Gonçalo Homem de Almeida Correia

The Added Value of Accounting For Users’ Flexibility and Information on the Potential of a Station-Based One-Way Car-Sharing System: An Application in Lisbon, Portugal

Car-sharing systems are an alternative to private transportation whereby a person may use an automobile without having to own the vehicle. The classical systems in Europe are organized in stations scattered around the city where a person may pick up a vehicle and afterward return it to the same station (round trip). Allowing a person to drop off the vehicle at any station, called one-way system, poses a significant logistics problem because it creates a significant stock imbalance at the stations, which means that there will be times when users will not have a vehicle available for their trip. Previous mathematical programming formulations have tried to overcome this limitation by optimizing trip selection and station location in a city in order to capture the best trips for balancing the system. But there was one main limitation: The users were assumed to be inflexible with respect to their choice of a station, and held to use only the one closest to their origin and destination. If the user is willing to use the second or even the third closest station the user could benefit from using real-time information on vehicle stocks at each station and be able to select the one with available capacity. In this article we extend a previous model for trip selection and station location that takes that aspect into account by considering more vehicle pick-up and drop-off station options and then apply it to a trip origin–destination matrix from the Lisbon region in Portugal. Through the extended formulation we were able to conclude that user flexibility allied with having information on vehicle stocks increases the profit of the company, as people will go directly to a station with a vehicle available, thus making the use of the fleet more efficient. Observing the size of the stations resulting from the model, we also concluded that the effect of information is enhanced by large car-sharing systems consisting of many small stations.

Analysis of the pattern and intensity of urban activities through aggregate cellphone usage

This study applies passive mobile positioning data such as Call Volume, Handover, and Erlang to detect the spatiotemporal distributions of urban activities. We obtained hourly aggregated cellphone data from a dataset of communications in Lisbon, Portugal. Fuzzy c-mean clustering algorithm was applied to the cellphone data to create clusters of locations with similar features in two aspects of activities: the pattern and intensity of urban activities along the hours of a day. In order to validate those clusters as actual predictors of human activity, we compared them with clusters formed using ground truth variables namely presence of people, buildings, points of interest, and bus and taxi movement. To identify the patterns of urban activities, the Erlang data provided a better match, with the ground truth giving 69% of overall accurate predictions. In the case of the intensity of activities, Handover data provided the highest match, with the ground truth yielding 80% of overall accuracy of predictions. Hence, the results demonstrate the potential of passive mobile positioning data in detecting intensity of activities that are superimposed on the different activity patterns, which is a fundamental piece of information for transportation and urban planning.

Designing an Automated Demand-Responsive Transport System: Fleet Size and Performance Analysis for a Campus–Train Station Service

The rapid development of automated vehicles and the introduction of shared mobility services hold great potential for the development of automated public transport systems. However, their expected performance under alternative conditions and with alternative design factors remains unknown. In this study, the operation of a new automated mobility service was simulated and evaluated. The proposed transport system was defined as an automated demand-responsive transport system (ADRTS), which consisted of a demand-responsive public transportation service providing sharable rides without fixed routes or timetables in automated vehicles. Requests for the ADRTS were combined if they shared the same pickup and drop-off locations, subject to vehicle capacity limitations. Rides were launched within a predefined maximum vehicle dwell time, and routes were chosen without detours. The proposed system was tested in a field operation pilot project of the operation of automated vehicles in Wageningen in the Netherlands under conditions with different demand patterns, vehicle capacities, and operational factors. The simulation model determined the minimal and optimal fleet size for operation of the shuttle service when total operational and travel costs were minimized and maximum passenger waiting time requirements were satisfied. The results indicated that the most effective way to reduce the system cost per passenger of the ADRTS in the presented case study was to increase the demand level, increase the share of passengers who arrive independently, and use adequate vehicle sizes and short vehicle dwell times.

Applying a Model for Trip Assignment and Dynamic Routing of Automated Taxis with Congestion: System Performance in the City of Delft, The Netherlands

This paper proposes a method of assigning trips to automated taxis (ATs) and designing the routes of those vehicles in an urban road network, and also considering the traffic congestion caused by this dynamic responsive service. The system is envisioned to provide a seamless door-to-door service within a city area for all passenger origins and destinations. An integer programming model is proposed to define the routing of the vehicles according to a profit maximization function, depending on the dynamic travel times, which varies with the ATs’ flow. This will be especially important when the number of automated vehicles (AVs) circulating on the roads is high enough that their routing will cause delays. This system should be able to serve not only the reserved travel requests, but also some real-time requests. A rolling horizon scheme is used to divide one day into several periods in which both the real-time and the booked demand will be considered together. The model was applied to the real size case study city of Delft, the Netherlands. The results allow assessing of the impact of the ATs movements on traffic congestion and the profitability of the system. From this case-study, it is possible to conclude that taking into account the effect of the vehicle flows on travel time leads to changes in the system profit, the satisfied percentage and the driving distance of the vehicles, which highlights the importance of this type of model in the assessment of the operational effects of ATs in the future.