Automated People Movers and Automated Transit Systems 2022

Low-speed automated vehicle (LSAV) shuttle technology has attracted attention for its potential to address first/last-mile links in public transit, improve connectivity in campus/office park environments, reduce human-induced crashes, reduce operating costs, and improve overall urban mobility. LSAV shuttle technology has seen an array of pilot tests and demonstrations around the world ranging from closed test sites to operating in mixed vehicular traffic. These demonstrations continue to provide evidence that LSAV shuttles can safely operate in these environments. The potential for these systems to fill some mobility and connectivity needs is clear and their capability to transport patrons safely has been documented. What has not been discussed in depth is a methodology for systematically and objectively identifying potential route locations using available data. To improve upon these past practices, this paper focuses on objectively identifying LSAV shuttle routes throughout the University of South Florida’s campus utilizing existing pedestrian paths, or mixed pedestrian environment, with readily available campus demand and mobility data. The study’s objective is to present a data-driven route selection methodology that accounts for the possible operational environment of these vehicles. The nature of LSAV shuttle people movers being able to operate outside of a typical roadway network creates a necessity to consider both trip distribution and route choice set models from the perspective of transit and pedestrian travel. Components of both modes were considered in the methodology and applied to generate routes utilizing readily available campus mobility and population data. The results demonstrate the most optimal routes are oriented on a line with the least deviation from higher density trip production locations (origins) to higher density trip attraction locations (destinations).

The Seattle Center Monorail was originally built for the 1962 Seattle World’s Fair to provide a link between downtown Seattle and the fairgrounds and it still operates today. It is an elevated straddle-beam monorail line that runs along 5th Avenue between the Westlake Center in Downtown Seattle and the Seattle Center with no intermediate stops. The 0.9-mi (1.4-km) line was constructed by Alweg Rapid Transit Systems and it opened to the public about one month before the start of the World’s Fair. The Seattle World’s Fair, also known as the Century 21 Exposition, was held April 21 to October 21, 1962, on a site that is now called Seattle Center. The fair saw the construction of the Space Needle, the monorail, several sports venues, and performing arts and exhibit buildings. Nearly 10 million people attended the fair and it was estimated that more than 8 million rode the monorail during the six months of the fair. The monorail has operated for over 60 years and has been declared a historic landmark and continues to be a popular tourist attraction. Today, it is owned by the City of Seattle and is operated by Seattle Monorail Services, a privately run business, and annually carries over 2 million passengers. This paper presents an overview of the Seattle World’s Fair and provides background, history, and operating characteristics of the famous monorail.

While representing only 2%–5% of the total cost of typical APM systems, the conductor rail system (CRS) is an integral part of an overall transit system. This paper chronicles the planning and execution of installing over 300 km of a large (4,500 A capacity) conductor rail system (CRS) in the urban environment of Bangkok, Thailand. This multi-year project involves the delivery and installation of over 4,500 t of material lifted to and installed on an elevated monorail guideway that crosses a major metropolis. The author explains the extensive planning, communication, coordination, flexibility, and perseverance necessary for execution of a project of this scale and scope.

Monolithic monorail guideway structures (MMGWS) have different performance unlike bridges due to the flexibility of the guideway beams. Assessment of seismic performance of structures is essential to determine the plastic hinge zones, assess the ductility, and predict the failure modes. In this paper, the seismic performance of MMGWS is analyzed using displacement-controlled pushover analysis (PA) using SAP2000. In non-linear PA, moment-curvature (MC) relationship is used to model user defined plastic hinge behavior for beam and column sections. Top node displacement is incremented step by step up to failure to obtain the pushover curve and the sequence of plastic hinges formation and failure of beams and columns were recorded. The analysis results including pushover curve, energy dissipation, hinge formation locations, and its case are discussed. The hinges were formed in piers prior to beams. Also, plastic hinge progression, damage states, and locations were affected by several parameters including pier height, reinforcement and stiffness, material properties, and span length.

This paper explores the various configurations of off-line stations and vehicle berthing that have been found to be fundamentally important parameters in the analysis of system performance, operations, and capacity. The proposed automated transit network system concept, which provides a direct ride between a passenger’s origin and destination station, is applicable to both fixed guideway automated transit and to systems with self-driving vehicles. Addressed at a conceptual level is the vehicle/station interface in terms of Americans with Disability Act compliance and station capacity concerns. The concept of open-edge platforms monitored by advanced sensing technology combined with artificial intelligence-powered perception for the purpose of protecting passengers from entering the active vehicle lanes is also discussed. Finally, the paper draws initial conclusions on how these station configurations, operational complexities, and associated costs can be better understood through appropriate research and development.

Regulations for automated people movers (APMs) differ greatly from passenger ropeway regulations. Passenger ropeways consist of rotating or reciprocating ropes or cables that individual cabins are attached to. The Whoosh Transportation System, a new form of cable transport, combines elements of APMs with a network of static cable and rail. Specific to the Whoosh system’s movement using cable infrastructure, individual vehicles traverse independently across stationary cable spans. The cables remain fixed and are used as static infrastructure, unlike traditional passenger ropeway systems where vehicles are clamped to a moving cable. The vehicles operate autonomously and control their own speed and navigation throughout the network. Navigation of the network is achieved by an onboard vehicle switching system, allowing each vehicle to choose a unique path between stations. This introduces vehicle merging and diverging, requiring a control system similar to the personal rapid transit category of APMs. This paper explores the regulatory pathway for a Whoosh Transport System installation at a business park in New Zealand, which requires compliance with New Zealand specific standards and sign off by the authority having jurisdiction.

Literature is available on the adoption of shared autonomous vehicles (SAVs), but most of it is theoretical and lacks data provided by people who have actually ridden in SAVs. This study utilizes the responses to a survey conducted during the deployment phase of an SAV pilot project in Arlington, Texas, to examine the attitudes of segments of the population who are users of the service. Each group was profiled, based on their attitudes, travel behavior, and sociodemographic characteristics. Two cluster segments were obtained, using the K-Means cluster analysis, and then the modes of transportation primarily used by each group were explored, using a one-way ANOVA to test the mean difference across the two clusters. The results indicated that the first cluster group (SAV Optimists) was comprised primarily of highly educated males between the ages of 18 and 34 years, whose daily travel consisted of either riding a bike or walking. The second cluster segment (SAV Pessimists) included young females with high school or some post-secondary education. The results suggested that females prefer private transportation and are less interested in riding SAVs or recommending them to others.

Autonomous vehicles (AVs) are expected to play a crucial role in achieving sustainable transportation in the near future, making it essential to acquire insights into the public’s acceptance of them so they can be successfully integrated into the existing infrastructure. This study uses data obtained from riders of RAPID (Rideshare Automation Payment Integration Demonstration), a shared autonomous vehicle (SAV) service in Arlington, Texas, to identify the factors that influence how frequently riders take advantage of SAV services. A structured survey was developed to gather data related to the riders’ general travel behavior, attitudes towards the SAV service, frequency of use, and sociodemographic characteristics. A conceptual framework was developed based on the relationship between the key variables and the frequency of SAV usage. Structural equation modelling was employed to explore the direct relationship between the users’ sociodemographic characteristics, SAV attitudes, and general travel behavior and the impact on frequency of use. The results indicated that race, trip purpose, waiting time, and the availability of a private vehicle significantly influence the use of RAPID. Our model also identifies how users’ perception of the service affects how often it is used. This study provides insight into SAV services and its use in low-density areas that will enable transportation planners and policymakers to develop policies and a transportation infrastructure that will enhance SAV operations universally.

Technology-driven mobility services such as shared autonomous vehicles (SAVs) are gaining in popularity, and many cities in the world are implementing pilot projects that provide flexible on-demand services. SAVs strengthen the accessibility and mobility of underserved populations and enhance the efficiency of existing transit services by offering first/last mile connectivity. Due to a lack of empirical data, most of the existing literature on SAVs is based on hypothetical scenarios and simulation models; literature on modeling actual data is still scant. This study, developed based on real-time ridership data from an SAV service platform, is intended to fill that gap. We use the cluster analysis technique to identify patterns in trips lengths and durations, trip purposes, and bookings and the results indicated differences in the segments of users. The findings of this study will be helpful in planning, implementation, and marketing future SAV projects.