of benchmarking in the use of accessibility in planning has been largely ignored. Particularly important to the evaluation of transportation planning outcomes is accessibility to employment as it is a key element and predictor of urban economic prosperity. This paper, building on the work of Alain Bertaud, proposes the Multimodal Accessibility Benchmark or MAB. The MAB has been designed to allow for a more accurate picture of the overall accessibility to jobs in a city and to be used in the evaluation of transportation intervention scenarios. It also provides a standard and benchmark for accessibility that is an easily interpretable indicator that can serve as a goal in accessibility-based transportation planning. It can be used to provide a clear idea on how overall accessibility will increase or decrease as a result of transportation infrastructure investments. A case study of the proposed implementation of a BRT system in Montreal, Canada is used as an empirical example of the use of the MAB. In the case study, the predicted multimodal accessibility to employment in the study area (and thereby the MAB) was found to increase as a result of the BRT implementation compared to the base case scenario.
Roadmap for Dockless Shared Micromobility Vehicle Data: Analysis Processes to Support E-Scooter Implementation and Management
Myriam Zakhem (email@example.com), Southern Methodist UniversityShow Abstract
Janille Smith-Colin, Southern Methodist University
This paper presents the dockless shared micromobility vehicle analysis roadmap (DSMV-AR), which includes a set of analysis processes intended to provide data-driven solutions to the many challenges resulting from the rapid growth of dockless shared micromobility vehicles across U.S. cities. Using data made available from a scooter vendor API and prepared using the MDS open data specification for micromobility services, the DSMV-AR offers a first of its kind preliminary analysis roadmap that can provide information related to when and where dockless scooters are used, vehicle underutilization, sidewalk cluttering, and parking location identification needs. Through a series of six steps, the DSMV-AR outlines analysis processes that transportation planners and mobility managers can use to support real-time decision making related to dockless vehicle implementation and management. The DSMV-AR analysis processes contribute to transportation practice by providing information on temporal trip patterns thus informing decisions about user demand; vehicle usage at different points of day and across geographic locations thus supporting city-wide rebalancing efforts; identifies areas of high demand parking need supporting efforts to assign free-floating parking areas and reduce vehicle clutter; and identifies high use street corridors thus assisting transportation planners in identifying locations for road infrastructure improvement, ordinance enforcement, and safety enhancement.
Modeling the Impact of Shared Autonomous Vehicles on Travel Behaviors
Nima Haghighi, Pasco CountyShow Abstract
Xiaoyue Cathy Liu, University of Utah
Zhiyan Yi, University of Utah
Autonomous Vehicles (AVs) have the potential to offer benefits and flexibility in travel, which can lead to significant reductions in the generalized travel cost, and possibly more demand. The combination of the AV technology with Mobility as a Service (MaaS) creates a new disruptive transportation mode – Shared Autonomous Vehicles (SAVs) that have the promise to re-define the transportation landscape by improving mobility and competing with conventional transportation modes. While it is foreseen that SAVs could potentially be on the market in the near future, the long-range transportation planning process has yet to account for their impact. We fill this gap by presenting a framework of modeling SAVs to seamlessly integrate them into the four-step travel demand models that are widely used by transportation agencies. Using the Wasatch Front region in the State of Utah as a case study, this paper presents such modeling effort for the year 2040 forecast horizon. Delineated by different combinations of trip growth rates and SAV market attractiveness, the designed scenarios revealed that SAVs could increase the total number of trips by 1% to 7%. SAVs could shift travel away from conventional transportation modes. It is estimated that SAVs will increase daily Vehicle Miles Traveled (VMT) by 4% to 9% across designed scenarios due to improved mobility of underserved populations and additional repositioning trips. The results will assist public agencies in understanding the impacts of SAVs on travel patterns to further consider the special needs of AV technology in long-range cost estimates and programming processes.
Comparing Regional Sustainability and Transportation Sustainability of MSAs in the US using Artificial Neural Network Clustering Techniques
Haiqing Liu (firstname.lastname@example.org), University of CincinnatiShow Abstract
Na Chen, University of Cincinnati
Xinhao Wang, University of Cincinnati
In past decades, regional sustainability and transportation sustainability have been intensely discussed and modeled. Though the use of indicators has been adopted in those models, debates still exist on what indicators should be used and how to optimize the number of indicators. It results in a lack of comprehensive method to assess and compare sustainability of a sub-system, such as transportation system, and the regional sustainability as a whole. This study first conducted a thorough literature review to identify indicators used to assess regional sustainability and transportation sustainability. Then, based on the available data, two sets of indicators for regional sustainability and transport sustainability were selected and calculated respectively for the 382 metropolitan statistical areas (MSAs) in the US. The self-organizing map, which is a type of artificial neural network using unsupervised learning techniques, was used to cluster the MSAs and compare their regional sustainability and transportation sustainability as well to investigate the relationships among indicators. The results show that MSAs with a higher score in regional sustainability do not necessarily have a higher score in transportation sustainability. Some MSAs that are geographically close to each other have similar scores in regional sustainability and transportation sustainability. These findings provide insights to decision-makers that the assessment of sustainability should consider both correlation and heterogeneity of different indicators within a region. Therefore, it is important to develop a comprehensive and efficient method to evaluate the role of sustainability in one urban system, such as transportation, in contributing to the overall regional sustainability.
Comprehensive Transportation Review in DC: A Parking, TDM, and Design-Focused Alternative to the Traffic Impact Study in a Transit-Rich Setting
Aaron Zimmerman, District Department of TransportationShow Abstract
Ryan Westrom, Ford Smart Mobility
Jamie Henson, Kittelson & Associates, Inc. (KAI)
Anna Chamberlin, District Department of Transportation
Traffic studies have typically been the tool of choice for engineers and planners attempting to weigh traffic impacts resulting from new development. However, they have typically disproportionately focused on automobile impacts. This auto-centric approach doesn’t fit urban settings or places who take a multimodal perspective. This mismatch has long been recognized, but an appropriate alternative has not coalesced. In the District of Columbia, a planning team at the District Department of Transportation (DDOT) set out to establish a new, more comprehensive, template for measuring multimodal transportation impacts from development. The process to establish and then mature this guidance outlined in this paper as well as the factors and variables included in this new review represent a significant leap forward for understanding the transportation impacts resulting from new development. Included in this guidance are new approaches for evaluating the appropriateness of proposed off-street parking supply, linking the proposed parking supply to induced demand for driving, and more consistent mitigation guidelines to allow greater leverage of traffic impacts to realize offsetting non-auto network improvements. This guidance can serve as a template for transportation agencies throughout North America, particularly in cities with high quality transit access.
Microscopic Traffic Simulation as a Decision Support System for Road Diet and Tactical Urbanism Strategies
Bernice Liu, California Polytechnic State UniversityShow Abstract
Amirarsalan Mehrara Molan, University of Mississippi
Anurag Pande, California Polytechnic State University, San Luis Obispo
Jonathan Howard, California Polytechnic State University
Serena Alexander, San Jose State University
Zhiliang Luo, California Polytechnic State University, San Luis Obispo
Urban street networks in the United States have been primarily designed for automobile traffic with negligible considerations to non-motorized transportation users. Due to environmental issues and quality of life concerns, communities are reclaiming street spaces for active modes and slowing the speeds in their downtown. Moreover, tactical urbanism, i.e., use of street space for innovative purposes other than moving automobile traffic, is becoming attractive due to reduced automobile travel demand and the need for outdoor activities in the age of COVID-19 pandemic. This study provides details of modeling an urban downtown network (the City of San Jose) using microscopic traffic simulation. The model is then applied to evaluate the effectiveness of street design changes at varying demand scenarios. The microsimulation approach was chosen because it allows for detailed modeling and visualization of the transportation networks, including movements of individual vehicles, bicyclists, and pedestrians. The street design change demonstrated here involves one-way to two-way street conversion, but the framework of network-wide impact evaluation may also be used for complete street conversions. The base conditions network was also tested under different travel demand reduction scenarios (10%, 20%, and 30%) to identify the corridors in the city network on which the tactical urbanism strategies (e.g., open-air dining) may be best accommodated. The study provides the framework for using a microscopic model as part of a decision support system to evaluate and effectively implement complete streets/tactical urbanism strategies.
Keeping up with the Car-dashians: A retrospective look at private mobility holdings in the shared mobility era
Rounaq Basu, Massachusetts Institute of Technology (MIT)Show Abstract
Joseph Ferreira, Massachusetts Institute of Technology (MIT)
Concerns about the adverse impacts of rising automobile ownership on society have motivated cities to explore strategies envisioning a car-lite future. Such explorations need to consider the possible impact of shared mobility services on reducing private vehicle holdings. We argue that traditional transport datasets are inadequate for this research direction, and present a retrospective survey that is theoretically grounded as a mobility biography. By administering this survey in Singapore, we hope to explore the dynamics of the decision to purchase vehicles, while integrating it with other urban long-term choices such as residential and job location changes, economic events, and changes in household composition. This study provides a descriptive summary of findings related to five research questions. We find that multi-generational households with higher wealth status, as proxied by dwelling ownership and income, are more likely to buy or trade cars, which is often accompanied by the addition of a new member to the household. Attitudinal desires for certain vehicle models or brands, and economic life-cycle events such as job promotions, are the most likely reasons for purchasing cars. Our findings for recent car-buyers and car-traders also contradict the popular hypothesis that frequent users of shared mobility services are less likely to own cars. Attitudinal explorations show how cars are perceived as a social status symbol, but also highlight the positive outlook of car-owning households towards shared mobility. Stated preference responses point to the possibility of reducing car ownership, if the service qualities of shared mobility and mass transit are improved in addition to providing integrated and reliable connections closer to homes and workplaces. Acknowledging the preliminary reports in this study, we hope to use this valuable data source to further our understanding of the dynamics of the vehicle ownership decision.
Measuring Changes in Multimodal Travel Behavior: What Is the Effect of Transport Supply Improvement?
Elodie Deschaintres (email@example.com), Ecole Polytechnique de MontrealShow Abstract
Catherine Morency, Ecole Polytechnique de Montreal
Trépanier Martin, Ecole Polytechnique de Montreal
Despite the desired transition towards sustainable and multimodal mobility, few tools have been developed either to quantify mode use diversity or to assess the effects of transportation systems enhancements on multimodal travel behaviors. This paper attempts to fill this gap by proposing a methodology to appraise the causal impact of transport supply improvement on the evolution of multimodality levels between 2013 and 2018 in Montreal (Quebec, Canada). First, the participants of two households travel surveys were clustered into types of people (PeTys) to overcome the cross-sectional nature of the data. This allowed to evaluate changes in travel behavior per type over a 5-year period. A variant of the Dalton index was then applied on a series of aggregated (weighted) intensities of use of several modes to measure multimodality. Various sensitivity analyses were carried out to determine the parameters of this indicator (sensitivity to the least used modes, intensity metric and mode independency). Finally, a difference-in-differences causal inference approach was explored to model the influence of the improvement of three alternative transport services (transit, bikesharing and station-based carsharing) on the evolution of modal variability by type of people. The results revealed that, after controlling for different socio-demographic and spatial attributes, increasing transport supply had a significant and positive impact on multimodality. This outcome is therefore good news for the mobility of the future as alternative modes of transport emerge.
Tools of the Trade: Assessing the Progress of Accessibility Evaluation Tools for Planning Practice
Fariba Siddiq (firstname.lastname@example.org), University of California, Los AngelesShow Abstract
Brian Taylor, University of California, Los Angeles
For the past quarter-century, a growing number of researchers and practitioners have argued for shifting from a mobility-centered approach of transportation planning to an access-focused approach. Much progress has been made during this time in developing and improving accessibility metrics and tools for both regional planning and project evaluation. To assess the state of this progress, we reviewed 45 different accessibility tools and evaluated them in light of their theoretical basis, data requirements, basic units of analysis, travel modes and trip purposes accounted for, and potential application to planning practice. Most of the tools focus on calculating the accessibility of places, while a few focus on travelers. Most of the tools are designed for regional scale planning and scenario evaluation, while tools for local development impact assessment at project level remain comparatively rare. Most of the tools account for accessibility facilitated by a single mode of transportation and no single tool developed to date takes into account all the factors thought to affect accessibility. While these tools have come a long way, more work is still needed to develop development impact assessment tools that can account for the many factors thought to affect accessibility, multimodal accessibility, the heterogeneity of travelers, and which can be spatially aggregated for site-and place-based analyses.
Mining Dockless Bikeshare Data for Deeper Transportation Planning Insights: Evidence from the Greater Boston Region.
Bita Sadeghinasr (email@example.com), Northeastern UniversityShow Abstract
Armin Akhavan, Northeastern University
Steven Gehrke, Northern Arizona University
Ryan (Qi) Wang, Northeastern University
Timothy Reardon, Metropolitan Area Planning Council
Emerging micromobility services provide not only new transportation options, but also valuable sources of data for researchers and planners seeking to improve transportation networks. Dockless systems for shared bicycles and electric scooters have recently began operating in many American cities, providing new travel behavior information. In this study, we analyze over 301,000 Lime dockless bikeshare trips—logging an estimated 380,000 miles—during the first 18 months following their introduction to Boston’s suburbs. By exploring systemwide spatiotemporal information related to trip origins, destination, and routes, we seek to: (1) understand the role that micromobility services play in providing transportation service to the Greater Boston region, (2) characterize route choices by assigning a “level of traffic stress” to observed bike trips and studying detour levels, and (3) identify highly-utilized but high-stress street segments that should be prioritized for bike infrastructure improvements. We found that 18% of the total miles traveled were on roads classified as “very-high-stress" and about 15% of trips started or ended at a transit station. Among other insights, we find that 75% of the routes were less than 25% longer than the shortest path and that less than 30% of all routes consisted of “very-high-stress” links for 75% of trips.
The Impact of Rivers and Lakes on the Expansion of Urban Traffic: A Case Study of Traffic Evolution in Wuhan, China in the Past Century
Ran Peng, Wuhan UniversityShow Abstract
Qing Liu, Wuhan University
Rivers and lakes have always had a profound impact on the expansion of urban traffic. We take the traffic evolution in Wuhan, China in the past century as an example to explore the relationship which is from "strong connection" to "weakening connection" to "restriction" to "reciprocity" between water and urban traffic, and then analyze the influence mechanism of rivers and lakes on different development stages of urban traffic. In this regard, we choose the road network of Wuhan in 1922, 1975, 1997 and 2019 as the research object, and analyzes the change characteristics of riverside traffic and lakeside traffic in the process of urban expansion through the setting of water buffers. We revise the traditional Shortest-Path Model and propose the new concepts of "Detour Index" and "Weighted Detour Index" to explain the road accessibility of each node in the city based on its own environmental characteristics, based on these we evaluate the possible impact of water as "obstacles" on the road network in different stages of urban development.
Can bike-sharing reduce auto-dependence in the long-term? An opportunity for sustainable mobility in Metro Boston
Rounaq Basu, Massachusetts Institute of Technology (MIT)Show Abstract
Joseph Ferreira, Massachusetts Institute of Technology (MIT)
Bike-sharing programs can prove to be an important tool for promoting sustainable mobility planning efforts in urban cores of larger metropolitan areas. However, the impact of bike-sharing of auto-dependence is not well-examined, wherein prior studies have relied on self-reported auto-substitution effects. We use a unique dataset containing millions of vehicle registration and inspection records in Massachusetts to examine the causal impact of bike-sharing on various metrics of auto-dependence in the inner core of Metro Boston. The spatial difference-in-differences framework is extended to accommodate a dynamic treatment area, as some bike-share stations are closed for the winter months due to inclement weather. We find that a new bike-share station reduces vehicle ownership per household by 3\%, vehicle miles traveled per person by 38\%, and CO2 emissions by 10\%. Vehicle ownership reductions are almost immediately realized within one quarter, possibly due to the self-selection effect. However, vehicle use and emission reductions take effect over longer periods of time, as individuals adjust to accommodating bike-sharing in their daily activity schedules. Additionally, we find strong evidence towards the contribution of bike-sharing to mass transit ridership through the provision of first-/last-mile connections to T stations. Auto-dependence reductions are at least twice as high as average impacts for cases where the connections to transit stations are less than 400 meters. Our findings are especially important in the post-COVID-19 era, as cities strive to make the increase in biking activity during the lockdown period more than a flash in the pan. This paper provides supporting evidence to encourage increased infrastructural, financial, and policy support for bike-sharing programs, especially in places that have captured a critical mass like Metro Boston.
If you build it, who will come? Simulating effects of changes in housing supply on car ownership in Los Angeles
Matthew Conway (firstname.lastname@example.org), Arizona State UniversityShow Abstract
Regions across the US are considering policies to increase housing supply to try to ease affordability problems. These policies will have transport effects as well as effects on the housing market. This paper uses an equilibrium sorting model to simulate where households will choose to live after a change to housing supply in the Los Angeles region, and then simulates how those residential locations affect their car ownership. The land use scenarios that promote more urban housing result in the lowest car ownership, but effect sizes are modest. This model form is promising for simulating the housing and transport policy implications of changes to housing supply.
A Life History-oriented Approach for Modeling Residential Mobility: A Hazard-based Latent Segment Duration Model
Muntahith Orvin, University of British ColumbiaShow Abstract
Mahmudur Fatmi, University of British Columbia
This study adopts a life history-oriented approach to investigate residential mobility decisions. A hazard-based latent segment duration (HLSD) model is developed using retrospective data. The model captures unobserved heterogeneity and accommodates the effects of repeated spell durations along the life-course of the households. The model accommodates the effects of life-trajectory dynamics by testing life-cycle events occurring at different life-domains. Several hazard models such as conventional, and random parameter models are developed considering multiple and single spell, as well as assuming various distributions. The model is also validated using a hold-out sample based on the predictive performance. Overall, multiple spell 2-segment HLSD model with Weibull distribution outperforms other models. The HLSD model is estimated for 2-segments; where segment 1 includes lower-income urban dwellers and segment 2 includes higher-income suburban dwellers. Parameter estimation results confirm the effects of life-cycle events, socio-demographics, and accessibility characteristics. For instance, households might be active in the housing market following the birth of a child, and loss of a job. Owners and households in larger sized dwellings are more likely to reside longer; whereas, households residing closer to CBD might have a shorter duration. The model confirms the existence of heterogeneity. For instance, urban dwellers might trigger a move following the marriage of a household member; in contrast, suburban dwellers might continue to have a longer duration of stay. Similar heterogeneity between the urban and suburban dwellers is confirmed for variables representing no vehicle ownership, and residence closer to workplace, educational, and health services.
Exploring Accessibility of Urban Parks Analyzing Students' Perceptions and Spatial Connectivity: A Case Study of Ames, Iowa
Shoaib Mahmud, Iowa State UniversityShow Abstract
Alenka Poplin, Iowa State University
Park and green spaces generate meaningful and evocative places in an urban setup. A significant aspect of quality of life in urban areas is access to parklands. The physical and perceived accessibility of urban parks has been explored in different studies applying qualitative methods or empirical measurements. A limited number of studies have investigated the spatial connectivity and visitors' perceptions of urban parks collectively. Furthermore, the layer of emotion is one of the less considered aspects of urban parks, which has a significant influence on placemaking. Acknowledging this gap, this study utilized Geographical Information System (GIS) to perform network-based connectivity analysis of the parks of the City of Ames, Iowa and compared the results with the visitors' perception of access and emotional connection to park. We conducted a questionnaire survey of the students to evaluate their subjective opinions of the parks and identify the locations of their preferred parks. The results indicate the geographic areas where parks are the most accessible as well as the other physical and non-physical factors which are essential to developing the vision of the community in park planning.
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