Arieh Schlote

Stochastic Park-and-Charge Balancing for Fully Electric and Plug-in Hybrid Vehicles

Motivated by the need to provide services to alleviate range anxiety of electric vehicles, we consider the problem of balancing charging demand across a network of charging stations. Our objective is to reduce the potential for excessively long queues to build up at some charging stations, although other charging stations are underutilized. A stochastic balancing algorithm is presented to achieve these goals. A further feature of this algorithm is that it is fully decentralized and facilitates a plug-and-play type of behavior. Using our system, the charging stations can join and leave the network without any changes to, or communication with, a centralized infrastructure. Analysis and simulations are presented to illustrate the efficacy of our algorithm.

Cooperative Regulation and Trading of Emissions Using Plug-in Hybrid Vehicles

We present a new approach to regulate traffic-related pollution in urban environments by utilizing hybrid vehicles. To do this, we orchestrate the way that each vehicle in a large fleet combines its two engines based on simple communication signals from a central infrastructure. Our approach can be viewed both as a control algorithm and as an optimization algorithm. The primary goal is to regulate emissions, and we discuss a number of control strategies to achieve this goal. Second, we want to allocate the available pollution budget in a fair way among the participating vehicles; again, we explore several different notions of fairness that can be achieved. The efficacy of our approach is exemplified both by the construction of a proof-of-concept vehicle and by extensive simulations, and is verified by mathematical analysis.

Traffic modelling framework for electric vehicles

This article reviews and improves a recently proposed model of road network dynamics. The model is also adapted and generalised to represent the patterns of battery consumption of electric vehicles travelling in the road network. Simulations from the mobility simulator SUMO are given to support and to illustrate the efficacy of the proposed approach. Applications relevant in the field of electric vehicles, such as optimal routing and traffic load control, are provided to illustrate how the proposed model can be used to address typical problems arising in contemporary road network planning and electric vehicle mobility.

A big-data model for multi-modal public transportation with application to macroscopic control and optimisation

This paper describes a Markov-chain-based approach to modelling multi-modal transportation networks. An advantage of the model is the ability to accommodate complex dynamics and handle huge amounts of data. The transition matrix of the Markov chain is built and the model is validated using the data extracted from a traffic simulator. A realistic test-case using multi-modal data from the city of London is given to further support the ability of the proposed methodology to handle big quantities of data. Then, we use the Markov chain as a control tool to improve the overall efficiency of a transportation network, and some practical examples are described to illustrate the potentials of the approach.

Markov Chain Based Emissions Models: A Precursor for Green Control

In this chapter we propose a new method of modeling urban pollutants arising from transportation networks. The efficacy of the proposed approach is demonstrated by means of a number of examples. Our models give rise to a number of surprising observations that are relevant for the regulation of pollution in urban networks: different actions are required for the control of different pollutants and low speed limits do not necessarily lead to low pollution.

On Closed-Loop Bicycle Availability Prediction

We study the effect of customer choices in bicycle-sharing systems based on bicycle availability predictions. We show that such systems may lead to flapping behavior between bicycle stations. The consequences of flapping instability include poor user experience and suboptimal usage of the available bicycle stock. We propose a simple assignment strategy aimed at eliminating flapping and balancing demand at each station based on actual availability.

Stochastically Balanced Parking and Charging for Fully Electric and Plug-in Hybrid Vehicles

Motivated by the need to reduce the potential for queuing at charging stations, and by the need to balance load across a number of charging stations, a stochastic algorithm is presented to balance load over a network of stations that also facilitates decentralised plug-and-play type behaviour that allows stations to provide charging services on an adhoc basis. Simulation results are presented to illustrate the efficacy of our algorithm.

A Google-like model for public transport

A recently introduced paradigm to describe road mobility networks is adapted to public transport networks. Preliminary description of the proposed approach and current on-going work in the topic are illustrated.

The effect of feedback in the assignment problem in shared bicycle systems

We study the effect of availability of occupation data on customer choices in bicycle sharing systems and the use of this data to build prediction systems. We show that if this data is given directly to customers, the overall performance of such a system can degrade. Solutions to this problem are presented.

A Markov-chain based model for a bike-sharing system

Bike-sharing systems are recently becoming ubiquitous in most cities, as an environmentally friendly alternative to other means of transportation. An optimal management of the bike-sharing service would in principle benefit from the availability of a mathematical model underlying the system. Accordingly, in this paper we propose a Markov-chain based approach to model the bike-sharing system, which we believe has a potential to develop alternative methods to implement classic control actions in a bike-sharing system (e.g., in terms of implementing alternative relocation strategies or planning advertising campaigns). The proposed methodology is validated on real data from the bike-sharing system in Boston, USA, and a first application of the proposed model is preliminarily illustrated in the paper.