Mixed-fleet single-terminal bus scheduling problem: Modelling, solution scheme and potential applications

in Omega: the International Journal of Management Science (in press)

Reducing pollutant emissions and promoting sustainable mobility solutions, including Public Transport (PT), are increasingly becoming key objectives for policymakers worldwide. In this work we develop an ... [more ▼]

Reducing pollutant emissions and promoting sustainable mobility solutions, including Public Transport (PT), are increasingly becoming key objectives for policymakers worldwide. In this work we develop an optimal vehicle scheduling approach for next generation PT systems, considering the instance of mixed electric / hybrid fleet. Our objective is that of investigating to what extent electrification, coupled with optimal fleet management, can yield operational cost savings for PT operators. We propose a Mixed In- teger Linear Program (MILP) to address the problem of optimal scheduling of a mixed fleet of electric and hybrid / non-electric buses, coupled with an ad-hoc decomposition scheme aimed at enhancing the scalability of the proposed MILP. Two case studies arising from the PT network of the city of Luxem- bourg are employed in order to validate the model; sensitivity analysis to fleet design parameters is performed, specifically in terms of fleet size and fleet composition. Conclusions point to the fact that careful modelling and handling of mixed-fleet conditions are necessary to achieve operational savings, and that marginal savings gradually reduce as more conventional buses are replaced by their electric counterparts. We believe the methodology proposed may be a key part of advanced decision support systems for policymakers and operators that are dealing with the on-going transition from conventional bus fleets towards greener transport solutions. [less ▲]

Investigating the effect of controller locations on centralised dynamic traffic control with equilibrium constraints

Scientific Conference (2019, September)

Principles for setting single line and multiline control based on network characteristics

Scientific Conference (2019, September)

Investigating the robustness of route-based sensor location policies under variable network demand

Scientific Conference (2019, June)

On the substitutability of traffic light and pricing controllers in transportation networks

Scientific Conference (2019, June)

Leveraging GIS Data and Topological Information to Infer Trip Chaining Behaviour at Macroscopic Level

Scientific Conference (2019, June)

One of the open challenges in transport modelling is to estimate within-day demand flows that reflect the complexity of individual activity-travel behaviour. While disaggregate (Activity-Based) demand ... [more ▼]

One of the open challenges in transport modelling is to estimate within-day demand flows that reflect the complexity of individual activity-travel behaviour. While disaggregate (Activity-Based) demand models can recreate realistic daily mobility patterns at an individual level, they usually require an accurate knowledge of individual user behaviour (i.e. via travel surveys), which is not always available. As a result, practitioners often turn to aggregate demand models, that have the advantage of being less demanding in terms of data but typically under represent the demand for secondary activities. In this work, we take research on within-day demand modelling one step forward by proposing a framework that combines traditional methodologies with heterogeneous data sources in order to explicitly represent trip chaining at an aggregated level. We show that the combination of web-based crowd sensed data, network data and behavioural constraints allows to capture complex spatial and temporal correlations between demand patterns. The methodology is applied on the classical Gravity model to show how to incorporate within-day dynamics. Yet, any alternative demand model can be adopted. In our case, Generation and Attraction are used to estimate the systematic demand, that is enriched of information about individual activity patterns, and then a novel definition of impedance function based on Hagestraand ellipse theory plays a central role in spatially distributing locations of trips using geographic relationships and constraints deriving from space-time behaviour. A case study for Luxembourg City has been presented to show the potential of the methodology: the choice of using data from a different spatial context to account for the temporal dimension has been validated through comparisons with official statistics. The results of simulating a workplace relocation show the advantages of this new approach in representing demand related to secondary activities. [less ▲]

Availability-based dynamic pricing on a round-trip carsharing service: an explorative analysis using agent-based simulation 

in Transportation Research Procedia (2019)

Carsharing companies aim to customize their service to increase fleet usage and revenues with different pricing schemes and offer types. Dynamic pricing policies can be designed to adjust and balance ... [more ▼]

Carsharing companies aim to customize their service to increase fleet usage and revenues with different pricing schemes and offer types. Dynamic pricing policies can be designed to adjust and balance temporally and spatially cars availability but may pose some question on customers’ fairness. In this paper, we propose an explorative analysis of how an availability-based dynamic pricing scheme impacts the demand and the supply performance. The policy is simulated in MATSim and compared to a fixed pricing policy scheme. This simulation consists of analyzing the behavior of a synthetic population of car-sharing members for Berlin and the surrounding region in which is applied an availability-based dynamic pricing in which price depends on vehicle availability in booking stations. Results show that when the dynamic pricing is applied there is a light decrease in the number of bookings and people with low value of time tend to abandon the carsharing mode in favor of other modes of transportation. [less ▲]

How Road and Mobile Networks Correlate: Estimating Urban Traffic Using Handovers

in IEEE Transactions on Intelligent Transportation Systems (2019)

We propose a novel way of linking mobile network signaling data to the state of the underlying urban road network. We show how a predictive model of traffic flows can be created from mobile network ... [more ▼]

We propose a novel way of linking mobile network signaling data to the state of the underlying urban road network. We show how a predictive model of traffic flows can be created from mobile network signaling data. To achieve this, we estimate the vehicular density inside specific areas using a polynomial function of the inner and exiting mobile phone handovers performed by the base stations covering those areas. We can then use the aggregated handovers as flow proxies alongside the density proxy to directly estimate an average velocity within an area. We evaluate the model in a simulation study of Luxembourg city and generalize our findings using a real-world data set extracted from the LTE network of a Luxembourg operator. By predicting the real traffic states as measured through floating car data, we achieve a mean absolute percentage error of 11.12%. Furthermore, in our study case, the approximations of the network macroscopic fundamental diagrams (MFD) of road network partitions can be generated. The analyzed data exhibit low variance with respect to a quadratic concave flow-density function, which is inline with the previous theoretical results on MFDs and are similar when estimated from simulation and real data. These results indicate that mobile signaling data can potentially be used to approximate MFDs of the underlying road network and contribute to better estimate road traffic states in urban congested networks. [less ▲]

A Big Data Demand Estimation Framework for Modelling of Urban Congested Networks

in CSUM 2018, AISC 879 proceedings (2019)

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

Scientific Conference (2019, January)

Reducing pollutant emissions and promoting sustainable mobility solutions, including Public Transport, are increasingly becoming key objectives for policymakers worldwide. In order to jointly achieve ... [more ▼]

Reducing pollutant emissions and promoting sustainable mobility solutions, including Public Transport, are increasingly becoming key objectives for policymakers worldwide. In order to jointly achieve these goals, careful consideration should be put on the operational cost and management of PT services, in order to promote the adoption of green mobility solutions and advanced management techniques by operators. In this work we develop a dynamic fleet management approach for next generation Public Transportation systems, considering the instance of mixed electric / hybrid fleet. Our objective is that of investigating to what extent electrification, coupled with optimal fleet management, can yield operational cost savings for PT operators, explicitly considering real-time disturbances, including delays, service disruptions etc. We propose a Mixed Integer Linear Program to address the problem of optimal scheduling of a mixed fleet of electric and hybrid / non-electric buses, and employ it as predictor in a Model Predictive Control approach. Test results based upon a real-life scenario showcase how the proposed approach is indeed capable of yielding a sizable reduction in operational costs, even when considerable disturbances arise from the underlying system. [less ▲]