Demand-response Transit Research Articles

Accessibilities by shared autonomous vehicles under different regulatory scenarios

The introduction of new mobility technologies and systems have historically shaped the patterns of settlements. With the advent of autonomous vehicles (AVs) and the implementation of ubiquitous mobility-as-a-service (MaaS) systems, it can be argued that there may be another fundamental change in terms of people's travel. Given the interaction of land use and transport, a stronger reliance of travel on AVs has the potential to change the layout of cities. In this study, we use accessibility measures to quantify and compare the utility that such services offer for citizens in an exemplaric case study using the transport simulation model MATSim. MATSim allows both (1) to model demand-response transit services and (2) to compute dynamic accessibilities based on various modes of transport. This study brings these functionalities together for the first time to assess such services by a measure that resembles the perspective of the individual citizen. We find that the changes in the operational model of the SAV (shared autonomous vehicle) fleet has significant effect on accessibilities provided by SAVs. Especially in a setup were a fleet is expected to fulfill a basic needs supply, the results are of high relevance under equity aspects.

A Methodology to Determine the Economically Feasible Capacity for Rural Demand Response Transit Systems

Transportation professionals generally use volume-to-capacity ratios as a standard measure of effectiveness to evaluate the operation of transportation facilities. Unfortunately, this commonly used measure has not been available for the analysis of rural demand response transit systems, as there has not been a clearly defìned methodology for determining the capacity of these systems. This article presents a methodology for determining the capacity of a rural demand response transit system using an economic constraint model and spatial data for the service area stored in a Geographic Information System (GIS). The methodology develops an equation that incorporates operating costs, transit need, route distance, and revenue to define an agency s potential service area, or capacity. To demonstrate the methodology, the article presents a case study for a transit agency in Northwest Alabama. The article concludes that the methodology presented can be applied to determine the economically feasible service area for a rural demand response transit system, thus allowing for development and use of volume-to-capacity ratios as a consistent measure of effectiveness to evaluate an agency’s operation.

Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization

To reduce the operating cost and running time of demand responsive transit between urban and rural areas, a DBSCAN K-means (DK-means) clustering algorithm, which is based on the density-based spatial clustering of applications with noise (DBSCAN) and K-means clustering algorithm, was proposed to cluster pre-processing and station optimization for passenger reservation demand and to design a new variable-route demand responsive transit service system that can promote urban–rural integration. Firstly, after preprocessing the reservation demand through DBSCAN clustering algorithm, K-means clustering algorithm was used to divide fixed sites and alternative sites. Then, a bus scheduling model was established, and a genetic simulated annealing algorithm was proposed to solve the model. Finally, the feasibility of the model was validated in the northern area of Yongcheng City, Henan Province, China. The results show that the optimized bus scheduling reduced the operating cost and running time by 9.5% and 9.0%, respectively, compared with those of the regional flexible bus, and 4.5% and 5.1%, respectively, compared with those of the variable-route demand response transit after K-means clustering for passenger preprocessing.

Developing a Method for Assessing National Demand-Response Transit Level of Service

Demand-response transit service is a major source of mobility for older adults and people with disabilities in both urban and rural areas in United States. Federal Transit Administration (FTA) grant programs under sections 5307, 5310, and 5311 all have components designed to increase the availability of paratransit or demand-response transit service. However, there is little information in the National Transit Database (NTD) or elsewhere about the extent of demand-response transit service coverage across the country. The lack of data makes it a challenge to identify gaps in service and unmet needs. The primary objective of this study was to fill the data gaps to the available NTD database to effectively determine the demand-response transit level of service. This study also developed a priority ranking procedure to identify where the greatest needs for service improvements exist in a state.

Flex-route demand response transit scheduling based on station optimization

In order to increase the proportion of passengers served by the flex-route demand response transit (DRT) and reduce its service cost, we propose an improved D-<italic>k</italic>-means clustering algorithm that based on DBSCAN clustering algorithm and <italic>k</italic>-means clustering algorithm, which could determine fixed stations and alternative stations according to passengers' needs. Besides, we design a flex-route demand response transit service system and establish the flex-route demand response transit scheduling model which is solved by an accurate algorithm. Finally, the feasibility of the scheduling model is verified by the example in the southern part of Jiexi County of Guangdong Province in China. The results indicate that the model serves 95.3% of passengers, and comparing with regional flexible bus, the operating cost and time are reduced by 9% and 5%, respectively. The algorithm is fast and stable.

Willingness to ride and perceptions of autonomous public transit

Abstract Autonomous vehicles (AVs) hold great promise to contribute to global sustainability by expanding access to mobility. The introduction of autonomous buses and shuttles could be a turning point for public mobility in the USA, but how autonomous public transit is perceived remains largely unknown. To fill this gap, this study analyzes the willingness to use autonomous buses and shuttles based on two surveys conducted in Michigan. These surveys were a phone-based random-sampling survey of the general public and an on-board intercept survey of public transit riders. We found that autonomous buses might increase willingness to use public transit. 15% of people, who occasionally ride or do not ride public transit, embrace the idea of using autonomous bus service, while fixed-route riders were more likely to accept AVs than demand-response transit riders. However, about half of the public transit riders were hesitant about riding in autonomous buses citing concerns over safety, no human, and distrust in technology. Willingness to ride was higher among younger males than it was for females, seniors, and people with mobility disabilities. In addition, our data suggests that riders’ satisfaction with their drivers - be it skill, professionalism, or friendliness - had no impact on willingness to ride in AVs. As AVs become ready for deployment, policymakers and public transportation service providers should consider AV acceptance among vulnerable individuals to bring AV benefits to all.

Evaluating Automated Demand Responsive Transit Using Microsimulation

Recent advancements in automated vehicle technology and the concurrent emergence of ride-hailing services have focused increasing attention on Automated Mobility-on-Demand (AMOD; a system of shared driverless taxis) as a potential solution for sustainable future urban mobility. However, the impacts of an unrestricted deployment of AMOD are as yet uncertain and likely to be context-specific; evidence with existing on-demand services suggests that they may lead to the cannibalization of mass-transit and increased traffic congestion. In this context, automated demand-responsive transit (also termed microtransit), which provides similar on-demand services (stop-to-stop or curbside) through higher capacity vehicles, may prove to be a promising substitute and/or complement. In this study, we evaluate the performance of such an automated demand response transit system (hereafter AMOD minibus) through agent-based simulations of the Singapore network. Towards this end, we extend SimMobility (an agent- and activity-based microsimulation laboratory) with the capability of modeling an AMOD minibus service including demand, supply and their interactions. On the demand side, we use an activity-based model system that draws on data from a stated-preferences survey conducted in Singapore. On the supply side, an insertion heuristic is applied to dynamically perform both the assignment of requests to vehicles and vehicle routing. Scenario simulations on the Singapore network (with an area-wide deployment of the AMOD services) indicate the potential benefits of an automated demand responsive transit service for local circulation, which can result in a reduction of Vehicle Kilometres Traveled of up to 50% (compared to the AMOD shared taxis) whilst satisfying the same demand, with a modest increase in average travel times.

Modeling frequency of rural demand response transit trips

Captive riders do not have many travel choices to meet the travel needs as fixed route transit services are not generally provided in rural areas. In many states, demand response transit (DRT) services are provided to meet such needs. However, state public agencies face the dilemma of whether to increase or decrease the service availability for on-call services. To enhance decision making of identifying what the causal factors related to DRT trips, the authors present a set of econometric models by integrating a sample DRT data with other explanatory variables such as land use, socio-economic, and demographic characteristics. Seven count data models including Poisson, Negative Binomial, Zero-inflated Poisson, Zero-inflated Negative Binomial (ZINB), Hurdle Poisson, Hurdle Negative Binomial, and ZINB Mixed Effect were developed to understand the factors that affect DRT trips. The ZINB Mixed Effect model that combines a zero-inflated negative binomial model with random effect was found to provide the best fit. A number of factors showed significant relationship with DRT trip frequency including distance, population density, elderly population, average income, and others. Further, the elasticity effects of these different factors were computed to quantify the magnitude of their impact on DRT. The proposed model can be helpful for transit agencies to predict the frequency of DRT trips and to provide adequate services in rural areas.

2495 - Role of Scheduled and Demand-Response Transit in Connecting People with Activities of Daily Living

Background President Trump's proposed Fiscal Year 2019 budget would make provision of Non-Emergency Medical Transportation through the Medicaid program optional. The Trump administration has granted waivers to Indiana, Kentucky and Iowa, allowing them to deny rides to non-disabled adults trying to access medical appointments. These adults likely include people with hypertension and diabetes in danger of kidney failure, asthma patients, and potentially people with tuberculosis. Health Care is a human service program. Luckily, the means by which people can be mobile and access health care are being transformed at near-warp speed. I’ll be speaking from nearly 40 years of experience planning, developing and overseeing public and human service agency demand-response transportation services and fixed-route bus operations; proposing and serving as a panel member on Transit Cooperative Research Program projects; and past board service for a center for independent living and a long term care coordinating council. Methods Transit has progressed from fixed-route fixed-stop train and bus services, dial-a-ride, scheduled standing-order, taxi and volunteer-based services to app-based exclusive-ride and shared-ride connections, bike-sharing, car-sharing, and scooters. Market segmentation and community engagement best practices enable transportation providers to offer services and information that will be absorbed and used. I’ll provide insight into mobility customers, the wide array of agencies and regulations subsidizing rides, and discuss a comprehensive community engagement program that leads to effective marketing. Technology is being transformed on several fronts beyond assigning rides to vehicles: mobile platforms for information and fare payment; pipelines to exchange structured data between mobility partners; and accounting and reporting software to support rideshare as well as service evaluation. With this presentation, I’ll describe the current spectrum of transit services, near to mid-term mobility enhancements under development, and promote a structure to sustainably provide mobility. Results Results and Trends are temporary. Transit ridership in the Washington DC metropolitan area and in much of the country has been declining substantially in the past few years. Uber, Lyft and similar services now provide almost the same number of rides as transit, though all require substantial subsidies. I look forward to a bounce-back as partnerships are developed and refined to enable each provider to efficiently and sustainable focus on fully-accessible market segments within a cooperatively coordinated structure. Conclusions Technology advances combined with basic transportation economics and adherence to civil rights laws, will steer transportation providers into offering cooperatively coordinated accessible mobility network. I’ll close with a list of resources.

Estimating Ridership of Rural Demand–Response Transit Services for the General Public

The general objective of this study was to develop a model for estimating ridership for rural demand–response transit services for the general public. A lack of data for demand–response service characteristics and geographic coverage has limited the estimation of such models. This study developed and estimated two models. The first was estimated with data from the 2013 rural National Transit Database, and the second was estimated with more detailed service data collected from surveys of transit agencies. Results showed that in addition to total population, demographic characteristics were important. Ridership was found to increase significantly when the percentage of the population composed of older adults or people without access to a vehicle increased. Both models showed a negative effect of fares on ridership. The second model analyzed the effects of service span and reservation requirements on ridership. Results showed that providing more days of service had an expected positive effect on ridership; allowing users to reserve rides on shorter notice also had a significant positive effect. Compared with previous research, the inclusion of a greater number of variables and more specific service information improved the performance of the models.

Public Transit Market Research of Low-Income Commuters Using Attitude-Based Market Segmentation Approach: Case Study of Fushun, China

In China, low-income commuters are usually concentrated in peripheral settlements outside downtown areas, where travel services are inadequately provided. These commuters are dependent on fewer travel options, considering their affordability. Based on the recognition that public transit is an important mode to enhance low-income commuters’ travel mobility, a comprehensive attitude-based market segmentation analysis was performed to identify distinct market segments to best serve the needs of each segment and to develop plans to increase transit usage. First, a detailed household survey was conducted in Fushun, China, to obtain commuters’ attitudes toward daily travel. Then, factor analysis was utilized to explore latent attitudinal factors. The structural equation modeling investigated the correlations between attitudes and public transit usage. The k-means clustering was then employed to partition the transit market into several subgroups. Finally, five segments of transit market with distinct attitudes were identified by three dividing variables, namely, the desire for comfort, the need for reliability, and environmental awareness. Low-income commuters in the same segment share homogeneous travel preferences while those in other segments possess different attitudes. The attitudinal characteristics, socioeconomic profile, and mode choice behavior in each segment were examined and discussed. Policies that best meet the needs of each submarket were proposed. These transit-related strategies included building a reliable operation environment, improving the level of service of existing facilities, implementing demand-response transit services, and providing public propaganda and education toward environmental protection.

Method for Estimating Statewide Transit Needs and Investment Priorities for Rural and Small Urban Areas

This study demonstrated a method for identifying statewide transit needs and gaps, prioritizing investment needs for statewide transit planning, collecting better data for the demand-response transit level of service, estimating costs of needed improvements, and projecting service needs based on projected population growth. The method was applied to the state of North Dakota and is transferable to any state, especially those with a large number of rural demand-response transit agencies that are not located within metropolitan planning areas. Currently, data in the National Transit Database are insufficient for analyzing the level of service for rural transit agencies because the database lacks information on geographic service area and span of service. A survey conducted for this study filled this data need by collecting detailed information regarding each agency’s service area and the number of days and hours of service being provided. These data combined with population and demographic data are useful for identifying gaps in transit service and prioritizing needed service improvements. The study also calculated per capita trips, vehicle miles, and vehicle hours provided in regions across the state. These performance measures were compared with benchmark values to identify areas in which the transit system may not be meeting the needs of the service area population, with the understanding that needs are not identical in all areas. The study estimated necessary increases in service and funding to meet target levels in different scenarios. Population projections were also considered for estimating needed increases in service and funding.

Changes in access to public transportation for cycle–transit users in response to service reductions

North American transit agencies have made large investments since the late 1990s in the coordination of bicycling and public transit services. A key goal in doing so has been to increase transit ridership by extending the geographic area from which riders can easily and quickly reach transit stops and stations. While it is widely hypothesized that being able to travel on transit vehicles with bicycles allows riders to access transit stops and stations from a larger geographic area, the empirical evidence of this is scanty. Information available for Northeast Ohio, where the Greater Cleveland Regional Transit Authority (GCRTA) operates rail, bus and demand response transit, presents an opportunity to address an important aspect of this issue. The availability of detailed long-term bicycle-on-bus boardings (BoBBs) data and the implementation of a series of service reductions in 2008, 2009 and 2010 offer an opportunity to ask the question: Do significant changes in geographic access to transit services result in significant changes to the numbers of cycle–transit users accessing transit buses? The evidence from GCRTA׳s service area provides some support for this conclusion, with the rates of utilization of bus bicycle racks increasing significantly over time and in slightly higher numbers for routes that saw the largest reductions in bus transit service.

Bringing Fixed-Route Transit Service to Small Cities and Towns: Potential Contribution of Existing Demand-Response Brokerages

The possibility is suggested of adding fixed-route transit service to many of the nation’s small cities and towns by using some of the money currently devoted to the provision of client-oriented demand-response transit to fund the new service. This can be done, this study finds, with very little additional expense and no loss of necessary demand-response service. The creation of small fixed routes is feasible, when the demand-response system is organized across a region by a brokerage (i.e., an organization paid on a capitated basis that assigns eligible riders to transportation providers). Research shows that both brokerages and fixed routes tend to lower Medicaid transportation costs. The thesis is that these two efficiency enhancers can be combined effectively in some of the nation’s small towns and cities, whenever capitated brokerages can provide the administrative apparatus and much of the funding for the fixed route. This thesis is illustrated with the results of a study conducted in a Kentucky city of 27,000. It found that a substantial number of those currently riding in demand-response vehicles to which they are assigned by a broker could be shifted to a proposed fixed route. The new route would be operated by the broker with funds from the capitation payments. The study’s findings suggest that capitated brokerages may create an opportunity for many of the nation’s smaller cities to obtain a fixed-route bus service at a modest, or perhaps at even no, additional cost.