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1
Cho, Seung-Wan, Yeong-Hyun Lim, Seong-Hyeon Ju, and Kyung-Min Seo. "Analyzing New Operation Strategy of Demand-Responsive Transports Using Discrete-Event Simulation Framework." Systems 13, no. 4 (2025): 303. https://doi.org/10.3390/systems13040303.
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Demand-responsive transport (DRT) provides flexible ride-sharing by dynamically adjusting routes based on real-time user demand, making it suitable for complex urban mobility needs. This study proposes a modular simulation framework based on the DEVS (Discrete Event System Specification) formalism and introduces an “express service” strategy that enables direct trips without intermediate stops. The framework supports scenario-based analysis using key performance indicators (KPIs) and allows for flexible testing of operational strategies. Two experiments were conducted: the first validated the simulation model under varying demand and fleet conditions; and the second assessed the impact of the express service. Results showed that express passengers experienced significantly shorter waiting and riding times, while standard passenger service remained stable. The strategy also improved operational efficiency under constrained resources. This study contributes to a configurable simulation platform for evaluating differentiated DRT services and provides practical insights for adaptive service planning, especially in urban settings where tiered mobility solutions are increasingly needed.
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Naeem, Nabih, Patrick Ratei, and Prajwal Shiva Prakasha. "Modelling and simulation of urban air mobility: an extendable approach." Journal of Physics: Conference Series 2526, no. 1 (2023): 012104. http://dx.doi.org/10.1088/1742-6596/2526/1/012104.
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Abstract This paper presents an extendable approach to the modelling and simulation of Urban Air Mobility (UAM), and dissemination criteria for system of systems simulation driven studies. UAM involves a multitude of complexities including the airspace, fleet, demand, and vertidrome management. Simulation is a key enabler for understanding these complexities and the interaction of the different stakeholders within the UAM paradigm. This work builds upon past research of the authors and presents a framework for simulation and modelling which includes the modelling of passenger demand, passenger mode choice, vehicle allocation for heterogenous fleets, route planning, deadheading, vertidrome scheduling, and flight scheduling with stop-overs. The approach presented in this work can be used to model both on-demand and scheduled operations, while the primary focus is placed on the former. Moreover, different methods can be implemented for the detailed modelling of the stakeholders, in addition to parametrically varying aspects such as the fleet size, number of vertidromes, and others. The aims of this paper are two, firstly to offer a framework for the modelling of UAM by breaking down its complexity systematically to simpler blocks, namely the stakeholders, the processes and interaction, through which the emergent behavior of the system of systems simulation may be more easily observed or understood. Secondly, it is to provide a clear method for dissemination of the modelling and simulation with the goal of establishing a common standard, demonstrated through the dissemination of the authors’ simulation to the reader.
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Martí, Pasqual, Alejandro Ibáñez, Vicente Julian, Paulo Novais, and Jaume Jordán. "Bus Ridership Prediction and Scenario Analysis through ML and Multi-Agent Simulations." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 13 (December 31, 2024): e31866. https://doi.org/10.14201/adcaij.31866.
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This paper introduces an innovative approach to predicting bus ridership andanalysing transportation scenarios through a fusion of machine learning (ML) techniques and multi-agent simulations. Utilising a comprehensive dataset from an urban bus system, we employ ML models to accurately forecast passenger flows, factoring in diverse variables such as weather conditions. The novelty of our method lies in the application of these predictions to generate detailed simulation scenarios, which are meticulously executed to evaluate the efficacy of public transportation services. Our research uniquely demonstrates the synergy between ML predictions and agent-based simulations, offering a robust tool for optimising urban mobility. The results reveal critical insights into resource allocation, service efficiency, and potential improvements in public transport systems. This study significantly advances the field by providing a practical framework for transportation providers to optimise services and address long-term challenges in urban mobility
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Asad, Syed Muhammad, Jawad Ahmad, Sajjad Hussain, Ahmed Zoha, Qammer Hussain Abbasi, and Muhammad Ali Imran. "Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning." Sensors 20, no. 9 (2020): 2629. http://dx.doi.org/10.3390/s20092629.
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Information and Communication Technology (ICT) enabled optimisation of train’s passenger traffic flows is a key consideration of transportation under Smart City planning (SCP). Traditional mobility prediction based optimisation and encryption approaches are reactive in nature; however, Artificial Intelligence (AI) driven proactive solutions are required for near real-time optimisation. Leveraging the historical passenger data recorded via Radio Frequency Identification (RFID) sensors installed at the train stations, mobility prediction models can be developed to support and improve the railway operational performance vis-a-vis 5G and beyond. In this paper we have analysed the passenger traffic flows based on an Access, Egress and Interchange (AEI) framework to support train infrastructure against congestion, accidents, overloading carriages and maintenance. This paper predominantly focuses on developing passenger flow predictions using Machine Learning (ML) along with a novel encryption model that is capable of handling the heavy passenger traffic flow in real-time. We have compared and reported the performance of various ML driven flow prediction models using real-world passenger flow data obtained from London Underground and Overground (LUO). Extensive spatio-temporal simulations leveraging realistic mobility prediction models show that an AEI framework can achieve 91.17% prediction accuracy along with secure and light-weight encryption capabilities. Security parameters such as correlation coefficient (<0.01), entropy (>7.70), number of pixel change rate (>99%), unified average change intensity (>33), contrast (>10), homogeneity (<0.3) and energy (<0.01) prove the efficacy of the proposed encryption scheme.
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LI, YANFEI, and ROBERT KOCHHAN. "POLICIES AND BUSINESS MODELS FOR THE ELECTRIC MOBILITY REVOLUTION: THE CASE STUDY ON SINGAPORE." Singapore Economic Review 62, no. 05 (2017): 1195–222. http://dx.doi.org/10.1142/s021759081550109x.
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A detailed total cost of ownership (TCO) model, with well-to-wheel carbon emissions assessment, is developed to analyze the economic competitiveness of battery electric vehicle (BEV) against conventional internal combustion engine (ICE) vehicles in Singapore. The model fully integrates the unique economic, institutional and social features of Singapore related to the ownership and usage of a passenger vehicle into consideration. Assuming current technologies and no change in the regulatory and policy framework, it is found that BEV is already economically competitive in certain niche markets of Singapore, such as small and midsize vehicles for car-sharing and corporate uses. In the near future, with technological progress, BEVs will become competitive in most parts of the Singapore passenger vehicle market, including small vehicles for household and small and midsize vehicles for car-sharing, corporate and taxi uses. However, certain supportive policies are called for, based on policy simulation results, to effectively accelerate the adoption of BEVs. Evidence also shows that supports should be given to the development of charging infrastructure at an early stage of BEV adoption.
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Mahmood, Bawan, and Jalil Kianfar. "Driver Behavior Models for Heavy Vehicles and Passenger Cars at a Work Zone." Sustainability 11, no. 21 (2019): 6007. http://dx.doi.org/10.3390/su11216007.
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Traffic impact assessment is a key step in the process of work zone planning and scheduling for transportation agencies. Microscopic traffic simulation models enable transportation agencies to conduct detailed analyses of work zone mobility performance measures during the planning and scheduling process. However, traffic simulation results are valid only when the simulation model is calibrated to replicate driver behavior that is observed in the field. Few studies have provided guidance on the calibration of traffic simulation models at work zones and have offered driver behavior parameters that reproduce capacity values that are observed in the field. This paper contributes to existing knowledge of work zone simulation by providing separate driver behavior model parameters for heavy vehicles and passenger vehicles. The driver behavior parameters replicate the flow and speed at the work zone taper and at roadway segments upstream of the work zone. A particle swarm optimization framework is proposed to improve the efficiency of the calibration process. The desired time headway was found to be 2.31 seconds for heavy vehicles and 1.53 seconds for passenger cars. The longitudinal following threshold was found to be 17.64 meters for heavy vehicles and 11.70 meters for passenger cars. The proposed parameters were tested against field data that had not previously been used in the calibration of driver behavior models. The average absolute relative error for flow rate at the taper was 10% and the mean absolute error was 54 veh/h/ln. The GEH statistic for the validation dataset was 1.48.
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Moffatt, Damian, and Hussein Dia. "Development and Evaluation of Simulation-Based Low Carbon Mobility Assessment Models." Future Transportation 1, no. 2 (2021): 134–53. http://dx.doi.org/10.3390/futuretransp1020009.
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The transport sector is a significant contributor to global emissions. In Australia, it is the third largest source of greenhouse gases and is responsible for around 17% of emissions with passenger cars accounting for around half of all transport emissions. Governments at all levels have identified a need for a reduction in transport carbon emissions to meet their net zero emissions targets. This research aims to help decision makers estimate the carbon footprint of transport networks within their jurisdictions and evaluate the impacts of emission-reduction interventions, through development of a simulation-based low carbon mobility assessment model. The model was developed based on a framework that integrates multiple mobility components including individual travel preferences, traffic simulation, and an assessment interface to create a seamless tool for the end-user. The feasibility of the assessment model was demonstrated in a case study for a local city council in Melbourne. In one of many scenarios reported in this paper, the model showed that maintaining current levels of emissions would require a 20% reduction in vehicle trips by 2030, and a much larger reduction would be required to reduce the levels of greenhouse gas emissions and achieve desired emissions reduction targets. The paper concludes with recommendations and future directions to extend the model’s capabilities and applications.
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Pratama, Rakha Rahmadani, Catur Hilman Adritya Haryo Bhakti Baskoro, Joga Dharma Setiawan, et al. "Non-linear model predictive control with single-shooting method for autonomous personal mobility vehicle." Journal of Mechatronics, Electrical Power, and Vehicular Technology 15, no. 2 (2024): 186–96. https://doi.org/10.55981/j.mev.2024.1105.
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The advancement of autonomous vehicle technology has markedly evolved during the last decades. Reliable vehicle control is one of the essential technologies in this domain. This study aims to develop a proposed method for controlling an autonomous personal mobility vehicle called SEATER (Single-passenger Electric Autonomous Transporter), using Non-linear Model Predictive Control (NMPC). We propose a single-shooting technique to solve the optimal control problem (OCP) via non-linear programming (NLP). The NMPC is applied to a non-holonomic vehicle with a differential drive setup. The vehicle utilizes odometry data as feedback to help guide it to its target position while complying with constraints, such as vehicle constraints and avoiding obstacles. To evaluate the method's performance, we have developed the SEATER model and testing environment in the Gazebo Simulation and implemented the NMPC via the Robot Operating System (ROS) framework. Several simulations have been done in both obstacle-free and obstacle-filled areas. Based on the simulation results, the NMPC approach effectively directed the vehicle to the desired pose while satisfying the set constraints. In addition, the results from this study have also pointed out the reliability and real-time performance of NMPC with a single-shooting method for controlling SEATER in the various tested scenarios.
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Ratei, Patrick, Nabih Naeem, and Prajwal Shiva Prakasha. "Development of an urban air mobility vehicle family concept by system of systems aircraft design and assessment." Journal of Physics: Conference Series 2526, no. 1 (2023): 012043. http://dx.doi.org/10.1088/1742-6596/2526/1/012043.
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Abstract The concept of urban air mobility promises a modern air taxi transport solution providing on-demand air mobility and hence time savings compared to congested terrestrial transportation in major cities and metropolitan areas. To make it a reality, vehicles, infrastructure, services, and operations must be developed simultaneously and cross-linked. These interconnections and interactions of the multitude of systems involved necessitate a system of systems approach, which is accounted for and implemented through agent-based simulations. Accordingly, the system of systems simulation framework is tuned to vehicle architecture as well as fleet design and assessment, thus allows to expand the aircraft design process by fleet operations and transport network perspectives. In this paper, the top level aircraft requirements of electric vertical take-off and landing vehicles are investigated by a fleet-centric approach. Herein, two disparate configurations, i.e. multirotor and tiltrotor, are modelled to depict representative wingless and winged configurations. The optimal design points are found and traded off by formulating different measures of effectiveness accounting for conversion of passenger requests, fleet energy consumption, and vehicle load factor. In summary, this study demonstrates the need for system of systems simulations to derive market-and operations-tailored vehicles and fleets. Furthermore, work on heterogeneous fleet compositions is required.
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Schweiger, Karolin, and Franz Knabe. "Vertidrome Airside Level of Service: Performance-Based Evaluation of Vertiport Airside Operations." Drones 7, no. 11 (2023): 671. http://dx.doi.org/10.3390/drones7110671.
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This paper presents the Vertidrome Airside Level of Service (VALoS) framework, a novel performance metric designed to evaluate airside traffic flow operations at vertidromes in the context of Urban Air Mobility (UAM). As the UAM industry rapidly evolves, the need for a comprehensive evaluation framework becomes increasingly important. The VALoS framework provides a performance-based approach to evaluating vertidrome traffic flow performance, considering metrics like average passenger delay, air taxi in-flight delay, and vertidrome punctuality. Unlike existing Level of Service approaches, the VALoS framework unifies the requirements of various stakeholders, the passenger, the air taxi operator, and the vertidrome operator each with their own performance metric and target. It provides a multi-faceted approach covering airside air and ground traffic flows, arrivals and departures, and performance changes during strategic planning and tactical execution phases. The VALoS is evaluated at 15-min intervals while considering changing stakeholder performance targets and operational uncertainties. For the reference use case, the study demonstrates the significant impact of short-term disruptions, while stochastic deviations can be neglected. Higher traffic volumes due to changing demand/capacity ratios result in higher VALoS variability. The VALoS framework, together with a fast-time simulation, provides a versatile method for exploring future vertidrome traffic flows and supporting strategic vertidrome airside planning and integration. This integrated approach is essential for the evolving UAM vertidrome industry; aligning the interests of different stakeholders and promoting sustainable and efficient vertidrome planning and operation.
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Panossian, Nadia V., Haitam Laarabi, Keith Moffat, et al. "Architecture for Co-Simulation of Transportation and Distribution Systems with Electric Vehicle Charging at Scale in the San Francisco Bay Area." Energies 16, no. 5 (2023): 2189. http://dx.doi.org/10.3390/en16052189.
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This work describes the Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI) architecture for the co-simulation of distribution and transportation systems to evaluate EV charging impacts on electric distribution systems of a large metropolitan area and the surrounding rural regions with high fidelity. The current co-simulation is applied to Oakland and Alameda, California, and in future work will be extended to the full San Francisco Bay Area. It uses the HELICS co-simulation framework to enable parallel instances of vetted grid and transportation software programs to interact at every model timestep, allowing high-fidelity simulations at a large scale. This enables not only the impacts of electrified transportation systems across a larger interconnected collection of distribution feeders to be evaluated, but also the feedbacks between the two systems, such as through control systems, to be captured and compared. The findings are that with moderate passenger EV adoption rates, inverter controls combined with some distribution system hardware upgrades can maintain grid voltages within ANSI C.84 range A limits of 0.95 to 1.05 p.u. without smart charging. However, EV charging control may be required for higher levels of charging or to reduce grid upgrades, and this will be explored in future work.
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Irimescu, Adrian, Bianca Maria Vaglieco, Simona Silvia Merola, Vasco Zollo, and Raffaele De Marinis. "Conversion of a Small-Size Passenger Car to Hydrogen Fueling: 0D/1D Simulation of EGR and Related Flow Limitations." Applied Sciences 14, no. 2 (2024): 844. http://dx.doi.org/10.3390/app14020844.
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Hydrogen is seen as a prime choice for complete replacement of gasoline so as to achieve zero-emissions energy and mobility. Combining the use of this alternative fuel with a circular economy approach for giving new life to the existing fleet of passenger cars ensures further benefits in terms of cost competitiveness. Transforming spark ignition (SI) engines to H2 power requires relatively minor changes and limited added components. Within this framework, the conversion of a small-size passenger car to hydrogen fueling was evaluated based on 0D/1D simulation. One of the methods to improve efficiency is to apply exhaust gas recirculation (EGR), which also lowers NOx emissions. Therefore, the previous version of the quasi-dimensional model was modified to include EGR and its effects on combustion. A dedicated laminar flame speed model was implemented for the specific properties of hydrogen, and a purpose-built sub-routine was implemented to correctly model the effects of residual gas at the start of combustion. Simulations were performed in several operating points representative of urban and highway driving. One of the main conclusions was that high-pressure recirculation was severely limited by the minimum flow requirements of the compressor. Low-pressure EGR ensured wider applicability and significant improvement of efficiency, especially during partial-load operation specific to urban use. Another benefit of recirculation was that pressure rise rates were predicted to be more contained and closer to the values expected for gasoline fueling. This was possible due to the high tolerance of H2 to the presence of residual gas.
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Iglesias, Ramon, Federico Rossi, Rick Zhang, and Marco Pavone. "A BCMP network approach to modeling and controlling autonomous mobility-on-demand systems." International Journal of Robotics Research 38, no. 2-3 (2018): 357–74. http://dx.doi.org/10.1177/0278364918780335.
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In this paper we present a queuing network approach to the problem of routing and rebalancing a fleet of self-driving vehicles providing on-demand mobility within a capacitated road network. We refer to such systems as autonomous mobility-on-demand (AMoD) systems. We first cast an AMoD system into a closed, multi-class Baskett–Chandy–Muntz–Palacios (BCMP) queuing network model capable of capturing the passenger arrival process, traffic, the state-of-charge of electric vehicles, and the availability of vehicles at the stations. Second, we propose a scalable method for the synthesis of routing and charging policies, with performance guarantees in the limit of large fleet sizes. Third, we explore the applicability of our theoretical results on a case study of Manhattan. Collectively, this paper provides a unifying framework for the analysis and control of AMoD systems, which provides a large set of modeling options (e.g. the inclusion of road capacities and charging constraints), and subsumes earlier Jackson and network flow models.
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Nahmias-Biran, Bat-Hen, Gabriel Dadashev, and Yedidya Levi. "Sustainable Automated Mobility-On-Demand Strategies in Dense Urban Areas: A Case Study of the Tel Aviv Metropolis in 2040." Sustainability 15, no. 22 (2023): 16037. http://dx.doi.org/10.3390/su152216037.
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The emergence of automated mobility-on-demand (AMoD) services in urban regions has underscored crucial issues concerning the sustainable advancement of urban mobility. In particular, the impact of various AMoD implementation strategies in dense, transit-oriented cities has yet to be investigated in a generalized manner. To address this gap, we quantify the effects of AMoD on trip patterns, congestion, and energy and emissions in a dense, transit-oriented prototype city via high-fidelity simulation. We employ an activity- and agent-based framework, with specific demand and supply considerations for both single and shared AMoD rides. Our findings suggest that, in densely populated, transit-oriented cities such as the Tel Aviv metropolis, AMoD contributes to higher congestion levels and increased passenger vehicle kilometers traveled (VKT). However, when AMoD is integrated with public transit systems or introduced alongside measures to reduce household car ownership, it helps alleviate the VKT impact. Furthermore, these combined approaches effectively counter the negative impact of AMoD on public transit ridership. None of the AMoD strategies analyzed in our study reduce the congestion effects of AMoD and all strategies cannibalize active mobility in dense, transit-oriented cities compared to the base case. Nevertheless, our analysis reveals that a policy leading to decreased car ownership proves to be a more efficient measure in curbing energy consumption and greenhouse gas emissions.
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Megantara, Tubagus Robbi, and Rizki Apriva Hidayana. "Matching Riders to Drivers Under Uncertain Wait Times in Ride-Hailing Systems: A Robust Optimization Approach with Box Uncertainty." International Journal of Mathematics, Statistics, and Computing 3, no. 2 (2025): 67–74. https://doi.org/10.46336/ijmsc.v3i2.202.
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The advent of ride-hailing systems has revolutionized urban mobility, yet efficient vehicle assignment remains challenging due to inherent uncertainties in passenger waiting times. This study addresses the ride-hailing matching problem under uncertain wait times, proposing a robust optimization model with a box uncertainty set to mitigate the impact of variability in service delivery. We first contextualize the problem by examining the evolution of transportation systems, emphasizing how ride-hailing services complicate traditional matching paradigms. Existing approaches often fail to account for real-world unpredictability, leading to suboptimal assignments. To bridge this gap, we formulate a data-driven robust optimization framework that bounds waiting time fluctuations within a box uncertainty set, ensuring reliable performance under worst-case scenarios. Using simulation data from Manhattan taxi trips, we compare our robust model against deterministic benchmarks, demonstrating its superiority in reducing average waiting times and enhancing system reliability, even under high uncertainty. Our results highlight the practical viability of robust optimization for ride-hailing platforms operating in dynamic environments.
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Ribeiro, Bruno, Maria João Nicolau, and Alexandre Santos. "Using Machine Learning on V2X Communications Data for VRU Collision Prediction." Sensors 23, no. 3 (2023): 1260. http://dx.doi.org/10.3390/s23031260.
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Intelligent Transportation Systems (ITSs) are systems that aim to provide innovative services for road users in order to improve traffic efficiency, mobility and safety. This aspect of safety is of utmost importance for Vulnerable Road Users (VRUs), as these users are typically more exposed to dangerous situations, and their vehicles also possess poorer safety mechanisms when in comparison to regular vehicles on the road. Implementing automatic safety solutions for VRU vehicles is challenging since they have high agility and it can be difficult to anticipate their behavior. However, if equipped with communication capabilities, the generated Vehicle-to-Anything (V2X) data can be leveraged by Machine Learning (ML) mechanisms in order to implement such automatic systems. This work proposes a VRU (motorcyclist) collision prediction system, utilizing stacked unidirectional Long Short-Term Memorys (LSTMs) on top of communication data that is generated using the VEINS simulation framework (coupling the Simulation of Urban MObility (SUMO) and Network Simulator 3 (ns-3) tools). The proposed system performed well in two different scenarios: in Scenario A, it predicted 96% of the collisions, averaging 4.53 s for Average Prediction Time (s) (APT) and with a Correct Decision Percentage (CDP) of 41% and 78 False Positives (FPs); in Scenario B, it predicted 95% of the collisions, with a 4.44 s APT, while the CDP was 43% with 68 FPs. The results show the effectiveness of the approach: using ML methods on V2X data allowed the prediction of most of the simulated accidents. Nonetheless, the presence of a relatively high number of FPs does not allow for the usage of automatic safety features (e.g., emergency breaking in the passenger vehicles); thus, collision avoidance must be achieved manually by the drivers.
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Olakanmi, Oladayo, and Sekoni Oluwaseun. "A Trust Based Secure and Privacy Aware Framework for Efficient Taxi and Car Sharing System." International Journal of Vehicular Telematics and Infotainment Systems 2, no. 1 (2018): 34–47. http://dx.doi.org/10.4018/ijvtis.2018010103.
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This article describes how taxi service is an essential means of mobility in many cities. Recent findings show that average automobile owners utilize their vehicles for only 5% of its time in a day. Therefore, the advent of autonomous vehicles and car sharing will make it possible for owners to engage their vehicles as taxis when not in use by utilizing its 95% free time for income generation. Sensitive private information is required to be released during a taxi service delivery, which may bring certain security and privacy issues and challenges. This may hinder the prospect of using autonomous vehicles as a form of taxi. As a result of these, the authors propose a secure and privacy-preserving taxi service framework for car sharing, which ensures protection of car owner and passengers personal details, e.g. identity, location, destination, etc. The authors developed a decay-based trust model for a framework in order to monitor and improve the quality of service rendered to passengers by vehicles. The decay-based trust model was simulated on the framework. The simulation of the decay-based trust model shows that it is a perfect model for rewarding vehicles which render good quality of service and blacklisting vehicles with frequent poor service delivery.
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Brunelli, Matteo, Chiara Caterina Ditta, and Maria Nadia Postorino. "A Framework to Develop Urban Aerial Networks by Using a Digital Twin Approach." Drones 6, no. 12 (2022): 387. http://dx.doi.org/10.3390/drones6120387.
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The new concept of Urban Air Mobility (UAM) and the emergent unmanned aerial vehicles are receiving more and more attention by several stakeholders for implementing new transport solutions. However, there are several issues to solve in order to implement successful UAM systems. Particularly, setting a suitable framework is central for including this new transportation system into the existing ones—both ground and aerial systems. Regulation and definition of aerial networks, but also the characterization of ground facilities (vertiports) to allow passengers and freight to access the services are among the most relevant issues to be discussed. To identify UAM transportation networks, suitably connected with ground transportation services, digital twin models could be adopted to support the modelling and simulation of existing—and expected—scenarios with constantly updated data for identifying solutions addressing the design and management of transport systems. In this perspective, a digital twin model applied to an existing urban context—the city of Bologna, in northern Italy—is presented in combination with a novel air transport network that includes the third dimension. The 3D Urban Air Network tries to satisfy the principle of linking origin/destination points by ensuring safe aerial paths and suitable aerial vehicle separations. It involves innovative dynamic links powered by a heuristic cost function. This work provides the initial framework to explore the integration of UAM services into realistic contexts, by avoiding the costs associated with flight simulations in reality. Moreover, it can be used for holistic analyses of UAM systems.
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Huang, Chenn-Jung, Kai-Wen Hu, and Cheng-Yang Hsieh. "Congestion-Aware Rideshare Dispatch for Shared Autonomous Electric Vehicle Fleets." Electronics 11, no. 16 (2022): 2591. http://dx.doi.org/10.3390/electronics11162591.
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The problem of traffic congestion caused by the fast-growing travel demands has been getting serious in urban areas. Meanwhile, the future of urban mobility has been foreseen as being electric, shared, and autonomous. Accordingly, the routing and charging strategies for fleets of shared autonomous electric vehicles (SAEVs) need to be carefully addressed to cope with the characteristics of the rideshare service operation of the SAEV fleets. In the literature, much work has been done to develop various traffic control strategies for alleviating the problem in urban traffic congestion. However, little research has proposed effective solutions that integrate the route of charging strategies for SAEV fleets with the urban traffic congestion problem. In this regard, this work presents an integrated framework that tackles the route and charging of SAEV fleets as well as the urban traffic congestion prevention issues. Notably, our contribution in this work not only proposes a joint solution for the problems of the urban traffic congestion control and rideshare dispatch of SAEV fleets, but also fills the gap of the routing and charging strategies for mixed privately owned EVs (PEV) and SAEV fleets in the literature. A general optimization framework is formulated, and effective heuristics are proposed to tackle the above-mentioned problems in this work. The feasibility and effectiveness of the proposed algorithms were evaluated through four different scenarios in the simulation. After applying the proposed algorithms, the traffic volumes of the oversaturated main arterial road were diverted to other less busy road sections, and the traveling times of EV passengers were decreased by 28% during peak periods. The simulation results reveal that the proposed algorithms not only provide a practical solution to prevent the problem in urban traffic congestion during rush hours, but also shorten the travel times of EV passengers effectively.
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Louati, Ali, Hassen Louati, Elham Kariri, et al. "Sustainable Smart Cities through Multi-Agent Reinforcement Learning-Based Cooperative Autonomous Vehicles." Sustainability 16, no. 5 (2024): 1779. http://dx.doi.org/10.3390/su16051779.
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As urban centers evolve into smart cities, sustainable mobility emerges as a cornerstone for ensuring environmental integrity and enhancing quality of life. Autonomous vehicles (AVs) play a pivotal role in this transformation, with the potential to significantly improve efficiency and safety, and reduce environmental impacts. This study introduces a novel Multi-Agent Actor–Critic (MA2C) algorithm tailored for multi-AV lane-changing in mixed-traffic scenarios, a critical component of intelligent transportation systems in smart cities. By incorporating a local reward system that values efficiency, safety, and passenger comfort, and a parameter-sharing scheme that encourages inter-agent collaboration, our MA2C algorithm presents a comprehensive approach to urban traffic management. The MA2C algorithm leverages reinforcement learning to optimize lane-changing decisions, ensuring optimal traffic flow and enhancing both environmental sustainability and urban living standards. The actor–critic architecture is refined to minimize variances in urban traffic conditions, enhancing predictability and safety. The study extends to simulating realistic human-driven vehicle (HDV) behavior using the Intelligent Driver Model (IDM) and the model of Minimizing Overall Braking Induced by Lane changes (MOBIL), contributing to more accurate and effective traffic management strategies. Empirical results indicate that the MA2C algorithm outperforms existing state-of-the-art models in managing lane changes, passenger comfort, and inter-vehicle cooperation, essential for the dynamic environment of smart cities. The success of the MA2C algorithm in facilitating seamless interaction between AVs and HDVs holds promise for more fluid urban traffic conditions, reduced congestion, and lower emissions. This research contributes to the growing body of knowledge on autonomous driving within the framework of sustainable smart cities, focusing on the integration of AVs into the urban fabric. It underscores the potential of machine learning and artificial intelligence in developing transportation systems that are not only efficient and safe but also sustainable, supporting the broader goals of creating resilient, adaptive, and environmentally friendly urban spaces.
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Qureshi, Kashif Naseer, Farooq Ul Islam, Omprakash Kaiwartya, Arvind Kumar, and Jaime Lloret. "Improved Road Segment-Based Geographical Routing Protocol for Vehicular Ad-hoc Networks." Electronics 9, no. 8 (2020): 1248. http://dx.doi.org/10.3390/electronics9081248.
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The real-time traffic information dissemination among on-road vehicles has been envisioned via realizing vehicular ad hoc networks (VANETs) as smart service-oriented roadside wireless sensor networks. The network enables various types of real-time traffic applications related to safety and infotainment for drivers and passengers. The information dissemination-centric routing protocols for vehicular networks have to dynamically adopt under the constrained network environment while considering the higher mobility of vehicular nodes and unpredictable physical topologies in the network. The issue needs to be addressed through smart and network-aware routing protocols. Geographical routing protocols have witnessed significant attention for information dissemination under these types of dynamic vehicular network environment. To this end, this paper presents an improved road segment-based geographical routing (ISR) protocol focusing on better head node selection for information dissemination. It divides the forwarding area into a number of road segments and selects a head node on each segment by focusing on traffic-aware information including the location, direction, and link quality-centric score for every vehicle on each road segment. Algorithms were developed for the complete process of head node selection and information dissemination among vehicles on the road segments. The simulation results attested the performance benefits of the proposed routing framework as compared to the state-of-the-art protocols considering dynamic vehicular traffic environment-related metrics.
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Klamser, Pascal P., Adrian Zachariae, Benjamin F. Maier, et al. "Inferring country-specific import risk of diseases from the world air transportation network." PLOS Computational Biology 20, no. 1 (2024): e1011775. http://dx.doi.org/10.1371/journal.pcbi.1011775.
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Disease propagation between countries strongly depends on their effective distance, a measure derived from the world air transportation network (WAN). It reduces the complex spreading patterns of a pandemic to a wave-like propagation from the outbreak country, establishing a linear relationship to the arrival time of the unmitigated spread of a disease. However, in the early stages of an outbreak, what concerns decision-makers in countries is understanding the relative risk of active cases arriving in their country—essentially, the likelihood that an active case boarding an airplane at the outbreak location will reach them. While there are data-fitted models available to estimate these risks, accurate mechanistic, parameter-free models are still lacking. Therefore, we introduce the ‘import risk’ model in this study, which defines import probabilities using the effective-distance framework. The model assumes that airline passengers are distributed along the shortest path tree that starts at the outbreak’s origin. In combination with a random walk, we account for all possible paths, thus inferring predominant connecting flights. Our model outperforms other mobility models, such as the radiation and gravity model with varying distance types, and it improves further if additional geographic information is included. The import risk model’s precision increases for countries with stronger connections within the WAN, and it reveals a geographic distance dependence that implies a pull- rather than a push-dynamic in the distribution process.
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Asad, Syed Muhammad, Shuja Ansari, Metin Ozturk, et al. "Mobility Management-Based Autonomous Energy-Aware Framework Using Machine Learning Approach in Dense Mobile Networks." Signals 1, no. 2 (2020): 170–87. http://dx.doi.org/10.3390/signals1020010.
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A paramount challenge of prohibiting increased CO2 emissions for network densification is to deliver the Fifth Generation (5G) cellular capacity and connectivity demands, while maintaining a greener, healthier and prosperous environment. Energy consumption is a demanding consideration in the 5G era to combat several challenges such as reactive mode of operation, high latency wake up times, incorrect user association with the cells, multiple cross-functional operation of Self-Organising Networks (SON), etc. To address this challenge, we propose a novel Mobility Management-Based Autonomous Energy-Aware Framework for analysing bus passengers ridership through statistical Machine Learning (ML) and proactive energy savings coupled with CO2 emissions in Heterogeneous Network (HetNet) architecture using Reinforcement Learning (RL). Furthermore, we compare and report various ML algorithms using bus passengers ridership obtained from London Overground (LO) dataset. Extensive spatiotemporal simulations show that our proposed framework can achieve up to 98.82% prediction accuracy and CO2 reduction gains of up to 31.83%.
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Yoshitake, Hiroshi, Yosuke Isono, and Motoki Shino. "Pedestrian Avoidance Method Considering Passenger Comfort for Autonomous Personal Mobility Vehicles." Journal of Robotics and Mechatronics 35, no. 2 (2023): 231–39. http://dx.doi.org/10.20965/jrm.2023.p0231.
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During the autonomous navigation of personal mobility vehicles (PMVs) in pedestrian spaces, avoiding collisions with pedestrians walking nearby is necessary. The avoidance paths of PMVs are affected by the behaviors of the pedestrians, which may also affect passenger comfort. Herein, a local path-planning method that considers passenger comfort to realize comfortable pedestrian avoidance during the autonomous navigation of PMVs in pedestrian spaces is proposed. First, the avoidance path and pedestrian behavior parameters affecting passenger comfort are investigated by evaluating passenger comfort in scenarios with different avoidance paths and pedestrian behaviors. Next, the requirements of a pedestrian avoidance method that considers passenger comfort are set based on the parameters affecting passenger comfort. Finally, a novel path-planning method that satisfies the requirements is proposed. The method is shown to generate comfortable paths in pedestrian spaces via a numerical simulation and participant experiment.
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Hogeveen, Peter, Maarten Steinbuch, Geert Verbong, and Auke Hoekstra. "Quantifying the Fleet Composition at Full Adoption of Shared Autonomous Electric Vehicles: An Agent-based Approach." Open Transportation Journal 15, no. 1 (2021): 47–60. http://dx.doi.org/10.2174/1874447802115010047.
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Aims: Exploring the impact of full adoption of fit-for-demand shared and autonomous electric vehicles on the passenger vehicle fleet of a society. Background: Shared Eutonomous Electric Vehicles (SAEVs) are expected to have a disruptive impact on the mobility sector. Reduced cost for mobility and increased accessibility will induce new mobility demand and the vehicles that provide it will be fit-for-demand vehicles. Both these aspects have been qualitatively covered in recent research, but there have not yet been attempts to quantify fleet compositions in scenarios where passenger transport is dominated by fit-for-demand, one-person autonomous vehicles. Objective: To quantify the composition of the future vehicle fleet when all passenger vehicles are autonomous, shared and fit-for-demand and where cheap and accessible mobility has significantly increased the mobility demand. Methods: An agent-based model is developed to model detailed travel dynamics of a large population. Numerical data is used to mimic actual driving motions in the Netherlands. Next, passenger vehicle trips are changed to trips with fit-for-demand vehicles, and new mobility demand is added in the form of longer tips, more frequent trips, modal shifts from public transport, redistribution of shared vehicles, and new user groups. Two scenarios are defined for the induced mobility demand from SAEVs, one scenario with limited increased mobility demand, and one scenario with more than double the current mobility demand. Three categories of fit-for-demand vehicles are stochastically mapped to all vehicle trips based on each trip's characteristics. The vehicle categories contain two one-person vehicle types and one multi-person vehicle type. Results: The simulations show that at full adoption of SAEVs, the maximum daily number of passenger vehicles on the road increases by 60% to 180%. However, the total fleet size could shrink by up to 90% if the increase in mobility demand is limited. An 80% reduction in fleet size is possible at more than doubling the current mobility demand. Additionally, about three-quarters of the SAEVs can be small one-person vehicles. Conclusion: Full adoption of fit-for-demand SAEVs is expected to induce new mobility demand. However, the results of this research indicate that there would be 80% to 90% less vehicles required in such a situation, and the vast majority would be one-person vehicles. Such vehicles are less resource-intense and, because of their size and electric drivetrains, are significantly more energy-efficient than the average current-day vehicle. This research indicates the massive potential of SAEVs to lower both the cost and the environmental impact of the mobility sector. Quantification of these environmental benefits and reduced mobility costs are proposed for further research.
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CHEN, Suxiao, Guangjie LIU, Shen GAO, Jiming LI, and Juan WU. "A Discrete-Event Simulation System for Estimating Passenger Flow in Urban Rail Transit." Promet - Traffic&Transportation 37, no. 2 (2025): 440–55. https://doi.org/10.7307/ptt.v37i2.768.
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Establishing simulation models is a widely used and effective approach for analysing passenger flow distribution in urban rail transit systems. Recently, multi-agent and discrete event-based simulation models have shown exceptional performance in studying passenger flow information within urban rail transit systems. While simulations of passengers and trains often yield satisfactory results, few models capture the overall operational status of urban rail transit systems. The complex interactions among stations, trains and passengers make it challenging to integrate these elements into a unified system framework. In this paper, we introduce a triple simulation framework that integrates stations, trains and passengers as foundational elements to comprehensively simulate the entire urban rail transit system and observe overall passenger flow distribution. Experimental results demonstrate that our system surpasses existing advanced simulation models, achieving an accuracy rate of 88.44% with a tolerance for a 30% deviation. To further illustrate the effectiveness of our framework in analysing passenger flows, we conducted experiments using the Nanjing Metro AFC dataset, analysing passenger flow distributions at stations and on trains.
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Wang, Zhi Qiang. "Urban Rail Transit Passenger Transport Microcosmic Simulation Model Framework Design." Applied Mechanics and Materials 253-255 (December 2012): 1249–53. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.1249.
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This paper from the network level design the urban rail transit passenger transport microcosmic simulation model. Take the model’s achieve and apply as a target, through analyse the simulation model’s class library and its properties, design the event driven model, optimize the model frame and etc work, develop the urban rail transit passenger transport microcosmic simulation system, and through the Suzhou Rail transit planning network’s calculation example, verified the system’s availability.
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Hogeveen, Peter, Maarten Steinbuch, Geert Verbong, and Auke Hoekstra. "The Energy Consumption of Passenger Vehicles in a Transformed Mobility System with Autonomous, Shared and Fit-For-Purpose Electric Vehicles in the Netherlands." Open Transportation Journal 15, no. 1 (2021): 201–9. http://dx.doi.org/10.2174/1874447802115010201.
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Aims: This article explores the tank-to-wheel energy consumption of passenger transport at full adoption of fit-for-purpose shared and autonomous electric vehicles. Background: The energy consumption of passenger transport is increasing every year. Electrification of vehicles reduces their energy consumption significantly but is not the only disruptive trend in mobility. Shared fleets and autonomous driving are also expected to have large impacts and lead to fleets with one-person fit-for-purpose vehicles. The energy consumption of passenger transport in such scenarios is rarely discussed and we have not yet seen attempts to quantify it. Objective: The objective of this study is to quantify the tank-to-wheel energy consumption of passenger transport when the vehicle fleet is comprised of shared autonomous and electric fit-for-purpose vehicles and where cheap and accessible mobility leads to significantly increased mobility demand. Methodology: The approach consists of four steps. First, describing the key characteristics of a future mobility system with fit-for-purpose shared autonomous electric vehicles. Second, estimating the vehicle miles traveled in such a scenario. Third, estimating the energy use of the fit-for-purpose vehicles. And last, multiplying the mileages and energy consumptions of the vehicles and scaling the results with the population of the Netherlands. Results: Our findings show that the daily tank-to-wheel energy consumption from Dutch passenger transport in full adoption scenarios of shared autonomous electric vehicles ranges from 700 Wh to 2200 Wh per capita. This implies a reduction of 90% to 70% compared to the current situation. Conclusion: Full adoption of shared autonomous electric vehicles could increase the vehicle-miles-travelled and thus energy use of passenger transport by 30% to 150%. Electrification of vehicles reduces energy consumption by 75%. Autonomous driving has the potential of reducing the energy consumption by up to 40% and implementing one-person fit-for-purpose vehicles by another 50% to 60%. For our case study of the Netherlands, this means that the current 600 TJ/day that is consumed by passenger vehicles will be reduced to about 50 to 150 TJ/day at full adoption of SAEVs.
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Duca, Gabriella, Barbara Trincone, Margarita Bagamanova, Peter Meincke, Raffaella Russo, and Vittorio Sangermano. "Passenger Dimensions in Sustainable Multimodal Mobility Services." Sustainability 14, no. 19 (2022): 12254. http://dx.doi.org/10.3390/su141912254.
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Seamless integration of air segment in the overall multimodal mobility chain is a key challenge to provide more efficient and sustainable transport services. Technology advances offer a unique opportunity to build a new generation of transport services able to match the evolving expectations and needs of society as a whole. In this context, the passenger-centric approach represents a method to inform the design of future mobility services, supporting quality of life, security and services to citizens traveling across Europe. Relying on the concepts of inclusive design, context of use and task analysis, in this article, we present a comprehensive methodological framework for the analysis of passenger characteristics to elicit features and requirements for future multimodal mobility services, including air leg, that are relevant from the perspective of passengers. The proposed methodology was applied to a series of specific use cases envisaged for three time horizons, 2025, 2035 and 2050, in the context of a European research project. Then, passenger-focused key performance indicators and related metrics were derived to be included in a validation step, with the aim of assessing the extent of benefit for passengers that can be achieved in the forecasted scenarios. The results of the study demonstrate the relevance of human variability in the design of public services, as well as the feasibility of personalized performance assessment of mobility services.
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Guolei, Tang, Zhao Xiaoyi, Zhao Zhuoyao, Yu Jingjing, Guo Lei, and Wang Yuhan. "Simulation-based Fuzzy Multiple Attribute Decision Making framework for an optimal apron layout for aRoll-on/Roll-off/Passenger terminal considering passenger service quality." SIMULATION 97, no. 7 (2021): 451–71. http://dx.doi.org/10.1177/0037549721999083.
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The apron of the Roll-on/Roll-off/Passenger (Ro-Pax) terminal is an accident-prone zone with high risk of traffic congestion and vehicle exhaust pollution in the peak season, which brings a bad experience to passengers and even endangers passengers’ health. This study aims to improve the passenger service quality in the peak season by rezoning the Ro-Pax terminal apron and its traffic organization. Thus, we establish a simulation-based Fuzzy Multiple Attribute Decision Making framework to evaluate the passenger service quality and distinguish an optimal layout of the terminal apron. This framework introduces three evaluation indicators (safety, convenience, and comfort and health) and 11 performance indexes to define the passenger service quality, and the values of these indexes are derived from an agent-based simulation model for the Ro-Pax terminal apron operation. Finally, an example of the proposed framework is presented in a case study to show how to select an improved layout of the Ro-Pax terminal apron considering passenger service quality. The result confirms the proposed framework is an effective tool to solve rezoning the Ro-Pax terminal apron and the proposed methodology can also be used to cope with similar problems.
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Han, Shaoyong, Qiang Guo, Kai Yu, Rende Li, Bing He, and Jian-Guo Liu. "Statistical mechanism of passenger mobility behaviors for different transportations." International Journal of Modern Physics C 31, no. 06 (2020): 2050082. http://dx.doi.org/10.1142/s0129183120500825.
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Passengers’ boarding time interval is of great significance for analysis of collective mobility behaviors. In this paper, we empirically investigate the boarding time interval of mobility behaviors based on three large-scale reservation records of passengers traveling by three different types of transportation from a travel agency platform, namely airplane, intercity bus and car rental. The statistical results show that similar properties exist in the passengers’ mobility behaviors, for example, there are similar burstiness [Formula: see text] and memory [Formula: see text] for different time interval distribution, which indicates that the passengers’ mobility behaviors are periodical. Furthermore, we present a probability model to regenerate the empirical results by assuming that the passengers’ next boarding time interval will generate between a short time of 1–7 days with probability [Formula: see text] and a random long time with probability [Formula: see text]. The simulation results show that the presented model can reproduce the burstiness and memory effect of the boarding time interval when [Formula: see text] for three empirical datasets, which suggests the periodical behaviors with the probability [Formula: see text]. This work helps in deeply understanding the regularity of human mobility behaviors.
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Hui, Yang, Shujie Su, and Hui Peng. "Evaluation of Subway Emergency Evacuation Based on Combined Theoretical and Simulation Methods." Applied Sciences 14, no. 24 (2024): 11580. https://doi.org/10.3390/app142411580.
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In this paper, a thorough investigation of the emergency evacuation capabilities of subway systems has been undertaken, employing a blend of theoretical models and simulation methodologies. Initially, a theoretical framework was established to estimate the evacuation duration for passengers transitioning from the train to a secure area while considering the spatial configuration and passenger flow dynamics of subway stations. Following this, a real-time visualization simulation model was developed, which integrates the dynamic aspects of passenger flow and the transportation capacity of evacuation bottlenecks across various segments. This model incorporates both spatial parameters and the travel behaviors of passengers. Ultimately, in accordance with actual operational needs, a simulation analysis was performed for substantial passenger volumes across three representative scenarios to assess the effectiveness and scientific validity of the theoretical calculation model. This study offers a foundational framework for the management of subway safety operations, facilitating the identification of evacuation bottlenecks and the implementation of emergency strategies for handling large passenger flows.
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Yoshitake, Hiroshi, Kenta Nishi, and Motoki Shino. "Autonomous Motion Planning in Pedestrian Space Considering Passenger Comfort." Journal of Robotics and Mechatronics 32, no. 3 (2020): 580–87. http://dx.doi.org/10.20965/jrm.2020.p0580.
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In this study, we proposed an autonomous motion planning method for improving passenger comfort while ensuring safety, particularly with respect to mobility scooters used by elderly people. We proposed a trajectory planner for restricting vehicle behaviors with large accelerations and jerks by selecting a safe trajectory from a set of preset trajectories. Then, based on this trajectory planner, we developed an autonomous motion planning method with four different driving modes, and evaluated the effectiveness of the method through a numerical simulation. The simulation results demonstrated that the proposed method increased comfort without compromising on safety.
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Mulenko, Olga, Vyacheslav Zadorozhniy, and Alexey Zabelin. "Sustainable mobility of passenger traffic with the application of the logistics, financial and clients services." E3S Web of Conferences 371 (2023): 04036. http://dx.doi.org/10.1051/e3sconf/202337104036.
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The development of the modern technologies for passenger transportation and the integration of rail routes with other modes of transport using logistics contributes to the increasing of the mobility of the people and the increasing of the economic efficiency in general. The market of the passenger transportation by rail on the North Caucasian railway is analyzed. Multimodal passenger transportation is one of the most effective tools for increasing the mobility of the people. Social factors are determined, the fundamental idea of the organizing of the multimodal passenger transportation using a mobile application is proposed, this allows to determine the optimal route for a passenger to travel to all specified points. Using research methods within the framework of a comparative and logical analysis, variants of digitalization of the multimodal passenger transportation services by the world's leading railway operators are considered. As a result, it was detected that a promising direction of multimodal transportation is the combination of the train routes not only with the regional bus routes, but also with the integration of the urban transport through the digitalization of services and the development of the customer services. The organization of the passenger service in multimodal transportations at transport hubs and options for a convenient and rational orientation of movement for passengers are considered.
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Alodhaibi, Sultan, Robert L. Burdett, and Prasad K. D. V. Yarlagadda. "A Framework for Sharing Staff between Outbound and Inbound Airport Processes." Mathematics 8, no. 6 (2020): 895. http://dx.doi.org/10.3390/math8060895.
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This paper proposes an advanced simulation-optimization approach to evaluate and optimize the passenger flows within international airports. This approach allocates resources intelligently during the simulation process and balances demand and service quality. The resource allocation performed by our Advanced Resource Management (ARM) algorithm was used to develop an integrated system for arranging resources, identifying the proper resources, and allocating them throughout the model. It was used to investigate the influences of different staff allocation techniques on the inbound and outbound processes of an airport terminal. The purpose of the proposed simulation-optimization approach is to enhance passenger satisfaction through ensuring reasonable wait times during processing at the lowest cost possible (minimal staff hours).
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Romano Alho, André, Takanori Sakai, Simon Oh, et al. "A Simulation-Based Evaluation of a Cargo-Hitching Service for E-Commerce Using Mobility-on-Demand Vehicles." Future Transportation 1, no. 3 (2021): 639–56. http://dx.doi.org/10.3390/futuretransp1030034.
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Time-sensitive parcel deliveries—shipments requested for delivery in a day or less—are an increasingly important aspect of urban logistics. It is challenging to deal with these deliveries from a carrier perspective. These require additional planning constraints, preventing the efficient consolidation of deliveries that is possible when demand is well known in advance. Furthermore, such time-sensitive deliveries are requested to a wider spatial scope than retail centers, including homes and offices. Therefore, an increase in such deliveries is considered to exacerbate negative externalities, such as congestion and emissions. One of the solutions is to leverage spare capacity in passenger transport modes. This concept is often denominated as cargo hitching. While there are various system designs, it is crucial that such a solution does not deteriorate the quality of service of passenger trips. This research aims to evaluate the use of mobility-on-demand (MOD) services that perform same-day parcel deliveries. To test the MOD-based solutions, we utilize a high-resolution agent- and activity-based simulation platform of passenger and freight flows. E-commerce demand carrier data collected in Singapore are used to characterize simulated parcel delivery demand. We explore operational scenarios that aim to minimize the adverse effects of fulfilling deliveries with MOD service vehicles on passenger flows. Adverse effects are measured in fulfillment, wait, and travel times. A case study on Singapore indicates that the MOD services have potential to fulfill a considerable amount of parcel deliveries and decrease freight vehicle traffic and total vehicle kilometers travelled without compromising the quality of MOD for passenger travel. Insights into the operational performance of the cargo-hitching service are also provided.
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Wang, Shen, Thomas Weber, Dieter Schramm, and Thorben Berns. "Simulation-Based Investigation of On-Demand Vehicle Deployment for Night Bus Routes Using the Monte Carlo Method." Future Transportation 4, no. 2 (2024): 380–408. http://dx.doi.org/10.3390/futuretransp4020019.
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Public transportation systems, including trams and buses, play a crucial role in urban traffic. However, these traditional modes of transport have some well-known drawbacks, such as long distances between stops, lengthy waiting times, and a lack of privacy. In response to these challenges, an innovative mobility concept called “FLAIT-train” offers potential solutions. The FLAIT-train operates on regular roads and aims to provide DOOR-2-DOOR transport, addressing the issues associated with fixed stops and offering increased accessibility and convenience. In its initial phase, the FLAIT-train operates on exclusive lanes, but it is designed to integrate with other traffic eventually. The vehicle technology of FLAIT-trains closely resembles that of battery electric autonomous vehicles. To assess whether FLAIT-trains can be used as a suitable alternative to conventional public transportation systems, this paper employs traffic simulations that consider key performance indicators, including the average waiting time per passenger, maximum waiting time of a single passenger, average in-vehicle time per passenger, and average occupancy rate of the vehicles. Using SUMO software (“Simulation of Urban Mobility”, version 1.12.0), a night bus service scenario is meticulously designed and generated. Within this scenario, both FLAIT-trains and conventional buses are simulated under identical conditions and based on statistical data.
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Bulková, Zdenka, Milan Dedík, Jozef Gašparík, and Rudolf Kampf. "Framework Proposal for Solving Problems in Railway Transport During the COVID-19 Pandemic." Transport technic and technology 18, no. 1 (2022): 1–8. http://dx.doi.org/10.2478/ttt-2022-0001.
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Abstract Since the beginning of 2020, the COVID-19 pandemic had a major impact on rail transport in the EU. The slowdown in the spread of the COVID-19 pandemic has been achieved by reducing the mobility of the population. The reduction in mobility has had an impact on passenger transport performance. The number of national rail passengers fell by as much as 90% during the first wave of the pandemic compared to the previous year. Several operators, especially new carriers, had to close down, while rail freight operators reported a dramatic drop in volumes as many sectors slowed or even stopped production as a result of the pandemic. The second wave of the autumn 2020 pandemic has forced many countries to take further restrictive measures regarding population mobility. The outbreak of the third wave of the pandemic has prevented a rapid recovery in rail transport, especially for passenger rail services. The paper focuses on the analysis of the impact of the pandemic and the measures put in place on the development of transport performance in rail transport in the Slovak Republic during the COVID-19 pandemic. The paper proposes operational and organizational measures against the spread of the COVID-19 pandemic in railway transport in the Slovak Republic.
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Yu, Chao, Haiying Li, Xinyue Xu, et al. "Data-Driven Approach for Passenger Mobility Pattern Recognition Using Spatiotemporal Embedding." Journal of Advanced Transportation 2021 (May 19, 2021): 1–21. http://dx.doi.org/10.1155/2021/5574093.
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Urban mobility pattern recognition has great potential in revealing human travel mechanism, discovering passenger travel purpose, and predicting and managing traffic demand. This paper aims to propose a data-driven method to identify metro passenger mobility patterns based on Automatic Fare Collection (AFC) data and geo-based data. First, Point of Information (POI) data within 500 meters of the metro stations are captured to characterize the spatial attributes of the stations. Especially, a fusion method of multisource geo-based data is proposed to convert raw POI data into weighted POI data considering service capabilities. Second, an unsupervised learning framework based on stacked auto-encoder (SAE) is designed to embed the spatiotemporal information of trips into low-dimensional dense trip vectors. In detail, the embedded spatiotemporal information includes spatial features (POI categories around the origin station and that around the destination station) and temporal features (start time, day of the week, and travel time). Third, a density-based clustering algorithm is introduced to identify passenger mobility patterns based on the embedded dense trip vectors. Finally, a case of Beijing metro network is used to verify the feasibility of the above methodology. The results show that the proposed method performs well in recognizing mobility patterns and outperforms the existing methods.
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Tamiyati, Lusifa Hafi. "Redesain Terminal Tipe A Poris Plawad di Kota Tangerang dengan Tema Arsitektur Hijau." Jurnal Arsitektur Wastu Padma 1, no. 1 (2023): 010–22. http://dx.doi.org/10.62024/jawp.v1i1.2.
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Plawad Terminal is a type A bus terminal which still serves public transportation vehicle mobility in the area around Cipondoh Tangerang District. The potential for passenger mobility and environmental conditions are sufficient to support its existence as a regional transportation facility. Currently, the physical and functional condition of the terminal does not meet the passenger and vehicle service requirements, so a redesign program is needed. The redesign program takes the theme of green architecture as part of improving the quality of the terminal, users and the surrounding environment. The problems raised are: (1) How does the terminal design concept accommodate passenger needs and vehicle movement? (2) How is the green architecture theme applied in building design? The discussion refers to a qualitative analytical-descriptive approach which is based on a theoretical framework and applied to design. From the results of the discussion, a design direction can be formulated, namely: (1) The design is focused on arranging the optimization layout for ease of vehicle movement and passenger space facilities which refer to the principles of walk, transit and connected (2) The design concept refers to energy conservation efforts through shape and spatial layout. Building.
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Krajnyk, Lybomyr, Mykhailo Burian, Olena Lanets та Vasyl Kokhan. "SMOOTHNESS OF MOVEMENT AS THE BASIS OF CAR COMFORT:FORMATION OF THE REGULATORY FRAMEWORK «VEHICLE ROAD СOMFORT»". Avtoshliakhovyk Ukrayiny 3, № 271 (2022): 2–7. http://dx.doi.org/10.33868/0365-8392-2022-3-271-2-7.
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The development and condition of the road network of Ukraine significantly lag behind the needs of society. At the same time, the road itself is the basic factor that forms the smoothness and driving comfort in the aspect of vibration loads of the driver and passengers. In addition, in the conditions of warfare, the mobility (that is, speed modes) of military vehicles off-road is extremely important, the maximum values of which are limited to reaching the maximum permissible levels of vibration loads on personnel (driver – passenger). As a result of practical tests, the results of vibration loads were obtained for the scheme “Road surface – Car – Passenger”. Based on these results, the foundations for the formation of a national regulatory framework regarding the permissible vibration loads during the transporting of passengers on roads and as well as conditions for limiting the speed of off-road vehicles were developed. Based on ISO 2631-1, according to the results of the research, regulatory requirements for the level of permissible vibration loads for city, suburban, and intercity buses, as well as a conformity assessment methodology had been defined. The issues of suspension requirements for off-road driving are considered in connection with the formation of the regulatory framework for military equipment as the basis for mobility from the conditions of vibration loads and the separation of requirements to military equipment designs, depending on the scope of use. By analogy with NATO countries, the above requirements are also necessary regarding the suspension, as a determining element of comfort and off-road mobility. This predetermines the development of a separate standard for military equipment. Keywords. smoothness, passenger transportation, highway, off-road driving, vibration loads.
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Preis, Lukas, and Mirko Hornung. "Vertiport Operations Modeling, Agent-Based Simulation and Parameter Value Specification." Electronics 11, no. 7 (2022): 1071. http://dx.doi.org/10.3390/electronics11071071.
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Urban air mobility (UAM) is the idea of creating a future mobility market through the introduction of a new mode of aerial transport with substantial travel time advantages. A key factor diminishing travel time savings is vertiport processes. So far, vertiport throughput capacity has only been studied in a static manner using analytical methods, which has been found to be insufficient. This paper wants to increase the level of understanding of operational dynamics on vertiport airfields by being the first to apply agent-based simulation. For this purpose, an existing vertiport model consisting of pads, gates and stands was refined through two means. First, a sensitivity study with over 100 simulations was executed shedding light on the driving processes on a vertiport airfield. Second, an expert interview series with 17 participants was conducted, letting the experts evaluate the model and specify relevant parameter values. Three main results should find mention here: (1) Pad operations were identified to be most impactful on passenger delays. (2) Pad and gate processes have a threshold capacity beyond which delays increase exponentially. (3) A refined vertiport model is presented, including the 27 most relevant parameters and their value specification. In conclusion, this paper finds that optimized vertiport airfield design is crucial to UAM operations, and dynamic passenger and vehicle interactions cannot be neglected.
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Mazzarino, Marco, and Lucio Rubini. "Smart Urban Planning: Evaluating Urban Logistics Performance of Innovative Solutions and Sustainable Policies in the Venice Lagoon—the Results of a Case Study." Sustainability 11, no. 17 (2019): 4580. http://dx.doi.org/10.3390/su11174580.
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Currently, remarkable gaps of operational, social and environmental efficiency and overall sub-optimization of the logistics and mobility systems exist in urban areas. There is then the need to promote and assess innovative transport solutions and policy-making within SUMPs (Sustainable Urban Mobility Plans) to deal with such critical issues in order to improve urban sustainability. The paper focuses on the case study of the Venice Lagoon, where islands—despite representing a relevant feature of urban planning—face a tremendous lack of accessibility, depopulation, social cohesion and they turn out to be poorly connected. By developing an original scenario-building methodological framework and performing data collection activities, the purpose of the paper consists of assessing the feasibility of a mixed passenger and freight transport system —sometimes called cargo hitching. Mixed passenger and freight systems/cargo hitching are considered as an innovative framework based on the integration of freight and passenger urban systems and resources to optimize the existing transport capacity, and thus, urban sustainability. Results show that the overall existing urban transport capacity can accommodate urban freight flows on main connections in the Lagoon. The reduction in spare public transport capacity, as well as in the number (and type) of circulating freight boats show—in various scenarios—the degree of optimization of the resulting urban network configuration and the positive impacts on urban sustainability. This paves the way for the regulatory framework to adopt proposed solutions.
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Kanaroglou, Pavlos, Ruben Mercado, Hanna Maoh, Antonio Páez, Darren M. Scott, and Bruce Newbold. "Simulation Framework for Analysis of Elderly Mobility Policies." Transportation Research Record: Journal of the Transportation Research Board 2078, no. 1 (2008): 62–71. http://dx.doi.org/10.3141/2078-09.
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Guo, Hongyu. "Self-calibrated transit service monitoring using automated collected data." Applied and Computational Engineering 74, no. 1 (2024): 194–205. http://dx.doi.org/10.54254/2755-2721/74/20240472.
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This paper proposes a self-calibrated transit service monitoring framework that aims to obtain the performance of a transit system using automated collected data. We first introduce an event-based transit simulation model, which allows the detailed simulation of passenger travel behavior in a transit system, including boarding, alighting, and transfer walking. To estimate passenger path choices, we assume the path choices can be modeled using a C-logit model, and propose a simulation-based optimization model to estimate the path choice parameters based on automated fare collection and automated vehicle location data. The path choices can be estimated on a daily basis, which enables the simulation model to adapt to dynamic passenger behavior changes, and output more accurate network performance indicators for regular service monitoring such as train load, passenger travel time, and crowding at platforms. The proposed system eliminates the need for conventional monitoring equipment such as cameras at platforms and scaling/weighing systems on trains. The Hong Kong Mass Transit Railway (MTR) system is used as the case study. Results show that the model can well estimate the path choice behavior of passengers in the system. The output passenger exit flows are closer to the actual one compared to the two benchmark models (shortest path and uniform path choice).
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Zakharov, D. A., and A. A. Fadyushin. "Changes of the urban population mobility at the development of infrastructure for public transport, cyclists and pedestrians." Вестник гражданских инженеров 17, no. 5 (2020): 187–93. http://dx.doi.org/10.23968/1999-5571-2020-17-5-187-193.
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The article examines the mobility of the large city population with 800 thousand inhabitants where there is no off-street transport. As a result of simulation modeling with a macroscopic transport model of the city, there was established the influence of the chargeable parking facilities` cost, the length of lanes for public transport and bike paths on the share of movements by private and public transport, cycling, pedestrian movements. An estimation of changes in passenger turnover at bus stops and passenger traffic was performed. The impact of the new pedestrian bridge across the river Tura is demonstrated in relation to the resident transportation routes change.
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Tang, M. C., J. Cao, D. Q. Gong, G. Xue, and B. T. Khoa. "Simulation analysis of dual-end queuing ride-hailing system considering driver-side queue management." Advances in Production Engineering & Management 19, no. 2 (2024): 268–80. http://dx.doi.org/10.14743/apem2024.2.506.
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Ride-hailing services have transformed urban transportation through convenience yet introduced new complexities around efficiency and traffic management. This study investigates the dual-queuing problem in ride-hailing from the driver perspective using a multi-agent simulation approach. The focus is dissecting the dynamics between driver search times and passenger wait times, which critically influence operational efficiency especially during peak demand. Exploring these interactions aims to uncover insights that could improve service efficiency and customer satisfaction. Addressing such ride-hailing challenges is vital not just for individual providers but also for advancing sustainable mobility across rapidly growing metropolitan regions. Enhanced efficiency connects to broader urban development narratives around livability, accessibility, and responsible mobility ecosystems.
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Bosso, Nicola, Antonio Gugliotta, and Nicolò Zampieri. "Design and Simulation of a Railway Vehicle for the Transport of People with Reduced Mobility." Shock and Vibration 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/9207639.
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One of the most important aspects of the design activity of passenger railway vehicles is the optimization of the comfort level that is often in contrast with other requirements, such as low weight, to reduce energy consumption, and high and flexible seating capacity. Due to the coach weight reduction, the car body structure becomes more susceptible to structural vibrations that affect the passenger comfort. In modern vehicles, seats are important elements to achieve the desired comfort, but in order to design and estimate the actual comfort level, the whole system must be considered, including the track excitations, a vehicle detailed dynamic model, and the coach and the seat flexibility. This paper describes a numerical model of a double-deck vehicle developed using a MB code that considers measured track irregularities, a detailed vehicle model, and a transfer function of the seat obtained by experimental tests on an optimized seat. In order to make the numerical model more realistic, the coach has been modeled as a flexible body to consider the effect of its natural frequencies. The work has been performed within the “CARITAS” project, whose aim is the design of a high comfort vehicle for people with reduced mobility.
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Zarina, Yusuf Shaikh, Prasadu Peddi Dr., and Rachana K. Somkunwar Dr. "A Hybrid Dynamic Toll Pricing Framework for Congestion Management and Revenue Generation for Optimizing Urban Mobility." International Journal of Advance and Applied Research 12, no. 4 (2025): 311–19. https://doi.org/10.5281/zenodo.15480192.
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<em>The need for new strategies to manage roads and increase traffic efficiency intensified as cities keep growing while traffic congestion increases. This investigation presents an entire study of hybrid dynamic toll pricing systems that manage road congestion effectively and maximizes revenue along with supporting urban environmental sustainability. Real-time adaptation of toll rates emerges from the researcher's implementation of predictive traffic models by Long Short-Term Memory (LSTM) networks together with Auto Regressive Integrated Moving Average (ARIMA) as well as Prophet to support geo-clustering techniques through DBSCAN. This system tailors toll rates to current traffic patterns alongside predicted traffic conditions. Q-learning reinforcement learning serves in this model as an automated system to establish optimal toll rate adjustments that maintain congestion reduction performance together with revenue targets. The trial simulations demonstrate that tow system produces multiple advantages such as better flow reduction combined with revenue growth as well as environmental advantages through emission decreases. Subsidy programs are suggested to support low-income passengers since the research evaluates the system's social implications. The research enables policy and transportation planning communities to create sustainability-based mobility guidelines through its provision of applicable information regarding enhanced road resource optimization and urban transportation systems.</em>
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Corradini, Flavio, Francesco De Angelis, Andrea Polini, Carlo Castagnari, Jacopo de Berardinis, and Giorgio Forcina. "Tangramob: An Agent-Based Simulation Framework for Validating Urban Smart Mobility Solutions." Journal of Intelligent Systems 29, no. 1 (2019): 1188–201. http://dx.doi.org/10.1515/jisys-2018-0321.
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Abstract Estimating the effects of introducing a range of smart mobility solutions within an urban area is a crucial concern in urban planning. The lack of a simulator for the assessment of mobility initiatives forces local public authorities and mobility service providers to base their decisions on guidelines derived from common heuristics and best practices. These approaches can help planners in shaping mobility solutions; however, given the high number of variables to consider, the effects are not guaranteed. Therefore, a solution conceived respecting the available guidelines can result in a failure in a different context. In particular, difficult aspects to consider are the interactions between different mobility services available in a given urban area and the acceptance of a given mobility initiative by the inhabitants of the area. In order to fill this gap, we introduce Tangramob, an agent-based simulation framework capable of assessing the impacts of a smart mobility initiative within an urban area of interest. Tangramob simulates how urban traffic is expected to evolve as citizens start experiencing newly offered traveling solutions. This allows decision makers to evaluate the efficacy of their initiatives, taking into account the current urban system. In this paper, we provide an overview of the simulation framework along with its design. To show the potential of Tangramob, three mobility initiatives are simulated and compared in the same scenario. This demonstrates how it is possible to perform comparative experiments so as to align mobility initiatives to the user goals.