Journal of Public Transportation最新文献

A terrorist attack on the public transportation system of a city can cripple its economy. Uninformed investments in countermeasures may result in a waste of resources if the risk is negligible. However, risks are difficult to quantify in an objective manner because of uncertainties, speculations, and subjective assumptions. This study contributes a probabilistic model, validated by ten different machine learning methods applied to the fusion of six heterogeneous datasets, to objectively quantify risks at different jurisdictional scales. The risk index is purposefully simple to quickly inform a proportional prioritization of resources to make fair investment decisions that stakeholders can easily understand, and to guide policy formulation. The main finding is that the risk indices among public transit jurisdictions in the United States distribute normally. This result enables agencies to evaluate the quality of their risk index calculations by detecting an outlier or a large deviation from the expected value.

Recently some US cities have launched pilot driverless shuttle programs, testing driverless shuttles on their roads. Using data collected in April 2020 from respondents in eight US cities, four with pilot driverless shuttle programs and four non-pilot control cities, we investigate the factors associated with residents’ attitudes towards driverless shuttles. We use confirmatory factor analysis to construct four latent variables representing respondent attitudes: safety confidence, software security concerns, technology familiarity and interest, and preference for human control. Then, we estimate levels of adoption using a structural equation model-based multigroup analysis. We find that individuals in pilot cities not only demonstrate greater acceptance of driverless shuttle programs but also have different determinants of acceptance compared with those in non-pilot cities. Notably, the effects of local transit access on driverless shuttle acceptance vary between pilot and non-pilot cities. These findings provide early insight into how driverless shuttles may be accepted by the broader population and what factors may influence the effectiveness of driverless shuttles as public transportation over the long term.

Public investment in transit increased following the Great Recession, yet transit use nationally mostly fell, even prior to the 2020 pandemic. We investigate this troubling disjuncture by comparing transit ridership trends during the 2010s in two of America’s largest regions: Greater Los Angeles and the San Francisco Bay Area. While both California regions lost transit riders, we see substantial differences in the scale, timing, geography, and modes of these declines. In the LA area, ridership fell longer and further, spread more across routes, times, and sub-regions and concentrated on the region’s dominant operator. In both regions, increasing auto access appears to have played a central role, albeit in different ways. Greater LA saw increased automobile ownership, particularly among high-propensity transit riders. In the Bay Area, as jobs and housing have dispersed, ridehail services like Lyft and Uber may have eroded non-commute transit use.

Transit service can be aligned and scheduled to allow passengers to transfer between routes that arrive together and provide the opportunity to connect. In modular networks, timed-connections at every intersection of routes are independent of frequency and do not require additional schedule time. Recovery time to accommodate variable traffic levels and transit speeds can be added as needed. Routes can be aligned over an incomplete grid of roads or provide additional transit capacity between major destinations. Routes with lower ridership can be scheduled with lower frequency of service. Portions of the same route can be served with different frequencies. A central grid of routes can branch as radial suburban routes. Modular networks can be applied to the constantly changing headways between faster, rapid buses and slower, local buses. In each case, timed-connections allow passengers to transfer between routes without delay.

Due to concerns about data quality, Automated Passenger Counting technology has rarely been used to analyze local ridership trends. This paper presents a novel framework to test the consistency and completeness of automated passenger count (APC) data in four cities. Weekday APC data are aggregated at the system level and compared with the National Transit Database between 2012 and 2018. In all four agencies, passenger counts closely follow the fluctuations observed in the national transit database. There is, however, a slight drift in two of the four agencies, possibly due to the diverging trends between weekday and weekend ridership. At the stop-level, missing and duplicate vehicle-trips are identified using schedule data from the General Transit Feed Specification. Missing and duplicate trips only concern a small proportion of stops, which can be eliminated using the proposed method. Overall, this research leads the way towards the analysis of factors affecting ridership on a tight spatial and temporal scale.

The taxi industry has changed dramatically during the last decade, as ridesourcing applications, ridesharing, and alternative pricing schemes have emerged, either as complementing or competitive services and strategies. After some years of familiarity with such trends, it is interesting to explore where the taxi industry stands with respect to possible service innovations. This paper explores the behavioral patterns of drivers, focusing on issues such as their preferred way of conducting business and their views on introducing taxi-sharing and dynamic pricing. Data collected from a face-to-face survey in Athens, Greece, are exploited and appropriate econometric models are developed for the purposes of the study. The analysis shows that young and/or educated drivers, as well as those who are familiar with new technologies, are more willing to accept innovations in taxi services. Results from a stated choice experiment show that on average 3.5 euros is the extra charge that the taxi market would accept to offer a taxi-sharing service. However, results reveal that the value of taxi-sharing varies across different groups of drivers. Overall, findings indicate that in the years to come, competition by other services (e.g., ridesharing) will force the taxi industry to adopt new models of operation and pricing.

Transit stops serve as crucial components of journeys for riders, but their condition is often left out of equity considerations. Two important empirical questions are what stop amenities, such as places to sit, clear signage, shelters for inclement weather, and unobstructed curbs are present, and how are they distributed across systems, which may reveal neighborhood or route-specific disparities. San Francisco, CA represents an ideal case for which to pursue this question, given it maintains a ‘transit first’ policy directive that mandates public space prioritize transit over private automobiles. An in-person census of 2964 street-level bus stops was conducted over three months, which finds that a majority of stops lack both seating and shelter of any kind, that route signage varies widely in format and legibility, and that roughly one third of all stops are obstructed by on-street parking, rendering them difficult to use and exposing riders to oncoming traffic. Stops in the city’s northern half are more likely to feature seating, shelter, and unobstructed curbs, whereas amenity “coldspots” nearly all lie within the city’s southern half. Stop amenities also vary sharply by bus route, such that routes with the longest headways (and thus waiting times) provide on average the least seating, shelter, and clear curbs. These three amenities – seating, shelter, and unobstructed curbs – are also present to a greater degree in Census tracts with higher shares of white residents. This census demonstrates that equity evaluations of transit must include stop amenities, which are often overlooked, can undermine transit’s attractiveness, and even compound long-standing imbalances in service quality for underserved communities. Furthermore, studies of this kind can inform where amenity upgrades should be prioritized, targeting those areas currently lacking in high-quality stops, and raising the minimum standard of stop amenities overall. Finally, given data collected in this census is almost entirely unavailable to riders within current trip-planning and wayfinding applications, this work raises the possibility of expanding transit-data standards to include amenity details.

Numerous studies have focused on the potential influence of land-use and built-environment features around rapid-transit stations (e.g. heavy or light rail or bus rapid-transit) as key determinants or mediators of patronage. Many find statistically significant associations, yet their effects are relatively weak as compared to demographic, socio-economic, service quality, and larger-scale network accessibility factors. Yet most studies have ignored areas surrounding stops on bus lines that feed into rapid-transit stations. This study examines Los Angeles’s multimodal transit network to understand how walkability around feeder bus-stops might affect boardings at LA Metro’s rapid-transit stations. A multilevel generalized linear model is implemented and fitted with bus-stop walkability data and relevant controls to explain the number of linked person-trips from feeder bus-stops to rapid-transit stations and how this might be associated with land use and design characteristics around feeder bus stops. Results indicate a weak but statistically significant influence and policy implications are discussed.

The emergence of transportation network companies (TNCs) has created new options for travelers and fierce competition for taxis and public transportation (PT). While the literature focuses either on TNCs or PT users, we contrast individuals/households who use only PT, only TNCs, or both by estimating a cross-nested logit on 2017 NHTS data. We analyzed both individuals (for consistency with most of the literature) and households (to account for intrahousehold travel dependencies). Our results show that the unit of analysis (individuals vs. households) does not matter much for our dataset. We found that individuals/households who use either PT or TNCs or both share socio-economic characteristics, reside in similar areas, and differ from individuals/households who use neither transit nor TNCs. In addition, individuals/households who use both PT and TNCs tend to be composed of Millennials and Generation Z, with a higher income, more education, no children, and fewer vehicles than drivers. Our findings highlight the danger for PT of entering into outsourcing agreements with TNCs, neglecting captive riders, and further exposing choice riders to TNCs.

This paper reports on the study of public support for transit in Southeast Michigan. The goal was to assess Willingness to Support Public Transit (WTST) among likely voters in the region. The study developed a new tool (WTST Index) that would allow planners, policy makers, and advocates to measure the likelihood of support for transit based on a reduced set of questions, through a formula that captures the gradient from attitudes (opinion) to behaviors (action). The study investigated how sociodemographic, sociopolitical, geographical, and opinion related variables impact WTST Index. The results highlighted how wealth, education, and political and ideological beliefs impact willingness to support transit. The U-shaped distribution of the WTST Index shows the divisiveness in support and uncovers the political and ideological dimensions of transit. The results provide guidance for understanding support for transit in comparable regions where transit initiatives and policies seek to expand or improve underused transit systems.