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The shortest tour distance for visiting all points exactly once and returning to the origin is computed by solving the well-known traveling salesman problem (TSP). Owing to the large computational effort needed for optimizing TSP tours, researchers have developed approximations that relate the average length of TSP tours to the number of points, n, visited per tour. The existing approximations are used in transportation system planning and evaluation for estimating the distance for vehicles with large capacities (e.g., delivery trucks) where n is relatively large. However, the average TSP tour lengths would be inaccurately estimated if the approximations were derived for a wide range of n values since the tour lengths increase at a decreasing rate (i.e., with [Formula: see text]) as n increases. A comprehensive review is presented here of existing studies approximating TSP tour lengths for low n values, which have become important for some recently favored delivery alternatives (e.g., deliveries by bikes, drones, and robots).

The Federal Highway Administration (FHWA) vehicle classification scheme is designed to serve various transportation needs such as pavement design, emission estimation, and transportation planning. Many transportation agencies rely on Weigh-In-Motion and Automatic Vehicle Classification sites to collect these essential vehicle classification counts. However, the spatial coverage of these detection sites across the highway network is limited by high installation and maintenance costs. One cost-effective approach has been the use of single inductive loop sensors as an alternative to obtaining FHWA vehicle classification data. However, most data sets used to develop such models are skewed since many classes associated with larger truck configurations are less commonly observed in the roadway network. This makes it more difficult to accurately classify under-represented classes, even though many of these minority classes may have disproportionately adverse effects on pavement infrastructure and the environment. Therefore, previous models have been unable to adequately classify under-represented classes, and the overall performance of the models is often masked by excellent classification accuracy of majority classes, such as passenger vehicles and five-axle tractor-trailers. To resolve the challenge of imbalanced data sets in the FHWA vehicle classification, this paper constructed a bootstrap aggregating deep neural network model on a truck-focused data set using single inductive loop signatures. The proposed method significantly improved the model performance on several truck classes, especially minority classes such as Classes 7 and 11 which were overlooked in previous research. The model was tested on a distinct data set obtained from four spatially independent sites and achieved an accuracy of 0.87 and an average F1 score of 0.72.

Stay-at-home policies in response to COVID-19 transformed high-volume arterials and highways into lower-volume roads, and reduced congestion during peak travel times. To learn from the effects of this transformation on traffic safety, an analysis of crash data in Ohio’s Franklin County, U.S., from February to May 2020 is presented, augmented by speed and network data. Crash characteristics such as type and time of day are analyzed during a period of stay-at-home guidelines, and two models are estimated: (i) a multinomial logistic regression that relates daily volume to crash severity; and (ii) a Bayesian hierarchical logistic regression model that relates increases in average road speeds to increased severity and the likelihood of a crash being fatal. The findings confirm that lower volumes are associated with higher severity. The opportunity of the pandemic response is taken to explore the mechanisms of this effect. It is shown that higher speeds were associated with more severe crashes, a lower proportion of crashes were observed during morning peaks, and there was a reduction in types of crashes that occur in congestion. It is also noted that there was an increase in the proportion of crashes related to intoxication and speeding. The importance of the findings lay in the risk to essential workers who were required to use the road system while others could telework from home. Possibilities of similar shocks to travel demand in the future, and that traffic volumes may not recover to previous levels, are discussed, and policies are recommended that could reduce the risk of incapacitating and fatal crashes for continuing road users.

A central theme of U.S. transportation planning policies is to reduce single-occupancy vehicle (SOV) trips and promote transit and non-motorized transportation by coordinating land-use planning and transportation demand management (TDM) programs. Cities often implement TDM programs by intervening with new development during the municipal permit review process. Seattle’s Transportation Management Program (TMP) under a joint Director’s Rule (DR) requires a commitment from developers to adopt select strategies from six TDM element categories: program management, physical improvements, bicycle/walking programs, employer-based incentives, transit and car/vanpooling, and parking management. TMP targets new developments and requires some TDM elements, recommends others, and leaves the rest to negotiation. The result is an individualized TMP agreement that is site-specific, reflecting both city policy and developer needs. This case study presents a qualitative analysis of the guiding eight DRs and 41 site-specific TMP agreements in Seattle’s Downtown and South Lake Union (SLU) area since 1988. Overall, a content analysis of TMP documents reveals that the average number of elements adopted in an agreement falls short of requirements set by DRs (34%–61%). Major findings include developer preference toward non-traditional TDM measures such as physical improvement of frontage and urban design features, as well as parking management. High-occupancy vehicle (HOV) elements showed higher adoption rates (59%–63%) over biking/walking programs (

Reduced transit capacity to accommodate social distancing during the COVID-19 pandemic was a sudden constraint that along with a large reduction in total travel volume and a shift in activity patterns contributed to abrupt changes in transportation mode shares across cities worldwide. There are major concerns that as the total travel demand rises back toward prepandemic levels, the overall transport system capacity with transit constraints will be insufficient for the increasing demand. This paper uses city-level scenario analysis to examine the potential increase in post-COVID-19 car use and the feasibility of shifting to active transportation, based on prepandemic mode shares and varying levels of reduction in transit capacity. An application of the analysis to a sample of cities in Europe and North America is presented. Mitigating an increase in driving requires a substantial increase in active transportation mode share, particularly in cities with high pre-COVID-19 transit ridership; however, such a shift may be possible based on the high percentage of short-distance motorized trips. The results highlight the importance of making active transportation attractive and reinforce the value of multimodal transportation systems as a strategy for urban resilience. This paper provides a strategic planning tool for policy makers facing challenging transportation system decisions in the aftermath of the COVID-19 pandemic.

This study demonstrates the critical need for strategic transportation planning for sustainable food waste management in urban regions. The majority of food waste is generated from non-industrial sectors (i.e., residential, commercial, and institutional sectors that cluster in cities), while over two-thirds of discarded food could have been edible. Food recovery programs (FRPs) can help reduce food wastage, avoid the negative impacts of landfill disposal, and mitigate food insecurities. Notably, FRPs shift long-haul waste transport services destined for a central landfill location to local, short, and time-sensitive pick-up and delivery trips, which have not been carefully studied. This study quantifies the economic efficiency (i.e., cost) and environmental impact (i.e., emissions) of urban FRP scenarios by incorporating the characteristics of surplus food pick-up options, fleet, urban built environment, and donor program participation rates. The results of an empirical study in Chicago, U.S., show that urban FRPs can be both economically efficient and environmentally beneficial, although the preferred strategy for FRPs may vary by neighborhood characteristics. With strategic planning and management, the transportation cost could be as low as $ 0.06/lb of recovered food. Meanwhile, diverting 1 ton of edible food from landfills, on average, reduces greenhouse gas emissions by 97%, or 0.51 MTCO2e. If the estimated volume of 10,000 tons of surplus food in Chicago can be fully recovered, the regional net emission reduction can reach 5,100 MTCO2e, which is equivalent to removing 1,110 vehicles from the roads. This study is anticipated to provide planning insights into initiating and scaling up city-wide FRPs.

Africa has not invested enough in its healthcare system, and China has been investing in and financing much of Africa’s transportation system. Many African countries’ fragile health and transportation systems have been further weakened by the COVID-19 pandemic. This literature review confirms the interdependence of the key functional areas of comprehensive development planning and the importance of building and maintaining a sound transportation infrastructure. With respect to partnerships with China, African nations need to strengthen government functional areas more comprehensively, considering all of the areas of development planning including trade as well as transportation and aid issues. It is all the more apparent given the COVID-19 pandemic that these trade deals need to include simultaneous heavy investments in healthcare, education, housing, public utilities (water and electricity), and economic development through improved supply chain management and the use of advanced digital technology. In addition to the deal structures for China’s investments in Africa’s transportation infrastructure, there are also opportunities to reimagine the African nations’ internal transportation spending. For example, there are models in the United States for using transportation funds to invest in health clinics in transit stations. The COVID-19 pandemic has brought this issue to bear, and it is a problem that can be rectified with “comprehensive” development planning that takes into account all of the key functional areas of planning: healthcare, environmental protection, safety, education, housing, economic development, and transportation. Five recommendations follow the literature review and discussion.

In Brazil, neglect and lack of data have complicated urban freight transportation planning. Belo Horizonte is one such example, where the administration’s disregard for urban freight transport has led to multiple negative externalities. Using the maximum entropy method, this study analyzed the influence of urban planning factors on warehouse location in the Belo Horizonte Metropolitan Region. Warehouse location was analyzed using 19 factors, with the results recorded on a map of logistic probabilities concerning locating warehouses. Given this is exploratory research, the model was validated by visual comparison to address the four hypotheses: (i) warehouses tend to be located near roads and railroads; (ii) warehouses tend to be located close to their consumer markets; (iii) warehouses tend to be located in urban areas; and (iv) warehouses tend to be located next to each other, creating clusters. These hypotheses were based on the extant literature and confirmed from observations of the Belo Horizonte Metropolitan Region. The results also demonstrated that the maximum entropy method can suitably model freight transportation in support of urban planning policy.

Context-based thinking is a transportation planning and design approach that aims to create infrastructure that serves diverse places and users. In Florida, context-based design is the approach Florida Department of Transportation (FDOT) has taken to implement its Statewide Complete Streets policy. FDOT intends to use the context of a roadway to better tailor design and planning solutions for the roadway, thereby putting the “right street in the right place.” To this end, FDOT developed a context classification system and guidance document for project corridors and has since applied the classification effort to the entire state road network. This paper shares a method that two FDOT districts, District One and District Five, used to accomplish their districtwide context classification efforts. The method leverages geographic information systems (GIS) to appropriately segment the road network, analyze connectivity, land use, and density measures, and evaluate the context classification for all state roads within each district. The resulting database is regularly updated using a GIS-based tool and serves as a rich source of information for FDOT and partner agency planners and designers.

Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.

This study looks at initial results from the Transportation Wallet for Residents of Affordable Housing pilot program launched by the City of Portland’s Bureau of Transportation. The program provides a set of transportation incentives for low-income participants, including a US$308 prepaid Visa card that could be applied to public transit or other transportation services, a free bike share membership, and access to discounted rates on several services. A survey was conducted with the program’s participants (278 total responses) to understand how they used the Transportation Wallet and how the program helped them use different transport modes to get around. The main findings include: (1) The financial support of this program encouraged some participants to use new mobility services (including Uber/Lyft, bike share, and e-scooter) that they had never used before; (2) the program increased access for participants, helping them make more trips and, for some, get to places they otherwise could not have gone; and (3) transportation fairs, where participants could learn about services and talk to providers, promoted both mode sign-up and mode usage, particularly for new mobility services and a reduced fare transit program. The survey results also point to some opportunities to improve the program. Participant feedback suggests that transportation agencies do more to streamline and educate participants on how to use new mobility services and coordinate different service providers to optimize seamless services for participants. The paper provides insights into the implementation and effectiveness of a transportation financial incentive program for low-income populations.

Since 1980, many have marveled at Los Angeles’“innovation” of funding transportation through ballot measures that are raising billions for transportation improvements. In fact, historically much transportation infrastructure in Los Angeles was financed by local voter-approved revenues. It began in 1868 with a narrowly approved $225,000 bond measure to build the region’s first railroad, followed by an 1876 measure to grant the Southern Pacific railroad a $602,000 subsidy to entice the company to route its transcontinental line through the region. Angeleno voted on an additional 23 different transportation-related ballot measures between the passage of the Good Roads Act (1908) and the end of the New Deal (1937)—a key period of Los Angeles’ history that saw dramatic population increase and with it political contention over the direction of the region’s growth. Overall, these early transportation measures fared well with voters. Of the 25 transportation-related ballot measures in Los Angeles County from 1860 to 1960, only seven (28%) failed to pass, a far better record than nontransportation measures of which 21 of 31 (71%) went down to defeat. Regardless of whether, as some contend, Los Angeles missed a golden opportunity to create the backbone of an effective transit system that would have reduced the need for automobiles and spending many billions on freeways, it is clear that local voters have long faced competing visions for the future of Los Angeles and arguments over whether to fund transportation systems to serve these visions.

New data, new technologies, and greater computational power are changing the possibilities for involving stakeholders in transportation planning. This article explores these possibilities by comparing deployments of an interactive web-based public transportation mapping system in four cities of the Global South. Structured workshops focused on different public transportation improvement projects and involved different types of stakeholders. Despite the differences across the workshops, they allow for some comparison of the effects. In relation to the technology itself, participants broadly agreed about its usefulness and usability. Pre-/post-workshop surveys suggest that participants learned about the transportation projects presented but reveal modest evidence in relation to other impacts, such as learning about the concept of accessibility, and gaining an appreciation for the potential broader urban system impacts of public transport projects. Knowledge among the participants tended to converge, and the tool helped promote dialog among participants and generated some empathy for others. The workshop experiences provide some hope for such tools in enhancing public transportation planning processes, globally, but “scaling up” the technology would need to overcome institutional, technical, and procedural challenges.

Pedestrian safety is a top priority within the transportation planning community as cities promote sustainable transportation, alternative travel modes, and healthy lifestyles. If people feel unsafe while walking, they will choose other modes of transportation if they are able. To prioritize safety, it is important to know where pedestrian crashes are occurring and with what severity. Using spatial statistical methods including nearest neighbor index, Moran’s I, local indicators of spatial autocorrelation (LISA), and Getis-Ord G-statistic, this study seeks to analyze the pedestrian fatality locations within the state of Ohio over a 10-year period (2007–2016) to identify hot spots, cold spots, and spatial patterns across three different spatial scales: county, census tract, and traffic analysis zone (TAZ). It seeks to understand the effects of aggregated data across these spatial scales on the outcome of the analysis and determine the most useful spatial scale at which to study pedestrian fatalities. The study concludes that spatial analyses at small scales are most informative. It goes on to recommend locations within Ohio for further analysis based on the resulting maps, including areas with outliers. As of writing this, there is no current statewide pedestrian fatality analysis for the state of Ohio.

Unsafe interactions among road users are often seen on the urban networks of India. The conventional transportation planning method deals with this problem by segregating different modes to minimize intermodal conflicts. However, another school of thought recommends that spaces should be designed to ensure minimum segregation between road users by removing physical barriers and eliminating rules. Once the rules are lifted, road users become attentive to their surroundings, and therefore spontaneous order emerges. The main objective of this study is to explore the yielding behavior of road users during conflicts and analyze the spontaneous order developed at intersections. Traffic data was collected from 12 signalized intersections in India. A total of 1,785 conflicts between pedestrians and vehicles were observed during peak hours at the intersection locations. The modal dominance was examined for whether a pedestrian yields to a vehicle or vice versa. Two multiple linear regression models were developed to analyze the effect of various factors (speed and volume of pedestrians and vehicles, and size of intersection) on the number of conflicts and the proximity of conflicts. The dominance of pedestrians and vehicles was predicted with the help of the binary logit model. The model results suggested that vehicle volume, type of vehicle, noncompliance with signals by pedestrians, number of pedestrians crossing in a group, type of pedestrian–vehicle interaction, and size of intersection are some of the major factors that are significant predictors of the dominance of road users.

Traditional transport planning methods are costly and require an advanced degree of understanding not only from the involved transport planning professionals, but also the politicians who must approve the resulting outcomes and transport interventions proposed that are based on these traditional methods. A different approach is proposed for small and medium-sized cities in developing countries that have less technical expertise and fewer financial resources to improve their public transport situation. This approach was trialed in a medium-sized city in West-Asia (Batumi, Georgia) and also in Central Asia, in a larger city (Bishkek, Kyrgyzstan). The planning interventions suggested in the medium-sized city were validated by an independent consultant at the request of Batumi City planning agency, using traditional transport planning methods, which shows promise for the new low-cost method proposed. With additional validation and research, it may be possible to expand and apply this method to South Asia, sub-Saharan Africa, and any other area of the world suffering from similar transport planning constraints to these developing regions. If successful, these planning methods could rapidly transform such cities and urban areas to become less carbon intensive and concurrently more efficient and comfortable for public transport users.

Transportation infrastructure around the world is under pressure to perform with ever-changing climate scenarios, unpredictable disasters, and stress on resources stemming from rapid urbanization and population growth. Current approaches to developing resilience applied to the transportation system focus primarily on engineering resilience and do not explicitly deal with deep uncertainties arising from climate change. This paper reviews adaptation, a critical aspect of a resilient system in an uncertain and changing environment, as applied in the transportation resilience literature. It compares and contrasts the status of adaptive resiliency in transportation with that in other fields to highlight gaps and research opportunities. The paper then presents Dynamic Adaptive Planning (DAP) as a method for dealing more effectively with deep uncertainty in decision making and offers an approach that combines economic analysis with DAP to enhance decision making under external uncertainties, such as natural disasters, with financial constraints. It presents a case study of the San Francisco–Oakland Bridge to demonstrate the economic benefits of DAP. This paper provides transportation practitioners with guidance on the application of DAP and insight into the economic benefits of such an approach to decision making in various settings including emergency response planning, long-range planning, maintenance and renewal planning, and operations planning. The paper also identifies areas for possible future research combining financial theory with DAP as important in developing more robust decision-making frameworks for handling deep uncertainty.

This research examines the history of transportation planning in African cities and how paratransit has been taken into account in the production of planning documents. On the rise since the 1980s, paratransit today is the most common motorized transportation mode in many African cities. The dominant approach among policymakers has been to limit paratransit, in some cases even to ban it. The question this research explores is how distrust of paratransit, and underappreciation of its intrinsic qualities, have been reflected in urban transportation plans. Having selected two cities—Cape Town, South Africa and Nairobi, Kenya—we conducted an in-depth analysis of planning documents at national and local levels. South Africa has a long tradition of transportation planning, with documentation available at the national, provincial, and municipal levels. In the 1990s, paratransit was a national-level concern. It gradually became a municipal issue with the implementation of Bus Rapid Transit (BRT). In Kenya, planning has a shorter history. Development agencies (e.g., Japanese International Cooperation Agency [JICA]) have played a key role in recent planning processes and encourage the formalization of paratransit. However, planning documents contain no explicit references to “matatus.” In both cities, the focus in the documents is still mainly on developing infrastructure rather than improving mobility. While the role of paratransit is increasingly recognized, this trend is still more apparent in regulation than in planning.

Estimation of vehicular emissions at network level is a prominent issue in transportation planning and management of urban areas. For large networks, macroscopic emission models are preferred because of their simplicity. However, these models do not consider traffic flow dynamics that significantly affect emissions production. This study proposes a network-level emission modeling framework based on the network-wide fundamental diagram (NFD), via integrating the NFD properties with an existing microscopic emission model. The NFD and microscopic emission models are estimated using microscopic and mesoscopic traffic simulation tools at different scales for various traffic compositions. The major contribution is to consider heterogeneous vehicle types with different emission generation rates in a network-level model. This framework is applied to the large-scale network of Chicago as well as its central business district. Non-linear and support vector regression models are developed using simulated trajectory data of 13 simulated scenarios. The results show a satisfactory calibration and successful validation with acceptable deviations from the underlying microscopic emissions model regardless of the simulation tool that is used to calibrate the network-level emissions model. The microscopic traffic simulation is appropriate for smaller networks, while mesoscopic traffic simulation is a proper means to calibrate models for larger networks. The proposed model is also used to demonstrate the relationship between macroscopic emissions and flow characteristics in the form of a network emissions diagram. The results of this study provide a tool for planners to analyze vehicular emissions in real time and find optimal policies to control the level of emissions in large cities.

The prediction of high-resolution hourly traffic volumes of a given roadway is essential for transportation planning. Traditionally, automatic traffic recorders (ATR) are used to collect these hourly volume data. These large datasets are time-series data characterized by long-term temporal dependencies and missing values. Regarding the temporal dependencies, all roadways are characterized by seasonal variations that can be weekly, monthly or yearly, depending on the cause of the variation. Traditional time-series forecasting models perform poorly when they encounter missing data in the dataset. To address this limitation, robust, recurrent neural network (RNN)-based, multi-step-ahead forecasting models are developed for time-series in this study. The simple RNN, the gated recurrent unit (GRU) and the long short-term memory (LSTM) units are used to develop the forecasting models and evaluate their performance. Two approaches are used to address the missing value issue: masking and imputation, in conjunction with the RNN models. Six different imputation algorithms are then used to identify the best model. The analysis indicates that the LSTM model performs better than simple RNN and GRU models, and imputation performs better than masking to predict future traffic volume. Based on analysis using 92 ATRs, the LSTM-Median model is deemed the best model in all scenarios for hourly traffic volume and annual average daily traffic (AADT) prediction, with an average root mean squared error (RMSE) of 274 and mean absolute percentage error (MAPE) of 18.91% for hourly traffic volume prediction and average RMSE of 824 and MAPE of 2.10% for AADT prediction.

While transportation planning professionals are interested in exploring ways to increase walking in daily travel, it would be interesting to know whether people desire to walk more or not. This paper addresses a gap in the literature by designing a conceptual framework in which it was hypothesized that commuters’ perception of the walking distance and their attitudes towards walking take a central role in forming their desire to walk more in traveling to/from work while considering the effect of various objective travel/environmental characteristics and individual/socioeconomic characteristics. Using data collected in 2015 from 432 working commuters in the city of Rasht, Iran, a structural equation model using an observed endogenous variable approach was applied to examine the conceptual framework of the study. Results showed that, as the perceived walking distance increases, the desire to walk more decreases. The total travel time to/from work, as an objective measure of travel, was found to have the greatest effect on someone’s perception of the walking distance. Results show that individuals with positive attitudes towards walking desired to walk more. Having positive attitudes towards walking also modified the negative effect of perception as individuals with higher positive attitudes towards walking also perceived the walking distance between their homes and work to be shorter. Among various factors, having at least one walking trip in traveling to/from work was found as the main determinant of commuters’ desire for more walking.

Even in a context of rapidly evolving transportation and information technologies, household travel surveys remain an essential source of information for transportation planning. Moreover, as planning authorities become increasingly concerned with reducing the use of the private car, travelers’ mode choice patterns should be reexamined. In this study, a machine learning algorithm (Random Forest) was employed to characterize the use of eight different travel modes observed in two consecutive household travel surveys undertaken in Montreal, Canada. The analysis incorporated roughly 160,000 observed trips. The Random Forest algorithm was trained on the 2008 survey data and applied to the 2013 survey. The usefulness of the algorithm was evaluated using two numerical representations: the confusion matrix and the importance matrix. The results of this evaluation showed that the Random Forest algorithm could generate a detailed and precise characterization of travel submarkets for four of the most commonly observed modes of travel (auto-drive, public transit, school bus, and walk) using 11 attributes of households, persons, and trips. However, the auto-passenger mode was difficult to characterize because of its dependence on unobserved intra-household interactions. The algorithm also had difficulty identifying users of rarely observed modes (park-and-ride, kiss-and-ride, bicycle), but performed better in this regard than a traditional mode choice model. Finally, traveler’s age and the spatial orientation of origin–destination pairs were found to be decisive factors in the use of the auto-drive mode. This finding, combined with the stability of mode choice patterns observed over 5 years, highlights the difficulty of significantly reducing automobile use.

Trip generation estimates are integral to assessing the transport impacts of land use developments. However, past efforts have predominantly focused on vehicle trips only. This paper provides an international literature review of multimodal trip generation associated with land use developments. A total of 153 publications were sourced as relevant to the review. The results show that although multimodal trip generation studies have been relatively scant, they have received greater attention in the last 10 years. A range of issues was identified with estimating and applying multimodal trip generation rates, not least was a lack of sufficient data and higher complexity in data collection compared with vehicle trip generation studies. Current knowledge gaps highlight opportunities to move toward greater international coordination and sharing of multimodal trip generation data, along with exploring the use of technology to assist with data collection. Key directions for the future include a fundamental change in paradigm to consistently account for multimodal trip generation, the development of an international multimodal trip generation database, and greater sensitivity testing in assessing the multimodal impacts of new land use developments.

Transportation planning has become increasingly interested in the institutional changes needed to implement sustainable transportation measures. This research looks at six U.S. metropolitan areas that are implementing or have recently implemented transit priority projects—Seattle, Portland, Denver, Chicago, New York, and Boston—using interviews with local planners, media accounts, and analyses of project documents. It explores what policy statements and arguments were used to support transit priority, the leadership needed to support implementation, innovative funding arrangements, and the institutional changes within agencies that led to a virtuous cycle of transit improvement. The research found that cities that talked about their transit priority as a way to accommodate growth without cars or to manage street space efficiently were more effective than those who put forth transit priority, and particularly bus rapid transit, as a cheap solution to improve transit. It also found that leadership within city transportation agencies was more important than elected official champions, and that transit agencies can use the promise of increased frequency to leverage city funding for street improvements. Lastly, it found that city streets agencies that are serious about prioritizing transit develop transit planning programs, staffing capabilities, and set regular meetings with transit agencies that they view as partners in improving transit.

Transportation planners increasingly use new forms of online public participation alongside traditional in-person approaches, including crowdsourcing tools capable of encouraging geographically specific input. Digital involvement may be particularly valuable in exploring methods to plan at a megaregional scale. Research is beginning to address digital inequalities, recognizing that broadband and smartphone access may restrict opportunities for disadvantaged groups. However, the geography and equity of participation remain pragmatic issues for practice and research. This paper reviews the geography and equity of the participation methods in Austin, Texas for active transportation (bicycling and pedestrian) through three approaches to co-produce informed plans: in-person meetings, public participation geographic information system (PPGIS), and an emerging smartphone platform that logs trips and encourages input on route quality. In addition to spatial analysis with standard deviational ellipses, we include qualitative case analysis to contextualize the geographic and equity implications of different participation approaches. Results show that both online techniques resulted in a larger geography for participation than in-person meetings, with the regional PPGIS covering the most area. However, review of the income levels in each area shows that use of the smartphone-based crowdsourcing platform was aligned with lowest-income areas. This study shows that online participation methods are not homogeneous regarding geography or equity. In some contexts, smartphone applications can help reach lower-income communities, even when compared with in-person meetings. Crowdsourcing tools can be valuable approaches to increase geography and equity of public participation in transportation planning.

The overall goal of this study is to assess the concept of sustainability in relation to the related concepts of “health” and “livability” that have emerged in transportation planning discourse. This study achieves the goal using an indicator-based case study, conducted for the El Paso metropolitan area in the United States. Data from the regional travel demand model and other sources were used to quantify a sustainability index, livability index, and health index for individual traffic analysis zones in the region, for four analysis years over a 30-year planning horizon. Each index was comprised of representative indicators, which were normalized and aggregated in accordance with common multi-criteria decision-making methods. The analysis results demonstrated little correlation between the quantified livability, sustainability, and health indices developed for the El Paso region. The indices also showed relatively low levels of change over time for a location. That is, the relative performance of a traffic analysis zone tended to stay the same, despite the modeled changes to the transportation system, demographics, and land use. The main implication of the research findings is that despite overlaps at a theoretical level, concepts such as livability and health cannot necessarily serve as proxies for sustainability when implemented in practice. The study also provides insight into the challenges of making meaningful change in the area of sustainability over time and highlights the influence of factors beyond transportation, such as land use and socio-economic issues.

Traditional methods used in transportation planning to identify bottlenecks and mobility issues, such as the Volume-to-Capacity Ratio and the Planning Time Index, have limited usefulness in identifying the exact location and extent of bottlenecks. Moreover, existing bottleneck identification tools flag all bottleneck types without distinction, despite the fact that strategies and resources used to address each type are different. This research first proposes a taxonomy of congestion to distinguish between different types of bottlenecks. It then describes a new methodology for identifying the location of recurring bottlenecks. Recurring bottleneck locations must experience a reduction in speed, an upstream accumulation of vehicles, and recurrence at the same location over three consecutive months. The methodology is currently used in long-term transportation planning and project selection in Colorado to identify and address the most severe bottleneck locations in the state.

Transportation planning in the United States is moving to widespread use of performance-based planning methods as new federal requirements for metropolitan planning organizations (MPOs) are implemented. In addition to requirements for measures of safety, infrastructure condition, and travel reliability, many MPOs are adopting additional performance measures including those of complex socioeconomic issues. This study explores current planning practice in using a performance-based approach to tackle such an issue: location affordability, defined as an affordable level of combined cost burdens for housing and transportation. A review of long-range transportation plans at 21 large MPOs provides information on how location affordability is represented in regional transportation plans, how it is defined and measured, and how it is integrated into the planning process. The results are interpreted using a theoretical framework designed to help match planning methods to the conditions of a planning situation. The framework, Christensen’s matrix of planning problems, provides insights into the use of performance measures for location affordability and how performance measurement programs can be designed to support more effective planning for this and similarly challenging issues.

The impacts of active transportation planning on equity are often overlooked, potentially leading to disparities in who receives benefits of infrastructure investment. This study examined income inequalities in spatial access to bicycling infrastructure in three mid-sized Canadian cities: Victoria and Kelowna (British Columbia), and Halifax (Nova Scotia), using non-spatial and spatial methods. We compiled municipal bicycling infrastructure data and calculated access to bicycling infrastructure (m/km2) for dissemination areas (DAs) within each city. We analyzed trends in access across median household income quintiles, and characterized spatial patterns using a local measure of spatial autocorrelation. DAs in Kelowna ( n = 168) had the greatest access to infrastructure (median infrastructure = 2,915 m/km2), followed by Victoria ( n = 386 DAs; median = 2,157 m/km2), and Halifax ( n = 312 DAs; median = 0 m/km2). Lower income areas in Victoria and Kelowna had greater access to infrastructure compared with higher income areas. The majority of DAs in Halifax had no infrastructure (59%), consistent across income quintiles. Spatial pattern analysis identified clusters of low income areas with poor access in each city, which may be targets for strategic, equitable investment. Although in many cities bicycling infrastructure planning is not driven by equity considerations, there is increasing political pressure to ensure equitable access to safe bicycling. Measuring and mapping trends in access to transportation resources from an equity perspective are requisite steps in the pathway toward healthy, sustainable cities for all.

Transportation investments determine the long-term success or failure of a transportation provider. It is therefore vital for decision makers to have an in-depth understanding of the alternatives available before they choose to invest. However, often, the process of evaluating alternatives is lengthy, costly, and contentious, particularly for transportation infrastructure investment decisions that are large, complex, and interconnected with other economic development and sustainability goals. Furthermore, transportation investments involve many decision makers, each with different priorities and expertise. Therefore, there is a need for transparent, accurate, flexible, and practicable decision-making aids that can handle the complex challenges facing the decision-making bodies of transportation providers and planning organizations. This paper introduces a new decision aid—the CLIOSjre Process—that combines insights from multicriteria decision analysis, multistakeholder negotiation theory, and uncertainty analysis. The CLIOSjre Process helps decision makers compare multiple alternatives across multiple objectives and seek an informed collective transportation investment decision. Unlike other multicriteria decision aids, the CLIOSjre Process accounts for differences of opinion among decision makers and is designed to facilitate constructive negotiation among them. Finally, the CLIOSjre Process formally accounts for sources of uncertainty inherent in these decisions. In this way, the CLIOSjre Process provides a unique and flexible framework for investment analysis that can adapt to changes in transportation alternatives, decision-maker priorities, and emerging real-world conditions. The usefulness of this new decision aid is illustrated for the East Japan Railway Company’s consideration of a transportation investment opportunity in high-speed rail development on the Northeast Corridor of the United States.

Deciding which projects, alternatives or investments to implement is a complex and important problem not only in transportation engineering, but in management, operations research and economics. Projects are interrelated if their benefits or costs depend on which other projects are implemented. Furthermore, in the network development problem analyzed here, the timing of projects also affects the benefits and costs of other projects. This paper presents a method for optimizing the selection and scheduling of interrelated improvements in road networks that explicitly considers intersections. The Frank Wolfe algorithm, which is modified here to consider intersections, is used for evaluating network improvements as well as for traffic assignment. Intersections are modelled with pseudo-links whose delays are estimated with Akcelik’s generalized model. The objective is to minimize the present value of total costs (including user time) by determining which projects should be selected and when they should be completed. A genetic algorithm is used for optimizing the sequence and schedule of projects.

Traveler intercept surveys are used to obtain essential data for transportation planning purposes in a variety of contexts and situations. This paper describes an application of the foot-in-the-door (FITD) compliance technique for traveler intercept surveys as a way to increase participation and improve the overall experience for both the traveler and the organization conducting the survey. Outcomes related to survey participation and response characteristics for three such surveys are presented. The two-stage FITD method described in this paper was successful at collecting a small amount of data from a majority of travelers at the locations being studied while minimizing the inconvenience to the traveler. A follow-up survey conducted via the Internet had a response rate that varied between the different case studies and also among different groups of travelers. Approximately one-third of follow-up survey responses were provided on the same day as the initial interaction between the traveler and the researcher, whereas one-quarter of respondents utilized a mobile device to complete the follow-up survey. The results of these case studies are informative for the development of traveler intercept surveys. Transportation planners are encouraged to consider the FITD technique described in this paper when developing traveler intercept surveys within their jurisdictions.

As connected and autonomous vehicle (CAV) technology continues to evolve and rapidly develop new capabilities, it is becoming increasingly important for transportation planners to consider the effects that these vehicles will have on the transportation network. It is evident that this trend has already started; over 60% of long-range transportation plans in the largest urban areas now include some discussion of CAVs, up from just 6% in 2015. There are also numerous CAV pilot programs currently underway that entail testing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) interaction in both isolated and real-world environments. In this review of the current assessments for CAV impacts, two primary trends are identified. First, there is a great deal of uncertainty that is not being explicitly considered and properly accounted for in the transportation-network planning process. Second, the predictions that are being made are not considering potential policy or planning actions that could shape or affect the impacts of CAVs. This paper provides a picture of how ongoing CAV research interacts with current transportation planning practices by examining how the methods, the ranges of predictions, and the different sources of uncertainty in each method impact the planning process and potential system outcomes. Finally, it will identify best practices from decision analysis to help plan the best possible future transportation networks.

This paper identifies how much was added to residential land values through the provision of bus rapid transit (BRT) service in Brisbane, Queensland, Australia, with regard to increases in land values because of the BRT system itself and the value of the network effect as a result of incremental additions to the transport infrastructure, a much more common feature of Australian cities. Difference-in-differences models were employed to explore whether there were significant differences between the treatment group (properties close to busway stations) and the control group (properties not close to busway stations) with regard to housing price changes (first difference) before and after the opening of busway stations (second difference). Two types of control groups were defined by using conventional buffer methods and propensity score matching. Model results show increases in property prices because of better accessibility to busways themselves, on the one hand, and on the other hand, additions to the value of land through the network effect of access to a greater service area as the BRT network is developed. The paper reports how different methods of identifying control areas to match given treatment areas had different results and discusses the implications of these results for a literature that has a variety of methods established for research.

As many cities are investing in street improvements or transportation infrastructure upgrades to provide better bike access or more complete bike networks, many practitioners, planners, and policy makers are seeking more conclusive evidence about the economic value of bike infrastructure and bike facilities. With the use of residential property values as indicators of consumer preferences for bicycle infrastructure, many scholars have shown the importance of green space and off-street bike trails as amenities valuable to property owners. However, empirical evidence on the relationship of on-street bike facilities and property values remains relatively inconsistent. The unique focus of this study was advanced bike facilities that represented higher levels of bike priority or bike infrastructure investments shown to be more desirable to a larger portion of the population. Through the separate estimation of ordinary least squares hedonic pricing models and spatial autoregressive hedonic models of single and multifamily properties, it was found that proximity to advanced bike facilities (measured by distance) had significant and positive effects on all property values, which highlighted household preferences for high-quality bike infrastructure. Furthermore, the study showed that the extensiveness of the bike network (measured by density) was a positive and statistically significant contributor to the prices for all property types, even after proximity was controlled for with respect to bike facilities and other property, neighborhood, and transaction characteristics. Finally, estimated coefficients were applied to assess the property value impacts of the Green Loop (i.e., the proposed Portland, Oregon, signature bike infrastructure concept), which illustrated the importance of considering the accessibility and the extensiveness of bike facility networks.

The objectives of this study were to integrate road networks to provide a comprehensive road network through the use of multiple public sources and to provide guides for performing a quality control assessment before delivering data and using the data for geospatial analysis. The study focused on the Fort Berthold Reservation in western North Dakota. A method is proposed to integrate multiple public road networks to support a tribal transportation agency. The workflow and techniques proposed are transferrable to other tribal transportation agencies. Depending on current data used for transportation planning and operations, the workflow and processes are subject to change from agency to agency, but the study provided general guidance for the agency. The study recommended that the tribal transportation agencies develop linear referencing systems on the proposed road network to adopt efficient asset management and version control. The linear referencing system should comply with state or federal guidelines for improved communication. To develop appropriate applications, the road network should include additional attributes based on the needs of the reservation.

Measures of accessibility have long been an important metric in regional transportation planning and modeling. However, new methods are needed to provide computationally efficient, multiscale, free, transparent, and customizable tools that harness open and disparate sources of transportation network data at fine spatial resolution over large geographic extents. This research presents a new open source tool, UrbanAccess, which uses a generalized and scalable methodology to measure transit accessibility with a multimodal network comprising both pedestrian and operational schedule transit networks at a fine spatial scale over large metropolitan extents. A typical use for this tool is illustrated in a case study that characterizes regional transit accessibility in the San Francisco Bay Area in California.

Several studies have rigorously documented the induced travel effect, in which added highway capacity leads to added vehicle travel. Despite the evidence, transportation planning practice does not fully account for this phenomenon, with the result that estimates of the potential congestion-reducing benefits of added highway capacity may be overstated and estimates of potential environmental impacts understated. In 2015, the California Department of Transportation (Caltrans) sponsored a review of applicable induced vehicle travel research that could inform transportation analysis guidance in response to new laws in California such as Senate Bill 743 (S.B. 743), which prohibits the use of vehicle level of service (LOS) and similar measures as the sole basis for determining significant transportation impacts under the California Environmental Quality Act. Instead, vehicle miles traveled was selected to replace LOS under S.B. 743, and along with the new metric there will be a requirement to account for induced travel effects in analysis of roadway capacity expansion projects. The Caltrans review revealed an inconsistent lexicon in academic research and among practitioners, questions about research applicability, limitations in the sensitivity of travel forecasting models, and confusion about the appropriate use of induced vehicle travel elasticities from research. This paper summarizes the Caltrans review and shares the findings to advance understanding of induced vehicle travel effects and suggest steps for additional research.

Timely and reliable truck activity data are of importance for transportation planning and investment analysis, traffic management, environmental and safety analyses, and operation and maintenance of infrastructure. Therefore, state and regional agencies have used various types of sensors to obtain truck flows. To measure accurate traffic flows with the use of sensors, considerable effort has been made to determine optimal sensor locations. The most common approach has been to focus on capturing the maximum number of origin–destinations (O-Ds) and routes at the sensor locations; this approach is referred to as the observability problem. However, when the long travel distances of trucks are considered, sensor locations that seek observability of O-Ds and routes may not acquire meaningful proportions of truck movements in a large-size truck network when budgets are limited. Therefore, this study provides a decision model that optimally locates sensors to capture the maximum O-D and route flow. A goal-programming approach with different weights that prioritize O-Ds or route flow, depending on a prior objective, was investigated. The proposed model was implemented in a real network in Los Angeles, California, with actual truck flow data obtained from sampled truck GPS trajectories. Results showed that significantly larger numbers of O-Ds and route flows were captured by the proposed flow-capturing model than with conventional observability approaches. In addition, trade-offs between O-D and route flow capturing depended on the applied weights and budgets. Particularly in the Los Angeles truck network, equally applied weight provided balanced capturing of O-D and route flow when sufficient numbers of sensors were located in the network. The proposed goal-programming approach can provide insights for researchers and practitioners for alternative sensor location solutions.

Shared mobility—ridesharing in particular—has become an important research topic in recent years because of the ability to relieve traffic congestion, reduce travel costs, and reduce energy consumption. However, researchers in transportation science still lack an understanding of how to incorporate ridesharing into the transportation planning process, specifically in the traffic assignment problem. This paper presents ridesharing user equilibrium (RUE) as a path-flow–based nonlinear complementarity problem with side constraints. This formulation is extended by considering the presence of high-occupancy toll lanes. A numerical example is given to illustrate the relationships of the path cost coefficients on RUE and on the occurrence of the Braess paradox. The performance of two tolling strategies is compared: one charges single-occupancy vehicles, and the other charges both single-occupancy vehicles and vehicles carrying only one passenger.

Planning for equitable outcomes in transportation requires the development of evaluation methods that support the integration of equity in planning processes. This paper presents a methodology for equity evaluation that addresses limitations in current practices by analyzing transportation system outcomes regionally for the full range of demographic groups over time to incorporate the results into performance-based planning processes. This work applied current equity evaluations and a proposed methodology to a case study of the Atlanta, Georgia, metropolitan region. The results illustrate the current methodological limitations and provide an application of the proposed method that shows its ability to address the limitations. The proposed methodology is a comprehensive approach to analyze and characterize transportation system impacts that can be incorporated into performance-based transportation planning at the regional level to improve equity of transportation outcomes.

There is an upward trend in wind energy production in Texas and a growing need to transport wind turbine components in that state. This paper proposes a methodology and an associated operational planning tool that can be used to develop optimal route plans for the transportation of wind turbine components on Texas roadways. The paper also recommends transportation infrastructure maintenance and improvement strategies, as well as more general multisector infrastructure improvements that will be needed to respond to the predicted growth of wind energy over time. Specifically, researchers predicted the amount of energy that will be installed in Texas from 2015 to 2025 and used the new planning tool, along with detailed knowledge of the wind energy production industry and the related supply chain, to find optimal routes for wind turbine components (minimizing both potential for road damage and driver delay). A methodology in which the planning tool is used for the analysis of several what-if scenarios is also proposed. Although the planning tool and the associated methodology were developed for Texas, they can be generalized to any other state after the underlying databases are updated.

Public transit is widely recognized to reduce urban traffic congestion, as it encourages automobile travelers off the road. However, streetcars have been criticized for causing traffic congestion because large trams must operate in mixed traffic on narrow, congested streets. At the same time, streetcars reduce congestion by encouraging automobile drivers to use trams. So what is the net effect of streetcars on congestion? This paper presents a new method for assessing the net traffic congestion effects associated with streetcar operations in Melbourne, Australia, which has the largest streetcar network in the world. Impacts were determined with the use of a traffic network model to compare congestion with trams and without trams. The positive impacts of trams were estimated by using mode shift from tram to automobile when tram services were removed. Negative impacts were explored by considering streetcar traffic operations, the impact of curbside tram stops, and the effect of exclusive priority tram lanes on traffic flow. Findings show that the streetcar network in inner Melbourne results in a net congestion benefit to traffic; a 3.4% decrease in vehicle time traveled and total delay on the road network was established. The streetcar network also contributes to reducing the number of moderately congested links by 16%. Areas for future research are suggested, such as exploring the spatial distribution of the mode shift to automobile and the long-term effect of trams on traffic.

Historically, latent demand—the activities and travel that are desired but unrealized because of constraints—has been examined from the standpoint of understanding the effects of proposed capacity or service improvements on travel demand. Drawing on work from a variety of theoretical perspectives, this paper presents a broader conceptual view of latent demand that provides a useful framework for researching and understanding these unmet needs. This view is important from an equity standpoint, as it provides insights into questions of transport disadvantage, social exclusion, and poverty. The framework presented here is theoretical in nature and untested empirically. This paper aims to promote discussion and ultimately a more developed theory that can inform transportation planning and forecasting. A better definition and quantification of latent (or induced) demand can aid transportation planners in better predicting the effects of future transportation investments and other social, economic, and technological changes.

Pickup and delivery operations are an essential part of urban goods movements. However, rapid urban growth, increasing demand, and higher customer expectations have amplified the challenges of urban freight movement. In recent years, the industry has emphasized improving last-mile operations with the intent of focusing on what has been described as the last leg of the supply chain. In this paper, it is suggested that solving urban freight challenges requires an even more granular scale than the last mile, that is, the last 800 ft. The necessary operations in the last 800 ft require integration of diverse stakeholders, public and private infrastructure, and a diverse set of infrastructure users with multiple, varied objectives. That complexity has led to a gap in the needs of delivery operations and the characteristics of receiving facilities (i.e., unloading and loading facilities and pickup–drop-off locations). This paper focuses on accessibility for pickup and dropoff operations, taking a closer look at urban goods movement in the last 800 ft from the final customer. The paper presents and analyzes previously documented approaches and measures used to study the challenges at the proposed scale. Finally, it proposes a more holistic approach to address accessibility for urban pickup–delivery operations at the microscale to help develop more comprehensive urban freight transportation planning.

To build on a project recently completed for the Oregon Department of Transportation (DOT), processes and tools were developed to prioritize the implementation of connected vehicle (CV) applications. Internal mechanisms for addressing CV development and deployment at the Oregon DOT were assessed; the technical maturity of each potential CV application was scanned, reviewed, and assessed; preliminary goals were developed; prospective CV applications were linked; and applications that fit with the potential role of the Oregon DOT in advancing these initiatives were refined, prioritized, and ranked. A shared vision and business plan that prioritizes CV applications for Oregon is recommended. An internal effort aimed at producing a small set of priority CV applications for further development is described. This effort culminated in a CV application prioritization workshop that included a priority mapping exercise, discussion of the CV concept, and an initial mapping of goals and applications. The workshop identified seven near-term priority CV applications for the Oregon DOT; 12 applications that the Oregon DOT will monitor (and possibly collaborate on with others in the future); and eight applications that the Oregon DOT will monitor but that will be led by others. The Oregon DOT has used the results of this effort as a springboard for hiring new staff dedicated to CV policy, forming a CV steering team, and launching a CV business plan. The process and tools can be used by other states and transportation agencies in their CV application prioritization processes.

Rapid transit systems (RTSs) will increasingly play an important role in the daily commute. However, RTSs are complex systems and are susceptible to degradation over time, and recurring RTS service disruptions are inevitable. Therefore, resilience should be considered in the design of an RTS network, to provide commuters alternative paths that enable them to work around service disruptions. This paper proposes a commuter-centric resilience index for RTS networks that is based on the concept of an acceptable commute time. The proposed index was applied to the Singapore Mass Rapid Transit network, and the findings revealed that the introduction of each new rail line increased the resilience of the RTS network. Ring lines or orbital lines appeared to be most effective in improving network resilience. The resilience index can also be determined for individual stations to help planners identify gaps in the RTS network and to provide useful insight for land use and transport planning. The proposed index would be applicable to RTS networks in other cities or regions, but while information on an RTS network can be sourced from the public domain, computation of the index requires the corresponding commuter trip data.

Accessibility analysis can have important implications for understanding social equity in transit planning. The emergence and the increasingly broad acceptance of the general transit feed specification (GTFS) format for transit route, stop, and schedule data have revolutionized transit accessibility research by providing researchers with a convenient, publicly available source of data interoperable with common geographic information system (GIS) software. Existing approaches to GTFS-based transit analysis, however, focus on currently operating transit systems. With major transit expansions across the nation and around the world increasing in number and ambition, understanding the accessibility impacts of proposed projects in their early planning stages is crucial to achieving the greatest possible social benefit from these massive public investments. This paper describes the development of a hypothetical transit network based on current GTFS data and proposed 2040 transit improvements for the Twin Cities region of Minneapolis–Saint Paul, Minnesota, as well as its use as a sketch planning tool in exploring the proposed system’s impacts on access to job vacancies from historically disadvantaged areas. This research demonstrates the importance of accessibility analysis in planning a transit system that increases opportunity for marginalized workers and concludes by calling for broader, easier access to accessibility analysis for practitioners and community groups to refine the early stages of the transit planning process and democratize an increasingly crucial transit planning tool.

Community resilience depends on the resilience of the lifeline infrastructure and the performance of the disaster-related functions of local governments. State and federal resilience plans and guidelines acknowledge the importance of the transportation system as a critical lifeline in planning for community resilience and in helping local governments to set recovery goals. However, a widely accepted definition of the resilience of the transportation system and a structure for its measurement are not available. This paper provides a literature review that summarizes the metrics used to assess the resilience of the transportation system and a categorization of the assessment approaches at three levels of analysis (the asset, network, and systems levels). Furthermore, this paper ties these metrics to relevant dimensions of community resilience. This work addresses a key first step required to enhance the efficiency of planning related to transportation system resilience by providing ( a) a standard terminology with which efforts to enhance the resilience of the transportation system can be developed, ( b) an approach to organize planning and research efforts related to the resilience of the transportation system, and ( c) identification of the gaps in measurement of the performance of the resilience of the transportation system.

Late-night transit service provides an important connection to jobs, entertainment venues, and other destinations in San Francisco, California, and other major cities. In 2016, the San Francisco County Transportation Authority led a comprehensive reexamination of the region’s late-night bus network, which provided service between about midnight and 5:00 a.m., while the region’s rail services were closed for maintenance. Previous literature established the general characteristics of late-night transit users and trip generators but did not develop and validate the use of a specific tool to plan service. Other researchers also developed transit propensity indexes (TPIs) with the use of demographic data for transit service in general but not specifically for the late-night period. A new approach was used to assess transit demand for late-night work trips by using Census Transportation Planning Package data to identify late-night work trips and combining those trip volumes with additional demographic factors associated with reliance on public transit to develop a late-night TPI. The hypothesis was that high TPI scores were an indicator of areas where late-night transit would attract strong ridership. The research team compared the index results with ridership on existing routes by using a stop-level regression analysis to validate that the TPI is predictive of ridership at a statistically significant level. It was concluded that the TPI together with productivity analysis of existing routes supported identification of potential late-night transit network changes to improve coverage in areas where riders would most need and use the service.