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on Transport Economics |
| By: | Boarnet, Marlon G.; Flores Moctezuma, David; Gross, James |
| Abstract: | This research project examines the link between job access and stop/station level transit ridership. Job access, following recent literature, is measured as the number of jobs that can be reached within a 30-minute transit travel time, including transfers and walk time to access jobs once exiting a transit station. Cumulative opportunity job access measures of this sort—i.e., the number of jobs that can be reached within 30 minutes—have become common in the recent access literature, and those measures have often focused on access via transit. Yet there have been few studies that examine the link between transit job access and transit ridership, and of those none that examine the link at a station or stop level. This study uses station and stop level ridership data for the Los Angeles Metro bus and rail system and the BART rail system in the San Francisco Bay Area. The research team calculated transit job access as jobs that can be reached within 30 minutes, using the Remix software tool. Regression analysis of 1,000 randomly selected Los Angeles bus stops reveals a robust relationship between stop-level ridership and job access. The association between transit job access and bus stop ridership (embarkations and disembarkations at the stop) is statistically significant. Converting that association into an elasticity, if the number of jobs accessible within 30-minutes were to increase by 1 percent, on average stop-level ridership would increase between 0.6 to 0.8 percent. The same association, with similar magnitudes, exists for Metro rail stations and BART rail stations, but due the smaller sample sizes, those relationships are not statistically significant when control variables are added to the regression. The findings show that job access is closely related to ridership at the bus stop level, suggesting transit agencies can increase job access by increasing bus frequency, reducing transfers, siting lines that connect job concentrations to residents, and by improving bus stop/rail station access/egress times. View the NCST Project Webpage |
| Keywords: | Engineering, Social and Behavioral Sciences, Accessibility, Job Access, Employment, Transit Ridership, Boarding & Alighting, Travel Time, Public Transit Networks |
| Date: | 2022–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt7t5876bw&r= |
| By: | Harold, Brian MBA; Rodier, Caroline PhD; Zhang, Yunwan MS; Harrison, Makenna; Yang, Grace; Phan, Christine |
| Abstract: | Pilot programs have been implemented in cities across the U.S. to address the first- and last- mile problem with door-to-door shared microtransit, ridehailing companies, and shared-ride operators with dynamic pick-up locations. The City of Rancho Cordova and Lyft partnered to launch one such pilot in the form of a discount-based door-to-door (D2D) coupon program named “Free $5 to Ride”. The program offers $5 credits to Lyft riders who start or end their trips at one of four Sacramento Regional Transit District (SacRT) light rail stations. The program was designed to reduce rider dependence on personal vehicles and increase the overall convenience of transit use in the region. UC Davis researchers conducted an evaluation of the “Free $5 to Ride” program during its operational period of May 2019 through June 2021. Researchers developed a participant survey and used survey data along with participant trip data, ridership data for the SacRT light rail, and ridership data for the Rancho CordoVan shuttle service to characterize the outcomes of the pilot program. The evaluation shows that the coupon program was generally well-received. Participation levels increased dramatically by early 2020, and while trip activity dropped at the onset of the COVID-19 pandemic, program activity remained fairly constant through the end of the program. Researchers encountered survey sampling limitations due to ridehailing customer engagement policies, suggesting that future evaluations of similar programs would benefit from increased data access, or modified policies allowing operators to conduct more extensive outreach in support of these studies. |
| Keywords: | Engineering, Ridesourcing, public transit, first mile/last mile, shared mobility, Sincentives, surveys, travel behavior, outreach, pilot studies |
| Date: | 2022–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt31s5451c&r= |
| By: | Darren Shannon; Grigorios Fountas |
| Abstract: | Modelling crash rates in an urban area requires a swathe of data regarding historical and prevailing traffic volumes and crash events and characteristics. Provided that the traffic volume of urban networks is largely defined by typical work and school commute patterns, crash rates can be determined with a reasonable degree of accuracy. However, this process becomes more complicated for an area that is frequently subject to peaks and troughs in traffic volume and crash events owing to exogenous events (for example, extreme weather) rather than typical commute patterns. One such area that is particularly exposed to exogenous events is Washington, DC, which has seen a large rise in crash events between 2009 and 2020. In this study, we adopt a forecasting model that embeds heterogeneity and temporal instability in its estimates in order to improve upon forecasting models currently used in transportation and road safety research. Specifically, we introduce a stochastic volatility model that aims to capture the nuances associated with crash rates in Washington, DC. We determine that this model can outperform conventional forecasting models, but it does not perform well in light of the unique travel patterns exhibited throughout the COVID-19 pandemic. Nevertheless, its adaptability to the idiosyncrasies of Washington, DC crash rates demonstrates its ability to accurately simulate localised crash rates processes, which can be further adapted in public policy contexts to form road safety targets. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.01729&r= |
| By: | Shilling, Fraser PhD; Waetjen, David PhD; Longcore, Travis PhD; Vickers, Winston DVM, MPVM; McDowell, Sean; Oke, Adetayo; Bass, Aaron; Stevens, Clark |
| Abstract: | Transportation and other agencies and organizations are increasingly planning and building under- and over-crossing structures for wildlife to traverse busy highways. However, if wildlife do not use these structures due to noise, light, and other factors, then the structures may have a low benefit to cost ratio. Several criteria are key for their success— sufficient safety and/or conservation need, cost, location, and anticipated use by wildlife. There is limited information in wildlife-crossing guidance on how wildlife biologists should advise designers, engineers, and architects on the use of structural and vegetation elements that could reduce noise and light disturbances. To address this problem, this study used field measurements and modeling of light and noise from traffic to inform and test the designs of two wildlife overcrossings. Wildlife-responsive designs were developed and tested for two crossings being considered or planned by California Department of Transportation in California. For the planned crossing of US 101 near the city of Agoura Hills (the Wallis-Annenberg crossing), the three designs consisted of noise/glare barriers; noise/glare barriers + berm; and noise/glare barriers + multiple berms. For the potential crossing of Interstate 15 south of Temecula, one design used noise/glare barriers of 3 different heights and the other had no barriers. Key limitations and opportunities for each design approach were identified. Creating “dark and quiet paths” using a combination of berms and noise/glare barriers could decrease disturbance in the crossing structure approach zones and increase the wildlife-responsiveness of the designs. |
| Keywords: | Engineering, Wildlife crossings, highway design, noise barriers, anti glare screens, benefit cost analysis |
| Date: | 2022–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt4vk0m9cs&r= |
| By: | Katsunobu Okamoto; Takuji Takemoto; Yoshimi Kawamoto; Sachiyo Kamimura |
| Abstract: | The purpose of this study is to identify the differences in SAPA user interest due to each route, traffic volumes and after COVID-19 by the daily feedback from them and to help develop SAPA plans. Food was the most common opinion. However, for the route, some showed interest in other options. For the traffic volume, the difference of interest was also shown in some heavy traffic areas, On the other hand, the changes in customer needs after the Covid-19 disaster were less changed. In addition, there were some differences between the tendency of the users' opinions and the drop-in factors of SAPA in the previous studies. The bias of the sample data and the collection of samples after the covid-19 are the issues in this research. Since the data may represent some part of users and, due to the covid-19, the demographics of users may change drastically in 2020. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2203.12858&r= |
| By: | Robert Huang; Matthew E. Kahn |
| Abstract: | Urban public transit agencies spend billions of dollars each year on workers, durable capital and energy to supply transportation services. During a time of rising concern about climate change, the urban public transit sector has not significantly reduced its carbon footprint. Using data for the nation’s transit agencies over the years 2002 to 2019, we benchmark U.S transit agencies with transit agencies in Germany and the United Kingdom. We study U.S urban public sector energy efficiency trends and explain the cross-sectional variation. We present a new operating profits metric that incorporates each transit agency’s annual total carbon emissions. |
| JEL: | H23 H41 H76 R4 |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:29900&r= |
| By: | FERNANDEZ LLORCA David (European Commission - JRC); GOMEZ Emilia (European Commission - JRC) |
| Abstract: | As Artificial Intelligence (AI) is the main enabler of Autonomous Vehicles (AVs), and autonomous mobility is a scenario of high-risk nature, sectorial regulations of AVs are expected to be aligned with the AI Act. Beyond requirements of safety and robustness, other important criteria to be considered include human agency and oversight, security, privacy, data governance, transparency, explainability, diversity, fairness, social and environmental wellbeing and accountability. These trustworthy requirements for AVs have a heterogeneous level of maturity and bring new research and development challenges in different areas. A specific analysis of the evaluation criteria for trustworthy AI in the context of autonomous driving is needed. There is a window of opportunity to define a European approach to AVs in future implementing acts, by including requirements of trustworthy AI systems in future procedures for the type-approval of AVs at EU level. |
| Keywords: | Artificial Intelligence, Autonomous Vehicles |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc128170&r= |
| By: | Giménez-Nadal; José Ignacio, Molina, José Alberto, Velilla; Jorge |
| Abstract: | Recent analyses have shown that commutes to and from work are not symmetric, suggesting that intermediate activities are at the root of these asymmetries. However, how intermediate activities interact with trips to and from work is an unexplored issue. Using data from the American Time Use Survey 2003-2019, we analyze what activities workers do while commuting, and compare measures of commuting when intermediate activities are included or excluded as part of the commuting trip. We show that commuting is underestimated if measured with the Time Use Survey lexicon. Such differences are especially significant in commuting from work. Furthermore, gender comparisons of commuting are affected by the inclusion of intermediate while commuting, with gender differences narrowing when intermediate activities are considered. Our results contribute to the analysis of commuting behavior, by proposing new identification strategies based on intermediate non-trip episodes, and by showing how commuting interacts with other non-commuting activities. |
| Keywords: | Commuting time,Trip behavior,Intermediate activity,Time use data,American Time Use Survey |
| JEL: | J22 R41 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:glodps:1080&r= |