A Two-Phase Optimization Model for Autonomous Electric Customized Bus Service Design

Guo, Rongge; Guan, Wei; Bhatnagar, Saumya; Vallati, Mauro

doi:10.1109/itsc55140.2022.9921783

Cited by 5 publications

(12 citation statements)

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“…The allocation procedure used here shares some elements with the operational strategies 2 and 3 proposed in [10], that insert passengers to en-route vehicles. However, here we do not consider modular features, increasing the importance of exploiting predictions to maximize the use of vehicles' space.…”

Section: En-route Vehicle Dispatchingmentioning

confidence: 99%

“…However, here we do not consider modular features, increasing the importance of exploiting predictions to maximize the use of vehicles' space. For the detailed dispatching and demand insertion algorithm the reader is referred to [10].…”

Section: En-route Vehicle Dispatchingmentioning

confidence: 99%

“…The dates have been selected to reduce the impact of external factors, such as COVID-19 and Chinese New Year. The map of the urban road network and the distribution of the 53 demand vertices is shown in the work of [9].…”

Section: A Experimental Settingsmentioning

confidence: 99%

“…Despite its significant potential, only limited studies have explored the use of AVs in CBs. Guo et al [9] integrated the AVs into CB systems and put forward a twophase optimization model to deal with reserved and real-time demands; then, they extended the autonomous customized bus (ACB) system by considering the modularity of AVs in dynamic scheduling [10], which improved the system performance in terms of capacity utilization.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Real-time Routing and Scheduling of On-demand Autonomous Customized Bus Systems

Guo,

Liu,

Bhatnagar

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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The integration of autonomous vehicles and ondemand customized bus systems is expected to be beneficial for responding to real-time demands. This paper investigates the autonomous customized bus (ACB) system that leverages passenger demand prediction to enhance service quality and vehicle utilization. A novel ACB service design optimization model that determines vehicle movements and passenger-tovehicle assignments is developed for the real-time routing and scheduling problem. Then, a rolling horizon approach, incorporating travel demand prediction, proactive dispatching and reactive adjustment, is proposed to address the studied problem. The performance of the introduced ACB system is evaluated using smartcard data from Beijing and the state-of-the-art machine learning algorithm. Results show that the proposed ACB system can effectively improve system performance and service level in terms of operating cost and passenger waiting time compared to reactive operations.

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Section: En-route Vehicle Dispatchingmentioning

confidence: 99%

Section: En-route Vehicle Dispatchingmentioning

confidence: 99%

Section: A Experimental Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-time Routing and Scheduling of On-demand Autonomous Customized Bus Systems

Guo,

Liu,

Bhatnagar

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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“…However, Connected and Autonomous Vehicle (CAV) technologies offering demand-responsive, doorto-door travel could change that by making buses more modern, attractive, visible and user-centric [3] and capable of offering accessibility, traffic reduction, environmental, air quality and energy consumption benefits [4]. CAVs supported the design and implementation of Customised Bus (CB) systems, that do not rely on fixed routes but adapt the path to the demand and requirements of passengers [5]. At the same time though this transition to CAVs, and CAVbased buses in particular, may also create a new layer of unprecedented risks that revolve around cybersecurity issues [6], [7], since the powerful notion of connectivity opens the door to increased opportunities for an adversary to implement a successful attack [8], [9].…”

Section: Introductionmentioning

confidence: 99%

One Bus is All it Takes: The Impact of Cyber Attacks in Customised Bus Services

Guo,

Nikitas,

Parkinson

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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Connected and Autonomous Vehicle (CAV) technologies have the potential to revolutionise public transport systems, making them more financially and environmentally sustainable, accessible, and user-centric. However, CAV-based bus services are vulnerable to cyber attacks manifesting in unwanted, deceitful behaviour. This includes behaviours such as suppression, overloading, and substitution, which could be used by malicious actors to disrupt service and cause significant damage to both the transport operator and passengers.This paper raises awareness about the potential impact of deceitful behaviour on transport systems by instantiating the ideas in a customised bus system example. More specifically, this work evaluates the impact of a deceitful vehicle in Customised Bus (CB) services, when considering (i) different customer demand profiles, (ii) at what point in planning and control the attack occurs, and (iii) whether the controller has detected the attack. Our empirical analysis shows how an attack aimed at compromising a single vehicle can lead to a surge in operating costs of up to 48%. Further, there is an increase in cost, distance travelled, and unserved passengers when the attack goes undetected. The results underscore the importance of preventing and detecting cyber attacks in the deployment of CAV-based solutions and emphasise the need for a proactive approach in managing the associated risks.

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Modular Autonomous Electric Vehicle Scheduling for Customized On-Demand Bus Services

Guo

Guan

Vallati

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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The emerging customized bus system based on modular autonomous electric vehicles (MAEVs) shows tremendous potential to improve the mobility, accessibility and environmental friendliness of a public transport system. However, the existing studies in this area almost focus on human-driven vehicles which face some striking limitations (e.g., restricted crew scheduling and fixed vehicle capacity) and can weaken the overall benefits. This paper proposes a two-phase optimization procedure to fully unleash the potential of MAEVs by leveraging the strengths of MAEVs, including automatic allocation and charging of modules. In the first phase, a mixed integer programming model is established in the space-time-state framework to jointly optimize the MAEV routing and charging, passenger-to-vehicle assignment and vehicle capacity management for reserved passengers. A Lagrangian relaxation algorithm is developed to solve the model efficiently. In the second phase, three dispatching strategies are designed and optimized by a dynamic dispatching procedure to properly adapt the operation of MAEVs to emerging travel demands. A case study conducted on a major urban area of Beijing, China, demonstrates the high efficiency of the MAEV adoption in terms of resource utilization and environmental friendliness across a range of travel demand distributions, vehicle supply and module capacity scenarios.

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A Two-Phase Optimization Model for Autonomous Electric Customized Bus Service Design

Cited by 5 publications

References 0 publications

Real-time Routing and Scheduling of On-demand Autonomous Customized Bus Systems

Real-time Routing and Scheduling of On-demand Autonomous Customized Bus Systems

One Bus is All it Takes: The Impact of Cyber Attacks in Customised Bus Services

Modular Autonomous Electric Vehicle Scheduling for Customized On-Demand Bus Services

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