Mixed hybrid and electric bus dynamic fleet management in urban networks: a model predictive control approach

Rinaldi, Marco; Picarelli, Erika; Laskaris, Georgios; D’Ariano, Andrea; Viti, Francesco

doi:10.1109/mtits.2019.8883387

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Early systems corrected vehicle direction by differential braking. Still, current systems actively control steering to maintain lane centering [78] by taking the dynamic and kinematics model of the vehicle into consideration to control the lateral motion of the vehicle [79][80][81][82][83][84]. Such a system is a great tool to prevent off-the-road crashes [85].…”

Section: On the Roadmentioning

confidence: 99%

Review of Autonomous Intelligent Vehicles for Urban Driving and Parking

Chan

Chin

2021

Electronics

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With the concept of Internet-of-Things, autonomous vehicles can provide higher driving efficiency, traffic safety, and freedom for the driver to perform other tasks. This paper first covers enabling technology involving a vehicle moving out of parking, traveling on the road, and parking at the destination. The development of autonomous vehicles relies on the data collected for deployment in actual road conditions. Research gaps and recommendations for autonomous intelligent vehicles are included. For example, a sudden obstacle while the autonomous vehicle executes the parking trajectory on the road is discussed. Several aspects of social problems, such as the liability of an accident affecting the autonomous vehicle, are described. A smart device to detect abnormal driving behaviors to prevent possible accidents is briefly discussed.

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Section: On the Roadmentioning

confidence: 99%

Review of Autonomous Intelligent Vehicles for Urban Driving and Parking

Chan

Chin

2021

Electronics

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“…[31,32], in preparation and support towards widespread Public Transport electrification. In this Section we present results stemming from our own recent research efforts [33][34][35] concerning the development of mixed-fleet vehicle scheduling models and algorithms tailored to the ongoing electrification of the bus fleet in the City of Luxembourg.…”

Section: Mixed Fleet Vehicle Scheduling and Charging Optimizationmentioning

confidence: 99%

Optimal Management of Electrified and Cooperative Bus Systems

Viti¹,

Rinaldi²,

Laskaris³

2021

Models and Technologies for Smart, Sustainable and Safe Transportation Systems

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This chapter presents an integrated management approach exploiting the potentials of the new Cooperative Intelligent Transportation Systems (C-ITS) to meet the requirements of the next generation Public Transport (PT). This approach considers the additional complexity of electrification—for instance electric busses need to periodically recharge during operation using dedicated infrastructure. This not only can impact service level, but also extend operating costs with complex electric charges. We develop new strategies explicitly optimizing the interactions within the PT ecosystem consisting of vehicles, traffic signals, and e-bus charging infrastructure. To achieve these goals, we rely on vehicle control rather than on the use of transit signal priority, which in congested urban scenarios can have negative effects on overall traffic performance. The main research challenges are in formulating and solving complex multi-objective optimization problems and real-time control. The proposed system is tested and evaluated in simulation showing the benefits of electrified and cooperative bus systems.

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“…The main goal of VSPs is to compute an optimal vehicle schedule that requires a minimum number of buses with limited driving ranges for a given charging infrastructure (cf. Li 2014, Paul & Yamada 2014, Wen et al 2016, Adler & Mirchandani 2017, van Kooten Niekerk et al 2017, Janovec & Koháni 2019, Rinaldi et al 2019a, Rinaldi et al 2019b, Tang et al 2019, Liu & Ceder 2020, Teng et al 2020.…”

Section: Vehicle Scheduling Problemsmentioning

confidence: 99%

A Concise Guide on the Integration of Battery Electric Buses into Urban Bus Networks

Dirks,

Wagner,

Schiffer

et al. 2021

Preprint

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With the increasing market penetration of battery-electric buses into urban bus networks, practitioners face many novel planning problems. As a result, the interest in optimizationbased decision-making for these planning problems increases but practitioners' requirements on planning solutions and current academic approaches often diverge. Against this background, this survey aims to provide a concise guide on optimization-based planning approaches for integrating battery-electric buses into urban bus networks for both practitioners and academics. First, we derive practitioners' requirements for integrating battery-electric buses from state-of-the-art specifications, project reports, and expert knowledge. Second, we analyze whether existing optimization-based planning models fulfill these practitioners' requirements. Based on this analysis, we carve out the existing gap between practice and research and discuss how to address these in future research.

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Mixed hybrid and electric bus dynamic fleet management in urban networks: a model predictive control approach

Cited by 3 publications

References 21 publications

Review of Autonomous Intelligent Vehicles for Urban Driving and Parking

Review of Autonomous Intelligent Vehicles for Urban Driving and Parking

Optimal Management of Electrified and Cooperative Bus Systems

A Concise Guide on the Integration of Battery Electric Buses into Urban Bus Networks

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