Future Transp., Volume 3, Issue 2 (June 2023) – 23 articles

Current urban mobility systems in Europe, characterized by high car mobility shares, have negative environmental and health impacts but struggle to mitigate these for fear of sacrificing accessibility. Ironically, before the car mobility transition (in the 1950s and 1960s in Western countries and the 1990s in Eastern Europe), most cities were accessible by walking, cycling, public transport, and by the few cars there were. Through a longitudinal case study of a medium-sized urban area in Clermont-Ferrand, France (1950–2022), this paper explores the potential to ‘de-transition’, i.e., to reverse the urban transition process towards ‘accessible, low-car cities’ by reshaping infrastructures to constrain car use whilst accommodating walking, cycling, and public transport. We answer the following questions: To what extent can cities reverse the urban car mobility transition? How could such a process be further encouraged? Our analysis adopts a social practices perspective and uses a mixed-methods approach by combining semi-structured interviews, a survey, and a document analysis. On the one hand, our findings highlight the difficulty of an urban modality shift to car alternatives: (1) the limited reach of public transformation networks (in Clermont-Ferrand, the tramline); (2) the fact that many feel unsafe or assume they need excellent health conditions to cycle, which is associated with leisure and sports; and (3) strong convictions concerning the usefulness of vehicle ownership, which is believed to maximise comfort. On the other hand, based on a historic analysis, we offer practical recommendations to de-transition to low-car urban areas: (1) the creation of an extensive regional tramway network; (2) the development of a full cycling network; and (3) the promotion of an extensive car-free city centre. Full article

The deployment of autonomous shuttles (ASs) holds health and safety benefits for people with and without disabilities. Transportation is critical in helping people with disabilities (PWDs) access health care, services, and jobs, but the current transportation system has not afforded them ubiquitous access. To understand the acceptance of ASs, we (1) quantified PWDs’ (N = 42) perceptions before and after riding in an AS (Level 4) and (2) developed a model of facilitators and barriers from 143 participants. For Objective 1, after riding in the AS, PWDs (n = 42) expressed increased Intention to Use (p < 0.001) and Acceptance (p < 0.001), and decreased Perceived Barriers (p < 0.001), compared with baseline. For Objective 2, four multiple linear regression models were conducted to predict the outcomes for Intention to Use, Perceived Barriers, Well-being, and Acceptance among all participants (N = 143). The results indicated that optimism and ease of use negatively predicted Perceived Barriers and positively predicted Intention to Use, Well-being, and Acceptance. Driving status (i.e., active driver) negatively predicted Intention to Use, Well-being, and Acceptance. Predictors of user Acceptance included optimism, perceived ease of use, driver status, and race/ethnicity—with 30.7% of the variance in Acceptance explained. We also recommended deployment strategies to project stakeholders. Full article

In recent years, the maintenance of multicomponent systems has been discussed in many papers. The aim of these studies is to use the maintenance duration of one component for the maintenance of other components to minimize the total maintenance cost of the system. The complexity of the maintenance of this kind of system is due to its structure and its large number of components. The present paper suggests a grouped maintenance policy for multicomponent systems in a finite planning horizon based on the systemic inspection feedback data. The system considered is periodically inspected. Then, the collected data are triply censored (left, right, and interval censored). The proposed grouped maintenance strategy starts by clustering the components into g clusters according to their degradation model. Then, an expectation minimization algorithm is applied to correct the censorship in the data and to associate a Weibull distribution with each cluster. The proposed grouped maintenance strategy begins by specifying an individual maintenance plan for each cluster by identifying an optimal replacement path. Then, this step is followed by finding an optimal grouping strategy using a genetic algorithm. The aim is to identify a point in time when the components can be maintained simultaneously. To illustrate the proposed strategy, the grouped maintenance policy is applied to the feedback data of the road markings of French National Road 4 (NR4) connecting Paris and Strasbourg. Full article

The COVID-19 pandemic caused a significant shift towards teleworking, resulting in changes in travel patterns. The relationship between teleworking and individual travel behavior is crucial for transportation planning and policymaking. Thereby, this article investigates the relationships between teleworking, COVID-19, and mobility patterns in Brazil during two different periods of the COVID-19 pandemic. Given the exceptional nature of recent events, it is crucial to conduct studies related to teleworking during the pandemic crisis to contribute relevant information and evidence to the literature on this phenomenon. Such research has the potential to provide new insights and perspectives that enhance our understanding of the challenges and opportunities associated with teleworking. Two different approaches were used in the methodological procedure. The first approach used exploratory analysis to study public aggregated data related to the beginning of the pandemic, followed by a cluster analysis and a multinomial logit model. The second approach, relative to a longer relaxation period of sanitary/health measures, collected disaggregated self-reported data using an online survey, which were analyzed through non-parametric tests. The results show a strong relationship between the teleworking regime and the frequency of trips, especially for work purposes. It also highlights the influence of economic development and the region of Brazil in the kind of work regime. The continuation of studies, such as this one, in current periods is important for analyzing possible impacts, such as the reduction in congestion, vehicle emissions, and to enable the use of teleworking as a demand management policy. Full article

Autonomous vehicles (AVs) have generated excitement for the future of transportation. Public transit agencies and companies (i.e., Uber) have begun developing shared autonomous transportation services. Most AV surveys focus on public opinion of perceived benefits and concerns of AVs but are not directly tied to field implementation of AVs. Experience and exposure to new technology affect adults’ perceptions and level of technology acceptance. As such, the Autonomous RideShare Services Survey (ARSSS) was developed to assess adults’ perceptions of AVs before and after being exposed to AVs. Face validity and content validity were established via focus groups and subject-matter experts (CVI = 0.95). Adults in the U.S. (N = 553) completed the ARSSS, and a subsample (N = 100) completed the survey again after two weeks. An exploratory and confirmatory factor analysis demonstrated that the ARSSS consists of three factors that can be used to reliably quantify users’ perceptions of AVs: (a) Intention to Use, Trust, and Safety (r = 0.85, p < 0.001, ICC = 0.99); (b) Potential Benefits (r = 0.70, p < 0.001, ICC = 0.97); and (c) Accessibility (r = 0.78, p < 0.001, ICC = 0.96) of AVs. These are key factors in predicting intention to use and acceptance of AVs. Results from the ARSSS may inform the acceptance among users of these AV technologies. Full article

Movement-specific vehicle classification and counting at traffic intersections is a crucial component of various traffic management activities. In this context, with recent advancements in computer-vision-based techniques, cameras have emerged as a reliable data source for extracting vehicular trajectories from traffic scenes. However, classifying these trajectories by movement type is quite challenging, as characteristics of motion trajectories obtained this way vary depending on camera calibrations. Although some existing methods have addressed such classification tasks with decent accuracies, the performance of these methods significantly relied on the manual specification of several regions of interest. In this study, we proposed an automated classification method for movement-specific classification (such as right-turn, left-turn and through movements) of vision-based vehicle trajectories. Our classification framework identifies different movement patterns observed in a traffic scene using an unsupervised hierarchical clustering technique. Thereafter, a similarity-based assignment strategy is adopted to assign incoming vehicle trajectories to identified movement groups. A new similarity measure was designed to overcome the inherent shortcomings of vision-based trajectories. Experimental results demonstrated the effectiveness of the proposed classification approach and its ability to adapt to different traffic scenarios without any manual intervention. Full article

Via the analysis of a set of parking and journey information for vehicles traveling to the parking site at the University of Brescia (Italy), we evaluated the possibility of managing the electric recharging of these vehicles, which are hypothesized to be electric. The paper investigates charging optimization techniques that can limit the charge power peaks and distribute the energy demand throughout the day. A cost assessment for an auxiliary system consisting of a photovoltaic energy source (PV) and battery stationary storage (BSS) is also carried out. Optimal power management at the station with PV and BSS is introduced, and the performance of two feedback controllers based on the optimized results is compared with that of a real-time management algorithm in the presence of randomness in charging requests and insolation. The results show that the BSS degradation cost plays a primary role in determining the strategy to adopt to minimize the operating expenditure of a charging station. Full article

Optimizing signal timing improves sustainability metrics (e.g., fuel consumption or “FC”). Historically, traffic agencies have retimed signal timing to improve mobility measures (e.g., delays). However, optimizing signals to reduce delays does not necessarily mitigate sustainability measures. Hence, this study introduces an approach that integrates a newly derived surrogate measure for FC, traffic microsimulation software, and a stochastic genetic algorithm. This approach optimizes signal timing to reduce the surrogate measure and reduce sustainability metrics. This study also evaluated the impact of heavy vehicles’ presence in a fleet on signal timing and FC savings. A 13-intersection arterial on Washington Street in the Chicago metro area served as a case study. Optimized signal timing delivered solutions that balanced both sustainability and mobility. The estimated excess FC savings ranged between 8 and 12% under moderate operating conditions, with no heavy vehicles, compared to the initial signal timing. The savings reached up to ~14% when many heavy vehicles existed on the side streets. Most of the improvements came without worsening traffic-mobility efficiency, which shows the possibility of a fair tradeoff between mobility and sustainability. All optimization scenarios showed that a slightly longer cycle length than the one implemented in the field is required to reduce FC. Full article

Traffic intersections throughout the United States combine fixed, semi-actuated, and fully actuated intersections. In the case of the semi-actuated and actuated intersections, uncertainties are considered in phase duration. These uncertainties are due to car waiting queues and pedestrian crossing. Intelligent transportation systems deployed in traffic infrastructure can communicate Signal and Phase Timing messages (SPaT) to vehicles approaching intersections. In the connected and automated vehicle ecosystem, the fuel savings potential has been explored. Prior studies have predominantly focused on fixed time control for the driver. However, in the case of actuated signals, there is a different and significant challenge due to the randomness caused by uncertainties. We have developed a predictive control using the SPaT information communicated from the actuated traffic intersections. The developed MPC-based algorithm was validated using model-based design platforms such as AMBER^®, Autonomie^®, MATLAB^®, and SIMULINK^®. It was observed that the proposed algorithm can save energy in a single phase, in multiple phase scenarios, and in compelled stopping at stop signs when employed considering communications. Full article

The influence of variable weather conditions on the performance of the battery that powers electric vehicles (EV) was studied and analyzed. This paper also deals with the effects that changes in the performance of the battery have on the driving range of the vehicle. An algorithm to evaluate the influence of temperature on the behavior of the battery and on the real driving range of electric vehicles was developed. Our theoretical approach was assessed in experimental tests run under operating conditions that reproduce real situations. A correction factor was obtained to match theoretical and experimental values with an accuracy higher than 98%. A linear relation between driving range and ambient temperature was observed from a simulation process, with a high regression coefficient. The relation shows that the driving range increases with ambient temperature. The ratio of the estimated driving range from the simulation process and the standard value for a reference temperature of 25 °C was obtained. The ratio shows that the global driving range can be increased by up to 29% in high temperatures associated with the summer season, while for very low temperatures, near −30 °C, the global driving range is reduced by 20%. The comparative analysis of the driving range for different temperatures shows that there is a reduction of about 18% for the low range of ambient temperatures, between −15 °C and 5 °C, while for medium temperatures, between 5 °C and 25 °C, the reduction in the driving range is only 4.6%. Finally, tests demonstrated that with a reduction in high temperatures from 25 °C to 35 °C, the driving range only reduced by about 0.4%. For higher temperatures, around 50 °C, the longest driving distance can be achieved, with a higher accuracy. Full article

Recent studies suggest that advances in rear seat occupant protection are trailing while frontal crash prevention technologies have disproportionately improved front seat occupant safety. Therefore, the first objective of this study is to identify the safest seat for children by estimating injury severity by seat position using current crash data. The second objective of this study is to demonstrate that Level-2 and above (i.e., L2+) AVs will be significantly different from current vehicles regarding child injury severity, and therefore it is essential to find the safest seat for children in L2+ AVs. This study utilized data from the National Automotive Sampling System (NASS) to estimate crash injury severity by seat position in children. This study used the Injury Severity Score (ISS) as its measure of crash severity. The mean ISS for restrained children sitting in the front passenger seat was 0.494 (for model year > 2000 vehicles). The mean ISSs for second-row left and second-row right seats were 0.374 and 0.322, respectively. The second-row middle seat had 162, 98, and 71% lower mean ISSs than the front passenger, second-row left, and second-row right seats, respectively. Overall, in both restrained and unrestrained scenarios, the safest seat for a child was the second-row middle seat. Full article

This paper expands a methodology, originally formulated for calculating the firm-level business value of Information Technology (IT), to that for a whole sector and applies it experimentally for the transport sector using statistical databases for several countries in Europe. The term “business value” means the quantified impact of a given technology, in this case, Information Technology, on the productivity of a sector or firm as measured by a given “yardstick” (e.g., sales, revenue, and many others). This is important to know in order to justify investments in a given technology or technological sector and for policy formulation or regulation. This paper proposes a novel methodology for calculating the business value of IT in a given sector. This is the first time that such a task is attempted because in the past most research was focused on calculating the business value at firm-level. The suggested methodology is then applied by use of panel data from the statistical records of national statistics for the transport sector. The results show that infrastructural investment in Information Technology has an impact on the transport sector’s output, in terms of its annual production value, of the order of 5–6%. Through sensitivity tests and a discussion of the results, it is estimated that the actual impact may probably be a bit higher but not much higher (in any case, something below the 10% figure). The application also shows that the suggested methodology can be applied for the estimation of the impact of any other technology or service on any given economic sector. Finally, a possible future conceptual model is presented for the expansion of the methodology to a more global and integrated level considering the interaction from other sectors as well as other technological and environmental factors. Full article

Demand-Responsive Transport (DRT) is one of the most valid solutions to tackle the problems affecting public transport today, both in urban and rural areas. Despite its undoubted advantages, it still remains underdeveloped compared to its great potential. The purpose of this paper is to understand the role that on-demand transport plays in the strategic choices of public transport authorities (PTAs): to this end, this study examined the DRT services of three geographically distant European cities, in order to test different social, cultural and regulatory backgrounds, examining their main characteristics. Tampere, Braunschweig and Genoa were selected for the purposes of this work; data and information were collected by viewing the official websites of public transport companies and by direct contact via mail/telephone with the managers responsible for on-call transport. The data collected were then analyzed based on specific Key Performance Indicators (KPIs) identified in academic literature. The results of this paper show that the role of on-call service in the strategic decisions of PTAs depends on the cultural context of reference; some cities focus more on urban services, others on rural transport. In all three case studies examined, on-demand transport is an important aspect of local mobility and with wide room for growth. Full article

One of the main challenges for ticketing in Mobility as a Service is the integration of the public and individual transport modes into a unified ticketing service. To realize this concept, a trustworthy identification of transport modes that is resilient to possible attacks is required. In this work, we propose two smartphone-based methods to seamlessly identify the use of trams, buses, subways, walking and bicycles, which are able to detect GNSS-based attacks and continue to provide a trustworthy identification of transport modes. We have recorded real-world measurements with commercial smartphones using the transport network in Munich and Paris. Our results show that it is possible to provide trustworthy identification of transport modes even when the system is under attack. In conclusion, in this work we demonstrate the vulnerability of smartphone-based ticketing to GNSS-based attacks, propose two methods to overcome this vulnerability and demonstrate the validity of our methods with real-world measurements. Full article

Assuming a full market penetration rate of connected and autonomous vehicles (CAVs) would provide an opportunity to remove costly and inefficient traffic lights from intersections, this paper presents a signal-free intersection control system relying on CAVs’ communicability. This method deploys a deep reinforcement learning algorithm and pixel reservation logic to avoid potential collisions and minimize the overall delay at the intersection. To facilitate a traffic-oriented assessment of the model, the proposed model’s application is coupled with VISSIM traffic microsimulation software, and its performance is compared with other intersection control systems, including fixed traffic lights, actuated traffic lights, and the Longest Queue First (LQF) control system. The simulation result revealed that the proposed model reduces delay by 50%, 29%, and 23% in moderate, high, and extreme volume regimes, respectively, compared to another signal-free control system. Noticeable improvements are also gained in travel time, fuel consumption, emission, and Surrogate Safety Measures. Full article

The goal of this paper is the evaluation of lithium-ion batteries that power electric vehicles (EVs) under variable climatic conditions to determine how the driving range of a vehicle is modified because of changes in battery performance caused by the variability of environmental conditions. The influence of sudden changes in ambient temperature on the performance of the battery that powers electric vehicles has been studied and analyzed. The study is focused on how trips across geographical zones with different climates affect the autonomy of an electric vehicle’s battery, and thus the driving range of the electric vehicle (EV). A model has been developed to reproduce on a laboratory scale the real conditions to which EVs are subject when circulating under fluctuating temperatures, which force the battery to operate in a transient or non-steady state. A simulation has been run for different climatic conditions to evaluate the performance of the battery and the driving range of the electric vehicle under variable operating conditions. A laboratory prototype has been designed and built to validate the modeling and to adjust the theoretical approach to experimental values through the corresponding correction factor in case significant deviations occur. The model has been validated for a simulated route that reproduces a real driving trip for specific geographical areas. The model indicates that there is a shortening in the global driving range of 43.5 km over a trip distance of 538 km, which means there is a reduction of 8%. The simulation has been applied to a specific geographical area in the nearby of the city of Lyon (France), for a temperature gap of 39 °C, from −6 °C to 33 °C, but can also be used for almost any other zones, although the reduction in driving range may vary because of specific climate conditions. Full article

Transportation Network Companies (TNCs) use online-enabled apps to provide on-demand transportation services. TNCs facilitate travelers to connect with drivers that can offer them rides for compensation using driver-owned vehicles. The ride requests can be for (a) individual or (b) shared rides. The latter, also known as ride-pooling services, accommodates requests of unrelated parties with origins and destinations along the same route who agree to share the same vehicle, usually at a discounted fare. Uber and Lyft offer ride-pooling services in select markets. Compared to individual ride requests, ride-pooling services hold better promise toward easing urban congestion by reducing the number of automobiles on the road. However, their impact on traffic operations is still not fully understood. Using Birmingham, AL as a case study, this research evaluated the impact that ride-pooling services have on traffic operations using a Multi-Agent Transport Simulation (MATSim) model of the Birmingham metro area. Scenarios were developed to simulate baseline conditions (no TNC service) and ride-pooling availability with two types of ride-pooling services, namely door-to-door (d2d) and stop-based (sB) service and three fleet sizes (200, 400, and 800 vehicles). The results indicate that when TNC vehicles are added to the network, the Vehicle Kilometers Traveled (VKT) decrease by up to 5.78% for the door-to-door (d2d) service, and up to 2.71% for stop-based (sB) services, as compared to the baseline scenario (no TNC service). The findings also suggest that an increase in the size of the ride-pooling fleet results in a rise in total ride-pooling service VKT, network-wide total VKT, and detour distance. However, increasing the size of the ride-pooling fleet also results in a decrease in the ride request rejection rates, thus benefiting the customers and decreasing the vehicle empty ratio which, in turn, benefits the TNC drivers. The results further suggest that a fleet of 200 ride-pooling vehicles can meet the current demand for service in the Birmingham region at all times, thus it is the optimal ride-pooling TNC fleet size for a medium-sized city such as Birmingham. Full article

Mobility as a Service (MaaS) is an innovative mobility service that aims to redesign the future of urban mobility by integrating multi-modal transportation and app-based technologies to enable seamless urban mobility. While MaaS pilot demonstrations and schemes implementation have taken place in different cities at a global level, and relevant studies focus on the MaaS barriers and users’ characteristics, the planning process for implementing MaaS is rarely presented. This paper summarizes the services to be integrated into the MaaS Athens’ demo site in Greece and describes the planning process that was followed to showcase the demo. The demo site is located within the urban area of Athens, including a public transport operator, a bike-sharing service, a taxi operator, and a municipality public transport operator. The demonstration runs developments in a real corridor that has the potential to prepare the MaaS eco-system deployment and market uptake. Three travel cases are planned: (1) Multimodal work trip; (2) MaaS for tourists; and (3) Interurban/urban interfaces, for work and shopping/leisure trips. The user journeys are defined in detail and the main information for each user journey is presented. The study concludes with challenges that were faced during the demo planning and recommendations for achieving the MaaS goals. Full article

The crucial gap is an important aspect of traffic characteristics that is used to assess the delay and capacity of individual car movements at priority junctions. Because traffic operations at priority junctions are complicated, many methods have been studied to find a more accurate critical gap. This research examines the standards established for these procedures throughout the previous seven decades, from Raff’s method in 1950 to the current day. These methods can be used anywhere in the world to determine the value of the critical gap for a mix of traffic, such as cars, vans, trucks, and motorcycles. The accuracy of these methods is assessed using factual data at two priority intersections, namely three-legged junctions (two-way stop-controlled (TWSC) with multilanes on main and minor streets). A total of 120 h of video camera recording was completed over the course of 5 working days in a week. This research identified Troutbeck’s, Wu’s, and Raff’s techniques as the most popular, offering consistent and robust critical gap values for both sides of the minor road at priority junctions. Full article

Autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) are expected to have a significant impact on highways, but their planning horizon impacts have not been fully studied in the literature. This study seeks to address this gap by investigating the impact of AVs/CAVs at different stages of adoption on long-range transportation planning horizons in the United States. Planners use travel demand forecasts to make important and expensive transportation supply investment decisions, and this study uses oversaturated traffic data from the NGSIM database to estimate the parameters of the Wiedemann car-following model for a basic freeway. Using data from the European-funded Coexist Project, we construct AV/CAV scenarios that incorporate various mixes of AV/CAV technologies, including cautious driving behavior (AV-Cautious) and more aggressive driving behavior (AV All-Knowing), and span multiple planning horizon planning years. Our findings suggest that the capacity impact of AVs will change based on their penetration in the vehicle fleet. For medium-term planning horizons, AVs will reduce capacities, whereas for long-term planning horizons and the buildout, capacities will be positively impacted. However, the impact of AVs/CAVs on highway capacity is subject to two main limitations, including the assumptions made in this study about the behavior of AVs/CAVs and the lack of consideration for AVs/CAVs in oversaturated traffic in previous literature. Future studies could explore these limitations in more detail and consider other factors, such as the impact of AVs/CAVs on travel demand and the potential for AVs/CAVs to affect mode share. Overall, this research provides valuable information for transportation planners and decision-makers to consider as they develop medium and long-term transportation plans and make informed decisions about the impact of AVs/CAVs. Full article

The transition to net-zero emission urban bus (ZEB) systems is receiving increased attention in research and policymaking. Most studies in this area focus on techno-economic aspects and the views of a narrow group of stakeholders. This offers limited insight into the range of barriers that constrain transitions in real-world contexts. This article offers a political-economic and multi-stakeholder perspective on the technical and non-technical barriers to ZEB transitions within the UK context. It develops a theory-guided empirical case study, informed by stakeholder theory perspectives and semi-structured interviews with stakeholders in the local bus transportation system. It finds that a transition to net zero will require addressing technical, policy, market, and cooperative barriers across sectors and policy levels. On the one hand, this relates to high costs and performance uncertainties over ZEB technology and infrastructure. On the other hand, it concerns unsustainable bus networks from passenger patronage and coordination perspectives, stakeholder cooperative gaps, and high car use and dependency in urban areas. Policy portfolios and stakeholder collaborations, beyond a ‘net-zero’ and sectoral focus, could tackle barriers to system-level change. Further application of the theoretical framework can contribute to a broader body of knowledge about transition barriers operating in different political and economic contexts. Full article

Autonomous vehicles (AV) hold great potential to increase road safety, reduce traffic congestion, and improve mobility systems. However, the deployment of AVs introduces new liability challenges when they are involved in car accidents. A new legal framework should be developed to tackle such a challenge. This paper proposes a legal framework, incorporating liability rules to rear-end crashes in mixed-traffic platoons with AVs and human-propelled vehicles (HV). We leverage a matrix game approach to understand interactions among players whose utility captures crash loss for drivers according to liability rules. We investigate how liability rules may impact the game equilibrium between vehicles and whether human drivers’ moral hazards arise if liability is not designed properly. We find that compared to the no-fault liability rule, contributory and comparative rules make road users have incentives to execute a smaller reaction time to improve road safety. There exists moral hazards for human drivers when risk-averse AV players are in the car platoon. Full article

E-cargo bikes, i.e., electric bicycles equipped with baskets for transporting goods, are perceived as an advantageous solution for urban last-mile logistics. Particularly for short distances and low volumes of goods, the use of e-cargo bikes is ideal in urban areas where access of conventional cars may be prohibited. In this context, this study presents an e-cargo bike routing model for urban deliveries. The problem is formulated as a Capacitated Vehicle Routing Problem (CVRP), which takes several aspects into account, such as restrictions on bicycle capacity, road grade and workload balancing. A mathematical model is constructed to solve the problem, and a Genetic Algorithm (GA) is applied. Results on a case study in Athens, Greece are presented and analyzed, highlighting the applicability of the model in real-world operations, yielding reasonable results in short computational times. Full article