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 ▲]

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 ▲]

Supply characteristics and membership choice in round-trip and free-floating carsharing systems

Scientific Conference (2019, June)

Analysis of Cooperative Bus Priority at Traffic Signals

in IEEE Transactions on Intelligent Transportation Systems (2019)

Incorporating trip chaining within online demand estimation

in Transportation Research. Part B, Methodological (2019)

Time-dependent Origin–Destination (OD) demand flows are fundamental inputs for Dy- namic Traffic Assignment (DTA) systems and real-time traffic management. This work in- troduces a novel state-space ... [more ▼]

Time-dependent Origin–Destination (OD) demand flows are fundamental inputs for Dy- namic Traffic Assignment (DTA) systems and real-time traffic management. This work in- troduces a novel state-space framework to estimate these demand flows in an online con- text. Specifically, we propose to explicitly include trip-chaining behavior within the state- space formulation, which is solved using the well-established Kalman Filtering technique. While existing works already consider structural information and recursive behavior within the online demand estimation problem, this information has been always considered at the OD level. In this study, we introduce this structural information by explicitly representing trip-chaining within the estimation framework. The advantage is twofold. First, all trips belonging to the same tour can be jointly calibrated. Second, given the estimation during a certain time interval, a prediction of the structural deviation over the whole day can be obtained without the need to run additional simulations. The effectiveness of the proposed methodology is demonstrated first on a toy network and then on a large real-world net- work. Results show that the model improves the prediction performance with respect to a conventional Kalman Filtering approach. We also show that, on the basis of the estimation of the morning commute, the model can be used to predict the evening commute without need of running additional simulations. [less ▲]

Towards Optimized Deployment of Electric Bus Systems Using Cooperative ITS

Poster (2019, January)

In this paper we analyze the impact of using cooperative intelligent transportation systems (C-ITS) to manage electrical bus systems. A simulation-based study is presented where three control strategies ... [more ▼]

In this paper we analyze the impact of using cooperative intelligent transportation systems (C-ITS) to manage electrical bus systems. A simulation-based study is presented where three control strategies are used to regulate the operations of a line, namely bus holding, Green Light Optimal Dwell Time Adaptation (GLODTA) and Transit Signal Priority (TSP). The results show, using a realistic scenario of a major line in Luxembourg City, that buses are efficiently operated without necessarily providing additional priority to public transport, hence without negatively affecting the capacity of the private vehicles system. Benefits in terms of headway regulations, energy consumption and travel time variance reductions are quantified. [less ▲]

Quasi-dynamic traffic assignment with spatial queueing, control and blocking back

in Transportation Research. Part B, Methodological (2019)

This paper introduces a steady-state, fixed (or inelastic) demand equilibrium model with explicit link-exit capacities, explicit bottleneck or queueing delays and explicit bounds on queue storage ... [more ▼]

This paper introduces a steady-state, fixed (or inelastic) demand equilibrium model with explicit link-exit capacities, explicit bottleneck or queueing delays and explicit bounds on queue storage capacities. The model is a quasi-dynamic model. The link model at the heart of this quasi-dynamic equilibrium model is a spatial queueing model, which takes account of the space taken up by queues both when there is no blocking back and also when there is blocking back. The paper shows that if this quasi-dynamic model is utilised then for any feasible demand there is an equilibrium solution, provided (i) queue storage capacities are large or (ii) prices are used to help impose capacity restrictions; the prices either remove queueing delays entirely or just reduce spatial queues sufficiently to ensure that blocking back does not occur at equilibrium. Similar results, but now involving the P0 control policy (introduced in Smith (1979a, 1987)) and two new variations of this policy (i.e., the spatial P0 control policy, and the biased spatial P0 control policy) are obtained. In these results, the control policies allow green-times to vary in response to prices as well as spatial queueing delays. These three policies are also tested on a small simple network. In these tests, the biased spatial version of P0 is much the best in reducing equilibrium delays (on this simple network). The paper further illustrates how the spatial queueing model works on simple networks with different merge models; it is demonstrated that equilibrium may be prevented by certain (fixed ratio) merge models. It is also shown in this case that equilibrium may be imposed on just the controlled area itself by a variety of (merge model, gating strategy) combinations. Opportunities for developing such combined gating and merging control strategies are finally discussed. [less ▲]

Incorporating trip chaining within online demand estimation

in Transportation Research Procedia (2019), 38

Time-dependent Origin–Destination (OD) demand flows are fundamental inputs for Dynamic Traffic Assignment (DTA) systems and real-time traffic management. This work introduces a novel state-space framework ... [more ▼]

Time-dependent Origin–Destination (OD) demand flows are fundamental inputs for Dynamic Traffic Assignment (DTA) systems and real-time traffic management. This work introduces a novel state-space framework to estimate these demand flows in an online context. Specifically, we propose to explicitly include trip-chaining behavior within the state-space formulation, which is solved using the well-established Kalman Filtering technique. While existing works already consider structural information and recursive behavior within the online demand estimation problem, this information has been always considered at the OD level. In this study, we introduce this structural information by explicitly representing trip-chaining within the estimation framework. The advantage is twofold. First, all trips belonging to the same tour can be jointly calibrated. Second, given the estimation during a certain time interval, a prediction of the structural deviation over the whole day can be obtained without the need to run additional simulations. The effectiveness of the proposed methodology is demonstrated first on a toy network and then on a large real-world network. Results show that the model improves the prediction performance with respect to a conventional Kalman Filtering approach. We also show that, on the basis of the estimation of the morning commute, the model can be used to predict the evening commute without need of running additional simulations. [less ▲]

Real time multiline holding control for networks with shared transit corridor

Scientific Conference (2018, September 05)