Mixed logical dynamical modeling and hybrid model predictive control of public transport operations

Sirmatel, Isik Ilber; Geroliminis, Nikolas

doi:10.1016/j.trb.2018.06.009

Cited by 49 publications

(14 citation statements)

References 30 publications

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“…This avoids the need to resort to an indirect proxy, such as e.g. bus spacings [28], to enforce headway regularity. Indeed, this particular proxy might not be adapted to certain settings, such as a route with a non-homogeneous distribution of bus stops.…”

Section: Bus Stops and Passengersmentioning

confidence: 99%

“…PI-controller: This controller operates along the same lines as the PI-controller presented in [28]. The main difference here is that instead of spacing errors, we consider the error between the current position of a given bus and the shifted position of the preceding bus.…”

Section: B Baselinesmentioning

confidence: 99%

“…This controller aims to minimize deviations from the time-table and to enforce regular headways. In [28], an hybrid MPC is used to regularize bus spacings instead, while maintaining a high commercial speed. But in all those papers, the aim is to fulfill a serviceoriented objective, and no attention is paid to the eco-driving of the bus fleet as a result.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bilevel Optimization for Bunching Mitigation and Eco-Driving of Electric Bus Lines

Lacombe

Gros

Murgovski

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Section: Bus Stops and Passengersmentioning

confidence: 99%

Section: B Baselinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bilevel Optimization for Bunching Mitigation and Eco-Driving of Electric Bus Lines

Lacombe

Gros

Murgovski

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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“…First, one bus is selected and its individual energy consumption is analyzed with different control strategies. In Figure 12 four bus trajectories are compared: holding control, PI control [48], energy-aware cheap control (d), and advanced balanced velocity control (f). The two benchmark control strategies (HCS and PI control) perform similarly in this metric as none of them considers energy consumption.…”

Section: Energy Consumptionmentioning

confidence: 99%

Energy-aware predictive control for electrified bus networks

2019

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For an urban bus network to operate efficiently, conflicting objectives have to be considered: providing sufficient service quality while keeping energy consumption low. The paper focuses on energy efficient operation of bus lines, where bus stops are densely placed, and buses operate frequently with possibility of bunching. The proposed decentralized, bus fleet control solution aims to combine four conflicting goals incorporated into a multi-objective, nonlinear cost function. The multi-objective optimization is solved under a receding horizon model predictive framework. The four conflicting objectives are as follows. One is ensuring periodicity of headways by watching leading and following vehicles i.e. eliminating bus bunching. Equal headways are only a necessary condition for keeping a static, predefined, periodic timetable. The second objective is timetable tracking. A conflicting objective to the former is minimizing passenger waiting time. When more than the expected passengers are waiting for the bus it is desirable to haste the vehicle in order to prevent bunching. The final objective is energy efficiency. To this end, an energy consumption model is formulated considering battery electric vehicles with recuperation during braking. Different weighting strategies are compared and evaluated through realistic scenarios, realized in a validated microscopic traffic simulation environment. Simulation results suggest 3 − 8% network level energy saving compared to bus holding control while maintaining punctuality and periodicity of buses. Formulating the multi-objective cost functionBuses operate on a route based on their timetable. When the schedule is tight, their trajectory shall be carefully planned while considering the schedule, passenger demand, and energy efficiency.The proposed bus velocity control algorithm (referred hereafter as control algorithm) creates an optimal trajectory within a predefined prediction horizon, considering the schedule, the number of passengers waiting and the location of other buses. The trade-off in the optimization is the total energy consumed, which is modeled as a physical energy consumption model. During operation, due to irregular dwell times and traffic disturbances, they tend to get out of sync with the schedule and start bunching. The goal of the control algorithm is to ensure timetable and

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“…To evaluate different transit control strategies, [95] compares eight scenarios in two different approaches from three dimensions: bus capacities, service frequencies and load profiles. Also, a hybrid dynamic model of a single bus line is presented with a dual objective of regularizing spacings and improving bus service speed [96]. To further improve the bus operation efficiency, many bus signal priority strategies have also been proposed [97][98] [99].…”

Section: Urban Transit Schedulementioning

confidence: 99%

Optimization and scheduling for a large-scale urban transportation system involving human factors

Zhang¹

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Mixed logical dynamical modeling and hybrid model predictive control of public transport operations

Cited by 49 publications

References 30 publications

Bilevel Optimization for Bunching Mitigation and Eco-Driving of Electric Bus Lines

Bilevel Optimization for Bunching Mitigation and Eco-Driving of Electric Bus Lines

Energy-aware predictive control for electrified bus networks

Optimization and scheduling for a large-scale urban transportation system involving human factors

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