Search

As a solution to the high greenhouse gas emissions and declining quality of life caused by private vehicles, the shared mobility hub is introduced. The shared mobility hub is a place where multiple modalities come together, including public transport and shared private mobility. As the shared mobility hub is a relatively new solution, limited research is available on the topic, especially on finding potentially suitable locations for allocating them. In this research, this knowledge gap is addressed by developing and testing a generic methodology to determine suitable locations for a specific type: the regional shared mobility hub. The regional shared mobility hub is located outside a city center being able to act as an intermodal point of transfer. The developed methodology is a combination of two existing methods: the GIS Multi-Criteria Analysis (MCA) and Multi-Actor Multi-Criteria Analysis (MAMCA) available in the literature. The method is able to score and weight different criteria which determine regional shared mobility hub suitability, taking the end-user (traveler), operator, and government perspectives into account in the weighting. Results are presented in multiple heat maps based on scenarios with varying stakeholder weight importance. The methodology developed consists of five criteria that measure location suitability (potential demand at a certain location, hub implementation costs, generalized travel costs from and to the hub, link to surroundings, and societal impact) measured by nine attributes. In this method, the choice is made for the Analytic Hierarchy Process (AHP) to determine the criteria weights. The developed methodology is applied to the region of Rotterdam (The Netherlands) to analyse if the methodology produces useful results for policy implementation. From multiple analyses, it appears that the methodology is suitable for tackling the location suitability determination problem, as it produces intuitive results.

Seamless, efficient transport intermodality is a key aspect of the transition of cities toward sustainable mobility. The new “shared mobility hub” concept is increasingly gaining attention as a potential way to make this urgently needed transition happen. The present paper aims to provide an insightful view of the promising concept of shared mobility hub, initially by looking into the existing literature on its definitions and how they are classified to form different typologies. Following that, a new, flexible typology framework for shared mobility hubs is proposed. This is then applied to several existing hubs in five different cities in The Netherlands (Amsterdam, Eindhoven and Helmond), Spain (Sant Cugat de Vallès, Metropolitan Area of Barcelona), and Portugal (Lisbon). As these countries have different shared mobility policies in place and their citizens have very different mobility habits, we were able to reflect on how the proposed typology functions in varied contexts. This paper contributes to the ongoing discussion of a very timely topic in Europe and worldwide, which is the need for the deployment of a new generation of mobility hubs, with an emphasis on shared mobility. We trust that the suggested typology can be useful to policy-makers, local authorities, and transport and urban planners, as it can help with the conducting of a first effective screening with regard to which type of hub is needed for each specific case. The research presented herein is part of the SmartHubs Project (2020–2022), funded by the EIT–KIC on Urban Mobility. Grant Agreement: GA2021 EIT UM.

Free-floating bike sharing is an innovative and sustainable travel mode, where shared bikes can be picked up and returned at any proper place on the streets and not just at docking stations. Nevertheless, in these systems, two major problems arise. One is the imbalance of free-floating shared bikes (FFSB) between zones due to one-way trips, the other is the damaged bikes that must be brought for repair. In this study, a modeling framework for dynamic relocating operational and damaged bikes is proposed that starts with predicting the number and location of shared bikes using deep learning algorithms. The demand forecasting model adopts the Encoder-Decoder architecture embedded with the attention mechanism to further enhance the model’s prediction ability and flexibility. Then, a data-driven optimization model for FFSB relocations is presented, where the multi-period optimization is applied to dynamically plan the relocation activities throughout the day. A new hybrid metaheuristic algorithm that incorporates variable neighborhood search (VNS) and enhanced simulated annealing (ESA) algorithm is developed for solving the relocating problem, in which satisfactory performance is observed from the numerical example. We test the proposed framework with the real-world FFSB data from Beijing, China. The results show that relocating both operational and damaged bikes timely decreases the probability of users finding damaged bikes in the system, but leads to higher relocation costs. For peak-hours, considering only the operational bikes for relocation is the most effective strategy given the limited relocation resources. It is urgent at those times of the day to focus on providing bikes to clients where they are undersupplied.

Shared automated vehicles are expected to be part of the supply of transportation systems in the future. Parallel to this evolution, there is the rapid penetration of battery electric vehicles (BEVs). The limitations in battery capacity and charging speed of BEVs can influence the planning and operation of shared automated electric vehicle (SAEV) systems. The design of such systems needs to include these limitations so that their viability is properly estimated. In this paper, we develop a space–time-energy flow-based integer programming (IP) model in support of the strategic design of a regional SAEV system. The proposed approach optimizes the fleet (size and composition) and charging facilities (number and location), while explicitly accounting for vehicle operations in aggregated terms (including movements with users, relocations, and charging times). The model is used to assess the impact of vehicle range and different types of chargers in the optimal design of an interurban SAEV transport system in the center of Portugal. Results show a reduction in profit as the vehicle range increases. In regards to energy, it is observed that the adoption of long-range vehicles reduces the energy spent in relocations, and increases the amount of energy charged at a lower price. Additionally, it is found that a system with long-range vehicles does not take advantage of having fast chargers. Concerning the chargers’ optimal location, systems using short-range vehicles have more chargers close to the main commuter trips attracting cities, while systems with long-range vehicles have the chargers nearby the homes of users.

Recently, the traditional taxi industry has been struggling to keep its market share, especially with the emergence of new transport network companies (e.g., Uber). One of the problems with traditional taxi services is the difficulty of matching the taxi demand to its supply when there is no phone-booking or other reservation system. From that perspective, the taxi driver?s experience is important in reaching the next passenger. A taxi driver with limited experience may not know the high-demand locations and times of taxi stands or street sections to visit after dropping off a passenger. This causes a large number of vacant taxi drivers to regularly cruise the roads to search for a passenger, contributing to congestion, pollution, and resource waste. We formulate the problem of a taxi driver?s next passenger pickup location as a destination choice problem. Vacant taxi trips between drop-off and pickup points are extracted from GPS records obtained from a taxi operator in Lisbon, Portugal, to understand the travel behavior of vacant taxi drivers. We have estimated destination choice models with a multinomial logit and a nested logit structure. It was found that passenger demand at the pickup area, hotspot locations, service location preference, and major transport hubs positively influence a taxi driver?s next choice of passenger pickup location. Results of this study provide insight regarding the factors that explain a taxi driver?s probability to choose a certain zone within a set of passenger pickup zones, contributing to a better understanding of taxi driver travel behavior.

AV subnetworks is a way to deal with automated traffic and its technological need that will likely increase during the AVs deployment period. This strategy carries many benefits, yet some inconveniences are worth to mention. One of them relies on the fact that the design of AV subnetworks is often in practice focused on mitigating congestion in the peak-hours. However, designing for the most congested hour can be quite delicate when such a strategy is fixed throughout the day. The remaining part of the day involves different mobility patterns and shifting trips patterns throughout the day, i.e., different Origin-Destination pairs. When such O-D pairs are inside these AV subnetworks, CV owners cannot drive, and therefore a new mode of transport is necessary. This paper focuses on the lower-level decision problem, i.e., the traffic distribution during the transition period while AVs are being deployed in urban areas and AV subnetworks are expanding. A nonlinear mathematical programming model is presented to perform the trip distribution, where walking appears as an alternative. The main objective of this paper is to study the impacts of AV subnetworks from a CV owners’ perspective. A novel formulation guarantees that CV trips starting inside AV subnetworks throughout the day aren’t ignored – this means an alternative mode of transport, in this case, walking. This paper evaluates throughout the day when such situations would likely occur in a case study of the city of Delft, in the Netherlands, in two scenarios with AV subnetworks. The experiments revealed that walking is somehow inevitable when AVs reach 75% of the vehicle fleet – increasing travel costs up to 26.0% and 43.8%.

Shared e-mobility is a category of emerging mobility services that includes electric carsharing, e-bike sharing, and e-scooter sharing. These services are expected to reduce the negative externalities of road transport in cities, which is currently dominated by fossil-fuel-powered private car trips. In order to better inform the development and promotion of these services and indicate directions for further research, we conducted a comprehensive review of existing literature on the three shared e-mobility modes focusing on their usage pattern, demand estimation, and potential impacts. We found that despite the different vehicle capabilities, all three shared e-mobility services are mainly used for short trips, and their current users are mostly male, middle-aged people with relatively high income and education. The demand of all shared e-mobility modes share many common predictors: they appeal to people with similar socio-demographic characteristics and generate higher demand in locations with better transport connectivity and more points of interest. Shared e-mobility services can potentially lead to positive impacts on transportation and the environment, such as reducing car use, car ownership, and greenhouse gas emissions. However, the magnitude of these benefits depends on the specific operational conditions of the services such as the fuel type and lifetime of shared vehicles. The impact of each shared e-mobility mode is also expected to be affected by other coexisting shared e-mobility modes due to both complementarity and competition. Future directions should include studying the competition between and integration of multiple shared e-mobility modes.

Equity considerations in transportation planning literature have received increasingly more attention in the previous decades. While there have been theoretical suggestions to base transportation planning methods on the philosophical principle of “sufficientarianism” (whereby everyone is entitled to a minimum level of a good or service), the proposed approaches have not yet been developed enough to be usable for policy decision-making. In this paper we aim to bridge this gap by operationalizing in a case study an indicator of equity based on the theoretical work of Martens (2017) which argues for sufficientarianism. The presented formalised methodology can identify and quantify equity issues in transportation, is flexible to different contexts, and is a transparent way to assess equity in transportation. The case study shows that data availability is an important constraint and that careful attention must be paid to various assumptions and choices made.

Among the attempts made worldwide to foster urban and transport sustainability, transit-oriented development (TOD) certainly is one of the most successful. Since the TOD concept appeared in the late 1980s, it has received increasing attention from researchers and practitioners as a way to merge together transport engineering and planning, land-use planning, and urban design for providing comprehensive solutions to contemporary urban problems. This attention has notably led to the publication of over 300 articles explicitly concerned with TOD in Web of Science journals, as well as to many implementations of the concept, some already completed and others underway (as, for example, the Grand Paris Project in France and Moscow Central Circle in Russia). Essentially, TOD can be described as land-use and transport planning that makes sustainable transport modes convenient and desirable, and that maximizes the efficiency of transport services by concentrating urban development around transit stations. However, as TOD projects started to be implemented worldwide, it became evident that their outcomes could be quite diverse, revealing that in practice the results of a project would depend on a wide variety of factors, trends and complex interrelations between them. In this article, we aim to provide a comprehensive, systematic and up-to-date review of TOD research achievements and challenges. We start by presenting the TOD concept, framing it in the theory of urban planning, and by describing the different typologies of TOD proposed in the literature. Then, we review the vast research dedicated to the study of TOD effects, distinguishing impacts on travel behavior, real-estate prices, residential location, urban form, and community life. The next subject we look at is TOD planning, focusing separately on policy issues and decision-support tools. In the final part of the article, based on the analysis of previous literature, we identify the main gaps and challenges that TOD research needs to address in the future.

A restructuring of the current mobility and transportation system seems to be inescapable, as a result of the increasing urbanization and challenges regarding global sustainability. The concept of Mobility-as-a-Service (MaaS) is regarded by policy-makers as an answer to the needed change. Generally speaking, MaaS is an online platform that enables users to plan, book and pay a trip out of a variety of transport modes, conventional and shared. However, in the literature, the potential impact of MaaS on mobility is still relatively unclear. This study, therefore, aims to provide insights into which factors influence the intention to use MaaS among private vehicle owners, who have until now been identified as relatively MaaS-averse travellers. Policy-makers are highly interested in this group to start using MaaS since their shift from private vehicles to other transport options might positively contribute to easing the congestion and environmental problems. In order to create some insights on possible travel behavioural shift and adoption of new systems, an empirical study has been conducted among (co–)owners of motorized vehicles (passenger car, electric passenger car, van, motorcycle; moped) that live in the Netherlands. The survey was based on a conceptual model that explains why people would use this new system (MaaS) and has asked respondents about their travel behaviour, socio-economic characteristics and attitudes towards MaaS. Using Latent Class Cluster Analysis (LCCA) five clusters in the sample population regarding the intention to use MaaS were identified. The cluster profiles show that private vehicle owners who often use public transport and active modes are most inclined to use MaaS, whereas the ‘conservative’ passenger car owners who use the car as their main mode of transport for all their trips (e.g. commuting, leisure) show a lower intention to use MaaS. As it can be expected that the societal benefits of MaaS will especially occur when these conservative car owners adopt MaaS, we conclude that, from a policy perspective, implementing MaaS could be less effective in reducing transport externalities (e.g. pollution and wasted time in congestion) as perhaps expected.

The continuing urbanization and corresponding increase in transport demand are putting pressure on the accessibility, safety, sustainability, livability, and efficiency of urbanized regions. Public transport is regarded as a sustainable mode of transport for these regions and therefore transport policies aim to increase its attractiveness. However, public transport is facing last-mile connectivity problems. The application of Autonomous Mobility on-Demand (AMoD) as a feeder service for public transport hubs can potentially improve the first and last-mile trip leg which increases the attractivity of public transport. However, will such a system be financially viable when applied in an urban area? and what kind of operation will lead to the highest system performance? In this research, this question is addressed by proposing a method that connects macro transport modeling and agent-based modeling (ABM). An existing gravity-based travel demand estimation model built in a macro simulation tool is used to predict passenger demand across all the OD pairs of a city. For those OD pairs that can use the AMoD as first /last mile this is modeled using an agent-based rationale to be able to simulate the behavior of passengers and vehicles within that specific area of the city. The simulation model is applied to the case study area of the south of Rotterdam, in The Netherlands, where metro Station Zuidplein and the rail Station Lombardijen function as two AMoD hubs. Using the case study, the impact of relocation, ridesharing, and charging strategy is assessed in regards to financial viability. Among other insights, results show that the AMoD service leads to a profit on a typical business day for the operating companies despite the high-quality level of the service (very low average waiting time for a vehicle). If this particular system would not consist of automated vehicles and one would have to pay a salary to drivers, it would not be possible to make a profit on a typical business day. Moreover, results show that activating dynamic ridesharing and using wireless fast chargers at the stations results in the most financially viable operation. Activating automatic relocations results in the most costly operation.

Few studies have analyzed the mobility impacts of TOD using a longitudinal research approach, and even fewer incorporated origin–destination (OD) information in the analysis. In this paper, we examine the impacts of a metro system with TOD features on the share of car and bus trips using Metro do Porto as a case study. The application of an autoregressive beta regression modeling approach to parish and OD data from two mobility surveys (2000 and 2017) allowed to detect changes in travel behavior accounting for pre-metro mode shares. Metro implementation was associated with strong reductions of the share of car trips in OD pairs with metro at both trip ends, especially when stations were TOD. Metro influence on the share of bus trips varied from neutral to positive, suggesting that bus and metro complement each other rather than compete for passengers.

The performance of ride-sourcing services such as Uber and Lyft is determined by the collective choices of individual drivers who are not only chauffeurs but private fleet providers. In such a context, ride-sourcing drivers are free to decide whether to accept or decline ride requests assigned by the ride-hailing platform. Drivers' ride acceptance behaviour can significantly influence system performance in terms of riders' waiting time (associated with the level of service), drivers' occupation rate and idle time (related to drivers' income), and platform revenue and reputation. Hence, it is of great importance to identify the underlying determinants of the ride acceptance behaviour of drivers. To this end, we collected a unique dataset from ride-sourcing drivers working in the United States and the Netherlands through a cross-sectional stated preference experiment designed based upon disparate information conveyed to the respondents. Using a choice modelling approach, we estimated the effects of various existing and hypothetical attributes influencing the ride acceptance choice. Employment status, experience level with the platform, and working shift are found to be the key individual-specific determinants. Part-time and beginning drivers who work on midweek days (Monday-Thursday) have a higher tendency to accept ride offers. Results also reveal that pickup time, which is the travel time between the driver's location and the rider's waiting spot, has a negative impact on ride acceptance. Moreover, the findings suggest that a guaranteed tip (i.e., the minimum amount of tip that is indicated upfront by the prospective rider, a feature that is currently not available) and an additional income due to surge pricing are valued noticeably higher than trip fare. The provided insights can be used to develop customised matching and pricing strategies to improve system efficiency., Comment: 25 pages, 4 figures

Electric car-sharing systems have attracted large attention in recent years as a new business model for achieving both economic and environmental benefits in urban areas. Among different types, the one considered in this paper is the so-called one-way car-sharing system whereby a user can begin and end a trip at any station of the system. At the same time, the Vehicle-to-Grid (V2G) concept is emerging as a possible innovative solution for smart power grid control. A management system that combines car-sharing system operations and V2G technology is a recent challenge for academia and industry. In this work, a mixed integer linear programming formulation is proposed to find the optimal management of electric vehicles in a one-way car-sharing system integrated with V2G technology. The proposed mathematical model allows finding the optimal start-of-day electric vehicles distribution that maximizes the total revenue obtained from system users and V2G profits through daily electric vehicles charging/discharging schedules. These schedules are based on mean daily users' electric vehicles requests and electricity prices. The model can be applied to evaluate the possible average daily profitability of V2G operations. In order to test the model performance, we applied it to a small-size test network and a real-size test network (the Delft network in the Netherlands). Under the model assumptions, the adoption of V2G technology allows to fully cover the daily charging costs due to users’ trips and to obtain V2G profits by taking advantage of electric vehicles unused time without significantly reducing the satisfied car-sharing system demand. Most of the energy purchased to charge the electric vehicles batteries is provided back to the grid during energy peak load demand, creating benefits also for energy providers.

The quality of the delivery service is a crucial asset for an e-grocer to create and maintain a loyal customer-base. With the rapid market growth of e-grocers over the last decade, there is an urgent need for e-grocer specific routing systems. Although stochastic and dynamic routing models are studied for a wide range of applications, e-grocer specific models are missing in the literature. This paper investigates the concept of flexible deliveries, which introduces differentiated time window sizes. This creates the possibility for real-time re-optimization of the sequence of customers in a trip in order to improve the on-time delivery performance. The potential of flexible deliveries is investigated by means of computational experiments in which historic trip instances from the Dutch e-grocer Picnic are used. It is shown that, when re-optimization is activated, on-time delivery performance is improved and this benefit is significant when flexible deliveries represent at least 10% of the deliveries. When 10% of the deliveries are flexible, the number of late deliveries can be reduced by up to 18% and the number of extreme late deliveries $\mathbf{(\geq\ 15\ {min\ late})}$ up to 27%. This improved on-time delivery performance comes at the cost of a maximum of 2% increase in the average time spent per delivery.

This paper aims to explore the performance of the autonomous mobility-on-demand system (AMoD) with the coordinated formation of vehicle platooning. In this study, an agent-based model (ABM) is developed to explicitly simulate the operations of platooning formation and interactions between shared automated vehicles (SAVs) and real-time travel requests. The objective is to capture the real-time behavior of SAVs as trip makers, and then assess the performance of the AMoD system with the mechanism of coordinated formation of platoons. We conclude that the impact of vehicle assignment strategies in the AMoD system with vehicle platooning formation predominately affects the average waiting time and system capacity to transport travelers as a whole; however, vehicle platooning, to some extent, could lengthen the travel time of platoon vehicles. The hold-on time (imposed delay) of leading vehicles in order to form a platoon could affect the average time delay of vehicles part of those platoons. The developed ABM provides the first insight into the impact of the pervasive formation of vehicle platooning on the performance of the AMoD system.

The possibility of having driverless cars on the streets seems to be more real than ever. In this paper, we focus on developing exact methods that can determine the effects of privately owned automated vehicles (AVs) and how switching to those vehicles is going to change mobility in urban environments. The considered problem determines the routes of family owned AVs that minimize the transportation costs of that family while considering the possibility of using public transport as an alternative for some trips. We introduce a novel exact linear formulation for this problem which includes a linearized traffic congestion model and which is able to solve the user and system optimum variant of the problem to optimality. The introduced formulation can easily be adapted to consider the current situation with conventional vehicles and a situation where not only the travel time costs of the driver but also costs of the other passengers are taken into account. The main advantage of our novel formulation is that the optimal results can be obtained to explore potential changes of flows with vehicle automation in small networks. We investigated the behavior of the system, given the described scenarios, by applying our formulation to a case study.

Carsharing has become a popular travel mode owing to its convenience of use, easy parking, and low cost of using a car by those who only need it occasionally. However, because of the inadequate location of carsharing stations (station-based systems) or vehicles (free-floating systems), effectively requiring expensive and complex relocation strategies, a number of customers are lost, and some carsharing companies are facing bankruptcy. This study proposes a data-driven, dynamic, multi-company relocation method, which aims to reduce relocation costs and increase profit in one-way carsharing station-based systems through cooperative strategies. The method starts from the prediction of carsharing inflows and outflows at each station throughout the day using a new deep learning algorithm designated as “the attention-enhanced temporal graph convolutional network”. It adopts an encoder-decoder structure to simultaneously capture the temporal and spatial carsharing usage patterns. A two-phase integer programming model is proposed to optimize the process of vehicle relocation and staff rebalancing with cooperative relocation strategies: the sharing of relocation staff, the sharing of vehicles and stations among the different companies. An adaptive large neighborhood search based heuristic approach is implemented to solve the two-phase model. Based on the 6-month travel records from four carsharing companies operating simultaneously in Fuzhou, China, the proposed model and cooperative strategies are assessed. The results show that the total profit of the four carsharing companies can be increased by 25.49% with the cooperation of staff and vehicles. In addition, we prospect the future relocation with automated vehicles, whereby the profit can be increased by 46.69% without the need to employ the relocation staff.

A bilevel nonlinear mathematical programing model is formulated to determine the optimal pricing and operator-based relocations in a one-way station-based carsharing system in competition with private cars. In the upper level, the carsharing operator determines the vehicle fleet, prices, and relocation operations with the objective of maximizing profits, considering the potential reaction of travelers. In the lower level, travelers choose travel modes from a cost-minimization perspective. Travel utilities are calculated through a logit model. The Karush–Kuhn–Tucker conditions are used to transform the bilevel model into a single-level model and then a genetic algorithm is proposed to solve it. Computational tests in four different scenarios show the combined strategy is the best one. The four scenarios are base, relocations, dynamic pricing, and a combination of relocations and pricing separately. The combined strategy can make the best trade-offs between the operator’s profit and the travelers’ cost.

This paper explores the potential integration of freight transport into a Mobility as a Service (MaaS) environment. MaaS is a user-oriented service concept providing door-to-door mobility solutions for people. It integrates services of multiple providers, enabling searching, booking, and payment through a single digital platform. Although MaaS is often presented as a great opportunity to increase the efficiency of the overall transport system, its application is constrained to passenger transport. The possibility of combining freight and passenger trips in MaaS might contribute to improving capacity utilization in passenger transport and to reducing freight movements in cities. The aim of the paper is to systematically explore the service models through which integration might take place and to qualitatively assess their potential impact. Based on a suitable conceptualization of what we called MaaS4PaF, i.e. MaaS for Passenger and Freight, we identify relevant logistics segments, propose service models and evaluate these from a multi-stakeholder and sustainability perspective. The analysis results in propositions about the fitness between on-demand freight and passenger systems combined in MaaS, as well as a related research agenda to further investigate the potential of this approach for future sustainable mobility.

Shared electric mobility hubs, or eHUBs, offer users access to a range of shared electric vehicles on demand. However, little is currently known about what the characteristics of potential users of this novel type of shared mobility are. This makes it difficult to plan the location of hubs and to provide facilities, which ultimately will determine their success. This paper therefore seeks to identify potential users based on an in-depth case study of a representative sample of the Municipality of Amsterdam population. The analysis employed an attitudinal market segmentation approach supported by the Theory of Diffusion of Innovations (DOI). The analysis identified four specific target groups, each with a different propensity to use eHUBs in the future. In our sample, two groups expressed an interest in using eHUBs. The first group consists of highly educated and non-car owning young adults (19% of the sample), whereas the second group shows a higher level of car ownership and a greater number of households with children (69% of the sample). The two remaining groups comprise the majority of laggards (52%), despite only representing 12% of the sample. They tend to be older, less educated, and live in a household without children. The four groups are further distinguished based on their current shared mobility use, traveler identity, and perceived barriers to using shared electric vehicles. Finally, general recommendations to practitioners and policymakers to increase the uptake of shared mobility, including paying attention to the availability, cost, and convenience of shared mobility options, are provided.

Shared automated vehicles (SAV) are expected to become part of on-demand transport systems in the future. In an interurban context, SAV systems may potentially contribute to significantly improve the availability and quality of public transport. In this paper, we propose a flow-based integer programming approach to design and assess the financial viability of a SAV interurban system. An integer programming model, based on profit maximization and allowing vehicle pooling, was developed to optimize the fleet size and vehicle movements of a typical working day. This model and its optimal results were the basis for assessing the financial viability of an interurban transport network in the central area of Portugal. Different levels of demand were tested and three different fleet compositions were considered: cars, minibuses, and a mixed fleet of both. The results showed that a mixed fleet of vehicles is the best option for profit maximization. The optimal share of each vehicle type, in this fleet composition, asymptotically converges to approximately 1/3 of cars and 2/3 of minibuses, as demand increases. The introduction of a SAV system in such a region for interurban trips can replace up to 18 private vehicles per SAV vehicle, and should be done with sequential expansions to guarantee a profit at the different stages. The break-even point, for the conditions of the case study, is between 1.5 and 2 passengers per vehicle, for a fleet of cars, and between 4 and 5 passengers per vehicle, for a fleet of minibuses.