Integrating Connected and Automated Shuttles With Other Mobility Systems: Challenges and Future Directions

Connected and Automated Shuttles (CAS) are emerging technologies recently introduced into urban intelligent transport systems to enhance their efficiency. However, the majority of existing studies primarily concentrate on their design and public acceptance, while their integration to complement more conventional mobility systems is largely ignored. Indeed, the integration of CAS with existing transport systems represents a promising opportunity to improve the overall efficiency and safety of existing mobility services, be it offered to citizens or companies. However, this integration presents significant challenges for mobility service operators, who must consider the potential impact of these new technologies on traffic patterns, infrastructure investments, and travel behavior. This paper reviews the current state of connected and automated shuttle’s technologies, including the different experiments and pilot projects in Europe for the transport of people, goods or both, in addition to the scientific efforts to build new services (e.g., optimisation and AI-based models). We discuss the challenges that mobility service providers face when planning for the long-term integration of CAS, and propose a digital twin approach to help overcome these challenges. The proposed approach is based on advanced simulation and modeling software that can create realistic 3D representations of transport systems and has the potential to simulate the impact of CAS on traffic patterns and infrastructure investments. This paper serves as a starting point for future investigations on the integration of CAS with the existing mobility systems by identifying research gaps, limitations and challenges, and potential areas of research to overcome these challenges and improve the effectiveness of future mobility services.

[1]  Tan Yigitcanlar,et al.  How Can Smart Mobility Bridge the First/Last Mile Gap? Empirical Evidence on Public Attitudes from Australia , 2022, SSRN Electronic Journal.

[2]  Jeppe Rich,et al.  Effects of autonomous first- and last mile transport in the transport chain , 2022, Transportation Research Interdisciplinary Perspectives.

[3]  Catherine Cleophas,et al.  Scheduling shared passenger and freight transport on a fixed infrastructure , 2022, European Journal of Operational Research.

[4]  S. Bitam,et al.  Roadside Unit Deployment in Internet of Vehicles Systems: A Survey , 2022, Sensors.

[5]  E. Xidias,et al.  Intelligent fleet management of autonomous vehicles for city logistics , 2022, Applied Intelligence.

[6]  Silvio Nocera,et al.  Integration of passenger and freight transport: A concept-centric literature review , 2021, Research in Transportation Business & Management.

[7]  Tim Leinmüller,et al.  Dynamic Scheduling and Routing for TSN based In-vehicle Networks , 2021, 2021 IEEE International Conference on Communications Workshops (ICC Workshops).

[8]  Mohammed Obaid,et al.  Macroscopic Traffic Simulation of Autonomous Vehicle Effects , 2021 .

[9]  Alexander H. Hübner,et al.  A mixed truck and robot delivery approach for the daily supply of customers , 2021, Eur. J. Oper. Res..

[10]  Alexander H. Hübner,et al.  Cost‐optimal truck‐and‐robot routing for last‐mile delivery , 2021, Networks.

[11]  Maarten Weyn,et al.  Comparing Localization Performance of IEEE 802.11p and LTE-V V2I Communications , 2021, Sensors.

[12]  Yujie Li,et al.  A Cooperative Control Framework for CAV Lane Change in a Mixed Traffic Environment , 2020, ArXiv.

[13]  K. Nagel,et al.  Potential of Private Autonomous Vehicles for Parcel Delivery , 2020 .

[14]  Ziran Wang,et al.  Augmented Reality-Based Advanced Driver-Assistance System for Connected Vehicles , 2020, 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[15]  Yujie Li,et al.  Leveraging Vehicle Connectivity and Autonomy to Stabilize Flow in Mixed Traffic Conditions: Accounting for Human-driven Vehicle Driver Behavioral Heterogeneity and Perception-reaction Time Delay , 2020, ArXiv.

[16]  Moshe Ben-Akiva,et al.  Assessing the impacts of automated mobility-on-demand through agent-based simulation: A study of Singapore , 2020, Transportation Research Part A: Policy and Practice.

[17]  Vaneet Aggarwal,et al.  FlexPool: A Distributed Model-Free Deep Reinforcement Learning Algorithm for Joint Passengers and Goods Transportation , 2020, IEEE Transactions on Intelligent Transportation Systems.

[18]  M. Ziefle,et al.  On the Road Again - Explanatory Factors for the Users' Willingness to Replace Private Cars by Autonomous on-Demand Shuttle Services , 2020, AHFE.

[19]  John H. L. Hansen,et al.  Sensor Fusion of Camera and Cloud Digital Twin Information for Intelligent Vehicles , 2020, 2020 IEEE Intelligent Vehicles Symposium (IV).

[20]  Ralf-Martin Soe,et al.  Mobility Acceptance Factors of an Automated Shuttle Bus Last-Mile Service , 2020, Sustainability.

[21]  Mohamed-Slim Alouini,et al.  Performance Evaluation of UAV-Enabled Cellular Networks With Battery-Limited Drones , 2020, IEEE Communications Letters.

[22]  Q. Lu,et al.  LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving , 2020, 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC).

[23]  Guoyuan Wu,et al.  Driver Behavior Modeling Using Game Engine and Real Vehicle: A Learning-Based Approach , 2020, IEEE Transactions on Intelligent Vehicles.

[24]  Xiaolei Ma,et al.  Joint optimization of scheduling and capacity for mixed traffic with autonomous and human-driven buses: A dynamic programming approach , 2020 .

[25]  You Li,et al.  Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems , 2020, IEEE Signal Processing Magazine.

[26]  Lorenza Giupponi,et al.  NR-U and IEEE 802.11 Technologies Coexistence in Unlicensed mmWave Spectrum: Models and Evaluation , 2020, IEEE Access.

[27]  Tom Van Woensel,et al.  Integrating autonomous delivery service into a passenger transportation system , 2020, Int. J. Prod. Res..

[28]  Rui Dinis,et al.  A Low Complexity Channel Estimation and Detection for Massive MIMO Using SC-FDE , 2020, Telecom.

[29]  Alejandro Tirachini,et al.  The economics of automated public transport: Effects on operator cost, travel time, fare and subsidy , 2020 .

[30]  Akshay Vij,et al.  Consumer preferences for on-demand transport in Australia , 2020 .

[31]  Annapaola Marconi,et al.  Autonomous Shuttle-as-a-Service (ASaaS): Challenges, Opportunities, and Social Implications , 2020, IEEE Transactions on Intelligent Transportation Systems.

[32]  Lee D. Han,et al.  Fully Autonomous Buses: A Literature Review and Future Research Directions , 2019 .

[33]  Avishai Ceder,et al.  Real-time schedule adjustments for autonomous public transport vehicles , 2019 .

[34]  Borja Nogales,et al.  VENUE: Virtualized Environment for Multi-UAV Network Emulation , 2019, IEEE Access.

[35]  I. Roche-Cerasi Public acceptance of driverless shuttles in Norway , 2019, Transportation Research Part F: Traffic Psychology and Behaviour.

[36]  Marco Mazzarino,et al.  Smart Urban Planning: Evaluating Urban Logistics Performance of Innovative Solutions and Sustainable Policies in the Venice Lagoon—the Results of a Case Study , 2019, Sustainability.

[37]  Yiheng Feng,et al.  New Simulation Tools for Training and Testing Automated Vehicles , 2019 .

[38]  Yasushi Asami,et al.  Urban land use policies for efficient autonomous on-demand transportation – a case study on the japanese island of izu oshima , 2019, International Journal of Transport Development and Integration.

[39]  Alireza Zourmand,et al.  Internet of Things (IoT) using LoRa technology , 2019, 2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS).

[40]  Alexander Carballo,et al.  A Survey of Autonomous Driving: Common Practices and Emerging Technologies , 2019, IEEE Access.

[41]  Haris Ballis,et al.  Simulating a rich ride-share mobility service using agent-based models , 2019, Transportation.

[42]  Oded Cats,et al.  Taking The Self-Driving Bus: A Passenger Choice Experiment , 2019, 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).

[43]  Markus Friedrich,et al.  Integrating ridesharing services with automated vehicles into macroscopic travel demand models , 2019, 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).

[44]  Guoyuan Wu,et al.  Cooperative Ramp Merging System: Agent-Based Modeling and Simulation Using Game Engine , 2019, SAE International Journal of Connected and Automated Vehicles.

[45]  Avishai Ceder,et al.  Autonomous shuttle bus service timetabling and vehicle scheduling using skip-stop tactic , 2019, Transportation Research Part C: Emerging Technologies.

[46]  Pedro J. Navarro,et al.  A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research , 2019, Sensors.

[47]  Fatima de L. P. Duarte-Figueiredo,et al.  A 5G V2X Ecosystem Providing Internet of Vehicles † , 2019, Sensors.

[48]  Henk Jan Bergveld,et al.  Mobility Impacts of Automated Driving and Shared Mobility : Explorative Model and Case Study of the Province of North-Holland , 2019 .

[49]  Stanislav M. Chankov,et al.  Drone-delivery Using Autonomous Mobility: An Innovative Approach to Future Last-mile Delivery Problems , 2018, 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[50]  Alexandre M. Bayen,et al.  Lagrangian Control through Deep-RL: Applications to Bottleneck Decongestion , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[51]  Seung-Hoon Hwang,et al.  Optimization of Cell Size in Ultra-Dense Networks with Multiattribute User Types and Different Frequency Bands , 2018, Wirel. Commun. Mob. Comput..

[52]  Mauro Bellone,et al.  State of the Art of Automated Buses , 2018, Sustainability.

[53]  Muhammad Ali Imran,et al.  Cognition-Inspired 5G Cellular Networks: A Review and the Road Ahead , 2018, IEEE Access.

[54]  Albert Y. S. Lam,et al.  Autonomous Vehicle Logistic System: Joint Routing and Charging Strategy , 2018, IEEE Transactions on Intelligent Transportation Systems.

[55]  Michael Hyland,et al.  Dynamic autonomous vehicle fleet operations: Optimization-based strategies to assign AVs to immediate traveler demand requests , 2018, Transportation Research Part C: Emerging Technologies.

[56]  K. Rehrl,et al.  Digibus©: results from the first self-driving shuttle trial on a public road in Austria , 2018, European Transport Research Review.

[57]  Tuncer Baykas,et al.  A new approach for coexistence of IEEE 802.11af and IEEE 802.22 systems , 2018, 2018 26th Signal Processing and Communications Applications Conference (SIU).

[58]  Annegret Wagler,et al.  Fleet management for autonomous vehicles using flows in time-expanded networks , 2017, TOP.

[59]  V. Koltun,et al.  CARLA: An Open Urban Driving Simulator , 2017, CoRL.

[60]  Thomas Engel,et al.  Characterizing driving environments through Bluetooth discovery , 2017, 2017 International Conference on Information and Communication Technology Convergence (ICTC).

[61]  C. Macharis,et al.  Crowd logistics: an opportunity for more sustainable urban freight transport? , 2017, European Transport Research Review.

[62]  Kay W. Axhausen,et al.  Autonomous vehicles: The next jump in accessibilities? , 2017 .

[63]  Ashish Kapoor,et al.  AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles , 2017, FSR.

[64]  Fabien Lehuédé,et al.  Optimization of a city logistics transportation system with mixed passengers and goods , 2017, EURO J. Transp. Logist..

[65]  Gerhard Fettweis,et al.  Interference-Free Pilots Insertion for MIMO-GFDM Channel Estimation , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[66]  Y. Wang,et al.  Conceptual design and prototyping of a Slim Semi-autonomous Bus Rapid Transit Vehicle , 2017, SoutheastCon 2017.

[67]  Oded Cats,et al.  Designing an Automated Demand-Responsive Transport System: Fleet Size and Performance Analysis for a Campus–Train Station Service , 2016 .

[68]  Kay W. Axhausen,et al.  Autonomous Vehicle Fleet Sizes Required to Serve Different Levels of Demand , 2016 .

[69]  Stephen D. Boyles,et al.  Impact of Autonomous Vehicles on Traffic Management: Case of Dynamic Lane Reversal , 2016 .

[70]  Rosaldo J. F. Rossetti,et al.  Towards the integration of electric buses in conventional bus fleets , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[71]  Albena Mihovska,et al.  Performance evaluation of IEEE 802.11ah systems , 2016, 2016 24th Telecommunications Forum (TELFOR).

[72]  Alireza Talebpour,et al.  Influence of connected and autonomous vehicles on traffic flow stability and throughput , 2016 .

[73]  Federico Cheli,et al.  Real time energy management strategy for a fast charging electric urban bus powered by hybrid energy storage system , 2016 .

[74]  Rico Krueger,et al.  Preferences for shared autonomous vehicles , 2016 .

[75]  Antonio Alfredo Ferreira Loureiro,et al.  Real-time path planning to prevent traffic jam through an intelligent transportation system , 2016, 2016 IEEE Symposium on Computers and Communication (ISCC).

[76]  Daniel J. Fagnant,et al.  Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations , 2015 .

[77]  Chase C. Murray,et al.  The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery , 2015 .

[78]  Matthias R. Brust,et al.  A networked swarm model for UAV deployment in the assessment of forest environments , 2015, 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP).

[79]  Antonio Pescapè,et al.  A consensus-based approach for platooning with inter-vehicular communications , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[80]  Xiaowen Chu,et al.  Autonomous-Vehicle Public Transportation System: Scheduling and Admission Control , 2015, IEEE Transactions on Intelligent Transportation Systems.

[81]  Elisabeth Uhlemann,et al.  Introducing Connected Vehicles [Connected Vehicles] , 2015, IEEE Vehicular Technology Magazine.

[82]  Hajo A. Reijers,et al.  The Share-a-Ride Problem: People and parcels sharing taxis , 2014, Eur. J. Oper. Res..

[83]  Woonhee Sung,et al.  GREENIFY , 2013 .

[84]  Daniel Krajzewicz,et al.  Recent Development and Applications of SUMO - Simulation of Urban MObility , 2012 .

[85]  Christian Bettstetter,et al.  Channel measurements over 802.11a-based UAV-to-ground links , 2011, 2011 IEEE GLOBECOM Workshops (GC Wkshps).

[86]  Franz S. Hover,et al.  Imaging sonar-aided navigation for autonomous underwater harbor surveillance , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[87]  Anna Trentini,et al.  Toward a Shared Urban Transport System Ensuring Passengers & Goods Cohabitation , 2010 .

[88]  E. Venezia Urban Travellers’ Mode Choice:Towards A New Culture For Urban Mobility , 2009 .

[89]  Vijay Subramanian,et al.  Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation , 2006 .

[90]  Michael Zyda,et al.  From visual simulation to virtual reality to games , 2005, Computer.

[91]  Holger Giese,et al.  Autonomous Shuttle System Case Study , 2003, Scenarios: Models, Transformations and Tools.

[92]  Tomader Mazri,et al.  Improving V2X-6G network capacity using a new UAV-based approach in a Cloud/ICN architecture, case Study: VANET network , 2021, E3S Web of Conferences.

[93]  N. van Oort,et al.  Deployment Scenarios for First/Last-Mile Operations With Driverless Shuttles Based on Literature Review and Stakeholder Survey , 2021, IEEE Open Journal of Intelligent Transportation Systems.

[94]  Francisco Rodrigo Porto Cavalcanti,et al.  A CDL-Based Channel Model With Dual-Polarized Antennas for 5G MIMO Systems in Rural Remote Areas , 2020, IEEE Access.

[95]  Krishna M. Gurumurthy,et al.  Modeling Americans’ autonomous vehicle preferences: A focus on dynamic ride-sharing, privacy & long-distance mode choices , 2020, Technological Forecasting and Social Change.

[96]  Luciano Leonel Mendes,et al.  Enhanced Remote Areas Communications: The Missing Scenario for 5G and Beyond 5G Networks , 2020, IEEE Access.

[97]  A. König,et al.  Perceived Safety: a necessary precondition for successful autonomous mobility services , 2019 .

[98]  G. Currie Lies, damned lies, AVs, shared mobility, and urban transit futures , 2018 .

[99]  Frederik Schulte,et al.  Integrating People and Freight Transportation Using Shared Autonomous Vehicles with Compartments , 2018 .

[100]  Prateek Bansal,et al.  Influence of choice experiment designs on eliciting preferences for autonomous vehicles , 2018 .

[101]  Oscar Bergqvist,et al.  Bus Line Optimisation Using Autonomous Minibuses , 2017 .

[102]  Matti Kutila,et al.  Public Support Measures for Connected and Automated Driving. Competitiveness Report 2017 , 2017 .

[103]  Nick Hounsell,et al.  Public Views towards Implementation of Automated Vehicles in Urban Areas , 2016 .

[104]  Tam Tijs Jansen Development of a design model for integrated passenger and freight transportation systems , 2014 .

[105]  V Veaceslav Ghilas,et al.  Integrating passenger and freight transportation : model formulation and insights , 2013 .

[106]  Peter Vortisch,et al.  Microscopic Traffic Flow Simulator VISSIM , 2010 .

[107]  Saudi Arabia,et al.  A COMPARATIVE ANALYSIS OF CURRENTLY USED MICROSCOPIC AND MACROSCOPIC TRAFFIC SIMULATION SOFTWARE , 2009 .

[108]  Jordi Casas,et al.  Dynamic Network Simulation with AIMSUN , 2005 .

[109]  Steven E. Shladover,et al.  Effects of Adaptive Cruise Control Systems on Highway Traffic Flow Capacity , 2002 .

[110]  Simon Robinson,et al.  The future of road transport , 2002 .

[111]  Nuno Guimarāes,et al.  The Pilot Sites , 1998 .

[112]  John C. Mankins,et al.  Technology Readiness Levels-A White Paper , 1995 .

[113]  Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles , 2022 .