Shuttle Service Research Articles

Enhancing Availability of Autonomous Shuttle Services: A Conceptual Approach toward Challenges and Opportunities

<div>Autonomous vehicle technologies have become increasingly popular over the last few years. One of their most important application is autonomous shuttle buses that could radically change public transport systems. In order to enhance the availability of shuttle service, this article outlines a series of interconnected challenges and innovative solutions to optimize the operation of autonomous shuttles based on the experience within the Shuttle Modellregion Oberfranken (SMO) project. The shuttle shall be able to work in every weather condition, including the robustness of the perception algorithm. Besides, the shuttle shall react to environmental changes, interact with other traffic participants, and ensure comfortable travel for passengers and awareness of VRUs. These challenging situations shall be solved alone or with a teleoperator’s help. Our analysis considers the basic sense–plan–act architecture for autonomous driving. Critical components like object detection, pedestrian tracking, trajectory planning, and teleoperation are analyzed, and concepts for overcoming the challenges mainly in these components are presented.</div>

Pedestrian shuttle service optimization for autonomous intersection management

Autonomous intersection management (AIM) has been widely investigated over the past two decades as a solution for signal-free intersections in a fully connected and autonomous environment. However, existing AIM approaches lack consideration for crossing pedestrians, especially in a way that enables pedestrians to cross streets safely while optimizing upcoming vehicles at the intersection to maintain traffic continuity. To address this issue, this paper proposes an Automated Pedestrian Shuttle (APS) service system to transport pedestrians across the street. Using a coupled space–time network modeling method, an optimization model, which incorporates pedestrian ride strategies and aims to minimize the total crossing time, is first established for the APS operation scheme. A Shuttle-by-Shuttle-Planning (SBSP) algorithm is developed for model solving to determine the APS operation route and the departure time from each station along the route. Then, a timing schedule optimization model is proposed to adjust the departure time of APS at each station and the time of main-lane vehicles entering an intersection to avoid vehicle conflicts. To ensure that the main-lane vehicles can enter the intersection on time, a trajectory optimization model is proposed to provide a specific driving strategy for each main-lane vehicle. A rolling horizon strategy is adopted to bridge the different optimization time windows. Finally, the effectiveness of the proposed APS service system is demonstrated through numerical simulation experiments. The results show that (1) the APS service can effectively help pedestrians cross the street with minor delays to main-lane vehicles; (2) The dynamic-route scheme is adaptable to the needs of randomly arriving pedestrians due to its demand-responsive nature; (3) The service efficiency of the system is related to the number of APSs, and the more APSs, the higher the service efficiency under the premise of unsaturation; (4) The designed SBSP algorithm significantly reduces the model-solving time compared to the commercial solver, making the APS service system potentially applicable for field implementation.

Joint Routing and Wireless Charging Scheduling for Electric Vehicles With Shuttle Services

Joint Routing and Wireless Charging Scheduling for Electric Vehicles With Shuttle Services

SP surveys to estimate Airport Shuttle demand in an Urban Air Mobility context

Electric vertical take-off and landing vehicles (eVTOL) are expected to be the key drivers for Urban Air Mobility (UAM) scenarios, by satisfying on-demand air travel needs in the short or mid-term. Despite the high number of eVTOL prototypes, nowadays only few studies have focused on UAM travel demand estimation, in particular Airport Shuttle demand estimation. The aim of this work is to use Stated Preference methods to collect data necessary to understand the main features of the potential UAM Airport Shuttle trip demand, also by calibrating some discrete choice models. Data were collected by both on-line surveys and face-to-face interviews, which captured mainly the Italian context. Three different Multinominal Logit models and a Mixed Logit model have been calibrated in order to identify the main variables driving people's choices for Airport Shuttle services. The results show the positive impact of income, air travel frequency and shared ride in increasing the willingness to use Airport Shuttle services. On the other hand, user that still prefers the ground transportation modes to reach the airport, high ratio between the number of cars and the driving licenses per family unit and the lack of experience with autonomous systems (i.e., driving assistance systems) seem to have a negative impact on people intention to use Airport Shuttle services.

Tools and Methodologies for the Analysis of Home-to-Work Shuttle Service Impacts: The ENEA “Casaccia” Case Study

Mobility management is a regulatory framework designed to streamline systematic mobility and mitigate energy, environmental and economic impacts. In this work, we propose a flexible methodology for evaluating the sustainability of home-to-work travel, providing a comprehensive and detailed ex post cost–benefit assessment. Specifically, we analyzed the effectiveness of the shuttle service operating in the ENEA “Casaccia” Research Centre in pre-pandemic times. Initially, we conducted an online survey to collect data with the aim of characterizing the travel behavior of the staff and reconstructing the multi-modal individual mobility patterns. Over 70% of the recipients, which amounted to about 950 individuals, completed the survey. Subsequently, we studied two alternative scenarios—with and without the shuttle service—comparing their total mileage, energy consumption, and pollutant emissions and performing an economic analysis. Our findings suggest that operating the service has a significant impact on air pollutants and greenhouse gas emissions, with reductions of 97% for volatile organic compounds, 72% for particulate matter, and 60% for carbon dioxide. Moreover, the cost–benefit analysis reveals that both users and the community reaped benefits from the provision of the collective service. These benefits are estimated to be almost EUR 1.35 M per year.

Introducing Autonomous Shuttle Services Based on Travel Patterns for the Elderly

The transportation-disadvantaged population is rapidly increasing through aging; moreover, the mobility of the elderly has increased due to life expectancy improvements and lifestyle changes. To respond to these changes, a customized mobility service specifically tailored to the travel patterns of the elderly was developed in this study. In particular, an autonomous shuttle service that can improve the operational efficiency and the accessibility of existing transportation methods was proposed by considering the travel characteristics of elderly people, who mainly travel short distances. To this end, a study was conducted in Seongnam City, Republic of Korea, where mobility support services for the elderly are insufficient. Using smart card data, the elderly were classified according to their travel patterns, with autonomous shuttle routes suggested for each travel purpose. We derived four clusters via the Gaussian mixture model clustering, with travel purposes classified according to the spatiotemporal travel patterns. Finally, the major routes for each travel purpose were selected, and feasible road paths for autonomous shuttle operation were suggested using the concept of ODD (operational design domain). This holistic methodology is expected to contribute to the development of autonomous mobility services for the elderly and for the establishment of welfare policies based on the smart card data.

Optimization of Powertrain Parameters of a Battery Electric Vehicle for Shuttle Service Usage

Optimization of Powertrain Parameters of a Battery Electric Vehicle for Shuttle Service Usage

Challenges and Prospect of Operating University Campus Shuttle Services in Lagos State University

Challenges and Prospect of Operating University Campus Shuttle Services in Lagos State University

The Impact of Community Shuttle Services on Traffic and Traffic-Related Air Pollution.

Community shuttle services have the potential to alleviate traffic congestion and reduce traffic pollution caused by massive short-distance taxi-hailing trips. However, few studies have evaluated and quantified the impact of community shuttle services on urban traffic and traffic-related air pollution. In this paper, we propose a complete framework to quantitatively assess the positive impacts of community shuttle services, including route design, traffic congestion alleviation, and air pollution reduction. During the design of community shuttle services, we developed a novel method to adaptively generate shuttle stops with maximum service capacity based on residents' origin-destination (OD) data, and designed shuttle routes with minimum mileage by genetic algorithm. For traffic congestion alleviation, we identified trips that can be shifted to shuttle services and their potential changes in traffic flow. The decrease in traffic flow can alleviate traffic congestion and indirectly reduce unnecessary pollutant emissions. In terms of environmental protection, we utilized the COPERT III model and the spatial kernel density estimation method to finely analyze the reduction in traffic emissions by eco-friendly transportation modes to support detailed policymaking regarding transportation environmental issues. Taking Chengdu, China as the study area, the results indicate that: (1) the adaptively generated shuttle stops are more responsive to the travel demands of crowds compared with the existing bus stops; (2) shuttle services can replace 30.36% of private trips and provide convenience for 50.2% of commuters; (3) such eco-friendly transportation can reduce traffic emissions by 28.01% overall, and approximately 42% within residential areas.

Evaluating the Level of the Shuttle Service Quality at Al al-Bayt University Campus Using the SERVQUAL Model

This paper mainly adopts descriptive analysis method and case study method and uses SERVQUAL model and statistical method to measure and evaluate the service quality level of shuttle bus of Al al-Bayt University. The study concluded that the order of the dimensions of measuring the quality of shuttle service was as follows: assurance, reliability, and empathy at a medium level; then tangibility followed by responsiveness at a low level for each. In general, the students' satisfaction level with the shuttle services provided at the campus was low, especially for the tangibility and responsiveness dimensions. This research is of value to transport service providers in understanding the gaps in and requirements of their service provision. To policy makers it highlights the dissatisfaction in the current levels of public transport service, as well as providing some indication of areas in which future interventions can be directed.

Going the Distance by Going Green: DLSU’s Transportation System pre-, during, and post-pandemic

sustainable operations agenda. Guided by this agenda, we formed partnerships to operationalize a greener transportation system. One of our initiatives is to partner with an electromechanical solutions provider in operationalizing our zero-emission vehicle (ZEV) policy with the use of electronic vehicles in our shuttle services before the pandemic. To reduce the number of private vehicles on campus, we also partnered with a bus company to provide a point-to-point service for our community members. During the pandemic, we continued to develop facilities and programs to encourage the use of bicycles by our community post-pandemic. With this, we look forward to further reducing the number of private vehicles on campus and the need to provide additional parking spaces. Keyword: Zero Emission Vehicles (ZEV), Shuttle services, Bicycles

Impact of Airspace Restrictions on Urban Air Mobility Airport Shuttle Service Route Feasibility

Initial urban air mobility (UAM) operations are anticipated to include airport shuttle services between airport terminals and city centers. UAM airport shuttle operations have the potential to improve the customer experience and provide airports with new revenue streams but risk interfering with current airport operations. In this paper, we develop a discrete, heuristics-based optimization framework to explore the impact of airspace constraints and the placement of vertiports in the airport terminal area on the feasibility of UAM shuttle operations. The framework is applied to the terminal areas of Hartsfield-Jackson Atlanta International Airport (ATL), Dallas Fort Worth International Airport (DFW), and Los Angeles International Airport (LAX). Small-scale UAM operations are found to be feasible at all three airports with current terminal-area procedures, particularly if UAM aircraft are classified as helicopters. Scaled-up operations are found to be most feasible at ATL because of the possibility of segregating UAM aircraft from commercial traffic, whereas scaled-up operations at LAX and DFW are expected to require the adoption of new technologies, procedures, and regulations.

Locating Off-campus Shuttle Service Terminals for a University using Address Cluster Analysis

Locating Off-campus Shuttle Service Terminals for a University using Address Cluster Analysis

Behavioral Intention and Socio-Demographic Moderators for the Acceptance of Autonomous Shuttle Services Bridging the Last-Mile to Public Transport

Behavioral Intention and Socio-Demographic Moderators for the Acceptance of Autonomous Shuttle Services Bridging the Last-Mile to Public Transport

Business model options for passenger urban air mobility.

While technological progress towards passenger urban air mobility (UAM) receives high attention, relevant business models for the deployment of these increasingly sophisticated passenger-carrying air vehicles for urban applications get less attention. However, especially in early market stages with high risks of investment and in respect to an often envisioned implementation of transport services in the proximity of urban settlements, it is important that the technology adds value to society. In this paper relevant perspectives to comprehend the current UAM business environment are presented. Possible operator models and customer segments are compiled and matched in three concrete business model approaches for passenger UAM. Scrutinising UAM Airport Shuttle Services shows that even concepts with sufficient demand might still not be adequate for a valid business case due to incomplete and a so far insufficient critical analysis of the overall operational environment. Second, a so far often unconsidered business model for a Company Shuttle Service is introduced, addressing the problem of scaling up UAM services with a dynamically expandable Business-to-Business (B2B) concept. Third, UAM will be discussed as part of public transport, introducing a business model that is inclusive and in strong line with public demands. Finally, these rather different implementation concepts unlock the space for an open-minded discussion on business models in the field of passenger UAM in general.

Real-Time Implementation of Smart Wireless Charging of On-Demand Shuttle Service for Demand Charge Mitigation

This paper presents a smart charge management strategy for an on-demand electric shuttle operating at the National Renewable Energy Laboratory (NREL) campus and supported by an inductive charger at the vehicle's waiting spot. A new control algorithm has been proposed for mitigating the demand charges incurred from the wireless charger. It monitors the shuttle, wireless charger, renewable energy generation, and other loads and regulates charging behavior for demand charge mitigation. Within the control algorithm, an energy prediction is made to estimate the mobility needs of the vehicle and maintain uninterrupted service during operation while still minimizing peak demand. The proposed controller is designed and optimized using a Simulink model for the entire system. It is then implemented and tested in real time at the NREL campus using online cloud services. Two vehicle-use cases—charge-sustaining and charge-depletion operation—are tested under different campus power profiles and drive cycles to assess the controller's performance. The proposed controller showed a robust performance under different driving scenarios, with high correlation between simulation and experimental data. The results show that proper demand response can be achieved, with an average of 94% reduction of charging loads during peak demand events.

User acceptance of mixed-traffic autonomous shuttles in Gothenburg, Sweden

A user acceptance questionnaire study was carried out during the first phase of the Shared Shuttle Services (S3) pilot project in Gothenburg Sweden. Autonomous vehicles in the form of shuttle buses (AV shuttles) embody three major developments in transportation: mobility solutions that are electrified, shared, and automated. The adoption of these three developments is closely connected to the UN SDG (11) Sustainable cities and communities and in order to achieve any broader societal benefits, it is crucial to understand and address user acceptance and adoption of these services in real-life settings. The questionnaire included attitudinal and context specific questions, resulting in overall high ratings except for perceived speed and comfort. Still, the main reasons for not wanting to use the AV shuttle services could be linked to performance expectancy, route reasons and effort expectancy. Future implementations of AV shuttles will need to address this in order to expect any widespread adoption.

A Green Demand-Responsive Airport Shuttle Service Problem with Time-Varying Speeds

This study proposes a multiobjective mixed integer linear programming (MOMILP) model for a demand-responsive airport shuttle service. The approach aims to assign a set of alternative fuel vehicles (AFVs) located at different depots to visit each demand point within the specified time and transport all of them to the airport. The proposed model effectively captures the interactions between path selection and environmental protection. Moreover, users with flexible pick-up time windows, the time-varying speed of vehicles on the road network, and the limited fuel for the route duration are also fully considered in this model. The work aims at simultaneously minimizing the operating cost, vehicle fuel consumption, and CO2 emissions. Since this task is an NP-hard problem, a heuristic-based nondominated sorting genetic algorithm (NSGA-II) is also presented to find Pareto optimal solutions in a reasonable amount of time. Finally, a real-world example is provided to illustrate the proposed methodology. The results demonstrate that the model not only selects an optimal depot for each AFV but also determines its route and timetable plan. A sensitivity analysis is also given to assess the effect of early/late arrival penalty weights and the number of AFVs on the model performance, and the difference in quality between the proposed and traditional models is compared.

Modelling Behaviour of Shuttle Service Users and Preference towards a Proposed Bus Rapid Transit Line

This paper analyses the behaviour of university students in their mode selection and their preference towards a proposed bus rapid transit (BRT) line in Karachi, Pakistan. This BRT line, known as Red Line, has a major portion of its route along the University Road. Most of the major higher education institutions of Karachi are located along University Road. A preference survey was conducted from the students to determine individual preferences towards attributes of existing travel modes and the proposed BRT line. The respondents were classified into three categories which included users of private vehicles, public transport, and university shuttle service. The existing literature lacks in behaviour modelling of shuttle service users. This research models the behaviour of shuttle users and compares their behaviour with the commuters of other transport modes. The influencing factors examined in this research included one-way travel time, travel fare, and level of comfort during the travel. The utility model developed in this research shows that students who use private vehicles value travel time more than the other attributes. On the contrary, the commuters of the shuttle service are more sensitive to travel fare. This research shows that the behaviour of shuttle users is significantly different than the other commuters. The multinomial logit (MNL) model was applied to estimate the number of trips expected to transfer to the proposed BRT line. Results show that the shuttle service users will be least influenced by the introduction of BRT, as the shuttle service offers subsidized fares with more convenience. The model developed in this paper can be used by the policymakers to make the BRT more attractive for students and the general public.

Pusher Tug and Barge Scheduling Problem in a Shuttle Service with a Short-range Distance

Pusher tug and barge system (PTBS) can be divided into two parts: an unmanned cargo-stowage part and a manned propelling-power part. While the barge is loading/unloading cargo at a terminal, the pusher tug can uncouple that barge and move to another terminal, and couple another barge to transport the next shipments. This paper examines the possibility of further efficient operation of PTBS for reducing related costs of barge transport in a shuttle service with a relatively short-range distance. We model a pusher tug and barge scheduling problem that can be used to simultaneously determine the optimal operation of pusher tug and barge. Based on numerical experiments, we found that the PTBS reduces the working time of tugs and therefore saves the barge related costs compared to conventional barge system.