Mitigating Bunching with Bus-Following Models and Bus-To-Bus Cooperation

Bus bunching is an instability problem where buses operating on high frequency public transport lines arrive at stops in bunches. In this work, we unveil that bus-following models can be used to design bus-to-bus cooperative control strategies and mitigate bunching. The use of bus-following models avoids the explicit modelling of bus-stops, which would render the resulting problem discrete, with events occurring at arbitrary time intervals. In a "follow-the-leader" two-bus system, bus-to-bus communication allows the driver of the following bus to observe (from a remote distance) the position and speed of a lead bus operating in the same transport line. The information transmitted from the lead bus is then used to control the speed of the follower to eliminate bunching. In this context, we first propose practical linear and nonlinear control laws to regulate space headways and speeds, which would lead to bunching cure. Then a combined state estimation and remote control scheme, which is based on the Linear-Quadratic Gaussian theory, is developed to capture the effect of bus stops, traffic disturbances, and randomness in passenger arrivals. To investigate the behaviour and performance of the developed approaches the 9-km 1-California line in San Francisco with about 50 arbitrary spaced bus stops is used. Simulations with real passenger data obtained from the San Francisco Municipal Transportation Agency are carried out. Results show bunching avoidance and significant improvements in terms of schedule reliability of bus services and delays. The proposed control is robust, scalable in terms of public transport network size, and thus easy to implement in real-world settings

Mobilitási szolgáltatások komplex automatizálási szintjei

A közlekedésben ismert és elfogadott automatizálási szintek elsősorban a járműirányításra fókuszálnak. Ugyanakkor a mobilitási szolgáltatások tervezése és használata is hatékonyabbá, illetve kényelmesebbé tehető az automatizálással. A mobilitási szolgáltatások automatizáltsági jellemzőit komplex módon leíró értékelő módszer a szolgáltatás tervezési, irányítási és utaskezelési funkciók jellemzésére alkalmazható

Optimal Control of Electric Bus Lines

Bus lines are inherently unstable systems, where any delay tends to be further amplified by the accrued passenger loads encountered at stops downstream. This self-reinforcing mechanism, when combined with the multiple sources of disturbances of an urban environment, can lead to the problem of bus bunching. To mitigate this, various types of control strategies have been proposed and some are routinely employed by transit agencies around the globe to improve service regularity. They range from simple rule-based ad-hoc solutions, to elaborate real-time prediction-based bus velocity control. However, most of these strategies only focus on service-related objectives, and often disregard the potential energy savings that could be achieved through the control intervention. Velocity-based control, in particular, is very suitable for eco-driving strategies, which can increase the energy efficiency of the transit system by adjusting the planned velocity trajectories of the vehicles based on the road and traffic conditions.This thesis proposes a scalable resolution method for the bus line regularity and eco-driving optimal control problem for electric buses. It is shown how this problem can be recast as a smooth nonlinear program by making some specific modelling choices, thus circumventing the need for integer decision variables to capture bus stop locations and avoiding the infamous complexity of mixed-integer programs. Since this nonlinear program is weakly coupled, a distributed optimization procedure can be used to solve it, through a bi-level decomposition of the optimization problem. As a result, the bulk of the computations needed can be carried out in parallel, possibly aboard each individual bus. The latter option reduces the communication loads as well as the amount of computations that need to be performed centrally, which makes the proposed resolution method scalable in the number of buses. Using the concept of receding horizons to introduce closed-loop control, the optimized control trajectories obtained were applied in a stochastic simulation environment and compared with classical holding and velocity control baselines. We report a faster dissipation of bus bunching by the proposed method as well as energy efficiency improvements of up to 9.3% over the baselines

Energy-aware predictive control for electrified bus networks

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 frequentlywith possibility of bunching. The proposed decentralized, bus\ua0 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, predefifined, periodic timetable. The second objective is timetable tracking, and passenger waiting time minimization. In case of high passenger demand, it is desirable to haste the bus 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. Alternative weighting strategies are compared and evaluated through realistic scenarios, in a calibrated microscopic traffic simulation environment. Simulation results confirm of 3-8% network level energy saving compared to bus holding control while maintaining punctuality and periodicity of buses

Bilevel optimization for bunching mitigation and eco-driving of electric bus lines

The problems of bus bunching mitigation and of the energy management of groups of vehicles are traditionally treated separately in the literature, and formulated in two different frameworks. The present work bridges this gap by formulating the optimal control problem of the bus line eco-driving and regularity control as a smooth, multi-objective nonlinear program. Since this nonlinear program only has few coupling variables, it is shown how it can be solved in parallel aboard each bus such that only a marginal amount of computations need to be carried out centrally. This leverages the decentralized structure of a bus line by enabling parallel computations and reducing the communication loads between the buses, which makes the problem resolution scalable in terms of the number of buses. Closed-loop control is then achieved by embedding this procedure in a model predictive control. Stochastic simulations based on real passengers and travel times data are realized for several scenarios with different levels of bunching for a line of electric buses. Our method achieves fast recoveries to regular headways as well as energy savings of up to 9.3% when compared with traditional holding or speed control baselines

Mitigating bunching with bus-following models and bus-to-bus cooperation

Bus bunching is an instability problem where buses operating on high-frequency public transport lines arrive at stops in bunches. This work unveils that bus-following models can be used to design bus-to-bus cooperative control strategies and mitigate bunching. The use of bus-following models avoids the explicit modelling of bus-stops, which would render the resulting problem discrete, with events occurring at arbitrary time intervals. In a follow-the-leader two-bus system, bus-to-bus communication allows the driver of the following bus to observe (from a remote distance) the position and speed of the leading bus operating in the same transport line. The information transmitted from the leader is then used to control the speed of the follower to eliminate bunching. A platoon of buses operating in the same transit line can be then controlled as leader-follower dyads . In this context, we propose practical control laws to regulate speeds, which would lead to bunching cure. A combined state estimation and remote control scheme is developed to capture the effect of disturbances and randomness in passenger arrivals. To investigate the performance of the developed schemes the 9-km 1-California line in San Francisco with about 50 arbitrary spaced bus stops is used. Simulations with empirical passenger data are carried out. Results show bunching avoidance and improvements in terms of schedule reliability of bus services and delays. The proposed control is robust, scalable in terms of transit network size, and thus easy to deploy by transit agencies to improve communication and guidance to drivers, and reduce costs

Mitigating Bunching with Bus-Following Models and Bus-to-Bus Cooperation

Bus bunching is an instability problem where buses operating on high-frequency public transport lines arrive at stops in bunches. This work unveils that bus-following models can be used to design bus-to-bus cooperative control strategies and mitigate bunching. The use of bus-following models avoids the explicit modelling of bus-stops, which would render the resulting problem discrete, with events occurring at arbitrary time intervals. In a follow-the-leader two-bus system, bus-to-bus communication allows the driver of the following bus to observe (from a remote distance) the position and speed of the leading bus operating in the same transport line. The information transmitted from the leader is then used to control the speed of the follower to eliminate bunching. A platoon of buses operating in the same transit line can be then controlled as leader-follower dyads. In this context, we propose practical control laws to regulate speeds, which would lead to bunching cure. A combined state estimation and remote control scheme is developed to capture the effect of disturbances and randomness in passenger arrivals. To investigate the performance of the developed schemes the 9-km 1-California line in San Francisco with about 50 arbitrary spaced bus stops is used. Simulations with empirical passenger data are carried out. Results show bunching avoidance and improvements in terms of schedule reliability of bus services and delays. The proposed control is robust, scalable in terms of transit network size, and thus easy to deploy by transit agencies to improve communication and guidance to drivers, and reduce costs. © 2000-2011 IEEE

Planeación dinámica e inteligente de rutas para sistemas de transporte BRT

Este trabajo plantea el desarrollo de un sistema multiagente de planeación y programación dinámica de rutas express para sistemas de transporte BRT. La novedad de la propuesta reside en la capacidad de adaptarse al comportamiento estocástico de la demanda mediante estrategias emergentes que conllevan a obtener una solución suboptima local. El objetivo del sistema es capturar información de movilidad de los pasajeros en tiempo real para posterior-mente detectar patrones de viaje y principales pares origen-destino. Cuando se presenta un deterioro en la calidad de servicio, el sistema inicia protocolos de ayuda entre los agentes con el propósito de incorporar paradas dinámicas en las rutas de los buses de acuerdo a las necesidades del sistema. Para evaluar el desempeño del sistema Smart-BRT se aplica un protocolo experimental simulando una troncal del sistema Transmilenio de la ciudad de Bogotá.This work proposes the design and development of an express dynamic route planning and scheduling multi-agent system for bus rapid transit systems. The novelty of this proposal lies in the stochastic demand adaptation capabilities through a series of emerging strategies that lead to a sub-optimal local solution. The objective of the system is to collect real-time mobility information from the passengers to subsequently estimate travel patterns and principal origin-destination matrices. When passengers experiment a low quality of service, the system reacts by initiating help protocols between agents, the final purpose is to add dynamic stops on the routes of the busses according to the system specific needs. The performance of the Smart-BRT system is assessed by mean of an experiment through a simulation of a corridor of Transmilenio BRT system in Bogotá city.Magíster en Ingeniería de Sistemas y ComputaciónMaestrí